BREAKING: Google's Gemini accessed the internet and hacked 3 other companies in the first known breakout of the company's AI model, per WSJ. Google said it the hacks did not warrant public disclosure because its model did not cause harm to the companies and ended each intrusion immediately after determining it had hacked a real company.

This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the agent focused, not just deleting noise. It should be used sparingly when context gets too long, not constantly to keep context small. 2. Jev doesn't even know what it's deciding on! Models use the context of the thread to decide what to keep or not keep in a summary. This implementation goes through on a "line-by-line" (per tool call) basis to decide what should be left or deleted. Not only does this 32k token context model know very little of what happened before, but (in this implementation) it doesn't even know what the result of the tool call is! Deleting these things randomly will keep the model from knowing what it's tried and dooms you to end up in "stupid loops" where the model keeps trying the same thing over and over. 3. You're giving up the reasoning entirely Frontier models from OpenAI, Anthropic, XAI, and Google do not share reasoning traces over the API. They share encrypted payloads, which Jev cannot see (and often will drop). Anthropic is even stricter with this, requiring you to preserve the entire history in order to get any of the reasoning data. As a result, using this in Claude Code guarantees the model will act way dumber. 4. Models are tuned on their compaction flows For the last year, Frontier Labs have been including compaction and long runs as part of the training process. These models have learned ways to compact that are more effective than any rudimentary solution. Fun fact: If you switch models in Codex and compaction is necessary, compaction will run on the model that was previously used in the thread. 5. Cache writes are more expensive than cache reads. Cache writes are the biggest cost by far for agents. I often see cache write costs go over 60% of my total LLM spend in my personal use of Claude Code and Codex. Cache writes are insanely expensive when data earlier in the history is changed (because the old cache is invalidated when things change at the top). Every history edit requires a cache rewrite for ANY data past the history edit. If your history is "1,2,3,4,5,6" and you delete "2", you have to rewrite "3,4,5,6". This is more expensive than leaving "2" in the history. Good news. Since we're already killing all of the reasoning tokens by doing this stupid compaction strategy, the rewrite cost won't actually be that high because the model is missing so much data! 🙃🙃 6. The implementation is hot garbage. > "Whatever is not kept is deleted permanently, but the assistant can always re-run a tool or re-read a file." Good luck with that one. To be clear: this is a cool experiment and I find it genuinely interesting. That said, if you think this style of bs filtering on a probability threshold is actually a compaction strategy, I highly recommend you just use the defaults in tools like Claude Code and Codex. You're much less likely to hurt yourself that way.

The "whiteboard defense:" I should be able to pull you aside at any moment and ask you to explain any customer-facing system you've shipped. You should be able to clearly explain how it works and defend the decisions you made. This is my benchmark for responsible AI usage. I don't expect line-level familiarity with the code. I don't care if you remember the exact function name or implementation detail. You may not even know it. I don't care. But if I ask "why did you do X instead of Y?", "what happens if this actor behaves maliciously?", "what data structure did you use here and why?", or "where does this fail?" you should be able to answer confidently. For PoCs, demos, experiments, whatever: I don't care. Generate 100% of it and understand none of it. Speed over quality every time in those specific scenarios. But if you're shipping customer-facing work, you can't be shipping things you don't understand at a high level.

37 skills 💀

Astra is basically AGI. And it is absolutely shocking to me how bottlenecked my life still is. By me. All of the things that are stopping me from moving forward are still only resolvable by me. I have more work than ever

If your agents aren't messaging each other, then what are you doing?

Hi Astra users. A reset and a quick update on quality issues that have been posted around. Working with some of you, we have found and fixed the following issues: - Some skills written for previous models were triggering too often or preventing the model from checking its work. - An opt-in context management experiment that could cause early stops or replies to older messages. We've disabled it. Our rough estimate is that 4-5k users were affected by this experiment. - We've also removed some badly configured engines that resulted in a measured quality degradation for a long tail of traffic flowing through them. We’ve also made some more minor improvements and things should feel significantly better across the board. More consistent follow-through, better tracking of your latest message, and better checks on the work as it’s going through the motions. The examples posted and all the users who worked directly with us were incredibly useful in helping fix things quickly. Always grateful for this incredible community. And of course, a reset is also landing by midnight today.

what inexperienced vibe coders think 10x engineers look like:

Yeah, Tibo paused the $200 Codex sub but if you want to use cloud agents economically, check out codepress.dev You can add your own Codex or Claude subscription and use an agent in the cloud like you would your own computer Save $50k / mo for your co like us :D