StockGPT
AI stock rankings, portfolio builder, and market alerts.
What it does
Investors are now dealing with more data, more noise, and faster-moving opportunities than ever before. Relying on manual research, scattered tools, and instinct alone isn’t just inefficient—it’s a disadvantage. StockGPT was built to change that. We use AI to process and interpret vast amounts of market data in real time. Our goal is simple: help investors make smarter decisions, faster—without needing to spend hours filtering through information.
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A live dashboard where you can watch GPT-4, Claude 3, and Gemini analyze market data and make daily stock trades with real money. Each AI explains its reasoning, and you can compare their different approaches to the same data. Link: https://trading.snagra.com?utm_source=hn (no signup required) What you can try right now: - Watch live trades from GPT-4, Claude 3, and Gemini - Read each AI's full analysis and reasoning - Compare their different interpretations of the same market data - Track their real-time performance and win rates - View historical trades and performance metrics…
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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