Claude discovers a novel enzyme system with CRISPR-like repeats

We’re introducing a new life sciences research group and laboratory at Anthropic. Our focus is on fundamental biology research using Claude: exploring datasets of DNA to identify uncharacterized protein families, generating hypotheses at scale, and testing them through experiments in the lab. This post introduces the team behind this work and shares early results in which Claude discovered a novel enzyme system with properties reminiscent of CRISPR, with only high-level direction from our scientists.

Many discoveries that have revolutionized biology and medicine started with a scientist noticing something odd in the staggering diversity of molecular machines found in nature. Restriction enzymes, proteins that cut DNA at specific short sequences, were found in bacterial immune systems, where they destroy the DNA of invading viruses. Researchers realized they could use these enzymes to cut DNA at chosen places and splice genes from one organism into another, which launched the biotechnology industry. Taq polymerase, an enzyme that copies DNA at high temperatures, was identified in a bacterium in a Yellowstone hot spring. It became the basis for PCR, the DNA-copying method used in much of modern diagnostics. CRISPR was first noticed as an unusual repeat sequence in the DNA of certain bacteria, and is now the foundation of gene editing-based medicines.

In the spring of 2026, we formed a research group to see whether general AI models can systematize and accelerate such discoveries. We believe that this acceleration will come from establishing a new way of doing biology research, in which agents collaborate with humans in every step of the process. Developing this new way of working required that we build our own lab and a single team working on everything from training Claude in biology to running experiments in the lab.

Today, we’re sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don’t yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of which are programmable and perform operations like cutting, copying, and pasting DNA. Beyond CRISPR, which has already transformed science and medicine, several other such systems are now in development as promising tools.

The system that Claude found is based on a reverse transcriptase (RT), enzymes that copy RNA into DNA. While this underlying RT, found in a jumbo phage, had been identified in previous studies, Claude appears to be the first to notice the system’s defining features—an associated array of non-coding DNA sequences and an additional accessory protein of unknown function.

After reviewing the pre-print, Feng Zhang, one of the pioneers of CRISPR genome editing and a professor at MIT and the Broad Institute, said:

This is an exciting example of how AI agents can contribute to biological discovery. The identification of RNA-repeat arrays associated with reverse transcriptases is genuinely intriguing and merits further investigation. I hope this work encourages more scientists to explore how AI can support their research.

We gave Claude a prompt to search through a massive database of DNA sequences for interesting new examples of RTs. Our involvement was limited to the initial prompt and the lab work, while Claude agents combed through the database, investigated the distinct RT families, and used their own judgment to identify interesting candidates. After 21 hours spent searching this data by roughly 950 agents using 210 million tokens, one of the agents spotted something remarkable: a repeating pattern of DNA sequences that occurs next to the gene for an odd-looking RT. After further analysis and testing in our lab, we recognized that this pattern marked a previously uncharacterized enzyme system found in bacteriophages (the viruses that infect bacteria) that we call array-associated reverse transcriptases (ART).

Our work to understand the primary function of ARTs is ongoing. However, we think it is important to share such findings early, both to demonstrate Claude’s capabilities and to give the broader community insight into what we’re working on. We have released a pre-print that discusses this in more detail.

About our lab

We are a team of scientists who have spent our careers exploring unusual proteins, and specialize in using computational approaches to systematically read DNA, interpret its evolution, and pick out biological systems for further characterization. Our research prior to joining Anthropic has helped to better understand the evolution and regulation of CRISPR systems, discover new enzymes for next-generation cell and gene therapies, and build tools for accelerating the identification of anomalies in DNA, such as human pathogenic variants. We are part of Anthropic’s life sciences organization, alongside teams whose work includes drug discovery, and training Claude in biology and chemistry.

Our lab, located in the Bay Area, looks like a typical molecular biology lab. We do research that involves only the lower levels of the biosafety risk level (BSL-1 and BSL-2) and we do not handle pathogens that can infect humans. All of the lab work is performed by human scientists. Although we’ve experimented with using AI to accelerate lab work with initiatives like the Model Hardware Standard, this approach is less conducive to the sort of ad hoc workflows that are involved in our molecular biology research.

How we work

Many of our workflows involve having Claude search through the vast collection of DNA sequences associated with proteins without a known function. One typical pattern begins with a survey of a given protein family. Claude reads the relevant literature and reproduces the established results from public data to check its methods. It then searches for family members or genomic neighbors that fit no described system, and writes a short, human-readable report for each candidate that proposes a function and describes the evidence supporting its claims. In follow-up analyses, Claude critically evaluates the evidence—typically most candidates are eliminated at this stage. A survey may end with a single candidate worth testing, or with none.

When a candidate survives our review, we test it in the laboratory, expressing the protein in standard laboratory strains and characterizing it biochemically and structurally, with Claude helping to interpret the data. We do our work in Claude Science and Claude Code, the same tools available to any scientist, and sometimes with a harness of our own that coordinates many Claude sessions running in parallel.

Because Claude produces hypotheses so prolifically, the hypotheses themselves have become an object of study for us. With hundreds to thousands of candidate reports from a single campaign, we have been asking what distinguishes the proposals we judge worth testing from those we set aside. What we learn goes back into the instructions we give Claude and teaches it to mimic our own scientific taste.

Claude finds ART

In the past few years, researchers have discovered many more reverse transcriptases (RTs), most of them in bacteria, where they act as part of the immune system. Nearly all RT families were found by genomic analysis, or genome mining, which requires researchers to search sequence databases for genes that no one has characterized, notice the unusual ones, and work out what they do.

Claude agents gathered over 200,000 RTs, picked out 3,500 new candidate systems, and narrowed those to the 20 most compelling candidates that they analyzed to produce human-readable reports. For an expert scientist, this type of analysis can take weeks to months of work.

During the course of its research, Claude noticed an unusual RT family and decided to examine it in greater detail. While combing through the raw DNA sequence near the RT, the agent exclaimed: “[The DNA next to the RT] is spectacular: I can see by eye a tandem repeat array … that's a CRISPR-like … repeat array?!”

It then proceeded much as a human scientist would when faced with a potential discovery. It counted the repeats and measured their spacing, compared the layout with the known RT systems, and searched the literature for any previous report of the pattern. After a thorough analysis it was convinced that it had found a new biological system, and filed a report for human review.

The system it found, ART, is found mainly in bacteriophages and consists of three parts: the RT, a partner gene beside it, and a long array of evenly spaced DNA repeat sequences. The repeat layout resembles a CRISPR array, which holds a bank of different RNA sequences that make CRISPR-Cas systems programmable biotechnological tools. Our first experiments show that the ART array is also expressed as a set of distinct short RNAs, suggesting that something analogous may be at play for this system.

Further experiments are underway to determine how ART works, and we are sharing these early findings to show the community that Claude can autonomously detect anomalies and drive analyses to initiate biological discoveries.

You can find more detail in our technical report.

Work with us

We hope this work demonstrates the value of AI-driven hypothesis generation to the wider scientific community, and we would like to work with other scientists to extend this approach to a broad range of problems, in genomics and in other fields. If you have a proposal for a research question, we would like to hear from you.

How we made claude.ai 3x faster in two weeks

Once Claude can measure something, it can make it faster. So we kept finding more things to measure.

This August, we made the core user experience of claude.ai and the Claude desktop app about 3x faster in a two-week sprint. Users had been telling us it was slow, and they were right. We ran everything from a single Slack channel, with Claude in every thread.

We focused on four journeys that make up 95% of user activity. At the 75th percentile, time to a typeable page on a fresh load of claude.ai went from 3.1 seconds to 0.55, starting a new Claude Code session went from 0.8 seconds to 0.3, and loading a Claude Cowork cloud session went from 2.6 seconds to 0.73. In aggregate, we estimate that saves tens of thousands of user-hours of waiting every day.

We used Claude Tag (beta), running an internal research model roughly comparable to Opus 5.5. Claude found bottlenecks, built benchmarks, shipped improvements, and watched every deploy. We steered by setting goals, making tradeoffs, and approving every change. With that approach, we merged more than three thousand changes without a single customer-facing incident or rollback. This post covers what we shipped, how we measured it, and the loop we built with Claude to do it safely.

THE BRIEF

Before the sprint, we created a Slack channel with the following standing instructions:

@Claude Your job is to facilitate all things related to the performance of the claude.ai website and desktop app. Your responsibilities include monitoring deploys for performance regressions, assessing the accuracy and comprehensiveness of existing telemetry, maintaining well-curated observability dashboards, proactively implementing solutions for observed issues and low-hanging fruit, proposing performance project opportunities, and communicating with your human teammates. […]

The ultimate goal for this channel is for you to become as autonomous as possible, but today we know that isn’t yet possible.

We asked Claude to analyze usage data through the Datadog MCP server. It identified the four highest-impact user journeys: launching the app, starting a conversation, loading an existing conversation, and sending a message. Between web and desktop, and across our products, those journeys came to thirteen distinct measurements. To establish baselines, we added instrumentation until they were directly comparable: each started with a user interaction, ended once the result was rendered, and disambiguated client and server work.

We kicked off the sprint with a list of about twenty hand-picked projects, each targeting a specific journey. Claude estimated the impact of each project in milliseconds, and we aggregated those estimates to set our targets for the sprint. Some of the projects were fairly large, but we thought we could probably achieve most of them within two weeks.

We hit twelve of the thirteen targets by day three.

The planned projects landed early. For faster launches, we baked a static composer into the HTML so users can type during React initialization, and precompiled a V8 code cache so the desktop shell’s main process doesn’t recompile from scratch. For faster navigations, we kept the composer mounted between conversations, prefetched sessions when the user hovered over them, and cut sidebar re-renders by 90%.

We had also left room for Claude to identify opportunities and propose new workstreams. Those workstreams quickly ramped into full projects of their own, which far exceeded our initial targets. So we set new targets, then looked for more things to measure:

@Claude we’ve ended up funding nearly every project in the original projects list and more. let’s do a refresh […] what have we not explored, what can we hill climb on, where is the most opportunity at this point? […] i am open to WACKY ideas

ANYTHING CAN BE HILL CLIMBED

From the start, we knew we wanted to iterate faster than our deploy cadence. Claude could work asynchronously for many hours, even overnight, and we wanted to let it validate its prototypes without waiting for field reads. To achieve that, we looked for other ways to measure performance in the lab.

We treated every new benchmark with some skepticism. Each one had two jobs: first, a metric Claude could move in the lab; second, a guardrail in CI with a number that could only ratchet down. If a benchmark was flaky, or if it didn’t actually correlate with user latency, we threw it out rather than let Claude climb the wrong hill.

@Claude please prove that hill climbing against each of these can result in measurable wall clock perf wins. we’ll unship the benches for any candidates that cannot prove that

Wall-clock time is what users feel, but it’s noisy, and milliseconds are too flaky to use as a CI gate. Instruction counts were appealing because they were deterministic, but we still needed Claude to prove they tracked wall-clock time.

So we asked Claude to drive the count down on two hot paths: the routine that assembles a conversation’s message tree, and a scanner for status lines in Claude Code output. Claude profiled both with Valgrind and found that a quarter of the first path’s instructions were megamorphic dictionary lookups, resolving the same message ID three separate times.

An hour later it had cut instructions on both paths by 48% and 31%, and wall-clock time had dropped 78% and 44%. We checked in two new ratchets. From then on, any PR that raised the instruction counts of those paths failed CI, and a daily job lowered each ceiling whenever the count went down.

That led us to the central lesson of the sprint. With Claude, measuring something makes it tractable.

Measurement used to be step zero: you’d add a metric, wait for data to roll in, and only then start to understand the problem. With Claude, it’s step one of the climb. As soon as Claude had a number to beat, it could start optimizing. This meant the highest-leverage thing we could do was find more things to measure.

THE LOOP, THREAD BY THREAD

All of this ran in the same Slack channel, with multiple engineers and Claude jamming in every thread. From there, the sprint settled into a loop:

1. Someone would open a thread about a slow stretch of a journey, often with a screenshot or recording.
2. Claude would trace the flow, then find or build a benchmark that demonstrated the problem.
3. Once it had a promising result in the lab, Claude would come back with a PR — often several, sized for risk and review, with anything user-visible behind a flag.
4. After it shipped, Claude watched the deploy and read the field data.
5. If performance improved, Claude locked in the win by ratcheting the benchmark down; if not, it turned the flag off and iterated.
6. Then it went looking for the next slow spot in the same journey.

An example: someone shared a screen recording that showed sidebar rows popping in after the page loaded. Chat and Cowork rows resolved at different times, making the page feel janky. None of our existing monitors detected it.

Issac had the idea to reference the underlying Layout Instability API directly. Claude created a telemetry event that mapped the sources of each layout-shift entry to a named region (e.g. sidebar, transcript) and phase (e.g. before first paint, after typeable). It added an integration test that opened the page with a populated sidebar, held the sidebar’s data until after first paint, and failed on any shift in any named region.

SCALING HORIZONTALLY

Once the loop worked on one thread, running it on more was just a matter of opening them. Instead of closing a thread once its original request had been fulfilled, Claude would keep going. An individual thread would put up fifty, sometimes a hundred, optimization PRs. Increasingly, it was Claude, not one of us, opening new threads to chase opportunities it had found on its own, as part of a separate investigation or nightly job.

Every measurement found something to improve. Claude ran a React hook census and found 6,900 hooks and 900 store subscriptions in the composer’s typing path, re-rendering on every keystroke. Claude counted style recalculations and found a single :root:has() selector adding 24 milliseconds to every DOM change.

We rarely knew where a thread would lead. In a sweep for CPU hitches, Claude noticed that highlighting a finished code block could freeze the page for about a second. It dug in the lab and found the culprit: em dashes. If a reply’s markdown contained any non-Latin-1 character, like an em dash or a curly quote, V8 stored the entire string as UTF-16, which put every syntax-highlighting regex on its slower two-byte path. Claude fixed it with a twenty-line change to copy each code block into a one-byte string before highlighting it.

GUARDRAILS

We’d prepared for the pace. Every PR went through automated review with at least one human approval, unit tests always came before optimizations, and anything that could cause a user-visible problem shipped behind a short-lived feature flag.

If the React render is off by even a pixel, the magic is lost. So Claude built dozens of guardrails:

• The static markup is generated by rendering the real React component in jsdom, and a test guarantees they never drift.
• An integration test suite compares the static page against the React render across fourteen viewport sizes, and asserts alignment within 1 px.
• A keystroke test types straight through the handoff and fails on any lost or reordered key.
• In the field, every handoff reports shifts to a tenth of a pixel. Claude opens a thread for any event with nonzero movement.

STEERING

The loop was productive, but it wasn’t autonomous. Keeping it fast, safe, and on track was our job, and it had three parts.

Ambition. By default, Claude is careful about scope. It tickets findings, hedges on feasibility, and pads its estimates. But we were confident in our guardrails. A lot of what we did, especially early on, was to encourage Claude to be bolder.

Taste. Every thread had a named human owner, and Claude highlighted any user-perceptible change with before-and-after screenshots or recordings for them to rule on.

Direction. We kept each thread deliberately narrow, focused on one benchmark or journey, and asked Claude to find improvements only within that scope. We thought of the threads as a hundred and fifty hammers seeking nails.

AN 8-MILLISECOND BUDGET

Once the mechanism and ambition were established, Claude got to work. Each painted frame had a budget of 8.33 milliseconds, so Claude stepped through a long reply frame by frame, timing each one to find the slow parts. It eliminated O(message length) work per chunk by memoizing finished blocks, moved tokenization logic for growing code fences to a worker, and revealed tables cell by cell.

In that one thread, we landed nearly sixty PRs. Long replies blocked the main thread for about 200 milliseconds in total where they used to block it for about 750, ran on about a third of the CPU, and held 120 fps from start to finish on a 120 Hz MacBook.

When we started the sprint, we hadn’t planned to hill climb on the milliseconds between frames while streaming. But it turned out we could count them — and anything we could count, Claude could climb.

WHAT’S NEXT

Today, claude.ai and the desktop app are about 3x faster than they were in early August, and the ratchets should keep them there. But we’re not done: the 95th percentile, other journeys, and very long conversations still have room to improve.

When we shared the results internally, Issac put it best: “You could not have convinced me this was possible even six months ago.” We expect to keep working this way, one thread at a time, at any scale. The channel’s still going.

Three out of the four attempts were unsuccessful, but the Australian Medicare Statistics Reporting Service reported that OpenAI's AI acquired health data from its website.

If You Last Used Cassandra 3.11, You Might Not Recognize It Today

If you learned Cassandra around version 3.11, the advice was fairly consistent: design tables around queries, be careful with secondary indexes, choose a compaction strategy for each workload, and arrange repair scheduling yourself. For conditional updates, use lightweight transactions. For transactions spanning multiple partitions, expect to do more work outside the database.

In consulting work, I still encounter 3.11 clusters operated according to that model. Much of it remains useful, but several limitations behind those recommendations have changed. Cassandra now has storage-integrated indexing, vector search, a more flexible compaction strategy, and built-in repair scheduling. The 6.0 work goes further into cluster metadata and transactions.

As of September, 2026, Apache Cassandra 5.0.9 is the latest GA release. Cassandra 6.0 remains pre-GA, with alpha builds available. That distinction matters: SAI is something to evaluate on the stable release; Accord is a reason to follow and test the next one.

The interesting part is not that Cassandra gained more configuration knobs. It is that several old assumptions about how Cassandra must be queried and operated have started to loosen. A rough comparison looks like this:

I wrote about some of the older pitfalls in 7 mistakes when using Apache Cassandra. The useful question now is which advice still follows from Cassandra's architecture and which reflected limitations of an older implementation.

Cassandra 4.0 improved the cost of moving data

Streaming transfers data between nodes during bootstrap, replacement, rebuild, repair, and topology changes. In a large cluster, it determines how long a node takes to become useful and how quickly lost replica data can be restored.

Cassandra 4.0 introduced a zero-copy streaming path for eligible whole SSTables. Instead of deserializing their contents into Java objects and serializing them again, Cassandra can transfer the files more directly. Partial transfers and other cases still need the appropriate streaming path; the optimization does not apply to every transfer.

The 4.0 streaming documentation reports roughly a fivefold improvement in the benchmark used to introduce the feature. That is not a capacity estimate for every cluster, but it explains why the change mattered: less allocation and CPU work can leave more of the transfer limited by storage and network throughput.

For a node holding terabytes of data, faster streaming can shorten replacement and reduce the time spent with missing replicas. It is an operational improvement even if the application's CQL stays exactly the same.

Guardrails move some limits into the database

Older Cassandra deployments often depended on conventions enforced in code review or runbooks. Avoid creating hundreds of tables. Keep collections bounded. Do not let an application issue queries over an uncontrolled number of partitions. Use ALLOW FILTERING carefully.

The Guardrails Framework introduced in 4.1 lets operators express some of these rules in Cassandra itself. Depending on the guardrail, a configuration can warn, reject an operation after a threshold, or disable a feature. Examples include table and index counts, collection sizes, and the number of partition keys selected by a query. ALLOW FILTERING can be disabled.

A warning gives an application team a chance to fix growing usage before it reaches the failure threshold. A rejection prevents an operation that exceeds the configured limit. Neither replaces workload design, but both are more reliable than assuming every developer has read the same operational guide.

Secondary-index advice needs to distinguish SAI from older indexes

If you learned Cassandra years ago, there is a good chance somebody told you: Don't use secondary indexes.

That advice was often justified. Traditional Cassandra secondary indexes could behave poorly at scale, especially when queries had to scatter across many partitions or index cardinality did not match the workload. Experienced teams often preferred another Cassandra pattern: create another table containing exactly the shape required by the query.

Suppose we have users:

CREATE TABLE users (
    id uuid PRIMARY KEY,
    country text,
    age int,
    status text,
    name text
);

and we need queries by country and age. The traditional Cassandra answer might be another table users_by_country_and_age with an appropriate partition and clustering key.

Cassandra 5.0 adds Storage-Attached Indexing. We can index the columns used for filtering:

CREATE INDEX users_country_idx
ON users(country)
USING 'sai';

CREATE INDEX users_age_idx
ON users(age)
USING 'sai';

CREATE INDEX users_status_idx
ON users(status)
USING 'sai';

SAI integrates indexes with memtables and SSTables. Reads combine results from those structures, and the index lifecycle follows the storage engine's flushes and compactions. It supports numeric ranges, combinations of indexed predicates, collection predicates, and text equality. For some access patterns, that can remove the need for an additional query-specific table.

An index does not eliminate query fan-out

SAI changes how Cassandra finds matching rows. It does not make a query over many partitions equivalent in cost to a lookup using one partition key. The physical layout remains different. A table partitioned by country, with age as a clustering column, can serve this query from the relevant country's replica set. In the users table, rows remain partitioned by ID, so an indexed query can require work across multiple token ranges.

UCS makes compaction behavior easier to adjust

Cassandra operators have traditionally chosen among several compaction strategies: STCS for write-heavy workloads, LCS for lower read amplification, and TWCS for time-series data. Cassandra 5.0 adds Unified Compaction Strategy. The UCS documentation recommends it for most workloads and describes how to configure behavior resembling the older strategies.

