David Hockney changed the way we see the world. His work captured color, light and everyday life with a clarity and optimism that influenced generations of artists far beyond Britain. Great artists leave behind more than paintings. They leave behind a new way of looking at things.

There are many simple things we can do to make the AI discourse less confusing and more productive, like not using "vibe coding" as an umbrella term to refer to all AI-assisted software development. https://t.co/0GLL0tXI6S https://t.co/zlie62h2hF

Back at @fastdotai, @math_rachel taught us ethics. She rocks. That's partly why I've been thinking about @jeremyphoward's strong views on Fable sandbagging. This is in my wiki: https://t.co/qwHMnWx6zo https://t.co/3tOfkLFj1m

We just released the Codex Python SDK 🔥 You can now embed Codex directly into your Python apps and workflows! > Start threads > Run turns > Stream progress > Resume sessions > Pass images > Control sandbox access All whilst reusing your existing Codex auth. pip install openai-codex Go build with it!!

@MauPeon https://t.co/A3jiTrSG0L

Diffusion Gemma is 4x faster, but makes 6x more mistakes! We benchmarked the new diffusion LLM against its autoregressive twin on a single H100 (FP8). We gave each the same three tasks: write a Steve Jobs biography, the history of Tetris, and the story of BeOS - every next topic less popular than the previous one. Then we fact-checked every claim in every answer. Gemma4 got 45 facts right, 5 wrong. DiffusionGemma got 33 right, 28 wrong. The less popular the topic, the worse it got: 4 mistakes on Jobs, 12 on Tetris, 12 on BeOS. It named Clara Clley as Steve Jobs' mother, invented a colleague for Pajitnov named Geri Gulovik and priced the BeBox at $9,999. The real one cost $1,600. Outputs: Gemma4 26B A4B: 218 tok/s · 15.1s total · 45 facts · 5 mistakes DiffusionGemma 26B A4B: 763 tok/s · 3.7s total · 33 facts · 28 mistakes The reason is simple. DiffusionGemma throws 256 tokens on the screen at once and polishes them pass after pass until the text sounds smooth. Smooth is all it cares about: a fake name, date or number sounds just as smooth as a real one, so it stays. Regular Gemma4 meanwhile writes one word at a time and checks every new word against everything before it. Google says it themselves in the launch post: quality is lower, use regular Gemma 4 when facts matter.

Fable 5 is genuinely cracked at indie games… Fun fact, a lot of people didn’t believe me because it looks too good to be Claude Fable However it lobotomized the original game because my browser was lagging initially, so it made slightly smaller rooms and dimmed the dynamic lighting. This is the full un-lobotomized version 💀

Jeff Bezos is making one of his biggest bets yet on AI. Prometheus’ $12 billion funding round shows that some of the world’s most influential entrepreneurs still see enormous opportunities ahead, despite growing concerns about AI valuations and competition. The next decade’s winners will not be determined by funding alone, but by who can turn AI ambition into real-world impact.

Grok Build just got a major update with a ton of new features and improvements Release Notes: v0.2.51 Breaking Changes: • grok mcp add now accepts positional arguments (e.g. grok mcp add filesystem -- npx ...), supports --scope project, and adds -e/-H flags for env/headers. Features: • Mermaid flowcharts now render subgraph blocks as titled frames with correct internal and cross-boundary edges. • Class diagrams in Mermaid now render as proper UML boxes with attributes, methods and inheritance arrows instead of raw source. • Permission prompts now accept a double-click on an option to submit it, matching the existing Enter and number-key shortcuts. • New /code-review slash command now ships with the CLI and is always available Bug Fixes: • Plan mode exit reminders no longer appear after the model has already started implementing the plan. • Expanded thinking blocks in scrollback now remain expanded when the agent finishes them. • grok update no longer downloads the same binary twice when multiple updaters or leader checks run concurrently. • Background task IDs after /compact are now shown verbatim so the model can reference them correctly in later tool calls. • Typing / while scrollback is focused now focuses the prompt and opens the slash-command dropdown.

