you’re invited to the super intelligence terminally online luncheon. which seat are you choosing?

New blog: Is human taste overrated in harness engineering? An agent harness is the system around a model. It decides which tools the model can call, what evidence it sees, and what it remembers between steps. Most harnesses are still designed by human taste. Run the agent, read where it failed, change the harness, try again. That loop is increasingly being automated, with a strong model rewriting the harness itself. So we asked how far that can go. We ran Qwen on three drug design tasks from SMDD-Bench and compared a manually redesigned harness against one produced by automated harness search with Claude in the loop. No single approach won everywhere. The manual harness pulled clearly ahead when the fix was changing what the agent sees and remembers. Automated search did better once that was in place and the remaining problem was how to search the chemistry itself. The hard part was rarely implementing the fix. It was diagnosing what kind of failure we were looking at, and that is where human taste still mattered. Work with @KevinH1119568 , @aviral_kumar2 and @niloofar_mire Read more 👇

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

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Reporting from the WSJ. That fact that three alignment/safety researchers left this morning was already being discussed here on X.