Petto
The AI desktop pet that judges everything you do
What it does
Petto sits on your screen and watches everything you do. Switch to YouTube mid-work? It sighs. Push a commit at 3am? Happy dance. Open incognito? It leaves. No questions. Every few minutes, Petto screenshots your screen, sends it to AI, and reacts — with a face, a quip, and zero mercy. → Windows app → Free for 24 hours → 5 pets, 5 personalities → Built in 7 days by a 19-year-old
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ScreenGeany AIMar 2026 · screengeany.ai · ▲93Ask AI about anything on your screen with a single hotkey
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Hey HN! Dinoki is a 6MB native AI assistant with pixel pets that live on your desktop. Built for power users who want privacy, performance – and a bit of fun. See it in action: https://www.youtube.com/watch?v=V_pv2BU0CZA What makes it different: - 6MB native app (SwiftUI for macOS, WPF for Windows) – starts instantly - Zero telemetry – your API keys and data stay local - Pixel art companions with physics-based behaviors - Flexible AI support – OpenAI, Anthropic, OpenRouter (300+ models), or Ollama (offline) Why pixel pets in an AI assistant? Beyond being fun, pixel art is…
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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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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