OpenAI once mulled buying AI chip startup Cerebras, but since the deal fell through, has turned to alternative hardware options.

Sam Altman and Ilya Sutskever Invested in Cerebras But OpenAI Snubbed Its AI Chips

Cleverly forging a path to the future, OpenAI’s Startup Fund emerges as a catalyst for breakthrough technologies. Instead of hoarding technology, it spreads its roots across the tech industry, enabling new ventures to blossom and grow.

How OpenAI's Startup Fund is Quietly Shaping the Future of Technology

Since its founding in 2021, OpenAI Startup Fund has invested in over a dozen startups, including many of the hottest startups in AI.

OpenAI’s startup empire: The companies backed by its venture fund | TechCrunch

A recent study from a team of cybersecurity researchers has revealed severe security flaws in commercial-grade Large Reasoning Models (LRMs), including OpenAI’s o1/o3 series,...

Sliver C2 Server Vulnerability Let Attackers Open a TCP connection to Read Traffic

Plus, OpenAI says ChatGPT's growth is surging despite the DeepSeek buzz

Apple's new iPhone, Nvidia's new tool, and Palantir stock tanks: AI news roundup

About 30 researchers and engineers at Thinking Machines Lab come from rivals such as OpenAI, Meta, and Mistral, with ex-OpenAI staff making up two-thirds of the team.

Ex-OpenAI CTO Mira Murati launches AI startup, recruits top talent from rivals – Computerworld

Other key executives on Murati’s team include John Schulman, who co-founded OpenAI in 2015 and will be chief scientist at Thinking Machines Lab, and Barret Zoph, who served as OpenAI’s vice president of research and will be the new startup’s CTO.

Former OpenAI CTO Murati to launch new AI startup | Financial Post

Artificial intelligence (AI) is increasingly being used by companies not just for cost-cutting, but also as a way to drive new revenue streams. Premium AI services, such as OpenAI’s subscription models, reflect the growing costs of delivering such technologies.

Which companies will win the AI race? One VC shares his take.

Former OpenAI CTO Mira Murati has announced her new startup. Unsurprisingly, it's focused on AI.

Thinking Machines Lab is ex-OpenAI CTO Mira Murati's new startup | TechCrunch

OpenAI

openai

How to Use Ink-ui to Build Beautiful CLI Tools Like OpenAI’s Codex

Hello JavaScript Enthusiasts! Welcome to a new edition of “This Week in JavaScript”! This Week in JavaScript Today, we're exploring OpenAI's groundbreaking new models, important TC39 decisions, and a new high-performance JavaScript engine—plus exciting tool updates you won't want to miss! GPT-4.1, o3 and o4-mini, and Codex CLI: OpenAI’s Smartest Developer Tools GPT-4.1, o3 and o4-mini, and Codex CLI: OpenAI’s Smartest Developer Tools Together, these models and tools mark a shift from passive code suggestions to active, tool-using AI assistants that understand codebases, generate clean diffs, analyze logs, and even operate within the terminal. For JavaScript, full-stack, and backend developers, the implications are huge. GPT-4.1: Smarter, more precise, and built for real codebases GPT-4.1: Smarter, more precise, and built for real codebases GPT-4.1 introduces major upgrades that solve long-standing pain points in coding workflows: GPT-4.1 GPT-4.1 A 1 million token context window allows the model to work with entire codebases, multi-file apps, or full project documentation in a single go.
On the SWE-bench benchmark, GPT-4.1 hits 54.6% accuracy—a jump from GPT-4’s 33%. That means it solves far more real-world engineering tasks out of the box.
The model is trained for diff-style editing, which means instead of rewriting entire files, it returns clean, minimal code changes that can be reviewed or merged directly. A 1 million token context window allows the model to work with entire codebases, multi-file apps, or full project documentation in a single go. 1 million token context window On the SWE-bench benchmark, GPT-4.1 hits 54.6% accuracy—a jump from GPT-4’s 33%. That means it solves far more real-world engineering tasks out of the box. SWE-bench benchmark 54.6% accuracy The model is trained for diff-style editing, which means instead of rewriting entire files, it returns clean, minimal code changes that can be reviewed or merged directly. diff-style editing o3 and o4-mini: Agents that Think, Plan, and Act o3 and o4-mini: Agents that Think, Plan, and Act The o-series models add a layer of intelligent behavior that goes beyond code generation. The o-series models The o-series models These models can: These models can: Browse the web to fetch documentation or troubleshoot errors
Analyze uploaded files, logs, or datasets using Python
Understand screenshots and generate matching code or visuals
Create and manipulate images using DALL·E—all within the same conversation Browse the web to fetch documentation or troubleshoot errors Analyze uploaded files, logs, or datasets using Python Understand screenshots and generate matching code or visuals Create and manipulate images using DALL·E—all within the same conversation What makes them stand out is agentic behavior: the ability to reason through a problem, choose the right tools, and take action across multiple steps. agentic behavior For development use cases, this means: For development use cases, this means: Generating a custom Express backend with route logic and middleware
Analyzing error logs from production and returning structured insights
Translating hand-drawn wireframes into deployable front-end layouts Generating a custom Express backend with route logic and middleware Analyzing error logs from production and returning structured insights Translating hand-drawn wireframes into deployable front-end layouts This level of coordination turns the models into powerful assistants across the full software workflow. Codex CLI: AI Agent in the Terminal Codex CLI: AI Agent in the Terminal Codex CLI is a lightweight coding agent that runs locally in the terminal and connects to the user’s file system. It uses GPT-4.1 or o3 to interact with files, execute commands, and edit code through natural language instructions. Codex CLI Codex CLI Key capabilities include: Key capabilities include: Writing new features with natural prompts
Searching and summarizing code across large projects
Running scripts and interpreting outputs
Generating tests, documentation, and config files with minimal input Writing new features with natural prompts Searching and summarizing code across large projects Running scripts and interpreting outputs Generating tests, documentation, and config files with minimal input Codex CLI is fully open-source, and OpenAI is supporting its growth with a $1 million fund for projects built on top of it. $1 million fund Why This Matters Why This Matters This launch moves AI tools from reactive helpers to active collaborators. Instead of suggesting code in isolation, the models now understand context, plan across multiple steps, and even execute tasks locally via Codex CLI. The improvements in accuracy, reasoning, and usability make it possible to: accuracy reasoning usability Build faster
Debug with fewer mistakes
Create more consistent, production-ready outputs Build faster Debug with fewer mistakes Create more consistent, production-ready outputs For developers working in JavaScript, TypeScript, Python, or mixed stacks, this release offers a toolkit that integrates directly into existing workflows—and makes AI a far more practical part of daily development. JavaScript Records and Tuples are Dead JavaScript Records and Tuples are Dead At this month’s TC39 meeting, a major proposal was officially withdrawn: Records and Tuples, a long-running effort to bring deeply immutable data structures to JavaScript, has been shelved after several years of exploration. Records and Tuples Records and Tuples , But while one door closes, several new ones open. Records and Tuples: Proposal Withdrawn After Stalling at Stage 2 Records and Tuples: Proposal Withdrawn After Stalling at Stage 2 The Records and Tuples proposal aimed to introduce new primitive types for immutable values using special syntax like #{ x: 1 } and #[1, 2, 3 ]. It promised value-based equality and structural sharing—features developers often reach for when modeling data. #{ x: 1 } #[1, 2, 3 ] But the core idea faced growing concerns: It introduced new primitive types to the language
It required new syntax that many found awkward
It posed challenges for developer tooling and interop It introduced new primitive types to the language new primitive types It required new syntax that many found awkward new syntax It posed challenges for developer tooling and interop Despite years of refinement, the proposal failed to gain enough support to progress beyond Stage 2. As a result, it’s been withdrawn entirely. The functionality it aimed to deliver may return in a different form through Composite Keys, which offer value-based equality using regular JavaScript objects. Composite Keys Enums: TypeScript-Style Enums Enter Stage 1 Enums: TypeScript-Style Enums Enter Stage 1 A new Enum proposal has entered Stage 1, with the goal of bringing built-in enum support to JavaScript. The idea is to introduce a native enum syntax that aligns closely with TypeScript’s existing enum feature. Enum proposal This could mean: Cleaner declaration of constant sets
Better runtime compatibility with TypeScript enums
Easier modeling of state machines, role types, or status codes Cleaner declaration of constant sets Better runtime compatibility with TypeScript enums Easier modeling of state machines, role types, or status codes While the exact design is still evolving, the proposal advancing to Stage 1 signals serious interest in making enums part of the language natively. Deferred Re-exports: More Efficient Module Loading Deferred Re-exports: More Efficient Module Loading JavaScript modules may soon get a performance upgrade. The Deferred Re-exports proposal has reached Stage 2 and introduces the ability to selectively lazy-load re-exports. Deferred Re-exports This means developers could write: export defer { heavyTool } from './expensive.js';