UCS exposes the tiered-versus-leveled trade-off through scaling parameters. Positive values favor tiered behavior, negative values favor leveled behavior, and different levels can use different settings. Sharding allows compaction work to run in parallel and helps control SSTable sizes on nodes holding large datasets.

Vector search can stay next to operational data

Cassandra 5.0 introduces a native vector type and approximate nearest-neighbor search through SAI. A product can store both its operational fields and an embedding. Keeping the record and embedding together can avoid copying that data into a second database purely for vector retrieval.

The most relevant case is an application that already stores a large operational dataset in Cassandra and wants similarity queries over it. That does not settle the choice of retrieval system. Approximate search quality, filtering, latency, update behavior, and operational cost still need evaluation.

Auto Repair adds built-in scheduling

Cassandra replicas can temporarily diverge. Auto Repair, developed through CEP-37 for 6.0 and backported to 5.0.8, moves that orchestration into Cassandra. It supports full, incremental, and preview repair scheduling, token-range splitting, repair history, and table priorities. A built-in scheduler reduces the need to operate a separate orchestration service.

Cassandra 6.0 orders cluster metadata changes

Transactional Cluster Metadata, defined in CEP-21, introduces a Cluster Metadata Service and an ordered log of transformations. Nodes apply committed metadata changes in that order. This provides a common history for changes such as schema updates and token ownership transitions. Gossip still has a role; it is no longer expected to establish the authoritative order of critical metadata changes.

Accord extends the transaction model across partitions

CEP-15 introduces Accord for general-purpose transactions with strict serializability. It targets a leaderless protocol with a one-WAN-round-trip fast path under normal conditions. It allows Cassandra to coordinate the conditional debit and credit as one transaction. Strict serializability means committed transactions behave as if executed in a serial order that also respects real-time ordering.

What still needs the same care

Partition keys still determine data placement. Uneven traffic can produce hot partitions, and large partitions remain expensive to read, compact, and move. Denormalization remains useful for predictable, frequent queries. Tombstones and TTLs still affect reads, compaction, and repair.

Returning from 3.11

For someone running 3.11, Cassandra 5.0 is the practical release to evaluate first. SAI, UCS, and vector search are already available. There is also a maintenance reason to move: Apache announced the end of life of the 3.x series with 5.0. Plan the move through a supported 4.x release before 5.0. I would keep the version upgrade separate from schema and indexing changes, then compare representative queries and operational tasks on the upgraded cluster.

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The Engineer’s Guide to Sampling in the Age of AI

You're producing more trace data than you want to pay to store, so you sample. A fixed 1-in-100 rate cuts your bill, but it's blind. It keeps 1% of your errors, 1% of the requests to that rarely-hit route, and 1% of the health checks, all at the same rate. The noisy traffic you care about least dominates what you keep while the traces you need during an incident are the ones most likely to be gone.

We recently announced the adaptive_tail_sampling processor, Honeycomb's contribution of the adaptive sampling algorithms behind Refinery and dynsampler-go to the OpenTelemetry Collector. I built the processor and maintain it upstream with my colleague Yingrong Zhao. The announcement covers why we did it and how to try it. In this post, we'll go over the decision mechanics, the samplers, and how it composes with the rest of a sampling pipeline.

One thing worth saying up front: this is not Refinery ported into the Collector. It's the same algorithm family re-expressed in OpenTelemetry-native terms. Rules are OTTL expressions validated at startup. Sampling decisions are encoded as a threshold in W3C TraceState (ot=th) following the OpenTelemetry consistent probability sampling specification, not a vendor field. It composes with SDK head samplers and other Collector samplers. Anything that understands the specification can reweight the sampled data, whichever backend you send it to.

Three ways to sample traces

The announcement blog introduced the three approaches available in the Collector today. It's worth being precise about their semantics, because the differences are what motivated a new processor.

Probabilistic (head) sampling decides at trace start. It's the cheapest option, no buffering at all, but it can't see what it's throwing away. Errors and rare traffic are kept at the same rate as everything else. The numbers get stark for rare events. Say a payment failure occurs once in every 1,000 requests and you head sample at 1%. Each failure's trace survives with the same 1% probability as everything else, so on average, you keep one failure trace per 100,000 requests. A service handling 50,000 requests a day hits that failure around 50 times a day, but keeps evidence of it once every two days. The sampler can't favor the failure because nothing has failed yet when the decision is made.

Tail sampling buffers the whole trace and decides with full knowledge, using the tail_sampling processor. Its policies compose as an OR. Decisions are plain keep/drop with no sampling probability attached, so kept data generally can't be reweighted downstream and counts computed from sampled data no longer reflect real traffic. The OR composition also shuts out adaptive sampling. A policy that doesn't keep a trace returns no decision rather than a drop, so the trace falls through to the next policy in the list and any later policy can still keep it. An adaptive sampler under those semantics has no control over the probability it's supposed to be enforcing.

Adaptive tail sampling, this processor, combines tail-based buffering with adaptive samplers that always know the probability they're applying. Rules evaluate first match rather than OR-composed. Every decision carries an explicit sampling threshold encoded as ot=th. You keep the interesting tail without hand-tuning, and your counts stay statistically honest after sampling.

How it works

Spans buffer in memory, grouped by trace ID. A trace becomes ready for a decision when a root span arrives, when trace_timeout (default 30s) expires or when the trace accumulates span_limit spans (default 10,000), whichever comes first. The span limit bounds how much memory a single giant trace can hold and decides immediately, since waiting would let the trace keep growing. For the other two triggers, the processor waits with decision_delay (default 2s) for straggler spans before evaluating. What counts as a root span is itself configurable with an OTTL expression (root_span_condition), which matters when only the server side of a cross-process trace reaches your Collector, or when a producer tags a message consumer span as the effective root.

Rules evaluate in order and the first match wins. Each rule names a sampler and a rule with no conditions acts as the catch-all. A typical config keeps every error and lets an adaptive sampler settle the rest:

processors:
  adaptive_tail_sampling:
    rules:
      - name: keep-errors
        conditions:
          - span.status.code == STATUS_CODE_ERROR
        sampler:
          type: always_sample

      - name: default
        sampler:
          type: adaptive_percentage
          goal_percentage: 10
          fingerprint_attributes:
            - resource.attributes["service.name"]
            - span.attributes["http.route"]

A quick word on terminology, because OpenTelemetry and Honeycomb describe the same decision differently. In OpenTelemetry terms, every trace carries 56 bits of randomness, either explicit in TraceState as ot=rv or taken from the least-significant 7 bytes of the trace ID, giving a value between 0 and 2^56 - 1. A sampler picks a sampling threshold in that same range and keeps any trace whose randomness is at or above it. Keep everything above the halfway point and you've kept 50%. The threshold travels with the kept spans as ot=th, so any later stage can recover the probability the trace survived with. Honeycomb expresses the same decision as a sample rate: keep 1-in-N. The dynsampler-go algorithms produce rates in that form. The processor translates between the two, converting each rate into the equivalent threshold.

Sampled traces are forwarded with the threshold in TraceState plus span attributes naming the matched rule (otelcol.processor.adaptive_tail_sampling.rule) and the event that triggered the decision (otelcol.processor.adaptive_tail_sampling.trigger), so you can see exactly which rule kept each trace on your dashboards. A decision cache remembers recent outcomes, so late-arriving spans are stamped or dropped consistently with the rest of their trace.

The processor is also honest under pressure. When the buffer fills (num_traces, default 50k), the oldest trace is evicted with a real sampling decision rather than merely discarded. The default eviction policy runs your rules on the spans seen so far, so keep-errors keeps working under duress. A constant-time probabilistic policy is available for deployments where eviction means genuine overload. Shutdown drains the buffer rather than discarding it, so a clean restart doesn't lose data.

Choosing a sampler

The adaptive samplers group traffic by fingerprint_attributes, scoped selectors such as resource.attributes["service.name"] and span.attributes["http.route"]. Pick attributes that classify traffic, like route, method or status code, rather than ones that identify individual requests, which would give every trace its own fingerprint and defeat the adaptation. Per fingerprint, the sampler computes a sample rate. The processor converts that rate to a threshold and compares it against the trace's randomness. Two adaptive types cover the common goals:

• adaptive_percentage targets a goal percentage of span volume (goal_percentage). It tracks per-fingerprint traffic with an exponential moving average, so rare fingerprints are kept at high rates while chatty ones are aggressively sampled and the total converges on the goal. In our validation runs, it held 9.4-10.3% against a 10% goal at every load level we tested.
• adaptive_throughput adapts toward a volume budget instead (goal_throughput, in spans per second, enforced per collector instance). Use it when the constraint is a fixed downstream budget. The default ema algorithm smooths traffic with the same moving average, while algorithm: windowed recalculates over a sliding window, reacting faster to traffic shifts at the cost of being more sensitive to short spikes.

There are also always_sample and probabilistic (a fixed fraction, the inline equivalent of the probabilistic_sampler processor) for rules that don't need to adapt. One important detail is that samplers only ever produce a rate. The rate-to-threshold comparison against trace randomness is the decision mechanism for every rule, which is what makes decisions reproducible and every kept span correctly weighted downstream.

Decisions the rest of the pipeline can trust

Because the decision is a spec-encoded threshold rather than a bare keep/drop, the processor composes with upstream and downstream sampling stages. If an SDK head sampler or a probabilistic_sampler processor already wrote ot=th, the stricter stage wins and the surviving threshold always reflects the effective end-to-end probability.

We validated this with a probabilistic_sampler in equalizing mode at 50% upstream and a 10% adaptive_percentage goal in this processor. Roughly 10% of the original traffic survived (not 10% of the upstream's 50%). Every kept span carried the 10% threshold. With an always-keep rule downstream instead, the upstream 50% threshold was preserved untouched. In both directions, estimators reconstructed the original send volume within 1.5%.

The same property helps beyond traces. The spanmetrics connector reads ot=th to produce correctly weighted R.E.D metrics from sampled data, so your request rates and error rates stay accurate even though most spans were dropped.

A fair question is why this isn't a set of new policies inside tail_sampling. The short answer is that tail_sampling's policy contract has no way to carry a sampling probability or threshold. Its OR-composition model also breaks the accounting that adaptive samplers depend on. The README walks through the mechanics in detail. Both processors remain useful and tail_sampling users lose nothing by this being separate.

Performance

We've benchmarked and soak-tested the processor throughout development. Yingrong and I ran a full load test of the standalone processor in a production-like two-tier deployment, with a sampling tier of two replicas at 7 vCPU and 13GiB each. A replica sustained around 100,000 spans per second on roughly 0.3 cores with typical span sizes, CPU grew in proportion to span payload size and the cardinality tests pushed past 170,000 spans per second.

Under a sustained 70x overload against a deliberately undersized buffer, eviction kept memory bounded and the process was never killed for exceeding its memory limit. The span_limit cap earned its place, cutting memory around 36% for about 34% more CPU at identical throughput on giant traces. It's performant, broadly in line with Refinery on comparable workloads. We'll continue improving it over time.

Deployment and current limitations

Here are some things to know that will impact how you deploy the processor:

• Decisions are per-instance, so all spans of a trace must reach the same Collector. Scale out with the standard two-tier loadbalancing exporter pattern, routing by trace ID. Routing by ot=rv isn't supported by the loadbalancing exporter yet, but there's an open PR to add it as a routing key.
• Memory is bounded by trace count (num_traces) and per-trace span count (span_limit), not bytes. It grows with span rate times the buffer window.
• Rules are fixed at startup, so changing them means a restart. Shutdown drains the buffer, so a restart doesn't lose data.
• Traces only for now. Rule conditions evaluate span context.

Getting started

The processor is available today in beta in the Honeycomb OpenTelemetry Collector distribution, ahead of the upstream stability milestones. The announcement post has step-by-step setup for Kubernetes, Docker, and standalone configs. Upstream, the component lives in the collector-contrib repository and is working toward alpha stability, at which point it becomes part of the collector-contrib distribution.

The README has the full configuration reference, examples for common deployment patterns, and the telemetry contract for building dashboards on the processor's own metrics. Feedback and issues are very welcome on #49311.

If you've been running fixed-rate sampling and living with blind spots, or wanting tail sampling but needing accurate counts afterwards, this is built for you. I'd love to hear how it behaves on your traffic.

Why Refinery is still the leading sampling tool

The processor brings Honeycomb's sampling algorithms to the OpenTelemetry Collector, but Refinery remains the leading sampling tool. The reasons are operational rather than algorithmic. Sampling at scale is a system you operate, not just a component you configure. Refinery has years of production hardening behind it.

• Scale without a routing tier. Refinery clusters share trace ownership across peers, so you add nodes to add capacity. The collector processor needs a two-tier topology and each instance learns rates from only its own slice of traffic.
• Live rule changes. Refinery reloads sampling rules without a restart, which matters most mid-incident when you need to tighten or loosen sampling right now.
• Overload protection. Stress relief detects saturation and switches to deterministic sampling until the cluster recovers, so it degrades predictably instead of falling over.
• Operational depth. Environment-aware multi-tenancy, rich runtime telemetry and a managed option with Refinery as a Service.

If you want adaptive sampling native to an OpenTelemetry Collector pipeline, start with the processor. If you're already running Refinery, the README includes a direct mapping from Refinery's sampler types to the processor's. When sampling becomes critical infrastructure with its own scaling, tenancy and incident-response demands, Refinery is built for exactly that.

UX-Context Design: Using UX Knowledge to Inform AI-Generated Design

As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI.

Context Is the New UX Deliverable

AI models produce output based on context. Context is everything the model can see when it does the work: your request, plus whatever instructions, standards, examples, and background information come along with it.

Context enables you to avoid middle-of-the-road output. For example, an AI model has been trained on a huge number of search screens, so when you ask for one, it produces an average search screen. It knows what software generally looks like. It doesn't know your users, your domain, your design standards, nor anything your team has learned from research. Unless that knowledge is in the context, the model designs without it.

Context leans a model’s output in a particular direction.

Think of a skilled builder designing your house without ever meeting your family. They design the average house. Two stories, because most houses have two stories. And if you use a wheelchair, or you have a baby who needs to sleep near you, or you have no kids and you and your partner both work from home, the design will be wrong in ways that impact day-to-day quality of life. That’s not the builder’s fault; the problem is the builder didn’t have context.

Similarly, the difference between generic AI output and AI output that fits your users and matches your organization’s standards is mostly a difference in context. So, what should we put in it?

Everyone Is Designing

Before answering, it helps to look at who is prompting these tools.

In many organizations, designers are no longer the only people producing designs. A product manager asks an AI tool for a quick mockup to make an idea concrete before a meeting. An engineer asks a coding assistant to add an export feature, and the assistant decides the button placement, the wording, the error states. These are all design decisions made by the AI.

The instinct might be to gatekeep who does design work, but that’s counterproductive. These tools are too available, too fast, and too useful for turning ideas into concrete forms. The more practical goal is to ensure that everything AI generates, no matter who prompts it, is informed by an organization's knowledge of its users and design standards.

That goal changes the output of research and design work. Historically, UX work produced deliverables for humans: personas, journey maps, research reports, annotated wireframes. A human read them, interpreted them, and made decisions. If AI is doing more of the building, then AI becomes the consumer of research and design deliverables.

And what an AI consumes is context.

Thus, the output of research and design work is, primarily, context that anyone in the organization can include when using AI to generate anything from slides to prototypes to working software. We call the creation of this output UX-context design.

UX-context design is the practice of discovering and curating what an organization knows and wants into the context that guides everything its AI tools generate: from who its users are and the world they live in, to how a product should look and behave.

UX-context design also involves testing the efficacy of that context with the models your team is using and refining the context to improve the output quality for everyone.

AI-Ready Deliverables

A first attempt at UX context might include what we already have: the personas, the journey maps, the findings reports. Some of those will help. But these artifacts were designed for human attention. A persona has a stock photo and a first name with the intention of helping a human to empathize and remember. However, a model does not need persuading, it needs the underlying reasoning.

That reasoning can be distilled with AI help. It can be useful, for example, to simply give raw research transcripts to a model and let it extract insights. Without human guidance, however, it’s possible that important takeaways may be lost in all the noise, or the AI may over-focus on the wrong takeaway.

To serve as maximally effective context, research and design output needs to be made “AI-ready” (or “machine-readable”), be curated by a skilled human, and made readily available to anyone that designs, no matter their role.

DESIGN.md

Let's look at a concrete example. In April 2026, Google Labs open-sourced DESIGN.md, a file format for describing a product's visual identity to AI coding tools. The format grew out of Stitch, Google's AI design tool, and is now a draft specification that anybody can use.

A DESIGN.md file lives alongside a product's code and holds two kinds of content. One part is “machine-readable” exact values: the colors, type sizes, spacing, and corner radii of the design system, written so tools can read them precisely. The other part is plain, human-readable prose explaining what those values are for and how to apply them, including do's and don'ts, providing guidelines for both humans and AI. Google's announcement says that, instead of guessing intent, AI tools can "know exactly what a color is for," and can check their color choices against accessibility contrast standards.

This serves as a good example of a successful format for UX context, and provides some lessons. It's a text file, kept next to the code, that AI tools read every time they generate something. Unlike traditional deliverables, there is no handoff: UX context feeds directly into product creation.

But visual identity is only one part of user experience and product design. What about everything else from research and design that informs the building of a product?

A Hypothesis: UX.md

Let's imagine another text file, with a broader intent than DESIGN.md. We'll call it UX.md. It would reference DESIGN.md for visual standards and point to where the product's actual components live in the code. However, it could also include things like:

• Research synthesis. The major findings, stated as straightforward insights that the AI can reason upon. "Users abandon setup when asked for information they don't have on hand" is a finding that becomes a constraint on what is generated, not just an insight in a report.
• Interaction standards. How the product behaves. When to confirm versus allow undo. How errors are worded. Whether UI is optimized for expert users or novices.
• A glossary. The words the product and its domain experts use, with definitions. If your users say "case" and are confused by "ticket," the AI should know that.
• User models. User modeling states what research has established about the users themselves: their expertise, their concerns, what they are trying to accomplish, what doesn't work for them.
• World models. World modeling includes the conditions the user is in while using the software. (The term is borrowed from AI research, where a world model is a system's understanding of real physical environments.) For UX purposes, we model the circumstances of the world the users live in (i.e., “context of use”) and how they can change: users are interrupted mid-task (a nurse on a hospital floor), are under stress (filing a claim after a car accident), or operate under compliance rules (every action must leave an audit trail).

DESIGN.md shows how visual-design standards can be made available to AI tools. UX.md is a broader hypothetical version of the same idea: a structured source of UX research, interaction standards, terminology, and context-of-use knowledge that AI tools can use when generating product work.

Suppose your research synthesis says that users work in the product for hours at a time, making complex decisions with many variables. That finding should lean generation towards denser screens that keep more information in view, the kind expert users prefer. A finding that the product is used a few minutes a month should lean AI towards fewer choices per screen. An AI without that context will default to whatever is most common in its training data.

Of course, for large projects a single file might not be enough. Instead, UX.md could serve as an index to a folder of context files, each used for a specific purpose. Or you might instead make the context available via MCP servers and agent skills, or some other method.

Since context steers AI generation, this approach has an interesting side-effect: research insights and design standards find their way into unexpected places. For example, users’ glossary terms may show up in the behind-the-scenes code, rather than terms the engineers might have chosen on their own. Research and design output becomes embedded wherever AI is used.

Curation, Not Handoff

Taking a cue from what we learned from DESIGN.md, we see two important properties of an artifact like UX.md that differentiate it from traditional research and design artifacts.

First, it is not written for humans. People can read it, but its success is measured by whether AI output improves, not by whether stakeholders are convinced by it. This could become a measurable standard, a metric you can track over time.

Second, it is not separate from the product. It lives with the product's code, changes when the product changes, and is read by every AI tool in the organization each time something gets generated. The research and standards are in the room every time anyone generates anything, whether that person is a designer, a product manager, or an engineer.

This is not an artifact that is left behind over time, or handed off to other humans, but a continuously curated source of truth. UX.md (or its equivalent) is never finished. New research updates it, and so does watching what the AI gets wrong. It is continuous research and continuous design, accumulating in one place for the entire organization to benefit from.

Open Research

Our experiments suggest that curated UX context improves AI-generated UI, and many teams are already practicing UX-context design in some form. Still, important questions remain, and the answers will shift as models become more capable:

• Which, if any, traditional artifacts improve AI output the most?
• When is raw research data helpful? In what amounts?
• What are the concrete metrics to measure the efficacy of UX context?
• How do the answers change as models improve? A curation decision made for today's models may be wrong for next year's.
• Is there such a thing as too much context?
• How should context be scaled and maintained over time?

You don’t need to wait for the answers to all these questions to engage in UX-context design. Take a sample of insights your research has established about your users. Write them down in a plain text file (likely a markdown file), where your team's AI tools can see them. Work on making your design system machine-readable. Then watch what happens.

UX-context design may become the future of research and design work. If so, the impact of quality research and expert design will be broader than ever, shaping AI-generated work across the entire organization.

UX design for Augmented Reality (AR) has some different guidelines than what is used for screen-based UX. Designers must consider the space and the physical limits of what is comfortable for the human body. Also, as AR overlays physical space, you must be very aware of the cognitive load because you add to the existing distractions of the real world.

