OddsGap AI
Find value bets with AI — compare predicted odds vs market
What it does
OddsGap AI compares AI-predicted match probabilities against real-time bookmaker odds to surface value bets. Instead of telling you who will win, it shows you where the market is mispriced — and uses Kelly Criterion to suggest optimal bet sizing. Built for data-driven bettors who want an edge, not a tip.
Does the same job
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AiGamingPredictionsJul 2026 · aigamingpredictions.com · ▲6AI-powered sports betting data and expected-value analysis
Picks AI: AI Sports Prediction & BettingFeb 2026 · ▲3Real-Time AI Predictions & Visual Insights for Sport Betting
- UBUsing bots and maths to beat the sportsbooks2023 · whop.com · ▲13
I have recently developed a bot capable of simultaneously scanning over 140+ sportsbooks around the globe. It analyzes millions of odds in real time and uses this data to find overpriced odds. The logic behind it is straightforward: it uses odds from all sportsbooks to calculate the probabilities of an event occurring. Then, with simple maths it determines if the expected value is positive, meaning the potential payout exceeds the risk. Unfortunately, sportsbooks limit your accounts once they realize that you are profitable and not a losing customer. So I have already been limited at more…
- AEAnalysing Euro 2024 betting odds with scraping and AI2024 · notebooks.nodescript.dev · ▲8
I created a bot which analyses Euro 2024 betting odds and performs some basic analysis based on the recent form of each team and provides advice on which odds are under/over-priced by email to all subscribers. The intelligence of the bot is admittedly rather basic but this simple app shows how easy it is to reliably scrape and parse structured data with AI and perform insightful analysis. The initial version of the bot, shown in the notebook took about 15 minutes to build. Disclosure: this bot is built on our new graph based #nocode platform NodeScript.dev, which we've recently brought…
- PCPredictionHunt – Compare probabilities across prediction marketsOct 2025 · predictionhunt.com · ▲12
Hey HN - my friend and I built Prediction Hunt, a site that aggregates data from prediction markets like Kalshi, Polymarket, and PredictIt into one simple dashboard. It updates every few minutes, shows probabilities for each event, and even highlights arbitrage opportunities when markets disagree. I made it after getting tired of flipping between tabs just to see how each market was pricing the same question. Would love feedback on the product, data accuracy, and any ideas for what you'd want to see next.
More ai this month
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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.
AI · 17d ago · simedw.com
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AI · 18d ago · company-app.joinastute.com
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
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
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
- FM
Dev tools · May 2026 · github.com