OpenAI 用模型挖出 PHP 核心漏洞

RT @ChrisMurphyCT: Ukraine was winning the war, so Trump kneecapped Ukraine to help Russia. I wish it weren’t that simple, but it is. Our…

RT @PabloTorre: I have greatly enjoyed watching America feel the same things I have felt about Vivek Ramaswamy since the fall of 2003

OpenAI models helped researchers—operating as part of our Patch the Planet initiative—discover critical security flaws in PHP, an open-source programming language used to run an estimated 70% of websites. Over the past two months, we led a coalition of maintainers, researchers, and security providers to prepare fixes and protections ahead of disclosure. This duration was necessary because the fix was quite intricate and required a major change to the PHP engine. Every site using PHP can now safely upgrade their version to stay secure, in addition to applying mitigations from various security vendors. Due to the severity of these flaws and PHP’s widespread use, we consider this bug to be one of the most impactful vulnerabilities in recent memory. It also marks a new phase, where AI can find security bugs with internet-scale impact. This is an example of the Defender’s Window in action: to our knowledge, frontier AI was able to find this vulnerability ahead of attackers, giving defenders time to prepare. https://t.co/TW3jrSbB3f

If you are wondering whether your website is affected, we have packaged a Codex skill to help check. Point your Codex at this repo to guide you through it: https://t.co/XUEbOwXuQC

I am very grateful Aleksander accepted our request to come take this on; the safety and security challenges in front of us as we confront superintelligence are immense, and he has an excellent set of skills to work on this.

GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. https://t.co/oHdF3ELsMj

RT @OfficialLoganK: In just the first 72 hours since Gemini 4 Argon launched, we processed ~25 trillion tokens, the demand for intelligence…

RT @jonlovett: Maybe the most disgusting political ad in living memory. And then this statement by the campaign embracing the message - ju…

OpenAI models are coming to Google Cloud. We're bringing our frontier models and Codex agentic workflows to Google Cloud's Model Garden, so enterprises can use our technology where they already build. https://t.co/gjuLVALjDm

RT @TVietor08: Hey @ADL who used the term recently? Anyone notable?

RT @GoogleDeepMind: Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it anim…

RT @justinbaragona: "He was never convicted" because he jumped bail and fled the state, leading to the judge issuing an arrest warrant agai…

This summer, we invited METR and Redwood Research to pilot an embedded evaluation of our internal monitoring system, providing deep access to our infrastructure over three weeks. Their findings helped strengthen safeguards as we expanded the system and will help shape future research collaborations with METR and other third party evaluators.

This will make language models generate text much faster. Normally, a model generates one token at a time. But with this tool, a smaller helper model guesses several upcoming tokens, and the main model checks those guesses together. When the guesses are right, you get several tokens in one pass. They made the code available, and you can run this with vLLM and SGLang.

RT @AdamHoltererer: Gemini is the funniest model https://t.co/m4NPJGByN0

RT @Dogi1313: ELON FANS WON'T LIKE THIS ONE... BUT HERE ARE 10 BIG THINGS ELON PROMISED THAT NEVER HAPPENED: The New York Times went thro…

RT @Fritschner: Vivek Ramaswamy is now accusing Acton of lying about childhood trauma including sexual abuse she experienced as a 12-year-o…

Honoured to be featured in the @WSJ Innovators Issue. I’ve always believed in the power of human ingenuity to solve our hardest problems. I’m confident we can collectively navigate the potential challenges of frontier models and usher in a new golden age of scientific discovery!

