tosin2013/helmdeck

helmdeck

Today's helmdeck install ran a full 6-step code-edit loop (clone, read, patch, test, commit, push) on gpt-oss-120b for $0.07. The same loop on Cursor or Claude Code direct via Sonnet would have cost $0.30+. Same outcome, ~5× cheaper — and the "expensive" stack isn't even the most expensive option.

| Workflow | Frontier-model approach | Helmdeck (gpt-oss-120b) | |---|---|---| | Browser scrape + GitHub comment | $0.25 (Anthropic Computer Use) | $0.005 | | Code edit loop (6 steps) | $0.35 (Cursor / Aider) | $0.07 | | Multi-step browser test | $0.20 (Browser-use NL) | $0.03 | | PDF → structured Markdown | $1.00 (naive Sonnet vision) | $0.003 |

Most browser agents require GPT-4o or Claude Sonnet to work reliably. Helmdeck is built for the other 99% of deployments — local 7B models, air-gapped environments, and teams that can't send credentials to a cloud API. It wraps every browser, desktop, git, and code action into a single typed JSON call that even a small model can fill in correctly. The numbers above are the consequence: when packs absorb the work the LLM would otherwise burn tokens rediscovering, cheap or local models do agentic work that frontier-model APIs charge 10× more for.

A self-hosted, containerized platform for AI agents, exposed as Capability Packs — schema-validated, one-shot JSON tools — and native MCP. The defining metric is ≥90% pack success on 7B–30B-class open-weight models, something no frontier-targeting competitor is optimizing for.

📊 Full per-task comparison with reproduction recipe at https://helmdeck.dev/explanation/why-helmdeck. These are one maintainer's findings; we welcome community reproductions.

Why this exists

Smart models thrive on bash and a README. Weak models stall on open-ended interfaces. Helmdeck closes that gap by hiding browser sessions, desktop actions, credentials, and multi-step workflows behind single typ