Dagens Vibes — 12. august 2026

Lenny Rachitsky@lennysan

I got early access to Grok Bot and I'm hooked. I haven't been this excited about a new AI product in a while. It's like OpenClaw, but super easy, reliable, and less scary to use. I think this will be a huge new product line for Cursor/Grok/SpaceX. I've already found so many ways to use it that have meaningfully made my life better: 1. Matchmaking people looking for jobs with companies who are hiring (see below) 2. Auto-replying to support emails (saves me hours!) 3. Scanning my credit card statements and finding recurring subscriptions to cancel 4. Sending me (really good!) briefs for upcoming podcast guests See below for my actual set of agents that I've been using and chat with daily. Great work on this team Grok Bot. (I'm not an investor in this, nor do I have any ties to this product/company. I'm just a fan!)

elie@eliebakouch

insane paper, they extracted reasoning from frontier models by asking less safeguarded models (luna, haiku, ...) to decrypt and output the decoded version of the encrypted reasoning block. while reading this post or the paper, keep in mind this is a somewhat "lossy" decryption, but they have experiments to test it and it seems pretty accurate some stuff i found really interesting (much more in the paper and especially the appendix): - they were able to extract tokens and personal information from publicly available traces with this method - gpt models are trying to be very efficient in reasoning (was already in an apollo research report), extremely short sentences like "need maybe not need." "avoid maybe not" (they don't have 5.6 sol in this section tho!) - they computed perplexity on hidden reasoning with different open models and conditioned them on closed model reasoning as well > K3 seems to be more similar than other open models to sol and opus > result that makes me a bit sus is that they say they attribute higher average perplexity to their own CoT than the CoT of other models which i find quite surprising (except K3 and K2.7) but figure 26 showing median doesn't really show that > also surprising: all the open models score very high perplexity on gpt traces (see screenshot in thread) - models sometimes have chain of thought in other languages like chinese, russian or japanese. since it's lossy decoding this might not be super accurate but still interesting - models are mostly honest in their CoT on the task they tried and there are examples of them scheming and realizing they could cheat. also interesting that these things are not often transparent in the summary that users have access to - summary length doesn't really scale with the number of reasoning tokens really great work, maybe one thing is that i would have loved to see more details on cot of recent models like fable (i don't think paper have that) and 5.6 sol, lot of the observation here are based on older model cot, which is still valuable but don't automatically transfer to more recent model

Jensen Huang@JensenHuang

Lightning strikes for continuous and long-run agents! Nemotron 3.5 Lightning is smart, fast, efficient and open.

Daniel San@dani_avila7

Anthropic shared a Dynamic Workflows cookbook worth reviewing Bookmark this one Claude writes the orchestration script and spawns up to 1,000 subagents per run https://t.co/1xDUs5OMEk

OpenAI@OpenAI

Now in preview: The ChatGPT desktop app for Linux. Use ChatGPT, ChatGPT Work, and Codex where you already work and build, with your projects and browser workflows on supported Linux systems. https://t.co/OtsPt5N5QC

Deedy@deedydas

Deep dive into AI text-watermarking and what EU's AI Act actually mandates about AI detectability. How it works: The SynthID paper from Demis and team in Nature is probably the best primer in the technology. Generally, most approaches involve sampling next tokens statistically differently with some random key while not distorting the semantics of the output. Detecting text watermarks can never be perfect. Longer passages are always easier to detect, only at ~400 tokens do you get a true positive of rate of ~90% when your false positive rate is <1%. Because small changes in the text can change whether the algorithm thinks it's AI or not, other approaches include semantic data in their watermarking scheme too. Industry status on the EU regulation: EU's AI Act Article 50(2) requires AI generations from all modalities to be AI-detectable, with practical implementation details still being finalized. The main exception is "AI systems perform an assistive function for standard editing or do not substantially alter the input data" although how they would distinguish between these cases is unclear. Article 113 says "It shall apply from 2 August 2026." Claude declared that they will comply. Gemini already uses SynthID for their text outputs (even though no production public detector exists for text outputs). OpenAI had developer watermarking a while ago but reportedly shelved it when 30% of their users said they'd use the product less. However, their support page says "our goal is to expand provenance signals to all modalities including text" (!) I've always maintained I think it's critical to know whether text comes from AI or a human (and thus support tech like Pangram). Judging by the comments on X, I might be in the minority. This comment sort of embodies the backlash: "I'm paying it to write for me in a way people and machines wouldn't detect it is AI written" In other words, users believe if their ideas go into using AI for expressing them, *they* should be given credit for the idea and not penalized for using AI. I'd counterclaim that penalization and provenance are distinct, and provenance is extremely critical because a) without provenance, you are more likely to be judged poorly and falsely accused of using AI when you didn't b) just like with food, fundamentally readers deserve the right to know where the text came from and reserve their own judgment on whether its still valuable to them or not!