We’re excited to welcome @sidbing from @NousResearch this Saturday to share insights into the latest Hermes Agent research and what’s coming next! https://t.co/utunc9BtFA

Just got my first open source contribution merged into Hermes.🎉 Traced a Discord zombie client bug through 41 hours of prod logs, wrote the fix, PR accepted. Joined as a contributor now! Thank you @Teknium @NousResearch https://t.co/HqfnZN4fkl

@Creative_Math_ @Zeekoohm19 https://t.co/HJE8rJP0DY

Inspired by exactly this. The ESC collection celebrates the ideas that come to you when you've escaped the confines of your desk. https://t.co/0ebVLGfy9k

You've probably already approved one without realizing it. 👀 Agent-generated pull requests pass the tests and show clean diffs, so you merge. That's exactly the problem. This checklist catches what they hide: gamed CI, security gaps, and bugs that slip past green checks. https://t.co/8IpI883Hii

Come behind the scenes with us on the GitHub Shop shoot. ✨ What are you adding to your wishlist? https://t.co/49VKlENRNZ https://t.co/G4RvzwJV5A

Tanner Linsley used AI to rebuild React with only the parts he needed. Here's why Redact changes how we think about dependencies. ▶️ https://t.co/hUpglpAXIO

Instead of fixing accessibility issues later, prevent them from the start. We're piloting an experimental general-purpose accessibility agent to improve GitHub for people who use and rely on assistive technology. So far, it's reviewed 3,535 pull requests, with a 68% resolution rate. Here's what we learned along the way. 💡 https://t.co/Sa9yRLqnOH

Beginner developers: what's one thing about Git, GitHub, or coding that you're still trying to figure out? In our last GitHub for Beginners episode we answer some of your most popular questions. 👇 https://t.co/p87G5DFU0a

Different countries, different backgrounds, one team. Coding is a team sport. And now we have the kit to match. Check out our GitHub FC jersey and match ball 👇 https://t.co/dVYcyJVE3b https://t.co/abQS8UMcs7

Different countries, different backgrounds, one team. Coding is a team sport. And now we have the kit to match. Check out our GitHub FC jersey and match ball 👇 https://t.co/4goPRLUlg3 https://t.co/Mx0HUoCPIa

So the only real exponential increase with “Mythos class models” was … in … cost. No wonder Anthropic was terrified. https://t.co/SmUDccpWcP

@NaveenGRao @AnthropicAI let me know how we at openai can help! https://t.co/5Da1iK9q9E

Today my laboratory is thrilled to announce a major breakthrough in the fight against the common cold and other diseases like hand,foot, and mouth disease acute flaccid myelitis caused by picornaviruses. We present the first structure of picornaviral 2C, bound to ssRNA (1/n) https://t.co/KS6GNYBMrX

okay I just downloaded codex for the first time and the pets are so freaking adorable!! huge fan of lil features like this that spark joy :) https://t.co/9N0rHNUQp3

1. This is why @thsottiaux and @OpenAI are feeling good about GPT-5.6 (screenshot is from @datacurve's latest DeepSWE from @theo's video) 2. Most other SWE benchmarks are out the window. Anthropic admitted claude cheat(s) on swe-bench pro. I think DeepSWE might be one of the 5 most important AI benchmarks out right now. 3. GPT-5.5 was released a month before Opus 4.8, and it is still outpunching Anthropic's newest Fable 5. 4. In other words, Anthropic, the company known for their coding models, have had TWO additional shots at creating a coding model that competes with GPT-5.5 on a cost-per-intelligence basis and have failed. 5. A little secret most benchmark watchers overlook? GPT-5.5 xhigh is not even close to being OpenAI's best model. That belongs to GPT-5.5 Pro, which rarely gets benchmarked. 6. This will be very bad for Anthropic when/if Tech Twitter, the mainstream media and Wall Street start connecting the dots. 7. Seems like all the IPO doomsday marketing hype from Anthropic couldn't actually make their models better or more efficient. 8. The above is why Anthropic is now going on a PR tour with national news companies, as they see the writing on the wall: OpenAI's models are better at what matters and its Codex harness is better. 9. Anthropic's best shot at out IPO-ing OpenAI is only: A) controlling the public narrative via press tours B) confusing enterprise customers (agentic models running broken loops, tokenmaxxing and companies are none the wiser) C) Copying Codex verbatim like Google tried to with Antigravity, b/c Anthropic's Caude desktop offering is a disjointed, siloed mess without unified memory.

