LayerFlow
AI Orchestration Platform Built to Run Your Entire Business
What it does
LayerFlow is different from existing AI automation and agent platforms because it is designed to move beyond simply generating AI responses or running isolated agents. It acts as an AI-powered execution layer that can understand a business objective, coordinate specialized agents, generate the required work, verify the output, and produce a usable deliverable.
No-code AI agents for business: an AI SDR for sales, a 24/7 support chatbot, and marketing automation on one platform. Connect your CRM and start free.
Run an AI sales assistant, a 24/7 customer support chatbot, and marketing automation from one no-code platform. Connect your CRM and stop doing the busywork by hand. Coordinate every department, tool, and process under one intelligent layer. AI makes decisions, not just rules. Real-time dashboards across every metric. See what's working, what's failing, and what to do next — instantly. Connect every tool, automate every process, and let AI handle the complexity across your entire business. Deploy specialized agents across every department. They learn from your data, make decisions, and improve continuously. Not if/then rules. Real reasoning. AI evaluates context, weighs options, and adapts…from layerflow.org
Does the same job
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- FAFlow – A dynamic task engine for building AI agents2024 · github.com · ▲160
I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…




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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, August 2026
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Life & fun · 10d ago · louisabraham.github.io


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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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