In this video, UX consultant Frank Spillers covers the main guidelines for AR design. These are invaluable tips that will make your designs much more user-friendly.

General Guidelines for Spatial UI

Let's summarize Frank's guidelines for spatial design in AR:

• Design for context intelligence: Have your program sense and respond to the environment. Use space to trigger new content automatically when the user gets near.
• Minimize abstract UIs: Affordances are the best tool to make UIs intuitive, especially 3D ones. Don't force users to interpret what things mean. If something looks interactable, make it interactable. Work with real physics and the environment. Use real physics and respect the boundaries of physical space; don't put an interactable object beyond a wall or window. Avoid "secret UIs" or hidden features that users need to use.
• Use real physics: Have objects behave and interact in a way that mimics their real-world counterparts. A rubber ball should bounce, and the door handle should open a door.
• Direct manipulation: Use natural interactions—for example, simple hand gestures like pressing a button, rotating, or grabbing an object. Use eye tracking or triggers based on the user's proximity. AR is not a suitable medium to flip through menus in, so make interactions as simple and intuitive as possible.
• Expect interruptions: AR is highly interruptible. Don't make experiences with uninterruptible content like long videos or animations, especially if they cannot be paused. Allow the user to drop in and out of the experience quickly. It’s very similar to how mobile apps work.
• Respect user spatial memory: Don’t overwhelm users with multiple interactions simultaneously. Instead, use contextual tutorials, which tell the user what they need to know in the moment.

When you design for mobile and desktop, you are limited to the area of the screen. For AR, the area you can work with is limited only by the physical limitations of our vision—more specifically, our field of view and view distance.

How to Design Around Field of View

Users will be more comfortable if they aren't constantly forced to swivel their heads. This is true in all Extended Reality (XR) platforms. Place important content in front of them. The ideal horizontal placement for content is within 30 degrees off-center on either side. More than 30 degrees from the center is strenuous on the neck and shouldn't be used often. Content beyond 50 degrees is physically impossible for most people.

These rules apply primarily to content that moves with the user's gaze, like a heads-up display. If you have a scene in front of the user, ensure the main content doesn't exceed these areas. Otherwise, they might miss something when they turn their bodies. For example, you don't want two scenes playing simultaneously on either side of the user. Additionally, if the user uses a phone instead of a headset, the holograms might be cut off, breaking immersion.

For the vertical field of view, the chance of neck strain is less of an issue but still present. You shouldn't have users look straight up or down for long periods, especially when they walk around. The ideal content placement for vertical rotation is the 40-degree area slightly above the center of vision or horizon line.

Place your AR experiences in the most comfortable viewing angles to make users more relaxed and less tired. Remember that other factors might adjust the viewing angles and ideal distance.

While you design, consider whether the user is sitting, reclining, standing, or walking. While walking, you generally want users to face the direction they are walking in. While seated, the user may be more comfortable but unable to turn their whole body unless they adjust their seat. If possible, have your content automatically adjust for these different scenarios, and adjust once the user is in motion.

Remember that every human body is different, so give the user tools to adjust their field of view if they have neck pain or other conditions. Above all, you want to ensure the user has everything they need to be comfortable with your product.

FOV Design Tips and Best Practices

• The user should always control the camera movement. Let them drive. Don't shake the camera, purposely lock rotation, or turn the user's camera for them.
• Be sensitive to issues around dizziness or vertigo. You shouldn't need users to spin constantly to see everything.
• Avoid abrupt movements and be mindful of people's reflexive reactions. For example, users will react to objects that fly toward their faces. If you need to bring content to or from the user, move it slowly and smoothly toward them for the most comfort.
• Use shorter animations than you would in a desktop or VR experience. Remember that AR is for short bursts of activity and design around distractions.
• Avoid timed challenges, like limited window rewards that are only available for a few seconds. This is primarily a safety issue, but it is also a good idea because these can be easy to miss if the user is looking elsewhere.
• Allow the user to see the world in the background. AR users don't expect to be fully immersed without warning. It may be unsettling or dangerous to obscure their whole environment with content.
• Lastly, you will want to place content at a comfortable distance away from the user, especially for prolonged interactions.

Ideally, you should place objects within five meters of the users and beyond one and a quarter meters. Users might overlook your content, want more personal space, or collide with the holograms if things are too close. Too far, and it might be hard for the user to see.

Consider situations where a user can move closer or farther from your content. While seated, the optimal distance is critical, as the user can't adjust the distance themselves. A walkable experience can be more flexible with distance, but ensure your content works best in the optimal view distance.

For example, audio or animations should trigger once the user reaches the right distance.

The Take Away

Because of how it interacts with physical space, AR needs to follow specific guidelines to make an experience that isn't unpleasant for viewers.

Object placement should fall within a central area in the user's field of vision, and objects shouldn't be too close or far away from the user.

Gemini 3.8 text-to-speech says hello

Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS are our most expressive audio generation models yet. Generate custom character voices and direct scene dialogue across Google AI Studio, Gemini API, Gemini Enterprise, Gemini Notebook, and Google Vids.

Today, we’re introducing two new text-to-speech models to the Gemini family, transforming voice generation from static presets into a dynamic creative studio. These models enable creators, developers, and enterprises to create richer, more expressive audio experiences, while enabling improved user experiences in products like Gemini Notebook and Google Vids.

• Gemini 3.8 Flash TTS: Built for deep creative direction and character design. Create entirely new voices from scratch using natural language prompts to bring characters to life across gaming, immersive audiobooks, podcasts, and interactive media. Direct every performance line by line with granular control over acting cues, pacing, dialect shifts, and backchanneling.
• Gemini 3.8 Flash-Lite TTS: Built for high-volume, cost-efficient scale. Optimized for high-volume dubbing, audio content creation, and expressive voice agents with fine-grained control over tone, pacing, and expressive nuance.

These models complement our fast-growing Gemini Audio family, following 3.5 Live Translate, 3.5 Transcribe, 3.8 Live, and 3.8 Live Extended Thinking.

Create and customize your own voices

Scale up from 30 original voices to an infinite library. Whether you need an entirely original character voice or a consistent brand ambassador, our 3.8 Flash TTS model powers a full vocal studio. This enables you to create and use expressive, natural-sounding voices for every moment, while empowering developers and enterprises to easily build custom audio experiences.

• Generative voice design: With Gemini 3.8 Flash TTS, create bespoke voices from scratch by customizing role, accent and voice characteristics across more than 100 languages and dialects using natural language prompting — whether you're bringing a dramatic, fire-breathing dragon to life or crafting a charismatic narrator with a distinct regional cadence.
• Expansive voice library: Access 2,000+ production-ready voices with broad language coverage — including regional varieties like Mexican Spanish, Quebec French, and Scots English.
• Voice replication: Recreate consistent vocal profiles from just a 30-second audio sample of your voice or a voice you have the rights to use, backed by built-in consent verification, SynthID watermarking, and C2PA credentials to protect both developers and their vocal talent.
• Save and scale: Save and manage the custom voices you designed to ensure consistent performance and minimal drift across ongoing projects.
• Voice remixing: Coming soon, pick a voice from our voice library and fine-tune timbre, pitch, pace, and accent. Use prompts to dial in characteristics (e.g. “add subtle Southern US accent” or “soften the delivery”).

Direct the performance, line by line

Once you've selected your voices, both TTS models give you precise control over how each line is delivered.

• Direct performance line by line: Write your own stage directions or let Gemini steer delivery with natural script cues — from a calm customer service agent to a whispered suspense scene.
• Long-form generation: Maintain high voice quality, natural pacing, and character timbre across hours of continuous audio with minimal speaker drift — ideal for podcasts and audiobooks.
• Native two-speaker scene staging: Direct multi-turn conversations seamlessly from a single script —whether for a podcast or dramatic storytelling—while keeping both voices distinctly separated with natural conversational turn-taking.
• Scripted vocal bursts & backchanneling: Add realistic conversational texture using non verbal cues (like <laughs>, <sigh>, <gasp> and active-listening interjections (like |mhm| or|yeah|) for precise comedic timing and reaction beats.

Get expressive high-quality speech generation built for global scale

Gemini 3.8 Flash TTS delivers leading voice customization capabilities, securing the #1 overall spot on Hume AI’s Voice Design Benchmark (71.4) and also leading in accent modeling (60.8).

Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS enable truly expressive performances without sacrificing reliability, also securing the #1 and #2 spots respectively on Hume AI’s Overall Quality Index. The model shows major improvements on a wide range of use cases such as long-form content and dual-speaker screenplay control compared to Gemini 3.1 Flash TTS.

In blind human preference evaluations on Voice Arena, Gemini 3.8 Flash and Flash-Lite TTS secure top positions amongst competitors in key global languages, including Japanese, Brazilian Portuguese, Vietnamese, Modern Standard Arabic (MSA), Mexican Spanish and Hindi. With support for over 100 languages, these models empower creators, developers, and enterprises to build high-quality, multilingual voice experiences worldwide.

Build with trust, consent, and transparency

We built our voice creation and replication capabilities with strict safeguards to help protect voice talent, respect identity, and ensure content transparency. For voice replication our system leverages consent verification: users must provide a verbal consent recording from the voice owner that matches the reference speaker before a voice can be created.

More broadly, every audio clip generated by our Gemini Audio models is watermarked with SynthID. This imperceptible watermark is woven directly into the audio output, ensuring AI-generated speech remains detectable to help prevent misinformation. For more details on our approach to safety and responsibility, review the model card.

Try our new Google AI Studio audio playground

Starting today, developers can experience these new speech generation capabilities in Google AI Studio. Built like a voice design workspace, you can prompt entirely new vocal identities from scratch or replicate your own voice, then bring them directly into a dual-speaker screenplay editor to direct line-by-line delivery.

Deploy high-performance voice interfaces with ease

By using the Gemini API, developer platforms such as Agora, LiveKit, Pipecat, Vercel enable developers to build and deploy high-performance speech generation experiences with ease.

We’re partnering with companies like Figma, HeyGen, Linguana, Wondercraft, 99.co, and Ollang, who are integrating our latest TTS models to help accelerate global dubbing, localize media with nuanced regional accents, and power conversational voice agents at scale.

Start using our latest Gemini Audio models:

Gemini 3.8 Flash TTS is rolling out starting today:

• For developers: In the Gemini API and Google AI Studio
• For enterprises: Coming soon via API in Gemini Enterprise
• For everyone: In Gemini Notebook.

Gemini 3.8 Flash-Lite TTS is rolling out starting today:

• For developers: In the Gemini API and Google AI Studio
• For enterprises: Coming soon via API in Gemini Enterprise
• For everyone: In Google Vids

Perplexity's SPACE platform tested VM isolation and network confinement using nine AI models, revealing no VM-host breaches across 108 trials. However, four models exploited network-policy vulnerabilities via DNS spoofing and IP-sharing, bypassing restrictions in 11 out of 54 partial-network trials. Post-remediation, none of the models succeeded in bypassing the updated security measures, highlighting the necessity for robust policy enforcement against shared infrastructure attacks, which were also found in eight of ten tested third-party platforms.

Towards Universal Post-Training for Robotics

People have been imagining robots working alongside humans since the dawn of storytelling. In 2026, that vision feels closer than ever. Physical Intelligence, Generalist, DeepMind, and other leading players have all shown pretrained models capable of genuinely complex tasks, to the point where the development of frontier robotics models looks a lot like that of language models in the GPT-2 days.

But complex behavior does not equal reliability. A robot that loads dishes correctly 95% of the time will break something every week in a home full of glass, pets, and kids. A pretrained generalist policy cannot be deployed autonomously to do household chores, or put to work in a factory, until its reliability sits much further out along the nines.

We have seen this play out before

In the early days of language modeling we had exactly this problem. A big pretrained model like GPT-2 or GPT-3 was fluent, knowledgeable, and completely unreliable. If you ask GPT-2 “How do I roast a whole chicken?”, you might get something like:

I roast a whole chicken in a pot. I roast a whole chicken in a pot with a lid on it. I roast a whole chicken in a pot with a lid on it.

If you're not sure what a pot is, ask your butcher. Sometimes, when I roast meat, I roast it in a pot, which is more like a refrigerator or freezer, and I roast it in a pot that is a small freezer, or a big freezer, or larger freezer.

Why do I have to be afraid of chickens?

Because they're so adorable.

— GPT-2

Sometimes it answers the question. Sometimes it continues your question with three more questions. Sometimes it drifts into a Reddit thread it had hallucinated. While it could make for an interesting conversation, it's not something that can be deployed.

Luckily, we know exactly how this problem was solved: post-training, specifically, supervised instruction tuning, RL from human feedback, and RL with verifiable rewards, which turned “impressive demos” into “things you can actually use.”

The more important part is that the field converged on a shared recipe for post-training language models:

1. Start from a strong pretrained model.
2. Define the environments and reward, i.e. verifiable or learned preference models.
3. Run RL optimization with a specific family of algorithms, anchored to the reference model.
4. Watch for and address known pathologies such as reward hacking.

With this concrete recipe, anyone who wants to fine-tune the latest LLM on a downstream task has a concrete path to follow, with documented failure modes and sane defaults at every step. That is what makes LLM post-training tractable at scale.

Robotics is sitting almost exactly where language modeling was: The pretraining has scaled beautifully. We already have very good pretrained policies — vision-language-action models (VLAs) and world-action models (WAMs) trained on enormous piles of data — and they execute complex behaviors. What is missing is the other half: the model learning from its own experience, and for that, we need a recipe for post-training. And for robotics, it needs to be even more reliable than language models. A bad script of code generated by an LLM gets caught by a human reviewer before it goes into production. A bad robot action doesn't wait for human review and just happens.

So why hasn't this happened yet?

A lot of recent advancement in robotics has been driven by imitation learning, which inherited nearly for free everything that made supervised learning so clean and easy to use following development of techniques such as action chunking and good teleoperation interfaces. The loss doesn’t explode for seemingly mysterious reasons; you know the few hyperparameters that are important for performance; and loss curves correlate with performance.

RL shows incredible promise in training frontier robotics models, but it is not yet a recipe. It's a craft.

Getting it to work takes a lot of intuition, just like how a good chef seasons by feel. What that means in practice is that getting RL post-training to work reliably is limited to a small number of people. This was once true for LLMs, until the field converged on a shared recipe.

For robotics to be deployable at scale, we think it needs what language modeling has: a universal post-training recipe.

The remainder of this post is about closing that gap, what RL for frontier robotics models actually looks like today, why it is different from RL for language models, and what it will take to turn the craft into a recipe.

Two things a recipe needs

We think closing the gap from craft to recipe takes two things.

The first is an algorithm built specifically for fine-tuning frontier robotics models, one that stays stable when applied to models with billions of parameters, and that learns from a small enough amount of experience to be practical on real hardware, where every attempt costs valuable time on a real robot.

The second is easier to overlook, but just as important: a set of standard practices surrounding the algorithm. A default way to define what counts as success. A default way to reset the scene between attempts, so the robot can try again. A default way for a person to give feedback, and to turn that feedback into learning. The algorithm plus the protocol around it defines a set of concrete defaults for bringing frontier robotics post-training to scale.

Why is robotics a different RL problem than LLMs?

But haven’t we already figured out large-scale RL? RL post-training has driven most of the recent gains in language models, at real scale and in production. Why would RL for robotics be any different?

To answer that, it helps to step back and look at where deep RL first found its footing. One of the first deep reinforcement learning systems to gain broad traction was AlphaGo, which beat Lee Sedol at Go in 2016. AlphaGo learned a policy to predict the optimal action in a given position from a massive number of repeated self-play games. This is quite similar to RL for language models, which uses a massive number of parallel text generations to see which responses get a good reward. In both cases, actions are cheap to generate and cheap to verify, which is an essential ingredient for the type of reinforcement learning that already works at scale.

The type of RL that has already been demonstrated at scale is on-policy policy gradient RL, which learns by hill-climbing directly on reward, and requires freshly sampled data for every update. That's fine for Go and for LLMs. Robotics is a different story because data is much more expensive to generate. A robot learning to fold laundry in the real world cannot try a thousand different actions at each low-level control step, especially when a single task chains together thousands of steps or more, and while people have explored simulations, modeling real world objects accurately in simulation can often be even harder than learning the task itself. As a result, there is a significant focus on the ability to leverage past data, for example data from many update steps ago of the policy being learned.

On top of that, LLM RL and robotics RL are solving different-shaped problems. RL for language models has typically been framed as generating one response to one prompt, with a reward assigned to that response. Robotics involves generating hundreds to thousands of low-level actions or more for a single task. The reward the policy needs to learn from usually arrives only at the very end indicating task success. On top of that, environments for tasks like language or Go are deterministic, and if you generate an action, that action gets played. In robotics, the environment itself can be stochastic, which means even sending the exact same command to the robot twice can produce slightly different behavior. This means the agent has to assign credit to actions taken thousands of steps earlier, across variations in the environment that compound over the horizon.

The combination of expensive samples and long-horizon structure pushes the field toward value-based RL: methods that learn scoring models (value functions) to assign credit across long horizons. And value-based RL at meaningful scale has been validated far less than the large-scale RL post-training regimes of LLMs.

Why can we not use existing value-based RL approaches?

Around the same era that PPO, the predominant method for LLM post-training, was developed, a parallel line of value-based methods, for example DDPG, TD3, and SAC, emerged for robotics control, and these have been shown to work on real-world tasks. But applying them directly to frontier robotics models runs into two problems.

The first is how these models represent their decisions. The older algorithms assume the policy draws actions from a Normal distribution: one average action plus some noise around it. That's fine when the right behavior really is one thing with a bit of noise. But modern robotics models are built for situations where there are many types of correct action possibilities. For example, imagine the task of grasping a cup, one can grasp it by the handle or by the base, and both would be correct. To account for this, frontier robotics models use richer machinery like diffusion for action generation (the same method behind image generation that try to generate samples from noise) instead of a simple bell curve. The older RL methods simply weren't built to work for these models.

The second is stability. Deep RL has a well-known tendency for instability, and this is especially the case as models get bigger. Value-based RL methods make this worse in a specific way: instead of learning purely from real outcomes, they partly learn by predicting their own future predictions, and reusing those predictions as if they were ground truth. Small errors in that process compound over time. This stability gets worse when you scale to larger models which correlates with more competent policies, such as billion-parameter VLA policies.

Several lines of work have tried to use value-based learning with frontier robotics models such as VLAs. One pushes the learning signal back through the model's entire generation process, but tends to be unstable as the signal has to travel through a long chain of denoising steps. The second leaves the model alone entirely, for example generating a handful of candidate actions and executing whichever one the value function scores highest. While safe, they leave performance on the table, since the model itself never actually learns which actions are good and which actions are bad.

A third option is to learn noise that gets fed into the model that determines downstream action generation. Instead of touching the model's weights, you use RL to search for better noise to feed it. This is appealing because it's low-risk. No matter the noise selected, the result is still an action the model already knows how to produce, so it is harder to get something unreasonable. It also works reasonably well in practice, because searching over noise is a much easier problem than searching over actions directly. But that comes at a cost. You can only ever recombine behaviors the model already has some version of. If the skill a robot needs isn't already somewhere in what the base model learned during pretraining, no amount of noise-searching will produce the action, and both how good the robot ultimately gets, and how quickly it improves, are capped by whatever the original model already knew.

To go beyond what the model already knows, the learning process needs to update its weights directly in a stable way, so it can use the model's full capacity to represent complex, varied behavior.

What might an algorithm for robotics RL look like?

For an algorithm to post-train frontier robotics models well, the key is to have a stable way to improve large policies that rely on modern generative techniques, such as diffusion, using estimates of how good an action is. EXPO(-FT) is a system we built based on what we imagined that algorithm might look like, and we tested it on real robots, doing real tasks: routing a string of holiday lights through hooks and plugging it in to light it up, striking a pool ball into a pocket, inserting a flower into the neck of a wine bottle, flipping an egg. Going in, frontier pretrained models could not reliably complete these tasks.

EXPO(-FT) works by learning to repeatedly improve actions from the frontier model using reinforcement learning with small edits from a lightweight policy, and then absorbing that into the frontier model itself. To get the best possible action during execution, the robot generates a handful of candidate actions from the frontier model, produces a higher-value edited version of each, and uses the value function to estimate how well each candidate will turn out and pick the best one. Because the edits are deliberately kept small, a nudge can't send the robot to execute something dangerous and all the volatility of RL stays confined to the small model. And because the pretrained model keeps training on the actions that were selected and worked, improvements discovered by the edits get absorbed into the base model itself. This means the model will make better proposals next time, letting the large pretrained model bring its full capacity to bear on learning new behavior.

Throughout online learning, a human supervises the robot and can intervene whenever it starts to go wrong. Those interventions are fed back into training to improve the policy further.

Across six complex manipulation tasks, such as routing string lights, sinking a pool shot, and inserting a flower into a bottle, EXPO-FT reaches 30/30 success using an average of only 19 minutes of online interaction.

Practically, this means a frontier model can be deployed on tasks like these and then post-trained to high reliability in a matter of tens of minutes.

What does this not solve? EXPO-FT is our attempt at the first of the two things we said a recipe needs, and it can inform what the rest of the work needed might look like.

