# earshot: Giving Coding Agents Ears — Hear, Review, and Make Sound in Pure Code, on CPU > Source: > Published: 2026-10-22 00:00:00+00:00 # earshot: Giving Coding Agents Ears — Hear, Review, and Make Sound in Pure Code, on CPU We’ve written a lot lately about agents gaining *senses* and *self-verification* — [Pixel Path screenshotting its own renders until the sky stops bleeding through buildings](https://themenonlab.blog/pixel-path-claude-code-skill-real-road-flythrough/), [vision models that draw to reason](https://themenonlab.blog/sketchvlm-vision-models-draw-reasoning/). **earshot** adds the one sense everyone forgets: hearing. The premise is sharp. AI coding agents can now write the code that renders a film, a product demo, or a game — *but they still cannot hear what they made.* They can’t tell a premium key press from a cheap one, notice that a hit lands 80 ms after the cut, or catch that the music swells in the wrong place. earshot fixes exactly that, and it runs **locally on CPU** as a CLI, Python API, and MCP server. ## Turning Sound Into Evidence The core idea is to convert audio into something an agent can *reason about in words and numbers*, not just play back. A sampling of the commands: - **`describe`** — what a file sounds like: calibrated tags (e.g.*“single mechanical keyboard key press, z +5.4”* ), closest alternative readings, AudioSet labels, and raw measurements (peak dBFS, brightness). - **`match`** — how well a description fits a sound, with a verdict (strong / plausible / weak / no match) and held-out evidence. - **`find`** — the best moments inside a long recording, returned as precise sample trims. - **`realness`** — is this a real/pro-produced recording or code-synthesized? With per-window scores. - **`timeline`** — a second-by-second*listening transcript* of a whole film: events, sections, loudness, beat grid, and how sound syncs to picture cuts. - **`sheet`** — an annotated PNG (frames, events, spectrogram, waveform, sync markers) for agents that read images. - **`critique`** — a launch-video scorecard against a profile of 7 professional launch films, with**timestamped fixes** (*“nothing reaches body level until 0:01.01”* ). That last one is the kicker: the agent doesn’t just hear, it gets graded against professional work and told what to fix, where. ## Making Sound in Code — and Hearing It Back The other half is generative, and it closes a feedback loop: - **`forge`** — synthesize a sound*entirely in code* , then improve it by listening to the result. (Every sound in the project’s 22-second launch film was made this way — no recordings, no music track.) - **`fit`** — translate a real recording into a` forge` recipe Python module that re-renders it, with knobs and morphs so the agent can vary it. - **`sfx` / `kit`** (new in 0.3) — a bank of**201 sound effects stored as code** that render by id in seconds, no models, no network.`sfx` aims a whoosh, hit, click, or note at a moment in the picture;`kit` renders a whole film’s effects with measured sync points. Storing effects as *code with fingerprints rather than audio files* is a neat touch — it’s diffable, version-controllable, and tiny. ## Why It Matters This is the same deep pattern we keep flagging: [software built to be operated by an AI agent](https://themenonlab.blog/solidcraft-open-source-fusion-360-rust-mcp-agents/), exposing an MCP server so the model gets a real tool instead of a guess. earshot gives an agent a *perception-and-critique loop for audio* — generate, listen, score, fix — the exact discipline that separates “the code ran” from “the result is actually good.” As agents produce more video, games, and demos, being deaf to their own output is a glaring blind spot. earshot is a thoughtful, honest (it ships VALIDATION and LIMITS docs), CPU-only answer to it.