Dictation that respects your stack.

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GitHub — Commit
~/appgitcommit -m

"

“”

Talk the change. Get a clean commit message.

The challenge

Coding agents got good. The cap is now how fast you can prompt them. Developers type at 40 words a minute and think at 400, so the agent gets a thin slice of the context that would have produced the better diff. The richest prompt, the one with edge cases, prior attempts, and the constraints you'd mention in a pairing session, is the one a developer can't be bothered to type out.

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How Epilude helps

Prompts that match how you think.

CursorCursor·Agent

Agent modeRun

Talk to Cursor, Claude, and ChatGPT.

Dictate prompts that ramble the way a pairing session does. Edge cases, prior attempts, constraints. All of it ends up in the prompt the agent sees.

CursorCursor·Composer

File reference tagged for the prompt automatically.

Code-Aware Dictation that survives syntax.Coming soon

camelCase, snake_case, kebab-case, file names, CLI commands. Talk about a file in Cursor and Epilude tags it for the prompt automatically.

GitHub
GitHub

Memoize search query to cut redundant fetches

epilude/app #482

src/components/SearchResults.tsx
function SearchResults({ query }) {
- const results = fetchUsers(query)
+ const results = useMemo(() => fetchUsers(query), [query])
ATLeave a review comment

CommentSubmit review

PRs at the speed you think them.

Dictate the review, the commit message, the issue. Talking is faster than typing a paragraph of context, and the context is the part that matters.

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Lives in your editor.

VS Code, Cursor, Windsurf, Xcode, WebStorm, Terminal. One hotkey; no Epilude window to open.

Every app you ship from.

Epilude lives inside the AI-IDE stack you already run — the PR description lands in the window you opened it from.

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…and every other app you write code, PRs, or docs in on your Mac. In any app you already write in, on your Mac.

GitHubGitHub

Add backpressure to the stream parser

This adds backpressure to the streaming tokenizer so we stop buffering the whole response in memory on slow consumers. The parser now pauses the upstream read when the output queue hits the high-water mark and resumes once it drains. I've also added a regression test that replays the 40MB payload that triggered the OOM in

Practical ways developers use Epilude

“In lib/queue.ts, swap the in-memory queue for the new bullmq adapter. Keep the public API identical. Don’t touch the worker pool config, that’s pinned by ops. Tag @yara on the PR.”

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