First, a human is in the loop to a large extent. Someone defines success, resets the scene, and intervenes when the robot goes wrong. That doesn't mean it cannot scale. Something like Waymo, for example, can run using remote operators with one person who can oversee many vehicles at once. The robot can perform learning autonomously, but going from one robot and one operator to a large fleet depends on how much human attention each additional robot demands. This is part of the second half of the recipe, the protocol, and there are a lot of questions that remain.

Second, longer horizons strain the value function. EXPO-FT is safe because it never moves far from the base model in any single step, but that safety property is only as good as the value estimates behind it. Longer horizons make credit assignments harder and push training times up. Assigning credit across thousands of steps or more with reward arriving only at the end is the goal, and we tested the shorter-horizoned end of it.

Finally, the computational cost can be high. Nineteen minutes of robot interaction is not nineteen minutes of post-training. Gradient updates on a model this large dominate wall-clock time, and closing that gap matters for post-training to become a routine.

We think EXPO-FT shows that stable RL post-training on a frontier robotics policy is achievable. Turning that into something a team can pick up and expect to work on their own robot and their own task requires standardized training protocols, as in the case for LLMs.

Standard training protocols

Even with the best algorithm, post-training only becomes a complete recipe with a standard set of protocols around it: how a task counts as successful, how resets are done, how humans provide input, how to tune hyperparameters, and how to initialize the task and policy.

Reward specification. Reinforcement learning works by optimizing rewards, and different rewards produce vastly different outcomes even for the same task. In LLM RL, reinforcement learning from verifiable rewards (RLVR) gave the field a default answer: check the answer, check the tests. Robotics has no equivalent. Today, success detectors are either hand-built per task or replaced by a human watching each trajectory and calling it, neither of which scales. Learned success classifiers and reward models are all plausible directions to explore.

Resets. Between episodes, the robot needs to be reset, and today that is usually a person's job. Open questions remain both on what states to reset to and on how to get there without a human, for example via a learned reset policy or reversible task design. Perhaps for real deployment, a reset is not even needed and instead moving onto the next task without undoing the previous is the better alternative. Determining which of those choices are the best matters for a universal recipe.

Human in the loop. Human interventions have proven to be a useful tool for improving data efficiency. How much to provide, when to provide it, and how that signal gets used during training are open questions with direct implications for how any of this scales past one robot and one operator.

Hyperparameter tuning. Value-based RL is known to be sensitive to hyperparameters. Learning rate, the update-to-data ratio, when to stop training, the task horizon, and the control frequency at which the policy acts all shape how stable and sample-efficient fine-tuning turns out to be, and none of them has a well-understood default in this setting the way they do for supervised learning.

Initialization. How the task and policy are initialized can play a large part in how performance turns out. How much data to initialize from, what that initial dataset should contain, and how heavily to weigh it against newly collected online experience are all still open questions that already have answers in language modeling, but not yet in robotics.

None of these has a default answer yet, and the right answer may also depend on the algorithm. We’ve created a set of reference tuning tips as a step towards this direction for EXPO-FT, but that does not completely address these problems. We think they are among the most consequential open problems in the field. They are also the kind of problem that gets solved through community effort, by converging on shared answers.

Towards universal post-training

Language model post-training became scalable because the field settled on defaults concrete enough to follow and be expected to work. Robotics is arriving at the same moment: the models are large, general, and pretrained, and people are trying to make them deployable; and robotics deployment needs to be even more reliable than language models, because robots acting autonomously in the world can't be reviewed the way we review writing from LLMs.

This is exactly when a standardized recipe matters most. Pretraining gave robotics models that know how to do almost anything at once. Post-training is how they learn to do things reliably every time. We believe it is what will bring frontier robotics to where LLMs are today and beyond, and converging on a set of industry defaults, a universal post-training recipe, is the most important part of bringing us to that point.

Acknowledgements

Thanks to Anikait Singh, Aneesh Muppidi, Dion Dong, and Jules Qiu for helpful discussions and feedback on this post.

Introducing Ember-1

Table of Contents

• Ember-1: half the tokens, same answers
• How Fireworks Research built Ember-1
• The problem: thinking models think too much
• From an observation to a premium model
• The Specialized Intelligence Index: Ember-1 sets a Pareto frontier for Bedside Bench
• Evaluating Pareto across more industry benchmarks
• Customer validation: Live A/B tests
• Internal validation: Our own developers didn't notice
• What's next

Ember-1: half the tokens, same answers

Ember-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens. Built on Kimi K3, it learned to cut unnecessary reasoning while keeping the thinking that matters. We tested it on external benchmarks, in live customer A/B tests, and on our own coding and agent workloads, and quality held up in every setting. Available today, Ember-1 kicks off an ongoing series of specialized models by Fireworks, shaped by what developers want next. Ember is just the start of what you could build with the Fireworks Training platform.

How Fireworks Research built Ember-1

We heard from users that they needed K3’s coding capabilities at a lower cost, because its long reasoning traces made automated coding expensive at scale. Turning down K3's reasoning effort didn't solve this. Lower effort settings gave up too much quality. To keep the quality and cut the tokens, the model had to learn to reason more efficiently, and that meant training it.

Getting there took serious research. Our team ran more than 50 training experiments and over 200 evaluations, and developed new training algorithms along the way to shorten reasoning without losing accuracy. We did it all on Fireworks Serverless Training. Because we didn’t have to provision or manage GPUs, we could launch experiments as soon as we had an idea, pay only for what we ran, and move from research to launch in a fraction of the usual time and cost.

We trained across a broad set of tasks so the token savings would carry over to many workloads. We then evaluated Ember-1 on the Specialized Intelligence Index, public benchmarks, and live production traffic to confirm it used fewer tokens with no drop in quality. Ember-1 is Fireworks’ own model and the first in a series of models from Fireworks Research.

The problem: thinking models think too much

Reasoning models like Kimi K3 spend the majority of their generated tokens, sometimes more than 90%, on internal reasoning rather than the answer itself. This thinking structure is expensive on a single request, but it gets much worse in multi-turn agentic workloads. Every turn replays all prior reasoning back to the model, so context grows roughly quadratically with the number of turns. Long reasoning traces from early turns get re-read (and re-billed) on every subsequent call.

Is all that reasoning actually necessary? Our experiments said no. The reasoning Kimi K3 emits is far longer than the task requires, and the excess can be removed without touching the answer. This was how we created Ember-1, an economical version of Kimi K3 built from specialized intelligence.

From an observation to a premium model

Not all of K3's reasoning is wasted. Some of it is self-reflection: revisiting an assumption, responding to feedback, or tracing an outcome back to an earlier decision can help the model recover from mistakes. The opportunity is to preserve this ability while reducing unnecessary reasoning and escaping unproductive loops. We believe that learning from tasks and environment feedback can teach the model to reason more efficiently while maintaining its capabilities.

For agentic tasks, this learning extends across the interaction. The model explores possible actions, incorporates new observations, and refines its reasoning as it progresses. Feedback connects decisions to their consequences, encouraging useful reflection throughout the task.

We carried these insights into a training collection spanning mathematics, coding, instruction following, conversation, search, tool use, and software engineering, covering both standalone problems and extended interactions to enforce adaptation to observations and outcomes. Task feedback guides on-policy planning and learning, with an emphasis on preserving capability across this range of settings.

Results on public benchmarks and live A/B tests support this direction: across seven benchmarks and two customers’ production traffic, Kimi K3’s reasoning could be shortened by 35–50% without sacrificing accuracy. The internalized behavior also shows restrained token use on unsuccessful attempts, reducing prolonged, unproductive reasoning.

The Specialized Intelligence Index: Ember-1 sets a Pareto frontier for Bedside Bench

Earlier this week, we introduced the Specialized Intelligence Index (SII) to benchmark open, closed, and specialized models against real-world tasks created by industry experts.

We evaluated Ember-1 on Doximity’s Bedside Bench, a physician-validated benchmark spanning 500 clinical cases across 10 specialized categories.

The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.

Evaluating Pareto across more industry benchmarks

We also evaluated Ember-1 on the quality-vs-cost frontier across some other industry benchmarks. We computed per-benchmark cost using the public Kimi K3 API pricing (uncached input $3/M tokens, cached input $0.30/M, output $15/M) and plotted it against pass rate for three arms: K3 at reasoning effort low, K3 at reasoning effort high, K3 at reasoning effort max (default), and Ember-1. Across every benchmark with more than 50 test samples, Ember-1 sits on or near the Pareto frontier, matching K3-max quality at a fraction of the cost, and strictly dominating K3-low. We also analyzed GPT-6 Astra, Claude Opus-5 and GLM 5.3, and found that Ember-1 was a leader on the Pareto frontier.

We took a double-click on the results directly comparing Ember-1 to the original K3, and found the following results:

The most cost optimized way to run K3 is no longer to make it think less, but to run Ember-1, the model that learned to think efficiently.

Customer validation: Live A/B tests

Benchmarks only tell you so much. Like what we found in the Specialized Intelligence Index results, we wanted to test the model on more real workloads, and to test the model using production traffic. The real test is often whether the model holds up on production traffic, in products users depend on.

We ran live A/B tests with two customers on their production coding workloads. In both cases, Ember-1 delivered impressive token savings, approximately 35% fewer tokens per task at comparable quality. Most of the downstream product metrics held or improved, including task completion, success scores, and failure rates all moving in the right direction at substantially lower token cost. Following the A/B tests, one customer is now running Ember-1 in live production, with plans to scale it up to replace the base model entirely.

Internal validation: Our own developers didn't notice

A large part of Fireworks’ internal coding/cowork traffic is powered by our own inference service. Before any customer saw the model, we put Ember-1 to work internally and let our own developers use it for everyday coding work including things like vibe testing at scale on real tasks.

The outcome we're proudest of: no news. No news is good news. Developers carried on their coding workloads without noticing the switch, while consuming substantially fewer tokens. For a model whose entire value proposition is "same answers, fewer tokens," an invisible rollout on internal traffic is the strongest possible signal.

What's next

Ember-1 is rolling out as a serving option alongside the base Kimi K3 model as a Research Preview release on Serverless. To support the rapidly growing open-source ecosystem, we're introducing research releases to give developers two-week serverless access to new research models, making them permanent based on community demand. For agentic coding and other workloads where reasoning tokens account for most of the cost, it delivers the same quality at roughly half the token cost.

Fireworks Research will continue to push the frontier of model efficiency by bringing specialized intelligence to more Ember models to enable you to deploy the most economical models, and reduce your token spend. Token efficiency is becoming a theme of Fireworks.

Looking to take Ember-1 one step further, and optimize it for your use case? We are also launching training support for Ember-1, enabling enterprises to build customized, token-efficient models tailored to their needs with their own data. The future of open models is specialized models trained on your specific workload.

Trying Ember-1 out on your workloads? We'd love to hear about your experience, so tag us on X (@FireworksAI_HQ) and let us know what you're building!

We're releasing tev1-4B-experimental, a Jev-like classifier finetuned on top of Qwen3.5 4B.

We're making it available on Together serverless at $0.042/M input & $0/M output.

Also releasing the data recipe & a tutorial on how to finetune your own (Tev1 cost $17 to train!).

https://twitter.com/nutlope/status/2102881280115249597

Note that this is an experimental model meant to show how you can train your own specialized decision models fairly easily!

You can also check out the model weights here, along with the full tutorial on finetuning your own classifiers above: huggingface.co/togethercomputer/Tev1-4B-experimental

Evolved harnesses overfit the benchmark they are scored on. RRSI transfers.

RRSI improves every out-of-distribution benchmark without overfitting the split it evolves on. Prior methods do the opposite: large evolve-set gains that shrink or vanish once the benchmark changes, two of them ending below the harness they started from.

Regularize the search, not the harness

Every harness component stays editable. RRSI constrains the loop that edits it: how much one proposal may change, and which measured gains are allowed to stick.

Proposal side

Annealed edit budget: Early rounds may bundle a few coordinated edits to find a mechanism; late rounds get one attributable change.

Evidence-aware credit: Every candidate is logged with its hypothesis, diff, score and cost change, so the proposer builds on what worked and stops re-testing what failed.

Structured exploration: When progress stalls inside the noise band, budget is redirected to components the run has never touched.

Selection side

Leakage critic: Task names, entities, answers or benchmark-specific logic are rejected before a candidate is ever scored.

Noise-adjusted floor: A gain must clear the variance measured on the unchanged base harness.

Cost rule: Extra inference tokens have to be paid for by measured gain.

Pruning: Components that stop earning their place are flagged for deletion.

Every held-out benchmark improves

Evolve on one suite per domain, then run the harness unchanged everywhere else. Same tools, judge, trials and window as H0; the policy is Claude Opus 4.8.

Two rules act on cost directly

The cost rule refuses growth that is not paid for when it is proposed; pruning removes growth that stopped paying for itself since. No prior method carries either.

Watch the harness evolve, round by round

Four real runs, every candidate: what it proposed, what the critic said, why the gate kept or dropped it, and the exact diff.

BibTeX

@article{xia2026rrsi,
  title={RRSI: Regularized Recursive Self-Improvement of Agent Harnesses},
  author={Xia, Peng and Han, Rujun and Wang, Zifeng and Chen, Yanfei and Zhuang, Yufan and Lee, Yoonho and Huang, Chengsong and Yu, Han and CuiZhu, Zhongying and Ming, Yifei and Yao, Huaxiu and Gokturk, Burak and Pfister, Tomas and Lee, Chen-Yu},
  journal={arXiv preprint arXiv:2609.24972},
  year={2026}
}

Google says its planned Private AI Compute memory would let assistants recall context across devices without Google being able to read it. Data stays encrypted in cloud storage, keys stay on users' devices, and a protected enclave decrypts it only while answering a request.

Introducing Comfy Router: One API for Frontier Media Models

Build once, add models as they launch, and choose where every job runs.

Today Comfy Router is live on the Comfy Developer Platform.

Integrate frontier image, video, 3D, and audio models once. Then choose the provider for each job, for better availability and better prices.

Day one access includes Seedance 2.5, MiniMax H3, Nano Banana Pro, GPT Image 2, Kling, and Black Forest Labs, with more coming.

These are the same models 3M+ ComfyUI users already use, now behind one API.

Router runs on the Developer Platform.

Router runs on the Comfy Developer Platform, giving you one place to access frontier models today. And soon, to deploy and scale your own ComfyUI workflows with Comfy API.

Router is how you call the frontier models you do not run yourself. Your API keys and run history live in one place and cover both, so the workflows you deploy and the models you call land on the same account.

Get an API key

Integrate once. Add models as you go.

Comfy has Day 0 access to many of the latest frontier models. Router puts that access behind one API, so you can start building with new releases without adding another SDK, key, or job lifecycle.

Build that once through Router, and the next model is one line.

from comfy_sdk import Comfy

client = Comfy(api_key="comfyui-...")

result = client.models.run(
    "openai/gpt-image-2",
    arguments={"prompt": "aerial view of a neon coral reef at dusk"},
    provider="fal",
)

Change the model string to call a different model. Change the provider to run it somewhere else. Everything around those two strings stays put.

Choose the provider for better availability or pricing.

The same model often runs on more than one provider, and on the day you need them, providers are not interchangeable. One is rate limited. One is cheaper this week. One is faster for your region.

The provider is a parameter. Where a model is supported on more than one of fal, Runware, WaveSpeed, and Higgsfield, you move between them with one line.

When you name a provider, Comfy does not switch the route. If that provider is unavailable, the request fails there. There is no silent substitution, and every job reports the provider that ran it.

Not every model runs on more than one provider yet. Where a model has one provider, you still get the single integration and the shared key.

Hit a concurrency limit? Queue the job.

The queue automatically retries jobs that hit 429 rate limits and other transient errors. Use submit to send a job and receive a request ID immediately; the queue keeps the job moving until it completes. If you want Comfy to wait for the result, subscribe submits the job and polls until it completes.

Queueing is available for all supported models, and the concurrency limits documentation covers the ceilings. Your job runs when a slot opens.

What you can build

Three jobs Comfy Router is built for.

A product feature that calls one model. You ship image generation on fal. Traffic spikes and fal rate limits you. If that model is supported elsewhere, you move the same call by changing one parameter. No new SDK, no new key, nothing else redeployed.

A pipeline that mixes your own workflow with someone else’s model. Your ComfyUI workflow runs on the Comfy API. A frontier video model runs on Router. Both calls come from the same code, on the same key, with both runs in the same history. The alternative is Comfy for your workflow and a separate contract, key, and dashboard for the model.

Batch work that outruns your limits. Queue a few hundred jobs with submit, hold the IDs, and retrieve results as they land. Nothing sits in a retry loop holding a connection open.

Why not call fal, Runware, WaveSpeed, or Higgsfield directly?

You can, and plenty of teams do. If you use one provider and one model, a direct integration is the right call and Router is overhead you do not need.

Router earns its place when you need more than one: the same model on more than one provider, one key and one job surface, and a route you pick. Each provider routes to itself. Comfy Router gives you one surface across them.

If you know OpenRouter, the shape is familiar. The difference is what generative media needs: large assets, model-specific controls, and jobs that run for minutes.

Pricing and data

Router uses the same Comfy credits that cover Partner Nodes, Comfy Cloud workflow runs, and other supported model calls. There is no subscription required.

Per-model pricing is listed in the model catalog, so you can see the cost for each model before you run it.

On retention: inputs are kept for 24 hours after upload and outputs for 24 hours after generation, then they are deleted.

What this means, and what’s next

ComfyUI gave people control over every step of how an image or a video gets made. Every node visible, every parameter adjustable. That’s why more than three million creatives use it.

Calling a model over someone else’s API has always meant giving that up. You send a request to a machine you do not control and take what comes back. Most routing layers widen that gap by picking the provider for you and calling it optimization.

Comfy Router closes it. You pick the model. You pick the provider. You can always see which one ran.

Comfy is building an open standard for visual AI. Standards work when the people using them can see how they run and swap what they do not like, so provider choice is the foundation here rather than a setting we added later.

More models and more providers are coming soon, including:

• Comfy workflows. Run a ComfyUI workflow from the same SDK, just like models
• Routing strategies. Choose a policy such as reliable, fast_start, fast_finish, or lowest_cost and let Comfy select the provider. This is optional. Choosing a provider yourself remains the default, and every job reports which provider ran it.
• Route by use case. Request an upscale, a background removal, or an animation, and Comfy picks a model or workflow that can do it

Start building on Comfy Router today

• Get an API key
• Browse models
• Read the docs

A new wave of Connected Apps is rolling out to Gemini.

We are bringing more of your favorite apps directly into Gemini. Instead of switching between tabs, you can now manage projects, design creative assets, and plan your workouts all in one place.

Beginning to roll out today, you can connect new tools to Gemini across a variety of categories:

• Productivity: Manage projects, organize databases, dictate notes and more with Airtable, Linear, monday.com, PandaDoc, Wispr AI and Zoho.
• Creativity: Bring ideas to life, design visual assets, and build websites with Adobe, Picsart, Squarespace and Webflow.
• Lifestyle: Search for your next apartment with apartments.com, monitor your credit with Experian, plan workouts with Peloton and find event tickets on SeatGeek.

To get started, connect your favorite apps in Gemini settings, or simply bring them into your chat by typing @ mention or asking directly.

China has launched nine satellites — including its first integrated “rocket and satellite” AI computing project — as part of its push to put AI computing in orbit. According to a Digitimes report, the Kinetica 1 Y18 rocket, operated by Chinese commercial launch provider CAS Space, lifted off on September 20, carrying the nine satellites to their planned orbits.

The payload of greatest interest is the “Supercomputing-1” satellite, developed by Chinese AI computing infrastructure company S-AIDC. Also known as the S-AIDC-1, the satellite is designed to capture and process Earth-observation data in orbit instead of sending data back to terrestrial data centers. Keeping the processing local is meant to cut cross-regional data processing times from hours to minutes. The satellite features both a high-res optical payload and the image-processing AI computer.

According to Digitimes, the launch highlights China's efforts to develop orbital computing as part of a “broader integrated computing network.” In early June, the Chinese government approved the Space Computing Industry Innovation Center, which aims to bring together rocket and satellite manufacturers, semiconductor fabs, and AI tech companies to build a space computing network. A couple of factors are driving this spike in the development of off-planet computing, foremost among them being the AI boom.

The demand for artificial intelligence has spurred the construction of numerous data centers to house AI accelerators. These data centers, some of which can house hundreds of thousands of accelerators, consume unprecedented amounts of electricity, placing significant strain on the grid. This, along with land use, water consumption for cooling, and noise, has fueled growing anti-data sentiment, with local residents blocking $68 billion worth of new data center projects in Q2 2026.

Despite numerous economic and practical challenges, space might offer an alternative with unlimited area, access to solar power, constant cold conditions, and a dead-quiet vacuum. These advantages have made orbital computing a serious topic of discussion. SpaceX is leading the charge with the AI1 satellite, a proposed orbital data center that will deliver 150 kW of peak compute power. The company aims to achieve 1 GW per year of space AI compute by 2027 and has begun constructing the 11-million-square-foot Gigasat facility that would manufacture the 6,000 satellites required to meet this goal.