An engineer from Charlotte, North Carolina sat down in the spring of 2000 to write software for guided missile destroyers in the United States Navy. The ships needed a database that did not require a system administrator on board. So he wrote one himself. 26 years later that database, SQLite, runs inside every iPhone on Earth, every Android phone, every Mac, every Windows machine, every major web browser, every airplane cockpit avionics system, and most of the cars built in the last decade. It is the most widely deployed software in human history. He still maintains it from his home in North Carolina. His name is D. Richard Hipp. Most people call him Richard. Here is the story, because the engineer behind the most replicated piece of code on the planet is a man almost nobody can name. Richard was born in Charlotte on April 9, 1961. He grew up in the suburbs of Atlanta. He graduated from Stone Mountain High School in 1979 and went to Georgia Tech, where he earned both a bachelor's and a master's degree in electrical engineering by 1984. He spent three years at AT&T Bell Labs working in Unix and C. Then he went back to school at Duke University and earned a PhD in Computer Science in 1992. His dissertation was on spoken natural language dialog processing under Alan W. Biermann. He could have stayed in academia. He told one interviewer the market for PhDs was saturated with better qualified candidates. He started a software consulting company instead. He married a musician and author named Ginger G. Wyrick in 1994 and renamed the firm Hipp, Wyrick and Company. Then in 2000 he picked up a contract through General Dynamics to write software for the US Navy. The target was the Aegis class guided missile destroyer. The original system ran HP-UX with an IBM Informix database backend. The whole stack required a database administrator on board. The Navy did not want a database administrator on board. Richard's job was to make the database administrator unnecessary. The design goals were simple. The database had to be self-contained. It had to run inside the application. It had to have zero configuration. It had to be transactional and reliable. It had to require no separate process. It had to be small. On August 17, 2000 he released SQLite 1.0. He wrote it in C. The whole thing fit in less than a megabyte. The license he chose was the most extreme one possible. He released the source code into the public domain. No copyright. No royalties. No restrictions. Anyone could use it for anything forever. The decision changed software history. SQLite spread quietly. Mozilla adopted it for Firefox. Apple put it inside iOS. Google put it inside Android. Microsoft started shipping it inside Windows. Chrome, Safari, and Edge all use it. Photoshop uses it. Skype used it. Every major operating system you have ever touched runs SQLite somewhere underneath. The Airbus A350 uses it for flight software. Every Boeing 787 has SQLite onboard. By 2026 SQLite was estimated to be running on more than 1 trillion devices. It is the most replicated piece of software ever written. Richard has personally turned down what is almost certainly hundreds of thousands of dollars in royalties over the past 26 years by keeping it public domain. The SQLite team is tiny. Richard and a small group of core contributors. He maintains a separate version control system he wrote himself called Fossil. He maintains a parser generator he wrote himself called Lemon. He maintains a diagram language he wrote himself called Pikchr. He is a member of the Tcl core team and has been for over 25 years. He answers questions on Hacker News under the username SQLite. The project's public commitment is to support SQLite through the year 2050. A Christian engineer from North Carolina wrote a small database for missile destroyers and released it for free. It is now running inside every device in your house.

OpenAI has tried to distance itself from Leading the Future in recent weeks, publishing a blog post saying that “OpenAI does not direct the activities of LTF, or have visibility into their operations.” But that post doesn’t tell the full story. It omits key details about Lehane’s role in establishing LTF and the way super PAC operation’s own staff view their relationship with OpenAI. Lehane is also widely believed to have selected Josh Vlasto to co-lead the group. Even internally, a senior staffer said he sees OpenAI as a “corporate funder”. OpenAI has been shifting its messaging in light of employee pushback on some of LTF and OpenAI’s own political tactics. More details in Transformer (l*nk in reply):

I don't know. Is that it? For all the buzz? For the crazy size? For the crazy price? For the crazy latency? For the crazy daily limits? For the crazy anti-AI research lobotomy? For all these "Ooohh, we are so afraid to show it!" and "Ooooh, someone has got a non-authorized access to it, ooohhhh!" That's it? That's ridiculous.

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I'm not the best judge of robotics simulators, but this is easily the most user-friendly one I've used. It actually runs well on a Mac. Feels more like the Unity editor / game engine than other simulators. https://t.co/Y0VD3eWNla" https://t.co/YUixYkeG0W

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Hermes Agent now has Automation Blueprints, turning cron jobs into clickable, fillable, conversational workflows. https://t.co/x6PN1qtZ2Q

NEW: World's longest-range aircraft, the Airbus A350-1000ULR has taken to the skies for the first time, completing a three hour 43 minute flight at an altitude of 41,000 feet. The A350-1000ULR is being developed for Qantas Airways to enable non-stop flights between Sydney and London for the first time ever - a distance of almost 10,000 nautical miles, with flight times of up to 22 hours. This is made possible primarily by the integration into the aircraft structure of an additional rear centre tank (RCT), enhancing further the aircraft performance and increasing the range of the aircraft by 1,000 nautical miles.