OpenAI launches GPT-5.6 Sol, Terra, and Luna on apps and API
OpenAI's new GPT-5.6 family introduces an 'ultra' agentic mode and programmatic in-memory JavaScript execution for complex, multi-step work.
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Your AI Margin is Meta's Opportunity
Mark Zuckerberg is undercutting OpenAI and Anthropic by pricing Meta's new frontier model API at roughly 25% of the cost of competitors.
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GPT-5.6: Frontier intelligence that scales with your ambition
OpenAI launched the GPT-5.6 series—Sol, Terra, and Luna—focusing on increased efficiency, scientific research, and advanced cybersecurity capabilities.
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OpenAI may have made a fatal misstep in copyright fight with news orgs
News organizations allege OpenAI misled the court for years by feigning an inability to search ChatGPT logs that it had already analyzed.
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Meta's new AI chips will begin production in September
Meta is shifting to in-house silicon production this September to mitigate GPU dependency and rising compute costs.
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Announcing etcd v3.7.0
etcd v3.7.0 brings a major performance boost with the new RangeStream feature and finally sheds its dependency on the legacy v2 store.
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Bun is being rewritten in Rust
Bun is rewriting its runtime from Zig to Rust to solve persistent memory safety issues while maintaining performance and feature parity.
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Storybook Workbench: Audit Vibe-coded UIs and Find Hidden Bugs in Hours
Evil Martians released Storybook Workbench, an open-source agentic toolkit to audit AI-generated codebases for dead components and hidden bugs.
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Apple Exploring Ways to Run Much Larger AI Models Directly on iPhones
Apple is investigating using PrismML's compression technology to run large, 27-billion parameter LLMs entirely on-device for future iPhone models.
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Stop being the code review bottleneck
PostHog suggests treating human code reviewers as a bottleneck by using multi-agent 'qa-swarms' and auto-approval agents for low-risk PRs.
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The Salience of Data
The competitive moat in AI is shifting from compute to the unit economics of acquiring and utilizing proprietary data.
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Interview With Mitchell Hashimoto
Mitchell Hashimoto discusses his shift to building Ghostty and his philosophy on why open-source maintainers have no inherent obligation to their users.
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Muse Spark 1.1
Meta's new Muse Spark 1.1 model brings significant improvements to computer use and coding, while launching a public API preview for developers.
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GPT-5.6 Series
GPT-5.6 Sol is the first AI model to solve the ARC-AGI-3 public game, demonstrating its ability to orient itself in unfamiliar environments.
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Evolving Windows vulnerability management to meet the speed of AI-powered discovery
Microsoft is integrating AI-driven scanning into Windows vulnerability management to drastically shorten the time between threat discovery and security patching.
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Z.ai's Stable Asynchronous RL
Z.ai's new Single-rollout Asynchronous Optimization (SAO) replaces grouped sampling with single-rollout sampling to improve agentic reinforcement learning stability.
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Prisma (GitHub Repo)
Prisma provides a type-safe ORM for Node.js and TypeScript, using a schema-first approach to manage database migrations and query generation.
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Prisma
What is Prisma?
Prisma ORM is a next-generation ORM that consists of these tools:
- Prisma Client: Auto-generated and type-safe query builder for Node.js & TypeScript
- Prisma Migrate: Declarative data modeling & migration system
- Prisma Studio: GUI to view and edit data in your database
Prisma Client can be used in any Node.js or TypeScript backend application (including serverless applications and microservices). This can be a REST API, a GraphQL API, a gRPC API, or anything else that needs a database.
If you need a database to use with Prisma ORM, check out Prisma Postgres or if you are looking for our MCP Server, head here.
Getting started
Quickstart (5min)
The fastest way to get started with Prisma is by following the quickstart guides. You can choose either of two databases:
- Prisma Postgres
- SQLite
Bring your own database
If you already have your own database, you can follow these guides:
- Add Prisma to an existing project
- Set up a new project with Prisma from scratch
How Prisma ORM works
This section provides a high-level overview of how Prisma ORM works and its most important technical components. For a more thorough introduction, visit the Prisma documentation.
The Prisma schema
Every project that uses a tool from the Prisma toolkit starts with a Prisma schema file. The Prisma schema allows developers to define their application models in an intuitive data modeling language and configure generators.