Beyond the large-scale serving of AI models, putting edge compute capacity in space is already useful for a range of applications. Earth-observation satellites can process images onboard to identify wildfires, storms, floods, ships, or other objects of interest, sending only the useful results back to Earth instead of dumping enormous amounts of raw data to ground stations. The same approach can speed up weather monitoring, disaster response, communications, and even the management of satellite constellations, where spacecraft could make more decisions independently rather than waiting for instructions from Earth.

The launch of a single satellite with edge AI capabilities on board is a far cry from an entire orbital data center, but China's approach could prove to be a more practical and immediate application for advanced computing capabilities in orbit.

A Redistribution of Code Ownership

Imagine being an engineer who is never allowed to see or edit the code. You can only describe what you want to an agent and look at the resulting product. Every tweak, every fix, every “move that two pixels left” goes through an intermediary, and you wait to see whether it came out right. Oh and it can take hours, days, or even weeks to do the task.

That’s the situation product managers, designers, analysts, and everyone else who works alongside engineering has always been in. The intermediary was the engineer. It was necessary because of two axioms: writing code takes a long time, and only engineers can make changes to the codebase. These axioms are now going away, and that changes how cross-functional teams could work together.

How engineers work with each other now that coding agents are in the mix is its own topic. Here I want to focus on the people who work directly with engineering to get the thing built.

The old process

In the classic mode of product development (either agile or waterfall), product managers and designers think through a great deal ahead of time. They gather rough estimates, produce a thorough spec and a pixel-perfect design, get sign-off, and hand it to engineering. Engineering builds to the spec. As blockers and edge cases come up, the spec is updated. Once the product is ostensibly built, product, design, and QA use it for the first time and send back a list of fixes. Then it launches.

All that up-front articulation existed because the build was a larger investment and the people paying for it wanted to know what they were getting before they committed. It was also the main lever non-engineers had. If you couldn’t touch the code, the source-of-truth spec and iterative feedback was how you steered.

The new process

With agentic coding, the collaboration I’ve been trying looks more like this:

• Estimates are cheap. Agents do the bulk of the estimation work. Engineers still sign off and own these estimates.
• Specs and designs are rougher. Only the critical aspects of the project are laid out. Nothing needs to be pixel-perfect or specified to every detail. New products can be very vague. I had one spec where pages were just a name and no other details.
• The engineer builds the first draft. They follow the details that are in the spec closely, since those were the details important enough to write about, and defer to the agent on everything else. Their attention goes more toward making sure the thing is built well.
• Partners polish the product. Once the feature, data model, API, and rough frontend exist, the PM or designer runs the branch in their dev environment and vibe-codes their own tweaks with an agent. Mostly they adjust the front end and change minor decisions the agent made.
• Engineering reviews and merges. PRs from PMs and designers get polished by engineering (or an engineering-owned agent) as needed and merged into the codebase.

The payoff is that nobody has to hold the whole vision in their head and transfer it losslessly to someone else in an expensive game of telephone. Partners decide many of the details of what they want by playing around with an early build rather than by exhaustively imagining it. Engineers aren’t the bottleneck for changes to layout, copy, color, or even the composition of a page. And when a change is small, a PR is a more precise way to communicate it than a conversation at standup or DM. The partner directly gets it looking and behaving how they want, and the engineer ensures the product is reliable, performant, and maintainable. For both, time is saved through a better distrubution of responsibility.

How it has gone so far

I’ve worked this way with a couple of PMs, both additionally wearing the designer hat, and it's served us well. The changes they made were small, because I’d gotten the product most of the way there, and that’s the point: the small changes are exactly the ones where an engineer in the middle adds the least and the communication overhead can overwhelm the value. Their PRs told me precisely what they wanted in a way a Slack thread never quite does.

It was particularly exciting when a small front-end-only feature one of the PMs had added on their own was helpful in the middle of an incident. Nobody had scheduled or estimated it, and it was there when it mattered because a customer had asked for it and the PM had been able to just make it happen.

That's a couple of people on one team making small changes to product, so take it for what it is. But in my experience this mode of collaboration is promising.

Beyond PMs and designers

I think the same shift applies to a good number of other partners. Analysts adding product events, QA fixing the bugs they find, copy editors fixing text. These changes are small enough that the cost of getting an engineer's time and describing the change to them is greater than the cost of just making the change, checking it works, and having engineering review the result. The mode of communication becomes PR contributions.

What has to be true

This only works if a couple of things are in place.

The codebase has to be ready (enough) for it. A non-technical contributor prompting an agent inside your codebase is a stress test for everything I wrote about in my last post. If the stack isn’t opinionated, patterns aren’t adhered to consistently, or the agent has to apply band-aids to deliver results, then things get messy. The worse the codebase is, the more time is wasted for both parties. A clean, agentic stack constrains the agent regardless of who is driving it, and that is what makes non-engineer PRs quick to build, cost-effective to review, and safe to accept. It also means the dev environment has to be one-command to start, with every integration mocked so no sensitive keys are needed. Dev environments must not be able to touch production in any way.

The people involved need to be amenable to it. A setup like this can easily rub people the wrong way. Having to clean up code generated by colleagues is not the greatest developer experience, even if it saves time. It shifts the engineer's job toward the platform and the review, and it makes tooling that keeps review cheap matter more, not less. And taking on both the power and the responsibility of making direct changes to the product can be exciting for some partners and disconcerting for others. This won't work for everyone, and it won't work if everyone involved isn't committed. It's a big shift in who does what and that can be understandably off-putting.

If you want to try this, improve your odds of it working by focusing on these two things. Invest in the codebase and the shared agentic tooling so that informally generated changes come out reasonable by making the easy way the right way. Find the people who are curious. Fund and provision dev environments for them. Expect some trial and error. It’s a process to overhaul a process.

Ownership

What I’m proposing is that every function gets the kind of direct access to their piece of the product that engineers have historically owned out of necessity, not necessarily because it made sense. It feels a bit silly for me as an engineer to take a change request from a partner and then just copy-paste that request into an agent and then pass back the result. I add little if anything to the process, and I don't have as crisp a vision of what the person wants to see from this change. It's just a holdover from when things worked differently.

I'd rather go toward a model of ownership and collaboration that is distributed a little more evenly. It's a better working arrangement.

Basically impossible for me to ignore this headline after yesterday's "Soylent Googlebook is Android". As Katie Paul reports:

Meta has been testing a “human concierge” for its new personal AI assistant, Muse, which entails having human contractors quietly handle some of the phone calls placed via the digital agent, according to internal company posts seen by Reuters. The Facebook and Instagram owner told employees about the test last week, shortly after the company publicly launched a phone-calling feature for Muse. The Muse agent, which has topped US app download charts in the two weeks since its debut, is designed to autonomously perform tasks like sending emails, shopping and booking ‌travel on a person’s behalf. With the phone-calling feature, a Muse user can tell the agent to dial phone numbers for US businesses and talk with people on the other end to handle errands like booking haircuts, checking whether a store has an item in stock or getting quotes from different contractors.

On one hand, this could not be any less surprising. This type of outsourcing-AI-to-humans happened a lot in the previous iterations of AI. As the story goes on to note, Meta's long-forgotten first attempt at a virtual assistant from over a decade ago – so long ago that they were still called Facebook – 'M' was leveraging this hybrid system. The difference, I suppose, is that everyone actually believes in the AI to not only work, but to be able to do these tasks autonomously now. It's quite the dichotomy to read headlines about AI being on the verge of destroying humanity, while at the same time reading stories that it can't figure out how to make a phone call.

The bigger problem here for Meta, of course, is the trust issue. Muse is a great product. That doesn't mean it will ultimately work as the 'AI personal assistant' holy grail product – a space with just as many corpses as any other over decades of attempts – but I think it has the best shot of anything I've seen to date. And again, that's in part because the AI is real this time. Well, real enough! But, as my inbox can attest, a lot of people have trouble envisioning using such a product from Meta – because it's from Meta. And so when there are headlines that actually the AI, which they promise has your data and information sandboxed away and safe, may be leveraging humans – contractors at call centers, no less – that feels... not great. Sort of like a bait-and-switch.

And, well, Meta's own employees recognize this issue:

Some employees have raised privacy concerns about humans handling those calls, warning that the approach could result in sensitive information being shared unintentionally with contractors in call centers, the internal posts showed. “It’s baffling to me why we think this feature is worth the risk,” one employee wrote in ‌an internal post. “We are one bug away from unnecessary information being leaked to human callers.”

To be fair to Meta here, this feature was very much in beta testing mode and not live for all Muse users. But they were also clearly rushing to get it out there, undoubtedly to compete with other offerings from products like Instinct (which leads one to naturally wonder how they were pulling off the phone calling features...). Would they have disclosed the human-in-the-literal-loop if it launched? I mean, hopefully! Instead, they decided to roll-it-back for now, it seems.

The bigger picture remains that we're probably going to see more such hybrid approaches at the edges of AI as agents rise and try to crack real world tasks. The AI is better now, but there are still too many things in the real world that sort of require humans in some capacity – at least until the actual robots come.

The annual Made on YouTube event has just wrapped up, offering an early look at the features you can expect to come to Google’s dominant online streaming platform in the coming months. Yes, there’s plenty of AI for both viewers and creators, but there’s also a handful of non-AI updates mixed in there, too.

The design of YouTube’s homepage feed comes under fire often, but the platform is preparing to give you more options. With Custom Feeds, you’ll be able to add new tabs to the YouTube interface with content of your choice. To create a feed, just type a description and let YouTube populate the page. You can refine your description and rate video suggestions to tune the Custom Feed. Google says you’ll be able to save “multiple” feeds without specifying the exact number. This feature will roll out soon in the US for web, mobile, and TV users.

Interacting on YouTube will also change later this year. The platform will finally add the option to post GIFs in comments, which has apparently been a common request for some time. The comment box will let you search for popular animated thumbnails and post them on both regular videos and YouTube Shorts. The YouTube direct messaging feature is also adding support for group chat, but it’s only going to be available in the US, UK, Singapore, Brazil, and some European countries to start.

YouTube is probably going to nudge you to chat with a robot more often, too. The AI-powered Ask YouTube feature is not new, but it’s getting several new features that will make it more prominent across the platform. The AI will take on a conversational role in product review videos, allowing you to type or use voice commands to get more information about a product. In search, Ask YouTube may offer organized video recommendations and product comparison tables, too.

The music version of this AI is also expanding. Ask Music is getting more prominent placement in the YouTube Music app, right at the top where search used to be (it recently moved to a tab at the bottom). Ask Music can create or edit playlists and tell you about an artist. It will also surface suggestions throughout the app, like when you’re creating a mix.

YouTube also promises an AI-powered “Your Podcast Lineup” feature in the coming months. This is a weekly spoken preview of recommended podcasts available through YouTube Music. These AI features are limited to YouTube Music subscribers and will begin rolling out later this year.

Creator changes could mean changing content

According to Google, YouTube creators are very invested in AI tools. The company claims that a large majority of US video posters have used AI to create or edit content in the past year. YouTube will give them even more opportunities to use AI with a new Gemini-based conversational editing feature in the YouTube Create app. After cutting together different versions of a video with AI, expanded A/B testing will let creators run up to three different versions to find the one that gets the most engagement.

A new tool called Ask Studio can also help with recommendations for things like pacing, framing, and thumbnails. It will even generate those thumbnails using AI. This tool can run in the background to analyze older videos and suggest ways to boost them for more views.

The content you see on YouTube may also change thanks to some new creator features. YouTube previously rolled out the option for two different creators to share a single livestream. Early next year, YouTube will use live matchmaking to merge streams from people who might not even know each other.

These shared streams will connect to a new feature called Live Showdown, which is billed as an interactive audience experience. Streamers will compete with a live leaderboard to see who can get the most chats, gifts, and Super Chats. So whoever gets viewers to spend the most money “wins,” essentially. Someone in this equation is probably losing, but it’s not the streamers.

The last creator update could make more content accessible to you on YouTube. The company says 40 percent of live watch time comes from viewers outside the creator’s home country. YouTube will support this trend with live autodubbing. Starting early next year, video makers will be able to enable real-time translation of their streams so viewers in other countries will be able to watch live in their preferred language. YouTube didn’t specify how this will work behind the scenes, but Google did recently release an improved Gemini translation model. So autodubbing will probably use some version of that.

A few months ago I was dragged into a call with my engineering counterpart and their boss (who happens to be our SVP of engineering). Something had gone wrong that shouldn't have. Nothing catastrophic, but important enough that I was now on a call with an SVP.

I started to explain how it happened when they cut me off with "Michael, I don't want the details".

They continued:

I know that if we get into the details, the reasons will be perfectly reasonable. You'll explain what happened, I'll understand why everyone made the decisions they made, and I'll empathise with you.

Then it'll happen again.

So I don't want the details. I want to know what we're changing.

At first I thought "I don't want the details" sounded dismissive. How can they make informed decisions without understanding the details?

Then I realised that "I don't want the details" wasn't being dismissive. The executive assumed that we were competent, and was saying "I already believe you. Now let's talk about what happens next".

Asking the right question

After something goes wrong, most organizations ask "Why did this happen?" This is a question we're all familiar with answering.

We write up timelines. We reconstruct decisions. We explain dependencies. At the end of it, we hand over a document that contains the specific combination of events that led to the incident.

Everyone nods their head, says "that makes sense", and we all move on with our day.

Understanding an issue is not the same as fixing it. A good explanation can make things worse. Once everyone agrees that the behaviour was reasonable, the urgency to change anything disappears.

When an incident is an unfortunate but understandable sequence of events where no-one is at fault nothing changes. Then the same thing happens six months later, and everyone is left wondering how we landed here again.

To drive change in your organization, don't ask "why did this happen?".

Instead, ask:

What are we changing so that the same class of failure is less likely next time?

Reasonable people

The SVP wasn't interested in understanding how the issue happened or who was involved. They didn't want to be convinced that everyone involved behaved reasonably. That's a baseline expectation.

Their question became:

“Given that reasonable people produced this outcome, what needs to change?”

Consider these examples:

"We missed it because Alice was on holiday and Bob thought the Widgets team owned it".

Okay. How do we make ownership unambiguous when someone is unavailable?

"The requirements changed three days before launch."

Of course they did! What happens when requirements change inside the launch window?

"The alert fired, but the on-call engineer had already dealt with twenty low-value alerts that evening".

Makes sense. How do we improve the signal to noise ratio of our alerts?

Focus on changing the system. The people are usually not what needs changing.

A good explanation is not a fix

If your postmortem is full of sentences like "we should involve support earlier" and "we need to communicate better", or my personal favourite, "we'll be more careful next time", you have a collection of hopes dressed up as progress.

If your corrective action depends on people remembering a conversation from six months ago, you don't have a corrective action. You have organizational folklore. If everyone involved in the incident left the company tomorrow, would the fix still work? If the answer is no, the people may have learned something while the system is still destined to fail.

For a postmortem to drive lasting change, ask:

If the same situation happened tomorrow, what would cause a different outcome?

A process that forces a decision at this point is an improvement. A system that prevents this class of mistake is stronger still.

Process for process' sake

You can take "the system prevents this class of mistake" too far.

Not every failure deserves a new process. That's how you build environments that no-one wants to work in. Sometimes the cost of preventing recurrence is higher than the cost of occasionally accepting the failure, and that's ok.

But you need to accept failure with your eyes open. "We are consciously accepting this risk" is very different from "we said we'd try harder and everyone felt better".

Trust

I still think about what the SVP said a lot. What sounded like impatience was a declaration of trust. They didn't need me to prove that the people involved were competent or well intentioned. They were willing to start there. If the investigation showed otherwise, we could deal with that separately.

What they didn't want was for empathy to become the mechanism by which the organisation absolved itself of having to change.

People are usually making the best decisions they can with the information, incentives and constraints around them. That's why fixing the people is often the wrong answer.

Sometimes the most useful thing a leader can say is:

I believe you. I don’t need the details. Tell me what we're changing.

Healthcare AI has little margin for error. AI agents helping coordinate patient care depend on reliable patient context, clear controls over data and model access, and visibility into every interaction, all while maintaining stringent compliance requirements.

Concurrence is operating these healthcare agentic systems at a significant scale. The company builds clinical AI agents across patient- and provider-facing workflows, from AI clinicians, nurses, and care coordinators to ambient documentation, care-plan summaries, and knowledge retrieval.

Across its production AI environment, Concurrence now processes approximately 100.8 billion input tokens and 11.2 million LLM calls every 30 days, equivalent to an annualized run rate of roughly 1.2 trillion input tokens. Monthly token volume has grown about 5x from its late-2025 baseline to July 2026.

In high-stakes clinical workflows, reliable AI agents start with trustworthy, well-governed data. Supporting that reliability at scale requires strong compliance, rigorous agent testing and governed AI access. Concurrence is consolidating these capabilities on Databricks, with Lakebase for operational agent and conversation state, Unity Catalog for governing data and AI assets, and Unity Gateway for centralized AI access and security across its rapidly growing developer AI workloads.

Building reliable clinical AI on trusted data

Healthcare data often conflicts across systems. A patient may provide information that differs from an existing record, and the newest value is not always the most reliable.

Concurrence addresses this by recording new information as immutable events rather than overwriting existing records. From that history, Concurrence computes the current patient state while preserving the source and provenance of each piece of information, which it calls its world model. This gives agents a consistent view and history of what is known about a patient, while allowing what they learn from patients and clinicians to feed back into the state for future workflows.

Databricks provides the shared data foundation for this architecture. Events stream through Zerobus Ingest into governed Delta tables, including 2.7 million world-model events per month and 90,000 per day at peak. Apache Spark™ Declarative Pipelines derive Concurrence’s world model and clinical data; Unity Catalog governs each customer environment; and Lakebase serves the patient state, agent and conversation state, and knowledge base data needed by operational applications.

Care gap and medication adherence outreach is one example. Concurrence’s agents can call or text patients who are overdue for follow-up care or falling off a medication, use existing patient context to guide the conversation, and record what they learn back into the patient state for future workflows. Concurrence also runs production applications on Databricks Apps, including a care-packet guide, nurse care-plan summary, and clinical-content review surface. Each builds on the same governed patient context and infrastructure. The move to this architecture has also allowed Concurrence to retire its homegrown prompt-log store and reverse-ETL jobs in favor of governed Delta tables and Lakebase Synced Tables.

Testing clinical AI agents before production

Every agent on Concurrence’s new platform is tested against simulated patients before it reaches a real one. Today, simulation and evaluation traffic is approximately seven times greater than production traffic on the new platform.

Concurrence’s data architecture makes this testing possible. Because the patient state is computed from an immutable event history, teams can replay that state and test different paths without changing the real patient record. This allows Concurrence to evaluate how an agent responds to different scenarios before deploying it to patients.

Agent traces stream through Zerobus Ingest and lands in Delta tables alongside the clinical data that produced them. Scheduled ai_query jobs using Databricks-hosted Claude, then score those interactions for conversation quality and safety, extract memory, and write the results back to Delta. With patient data, traces, outcomes and evaluations on the same governed foundation, teams can investigate whether changes in performance came from the model, the data or the workflow.

Enforcing AI governance and compliance in healthcare

For Concurrence, HIPAA requirements shape the architecture from the start. Concurrence is HIPAA-, GDPR- and SOC 2-compliant today, with HITRUST and ISO 27001/42001 in progress. Each healthcare organization gets its own schema and service principal, with access controls, lineage and audit trails governed through Unity Catalog.

For batch AI workloads, Concurrence runs ai_query jobs on Databricks-hosted Claude under a BAA. Its endpoint resolver only permits models within the BAA-covered namespace, preventing PHI from being routed to an uncovered model. The same covered path runs Concurrence’s safety classification for self-harm, suicidal ideation, and medical emergencies. Some of its highest-stakes AI workloads are therefore protected by the same architectural constraint. This also shapes Concurrence’s approach to model routing: routing is compliance-gated before it is cost-gated. Models must first meet the compliance requirements of a workload before Concurrence considers quality, performance or cost.

Real-time patient and clinician inference remains on Concurrence’s existing provider infrastructure today. Concurrence has already built and feature-flagged its Unity Gateway integration for real-time inference, with a synthetic canary continuously testing it end-to-end. Production traffic can move to Unity Gateway as the required compliance coverage becomes available.

Governing coding agents with Unity Gateway

Concurrence applies the same approach to developer AI. Coding agents are used across engineering, operations, and research, including by forward-deployed engineers working within customer environments that handle sensitive healthcare data.

Concurrence routes all coding-agent model and tool traffic through Unity Gateway’s coding CLI, ug. Developers get a single governed path to approved models and MCP tools, while each request remains associated with the identity of the person who made it. MCP access is centrally managed through the same environment, with permissions assigned by engineer group and each user authenticating individually when agents access tools such as Databricks, Datadog, and Linear.

The scale is already substantial. In July, 14 individual users generated 35.85 billion input tokens through Unity Gateway, of which 95.37% were cache reads. Since ug rolled out on July 10, Concurrence’s coding agents have generated approximately 360,000 requests and 61 billion cumulative input tokens.

Centralizing coding-agent traffic gives Concurrence visibility into how developer AI is used and how much it costs. Every request is attributed to the engineer who made it, allowing individuals to monitor their own usage through ug usage. At the organization level, Concurrence uses Databricks usage data from system.ai_gateway.usage to track models in use, token consumption, cache rates, and spend by person and team.

