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Sprout
The Problem Token cost is now a real budget line. Uber burned its entire 2026 AI coding budget in four months. Microsoft cancelled Claude Code licences for thousands of engineers after costs hit $2,000 per person per month. The root cause: coding agents re-read the entire codebase from scratch on every turn — burning 40,000+ tokens before they write a single line. Meanwhile, general software engineering roles are down 49% while AI engineer roles are up 59%. The engineers who survive are the ones who make AI work efficiently — not around them. The Solution Sprout fixes the token problem: map the codebase once into a compact ~800-token snapshot, then reference only that snapshot on every agent run. That single change drops a typical full pipeline run from ~57,000 tokens to ~1,620 — roughly 97% less — the difference between a $0.80 run and a $0.04 run, multiplied across hundreds of builds per day. What Sprout Does Paste any GitHub owner/repo slug. Sprout fetches the full recursive file tree (no clone required), domain-tags every file instantly using fast regex heuristics — UI, API, Database, Security, Validation — and renders an interactive D3 force graph. Hover any node for a 120-token AI summary. Click for a deeper plain-English breakdown (function, inputs, outputs, process). Ask free-form questions about any file. Describe a goal in plain English — "build a task manager with a database" — and a deterministic planner computes the exact pipeline phases without any routing LLM call. The pipeline runs: map activation → database schema generation → React UI code generation → automated Monitor and Security review gates → one-click GitHub push. Generated files render live in a Sandpack sandbox before anything touches the repo. If a review gate fails, the offending agent self-heals automatically — re-running with the feedback injected as a fix instruction — with no human loop required. The security gate covers 16 static rules (hardcoded secrets, eval(), XSS vectors, SQL injection, server-only imports in client components) plus a 5-check LLM layer for contextual vulnerabilities regex cannot catch. Target Users Developers onboarding to unfamiliar codebases, indie builders who want to ship features without burning through AI credits, and engineering teams that need auditable, cost-controlled AI-assisted code generation with GitHub integration baked in. What Makes It Unique Most AI coding tools treat context as a cost center they ignore. Sprout treats it as the core product: the compact repoMap snapshot, the deterministic planner, the per-repo run history that persists prior build context across sessions, and the two-layer self-healing review pipeline are all designed around the principle that the agent should spend tokens on generation — not on re-reading what it already knows.
27 Sep 2026
