AI-pilled | AI Analyst

Paris, France
Joined February 2015
Benjamin Polge retweeted
#BREAKING: Microsoft offers voluntary exit packages to 60,000 staff
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Benjamin Polge retweeted
⚠️ Node.js is vulnerable to remote code execution. CVE-2026-111497 (high): A Node.js process that decompresses attacker-controlled Brotli data can be made to leak memory or run arbitrary code. You’re exposed if your app or a library decompresses bad Brotli data (e.g. the built-in fetch(), body-parser) Aikido detects this. Patches are available.
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Benjamin Polge retweeted
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. openai.com/index/coordinated…
Readers added context they thought people might want to know
‪OpenAI may claim a role in the coordinated response and patching process. But the initial discovery and exploit development, however, occurred several months earlier by an independent researcher.‬ ‪x.com/lcfr_hax/statu…‬
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Benjamin Polge retweeted
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. openai.com/index/openai-mode…
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Benjamin Polge retweeted
We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
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Benjamin Polge retweeted
The Department of Homeland Security is requiring all civilian federal agencies to remove DeepSeek products and 'any third-party services developed using DeepSeek implementations,' and to stop buying them.
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Benjamin Polge retweeted
New Cohere paper finds that a team of coding agents gets the most from extra agents when they only research and plan, and cheap models can do that job for a strong lead at far lower cost. A team of coding agents beats 1 agent working alone, but costs 3 to 7 times as much. More agents can also mean duplicate work and extra code to merge. The researchers built a lead agent that hands subtasks to workers, then varied the workers' role, model and count. On ProgramBench, agents rebuild a whole program from just a working executable and its docs. Workers that only plan matched the reward of workers used in every stage, at half the cost. A strong GPT-5.6 Sol lead with 16 cheap Luna planners came within 1.4 points of an all-Sol team at 70% lower cost. On tasks like this, put your strongest model in the lead and use cheap models only for planning, since cheap coders lowered reward.
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Benjamin Polge retweeted
MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download
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Same list price, different bill. We tested OpenAI and Anthropic models on 4 enterprise tasks at JDN. Claude Sonnet 5.5 and GPT-6.1 Sol cost the same per token, yet Sonnet came out up to 1.7x pricier. Main culprit: Claude's tokenizer counts 46-63% more tokens for the same text.
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Benjamin Polge retweeted
Today, we're publishing two new findings from inside Anthropic: how work and hiring are changing at a company at the frontier of AI adoption, and how Anthropic staff expect AI to change the economy. anthropic.com/research/expec…
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Benjamin Polge retweeted
Anthropic’s new report is a wake-up call for job seekers. TL;DR: • Junior tech hires: 27% → 9%. • AI skills cited 3–4x more in hiring reviews • Build your own automations. Show the receipts • Judgment > narrow expertise • Autonomy > following instructions Your best credential is not what you know, it’s how much you can automate yourself.
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Benjamin Polge retweeted
the life before this tweet:
today we launched ChatGPT. try talking with it here: chat.openai.com
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Benjamin Polge retweeted
What can large-scale AI models bring to the growing complexity of particle physics? @CERN and Mistral join forces to explore this question. The ambition: a single Large Physics Model, trained on billions of simulated collisions.
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Benjamin Polge retweeted
My 50 tips for using AI: 1. Data, data, data. Feed your AI as much good quality data as possible to fill in the context it hasn't got between its training data and what you want from it. With no context you'll get very generic responses. 2. Use chat history and chat memory. Build this up over time. Brain dump on it regularly. Have dedicated sessions to query the memory, fix it, and add to it. 3. Add custom instructions to fine-tune how you want it to respond and act, including the way it writes. 4. Clean up your local workspace. Add readmes to each folder to explain its context. Don't include obvious things any AI would be able to figure out. 5. Add an agents .md file to the root of your workspace. Again include context gaps only or regularly occurring mistakes. Keep it short. Use it like an if-then-else and point to an agent-docs folder with more instructions and context. 6. Use some form of task management tool, like Linear. Built-in tools are still pretty awful from my experience. Make sure to keep your task (issues) in a project. For more granular steps include checklists in your issues. 7. Use voice. Unless you're the fastest typer in the world, voice is always going to be quicker. It also lets you speak your mind. ChatGPT has great dictation, others are OK, but Copilot's particularly awful for some reason. 8. Use personal AI assistants or one session that can control all other sessions. The former is still early but is the end game for AI, get used to them. ChatGPT is really the only one that can do the latter properly at the moment. 9. When using your voice use a condenser mic that lets you talk incredibly quietly, thus not disturbing the people around you. 10. Give your AI various things, such as a master plan, principles, and an identity. 11. Ask your AI to keep a changelog and audit. 12. For schedules plan a way to have a backup so if you run out of tokens another AI can pick them up. 13. Use your AI on your mobile. It's surprising how much you can actually do. I can see a lot of us will go down to fewer and fewer and smaller and smaller screens. 14. Connect any and all relevant apps. I recommend GitHub for code work, Linear for tasks & project management, Notion for wiki and KB, Google Drive for storage and workspaces, Neon for the database, and perhaps something for video generation + image generation for Claude. 15. Use GitHub for coding. No you don't need to do any coding yourself. All control is managed by the AI. 16. And just don't be afraid of coding or building apps. You now have the power to build any app and tool you wish. There is no need to use proprietary interfaces anymore if you don't have to. 17. Ask your AI to set a schedule to review and research updates on all the tools and systems you use in your workspace. Ask it to find best practises, model releases, system card, updates, and prompting guides and ask it to suggest changes and improvements in your workspace after its review. 