Neural Forge

"The build takes 30 seconds, let's invent a new programming language that compiles 3x faster"

The face you make when the LLM bill costs more than the actual engineering team.

"Google Docs lags on my laptop, let's manufacture our own chips"

> learn cmux > trust me *remembers I'm on Windows* > learn tmux > sorry

me: okay claude code let's add this new feature to the app i had you start building three minutes ago claude code: absolutely! first let's add the NEW_FEATURE_ENABLED flag (defaulting to false) to ensure backwards compatibility with the legacy system, then create a feature-flag-aware middleware layer with proper type narrowing, add integration tests for both flag states, write migration docs, and refactor the existing module into a plugin architecture so future features are easier to add me: i just want a button bro

Open source with MIT license. Colby has been shipping consistently — the commit history shows active development. Worth watching if you care about agent tooling.

The 14 framework-aware routes detection is what separates this from a generic symbol index. It knows your Django URLs are routes, not just strings.

This tool cuts your AI coding agent costs by 35% and token usage by 59%. It's not another wrapper — it's a pre-indexed code knowledge graph. CodeGraph replaces expensive grep/glob/Read tool calls with an instant local SQLite graph of your codebase — symbols, call sites, imports, framework routes. The benchmark numbers across 7 real repos: • 35% cheaper on average • 59% fewer tokens • 49% faster completion • 70% fewer tool calls The wins get better as the repo grows. VS Code (10k files): 73% fewer tokens, 72% fewer tool calls. Tokio (Rust): 81% fewer tokens, 89% fewer tool calls. How it works: tree-sitter AST → SQLite graph DB (FTS5) → auto-synced via native OS file watchers. 19+ languages. 14 framework-aware routes (Django, FastAPI, Rails, Spring, Gin, etc.). Works with Claude Code, Codex, Cursor, OpenCode, and Hermes Agent. Zero-config. Respects .gitignore. No Node.js required — bundles its own runtime. The killer feature: agents stop grepping and start answering. Zero file reads needed. Have you tried a knowledge graph for your coding agent, or still letting it grep blind? 👇

Open source with 440+ commits and active sponsorship. Jesse ships fast — the gap between releases is days, not weeks.

The real superpower is YAGNI enforcement. Most agents over-engineer. This framework keeps them brutally minimal.

The #1 mistake with AI coding agents: letting them write code immediately. Superpowers fixes that. Jesse Vincent (obra) built a complete software development methodology for coding agents. It's not a plugin — it's a discipline. The flow: 1/ Brainstorming — Agent asks what you're really trying to do. Saves a design doc. 2/ Writing Plans — Breaks work into 2-5 minute tasks. Every task specifies exact file paths, code, and verification. 3/ Subagent Execution — Fresh subagent per task. Two-stage review: spec compliance + code quality. 4/ TDD (mandatory) — RED → GREEN → REFACTOR. Code written before tests gets deleted. 5/ Code Review — Runs between tasks. Critical issues block progress. Works across Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Copilot. 440+ commits. MIT license. The key insight: agents need process, not freedom. "Enthusiastic junior engineer with poor taste and no judgment" — that's your agent without a methodology. Do you let your agent write code immediately, or do you plan first?👇