incredible work on alignment steganography from anthropic fellows i've been looking for a straussian explanation of why china keeps publishing open models out of the goodness of their hearts if you do stuff like use open models to, idk, clean *ahem* synthetically paraphrase your data to textbook quality you may very well import biases you can't detect until long after it's too late. so if you want to export your value system to the rest of the world this is the most powerful Soft Power tool invented since Hollywood. to be super clear we have no actual proof of this motivating any of the chinese labs. but this paper is a clear step towards a possible explanation.

congrats to Bee on getting picked up by Amazon; similar to Blink, Ring, Eero and ofc the new Claude + Nova + Alexa i think @panos_panay is putting together a pretty solid second act of amazon’s ai hardware strategy knew bee was winning when @dharmesh showed up to his @latentspacepod rocking one

the reason llm analysis (and regulation, and PMing) is hard* is that the relevant DIMENSIONS keep moving with each generation of frontier model; it is not enough to just put your x or y axis in log scale and track scaling laws, you have to actually do the work to think about how models are structurally different in 2025 vs 2024 vs 2023 and so on eg everyone focused on elo for 2 years, elo gets gamed and loses credibility everyone focused on price per tokens for 3 years, reasoning models have 10-40x variation in output tokens per task, price per token loses meaning collect data all you want but if you are just collecting pristine time series you can lose sight of the bigger picture *(and why statements like “ai engineer is not a thing because all software engineers are ai engineers” are cope and will never be right except in the most trivial sense)

trying to find the tweet that's like: Mankind's three current grand quests are: - Build God - Explore Stars - Defeat Death does anyone know who said that? @xai @grok is failing me

whoa, shoutout to @tferriss for featuring Hyperstitions of Moloch on Five Bullet Friday!

I will die on this hill that o1-preview is a far better summarizer than o1 or o3 and the world is about to lose access to a great model that was never properly pulled into production

🆕 Releasing our entire RL + Reasoning track! featuring: • @willccbb, Prime Intellect • @GregKamradt, Arc Prize • @natolambert, AI2/Interconnects • @corbtt, OpenPipe • @achowdhery, Reflection • @ryanmart3n, Bespoke • @ChrSzegedy, Morph with special 3 hour workshop from: @danielhanchen of Unsloth! start here: Happy weekend watching! and thanks to @OpenPipeAI for supporting and hosting this track!

if, as @sgrove proposes, specs are the code of the future, then what is debugging? 1) spec compilation is the process of a coding agent turning specs into code 2) more and more “compilation” will be unattended, less watching the agent work diff by diff, more spec in, code out 3) type errors -> truth errors : most debugging will be digging through research and implementation plans in markdown to find the one line of incorrect context that makes the coding agent fail to succeed when implementing. Test suites will, among other things, check for truth and logical consistency. 4) there is a new higher order flavor of “attaching a step debugger” which is watching the agent implement a plan step by step to pinpoint the logic error in the spec. When you find an error when stepping through a program line by line, you change the code, restart the process, and repeat until it’s working. When you find an error in a *spec* while stepping through an implementation, you go upstream, fix the spec, and restart the *implementation*

Hear @GregKamradt talk about ARC-AGI-3 with @swyx and @FanaHOVA on @latentspacepod * Why interactive benchmarks? * Defining Intelligence * Play through of ARC-AGI-3 games

we're releasing one track a day from the @aidotengineer conf now*. yesterday's RecSys track was a big hit - but by far the hottest track was our coverage of the state of MCP, hosted by @Calclavia personal fave slide is this where i realized @AnthropicAI dogfoods MCP -way- harder than i initially thought from our podcast with @dsp_ and @jspahrsummers take a look at these talks and give your fave speakers a shoutout! *most already available as "unlisted" via the "Complete Playlist" if you search