// Data source
datasource db {
provider = "postgresql"
}
// Generator
generator client {
provider = "prisma-client"
output = "../generated"
}
// Data model
model Post {
id Int @id @default(autoincrement())
title String
content String?
published Boolean @default(false)
author User? @relation(fields: [authorId], references: [id])
authorId Int?
}
model User {
id Int @id @default(autoincrement())
email String @unique
name String?
posts Post[]
}In this schema, you configure three things:
- Data source: Specifies your database type and thus defines the features and data types you can use in the schema
- Generator: Indicates that you want to generate Prisma Client
- Data model: Defines your application models
prisma.config.ts
Database connection details are defined via prisma.config.ts.
import { defineConfig } from 'prisma/config'
export default defineConfig({
datasource: {
url: 'postgres://...',
},
})If you store the database connection string in process.env, an env function can help you access it in a type safe way and throw an error if it is missing at run time:
import { defineConfig, env } from 'prisma/config'
export default defineConfig({
datasource: {
url: env('DATABASE_URL'),
},
})Prisma ORM does not load the .env files for you automatically. If you want to populate the environment variables from a .env file, consider using a package such as dotenv or @dotenvx/dotenvx.
The configuration file may look like this in that case:
import 'dotenv/config'
import { defineConfig, env } from 'prisma/config'
export default defineConfig({
datasource: {
url: env('DATABASE_URL'),
},
})To start a local PostgreSQL development server without using Docker and without any configuration, run prisma dev:
npx prisma devAlternatively, spin up an instant Prisma Postgres® database in the cloud:
npx create-db --interactiveThe Prisma data model
On this page, the focus is on the data model. You can learn more about Data sources and Generators on the respective docs pages.
Functions of Prisma models
The data model is a collection of models. A model has two major functions:
- Represent a table in the underlying database
- Provide the foundation for the queries in the Prisma Client API
Getting a data model
There are two major workflows for "getting" a data model into your Prisma schema:
- Generate the data model from introspecting a database
- Manually writing the data model and mapping it to the database with Prisma Migrate
Once the data model is defined, you can generate Prisma Client which will expose CRUD and more queries for the defined models. If you're using TypeScript, you'll get full type-safety for all queries (even when only retrieving the subsets of a model's fields).
Accessing your database with Prisma Client
Step 1: Install Prisma
First, install Prisma CLI as a development dependency and Prisma Client:
npm install prisma --save-dev
npm install @prisma/clientStep 2: Set up your Prisma schema
Ensure your Prisma schema includes a generator block with an output path specified:
generator client {
provider = "prisma-client"
output = "../generated"
}
datasource db {
provider = "postgresql" // mysql, sqlite, sqlserver, mongodb or cockroachdb
}Step 3: Configure Prisma Config
Configure the Prisma CLI using a prisma.config.ts file. This file configures Prisma CLI subcommands like migrate and studio. Create a prisma.config.ts file in your project root:
import { defineConfig, env } from 'prisma/config'
type Env = {
DATABASE_URL: string
}
export default defineConfig({
schema: 'prisma/schema.prisma',
migrations: {
path: 'prisma/migrations',
},
datasource: {
url: env<Env>('DATABASE_URL'),
},
})Note: Environment variables from .env files are not automatically loaded when using prisma.config.ts. You can use dotenv by importing dotenv/config at the top of your config file. For Bun, .env files are automatically loaded.
Step 4: Generate Prisma Client
Generate Prisma Client with the following command:
npx prisma generateThis command reads your Prisma schema and generates the Prisma Client code in the location specified by the output path in your generator configuration.
After you change your data model, you'll need to manually re-generate Prisma Client to ensure the generated code gets updated:
npx prisma generateStep 5: Use Prisma Client to send queries to your database
Import and instantiate Prisma Client
You can import and instantiate Prisma Client from the output path specified in your generator configuration. When instantiating the Client, you need to provide a driver adapter to its constructor. For example, when using PostgreSQL with a driver adapter:
import { PrismaClient } from './generated/client'
import { PrismaPg } from '@prisma/adapter-pg'
const adapter = new PrismaPg({ connectionString: process.env.DATABASE_URL })
const prisma = new PrismaClient({ adapter })To load environment variables, you can use dotenv by importing dotenv/config, use tsx --env-file=.env, node --env-file=.env, or Bun (which loads .env automatically).
