Fascinating technology Who is Lenn?

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AI self-preferencing in algorithmic hiring tl;dr Using AI to craft your resume leads to better shortlisting rates "The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting." source: https://arxiv.org/abs/2509.00462 https://x.com/heynavtoor/status/2048088874686300431

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Interesting and unexpected product announcement from OpenSubtitles: an AI-feature-packed media player with everything from instant subtitle matching to auto-translation, and even 4K upscaling. I’d love to see this as an SDK/plugin that could be built into other players, but it’s interesting nonetheless. https://rayplayer.com/en https://rayplayer.com/en

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Anyone played with Osaurus yet? https://github.com/osaurus-ai/osaurus

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cooking something big

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A comprehensive security reference distilled from 150+ sources to help LLMs generate safer code https://github.com/Arcanum-Sec/sec-context

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A tool that removes censorship from open-weight LLMs https://github.com/elder-plinius/OBLITERATUS

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Teaching LLMs to reason like Bayesians Google compressed a classical symbolic Bayesian model into a neural network via supervised fine-tuning. Generalization: Models trained only on synthetic flight data successfully transferred their probabilistic reasoning to entirely different domains like hotel recommendations and real-world web shopping. This suggests the LLMs internalized general Bayesian reasoning principles, not just task-specific patterns. The right training signal (demonstrations of how to reason, not just correct answers) can unlock capabilities that prompting alone can't. Read more: https://research.google/blog/teaching-llms-to-reason-like-bayesians/ P.S. There has been so much exciting foundational research lately that I'm more convinced than ever that there is not only no wall but that progress will accelerate. Three of many examples: 1. SOAR ("Teaching Models to Teach Themselves"), which shows that an AI model can generate useful intermediate problems (stepping stones) for tasks it cannot itself solve. https://arxiv.org/abs/2601.18778 2. QED-Nano, a tiny 4B parameter model, was trained to write Olympiad-level mathematical proofs that compete with models 50x its size. The key technique: instead of reasoning in one long pass, the model reasons in cycles: thinking, summarizing what it's learned, then thinking again conditioned on that summary. This lets it reason effectively across 1.5 million tokens without losing the thread. https://huggingface.co/spaces/lm-provers/qed-nano-blogpost 3. Recursive Language Models (RLMs): Instead of stuffing everything into the model's context window where it degrades, the model treats its input as an external object it can programmatically slice, examine, and recursively call itself on, handling inputs 100x larger than its context window. https://arxiv.org/abs/2512.24601

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The L in "LLM" Stands for Lying https://acko.net/blog/the-l-in-llm-stands-for-lying/

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On of my favourite tricks is to tell Claude to duplicate every change it makes to claude[dot]md files to agents[dot]md, so that if I ever need to make tweaks using a different agent, it adheres to the same set of guidelines. Works decently well.

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Imbue open-sourced "Darwinian Evolver" An LLM-powered evolutionary framework that optimizes code and prompts by treating them like organisms in a population. The technique maintains a pool of candidate solutions and uses an LLM to propose targeted mutations, scores them, keeps the fittest, and repeats. It doesn't need the LLM to succeed every time, just often enough that beneficial changes accumulate over generations. The framework is problem-agnostic and works on anything an LLM can read and a scoring function can evaluate. To demonstrate it, they applied it to ARC-AGI-2, the notoriously hard visual reasoning benchmark. The results are striking. Evolution boosted the open-weights model Kimi K2.5 from 12% to 34% (nearly 3× improvement), Gemini 3 Flash from 34% to 61%, and pushed Gemini 3.1 Pro to 95%. The Kimi result is the best open-weights ARC-AGI-2 score to date, and the Gemini 3.1 Pro result approaches the current state of the art. 1. How Evolver works: https://imbue.com/research/2026-02-27-darwinian-evolver/ 2. How Evolver set a record on ARC-AGI: https://imbue.com/research/2026-02-27-arc-agi-2-evolution/ 3. Code: https://github.com/imbue-ai/darwinian_evolver

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I’m just spitballing of how to distribute AI compute so that it’s not in big data centres. This, if replacing PoW in an existing cryptocurrency, could be a sort of two birds one stone solution.

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Any thoughts on Goose?

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From a handful of comments, LLMs can infer where you live, what you do, and your interests; then search for you on the web. https://arxiv.org/abs/2602.16800

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I think China is gonna deliver the double whammy of AI models that don’t need as much RAM, and cheap RAM that competes neck and neck with the big players (look up CXMT). As a result I think by end of year RAM prices will be through the floor.

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