RT @samstein: Christie with a conspicuous RT https://t.co/AY4GtWYIwq

For some people, coding was the only valuable skill they had. If you made your money by selling your ability to write JavaScript, CSS, Python, or whatever else, I understand why you are pissed now. "Writing code" is worth very little today. The sad part is that learning to code wasn't easy. It takes years for someone to become a skilled programmer. Imagine watching all of those years go down the drain. If you are a programmer (I mean, just someone who knows how to write code in a particular programming language), your days are numbered. If you still think AI can't do your job, you are just in denial. But honestly, writing code was never that valuable anyway. If you were a code pusher, you already knew that the best people at your company weren't just writing code. They came up with new ideas, designed solutions, architected whole systems, knew how to troubleshoot and fix problems, found different ways to build better software, and, in general, spent almost no time writing code. Not only are those people thriving today, but they're also the ones who will ride the AI wave and learn to bend it to their will. So don't cling too long to what you lost; embrace what's already here. AI is a tide that lifts all boats, but you have to be open to it.

RT @kyutai_labs: Pocket TTS has been getting updates those last few days. While it was ~100M parameters, we release smaller versions, down…

A programmer and a software engineer aren’t the same. I’ve known many people who made a living writing code, but they were always a cog in a larger process they never understood. Frontier models can do that job today. 1000x better. Software engineers can write code, but they always understood code was a means to an end. Software engineering is alive and well.

RT @AndrewJTobias: "Byrnes told CNN he obtained the video from a voter who saw it as part of a polling call and recorded it."

Here is an open-weight coding model you can run locally on your computer. It's a 40B parameter model with a mixture-of-experts architecture. It activates 2.5B parameters per generated token. 512K context window. This thing runs on regular computers! 125 tokens/sec on an M4 MacBook Pro. Good alternative to closed models, but make sure you check their usage guidance to understand the trade-offs.

Redis is cutting down the cost of running AI applications: 1. Semantic caching — Reuses generated answers from previous requests as long as they are sufficiently similar 2. Persistent memory — Retrieves relevant conversation context instead of requiring the full history in every prompt 3. Storage tiering — Moves less-used data from RAM to SSD Here is how each of these works: SEMANTIC CACHING - Using Redis LangCache • Your app sends a request to Redis • Redis embeds it • It then compares it with previous embedded requests • If it finds a match, it returns the stored answer • If there's no match, Redis calls the LLM PERSISTENT MEMORY - Using Redis Agent Memory • Your app sends a request to Redis • Redis extracts facts about the conversation in the background • It embeds those facts and stores them as long-term memory • Your app can now search that memory for relevant context STORAGE TIERING - Using Redis Flex • Redis keeps frequently accessed data in RAM • It automatically moves less-used data to SSD • When your app requests data, Redis brings it back into RAM • Redis adjusts where to store data as access patterns change • Your app keeps using the same Redis API Basically, your applications will make fewer repeated calls to the model, use smaller prompts, and need less RAM to do the same. Here is more information about this: https://t.co/uKjrSTy4YL Thanks to the @Redisinc team for partnering with me on this post.

You can now sell your data to train AI. You can package any datasets you think are valuable, describe them, and set a price. Then, you upload them to this marketplace. And just like this, a completely new business model starts.

I haven't talked to a single company that thinks it needs a better model to do what it wants. I think we have far more "intelligence" than we can use, and the biggest problem today is organizing knowledge and using it with the models we have. These guys are doing something cool: They connect your code with your company's knowledge base. Basically, when you merge a pull request, Falconer detects which docs are now outdated and prepares updates.

Please stop whatever you are doing and take the recent news about cyber vulnerabilities seriously. These events are now happening every 2-3 weeks. Your best next step: • Ask your agent how to protect your online accounts. • Be paranoid. • Do everything the agent says, even when you think it's a little too much. Here is another idea: this is a hosted service that gives your coding agent access to recent software vulnerability reports. If you connect it through MCP, your agent will automatically check for new vulnerabilities and help you patch them before you have to read about them on social media.

I love the idea of an agent reviewing all my social activity and identifying who is worth following and replying to. At some point, you can't reply to everyone, and you miss a ton of good opportunities because of it. Now, with so many AI replies, the problem is even worse. This seems aimed at companies, but I'd love to use it myself to see how much better my networking results can be on social media.

People are in denial. They literally think they will wake up tomorrow and all the AI-generated code will somehow be useless, and we'll go back to typing code by hand like Neanderthals.