export { fastTool } from './light.js'; export defer { heavyTool } from './expensive.js';

export { fastTool } from './light.js'; With this syntax, expensive.js will only load if heavyTool is actually imported. This opens the door to better tree-shaking, faster startup times, and smarter bundling strategies, especially in large libraries and frameworks. tree-shaking Map Upsert: Cleaner and Faster Key Management Map Upsert: Cleaner and Faster Key Management The Upsert proposal is nearing Stage 3 and would give developers a long-awaited method: Upsert proposal Map.prototype.getOrInsert Map.prototype.getOrInsert This pattern: if (!map.has(key)) {

map.set(key, defaultValue);

}

return map.get(key); if (!map.has(key)) {

map.set(key, defaultValue);

}

return map.get(key); Could become a single line: map.getOrInsert(key, () => defaultValue); map.getOrInsert(key, () => defaultValue); It’s more concise, avoids repeated lookups, and mirrors behavior found in other languages like Python and Rust. Great for caches, counters, and any scenario where maps are used as dynamic stores. Composite Keys: Value-Based Equality Without New Primitives Composite Keys: Value-Based Equality Without New Primitives Stepping in as a spiritual successor to Records and Tuples, Composite Keys have now entered Stage 1. They offer a new way to create objects that behave like value types—meaning two objects with the same structure and values will be considered equal. Composite Keys const one = Composite({ x: 1, y: 2 });

const two = Composite({ x: 1, y: 2 });

Composite.equal(one, two); // true const one = Composite({ x: 1, y: 2 });

const two = Composite({ x: 1, y: 2 });

Composite.equal(one, two); // true These can be used as Map or Set keys with value-based comparison, something that’s currently impossible with standard objects. If successful, this proposal could finally give JavaScript a clean and efficient way to model compound keys without resorting to stringification hacks. Map or Set keys What This Means for Developers What This Means for Developers The withdrawal of Records and Tuples may seem like a setback, but the momentum around Enums, Upsert, Composites, and smarter module loading shows TC39 is still actively evolving JavaScript toward more ergonomic, high-performance patterns. Many of these changes align closely with TypeScript workflows, making the future of JavaScript feel more familiar, more efficient, and more expressive. Keep an eye on these proposals. If they hit Stage 3 or 4 in the coming months, they could soon land in browsers, Node, or your next bundler release.Battle-Tested Node.js Testing Playbook Battle-Tested Node.js Testing Playbook Backend testing doesn’t have to be painful—and this GitHub repo proves it. Written like a no-fluff manual, it compiles over 50 hands-on strategies from real-world consulting work into one actionable playbook. Backend testing doesn’t have to be painful Backend testing doesn’t have to be painful What’s inside: What’s inside: Reliable patterns for mocking, Dockerized databases, flaky queue simulations, and message brokers
Test structures built around API routes and behavior-driven outcomes
A showcase Node.js app with 40+ tests (yes, including the DB) that all run in under five seconds Reliable patterns for mocking, Dockerized databases, flaky queue simulations, and message brokers Test structures built around API routes and behavior-driven outcomes A showcase Node.js app with 40+ tests (yes, including the DB) that all run in under five seconds If you're tired of flaky test suites and want a setup that runs fast and scales cleanly, this guide is gold. Hako: A High-Performance JavaScript Engine in a 800KB Box Hako: A High-Performance JavaScript Engine in a 800KB Box Hako is a new JavaScript engine focused on portability, performance, and security. Forked from PrimJS (an optimized QuickJS), it’s designed for embedding into any application with minimal overhead and maximum control. Hako Hako PrimJS Key highlights: Key highlights: WebAssembly-first architecture: Hako compiles to WASM, making it platform-agnostic and embeddable in any environment that supports WebAssembly. No need for native builds or complex bindings.
Built-in sandboxing and resource control: Runs securely within a WASM sandbox, with support for memory limits, execution quotas, and fail-fast controls to prevent abuse or denial-of-service scenarios.
Compact and fast: Weighs in at just ~800KB, starts quickly, and supports spinning up hundreds of lightweight VM instances. Ideal for plugins, serverless functions, and edge runtimes.
28% faster than QuickJS: Thanks to an improved interpreter and true garbage collection, Hako outperforms QuickJS in key benchmarks while retaining similar language coverage.
Modern JavaScript support: Supports ES2019+ features like modules, async/await, generators, BigInt, and more. Runs most modern JS code without issues.
Built-in debugging tools: Offers Chrome DevTools Protocol (CDP) support, allowing remote debugging via Chrome or VS Code. Rare among lightweight JS engines. WebAssembly-first architecture: Hako compiles to WASM, making it platform-agnostic and embeddable in any environment that supports WebAssembly. No need for native builds or complex bindings. WebAssembly-first architecture WASM Built-in sandboxing and resource control: Runs securely within a WASM sandbox, with support for memory limits, execution quotas, and fail-fast controls to prevent abuse or denial-of-service scenarios. Built-in sandboxing and resource control memory limits execution quotas fail-fast controls Compact and fast: Weighs in at just ~800KB, starts quickly, and supports spinning up hundreds of lightweight VM instances. Ideal for plugins, serverless functions, and edge runtimes. Compact and fast ~800KB 28% faster than QuickJS: Thanks to an improved interpreter and true garbage collection, Hako outperforms QuickJS in key benchmarks while retaining similar language coverage. 28% faster than QuickJS Modern JavaScript support: Supports ES2019+ features like modules, async/await, generators, BigInt, and more. Runs most modern JS code without issues. Modern JavaScript support Built-in debugging tools: Offers Chrome DevTools Protocol (CDP) support, allowing remote debugging via Chrome or VS Code. Rare among lightweight JS engines. Built-in debugging tools Chrome DevTools Protocol (CDP) Hako is best suited for developers building plugin systems, secure code sandboxes, lightweight REPLs, or cross-platform applications where running modern JS safely and efficiently is essential, and is faster than QuickJS, safer than Duktape, far lighter than V8, and more portable than Hermes. Tools & Releases You Should Know About Tools & Releases You Should Know About Let's speed-run through some of the other big tool updates this week! Astro 5.7: Introduces an experimental Fonts API supporting Google, Fontsource, and local files, plus a now-stable Sessions API for server-side data storage without cookies. SVG files can now be used directly as components.
WebStorm 2025.1: JetBrains' first major release of the year brings Claude model support to their AI Assistant, local model connections through Ollama, and improved Angular 17.2 signal query support.
tldts 7.0: This URL parsing utility now processes domains in microseconds with full Unicode compatibility, while adding email parsing and IPv6 detection.
Redux Toolkit 2.7: Adds Standard Schema validation support for RTK Query endpoints and fixes several bugs with infinite queries.
Babylon.js 8.3: Graphics improvements include native copy texture protocol and new post-processes like FXAA and film grain.
Bun v1.2.10: A mind-blowing performance boost for setImmediate (74 seconds → 4 milliseconds) makes Next.js builds 10% faster on macOS. Astro 5.7: Introduces an experimental Fonts API supporting Google, Fontsource, and local files, plus a now-stable Sessions API for server-side data storage without cookies. SVG files can now be used directly as components. Astro 5.7: WebStorm 2025.1: JetBrains' first major release of the year brings Claude model support to their AI Assistant, local model connections through Ollama, and improved Angular 17.2 signal query support. WebStorm 2025.1: tldts 7.0: This URL parsing utility now processes domains in microseconds with full Unicode compatibility, while adding email parsing and IPv6 detection. tldts 7.0: Redux Toolkit 2.7: Adds Standard Schema validation support for RTK Query endpoints and fixes several bugs with infinite queries. Redux Toolkit 2.7: Babylon.js 8.3: Graphics improvements include native copy texture protocol and new post-processes like FXAA and film grain. Babylon.js 8.3: Bun v1.2.10: A mind-blowing performance boost for setImmediate (74 seconds → 4 milliseconds) makes Next.js builds 10% faster on macOS. Bun v1.2.10: And that's it for the thirty-first issue of "This Week in JavaScript." This Week in JavaScript. Feel free to share this newsletter with a fellow developer, and make sure you're following for more weekly updates. Until next time, happy coding!

