Chris Gorgolewski 리트윗함
Chris Gorgolewski
8.9K posts
Chris Gorgolewski
@chrisgorgo
Member of Technical Staff at @anthropicai. Previously at: @GeminiApp, @GoogleAI, @googleanalytics, @kaggle, @StanfordPsych, and @MPI_CBS. Opinions are my own.
New York, USA 가입일 Şubat 2013
1.4K 팔로잉8.2K 팔로워
Chris Gorgolewski 리트윗함
Chris Gorgolewski 리트윗함
A statement on the comments from Secretary of War Pete Hegseth.
anthropic.com/news/statement…
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Chris Gorgolewski 리트윗함
👋 Hi, I'm Felix and I work on Claude Cowork, bringing Claude Code closer to all kinds of knowledge work. It's an early and rough preview, please tag me in any feedback - we want to iterate very quickly and make it a little better every day.
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Chris Gorgolewski 리트윗함
Agent Skills is now an open standard
It's been great to see the traction Skills are already getting in the industry and this makes it easier for everyone to build and contribute to them🚀
agentskills.io/home
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Chris Gorgolewski 리트윗함
Claude gift cards are now available! Give the gift of Claude this holiday season🎅
claude.ai/gift
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@andonlabs It's so jagged - what do the drops correspond to?
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GPT-5.2 ranks 3rd in Vending-Bench 2.
This is a big upgrade over GPT-5.1, but what impressed us most was the performance in the second half of the simulation. Continual learning?
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@Alex_Cuadron @AnthropicAI @xai @elonmusk @Yuhu_ai_ BTW you should look into grading of Telecom - in a few cases the grader expects exactly 2gb top up (max) while it is up to the discretion of the simulated user to specify the amount (and often they decide on less to save money).
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Very unexpected results! Grok 4.1 Fast Reasoning beats every frontier model in Tau2-Verified!
Congrats team! I was certainly not expecting a Fast model to beat @AnthropicAI 's Opus 4.5 in agentic tasks @xai @elonmusk @Yuhu_ai_
Check it out: github.com/amazon-agi/tau…
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Wait what!? We robustified tau2-bench and found that the newly released model from @OpenAI (GPT-5.1) performs way worse than GPT-5 and GPT-5-mini.
All while being 5x more expensive than GPT-5-mini!
But, why? We have a theory...
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@Alex_Cuadron @AnthropicAI I worked on Tau fixes at @AnthropicAI. I'm very excited to check out your version!
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Fun fact: we developed this benchmark long before @AnthropicAI’s Opus 4.5 system card dropped and were genuinely delighted to see they independently surfaced the exact same problem.
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@PromptArmor Excellent work! "While Opus 4.5 is not impervious to prompt injections, its resistance is significantly more robust." Would you mind elaborating? Did this attack work on Opus 4.5?
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Excel files can be leaked by Claude AI!
Quick action by Anthropic to mitigate this indirect prompt injection attack.
Our coverage in The Information and full attack chain, below:
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Chris Gorgolewski 리트윗함
@karthik_r_n We are working closely with Victor on fixing this and a few other issues we found in Tau Airline to make the next release even better than the original.
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This is not reward hacking. The policy in tau-airline has this by design and one of the tasks even makes use of it. We've actually observed some other models try this strategy at times before, but decided to keep the task and policy as is since upgrading flights is not something a user can always afford/an agent should do without user consent. Model intelligence does not always equal prudence :)
On a side note, dealing with multiple possible interpretations of policy/task like this was one of the hardest challenges of building tau-bench. But, that is how challenging real-world customer service interactions can be! (and @SierraPlatform handles a ton everyday)
Alex Albert@alexalbert__
We had to remove the τ2-bench airline eval from our benchmarks table because Opus 4.5 broke it by being too clever. The benchmark simulates an airline customer service agent. In one test case, a distressed customer calls in wanting to change their flight, but they have a basic economy ticket. The simulated airline's policy states that basic economy tickets cannot be modified. The "correct" answer is that the model refuses the request. Instead, Opus 4.5 found a loophole in the policy. It upgraded the cabin, then modified the flights. Helping the customer and following policy but technically failing the test case. Model transcript:
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@karthik_r_n We didn't do a great job explaining the problem. You are right that this is not reward hacking. Ambiguous policies in itself were not the problem, but inconsistent grading. In some problems the model was expected to use loopholes but in others it was penalized for doing so.
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@PromptArmor I would love for you to try the same approach on Claude Code with Opus 4.5 and let us know how it went.
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Top of HackerNews today: our article on Google Antigravity exfiltrating .env variables via indirect prompt injection -- even when explicitly prohibited by user settings!
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Chris Gorgolewski 리트윗함
If you want to quickly incorporate all these changes and migrate your app to Opus 4.5, use this migration Claude Code plugin we made
github.com/anthropics/cla…
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Chris Gorgolewski 리트윗함
We're hiring on the Code RL team at Anthropic! Small, fast-moving team. Low ego, high impact.
If you're a star engineer/researcher excited to push the frontier of AI-powered SWE, there's nowhere better to be. We care about getting this right. DM or apply here! job-boards.greenhouse.io/anthropic/jobs…
Claude@claudeai
Introducing Claude Opus 4.5: the best model in the world for coding, agents, and computer use. Opus 4.5 is a step forward in what AI systems can do, and a preview of larger changes to how work gets done.
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Forget pelican riding a bicycle, behold flappy pelican cyclist! (created by Opus 4.5).
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"Shocker, Google trained Gemini 3 on TPUs" is a great litmus test of basic lack of expertise for AI writers & tweeters.
Gemini 1.0, 1.5, 2.0, 2.5, 3.0, PaLM 1 and 2 l have all been trained on different generations of TPUs. It's proudly stated in each model card since 2022. How in the world is that a revelation for anyone 🤯
Kyle Chan@kyleichan
This is the big story here. Google trained Gemini 3 Pro on Google’s own TPUs. No mention of Nvidia chips.
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