
StyleMatch
Know if a clothing item suits your skin tone before you buy
What it does
Every Indian shopping site shows clothes on fair-skinned studio models. No way to know if it'll suit your actual skin tone before buying. StyleMatch fixes that. Set your skin tone, body type, and style once — it scores every product on Myntra, Amazon, Nykaa, AJIO and Snitch out of 100% and tells you why it works or doesn't for your complexion. Built specifically for Indian skin tones. Free, no account needed, works in your browser.
Does the same job
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- Glowara Skin Tone Matcher Jun 2026 · glowara.in · ▲2
This tool will match the jewelry which suits best on you
- SCShop Clothes with Models That Match Your Body Shape2024 · thebodymatch.com · ▲13
This is one of those fix a problem you can’t ignore projects. Like most online shoppers, I often found myself frustrated: clothes look great on models but disappoint when I try them on. It’s not the clothes, it’s the body shape mismatch. So, I spent the last few months building TheBodyMatch, a platform where clothes are showcased on models who share your body shape. You get a more relatable and confident shopping experience because seeing is believing, especially when the model reflects you. It’s still early days. Think of this as a beta where feedback is gold. I’d love for you to try it out…

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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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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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