OpenAI Releases Its Smartest Developer Tools Yet

feature image: studio Ghibli style recreation of the famous photo of Mira Murati (left), Sam Altman (standing), Greg Brockman, and Ilya Sutskever (right), by ChatGPT (duh) feature image: studio Ghibli style recreation of the famous photo of Mira Murati (left), Sam Altman (standing), Greg Brockman, and Ilya Sutskever (right), by ChatGPT (duh) Remember that weird weekend in November 2023 when Sam Altman got fired from OpenAI and then… sort of hired by Microsoft and then… reinstated at OpenAI in less than a week? I wrote three posts about that drama in between Thanksgiving madness and oh my goodness, SO MUCH has happened since. If I were to catch you up on all things OpenAI and AI related, it would take a book. (For one, I bet if you are reading this story, your relationship with ChatGPT or similar chatbot has significantly changed since 2023. You are probably too distracted to read my article in full and will go to said chatbot to ask for a summary LOL) Sam Altman got fired from OpenAI sort of hired by Microsoft reinstated at OpenAI I wrote three posts about that drama In any case….. In any case….. Now, over a year later, we finally have SOME clarity—thanks to new reporting by Wall Street Journal reporter Keach Hagey, who hunted down all the major sources in the story for over a year, including Sam Altman himself, and conducted over 250 interviews! Shout out to the good folks at the Hard Fork Podcast whose interview finally gave me some much needed closure (ish) on this topic! https://x.com/sama/status/1908163013192069460?embedable=true https://x.com/sama/status/1908163013192069460?embedable=true Tweet above: Sam Altman confirming the authenticity of the book Tweet above: Sam Altman confirming the authenticity of the book So without further ado, here’s what actually happened, what’s changed since then, and how everyone’s kind of regretting everything... but also getting richer? Part 1: What Happened Since (And Why It Matters Even More Now) Since the week after Sam got fired, then unfired in November of 2023, here are a non-exhaustive list of what has happened in that world. The board got reshuffled. Bret Taylor, Larry Summers, and Adam D’Angelo became the new grown-ups in the room. Everyone promised to work on "governance" (lol).
Investigation confirmed the vibes. A law firm (WilmerHale - not to brag but a lot of UWC graduates work here. UWC of course is the education system that trained Mira Murati, and yours truly lol) hired by OpenAI concluded the board's firing of Altman was due to a "loss of trust," not a smoking gun about AI safety or some secret doomsday project. New Board agreed with the findings. Essentially, both old Board and new Board had the same facts; yet the latter had much more favorable views of Sam than the former.
Key people left. Ilya Sutskever (co-founder and chief scientist) and Mira Murati (CTO) both left OpenAI for their own ventures in May and September of 2024, respectively. Ilya now runs a new startup called Safe Superintelligence Inc., which already hit a $30B valuation, while Mira founded Thinking Machines Lab, which aimed at a $9B valuation with $1B raised.
Altman rejoined the board. Because of course he did.
OpenAI went full steam ahead. The tender offer (where employees get to cash out some equity) went through. Sam’s reinstatement as CEO somehow “coincided” with a valuation jump from around $30B to $90B post-chaos. OpenAI is still the belle of the AI ball, and Sam Altman remains its unshakable poster child.
OpenAI is trying to ditch its nonprofit origins. The company is lobbying to loosen the governance shackles imposed by its nonprofit parent—because, you know, it’s really hard to scale capitalism when you technically still report to a nonprofit mission. It’s not like anyone else at that point was still fooled about OpenAI profit mode.
Elon Musk is trolling from the sidelines. Musk (one of OpenAI cofounders to refresh your memory) reportedly offered to buy OpenAI for $97.4 billion, which of course Altman rejected, citing how OpenAI is "not for sale," but the point was made. Musk is of course too busy dealing with “reshaping" the entire US government to close the deal.
AI is going mainstream at breakneck speed. ChatGPT is basically the new Google for a huge chunk of the population, and the recent rollout of GPT-4 Turbo (with native image generation capabilities baked right in, like the 2 images you could see above) only poured rocket fuel on the adoption curve. Everyone from students to startups is using it, often daily, like it’s no big deal. The board got reshuffled. Bret Taylor, Larry Summers, and Adam D’Angelo became the new grown-ups in the room. Everyone promised to work on "governance" (lol). The board got reshuffled. Investigation confirmed the vibes. A law firm (WilmerHale - not to brag but a lot of UWC graduates work here. UWC of course is the education system that trained Mira Murati, and yours truly lol) hired by OpenAI concluded the board's firing of Altman was due to a "loss of trust," not a smoking gun about AI safety or some secret doomsday project. New Board agreed with the findings. Essentially, both old Board and new Board had the same facts; yet the latter had much more favorable views of Sam than the former. Investigation confirmed the vibes. Key people left. Ilya Sutskever (co-founder and chief scientist) and Mira Murati (CTO) both left OpenAI for their own ventures in May and September of 2024, respectively. Ilya now runs a new startup called Safe Superintelligence Inc., which already hit a $30B valuation, while Mira founded Thinking Machines Lab, which aimed at a $9B valuation with $1B raised. Key people left. Altman rejoined the board. Because of course he did. Altman rejoined the board. OpenAI went full steam ahead. The tender offer (where employees get to cash out some equity) went through. Sam’s reinstatement as CEO somehow “coincided” with a valuation jump from around $30B to $90B post-chaos. OpenAI is still the belle of the AI ball, and Sam Altman remains its unshakable poster child. OpenAI went full steam ahead. OpenAI is trying to ditch its nonprofit origins. The company is lobbying to loosen the governance shackles imposed by its nonprofit parent—because, you know, it’s really hard to scale capitalism when you technically still report to a nonprofit mission. It’s not like anyone else at that point was still fooled about OpenAI profit mode. OpenAI is trying to ditch its nonprofit origins. Elon Musk is trolling from the sidelines. Musk (one of OpenAI cofounders to refresh your memory) reportedly offered to buy OpenAI for $97.4 billion, which of course Altman rejected, citing how OpenAI is "not for sale," but the point was made. Musk is of course too busy dealing with “reshaping" the entire US government to close the deal. Elon Musk is trolling from the sidelines. AI is going mainstream at breakneck speed. ChatGPT is basically the new Google for a huge chunk of the population, and the recent rollout of GPT-4 Turbo (with native image generation capabilities baked right in, like the 2 images you could see above) only poured rocket fuel on the adoption curve. Everyone from students to startups is using it, often daily, like it’s no big deal. AI is going mainstream at breakneck speed. Part 2: So what actually caused the firing of Sam Altman? Thanks to Keach Hagey’s new book, coming out in a few weeks, we finally got a sneak peek at the full(ish) story—on the record, from the key players. Contrary to popular belief at the time (AGI is getting close!!!!), it really was about miscommunication, personalities, and interpersonal dynamics in a fast moving industry. The through-line though, was money. was Ilya and Mira were instrumental in flagging Sam’s behavior to the board multiple times prior to the firing. Ilya had major issues with how Sam approached safety/super-alignment research, for years at that point. Independent of Ilya, Mira had also come to the Board with screenshots from her Slack of what she saw as patterns of toxic behaviors (which she apparently had brought up to Sam himself already), for example telling whatever he thinks one wants to hear then promptly going back on his words behind their backs; or siding with Greg Brockman who was supposed to report to Mira on some of his disruptive tendency in teamwork. Both Ilya and Mira were top dogs within OpenAI, one being the cofounder/main researcher, the other Chief Technology Officer, so the Board trusted their judgment over Sam’s. One Board member also personally experienced this pattern of toxic behavior herself (presumably Helen Toner, who published a research paper with more favorable view on Anthropic than OpenAI, its competitor)
They regretted it almost immediately. Both Mira and Ilya expressed their regret almost overnight. In Mira’s telling, she thought the Board would simply get Sam an executive coach as opposed to replacing him with HER. Definitely a bad look if you value organizational harmony. Ilya also did not understand the extent to which the next thing would happen, which was…
Employees almost fully sided with Sam. Why? Not just because he’s charismatic and powerful, but because their own paydays would be jeopardized. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. It was a no-brainer to everyone that if Sam was fired, the offer was gonna go away. So essentially, they had to choose between dealing with toxic leadership vs. losing millions. Stuck in between this rock and a hard place - guess which one people chose 😉
The rest is history. Sam got reinstated. Ilya and Mira promptly left. And many, many other OpenAI influential people have also departed since. Ilya and Mira were instrumental in flagging Sam’s behavior to the board multiple times prior to the firing. Ilya had major issues with how Sam approached safety/super-alignment research, for years at that point. Independent of Ilya, Mira had also come to the Board with screenshots from her Slack of what she saw as patterns of toxic behaviors (which she apparently had brought up to Sam himself already), for example telling whatever he thinks one wants to hear then promptly going back on his words behind their backs; or siding with Greg Brockman who was supposed to report to Mira on some of his disruptive tendency in teamwork. Both Ilya and Mira were top dogs within OpenAI, one being the cofounder/main researcher, the other Chief Technology Officer, so the Board trusted their judgment over Sam’s. One Board member also personally experienced this pattern of toxic behavior herself (presumably Helen Toner, who published a research paper with more favorable view on Anthropic than OpenAI, its competitor) Ilya and Mira were instrumental They regretted it almost immediately. Both Mira and Ilya expressed their regret almost overnight. In Mira’s telling, she thought the Board would simply get Sam an executive coach as opposed to replacing him with HER. Definitely a bad look if you value organizational harmony. Ilya also did not understand the extent to which the next thing would happen, which was… They regretted it almost immediately. Employees almost fully sided with Sam. Why? Not just because he’s charismatic and powerful, but because their own paydays would be jeopardized. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. It was a no-brainer to everyone that if Sam was fired, the offer was gonna go away. So essentially, they had to choose between dealing with toxic leadership vs. losing millions. Stuck in between this rock and a hard place - guess which one people chose 😉 Employees almost fully sided with Sam. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. The rest is history. Sam got reinstated. Ilya and Mira promptly left. And many, many other OpenAI influential people have also departed since. Part 3: Why come clean now? The book also revealed some major details about the big question in probably everyone’s mind, which is: Why now? Why not then? It was such a secretive and confusing firing. Even after WilmerHale and the new Board came out with the new findings, and after many reportings about subsequent departures of key OpenAI people, nobody still really knows WHY he was fired in the first place. And we finally have some answers about why now. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI.


Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI. Sam initially didn’t want to talk he must think he might have lost a tiny battle, but won the fight. Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam wants to be remembered as one of the greats America Equity here Part 4: So what now? I feel like I finally understand everything. It looks like the same traits that make Sam so effective at raising money and pushing product are the ones that make him really hard to work with or trust. It’s no coincidence that literally every major player in the AI community has had some kind of messy breakup with the guy, including his own sister (not a AI player, but I just thought it’s noteworthy to point out that his own sister is also suing him). really hard Yet, at the end of the day, everyone involved kinda moved on quickly from the whole incident. The board regrets how they fired him. Ilya and Mira regret triggering the chaos. Employees felt trapped between values and financial upside. Meanwhile, Sam just... wins again. The governance experiment failed. The board was supposed to act as a counterweight to Sam’s unchecked power. But when push came to shove, the company sided with the founder. Like they always do. The nonprofit structure was supposed to safeguard humanity from corporate overreach. Instead, it caved under the same pressures it was built to resist. he nonprofit structure was supposed to safeguard humanity from corporate overreach. Instead, it caved under the same pressures it was built to resist. So what now? We’re back where we started—Sam Altman at the helm, OpenAI chasing AGI, the board trying to stay relevant, and the rest of us watching from the sidelines. Only now we know more about what actually went down—and how fragile the whole setup really was. OpenAI was supposed to be structured differently. Instead, it played out like every other Silicon Valley drama. Power won. Money won. And the guy who believes he’s building the future didn’t just survive—he became inevitable. Boom. Done.

OpenAI Saga Part 4: The firing & unfiring of CEO Sam Altman FINALLY explained

OpenAI Saga Část 4: Vyhození a odvolání generálního ředitele Sama Altmana KONEČNĚ vysvětleno

OpenAI Saga Parte 4: El despido y el despido del CEO Sam Altman finalmente explicado

Saga OpenAI Partie 4 : Le licenciement et le retour du PDG Sam Altman ENFIN expliqués

OpenAI Saga Part 4: Pembongkaran dan pembongkaran CEO Sam Altman Akhirnya dijelaskan

OpenAI Saga Part 4: CEO サム・アルトマン(Sam Altman)の解雇と解雇

OpenAI Saga ნაწილი 4: აღმასრულებელი დირექტორის სემ ალტმანის გათავისუფლება და გათავისუფლება საბოლოოდ განმარტა

OpenAI Saga Part 4: Ny famotsorana sy ny famotsorana ny CEO Sam Altman farany mazava

OpenAI Saga Część 4: W KOŃCU wyjaśniono zwolnienie i odwołanie CEO Sama Altmana

OpenAI Saga Part 4: The firing & unfiring of CEO Sam Altman இறுதியாக விளக்கப்பட்டது

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OpenAI Saga Bölüm 4: CEO Sam Altman'ın kovulması ve kovulmasının SONUNDA açıklanması

OpenAI Saga Phần 4: Sự sa thải và không sa thải của CEO Sam Altman cuối cùng đã giải thích