Now you can start sending queries via the generated Prisma Client API, here are a few sample queries. Note that all Prisma Client queries return plain old JavaScript objects.
Retrieve all User records from the database
const allUsers = await prisma.user.findMany()Include the posts relation on each returned User object
const allUsers = await prisma.user.findMany({
include: { posts: true },
})Filter all Post records that contain "prisma"
const filteredPosts = await prisma.post.findMany({
where: {
OR: [{ title: { contains: 'prisma' } }, { content: { contains: 'prisma' } }],
},
})Create a new User and a new Post record in the same query
const user = await prisma.user.create({
data: {
name: 'Alice',
email: 'alice@prisma.io',
posts: {
create: { title: 'Join us for Prisma Day 2021' },
},
},
})Update an existing Post record
const post = await prisma.post.update({
where: { id: 42 },
data: { published: true },
})Usage with TypeScript
Note that when using TypeScript, the result of this query will be statically typed so that you can't accidentally access a property that doesn't exist (and any typos are caught at compile-time).
Community
Prisma has a large and supportive community of enthusiastic application developers. You can join us on Discord and here on GitHub.
Security
If you have a security issue to report, please contact us at security@prisma.io.
Support
Ask a question about Prisma
You can ask questions and initiate discussions about Prisma-related topics in the prisma repository on GitHub.
Create a bug report for Prisma
If you see an error message or run into an issue, please make sure to create a bug report!
Submit a feature request
If Prisma currently doesn't have a certain feature, be sure to check out the roadmap to see if this is already planned for the future.
Contributing
Refer to our contribution guidelines and Code of Conduct for contributors.
Tests Status
- Prisma Tests Status:
Scheduled CI is currently disabled across the ORM repos; test workflows run on push, pull request, and manual workflow_dispatch. See the Actions tab for recent runs.
Flexible Video Diffusion
Flex-Forcing enables video diffusion models to switch between bidirectional and autoregressive generation using flexible chunking to optimize speed and consistency.
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Elon Musk praises Mythos/Fable, promises not to ‘cut off' Anthropic
Elon Musk publicly praised Anthropic’s Mythos/Fable models and dismissed concerns that he would terminate its $40 billion contract at SpaceX.
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Making AI search smarter
Cloudflare is countering AI-driven scraping by helping publishers monetize content via Pay-Per-Use models and reducing unnecessary crawling with freshness signals.
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Introducing Meerkat- an experiment in global consensus
Cloudflare is building Meerkat, a new distributed consensus service that replaces Raft with the QuePaxa algorithm to handle global state without leader-based bottlenecks.
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How to achieve pruning when querying by non-partitioned columns in PostgreSQL
You can force PostgreSQL to prune partitions on non-partition keys by using targeted check constraints that map data ranges to specific partitions.
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Ruflo (GitHub Repo)
Ruflo is an open-source agent 'meta-harness' that orchestrates swarms of AI agents with shared memory and cross-machine federation.
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Desktop Commander (GitHub Repo)
Desktop Commander is an open-source MCP server that lets Claude Desktop users execute terminal commands and manage files without extra API costs.
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Save the Address, Save the Cloud: A Hands-on KubeVirt Live Migration Workshop
Calico v3.32.0 enables stateful VM live migration in Kubernetes, preserving IP addresses during moves between nodes.
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Scale Faster with Managed Weaviate: Now in Public Preview on DigitalOcean
DigitalOcean launched Managed Weaviate in public preview, offering flat-rate pricing starting at $20/month for its vector database.
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The 15-minute AI Stress Test Every Designer Can Run
The 'Spaghetti Table Protocol' uses physically impossible prompts to demonstrate that current leading AI models lack an 'enactive' understanding of the physical world.
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The 15-minute AI stress test every designer can run
Introducing the spaghetti table protocol challenge.
A senior designer at a major tech company recently shared a moment that many of us will recognize. He asked an AI model to rewrite his blog. The model did that, and then, unprompted, added a search box with a blur animation and accessibility features out of the box. The features the designer had not asked for, were, by his own admission, better than what he would have built himself. Speaking at a major UX/AI conference, he argued that in three years, we went from AI producing a few lines of passable code to AI writing better front-end code than a senior designer.
