Dexicle
The AI SDLC platform that remembers everything.
What it does
Dexicle is an AI SDLC platform built around one idea: your AI shouldn't forget everything between sessions. Every agent Claude, Codex, or any engine reads and writes to a shared, persistent knowledge base that learns your codebase, conventions, and decisions, then consolidates them automatically via "dream" cycles. Most AI coding tools reset to zero each session. Dexicle compounds. Tickets, tests, docs, design, and deploys all run through agents that remember your team.
Dexicle is an AI-powered software development platform. Ask everyday questions, plan structured work, and build software — discovery to delivery — in one thread with versioned specs and tracked tasks.
Dexicle turns a conversation into shipped software. Ask in plain language — it researches, plans the specs, splits the work, and builds, with you reviewing every step. AI can already write most of the code. The bottleneck has moved to everything around it — deciding, specifying, testing, tracking, and documenting. That's the part Dexicle owns. Research, specs, tickets, tests, and docs grow out of one conversation — not five disconnected tools. Nothing gets lost in handoffs, because there are no handoffs. Requirements and test suites are living, versioned artifacts that evolve with the code — not wiki pages that rot the day they're written. The team's job shifts from typing to directing:…from dexicle.com
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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.
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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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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


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.
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