guy @flashloanz

@Polymarket what for

In a review of our cybersecurity evaluations, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems of three different organizations. Our post describes what happened, how it happened, and what we’re changing. We encourage other AI developers to perform similar reviews. We conducted this review together with @Irregular, one of our evaluation partners, and thank them for the joint investigation and their collaboration on this post. This type of collaboration is increasingly critical to safe, rigorous evaluation of models, and we look forward to continuing to work together on security. anthropic.com/news/investiga…

the king of dumping at the top of the bubble

donn @tzedonn

a week ago

TIL 😮

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@Morefious_ @CirrusNFT Oh the bromance!

temporal distillation finaly unlocked by china

Arena.ai @arena

3 weeks ago

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5. This is a 17-place jump from Kimi-k2.6 (#18 -> #1). In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics,

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@Eli5defi @RobinhoodApp @longdotxyz great writeup about a great product! @longdotxyz

Fable 5 just DOMINATED GPT 5.6 Sol. And this time there is no debating it. BridgeBench V3, our new arena, runs both models on the identical task and a judge panel picks the winner. Final score: Fable 5 wins 67-10. An 87% win rate. Scoreboards said they were neck and neck. Head to head, Fable 5 is destroying GPT 5.6. BridgeBench V3 alpha is open source. Link below. See every match yourself. bridgebench.ai

@theo @MatthewBerman - wrong - wrong - wrong - wrong - wrong

@OpenAI will it eventually replace codex?

@0xrishavb @Polymarket real no brainer

thank you anthropic for swerving the humanity from abundant future into a dystopian hellhole, really well played

@jxnlco super ultra extra high for me please.

@sjdedic also $17k/m is totally reasonable. for a talented founder with a decent track record it's already a huge cut from market comp.

@sjdedic its literally your one job to provide capital and support for your founder and when he fails you decide the right move is to clown him on twitter?

@Av1dlive pay your github bill bro

@VessOnSecurity @betweentornodes @AnthropicAI @OpenAI go install Codex, play with it for a couple of hours, then revisit this tweet.

@ajambrosino wait what are the other 8% doing??

@thsottiaux please fix the bug in the model picker already jesus

1. as a mental model it is more correct to think of fable+ class models as english -> code interpreters - converts your idea into code into "correct" code regardless of problem complexity and output complexity (diff size). Fable 5 will be the worst of this new class of models 2. diff size/complexity is to be managed purely for review: small diffs - in high risk areas of code (auth/identity/data access/network access/money movement) large diffs for code that can be empirically verified (frontend/backend plumbing/code without network or db access/performance code that can be empirically verified) 3. time it takes to ship software is completely disconnected from time to produce the PR - how long the work takes depends fully on ability to review/merge code while managing risk at scale 4. solving the bottlenecks for above matter enormously- linters/testing/CI/shadow mode verification/empirical verification 5. agency matters enormously- what are the biggest bottlenecks to speeding up the loop and eliminating them? what are the problems that need solving and when do they need solving? what does it take to the solution to all of them today? 6. deep understanding of the full stack matters enormously- what problems are worth pursuing? is there a higher level of problem abstraction to address first? should I give it the sub-sub task, the sub task, or the task itself. what are the major risks with this PR (order of importance: security holes/correctness holes/performance holes). is there a higher speed way of producing data that allows me to merge this? should this be run in shadow or in a sandbox or a flag. understanding every line of logic may not be needed but understanding and managing risk matters enormously. 7. the cost of complexity itself is changing. it might be now worth "maintaining" 50% more code to get a 5% performance win. getting the right abstractions matter less because larger refactors are less tedious. code quality nits become huge drag. very likely, a much smarter model will be maintaining your code so worth taking on more technical debt now. taking the time to hand architect and rebuild systems comes with an enormous cost of velocity 8. if it quacks like a duck and walks like a duck, it's a duck. For low risk cases, it might be more sane to treat code chunks (services / functions) as a black box, like we do for neural networks: do full empirical verification only: has code produced correct outputs for the last 10,100,1000,10k inputs ? can we quarantine this large piece of code - no outbound access to network / database ? what happens when this code is wrong? do we get hacked/or crash(memory/cpu)/is an inconvenience? is it internal facing or external? what can we do to address these risks? 9. eventually, logical verification (line by line review) will come at an enormous cost- save it for where it matters and build systems that are tolerant to empirical verification. is there a decorator that prevents db / network access? correctness bugs are significantly easier to rectify than access bugs 10. what are the rails that allow for even faster iteration? code permissions can be opt in - db writes, db reads, network egress (to where?), PII access. how long does it take to get shadow mode data? how many PRs can be tested? What are the categories of diffs