Flatten codebases into one AI-ready file for local LLM workflows
Flatten a project's directory and source into a single, structured file optimized for Large Language Models, with explainable diagnostics and CLI control. Eugene Nesvetaev's Windows utility runs on Node.js or Bun and targets developers, engineers, and AI researchers. Key capabilities include local-only context compilation, built-in why/doctor diagnostics, and a command-line interface that integrates into developer workflows for staged context delivery to coding assistants.
What fln does and how it fits developer workflows
fln consolidates an entire codebase into one file designed for LLM consumption, producing an explainable context artifact rather than embeddings. The tool runs as a CLI and exports a structured, single-file snapshot, which you can feed to an AI coding agent or include in code-review automation. This suits workflows where a reproducible, local context bundle is preferable to remote services.
How it performs and its runtime characteristics
The implementation uses TypeScript and executes on Node.js or Bun, which gives high-performance processing for larger repositories. Because it runs locally and avoids external API calls or embedding steps, the main system impact is the runtime of your chosen JavaScript engine. Expect CPU and disk I/O proportional to repository size; the tool's architecture eliminates network latency from context generation.
Safety, explainability, and privacy assurances
fln operates without sending data to external servers, preserving source code on the machine and removing the need for vector databases or cloud embeddings. It exposes a why/doctor diagnostic feature that explains why particular files were included or excluded, supplying traceable selection logic users can inspect before handing context to an AI agent.
Interface, usability, and technical requirements
The tool is command-line driven, so basic shell familiarity is required for integration into scripts or CI steps. Installation depends on a JavaScript runtime; typical deployment paths include npm or running from a cloned repository under Node.js or Bun. The CLI approach favors automation and scripting over graphical guidance, which aligns with the target technical audience but demands care when selecting repository filters and scopes.
Best for technically proficient teams wanting local, auditable AI context
The tool serves developers and researchers who need a local, explainable way to prepare large-scale code context for AI models; expect predictable, runtime-bound processing because it runs on Node.js or Bun. A practical tip: run it inside a managed runtime environment such as nvm or a dedicated container to isolate dependencies and reproduce results across machines. The tool is focused and suited to scripted workflows rather than casual, GUI-driven use.