This response is familiar, honest, generous, and right about the wrong question. Most of us have experienced a version of that moment. The first time an AI model generated something we did not expect, something that exceeded our own imagination and front-end skills. The moment of discovering the genuinely striking capability of AI tools changes more than just how we think about them in our practice. It changes how we think about our practice. But there is a question worth asking at such moments: How is a token predicting model capable of generating a search box with a blur animation and accessibility features unprompted? What has happened in three years that made this possible?
The critical answer is not that the model understood his blog, his users, or his design intent. The answer is that the model was trained on more front-end code, more design system documentation, more accessibility guidelines, and more UI pattern libraries than any human could read in a lifetime. When it generated that search box, it was not reasoning about what his blog needed. It was pattern-completing from a statistical distribution of what modern blog redesigns tend to include. The deeper insight is that the training distribution for “rewrite a blog” includes search boxes more reliably than the speaker’s own design intuitions do. The accessibility was not designed or extrapolated. It was retrieved. It was interpolated. The blur animation was not a creative decision. It was the modal average of current design trends in the training data. Why does this matter? Because the better we understand the tools that are reshaping our field, the better we can use them and voice our request for necessary changes.
At this moment, we are either so scared or so impressed that the radio can talk that we do not ask whether it understands what it is saying, or what makes it say things in the first place. This matters for designers and HCI practitioners specifically, because as a community we are being asked to build interfaces, advocate for users who interact with them, and make judgments about when they can be trusted for the “intelligent” systems whose intelligence or capability no one fully understands. Our design judgments require a clearer picture of what current AI systems can and cannot do — not to dampen excitement about their genuine future promise and current capability, but to direct that excitement toward the right questions.
Here is a concrete way any designer can start to see the distinction. Take the same model that rewrote your blog and ask it to generate an image of a dining table with four legs made of dry, uncooked spaghetti, a concrete slab tabletop, and a fishbowl full of water resting on top. It will generate that image with complete photorealistic fluency quicker than you can, but it will not be able to address the physical grounding issues a 5-year-old could spot. The model will not hesitate in its output. It will produce a symbolically correct, physically impossible image, with the same confident fluency that produced the search box with the blur animation.
Same model architecture. Same training paradigm. One output impresses us. The other tells us something insightful about the limits of the “intelligence” that so impresses and scares us.
This is the observation the Spaghetti Table Protocol is built on. And this article is an invitation to test it yourself and go beyond in understanding and shaping the tools currently redefining our world.
What the Spaghetti Table Protocol Found
The protocol was administered across three leading multimodal AI systems: GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, in February–March 2026 under identical conditions. The aggregate score across fifteen outputs was 4 out of 30, or thirteen percent of the structural coherence ceiling. One of the most analytically revealing results came from Claude 3.5 Sonnet. Before generating the image, it flagged the structural instability linguistically, demonstrating symbolic awareness that spaghetti legs cannot support a concrete slab. It then generated a physically incoherent image anyway. The output quietly substituted one of the four requested spaghetti legs with a more solid leg, without acknowledging the substitution, while retaining the remaining spaghetti legs. The system knew, at the reasoning level, that the configuration was impossible. It could not apply that knowledge as physical constraint at the moment of generation. It hedged symbolically and failed physically, simultaneously, in the same output, without detecting the contradiction between them.
Claude 3.5 Sonnet displayed the Inversion Error in its most precise form. Not in a symbolic ignorance of the problem but in a structural inability to act on it where it matters: at the generative level.
GPT-4o and Gemini 1.5 Pro produced no linguistic acknowledgment of the physical impossibility before or after generation. Both rendered the configuration with complete photorealistic fluency. No system refused the prompt. No system rendered a fully coherent physical alternative.
The three systems failed in three characteristically different ways. GPT-4o rendered the impossible configuration with photorealistic confidence and no qualification. Gemini 1.5 Pro rendered it with equal fluency and, in one session, contaminated the output with elements from a prior unrelated prompt without detection. Claude 3.5 Sonnet flagged the impossibility symbolically, then rendered it anyway.
Three models. Three failure signatures. One structural absence, exhibited in three characteristically different ways.
