SpecDD

What it does

SpecDD is an open-source framework for specification-driven development with AI. Unlike existing AI coding workflows built around prompts, chat history, or massive docs folders, SpecDD keeps small structured specs close to the code they describe. The goal is to preserve intent before, during, and after implementation, reducing correction loops, architectural drift, and “plausible but wrong” AI-generated code.

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  Spec Coding by CapacityJan 2026 · ▲312

  Vibe Coding with a planning assistant to build with clarity

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  Specfy2023 · ▲155

  Open source stack intelligence platform

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  specs.mdJan 2026 · ▲13

  spec-driven AI development with pluggable flows

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  Dify.AI2023 · ▲261

  Open-source platform for LLMOps, define your AI-native apps

• SD
  Spec-Driven Development Workflow for Claude CodeMay 2026 · ▲20

  Spec Driven Development approach allows to squeeze more from coding agents thanks to few strong concepts: - decomposition across two dimensions. first you generate specs in multiple steps (requirements, code analysis, design), than you split task into multiple subtasks and implement them one by one - you clear context between every step - after spec generation and after subtask implementation. this helps keep cost low and context clear and focused which boost performance - specs written to disk help with information persistency - delivering specs layer by layer help to catch early when agent…

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  super-spec.skillJul 2026 · github.com · ▲3

  Stop vibe coding. Start spec-driven development.

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  I trained a 125M model to autocomplete piano on-device▲598

  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.

  AI · 17d ago · simedw.com

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  Astute▲585

  Automate your B2B brand going viral, with new media creators

  AI · 19d ago · company-app.joinastute.com

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  Grok Bot▲547

  AI teammates that you can give real work to

  AI · 26d ago · x.ai

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  Needle2: 14MB agentic LLM for phones, wearables, smart home and robots▲537

  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…

  AI · 27d ago · cactuscompute.com

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  Monid▲474

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  Clara AI SDR▲460

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  Brew ▲905

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  Kilo Code for VS Code▲897

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  Dev tools · May 2026 · kilo.ai

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  StoreClaw▲805

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  AI · May 2026 · storeclaw.ai

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  PollyReach▲796

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  AI · May 2026 · pollyreach.ai

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  Needle: We Distilled Gemini Tool Calling into a 26M Model▲776

  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…

  Life & fun · May 2026 · github.com

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  Files.md – Open-source alternative to Obsidian▲730

  Dev tools · May 2026 · github.com