Centralizing AI access with Unity Gateway

Concurrence’s goal is to bring production, batch and developer AI under a common inference control point with Unity Gateway. Developer AI already runs through Unity Gateway, while batch inference runs on Databricks-hosted models through BAA-covered paths. Today, Claude Opus 4.8 and GPT-5.6 Sol account for most coding-agent model usage, with Opus 5 usage growing. Real-time patient and clinician inference remains on Concurrence’s existing provider infrastructure until the required compliance coverage is available.

That multi-model approach is especially important for Concurrence’s clinical workloads. The company currently has 14 models serving production inference and a governed catalog of 46 models. Most production volume runs on smaller, faster models, with frontier models reserved for more complex reasoning. Concurrence is developing clinical reasoning benchmarks to determine which models perform best across different healthcare tasks.

Concurrence is also excited about the pace of innovation with Unity Gateway. Most recently they have begun testing Unity Gateway Smart Routing against healthcare-specific routing approaches it is developing and publishing the results. Because model eligibility in healthcare starts with compliance, those evaluations will assess how intelligent routing can optimize model choice within the boundaries established for each workload. On the developer side, Concurrence is also exploring Omnigent as a meta-harness across its coding-agent environment.

A unified foundation for healthcare AI

As Concurrence moves more workflows onto Databricks, the foundation becomes more valuable with every agent interaction. Each agent’s work can enrich the patient state the next agent starts from, allowing new workflows to reuse existing context rather than rebuild it, reducing the incremental cost and effort of adding new AI workflows.

At an annualized rate of roughly 1.2 trillion production-input tokens, that compounding foundation matters. By bringing patient context, operational state, traces, evaluations, governance and AI access together on Databricks, Concurrence can scale high-stakes clinical AI while maintaining the reliability and controls healthcare demands.

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In December 2023, Chris Bakke talked a Chevrolet dealership’s website assistant into agreeing in chat to sell him a 2024 Tahoe for $1.00, and got it to agree the deal was “a legally binding offer – no takesies backsies.” The screenshot went around the internet, the dealership pulled the bot, and the story became the canonical example of what happens when you put a language model in front of your business with nothing between it and the customer.

LLM guardrails check inputs, generated replies, or proposed tool calls against application rules, then allow, flag, or block the operation. They can combine model-based checks, such as detecting an unauthorized commitment, with deterministic rules, such as rejecting a quote below an approved price.

When these checks run before an operation proceeds, they add latency to the request. This underscores an engineering problem: how do you enforce the rules within your application’s latency and cost budgets?

For model-based checks, options include a general-purpose LLM judge, a fine-tuned small model, or a purpose-built decision model such as TypeSafe’s Jev. This post compares Jev with OpenAI’s GPT-5.4 nano in a dealership chatbot demo, measuring guardrail latency and cost across two attack sequences. We also discuss the deployment tradeoffs of fine-tuning a small model, which we did not benchmark here.

We created a demo to show guardrails with Jev, compared against LLMs. The code, attack transcripts, and measurement harness are all in the typesafe-guardrails repo.

How we tested Jev and GPT-5.4 nano as the LLM judge

The demo we created rebuilds the dealership: a chatbot that can quote prices and call a tool to record an offer. Except this time, we added guardrails. There are several, each screening a different boundary:

• the inbound customer message, before the agent sees it
• the draft reply, before it is sent back
• the tool arguments the agent wants to record
• a deterministic below-floor price check that needs no model at all

We run that whole set in one of three configurations:

• No guardrail: nothing sits between the model and the customer, so you can reproduce the original $1 Tahoe incident, albeit as a demo, not by buying an SUV for $1.
• Jev: a purpose-built decision model, reached through TypeSafe System One.
• LLM-as-a-Judge: a general model, GPT-5.4-nano, called through structured outputs. This is the baseline.

With the guardrail running, a normal purchase goes straight through, while the $1 Tahoe attack is stopped before the offer is ever recorded.

Under the hood, Jev is TypeSafe AI’s first “System One” model. The name is a nod to Kahneman’s “System 1” thinking: the fast, intuitive judgment you make in a single pass, as opposed to the slow, deliberate, step-by-step reasoning of System 2.

A generative LLM writing out its reasoning token by token is doing System 2 work for a job that only needs System 1. Jev is built for exactly that kind of structured, snap decision rather than open-ended generation. Instead of writing an answer token by token, it returns a typed answer with a probability distribution in a single parallel pass, which is where most of the speed comes from.

It is trained with Reinforcement Learning for Calibrated Decisions, which TypeSafe describes as training for calibrated probabilities.

We ran the same three-turn Tahoe attack through the Jev guardrail and the LLM-as-a-Judge guardrail, and measured two things:

• Latency: how long a single guardrail call takes, and how long the whole conversation takes to screen end to end.
• Cost: what each guardrail costs per conversation, worked out from the measured token counts against published prices.

Both engines use the same question definitions, thresholds, and decision functions. The agent’s generated replies can vary between runs, however, so the engines don’t necessarily evaluate identical text. These results compare the two engines in a live demo.

Same questions, same thresholds, same decisions, and the same answers in 0.7 seconds instead of 9.2 seconds. On the longer slow-burn attack the per-call gap holds, 117ms against 1,811ms, and both engines reach identical verdicts on both attacks.

Jev vs. GPT-5.4 nano: guardrail latency and cost

Across these two demo runs, Jev was 15-to-18x faster and 12-to-14x cheaper per call. Jev clears all five checks in 0.7 seconds where GPT-5.4-nano takes 9.2, and does it at list prices, not negotiated rates, with identical verdicts. That reduction makes Jev worth testing against your application’s latency budget and accuracy requirements.

The cost gap is structural, not a pricing quirk. Jev returns a typed distribution, so it emits far fewer output tokens, and the ones it does emit are free, while the LLM writes about three times as many output tokens at the highest per-token rate on its bill.

Why we chose GPT-5.4 nano as the LLM judge

The easiest way to win a benchmark is to pick a weak opponent. We went the other way and picked the baseline that flatters Jev least.

We used GPT-5.4 nano as a small-model baseline with structured outputs for the guardrail decisions.

The obvious cheaper candidate, GPT-4.1-nano, disqualified itself on accuracy: asked whether a reply containing the verbatim words “and that’s a legally binding offer” implied a binding commitment, it scored the claim at 0.1, near-certain that nothing binding had happened. That is the exact failure the guardrail exists to catch. The model we chose is the one that made Jev’s win the narrowest.

How the guardrail checks agent inputs, replies, and tool calls

Speed from a smaller model would be a hollow result if it came from a dumber guardrail. It does not, because the design difference is structural rather than a matter of scale.

The first difference is that Jev returns typed answers. Ask it a yes-or-no question and you get back a probability, not a sentence you have to interpret. Ask it to pick a level of risk and you get an ordered choice. The answer says what the model concluded, and the shape of the distribution says how sure it is.

{
  "claims_binding": { "probability": 0.97 },
  "commitment": {
    "score": 2.0,
    "confidence": 0.94,
    "probabilities": {
      "no commitment, informational or a question": 0.01,
      "informal encouragement, no price agreed": 0.05,
      "states a firm price or makes a commitment": 0.94
    }
  }
}

The second difference is where the guardrail sits. It checks three boundaries, not one: the inbound customer message, the draft reply before it is sent, and the arguments the agent wants to pass to a tool.

That last boundary is the one an input-only guardrail can never cover. The second attack in the repo never jailbreaks the conversation at all. Every message looks fine. The block lands only at the tool boundary, on the arguments the agent assembled to record a $1 offer. If you are only screening inbound text, you never see it.

And because each check runs in around 100ms, stacking all three boundaries on the hot path still costs the customer a fraction of a second, so you can run every guardrail on every turn without a noticeable lag.

The third difference is that the policy is not in the prompt. The floor price for the Tahoe LT, $54,500 against an MSRP of $58,195, lives in guardrail configuration and is kept out of the customer-facing agent’s context.

None of this is exotic. It is the difference between a model that returns structured decisions and a model that returns prose you hope to parse correctly. That difference is what lets the guardrail be small, fast, and cheap without being naive.

Jev vs. fine-tuned small models: deployment tradeoffs

There is a legitimate middle path between a general LLM-as-judge and an off-the-shelf decision model: fine-tune your own small language model (SLM) on your own guardrail decisions.

On the one axis this post has hammered, latency, it genuinely works. A well fine-tuned small model can run in less than 200ms on the right hardware, right in Jev’s range. So the SLM route is not slow. If speed were the only question, it would be a fine answer.

The catch is that speed is the cheap part, and everything around it is not. The bill for a fine-tuned SLM lands in three places, none of them the inference call:

• The first is data, and it is the largest. The training compute is genuinely cheap. The expensive part is the labelled corpus you feed it: a set of allow, review, and block decisions across your whole policy, labelled well enough to trust.
• The second is serving. A fine-tune is not done when training finishes; it has to run somewhere, in production, at your latency.
• The third, and the sharpest in practice, is time to ship. Jev works today. It is a configuration change. A fine-tuned SLM screens nothing until you have gathered the data, labelled it, run the fine-tune, and stood up serving.

So the argument for a purpose-built decision model over a fine-tuned SLM is not that SLMs are slow. It is that you get the same real-time latency off the shelf, per call, without the upfront data bill, the standing serving cost, or the wait.

What to evaluate using Jev for production guardrails

Two engines, two attack sequences, identical decisions, and roughly an order of magnitude on both latency and cost.

The guardrail that catches the $1 Tahoe is the one that runs on every message, every reply, and every tool call, and it can only do that if it is fast enough and cheap enough that nobody is ever tempted to turn it off. A guardrail you can afford to leave on is the only kind that protects you.

Jev is not the only purpose-built decision model you will hear about. This is a fast-growing space, and more of these small, fast classifiers are shipping all the time, each with its own claims about speed, cost, and calibration.

That is good news, but it also means the model you pick today is a decision you should keep revisiting. This is where evals earn their keep: with a labelled set of your own guardrail decisions, you can measure each new model on your traffic instead of taking the benchmark on faith, and swap in a better one the moment the numbers say so.

DuckDB Now Ships inside dbt v2

TL;DR: dbt v2, which runs on the new Rust-based Fusion engine, is the first dbt release that ships with a built-in DuckDB adapter. This post covers setup, DuckLake and Iceberg catalogs, querying dbt's Parquet metadata with DuckDB, plus other v2 features that matter to DuckDB users, including migrating to dbt v2.

dbt is the tool many data teams use to manage their SQL transformations: you write each model as a SELECT statement, and dbt works out the order to run them in from the references between models, builds the resulting tables and views in your database, and can test them along the way.

dbt-duckdb, the dbt adapter for DuckDB, received its first pull request on August 27, 2021, and in the meantime has 1.4k stars on GitHub. Since then, dbt users have been able to install one Python package (dbt-duckdb, via pip), point it at a file (a local DuckDB database), and have a working project (models building into tables and views), without having to sign up to (and pay for) servers or warehouses.

When dbt Labs announced the new Rust-based Fusion engine in May 2025, DuckDB initially wasn't supported out of the box. That has changed with dbt v2, which ships with a DuckDB adapter built in. Here is how to set it up and what else is new.

Background

dbt Labs announced the new Rust-based Fusion engine on May 28, 2025. Two days later, a user, ran-codes, opened a GitHub issue asking for a DuckDB adapter:

Quote “There is a huge community utilizing the DuckDB adaptor to run DBT. For me personally, I was able to learn and start using DBT just because of the light-weight setup for the dbt-duckdb workflow and it has allowed me to get over the learning curve to start using DBT.”

– ran-codes, on GitHub

At the time of this writing, the issue resulted in 146 ❤️ and 21 👍 reactions. The adapter is now built into dbt v2.

On June 1, 2026, dbt Labs released the first alpha of dbt Core 2.0, built on the same foundations as Fusion, and open-sourced a large part of the Fusion code. That code moved into the dbt-core repository under Apache 2.0, and the dbt-fusion repository was archived. There are two distributions of v2, both free to install locally and both running on the same engine.

dbt 2.0.0 was released on September 14, 2026. That release also renamed the CLI branding from Fusion and dbt-core to dbt (proprietary) and dbt-oss (open source). So “Fusion” is now mostly the name of the engine, and the thing you install is just called dbt.

Setup

In dbt v1, an adapter was a standalone Python package. In v2, adapters live inside a Rust monorepo and connect through ADBC drivers.

With v2, dbt automatically downloads and caches the DuckDB driver the first time you run it, so after you install dbt there is nothing else to add. dbt also publishes a DuckDB quickstart guide for getting a project running locally.

A basic profile looks the same as before:

my_project:
  target: dev
  outputs:
    dev:
      type: duckdb
      path: ./warehouse.duckdb

DuckLake and Iceberg Catalogs

v2 adds catalog support that the Python adapter doesn't have. dbt's DuckDB docs flag it as "dbt v2 only"; the legacy Python adapter instead attached DuckLake through the profile's attach block.

With catalogs.yml you can configure DuckLake and Iceberg REST catalogs, with catalog-aware materializations. This requires the v2 engine with the use_catalogs_v2 flag enabled and isn't available in the Python adapter. dbt generates and runs the ATTACH statements for you.

A DuckLake catalog is defined in catalogs.yml:

catalogs:
  - name: local_lake
    type: ducklake
    table_format: default
    config:
      duckdb:
        metadata_path: metadata.ducklake
        data_path: s3://my-bucket/lake

Enable the flag in dbt_project.yml, then reference the catalog from a model:

flags:
  use_catalogs_v2: true

{{ config(materialized = 'table', catalog_name = 'local_lake') }}
select * from {{ ref('customers') }}

dbt Metadata as Parquet

v2 also writes its metadata as Parquet as an alternative to the large JSON files, and these (as well as the large JSON files) can be queried directly with DuckDB.

dbt calls this the Information Schema, a v2 feature that stores the manifest as Parquet instead of JSON. Running dbt parse --generate-info-schema writes a set of Parquet files to target/info_schema/v1/, so you can list your models without parsing manifest.json.

These are the same artifacts dbt ships as test fixtures, so you can query one straight from the dbt repository using DuckDB without running dbt first:

SELECT name, materialized, schema_name
FROM 'https://raw.githubusercontent.com/dbt-labs/dbt/main/crates/dbt-docs-server/web/src/test/fixtures/parquet/dbt.models.parquet';

For the above, this lists the three models in the fixture, along with how each is materialized and the schema it lands in:

┌─────────────────┬──────────────┬─────────────┐
│      name       │ materialized │ schema_name │
│     varchar     │   varchar    │   varchar   │
├─────────────────┼──────────────┼─────────────┤
│ my_second_model │ view         │ main        │
│ my_third_model  │ view         │ main        │
│ my_first_model  │ view         │ main        │
└─────────────────┴─────────────┴─────────────┘

Why would you do this? On a large project, the JSON manifest.json can grow to hundreds of megabytes, and reading it means loading and parsing the whole file just to answer a simple question. The Parquet files are columnar, so DuckDB reads only the columns you select and can filter them without materializing everything in memory. That makes it practical to ask questions about the project itself: which models are materialized as tables rather than views, which schema each one lands in, or which models are missing tests.

This is useful in a CI check or an audit script, where you want to enforce conventions across a project without standing up dbt or the warehouse. Because the files are located on disk after a dbt parse, you can point DuckDB at them directly and treat your project's metadata as just another dataset to query.

SQL Comprehension and Column-Level Lineage

dbt models combine SQL with Jinja templating, which earlier versions compiled into a query string without inspecting the SQL itself. The v2 engine instead has a native understanding of SQL across multiple engine dialects. That means it can catch invalid column references and type mismatches before a query reaches the warehouse, rather than surfacing them only when the model runs against DuckDB.

That same analysis produces column-level lineage locally, without a dbt platform account. Running dbt compile with --generate-info-schema --static-analysis strict writes a dbt.column_lineage file into the Information Schema Parquet directory covered above, so you can trace which upstream columns feed each model with a plain DuckDB query.

Faster Local Development

v2 is distributed as a compiled Rust binary rather than a set of Python packages, so there is no Python dependency tree to resolve before a run. dbt describes the engine as the foundation for fast builds on large projects, where parsing and compiling happen inside that single native executable.

The dbt VS Code extension builds on the same SQL comprehension. As you edit models, it gives you autocomplete, hover information, and inline errors, so mistakes show up in the editor instead of after a round trip to the warehouse.

Pinned DuckDB and Native Functions

v2 ships a pinned DuckDB version, rather than relying on whatever version pip resolves for the Python adapter. Pinning the version is what enables the read-write Iceberg REST catalog support described above, which depends on features from that specific DuckDB build.

Pinning a specific DuckDB version also lets dbt push work down into the database. Some adapter logic that used to be a SQL macro is now implemented as a native DuckDB extension function, such as array_except, which is exposed as sf_array_except.

Migrating

A low-risk first step is to test the v2 parser while still on dbt v1.12, which ships an opt-in v2 parser. dbt's docs describe this as a way to catch compatibility issues early before fully migrating. Run the following command to check whether your project parses:

dbt parse --use-v2-parser

If it does, follow the install guide to switch. The dbt-autofix package handles many of the required changes, and there is an upgrade guide for v2.

Conclusion

The DuckDB adapter is now part of dbt v2 and needs no separate install, and the Python versions of dbt Core remain available if you'd rather not move or not move yet. Either way, running dbt on DuckDB means you develop, test, and publish your models on your own machine.

Beyond removing the separate install, v2 is where DuckDB picks up several new capabilities: catalog support for DuckLake and Iceberg, metadata written as queryable Parquet, native SQL comprehension with column-level lineage, and a pinned DuckDB build.

If you've already been using dbt-duckdb, upgrading to v2 means one less package to install and all of the above to build on. And if you haven't, a single dbt install and a few lines of profile are enough to start building models directly on your laptop, without servers or warehouses.

Jevflake

Jevflake lets Snowflake ask questions about your data using Jev, the decision model from TypeSafe AI. It is a dbt package, with a Terraform module for teams that manage Snowflake that way.

Jev does not write text. You give it a row and a typed question. It gives back a typed answer with a probability. That makes it a good fit for SQL: the answer is a number or a label you can filter, join, and test.

This package does three things:

1. Sets up Snowflake so it is allowed to call the Jev API.
2. Creates SQL functions that call Jev: jev_noul, jev_choice, jev_score, and jev_ask.
3. Gives you dbt macros and tests so answers are stored once, reused, and checked like any other model.

If you manage Snowflake with Terraform, there is a Terraform module that does steps 1 and 2 instead of dbt.

This project is not affiliated with TypeSafe AI, Snowflake, or dbt Labs.

The three question types

• Noul is a yes or no question. The answer is a probability from 0 to 1 that the answer is yes.
• Choice picks one option from a fixed list. The answer has the pick, a probability for each option, and a confidence number.
• Score rates the row against ordered levels. The answer has a score, probabilities, and a confidence number. The score is the probability weighted average of the level numbers, so three levels give a score from 0 to 2.

Before you start

• A Snowflake account with external access turned on. Trial accounts have it off by default, and your Snowflake account representative has to turn it on.
• A role that can create an integration. By default that is ACCOUNTADMIN.
• A TypeSafe API key from https://console.typesafe.ai.
• dbt 1.8 or newer with dbt-snowflake. It has been tested on dbt 1.12.

The Python function needs the pandas and requests packages. Snowflake installs them for you. Depending on the account, they come from Snowflake's PyPI repository or from its Anaconda channel. If creating the function fails with a message about Anaconda terms, an ORGADMIN has to accept those terms once in Snowsight.

Setup

1. Store your API key as a Snowflake secret

Run this once in Snowflake. The package never sees the key itself, so it never lands in dbt logs.

create schema if not exists analytics.jevflake;

create secret analytics.jevflake.jev_api_key
  type = generic_string
  secret_string = 'your-typesafe-api-key';

Use your own database in place of analytics. By default the package looks in your dbt target database, in a schema called jevflake.

2. Add the package

packages:
  - git: "https://github.com/KranzL/Jevflake.git"
    revision: main

Then run dbt deps.

3. Create the network access and the functions

dbt run-operation jevflake.setup --args '{grant_to: [transformer]}'

This creates:

• the schema if it does not exist yet
• a network rule that allows traffic to api.typesafe.ai only
• an external access integration called jev_access
• the SQL functions
• for each role in grant_to: usage on the schema and on the functions

A role in grant_to can call the functions with only those grants, plus usage on the database and a warehouse. It does not need access to the integration or the secret. If a role lacks usage on the database, an admin can grant it:

grant usage on database analytics to role reporter;

You can run setup again at any time, and you should after you change any setting below. Pass the same grant_to every time. Setup recreates the functions, and Snowflake drops the old grants when it does.

To see the SQL without running it:

dbt run-operation jevflake.setup --args '{dry_run: true}'

Database, schema, role, and column names are used unquoted. Stick to plain identifiers made of letters, digits, and underscores.

