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Macrokit Studio
A tiny local model does frontier-grade work — free, no key
Macrokit Studio is a free, open demo: a small model in your browser does GitHub-maintainer work by running macros that a strong model encoded ahead of time. No signup, no API key, no server — nothing leaves your machine (open the network tab). It's an open format for macros plus free tools to build and run them. Apache 2.0, fully open.
First, just try it — no signup, no API key, nothing to install: open https://studio.macrokit.dev and a tiny model does GitHub-maintainer work right in your browser. Open your network tab and you'll see nothing hits a server and nothing's billed.
The honest version of what Macrokit is and why it exists.
Most "agent" setups ask the model to reason through a multi-step workflow at runtime. A weak or local model loses the thread by step three — so people reach for a frontier API, and then the bill (or data-residency rules, or an air-gapped network) becomes the ceiling.
Macrokit flips the order. At design time, a strong model encodes a workflow once as a deterministic macro. At runtime, your weak/local model only classifies intent and dispatches that macro — no live multi-step reasoning. The hard part happens once, offline; the cheap part runs everywhere.
The piece I'm proudest of isn't the runtime, it's the CLI: macrokit gate reads your session logs and fails the build if an engineer reasoned at runtime instead of encoding a macro. It's a ratchet — your macro library compounds instead of rotting.
On the numbers: a 7B local model (Qwen 2.5, 4-bit, on a 16GB MacBook) scored 94.5% on a pre-registered 100-task intent-routing benchmark, with zero bail-outs. I published the failed first run (53.5%) right next to the fixed one — the methodology is fully open.
Apache-2.0, TypeScript, bring-your-own-model (OpenAI-compatible + Ollama out of the box). Repo + benchmark in the links.
Happy to get into any of it — the design-time/runtime split, the benchmark, or why this isn't "just LangChain with extra steps."
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About Macrokit Studio on Product Hunt
“A tiny local model does frontier-grade work — free, no key”
Macrokit Studio was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #142 on the daily leaderboard. Macrokit Studio is a free, open demo: a small model in your browser does GitHub-maintainer work by running macros that a strong model encoded ahead of time. No signup, no API key, no server — nothing leaves your machine (open the network tab). It's an open format for macros plus free tools to build and run them. Apache 2.0, fully open.
Macrokit Studio was featured in Developer Tools (514k followers), Artificial Intelligence (471k followers) and SDK (773 followers) on Product Hunt. Together, these topics include over 172.7k products, making this a competitive space to launch in.
Who hunted Macrokit Studio?
Macrokit Studio was hunted by Cheng Qian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Cheng Qian
Hey Product Hunt 👋
First, just try it — no signup, no API key, nothing to install: open https://studio.macrokit.dev and a tiny model does GitHub-maintainer work right in your browser. Open your network tab and you'll see nothing hits a server and nothing's billed.
The honest version of what Macrokit is and why it exists.
Most "agent" setups ask the model to reason through a multi-step workflow at runtime. A weak or local model loses the thread by step three — so people reach for a frontier API, and then the bill (or data-residency rules, or an air-gapped network) becomes the ceiling.
Macrokit flips the order. At design time, a strong model encodes a workflow once as a deterministic macro. At runtime, your weak/local model only classifies intent and dispatches that macro — no live multi-step reasoning. The hard part happens once, offline; the cheap part runs everywhere.
The piece I'm proudest of isn't the runtime, it's the CLI: macrokit gate reads your session logs and fails the build if an engineer reasoned at runtime instead of encoding a macro. It's a ratchet — your macro library compounds instead of rotting.
On the numbers: a 7B local model (Qwen 2.5, 4-bit, on a 16GB MacBook) scored 94.5% on a pre-registered 100-task intent-routing benchmark, with zero bail-outs. I published the failed first run (53.5%) right next to the fixed one — the methodology is fully open.
Apache-2.0, TypeScript, bring-your-own-model (OpenAI-compatible + Ollama out of the box). Repo + benchmark in the links.
Happy to get into any of it — the design-time/runtime split, the benchmark, or why this isn't "just LangChain with extra steps."