Hello AI Enthusiasts! Welcome to the ninth edition of "This Week in AI Engineering"! "This Week in AI Engineering" OpenAI launched a $50M funding connecting 15 research institutions, Inception Labs released Mercury with speeds 10x faster than current LLMs, Cohere For AI unveiled Aya Vision for multilingual capabilities, and Alibaba's QwQ-32B matches DeepSeek-R1 with far fewer parameters. With this, we’ll also be talking about some must-know tools to make developing AI agents and apps easier. NextGenAI: OpenAI's $50M Consortium Connecting 15 Research Institutions NextGenAI: OpenAI's $50M Consortium Connecting 15 Research Institutions OpenAI has launched NextGenAI, an alliance uniting 15 leading research institutions with $50M in funding to accelerate scientific breakthroughs and transform education through AI. The initiative provides research grants, compute resources, and API access to support academic innovation across disciplines. NextGenAI NextGenAI Technical Architecture: Technical Architecture: API Integration: Direct access for model training, fine-tuning, and application development
Resource Allocation: Dedicated compute resources for university-led AI model development API Integration: Direct access for model training, fine-tuning, and application development API Integration: Resource Allocation: Dedicated compute resources for university-led AI model development Resource Allocation: Key Capabilities: Key Capabilities: Cross-Institutional Collaboration: Shared resources and findings across consortium members
Educational Enhancement: Student access to hands-on AI model training and application development
Research Acceleration: AI-powered manufacturing, energy, and healthcare advancement at Ohio State
Knowledge Accessibility: Boston Public Library digitizing public domain materials for broader access Cross-Institutional Collaboration: Shared resources and findings across consortium members Cross-Institutional Collaboration: Educational Enhancement: Student access to hands-on AI model training and application development Educational Enhancement: Research Acceleration: AI-powered manufacturing, energy, and healthcare advancement at Ohio State Research Acceleration: Knowledge Accessibility: Boston Public Library digitizing public domain materials for broader access Knowledge Accessibility: Implementation Focus: Implementation Focus: Medical Research: Harvard and Boston Children's Hospital accelerating rare disease diagnostics
Scientific Discovery: Duke University conducting metascience research to identify high-impact AI fields
Educational Development: Texas A&M implementing Generative AI Literacy Initiative
Historical Preservation: Oxford digitizing rare texts at Bodleian Library with OpenAI's API Medical Research: Harvard and Boston Children's Hospital accelerating rare disease diagnostics Medical Research: Scientific Discovery: Duke University conducting metascience research to identify high-impact AI fields Scientific Discovery: Educational Development: Texas A&M implementing Generative AI Literacy Initiative Educational Development: Historical Preservation: Oxford digitizing rare texts at Bodleian Library with OpenAI's API Historical Preservation: The initiative expands on OpenAI's educational commitment following ChatGPT Edu's launch in May 2024. NextGenAI focuses specifically on providing API-level access and research funding to drive innovations in scientific research, university operations, and educational methodologies. Mercury: Inception Labs Launches 10x Faster Diffusion LLMs Mercury: Inception Labs Launches 10x Faster Diffusion LLMs Inception Labs has released Mercury, the first commercial-scale diffusion large language model (dLLM) family that achieves output speeds faster than DeepSeek Coder V2 Lite, GPT-4o Mini, and Claude 3.5 Haiku. The technology demonstrates breakthrough performance with Mercury Coder running at over 1000 tokens per second on standard NVIDIA H100s. Mercury Mercury Technical Architecture: Technical Architecture: Generation Method: Coarse-to-fine diffusion process instead of traditional autoregressive generation
Processing Pipeline: Transformer-based neural network that modifies multiple tokens in parallel
Hardware Support: Compatible with existing NVIDIA GPUs without requiring specialized chips
Deployment Options: Available via API and on-premise installations with fine-tuning support Generation Method: Coarse-to-fine diffusion process instead of traditional autoregressive generation Generation Method: Processing Pipeline: Transformer-based neural network that modifies multiple tokens in parallel Processing Pipeline: Hardware Support: Compatible with existing NVIDIA GPUs without requiring specialized chips Hardware Support: Deployment Options: Available via API and on-premise installations with fine-tuning support Deployment Options: Performance Metrics: Performance Metrics: Throughput: 1109 tokens/second for Mercury Coder Mini vs 59 tokens/second for GPT-4o Mini
HumanEval: 88.0% for Mercury Coder Mini, matching GPT-4o Mini's 88.0%
EvalPlus: 78.6% for Mercury Coder Mini vs 78.5% for GPT-4o Mini
Fill-in-the-Middle: 82.2% for Mercury Coder Mini, significantly outperforming GPT-4o Mini's 60.9% Throughput: 1109 tokens/second for Mercury Coder Mini vs 59 tokens/second for GPT-4o Mini Throughput: HumanEval: 88.0% for Mercury Coder Mini, matching GPT-4o Mini's 88.0% HumanEval: EvalPlus: 78.6% for Mercury Coder Mini vs 78.5% for GPT-4o Mini EvalPlus: Fill-in-the-Middle: 82.2% for Mercury Coder Mini, significantly outperforming GPT-4o Mini's 60.9% Fill-in-the-Middle: Comparative Analysis: Comparative Analysis: Speed Advantage: 20x faster than some frontier models running below 50 tokens/second
Quality Benchmarks: Mercury Coder Small scores 90.0% on HumanEval, tied with Gemini 2.0 Flash-Lite
User Preference: Second place in Copilot Arena, outperforming GPT-4o Mini and Gemini 1.5-Flash
Efficiency Gain: 5-10x reduction in inference costs while maintaining competitive code quality Speed Advantage: 20x faster than some frontier models running below 50 tokens/second Speed Advantage: Quality Benchmarks: Mercury Coder Small scores 90.0% on HumanEval, tied with Gemini 2.0 Flash-Lite Quality Benchmarks: User Preference: Second place in Copilot Arena, outperforming GPT-4o Mini and Gemini 1.5-Flash User Preference: Efficiency Gain: 5-10x reduction in inference costs while maintaining competitive code quality Efficiency Gain: The models enable new capabilities for latency-sensitive applications that previously required compromising on model quality to meet speed requirements. Mercury's architecture allows continuous refinement of outputs to correct mistakes and hallucinations, similar to approaches used in leading image and video generation systems. Aya Vision: Cohere For AI Launches State-of-the-Art Multilingual Vision Model Aya Vision: Cohere For AI Launches State-of-the-Art Multilingual Vision Model Cohere For AI has released Aya Vision, an advanced open-weight vision model that significantly expands multilingual and multimodal capabilities across 23 languages spoken by over half the world's population. The model excels in image captioning, visual question answering, and cross-modal translation tasks. Aya Vision Aya Vision Technical Architecture: Technical Architecture: Parameter Variants: Available in 8B and 32B parameter configurations
Language Support: Processes 23 languages with consistent performance across linguistic domains
Training Methodology: Combines synthetic annotations, translation rephrasing, and multimodal merging
Processing Pipeline: Unified image and text understanding with cross-modal translation capabilities Parameter Variants: Available in 8B and 32B parameter configurations Parameter Variants: Language Support: Processes 23 languages with consistent performance across linguistic domains Language Support: Training Methodology: Combines synthetic annotations, translation rephrasing, and multimodal merging Training Methodology: Processing Pipeline: Unified image and text understanding with cross-modal translation capabilities Processing Pipeline: Performance Metrics: Performance Metrics: AyaVisionBench: Aya Vision 8B achieves up to 70% win rates against comparable models
m-WildVision: 79% win rate in multilingual vision tasks for the 8B variant
Cross-Model Comparison: Aya Vision 8B outperforms Llama-3.2 90B Vision with 63% win rates