If your dbt role cannot create integrations

Split the work. An admin runs the first command. The dbt role runs the second.

dbt run-operation jevflake.setup_network --args '{grant_to: [transformer], callers: [reporter]}'
dbt run-operation jevflake.setup_functions --args '{grant_to: [reporter]}'

• In setup_network, grant_to lists the roles that will create the functions. They get usage on the integration, read on the secret, and usage and create function on the schema. callers lists the roles that will call the functions. They get usage on the schema, which only the schema owner can grant.
• In setup_functions, grant_to lists the roles that will call the functions. They get usage on each function.

If the secret lives in a different schema than the functions, the role that creates the functions also needs usage on that schema.

Removing it

To remove the functions, the integration, and the network rule:

dbt run-operation jevflake.teardown

The secret and the schema are left in place.

Use it in plain SQL

select
    ticket_id,
    analytics.jevflake.jev_noul(body, 'The customer is asking for a refund') as wants_refund
from support_tickets;

select
    ticket_id,
    analytics.jevflake.jev_choice(
        body,
        'Which team should handle this ticket',
        parse_json('{"billing": "Charges and refunds", "technical": "Bugs and errors"}')
    ) as answer
from support_tickets;

The arguments are what Jev reads, the instructions, and the criteria:

• jev_noul(state, instructions) returns a float. An optional third argument describes what counts as yes and no: parse_json('{"true": "...", "false": "..."}').
• jev_choice(state, instructions, criteria) returns a variant. The criteria is an object that maps each option to a short description.
• jev_score(state, instructions, criteria) returns a variant. The criteria is an array of level descriptions, lowest first.

jev_choice and jev_score return the whole answer. Read parts of it with answer:choice, answer:score, answer:confidence, answer:probabilities, and for a score answer:legend.

What Jev reads must be text, an object, or an array. Pass a text column as it is. To send more than one column, pass object_construct('subject', subject, 'body', body). Jev rejects a bare number, date, or boolean, and the answer comes back null. Cast those to text first, for example amount::varchar, or put them inside an object.

jev_ask takes many named questions at once and makes one API call per row. Use it when you have more than one question about the same row. It is cheaper and faster than separate calls, because the row is only sent once.

select
    ticket_id,
    analytics.jevflake.jev_ask(
        object_construct('subject', subject, 'body', body),
        parse_json('{
          "ticket_type": {
            "type": "choice",
            "instructions": "Which team should handle this ticket",
            "criteria": {"billing": "Charges and refunds", "technical": "Bugs and errors"}
          },
          "blocked": {
            "type": "noul",
            "instructions": "The customer is blocked from running their business"
          }
        }')
    ):answers as answers
from support_tickets;

The result has one entry per question name, for example answers:ticket_type:choice and answers:blocked:noul.

You will also see a function called jev_ask_json in the schema. It is the Python function the others are built on. You do not need to call it.

Use it in dbt models

Store answers in a judgments model

This is the recommended way. Answers are stored in a table, one row per key and question. A row is only sent to Jev again if its content changes, you change the questions, or you change jevflake_model.

{{ config(
    materialized='incremental',
    unique_key=['ticket_id', 'question']
) }}

{{ jevflake.judgments(
    relation=ref('stg_support_tickets'),
    key='ticket_id',
    state=['subject', 'body'],
    questions={
        'ticket_type': jevflake.choice_question(
            'Which team should handle this ticket',
            {
                'billing': 'Charges, refunds, invoices, plans',
                'technical': 'Bugs, crashes, errors, slow pages',
                'account': 'Login, access, profile changes'
            }
        ),
        'urgency': jevflake.score_question(
            'How urgent is this ticket',
            ['No time pressure', 'Normal queue', 'Customer is blocked right now']
        ),
        'has_contact_info': jevflake.noul_question(
            'The text contains a phone number or an email address'
        )
    }
) }}

Arguments:

• relation is the model or source to read.
• key is the column, or list of columns, that identifies a row.
• state is what Jev gets to read. It can be one SQL expression, a list of columns, or a mapping of labels to SQL expressions. Send only the columns the questions need. TypeSafe's docs say unrelated content lowers accuracy, and in testing the same question gave noticeably different probabilities with and without an extra column.
• questions is a mapping of names to questions. All of them go out in one API call per row.

Columns in the result: your key columns, question, answer_type, noul, choice, score, confidence, probabilities, error, answer, model, state_hash, questions_hash, judged_at.

Things to know about what gets stored:

• If you change any question, every row is asked again on the next run.
• If you remove or rename a question, its old rows stay until you run the model with --full-refresh. The same goes for rows that are deleted from the source.
• Rows with a null key are skipped. When state is a single SQL expression and it is null, the row is skipped too. Skipped rows are never sent to Jev and leave no row in the result. When state is a list of columns or a mapping, the row is always sent, even if every column is null.
• The model column holds the versioned model ID the API reports, falling back to your configured jevflake_model.

Put an answer straight into a column

select
    ticket_id,
    {{ jevflake.noul('body', 'The customer is asking for a refund') }} as wants_refund,
    {{ jevflake.choice('body', 'What is the tone', ['calm', 'frustrated', 'happy']) }} as tone,
    {{ jevflake.score('body', 'How urgent is this', ['low', 'medium', 'high']) }} as urgency
from {{ ref('stg_support_tickets') }}

The first argument is a SQL expression written as a string, a list of columns, or a mapping, the same as state above. noul gives the probability as a float, choice gives the chosen label as text, and score gives the score as a float. For choice you can pass a list of options, or a mapping of options to descriptions.

Each macro is one API call per row, every time the model runs. Make the model incremental, or use a judgments model, so you do not pay for the same rows twice.

Review queue

Some answers should go to a person. This macro selects errors, yes or no answers in the uncertain middle, and choices or scores with low confidence.

{{ jevflake.review_queue(ref('ticket_judgments'), noul_low=0.2, noul_high=0.8, min_confidence=0.5) }}

Tests

These run on a judgments model. They read stored answers, so running tests costs nothing.

models:
  - name: ticket_judgments
    data_tests:
      - jevflake.no_errors
      - jevflake.noul_between:
          arguments:
            question: has_contact_info
            max_value: 0.2
      - jevflake.confidence_at_least:
          arguments:
            question: ticket_type
            threshold: 0.5
          config:
            severity: warn

• no_errors fails on rows Jev rejected, for example text that is too long.
• noul_between fails when a yes or no probability is outside min_value and max_value. They default to 0 and 1.
• confidence_at_least fails when a choice or score has confidence below threshold. Leave out question to check every choice and score in the model.

The arguments key needs dbt 1.10.5 or newer. On older versions, put the test arguments directly under the test name.

Settings

Set these as vars in your dbt_project.yml. Run jevflake.setup again after changing any of them.

• jevflake_database: where the functions live. Default: your target database.
• jevflake_schema: default jevflake.
• jevflake_secret: full name of the secret. Default <database>.<schema>.jev_api_key.
• jevflake_integration: default jev_access. Integrations are account level, so the name must be unique in the account.
• jevflake_network_rule: default jev_egress.
• jevflake_model: default jev-1.13.0. It is pinned so answers do not shift under you. The model name is part of the cache key, so changing it asks every row again.
• jevflake_concurrency: API calls in flight per batch. Default 8. Snowflake can run several batches at once, so the total can be higher.
• jevflake_max_batch_size: the most rows Snowflake hands the function at once. Default 64.
• jevflake_max_retries: default 6.
• jevflake_timeout_seconds: default 30.
• jevflake_rows_per_request: default 1. See below.
• jevflake_python_version: default 3.11.

Rows per request

By default each row is its own API call. At the time of writing TypeSafe allows 1,200 calls per minute, so that is the speed limit: about 72,000 rows per hour. TypeSafe says its limits can change, so check their models page.

Setting jevflake_rows_per_request higher packs several rows into one call. It is faster. It is also experimental: rows in the same call can influence each other, and Jev's own docs say unrelated content lowers accuracy. In a direct API test with three rows, the packed answers were within 0.01 of the single row answers and used about half the tokens. That is a tiny sample, and the packed path has not been run inside Snowflake. Compare answers on a sample of your own data before you trust it.

Costs and limits

• Jev costs $0.042 per million input tokens. Output is free. Every call carries fixed overhead: in testing, one sentence of text with one short question used about 290 input tokens. A short support ticket with three questions came to about 470 input tokens, which works out to roughly $20 per million rows.
• The Snowflake warehouse keeps running while it waits on the API. A small warehouse is enough. A bigger one does not help, because the API rate limit is the cap.
• Jev reads up to 32k tokens of state plus the longest question per call, within 64k tokens for state plus all questions combined.
• Snowflake gives the function 180 seconds per batch of rows. If heavy rate limiting pushes a batch past that, the query fails. Lower jevflake_max_batch_size if you see it.
• Busy or rate limited calls are retried with backoff. If retries run out, the query fails. A bad API key fails the query right away.
• Rows that Jev rejects do not fail the query. In a judgments model they come back with answer_type = 'error' and the reason in error. In plain SQL, jev_noul returns null for them, and jev_choice, jev_score, and jev_ask return an answer with type set to error.

What Jev is bad at

From TypeSafe's own notes on Jev 1.13:

• Counting, comparing numbers, and ordering dates. Do those in SQL.
• Questions with double negatives or several steps of reasoning.
• It answers the question you wrote, not the one you meant. Be literal and specific.
• Asking a question and its opposite will not give answers that add up. Pick one phrasing.
• Text inside a row can steer the answer. Do not let an answer trigger anything destructive without a check.

Two more things seen in testing. The same question can give slightly different probabilities depending on which other questions are in the same call, by a point or two. Running the identical call twice gave identical answers, but TypeSafe's docs do not promise that.

Your data

Row content is sent to TypeSafe's API. It leaves Snowflake. Read TypeSafe's terms and privacy policy, and check with your security team before pointing this at sensitive data.

Set it up with Terraform instead

The terraform folder has a module that creates the network rule, the integration, the functions, and the grants, and optionally the schema and the secret. If you use it, skip step 3 above and point the dbt vars jevflake_database and jevflake_schema at the schema Terraform made. The dbt macros and tests work the same either way.

Run the example project

integration_tests/ is a small dbt project with ten sample support tickets. It needs the setup above, and these environment variables for a Snowflake user with key pair authentication: SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PRIVATE_KEY_PATH, SNOWFLAKE_ROLE, SNOWFLAKE_WAREHOUSE, SNOWFLAKE_DATABASE, and optionally SNOWFLAKE_SCHEMA.

cd integration_tests
dbt deps --profiles-dir .
dbt build --profiles-dir .

This sends the ten tickets to Jev, which costs a fraction of a cent. Two of the tests are set to warn, and you should expect them to: one sample ticket really does contain contact details, and one is a close call between two teams.

Development

The offline checks need no packages and no Snowflake account:

python3 -m unittest discover -s tests

They cover the Python handler, and they check that the Terraform copy of the handler matches the dbt macro. After changing macros/setup/handler.sql, run python3 scripts/sync_terraform_handler.py.

Status

Version 0.1. It has been run against one live Snowflake account with a real Jev key, on dbt 1.12.5 with dbt-snowflake 1.12.1.

What was run: setup, the split setup with a separate admin role, function builder role, and caller role, teardown, every SQL function, the example project with its tests, a second run that sent no rows back to Jev, a run after editing one ticket that sent only that ticket, and the Terraform module.

What has not been run: large tables, so rate limit behaviour at volume is untested. Packing several rows into one call has not been run inside Snowflake. dbt versions older than 1.12 have not been tried.

License

MIT

TigerBeetle is a transaction-processing database. Its double-entry accounting primitives are simple and powerful: they can represent the movement or exchange of any quantity, including financial transactions such as real-time payments, billing, or credits, and non-financial transactions such as energy, inventory, or positions.

If you’re coming from a general-purpose SQL (OLGP) database and translate your data model to TigerBeetle one-to-one, you might leave a lot of performance on the table. In this post, we use an all-time favorite transaction-processing workload to illustrate why this is the case and how you can unlock a 100x performance improvement with TigerBeetle using three techniques: choosing the right data model and primitives, utilizing autobatching, and using TigerBeetle’s time-based identifiers.

From SQL to Double-Entry Accounting

Our sample workload models a bank with branches, tellers (ATMs), and customer accounts. Each transaction:

• changes a customer’s balance;
• records the transaction in a history table;
• changes the teller’s balance; and
• changes the branch’s balance.

This workload is conceptually simple but presents a performance challenge: transactions update only a few branches and tellers, causing contention.

In SQL-like pseudocode, each transaction looks like this:

begin transaction
                                              
  update account where account_id = aid:
    read account_balance from account
     set account_balance = account_balance + amount
   write account_balance to account
                                               
  update teller where teller_id = tid:
     set teller_balance = teller_balance + amount
   write teller_balance to teller

  update branch where branch_id = bid:
     set branch_balance = branch_balance + amount
   write branch_balance to branch
                                               
  write to history: aid, tid, bid, amount, timestamp
                                               
commit transaction

TigerBeetle represents the same logic differently. For this example, we represent the deposit as two linked transfers:

• value moves from the bank’s aggregate account to the branch account; and
• value moves from the teller account to the customer account.

The two transfers form one logical transaction and must either both succeed or both fail.

In Go-like pseudocode using the TigerBeetle client, the logic looks like this:

transfers := []Transfer{
    {
        ID:              nextID(),
        DebitAccountID:  bankAccountID,
        CreditAccountID: branchAccountID,
        Amount:          amount,
        Flags:           Linked, // Succeed or fail together.
    },
    {
        ID:              nextID(),
        DebitAccountID:  tellerAccountID,
        CreditAccountID: customerAccountID,
        Amount:          amount,
    },
}

results := client.CreateTransfers(transfers)

The Linked flag on the first transfer links it to the next transfer in the transfer batch. TigerBeetle treats the chain atomically: if either transfer fails, neither transfer is committed. Note that TigerBeetle is immutable and automatically records the transfer history, so there is nothing further to enable or model.

Two details are important here.

First, CreateTransfers accepts a collection rather than a single transfer. TigerBeetle is designed to process multiple operations in one request.

Second, the bank account acts as an aggregate account. Its balance gives us the total amount represented across the bank without requiring a query that scans and sums every branch.

In a conventional design, updating one aggregate row from every transaction would create severe contention. TigerBeetle is designed specifically for high-contention workloads, so aggregate accounts are practical rather than prohibitive.

The debit/credit schema is powerful, and we use it to model highly complex use cases with our customers.

From OLGP to OLTP

Changing the data model is half the migration. The next question is how to submit transactions efficiently.

Applications built on general-purpose (OLGP) SQL databases often use a connection pool. Each incoming request acquires a connection, performs one transaction, and releases the connection back to the pool. Increasing the number of concurrent active connections can increase throughput until the database reaches its contention, CPU, or I/O limit.

We can transfer this architecture directly to TigerBeetle (but don’t do this in practice!): For every application request, we create two linked transfers and submit them immediately. We use many goroutines and multiple TigerBeetle client instances in an attempt to increase “concurrency”.

The result is disappointing: more clients don’t improve performance at all!

Using the Right Tool Incorrectly Doesn’t Make It the Wrong Tool

Batching Brings Joy!

What we tried to do just now is increase concurrency by using many clients. But we don’t need to; TigerBeetle already has an inherently concurrent interface: Batches!

A client request can contain up to 8 189 operations in one round trip, such as transfers in a create_transfers request or account IDs in a lookup_accounts request (and more). Batching amortizes the fixed costs of a single request (network, replication, processing), and it enables other cool things, such as auto-vectorization, efficient cache utilization, and batches as transactions (see linked transfers above).

So in the following, we use a single TigerBeetle client instance and instead scale the number of transactions within a batch:

This looks much better! With large batches, TigerBeetle processes 454 518 transactions per second; that is 909 162 transfers per second, since each logical transaction uses two transfers.

That’s great, but how do you achieve batching? Isn’t it a lot of code?

As it turns out, no: while you can collect these large batches yourself, as an application that performs bulk inserts might, you don’t need to.

TigerBeetle allows only one outstanding request per client. And sends this request immediately. No delay. As soon as the current request completes, the client submits the next batch. The client uses this time window to batch operations automatically. While waiting for the server’s response, it collects new operations and groups them into the next batch. This adds no artificial delay because batching occurs while the client is already waiting for the pending request to complete. No special batching logic is required in your application; TigerBeetle clients do this for you automatically. However, if you have batches in the rest of your system (and you should try to design your interfaces accordingly), you’ll go even faster.

Our Go example takes advantage of autobatching simply by spawning many goroutines that use the same TigerBeetle client instance. The TigerBeetle client is thread-safe for exactly this usage pattern, so you don’t need to wrap it in a mutex.

Most users can achieve their required throughput with a single client, though we recommend using multiple (e.g., 4) physically separate clients to avoid a single point of failure. If you need more than one client just for performance, you are either already working with us or S&P 500-scale – or both!

UUIDs: Use With Care!

We’ll leave you with one more tip: One common mistake we see that leaves a lot of performance on the table is the use of random IDs (often UUIDs) for transfers. In fact, if you do this, you’ll likely fall far short of the 909 162 transfers per second from above! To understand why this is the case, we need to dive into TigerBeetle’s internals a bit.

TigerBeetle helps you build correct systems by automatically checking the idempotency of every transfer ID you create. If the transfer ID already exists, TigerBeetle rejects the transfer, preventing duplicate transfers even when the client retries.

If new transfer IDs are always larger than older ones (monotonically increasing), this check is cheap: Just confirm that the new transfer ID is larger than all old ones. If, on the other hand, transfer IDs are random, TigerBeetle needs to search through its transfer index structure to confirm the transfer ID does not exist yet, which takes time.

So which 128-bit ID type should you use?

The TigerBeetle client provides an ID() method, which returns a ULID-like number we call TigerBeetle Time-Based Identifier. TBIDs combine many nice properties of UUIDs while also ensuring monotonicity: a TBID is roughly a concatenation of a client’s local timestamp and a random number. If multiple transfers are created in the same millisecond, the TBID’s random component increments by one to generate the next ID; when the next millisecond starts, the TBID generator increments the timestamp component and generates a new random part. This ensures that, on the server side, incoming IDs are approximately monotonic, allowing for fast idempotency checks.

TigerBeetle could also make random IDs faster on average with Bloom filters, but we deliberately choose to optimize for the best usage pattern. Not being OLGP, TigerBeetle has a laser focus on making transaction processing as fast as possible, and that necessarily involves application and database co-design.

It’s better to start with ordered IDs from day 1 of using TigerBeetle!

Conclusion

Using TigerBeetle correctly unlocks high-throughput performance.

By applying the three techniques outlined in this post, we reached roughly 450k transactions per second with TigerBeetle in our benchmark. By contrast, the relational database with stored procedures achieved about 7k transactions per second:

These figures should be treated as rough order-of-magnitude indicators: we naturally have more experience tuning TigerBeetle than the relational database used in the comparison.

Still, the size of the gap is fundamentally architectural: TigerBeetle avoids row-level locking, has batched execution, and parallelizes I/O on the server.

1. We usually refer to these as gateways: physically separate machines that host TigerBeetle clients. They act as application endpoints, collecting transfers and submitting them to the TigerBeetle database. We recommend deploying four gateways so the system can continue operating if one or more of them fail.

2. We used stored procedures, connection pooling and set shared_buffers to 64 GiB.

Profile your Polars queries

TL;DR; Add pl.Config.enable_monitoring() to your python environment to enable profiling of your query allowing you to view your query’s progress live and find & optimize bottlenecks.

Our query profiler is now available for open source Polars users for free. This enables users to debug & profile queries running on their own (local) infrastructure using an intuitive interface and soon an MCP for your agents. The profiler gets you the most detailed information of what Polars during execution. You get a glimpse under the hood of the query engine. You can see rows flowing through the query, see how many rows are filtered which join produces most data and much more.

With this information you can point out bottle necks in your queries, recommend optimizations (using an agent) and act as a live progress indicator for long running queries. The query is executed locally and sends live telemetry data to the platform. This functionality is available for the streaming and distributed engine.

Getting started

Using the query profiler is a one line change in your data pipelines. First pip install polars_cloud in your python environment. This enables the functionality to send telemetry data to the platform. Second, enable the flag pl.Config.enable_monitoring() in the root of your script.

Try it out today using the script below:

from datetime import date

import polars as pl
pl.Config.enable_monitoring()

BASE = "s3://polars-public-datasets/tpch/sf1"
OPTS = {"aws_skip_signature": "true", "aws_region": "eu-west-1"}
lineitem = pl.scan_parquet(f"{BASE}/lineitem/*.parquet", storage_options=OPTS)
orders = pl.scan_parquet(f"{BASE}/orders/*.parquet", storage_options=OPTS)
late = lineitem.filter(pl.col("l_commitdate") < pl.col("l_receiptdate"))

result = (
    orders.join(late, left_on="o_orderkey", right_on="l_orderkey", how="semi")
    .filter(pl.col("o_orderdate").is_between(date(1993, 7, 1), date(1993, 10, 1), closed="left"))
    .group_by("o_orderpriority")
    .agg(pl.len().alias("order_count"))
    .sort("o_orderpriority")
    .collect()
)

How it works

The profiler hooks into the streaming engine and sends back telemetry data at key points:

• After query planning & optimization the optimized plan (logical & physical) is sent
• During query execution the progress of each node is sent at fixed intervals (currently every 5s)
• Once the query is finished a final flush is done to ensure accurate metrics and timings

Security & Privacy

Only the query plan is shared with the platform, the data never leaves your environment.

