
世界杯 2026 / WC 2026
2026 World Cup H2H Predictor
What it does
2026世界杯H2H对战预测系统 — Poisson xG模型 + Elo差值 + 玄学因子(易经/道德经)。支持任意两队胜平负预测 + 比分概率矩阵。最独特的是将欧冠决赛球员心态数据映射到世界杯框架,并接入Polymarket实时赔率对比,找出市场低估机会。移动端优先,实时运行。
Does the same job
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World Cup 2022 Score Predictor by Guul2022 · ▲137Online World Cup 2022 score prediction game for Slack
- WCWorld Cup API for 20182018 · worldcup.sfg.io · ▲141



World Cup 2026 PredictorJun 2026 · 2026fifaworldcupsimulator.online · ▲22026 FIFA World Cup Simulator | Interactive Draw Ceremony
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
Astute▲585Automate your B2B brand going viral, with new media creators
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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
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