
Intent Driven Development - Book Launch
A Discipline for Building Software with AI
What it does
Intent-Driven Development: Stop vibing with AI. Start engineering. You're coding with ChatGPT or Claude. Hour 1: amazing. Hour 3: outputs drift, bugs appear, iterations pile up. This book teaches the FORGE methodology, a systematic approach to AI-assisted development that maintains speed while adding the structure you actually need. Inside: • The 5-phase FORGE process • Test-driven development with AI • Prompts that consistently work • Team practices & code review
Does the same job
all alternatives →- FGForge – Guardrails take an 8B model from 53% to 99% on agentic tasksMay 2026 · github.com · ▲687
Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…

- COContinue – Open-source coding autopilot2023 · github.com · ▲298
Hi HN, we’re Nate and Ty, co-founders of Continue, an open-source autopilot for software development built to be deeply customizable and continuously learn from development data. It consists of an extended language server and (to start) a VS Code extension. Our GitHub is https://github.com/continuedev/continue. You can watch a demo of Continue and download the extension at https://continue.dev — — — A growing number of developers are replacing Google + Stack Overflow with Large Language Models (LLMs) as their primary approach to get help, similar to how…

- IBI built a wizard to turn ideas into AI coding agent-ready specsNov 2025 · vibescaffold.dev · ▲71
I created vibescaffold.dev. It is a wizard-style AI tool that will guide you from idea → vision → tech spec → implementation plan. It will generate all the documents necessary for AI coding agents to understand & iteratively execute on your vision. How it works: - Step 1: Define your product vision and MVP - Step 2: AI helps create technical architecture and data models - Step 3: Generate a staged development plan - Step 4: Create an AGENTS.md for automated workflows I've used AI coding tools for awhile. Before this workflow (and now, this tool), I kept getting "close but not quite" results…
- ODOutline Driven Development – New AI-Assisted Coding Paradigm; BNNov 2025 · github.com · ▲11
The Problem "Vibing" with LLMs is often too shallow for complex logic, while writing full specifications is cognitively expensive and slow. We need a middle ground that mimics how human programmers gather context—scanning structure before diving into details. The Solution: Outline Driven Development (ODD) I've built a "batteries-included" kit for Gemini/Claude/Codex that uses AST analysis to understand code structure rather than just raw text. This relies on a hyper-optimized Rust toolchain (`ast-grep`, `ripgrep`, `jj`, etc.) to feed precise, structural context to the agent. 1. The…
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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…
AI · 27d ago · cactuscompute.com