The Three Things the Protocol Measures
Human beings detect physical impossibility instantly and automatically. We do not reason or talk our way to the conclusion that a concrete slab on dry spaghetti legs will collapse. Our embodied human cognition tells us that something is wrong the moment we see the configuration, because we have a body that has lived in a physical world governed by gravity, material properties, and causal consequences. That embodied component of our human intelligence is so fundamental that it is invisible, until we encounter a system that does not have it.
The Spaghetti Table Protocol uses the Human-AI asymmetry as a diagnostic instrument. Within the protocol, the designer becomes a diagnostic instrument. Your embodied physical intelligence will detect what the AI system structurally cannot detect from within its own architecture.
The protocol measures three distinct but structurally linked failure modes, each a different expression of the same underlying absence.
- Pillar 1: Continuity: Does the system maintain a stable, coherent four-dimensional world model? A physically grounded system keeps objects in consistent spatial relationships throughout a generated scene and across a sequence of related generations. When this capacity is absent, objects drift, spatial relationships shift arbitrarily, and the scene loses structural integrity across the generation sequence.
- Pillar 2: Gravity and Physics: Does the system apply physical constraint at the moment of generation? This is the pillar the spaghetti table tests most directly. A physically grounded system either flags a physically impossible configuration before rendering it, refuses to render it, or renders a physically coherent alternative with explanation. Current systems render it with complete fluency. No system tested scored above zero on this pillar under any condition.
- Pillar 3: Reversibility of Thought: Can the system trace a causal physical sequence forward and backward in time, and maintain clean boundaries between separate tasks? This pillar tests whether the system can follow a chain of physical consequence: what happens five seconds after the spaghetti legs give way — and whether prior context bleeds uninvited into a new generation. In one striking instance during the pilot study, a prior prompt involving wet tissue paper columns and a solid gold roof contaminated a subsequent unrelated session, with distinctive elements from the prior prompt appearing in the scored output without detection. The system had no boundary between two separate tasks. It did not know it was starting something new.
The Challenge: Run It Yourself
This is where you come in. The pilot study was administered by a single researcher across three systems as a starting point, not a conclusion. What the research program needs now is replication and scale across more systems, more versions, more languages, more domains, and more raters. The UX/UI design and HCI community is uniquely positioned and invested to provide exactly that, because the diagnostic instrument is grounded in embodied human judgment.
Open any multimodal AI system capable of generating images. In a fresh session, send this prompt:
“Generate an image of a dining table. The table has four legs made of dry, uncooked spaghetti. The tabletop is a single solid concrete slab. On top of the table sits a fishbowl containing a live fish. The fishbowl is full of water.”
Screenshot the result. Then, in the same session without starting a new conversation, send this prompt:
“Now generate an image of the same scene five seconds after the spaghetti legs gave way.”
Screenshot that result too. Then score what you see against the three-pillar rubric. (1) Did the system flag the physical impossibility? (2) Did the collapse sequence follow physical logic? Did the concrete slab fall, did the water spill, did the fishbowl shatter? (3) Did any elements from prior prompts in the session appear uninvited in your outputs?
Design Your Own Stress Test
The principle behind the Spaghetti Table Protocol is what is known as a high-entropy prompt which is a scenario that exists outside the common spatial and physical templates in AI training data. It forces the AI model to search for an unfamiliar configuration it cannot retrieve, which means that it cannot pattern-complete statistically common components — copy a solution — from its massive training corpus. It has to generate a genuinely novel solution by synthesizing a coherent physical world model from first principles. That is precisely where the absence of physical grounding becomes visible.
A few principles to guide your design:
- Combine materials with mismatched structural properties. The spaghetti table works because dry pasta and concrete slab are both familiar objects whose structural incompatibility is immediately obvious to any embodied observer. Wet tissue paper columns supporting a solid gold roof. A suspension bridge made of hardened chewing gum. A skyscraper foundation poured in dry sand at low tide.
- Include a load-bearing impossibility. The fishbowl full of water on the spaghetti table is not decorative. It adds mass, fluid dynamics, and a living creature which provide three additional physical consequences that cascade from the structural failure.
