Snap Analyzer
Instant AI chart analysis, entry signals & price targets
What it does
Snap Analyzer AI uses computer vision to turn chart screenshots into actionable trading plans. Get instant support & resistance levels, entry zones, stop-loss invalidations, and profit targets across Forex, Crypto, and Stocks.
Institutional AI chart analyzer. Upload any chart screenshot to get 3 instant trade plans (Scalp, Swing & Long-Term) with exact entry, stop-loss, and multi-tier profit targets.
Upload chart screenshots (Forex, Crypto, Stocks) to generate structural signals, order block targets, and stop losses. Information is for pattern reference and does not constitute advice. Leverage and market risk require independent caution. Supports PNG, JPG, JPEG. Ensure candles, price axes, and indicators are clearly visible. Snap Analyzer AI transforms static candlestick screenshots into actionable trade setups. Our vision engine scans multi-timeframe candle geometries, fair value gaps, and liquidity sweeps across global markets. EUR/USD, GBP/JPY, AUD/USD and major FX crosses. Evaluates session liquidity sweeps and institutional order blocks. Bitcoin (BTC), Ethereum (ETH), and altcoin…from snapanalyzer.com
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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…
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
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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