The Buffett Screen
Screen any stock through Warren Buffett's eyes
What it does
The Buffett Screen is an AI-powered stock screener that evaluates any publicly traded stock against Warren Buffett's 10 core value investing criteria — including moat, ROE, debt, free cash flow, and margin of safety. Enter a ticker. Get a BUY, WATCH, or NO BUY verdict, a Buffett conviction score out of 100, and a plain-English analysis written in Buffett's own voice. Reach Takarudana Mapendembe by email, or connect on LinkedIn, X, Facebook, and Medium.
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Hey HN, I’m Dan. I’ve been working on forecasting for the last six years at Google, then Metaculus, and now at FutureSearch. For a long time, I thought prediction markets, “superforecasting”, and AI forecasting techniques had nothing to say about the stock market. Stock prices already reflect the collective wisdom of investors. The stock market is basically a prediction market already. Recently, though, AI forecasting has gotten competitive with human forecasters. And we’ve found a way of modeling long-term company outcomes that is amenable to our forecasting approach. Iteration by…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com