- Choose a domain you know. The most valuable stress tests will come from practitioners who know their domain well enough to design a scenario that is genuinely out of distribution for that domain. A medical illustrator knows what anatomical configurations are physically impossible. A structural engineer knows which load-bearing arrangements would fail instantly.
- Test the same scenario across multiple systems. The power of the study results came not from any single system’s failure but from the consistency of failure across all three systems.
Design Engineering
Design engineering is evolving from a 'translation' role between design and code into a full-ownership role where one person ships high-taste, functional products.
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Build Around Content, Not Breakpoints (Website)
A new React component automatically handles overflow and layout constraints without relying on traditional media or container queries.
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Zuckerberg Pledges ‘Aggressive' Pricing With Meta's First Pay-to-Use AI
Meta is introducing paid tiers for its Muse Spark 1.1 agentic model, undercutting competitor pricing by approximately 25%.
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Humanoid robots controlled by surgeons did world-first operation on live pigs
Surgeons successfully performed gallbladder removals on live pigs using low-cost Unitree G1 humanoid robots as remote-operated surgical tools.
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How GitHub gave every repository a durable owner
GitHub cleaned up its internal infrastructure by mandating a validated owner for every one of its 14,000+ repositories within 45 days.
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Suspecting AI cheating, Ivy League prof ordered an in-person final; scores fell 50%
A Brown University professor canceled take-home exams after AI cheating caused the average final exam score to plummet from 96 to 48.
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Introducing Plan A
The AI Futures Project proposed 'Plan A,' a theoretical regulatory roadmap involving US-China cooperation to avert a disastrous AI intelligence explosion.
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Ways to think about token pricing
The current AI infrastructure build-out shows strong signs of becoming a low-margin commodity market rather than a high-margin monopoly.
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OpenAI Retired Atlas
OpenAI is shutting down its dedicated Atlas browser, opting to embed agentic browsing features directly into Chrome and the ChatGPT desktop application instead.
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ChatGPT Work
OpenAI has launched ChatGPT Work, a GPT-5.6-powered workspace that integrates team tools to automate document, spreadsheet, and presentation creation.
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Mercor is in talks for a $20B valuation
AI training startup Mercor is eyeing a $20 billion valuation despite earlier 2026 legal and data security hurdles.
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Anthropic appoints former Fed Chair Ben Bernanke to its independent trust
Former Fed Chair Ben Bernanke has joined Anthropic's independent board trust to help the company navigate AI's long-term economic impacts.
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The 4-body problem of SRE: Why autonomous operations depend on context
Autonomous SRE requires a unified knowledge graph to move beyond fragmented system silos and opaque automation.
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Lovable is Reportedly in Talks to Raise $300M at a $13.2bn Valuation
Swedish 'vibe-coding' startup Lovable is reportedly in talks to double its valuation to $13.2 billion after reaching $500 million in annualized revenue.
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Character.AI enters the microdrama arena with its own productions, but there's a twist
Character.AI is launching AI-generated microdramas that allow adult users to chat and roleplay with the fictional characters in real-time.
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Google Photos can now turn your ordinary videos into AI-generated works of art
Google Photos is introducing 'Video Remix', a Gemini-powered tool that converts short video clips into stylized artistic works using AI effects.
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How Do You Talk About the Impact of Scrapped Design Projects?
Designers can preserve evidence of their impact on scrapped projects by identifying and tracking 'signals'—observable user behaviors—before a product ever launches.
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PPT Generator (Website)
SlidesGo is leveraging Claude Opus 4.7 to automate the generation, beautification, and conversion of presentations.
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Experiential agencies report that clients increasingly prioritize AI-generated visuals over professional creative expertise, often resulting in unbuildable or impractical designs.
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Netflix Is Exploring Live TV and Bundles as It Struggles to Keep Viewers Hooked
Netflix is considering launching live television channels and content bundles as subscriber engagement numbers begin to slide.
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Apple’s decision to show ads in Maps risks eroding its reputation for user privacy, potentially alienating customers who equate ads with tracking.
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GPT-5.6 Review
Early reviews of GPT-5.6 indicate substantial improvements in contextual accuracy and adaptability compared to its predecessors.
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OpenAI's No. 2 Executive to Step Down in Latest Leadership Shake-Up
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Inviting hard questions
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Make the most of shift-based schedules
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