Efficiency Ratio: 32B model outperforms models 2x its size (Llama-3.2 90B, Molmo 72B) AyaVisionBench: Aya Vision 8B achieves up to 70% win rates against comparable models AyaVisionBench: m-WildVision: 79% win rate in multilingual vision tasks for the 8B variant m-WildVision: Cross-Model Comparison: Aya Vision 8B outperforms Llama-3.2 90B Vision with 63% win rates Cross-Model Comparison: Efficiency Ratio: 32B model outperforms models 2x its size (Llama-3.2 90B, Molmo 72B) Efficiency Ratio: Development Features: Development Features: Iterative Improvement: Performance scaling from 40.9% to 79.1% win rates through technical refinements
Evaluation Framework: Open-sourced Aya Vision Benchmark for multilingual multimodal assessment
Resource Efficiency: Optimized for researchers with limited computation resources
Accessibility Focus: Free access via WhatsApp integration for global usability Iterative Improvement: Performance scaling from 40.9% to 79.1% win rates through technical refinements Iterative Improvement: Evaluation Framework: Open-sourced Aya Vision Benchmark for multilingual multimodal assessment Evaluation Framework: Resource Efficiency: Optimized for researchers with limited computation resources Resource Efficiency: Accessibility Focus: Free access via WhatsApp integration for global usability Accessibility Focus: The release includes open-weights for both model sizes on Kaggle and Hugging Face, continuing Cohere's expansion of multilingual AI research that began with the Aya initiative two years ago. The model builds upon Aya Expanse, supporting research collaboration across 3,000 researchers from 119 countries. QwQ-32B: Alibaba's New Reasoning Model Achieves DeepSeek-R1 Level Performance QwQ-32B: Alibaba's New Reasoning Model Achieves DeepSeek-R1 Level Performance Alibaba has released QwQ-32B, a new open-source reinforcement learning (RL) enhanced language model that achieves performance comparable to DeepSeek-R1 despite using significantly fewer parameters. The model demonstrates that strategic RL applications can dramatically close the performance gap with much larger models. QwQ-32B QwQ-32B Technical Architecture: Technical Architecture: Parameter Size: 32B parameters versus DeepSeek-R1's 671B (37B activated)
Training Pipeline: Two-stage reinforcement learning with outcome-based rewards
First Stage: Math and coding task optimization using accuracy verifiers
Second Stage: General capability enhancement with reward models and rule-based verifiers
License: Apache 2.0 open-source availability Parameter Size: 32B parameters versus DeepSeek-R1's 671B (37B activated) Parameter Size: Training Pipeline: Two-stage reinforcement learning with outcome-based rewards Training Pipeline: First Stage: Math and coding task optimization using accuracy verifiers First Stage: Second Stage: General capability enhancement with reward models and rule-based verifiers Second Stage: License: Apache 2.0 open-source availability License: Performance Metrics: Performance Metrics: AIME24: 79.5% accuracy, matching DeepSeek-R1's 79.8%
LiveCodeBench: 63.4% score compared to DeepSeek-R1's 65.9%
LiveBench: 73.1% performance versus 71.6% for DeepSeek-R1
IFEval: 83.9% accuracy, comparable to R1's 83.3%
BFCL: 65.4% score, outperforming R1's 60.3% AIME24: 79.5% accuracy, matching DeepSeek-R1's 79.8% AIME24: LiveCodeBench: 63.4% score compared to DeepSeek-R1's 65.9% LiveCodeBench: LiveBench: 73.1% performance versus 71.6% for DeepSeek-R1 LiveBench: IFEval: 83.9% accuracy, comparable to R1's 83.3% IFEval: BFCL: 65.4% score, outperforming R1's 60.3% BFCL: Integration Capabilities: Integration Capabilities: Tool Use: Built-in agent capabilities for environmental interaction
Adaptive Reasoning: Dynamic thought process adjustment based on feedback
API Access: Available through Hugging Face, ModelScope, and Alibaba Cloud DashScope
Deployment: Accessible via Qwen Chat with Python integration examples Tool Use: Built-in agent capabilities for environmental interaction Tool Use: Adaptive Reasoning: Dynamic thought process adjustment based on feedback Adaptive Reasoning: API Access: Available through Hugging Face, ModelScope, and Alibaba Cloud DashScope API Access: Deployment: Accessible via Qwen Chat with Python integration examples Deployment: Alibaba's team identifies this as an initial step toward developing more capable AGI systems by combining stronger foundation models with scaled RL and computational resources. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About BoringUI is a technical platform that automates the creation of user interfaces (UIs) from JSON data. It generates UIs in HTML and Tailwind CSS, allowing users to copy and share the code via links. This tool streamlines UI development by providing a straightforward method to transform data into functional interfaces, enhancing productivity and collaboration among developers.
ChatWithGit is a specialized search engine designed to efficiently scan and index public GitHub repositories. It enables users to quickly find specific code snippets, files, or functionalities within the vast ecosystem of open-source projects. By offering targeted search capabilities, GitSearch helps developers discover and leverage existing code, contributing to increased efficiency and collaboration in software development.
DiffBlue is an enterprise-grade AI solution designed to automate unit test generation and management for complex Java code. Unlike LLM-driven assistants, it uses reinforcement learning to produce reliable, executable, and correct test code, ensuring IP security through on-premises operation. It integrates into IntelliJ and CI pipelines, generating tests 250x faster than manual methods and increasing code coverage. Diffblue Cover aims to
Swimm is an AI-powered platform designed to help developers understand and modernize complex mainframe codebases. It leverages AI to uncover code insights, generate documentation for languages like COBOL and Assembly, and extract business logic from legacy systems. Swimm provides features such as visualizing program flows, identifying dependencies, and assessing the impact of changes. It aims to reduce mainframe complexity, create missing specs, and ensure secure, compliant, and scalable operations, with options for both cloud and on-premises deployment. BoringUI is a technical platform that automates the creation of user interfaces (UIs) from JSON data. It generates UIs in HTML and Tailwind CSS, allowing users to copy and share the code via links. This tool streamlines UI development by providing a straightforward method to transform data into functional interfaces, enhancing productivity and collaboration among developers. BoringUI ChatWithGit is a specialized search engine designed to efficiently scan and index public GitHub repositories. It enables users to quickly find specific code snippets, files, or functionalities within the vast ecosystem of open-source projects. By offering targeted search capabilities, GitSearch helps developers discover and leverage existing code, contributing to increased efficiency and collaboration in software development. ChatWithGit DiffBlue is an enterprise-grade AI solution designed to automate unit test generation and management for complex Java code. Unlike LLM-driven assistants, it uses reinforcement learning to produce reliable, executable, and correct test code, ensuring IP security through on-premises operation. It integrates into IntelliJ and CI pipelines, generating tests 250x faster than manual methods and increasing code coverage. Diffblue Cover aims to DiffBlue Swimm is an AI-powered platform designed to help developers understand and modernize complex mainframe codebases. It leverages AI to uncover code insights, generate documentation for languages like COBOL and Assembly, and extract business logic from legacy systems. Swimm provides features such as visualizing program flows, identifying dependencies, and assessing the impact of changes. It aims to reduce mainframe complexity, create missing specs, and ensure secure, compliant, and scalable operations, with options for both cloud and on-premises deployment. Swimm And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this with your fellow AI enthusiasts and follow for the latest weekly updates. Until next time, happy building!