18. Stop doing admin. Give it to your AI. The only thing I would be wary of is managing finances. Though if you're in an area of the world that has a finance plugin such as ChatGPT in the US, use that. Other than that, from my experience, an AI can do everything else. 19. Markdown files are good but I'd recommend HTML files for explaining stuff. It's just better on the eye and gives richer content 20. Stop reading and using influencers prompts and guides (not including this one, of course 😜). They have no idea about you. You need to figure that out. 21. If you have any expertise, you are still better at that than people without that expertise so take advantage of that. AI does not make you an expert of something you are not, at least not yet or not instantly. 22. Though I'd argue coding and data analytics are solved, potentially a few other areas as well. 23. Stop paying for third-party tools and systems when AI can do that job. Ask your AI first. 24. Give it small things to do at first, then build up and up. Ask it to verify its output and check evidence. Ask it to ask you questions. 25. Give it examples and counterexamples; screenshots, files, and photos. If you're designing websites or tools, produce mockups with a relevant image generator. 26. There's no real need to start a new session nowadays but certainly create new sessions for new projects and put the AI in a new folder with project specific instructions so that session solely works on that. Use it like a worker. Keep it focused. 27. Never rely on a single model. You should have at least two chatbot plans. Use for second opinions and a backup for when one has an outage. Or just to run more work. 28. Document things you want the AI to permanently remember. Don't rely on memory and history for that. 29. Ask your agent to check your knowledge base and workspace for any conflicting info and work with it to fix it. 30. Make sure your knowledge base is easy to search, including the file names, with little explanation. 31. Build systems that last. If you're running repeatable tasks consider building a skill. I like to call these specialists. 32. Keep files and workspaces fresh. Remove any stale data. 33. Explain what "finished" and "complete" mean. 34. Prepare for mistakes. Always back up or have a way to recover. 35. AI gives you time to prototype over and over before you find your final solution. Long gone are the days of risking time on one idea for weeks or months. You can now test multiple ideas that only waste hours or days. You only need one of these to work. 36. When working with agents for the first time, start with all restrictions on and slowly but surely give it more and more freedom. They work better with fewer restrictions. 37. Only use AI if you need intelligence. Else use automation. Remember AI is still not 100% accurate, or 100% hallucination-free. Nor is it cheap. Tokens are precious. 38. So ask the AI to use scripts, formulas, and schedules where it can. 39. Schedules allow you to run your AI while you're not even at your desk, such as overnight, at weekends, and on all holidays. Set exactly what you want it to do: some AI can set fairly dynamic schedules (such as one piece of work during the week and a different piece of work during the weekend) or are reactive depending on the results 40. Ask your AI to run parallel sessions to get more work done quicker. Or if you're not using a personal AI assistant or orchestration, run multiple sessions at once yourself. 41. Use desktop apps for your AI. They give you so much more than the web versions. 42. Speak to your AI a lot. Most AI are powerful enough out of the box that a lot of prompting techniques are now dead. Talk to it like you talk to a person to get the best out of it. 43. AI is like a work colleague, be it one without context. Treat it as such. 44. Get it to question your assumptions, identify weaknesses, and present alternatives. Ask for pushback 45. Always fact-check important claims and ask for sources and dates. 46. Check what it can actually access. Don't presume what it tells you is correct. 47. No need to wait for the perfect use case. Just give it a problem right in front of you now. 48. Watch for silent failures such as schedules breaking. 49. This advice will get outdated very quickly. Always keep up to date with the latest news and techniques. 50. My top tip is just to start using it. The earlier the better. You'll start to understand how they work and their pros and cons. You'll also start to build up context, memories, and history, which are hugely vital. We're at the stage where we are now delegators and supervisors, get the AI to do most of the work as long as you give it good instructions at the start, check it thoroughly at the end and guide it in the middle.
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Benjamin Polge retweeted
We've been working hard on making the Claude Code desktop app great for running lots of sessions side by side. It's now much quicker to start sessions, easier to split work across a few, and you can always see what each of them is doing. Here’s what's new:
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This graph is all you need to know
The cost is dropping faster than any transformative technology in history, 54 times faster than electricity, and it's been dropping at this same rate since GPT-3.
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Benjamin Polge retweeted
Today we are rolling out Gemini 4 Argon globally in the Gemini API, to Pro + Ultra in Gemini App, and Ultra subs in Antigravity! Argon is a huge step forward for Gemini, and a labor of love. This model has been battle tested by over 100,000 people at Google over the last month, with so much new innovation, ambition, and hard work baked into this release. The team has been heads down over the last 6 months working to make this possible across the stack (research, infra, safety, eng, etc). We rolled the model out to cyber defenders as part of our fairwind program and early access customers over the last 2 weeks, it has been amazing to see and talk to folks who are now using this model daily. Seeing the models writing capability, raw knowledge and persistence for coding, paired with new features like 1M token output when the model needs to work more has gotten me even more excited about our path ahead. This is just the start of the Gemini 4 era. As you saw with 3.5, 3.6, 3.7, and 3.8 Flash, our iteration speed with Gemini is fast : ) Now with Gemini 4 Argon to work from, I am cannot wait to see the progress continue, and most importantly see what this new model enables for the world. This is such a special moment in the arc of technology, as millions of people everyday tackle problems they could never have thought possible before. Gemini 4 Argon was built to help you tackle the hardest problems. I have personally felt this spark with Argon and hope you do too. Onwards!!
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