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Notes from the studio.
How Frenchie works, what we picked and why, how MCP is quietly reshaping agent tooling. No fluff. RSS available.
How Frenchie Method keeps agent work easy to review
Fast agent work is only useful when a human can understand what changed, why it changed, and how it was checked.
Read the post →Why test cases come before implementation plans
Frenchie Method asks for proof before planning the code change, because the plan should be shaped by how the team will verify it.
Read the post →Why Frenchie Method uses worktrees
Worktrees give fast agent work a clean place to happen, so parallel tasks do not contaminate the main checkout.
Read the post →Document extraction, without the heavy platform
Frenchie keeps extraction intentionally small: read the file, return Markdown, let the agent do the reasoning.
Read the post →Excel to Markdown for AI agents
Why agents need spreadsheet extraction before they can reason over workbooks, and how Frenchie turns XLSX files into sheet-aware Markdown.
Read the post →Pandoc, Unstructured, or Frenchie?
A practical guide to three different extraction shapes: local conversion, document processing infrastructure, and MCP-native file extraction for agents.
Read the post →Read, listen, create: why your AI agent needs all three
Your agent already reasons. Give it three hands: read files, listen to recordings, create images. How image generation completes the Frenchie toolkit.
Read the post →Why we built Frenchie: the MCP tools gap
Agents write code and reason about systems. Drop a scanned PDF or a 30-minute recording in front of one and most hit a wall. That wall is why Frenchie exists.
Read the post →How Frenchie handles 30-minute audio without blocking your agent
Sync transcription freezes your agent for five minutes. Async job handling is the detail that makes transcription usable inside a live agent workflow.
Read the post →Inside Frenchie: from scanned PDF to clean Markdown in 3 seconds
What happens between dropping a scanned contract into your agent and getting back searchable Markdown with tables and figures intact. The OCR pipeline.
Read the post →MCP for beginners: what it is and why Frenchie picks it
Model Context Protocol is the quiet standard reshaping how AI agents get tools. What it actually is, why it matters, why Frenchie bet the whole product on it.
Read the post →Pay-per-use vs subscription: why Frenchie ships flat $1 = 100 credits
Every SaaS eventually debates usage-based vs subscription pricing. For a narrow tool like Frenchie, the answer was clear before the first line of billing code.
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The best way to know us is to try what we build.
100 free credits, no card. Read a post, drop a file, see if Frenchie earns a spot in your stack.