OpenAI Launches $50 million AI fund

Hello JavaScript Enthusiasts! Welcome to a new edition of "This Week in JavaScript"! A lot happened this week, let’s take a look at OpenAI's groundbreaking GPT-4.5 model, Doom running in TypeScript, major Bun updates, Firefox's privacy controversy, and the hottest tool updates in the dev community! OpenAI Releases GPT-4.5 Orion OpenAI has launched its most advanced model yet, GPT-4.5, code-named "Orion." And devs, this one’s going to change your workflow! Why it matters: AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer.
Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs!
Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. Though it comes at a higher cost, GPT-4.5's accuracy and enhanced problem-solving make it a valuable tool for developers working on creative and complex projects. Doom Runs in TypeScript’s Type System In what might be the wildest engineering feat of the year, Dimitri Mitropoulos has successfully run Doom in TypeScript’s Type system. The Setup: Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types.
Technical Feat: It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! This project is a technical marvel, showcasing the creative potential of TypeScript, even though it's unlikely you’ll be running games in your type definitions anytime soon. It pushes the boundaries of what’s possible with TypeScript. Bun 1.2.3 and 1.2.4 Double Release Bun versions 1.2.3 and 1.2.4 are here, packed with updates designed to improve your development experience! Key highlights: Frontend Bundling: Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init.
Built-in Routing: For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing.
SQL Enhancements: Bun.SQL now has better array support and multi-statement queries.
MacOS Speed Boost: A 60% increase in build speed for macOS apps, with new code-signing options. These updates make Bun an even more attractive option for JavaScript developers building full-stack apps. Firefox’s Privacy Controversy Mozilla sparked controversy this week with its updated Terms of Use and Privacy Notice. It suggested that Mozilla could use data from Firefox input, which worried many developers. What you need to know: Clarifications: Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified.
Opt-Out: If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Tools & Releases You Should Know About Let’s take a quick look at the latest tools making waves in the JavaScript world this week! Next.js 15.2: Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements.
Astro 5.4: New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization!
ESLint v9.21.0: Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules.
Ember 6.2: A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive!
Angular 19.2: New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. And that's it for the twenty-fourth issue of "This Week in JavaScript." If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious! Hello JavaScript Enthusiasts! Welcome to a new edition of " This Week in JavaScript "! This Week in JavaScript A lot happened this week, let’s take a look at OpenAI's groundbreaking GPT-4.5 model, Doom running in TypeScript, major Bun updates, Firefox's privacy controversy, and the hottest tool updates in the dev community! OpenAI Releases GPT-4.5 Orion OpenAI Releases GPT-4.5 Orion OpenAI has launched its most advanced model yet , GPT-4.5, code-named "Orion." And devs, this one’s going to change your workflow! launched its most advanced model yet launched its most advanced model yet Why it matters: Why it matters: AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer. Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs! Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. AI-Powered Development: GPT-4.5 outshines its predecessor in coding tasks, with higher scores on benchmarks like SWE-Lancer. AI-Powered Development: Fewer Mistakes: This model reduces hallucinations in code generation, leading to fewer bugs! Fewer Mistakes: Improved Persuasion: It excels in creative and human-centric tasks, performing better in persuasive scenarios. Improved Persuasion: Though it comes at a higher cost, GPT-4.5's accuracy and enhanced problem-solving make it a valuable tool for developers working on creative and complex projects. Doom Runs in TypeScript’s Type System Doom Runs in TypeScript’s Type System In what might be the wildest engineering feat of the year, Dimitri Mitropoulos has successfully run Doom in TypeScript’s Type system. Dimitri Mitropoulos has successfully run Doom Dimitri Mitropoulos has successfully run Doom The Setup: Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types. Technical Feat: It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! The Setup : Using TypeScript’s Turing-complete type system, Mitropoulos ran the 1993 classic game entirely within types. The Setup Technical Feat : It took 177 terabytes of type definitions processed over 12 days just to get the first frame running! Technical Feat This project is a technical marvel, showcasing the creative potential of TypeScript, even though it's unlikely you’ll be running games in your type definitions anytime soon. It pushes the boundaries of what’s possible with TypeScript. Bun 1.2.3 and 1.2.4 Double Release Bun 1.2.3 and 1.2.4 Double Release Bun versions 1.2.3 and 1.2.4 are here, packed with updates designed to improve your development experience! Bun versions 1.2.3 1.2.3 1.2.4 1.2.4 Key highlights: Key highlights: Frontend Bundling: Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init. Built-in Routing: For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing. SQL Enhancements: Bun.SQL now has better array support and multi-statement queries. MacOS Speed Boost: A 60% increase in build speed for macOS apps, with new code-signing options. Frontend Bundling : Just run bun ./index.html and let Bun handle the rest. It simplifies starting new projects with tools like React using bun init. Frontend Bundling Built-in Routing : For backend devs, dynamic path parameters in Bun.serve() enable quick and efficient routing. Built-in Routing SQL Enhancements : Bun.SQL now has better array support and multi-statement queries. SQL Enhancements MacOS Speed Boost : A 60% increase in build speed for macOS apps, with new code-signing options. MacOS Speed Boost These updates make Bun an even more attractive option for JavaScript developers building full-stack apps. Firefox’s Privacy Controversy Firefox’s Privacy Controversy Mozilla sparked controversy this week with its updated Terms of Use and Privacy Notice. It suggested that Mozilla could use data from Firefox input, which worried many developers. Mozilla sparked controversy Mozilla sparked controversy What you need to know: What you need to know: Clarifications: Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified. Opt-Out: If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Clarifications : Mozilla has reassured users that no personal data is used for AI training, and ad data is de-identified. Clarifications Opt-Out : If you’re concerned, Firefox allows users to opt out of technical data collection in the settings, especially important for sensitive dev projects. Opt-Out Tools & Releases You Should Know About Tools & Releases You Should Know About Let’s take a quick look at the latest tools making waves in the JavaScript world this week! Next.js 15.2: Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements. Astro 5.4: New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization! ESLint v9.21.0: Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules. Ember 6.2: A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive! Angular 19.2: New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. Next.js 15.2 : Redesigned debugging, experimental React View Transitions, and improved Node.js runtime in middleware. Faster stack traces and smoother rendering with Turbopack improvements. Next.js 15.2 Next.js 15.2 Astro 5.4 : New remote image optimization in Markdown, experimental responsive image support, and enhanced security features. Markdown images now get automatic optimization! Astro 5.4 Astro 5.4 ESLint v9.21.0 : Introduces the --ext CLI option, helping you lint specific file extensions more easily, along with improved metadata for deprecated rules. ESLint v9.21.0 ESLint v9.21.0 Ember 6.2 : A key fix resolves a TypeScript issue that had been affecting developers, while Ember CLI now allows app creation in any directory. And EmberData continues rebranding to WarpDrive! Ember 6.2 Ember 6.2 Angular 19.2 : New experimental httpResource, better TypeScript 5.8 support, and core enhancements like streaming resources and improved form validator sets. The update addresses race conditions, change detection timing issues, and animation handling on the server, plus a new migration tool that automatically converts templates to self-closing tags to save you time and improve code consistency. Angular 19.2 Angular 19.2 And that's it for the twenty-fourth issue of " This Week in JavaScript. " This Week in JavaScript. If you found these JavaScript updates useful, give this post an upvote to help others discover it. Don’t forget to share it with your fellow developers, and keep an eye out for more insights in the JavaScript world. Until next time, keep coding and stay curious!

Bun just got 60% faster, Doom now runs on TypeScript, and OpenAI launched GPT 4.5 - This Week in JS

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DeepSeek's Janus Pro, OpenAI o3-mini, Mistral's 24B Parameter Model, and More

Is OpenAI’s o3 Finally Thinking Like a Human?

Dink OpenAI se o3 uiteindelik soos 'n mens?

Най-накрая o3 на OpenAI мисли ли като човек?

¿El o3 de OpenAI finalmente piensa como un humano?

האם ה-o3 של OpenAI סוף סוף חושב כמו אדם?

क्या OpenAI का o3 अंततः मानव की तरह सोचने लगा है?

Apakah o3 OpenAI Akhirnya Berpikir Seperti Manusia?

OpenAI の o3 はついに人間のように考えるようになったのか?

តើ o3 របស់ OpenAI ទីបំផុតគិតដូចមនុស្សទេ?

Adakah o3 OpenAI Akhirnya Berfikir Seperti Manusia?

के OpenAI को o3 अन्ततः मानव जस्तै सोच्दै छ?

Неужели o3 от OpenAI наконец-то думает как человек?

Да ли ОпенАИ-ов о3 коначно размишља као човек?

