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salma-remyx
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reacted to Quazim0t0's post with 🔥 about 21 hours ago
I never really posted about my DaisyChain project because it's still work in progress. I decided to post a small bit about it and the demo. DaisyChain Genomics: four small DNA/RNA specialists chained behind a learned router that behave like one big genomics model, at ~7× less active compute. I built a modular genomics model chasing a 500M-parameter foundation model, then caught myself measuring it wrong. Here's the honest version. DaisyChain is a different bet: instead of one monolithic DNA model, it's four ~74M specialists (eukaryote, prokaryote, mRNA, splice) chained behind a learned router, each distilled per-domain from HuggingFaceBio's Carbon-500M. Every specialist reports how surprised it is (bits/base) and the router hands each sequence to the link most at home with it. In lineage it's a cluster Branch-Train-Merge mixture of experts, so you can chain on a new domain without retraining the others. The pitch: ~295M total params (under Carbon-500M), but only one ~74M specialist runs per query, so ~7× cheaper per token, routing at 100% held-out. The mistake: Carbon works in 6-mers, and I'd been scoring likelihood as 6-mer cross-entropy. By that number I was +0.043 bits/base behind, splice even "beating" Carbon. But Carbon scores at the base-pair level, which is harder and more honest. Re-run their way: Real gap: 1.862 vs 1.787 bits/base, +0.089 behind, not +0.043 No domain actually beats Carbon; the "splice win" was an artifact Seq recovery: euk 31.5% vs 38.9%, bacteria 40.9% vs 54.1% DaisyChain is still behind Carbon-500M (itself a draft model, not built to top benchmarks), but by a number I can defend, and the gap closes with every per-domain pass. 🌼 https://huggingface.co/DaisyChainAI https://huggingface.co/spaces/DaisyChainAI/Daisychain-Genomics-Demo https://huggingface.co/DaisyChainAI/daisychain-genomics
reacted to NatalieY's post with 🔥 3 days ago
Aiden: a physical AI agent that controls phones over USB HID Most GUI agent work assumes the agent lives inside the device or drives it through a debugging interface. We went the other way. Aiden is a small board that sits outside the host. It captures the screen over HDMI-to-CSI, runs the agent loop on-device, and sends actions back as a standard USB HID device — the host sees a keyboard and a mouse, nothing else. No app install, no root, no ADB, no cloud. Runtime is Go. Frame capture, full-duplex audio with VAD, the agent loop, and HID output all run as independent goroutines. There's no backend — nothing leaves the device, which is the only defensible design when the input is a live feed of someone's phone screen. Open questions we haven't solved: · Action verification — inferring success from a re-read of the screen breaks when loading states lie · Prompt injection — an agent that reads screens reads whatever an attacker puts on them · iOS pointer control requires AssistiveTouch Repo, including the HID gadget config and capture pipeline: github.com/AidenAI-IO/aiden-hardware-demo Wrote up how this differs from cloud-based computer use agents here: https://aidenai.io/blog/mobile-ai-agent-vs-computer-use-agent-whats-the-difference/ Note: current hardware is a dev board, not a finished product.
reacted to sergiopaniego's post with 🔥 5 days ago
Frontier models use distillation as a step of their post-training pipelines. In 2026 it has three jobs: compress a big model into a small one, merge RL experts into a single model, and let a model teach itself. I wrote up which frontier models use each one and how: https://huggingface.co/blog/sergiopaniego/distillation-2026 It pairs with Class 2 of the Training an Agent series Ben and I are doing, where we teach these techniques hands-on with TRL!
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