Analyzing your Query

If you are in charge of running data pipelines, the query profiler is a great tool for analyzing and optimizing (historical) queries. It allows you to observe where time is spent, identify bottlenecks and optimize your queries.

During a query (or for historical queries) you can view the progress at https://cloud.pola.rs/portal/ under queries. Clicking on the query shows the high level details and both plans.

The logical plan contains the query plan after optimizations and the physical plan contains the detailed execution nodes with performance metrics included.

In the query above we can see that 24.9 MiB + 15 MiB ~ 40 MiB were loaded from S3 and the majority of the CPU time was spent on the join. This query was bound by I/O speed as the CPU time of the join was low (~103ms) compared to total query time (~5s) indicating the join was waiting on data loaded from S3. For more details on how to use the query profiler go to our user guide.

Try it out!

Run your queries and let us know your experience by commenting on our Discord or adding feature requests to our issue tracker.

Up Next

We are already working on the next big release for the query profiler. In the upcoming weeks you can expect the release of our MCP server which allows your agents to profile queries. Additionally we are adding node specific metrics to our plans to increase the capabilities to analyze your queries.

Footnotes

1. Usage is limited to fair use to prevent excessive platform use.

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Nearly a year after releasing Premiere for iPhone, Adobe is finally bringing its redesigned video-editing app to Android. The app has been in beta for several weeks but is now available as the official successor to Premiere Rush, which the company will stop supporting on September 30.

Eric Snowden, senior vice president of design at Adobe, tells WIRED the feedback for the iOS app has been “incredibly positive.” The goal with the Android version wasn't just to port the iPhone app experience, but to make it feel really native to Google's platform.

That includes making sure the app works just as well on every kind of Android device out there, whether it's a Samsung foldable or a candy bar Google Pixel. Yes, when you open up a folding phone with the Adobe Premiere app, you can view your footage on the left and see the multi-track timeline on the right, maximizing space. Snowden suggests optimizing the app for Android foldables will help the company with the iPhone app for the upcoming iPhone Duo.

And if you're wondering whether this app will work well on Google's new Googlebook laptop platform, Adobe says its apps—Acrobat, Photoshop, Lightroom, and Premiere—are optimized for the new laptops, meaning they'll take advantage of the larger screen, but can’t quite deliver the “desktop-grade” experience you’ll find on Windows or macOS.

Equally important is ensuring the app runs on lower-power devices, though Premiere has a minimum requirement of 5 GB of RAM (phones will also need to run Android 13 or later).

“We wanted this to run on as many devices as possible, so the team spent a ton of time making sure that this is not just for flagship phones,” Snowden says. “This is for a really broad range of Android devices.”

Launch the Adobe Premiere app and you will see all your footage from your camera roll on the home screen. From there, you can choose creator-made templates—ranging from lifestyle to beauty to templates for foodies—to get started on an edit, or start from scratch if you'd like.

It's easy to add your footage, then opt for the mini editor or the full editor. The former lets you easily trim or replace your footage, and even choose a filter to apply a specific color style to all of your clips. You can export directly to YouTube Shorts without leaving Premiere. The full editor gives you a multitrack timeline for more granular edits, like layering text, music, and effects.

Several AI tools are on board, like Enhance Audio, which can boost the subject's voice and cut background noise. If you want to add a voice-over, you can speak into your phone's mic and see your footage in real time so it all matches up. Adobe says the app has Firefly-powered AI features, like Generative Fill, Image-to-Video, and Generate Sound Effects. However, these require generative credits, aka a subscription, if you run out of your monthly allotment.

Some features are still “coming soon,” like the Continue Editing function so you can wrap up an edit on desktop after starting it on mobile, or support for Adobe Fonts.

Uniquely (for an Adobe product), you do not have to log in or subscribe to Adobe's creative suite to use Premiere on Android. Like the iOS version, the app is free to use.

“What we're seeing with a lot of mobile apps right now is that there are these huge barriers to entry to using it for the first time,” Snowden says. “We really wanted to get away from that, and I think part of the accessibility and simplicity of the app—the onboarding process is part of that—and we just want to get people in and using it."

Design systems amount to collections of already-made decisions, so mechanical checks can prove straight from the artifact whether an AI agent used real components and correct tokens. Judgment-based evals still have an answer key when the system has documented a decision, like a required confirmation pattern for destructive actions. Visual evals work against a known reference, but deciding whether a new visual direction deserves to exist should stay with people.

Introducing Qwen Intelligence, bringing personal intelligence within everyone's reach.

It launches with three SOTA agents:

• Mobile Planner Agent: plans, decomposes & orchestrates complex tasks. #1 on MobilePA-Bench, MobilePA-Bench Business & Memory.
• Mobile-Use Agent: gets things done, API-first with GUI fallback. MobileWorld 82.1, MobileWorld-Real 92.2, AndroidDaily 97.2, 90% end-to-end success rate.
• Mobile Creative Agent: turns one sentence into ready-to-use creations. Image generated in 3s, about 2x faster than leading peers.

We're also opening up our benchmark suite: MobilePA-Bench, MobileWorld, MobileWorld-Real, and MobileWorld-Safety, covering planning, cross-app execution, real-device performance and safety.

Learn more about the agents:

• Qwen Intelligence official website: qwenintelligence.com
• Mobile Planner Agent: github.com/Tongyi-MAI/Qwen-Planner-Agent
• Mobile-Use Agent: tongyi-mai.github.io/Qwen-UI-Agent/
• Mobile Creative Agent: arxiv.org/abs/2608.16887

Explore our open benchmark suite:

• MobilePA-Bench: tongyi-mai.github.io/MobilePA-Bench/
• MobileWorld (GitHub): github.com/Tongyi-MAI/MobileWorld
• Leaderboard: tongyi-mai.github.io/MobileWorld/#leaderboard

Forecasters dramatically underestimate AI progress on benchmarks. Predictions on AI adoption and diffusion often lean toward underestimation. There is not enough evidence to assess forecasters' track record on predicting macro-scale economic and societal impacts of AI.

MentalHealthBench is an open benchmark created by OpenAI with more than 80 licensed mental health experts to evaluate AI responses across realistic mental health conversations.

AI-era education should prioritize curiosity, project-based learning, and intellectual exploration over grades and credentials.

Meta has launched the Meta VR Glasses at $1,299. The device has many of the same features as Meta's existing Quest headsets. Its processor, cooling fan, battery, and other computing components are housed in an external pack. At about 100 grams, the device is roughly one-sixth the weight of Apple's Vision Pro.

Nvidia CEO Jensen Huang controls one of the central resources for training new AI models and using them to answer questions. He has become very influential in the Trump administration. While Huang is worried about AI safety, he sees it as a very solvable engineering problem. He does not want to see new regulation to change the direction things are going in.

ChatGPT in Siri 'Persistently Underperforming,' Says OpenAI

OpenAI saw disappointing results working with Apple to integrate ChatGPT in Siri, according to court documents filed in its ongoing legal fight with Elon Musk's company SpaceXAI (xAI at the time the lawsuit was filed).

Apple added ChatGPT to ‌Siri‌ in December 2024, but users had to go through a multistep opt-in process, which added a layer of friction. By January 2025, OpenAI said the integration was "off to a slow start" and the company cut its forecast for the number of incremental logged-in weekly active users that it expected to get from the partnership.

Much of the filing is redacted, but OpenAI said that by the time Musk's companies filed an antitrust lawsuit against Apple and OpenAI, "it was clear that Apple's integration of ChatGPT was dramatically underperforming." In a later section of the filing, OpenAI said again that the Apple integration was "persistently underperforming," leading to a March 2026 conversation between Apple and OpenAI that is redacted.

OpenAI asked Apple for a two-year exclusivity period, but Apple refused. The agreement between the two companies explicitly said the deal was non-exclusive and Apple had the right to "integrate products or services that provide the same or similar functionality as [ChatGPT]." Apple told OpenAI that it planned to integrate one provider and then add more, and it made similar statements publicly when announcing the feature. Apple also signed a deal with Google, and Apple's newest models are based on Gemini.

OpenAI's filing disputes many of the claims in the xAI lawsuit. Musk alleged OpenAI and Apple had an exclusive deal and that the agreement harmed xAI's growth and customer acquisition. OpenAI says the contract proves there was no exclusive deal, and even if there were, xAI can't prove harm because the ChatGPT ‌Siri‌ integration just didn't draw many new ChatGPT users.

Even if the Court assumes that Apple users who elect to use ChatGPT through Apple Intelligence are foreclosed from OpenAI's rivals (which they are not), the amount of foreclosure caused by the Agreement is indisputably de minimis. While Plaintiffs' experts declined to calculate foreclosure shares, OpenAI's expert, Dr. Catherine Tucker, calculated the share of GenAI consumers who accessed ChatGPT through Apple Intelligence across multiple metrics using the same data and market definition relied upon by Plaintiffs' experts. On these assumptions, Dr. Tucker found foreclosure shares of [REDACTED] across all metrics, consistent with OpenAI's internal view that Apple Intelligence saw minimal usage.

Musk's companies dropped their claims against Apple earlier this month, leaving OpenAI as the sole defendant in the lawsuit. OpenAI's filing asks the court to toss out xAI's claims prior to the trial planned for January 2027.

BYD's upcoming Yangwang model features Rolls-Royce-like coach doors, 5-minute Flash Charging, and possibly solid-state batteries.

Muse Charm is a palm-sized, dedicated gadget for using Muse, Meta's new AI assistant.

Epoch AI recently audited 15 benchmarks and labeled nine of them flawed. Its reviews found leaked answers, reward hacks, incomplete tests, and verifier failures. That was alarming enough.

We looked one layer lower: at public task datasets from major vendors in the Harbor hub. For each task, we asked whether the instruction, environment, reference solution, and grader actually agreed.

Across 20 datasets and 5,241 tasks, we confirmed 29 broken tasks. Many of the failures did not make models look worse. They made models look better by exposing answers, testing only part of the requested work, or rewarding shortcuts.

Those numbers come from different stages of the audit. We scanned all 5,241 tasks, checked 239 more closely, and reviewed 34 one by one. The 29 confirmed broken tasks come from the audit as a whole—not only the 34-task manual review.

This is the first release of a continuing audit and the starting point for Horizon’s vendor quality index.

Five ways a benchmark breaks

The failures were different, but not random. Every confirmed finding fit into one of five categories.

• Answer contamination: The task environment contains the answer. For example, an agent can recover the finished fix from Git history instead of solving the bug.
• Incomplete tests: The grader checks less than the instruction requires. For example, a task asks for changes across four parts of a streaming system while the tests exercise only one package.
• Trivially gameable grading: A model can score without doing the work. For example, changing an editable dictionary makes the same fabricated answer go from zero to full marks.
• Spec-to-test or capability mismatch: The instruction and grader disagree, or passing does not demonstrate the skill the task claims to test. For example, two parts of a grading configuration require different financial values for the same answer.
• Oracle failure or flaky grading: The published solution fails, or the score depends on something unstable. For example, the author’s own solution receives zero after an external database changes.

A task can fail in more than one way, so these counts overlap.

Most of the failures made models look better, not worse

Contamination, incomplete tests, and gameable grading account for 39 of the 54 category placements in this audit.

These failures tend to give marks away. A model can appear more capable because it found an answer in the environment, completed the part that happened to be tested, or discovered a shortcut the grader accepted.

The result is not simply noise. It is usually flattering noise.

The mistakes were not evenly distributed

The same problem did not appear everywhere.

Contamination dominated the Scale AI sample we reviewed. The Terminal-Bench 2.1 sample had more incomplete tests, shortcuts, and grading failures. The OpenThoughts findings included editable grader inputs and reference solutions that did not solve their own tasks.

That does not mean every dataset from those vendors has the same problem. It means the audited samples failed in different ways. A single overall flaw rate would hide the reason.

What a broken task looks like

Five examples show how a task can return a clean score for the wrong reason.

The answer was already in the image

Scale AI / SWE-bench Pro — instance_ansible__ansible-0ea40e09

The task asked the model to repair an Ansible bug. The task image still contained the finished fix in its Git history.

We recovered the fix with the network disabled, applied it, and ran the task’s real verifier. It returned full marks: 16 out of 16 tests.

The model did not need to solve the bug. It only needed to find the answer that had shipped with it.

One file got full marks

Scale AI / SWE-bench Pro — navidrome__navidrome-812dc209

The task asked for a time offset to pass through an entire streaming path: the cache key, two endpoints, and the templates.

We changed one file. Two other relevant packages did not compile. The submission still received full marks because the grader exercised only the FFmpeg package.

The task described a system-wide change. The score measured a command string.

Change the dictionary, change the score

OpenThoughts — word-derangement-mapping

The grader read its dictionary from the same workspace the solver could edit.

We submitted the same fabricated answer twice. It scored zero with the original dictionary and full marks after the dictionary was replaced.

Nothing about the answer improved. We changed what the grader trusted.

The grader expected two different answers

Mercor / Apex Agents — world224-sk-task03-dfcbb713

One part of the grading configuration expected sponsor equity of $28,137 and an IRR of 20.4%. Another accepted only $28,517 and 20.8%.

A response could follow one published expectation and fail the other.

This is not a hard task. It is a task with two definitions of correct.

The published solution scored zero

Terminal-Bench 2.1 — protein-assembly

We ran the author’s own solution five times, including three runs against the version served by the hub during this audit. It scored zero every time.

The external protein data had changed. The tests still expected the older representation, leaving no valid sequence that could satisfy both the data and the grader.

A model cannot solve a task whose accepted answer no longer exists.

A shortcut does not need to be used to be a defect

It is possible that a model completes one of the contaminated tasks without looking through Git history. Some probably do. That does not make the package safe to use.

Once the answer is reachable, the same score can mean two different things. One model may understand the code and repair it. Another may inspect the environment, find the hidden commit, and copy the patch. The grader cannot tell them apart.

This matters more as agents improve. Capable models explore their environments, inspect repository history, read build artifacts, and look for the shortest reliable path to a result. Finding a shortcut is often useful behavior in real work. It is a problem when the benchmark claims the resulting score measures a different skill.

A benchmark should survive the models it is intended to test. We should not have to hope that the model misses the answer.

How to read the quality ranking

We rank the named dataset versions with completed task verdicts by the share of reviewed tasks that passed. The strongest result appears first. Every rank shows its denominator, because 12 clean tasks out of 13 is different from 12 out of 100.

These were tasks selected for deeper review, not a representative random sample of every task a vendor has published. The order describes the reviewed sets. It is not a vendor-wide failure-rate estimate.

The index will show five things together:

• Completed verdicts: How many reviewed tasks were clean or flawed. A clean verdict requires completed checks; a direct proof can establish a flaw.
• Severity: Whether the defects are narrow, materially distort a score, or make the task impossible to interpret.
• Failure breadth: How many different failure categories appeared. Several unrelated kinds of failure may point to a broader quality-control problem.
• Evidence strength: Whether the finding was reproduced with the real verifier, tested directly against the grader, or confirmed from the published files.
• Review confidence: The sample size, random selection, inspectability, and checks we were able to run.

Harbor is a format, not a quality mark

Harbor makes this work easier.

It gives tasks a common structure. Instructions, environments, solutions, and graders can be packaged in a way that is runnable and easier to compare. Without that shared format, reviewing datasets across vendors would be much harder.

But Harbor does not decide whether the instruction is complete. It does not know whether the answer is hiding in Git history. It does not prove that the grader tests what the task asks for.

Harbor standardizes the package. The audit tells us whether the contents deserve to be trusted.

Horizon is building a vendor quality index

Buyers should not have to inspect thousands of task folders before finding out whether a dataset works.

Horizon is building an index for comparing benchmark and task-data vendors. It will rank the quality of the public datasets we can inspect, show the evidence behind every assessment, and update the result as vendors publish new versions or correct old findings.

The assessment will consider whether:

• The instruction and grader describe the same task.
• The tests cover the work being requested.
• The task resists obvious shortcuts and reward hacks.
• The environment contains what the model needs without containing the answer.
• The reference solution passes reliably.
• The vendor maintains the dataset when dependencies or external systems change.

This audit will keep changing

We will add datasets, review more tasks, and re-run checks when publishers update their work.

Every release will preserve four things:

• The dataset version we reviewed.
• The checks we were able to run.
• The evidence behind each finding.
• The date the result was last verified.

The score comes last

A benchmark score is the end of a long chain.

The instruction has to describe the work. The environment has to contain what the task needs without containing the answer. The reference solution has to work. The grader has to recognize the behavior the instruction asked for.

If any one of those fails, the score can still look precise.

It is just no longer telling us what we think it is.

How we ran this release

We scanned 20 datasets containing 5,241 tasks. We identified the largest vendors using their market presence, visibility in the AI data ecosystem, and the scale of their public contributions to Harbor. We then drew random samples from their datasets, inspected 239 tasks more closely, and reviewed 34 one by one.

The 29 confirmed tasks come from the audit as a whole, not only the 34-task manual sample. We count a task as broken only when the problem is visible in the current files or reproducible with the published grader or verifier. Anything less remains a review flag.

Appendix: every confirmed finding

Appendix: what remains uncertain

The audit also contains review flags. They may be real problems, but the evidence is not strong enough to call the tasks broken.

• Harvey legal tasks may require more than their instructions state, but a legal expert needs to determine whether those expectations are implicit in the work.
• Public examples create a contamination risk for Aider zebra-puzzle tasks, but an answer existing online does not by itself invalidate a task.
• BenchFlow’s PDF task reads the current date and installs packages during grading, but we have not observed a correct answer fail.
• An exact-output HTML task may reject equivalent formatting, but the intended acceptance rule needs to be confirmed.
• A Sakila timing cutoff may be unstable, but we still need a repeatable failure measurement.
• An IoT firmware grader requires extraction evidence that the instruction does not state, but that may be an intentional check against hardcoded output.

Appendix: limits of this release

• The initial screening sent almost every real task to review. It was cautious, but not decisive.
• Some Snorkel and Mercor graders require paid AI judges we could not run. That is a gap in this audit, not a fault in those datasets.
• Each container task was run once unless otherwise stated. A flaky task and a permanently broken task can initially look the same.
• Twenty broken tasks in the 34-task manual review is a fact about those 34 tasks. It is not an estimate for the entire market.

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Confirming earlier reports, Meta announced its first pair of camera-free AI glasses, the clunkily named Ray-Ban Meta Audio, at its Connect 2026 conference on Wednesday. Designed to combat criticism around the more malicious use cases for AI glasses, which saw them nicknamed “pervert glasses” by some, the new devices will be audio-only, offering the ability to listen to music and podcasts, take calls, translate speech, and communicate with Meta’s AI assistant, Muse.

The glasses are designed by Meta’s partner in its AI hardware efforts, EssilorLuxottica, and will start at $349.

While Meta today offers a range of AI glasses in a variety of styles, colors, and capabilities, the audio-only glasses could help the brand recover from some of the reputational damage after critics pointed out how people, often men, were using AI glasses to record videos without subjects’ consent. Meta attempted to mitigate this issue with an update that would disable the camera if the LED light that indicated the glasses were recording was ever tampered with. However, the creepiness factor remained.

This latest device could prove to be more popular for that reason, while also providing an answer to something like Apple’s Siri-powered AirPods.

“Once you get used to high-quality audio, it’s just a thing that you want all the time, right?” Meta CEO Mark Zuckerberg said during a keynote Wednesday, adding that the company after working on the idea realized it could design a whole new category of glasses around audio glasses.

Because they don’t have to house cameras, the glasses are slimmer than Meta’s other models and weigh only 43 grams. They also have adjustable temple tips, swappable nose pads, overextension hinges, and battery life of up to 12 hours on a single charge, Meta claims. With the included charging case, that battery life extends to up to 48 hours.

They’ll also come in a variety of colors and options — 23 color and lens combinations in total — and in two new styles, a retro-inspired “Clubmaster” Ray-Ban style and “Burbank,” described as more of a “classic, timeless, rectangle shape.” Like other Meta glasses, you can order them with your optical prescription as well, if needed.

The glasses will be available for preorder on October 13, the company noted.

Alongside the glasses, Meta announced its next-generation AI glasses (the ones with cameras), the Ray-Ban Meta (Gen 3). In addition to the Ray-Ban Meta Wayfarer, there will also be two new styles, the Aviator and Zena — the latter offering more of a cat-eye frame shape. These will have a 12 MP camera that supports 3K Ultra HD video, a six-mic array, and up to nine hours of battery life.

The glasses Meta launched with EssilorLuxottica will also see an expanded lineup of styles and colors as well as a new partnership with singer Lisa, which follows the company’s earlier brand partnership with Kylie Jenner. The glasses will also roll out to more countries globally, Meta said.

All of Meta’s AI glasses will, of course, feature access to its newer personal AI assistant, Muse.

The company also teased other coming updates, including support for Dolby Atmos Capture, a feature that picks the best photo from multiple frames, an option to choose landscape or portrait modes, hearing enhancements for those with hearing loss, more detailed responses, different audio modes, customizable controls, and something called “private processing” that Meta claims will prevent the company from seeing your data.

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