StartupKillr
Find what could kill your startup idea
What it does
StartupKillr is an AI startup idea validator for early founders. Paste your idea and get a blunt risk analysis across buyer pain, market clarity, distribution, monetization, competition, and execution traps. It helps you spot what could kill the idea before you spend months building the wrong thing.
Does the same job
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Startup Coroner Jun 2026 · startupcoroner.online · ▲3AI pre-mortem generator to find your startup's fatal flaw
- LGLet Grumpy AI Roast Your Startup Idea2024 · maker-list.com · ▲17
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I was working on validating some of my own project ideas. While trying to find how to validate my idea, I realized the process itself could be turned into a tool. A few late nights later, I had something that takes any startup idea, fetches discussions, summarizes sentiment, and gives a quick “validation score.” It’s very rough, but it works, and it’s already making me rethink a few of my own ideas. It's still a work in progress. I don't actually know what I'm doing, but I know it's worth it. Honest feedback welcomed! Live demo here: https://validationly.com/
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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…
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Dev tools · May 2026 · github.com