Googled Tricked OpenAI Into Thinking It Was Ahead (It Wasn't)

Google engañó a OpenAI haciéndole creer que estaba por delante (no lo estaba)

Googled je prevario OpenAI da misli da je ispred (nije)

Googled te twonpe OpenAI pou l te panse li te devan (li pa t)

Googled menipu OpenAI dengan berpikir bahwa ia unggul (padahal tidak)

Googled は OpenAI を騙して自分たちが先行していると信じ込ませた (実際はそうではなかった)

Google OpenAI-ді алда деп ойлап, алдады (ол емес)

Google piemānīja OpenAI, liekot domāt, ka tas ir priekšā (tā nebija)

गुगलले ओपनएआईलाई ठग्यो यो अगाडि थियो भनेर सोच्दै (यो थिएन)

Google обманул OpenAI, заставив его думать, что он впереди (но это не так)

Google podviedol OpenAI, aby si myslel, že to bolo dopredu (nebolo)

Googled Yakanyengedza OpenAI Kufunga Kuti Yaive Mberi (Yanga isiri)

Googled Ilidanganya OpenAI Kufikiria Ilikuwa Mbele (Haikuwa)

OpenAI has recently announced a new subscription plan called ChatGPT Pro, priced at $200 per month. This plan includes access to the advanced o1 model, which now supports images and has improved skills in mathematics and coding. As someone who has been following these developments closely, here are my thoughts on this new offering. Exciting Updates with the o1 Model I think it's really cool that OpenAI is making progress again. Recently, there were concerns that AI development had hit a plateau because it required much more power for even small breakthroughs. With the new o1 model, these fears are eased. The o1 model's ability to handle images and perform better in math and coding is impressive. It shows that AI is still advancing and becoming more capable. Is ChatGPT Pro Worth the $200 Monthly Fee? The price of $200 per month for ChatGPT Pro seems reasonable to me. There will definitely be people who are willing to pay this without hesitation. However, the big question is whether the service is good enough to keep subscribers. Some online reviews mention that ChatGPT Pro is about 11% better in coding compared to the standard version. But considering it's usually 10 times more expensive than the regular subscription, it might not be the best deal for everyone. For some, a cheaper alternative like Claude 3.5 might be more appealing. I'm waiting to see what the first users have to say before making a final judgment. Benefits of the o1 Pro Mode Access to the o1 pro mode is a major plus for users who work with more complex queries. I believe this will have a positive impact, especially for those in scientific fields like math, physics, chemistry, and medicine. With more powerful reasoning capabilities, the o1 pro mode can help achieve breakthroughs and solve difficult problems more efficiently. o1 Model Now Available to All Paid Users Finally, OpenAI decided to make the o1 model available to all paid users and move it out of the preview stage. I'm really excited about this change and can't wait to try it out. It's great to see OpenAI making advanced models more accessible, allowing more people to benefit from their capabilities. New Features: Voice Module and Better Responses ChatGPT Pro also offers unlimited access to the voice module and improved answers to complex questions. However, I'm not sure how many users will regularly use the unlimited voice feature. It might be useful for some, but it may not be a deciding factor for everyone. Rumors of a $2,000 Monthly Subscription There are also rumors about a $2,000 per month subscription. I can see this being a reality for professional, governmental, and scientific users. It would be something between the regular web version and API access, possibly providing access to AGI (Artificial General Intelligence) that can work on tasks for hours. This higher-tier subscription could be very valuable for those who need advanced AI capabilities for their work. Conclusion OpenAI's ChatGPT Pro offers exciting new features and improved models like o1, but the high price tag may limit its appeal to a broader audience. It will be interesting to see how users respond and whether the benefits justify the cost. For those who need advanced AI for complex tasks, ChatGPT Pro could be a great investment. However, others might prefer sticking with more affordable options until they see more feedback from early adopters. OpenAI has recently announced a new subscription plan called ChatGPT Pro , priced at $200 per month . This plan includes access to the advanced o1 model , which now supports images and has improved skills in mathematics and coding. As someone who has been following these developments closely, here are my thoughts on this new offering. ChatGPT Pro ChatGPT Pro $200 per month o1 model Exciting Updates with the o1 Model Exciting Updates with the o1 Model I think it's really cool that OpenAI is making progress again. Recently, there were concerns that AI development had hit a plateau because it required much more power for even small breakthroughs. With the new o1 model, these fears are eased. The o1 model's ability to handle images and perform better in math and coding is impressive. It shows that AI is still advancing and becoming more capable. Is ChatGPT Pro Worth the $200 Monthly Fee? Is ChatGPT Pro Worth the $200 Monthly Fee? The price of $200 per month for ChatGPT Pro seems reasonable to me. There will definitely be people who are willing to pay this without hesitation. However, the big question is whether the service is good enough to keep subscribers. Some online reviews mention that ChatGPT Pro is about 11% better in coding compared to the standard version. But considering it's usually 10 times more expensive than the regular subscription, it might not be the best deal for everyone. For some, a cheaper alternative like Claude 3.5 might be more appealing. I'm waiting to see what the first users have to say before making a final judgment. 11% better in coding 10 times more expensive Claude 3.5 Benefits of the o1 Pro Mode Benefits of the o1 Pro Mode Access to the o1 pro mode is a major plus for users who work with more complex queries. I believe this will have a positive impact, especially for those in scientific fields like math, physics, chemistry, and medicine. With more powerful reasoning capabilities, the o1 pro mode can help achieve breakthroughs and solve difficult problems more efficiently. o1 pro mode o1 Model Now Available to All Paid Users o1 Model Now Available to All Paid Users Finally, OpenAI decided to make the o1 model available to all paid users and move it out of the preview stage. I'm really excited about this change and can't wait to try it out. It's great to see OpenAI making advanced models more accessible, allowing more people to benefit from their capabilities. New Features: Voice Module and Better Responses New Features: Voice Module and Better Responses ChatGPT Pro also offers unlimited access to the voice module and improved answers to complex questions. However, I'm not sure how many users will regularly use the unlimited voice feature. It might be useful for some, but it may not be a deciding factor for everyone. Rumors of a $2,000 Monthly Subscription Rumors of a $2,000 Monthly Subscription There are also rumors about a $2,000 per month subscription. I can see this being a reality for professional, governmental, and scientific users. It would be something between the regular web version and API access, possibly providing access to AGI (Artificial General Intelligence) that can work on tasks for hours. This higher-tier subscription could be very valuable for those who need advanced AI capabilities for their work. $2,000 per month AGI (Artificial General Intelligence) Conclusion Conclusion OpenAI's ChatGPT Pro offers exciting new features and improved models like o1, but the high price tag may limit its appeal to a broader audience. It will be interesting to see how users respond and whether the benefits justify the cost. For those who need advanced AI for complex tasks, ChatGPT Pro could be a great investment. However, others might prefer sticking with more affordable options until they see more feedback from early adopters.

OpenAI Launches ChatGPT Pro: Is the $200 Monthly Subscription Worth It?

OpenAI ChatGPT Proን ይጀምራል፡ የ$200 ወርሃዊ የደንበኝነት ምዝገባ ዋጋ አለው?

OpenAI ChatGPT Pro-nu işə salır: 200 dollarlıq aylıq abunə buna dəyərmi?

OpenAI пуска ChatGPT Pro: Заслужава ли си месечният абонамент от $200?

OpenAI lancerer ChatGPT Pro: Er det $200 månedlige abonnement værd?

OpenAI lanza ChatGPT Pro: ¿Vale la pena la suscripción mensual de $200?

OpenAI-k ChatGPT Pro abiarazten du: merezi al du 200 $ hileko harpidetzak?

OpenAI lansira ChatGPT Pro: Isplati li se mjesečna pretplata od 200 USD?

OpenAI משיקה את ChatGPT Pro: האם המנוי החודשי של $200 שווה את זה?

OpenAI が ChatGPT Pro をリリース: 月額 200 ドルのサブスクリプションは価値があるか?

OpenAI го лансираше ChatGPT Pro: Дали вреди месечната претплата од 200 долари?

OpenAI د ChatGPT پرو پیل کوي: ایا د $ 200 میاشتنۍ ګډون ارزښت لري؟

OpenAI Inotangisa ChatGPT Pro: Iyo $200 yeMwedzi Kunyoreswa Kwakakodzera Iko?

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