Startup Funding Intelligence API
AI-powered SEC filing data — find funded startups instantly
What it does
We built an API that pulls SEC Form D filings 3x daily and enriches every startup funding round with Claude AI. Each filing gets: signal score (HIGH/MEDIUM/LOW), funding stage detection, sales intelligence summary, buyer intent predictions, and competitor mapping. Built for B2B sales teams finding companies with fresh budget. Free tier available on RapidAPI. Data source: official SEC EDGAR government database.
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- IBI built an AI tool to analyze SEC filings the minute they're released2024 · docdelta.ca · ▲62
Hello everyone, I built this tool on Next/Node to automatically analyze new filings from the SEC and probe the Edgar API for new filings 24/7. We use AI to analyze the filings the second they are released. Free accounts to look at real filings (automatically updated) are available to any who sign up. If you have any questions feel free to ask.
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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
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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