u/OtherwisePush6424

Over the past year I've been building a set of tools around making fetch more reliable in production and more testable in development. They're now all on one site: fetchkit.org

The tools:

• ffetch (@fetchkit/ffetch) - drop-in fetch wrapper with timeouts, retries, backoff, circuit breaker, bulkhead, and typed errors. Plugin-based so you only pay for what you use.
• chaos-fetch (@fetchkit/chaos-fetch) - composable fetch middleware for injecting latency, failures, throttling, rate limits, and mocks into tests. Vitest/Jest compatible, no proxy needed. Also has a golang port.
• chaos-proxy - YAML-configured HTTP proxy that injects chaos at the transport level. Works with any language/client. Available in Node.js and Go.

The arena:

chaos-fetch powers a live browser benchmark at fetchkit.org/ffetch-demo/ that runs fetch, axios, ky, and ffetch side-by-side under identical chaos conditions (latency, failures, drops, rate limiting) and compares reliability scores, error rates, and latency percentiles in real time. No install, opens directly in the browser.

The chaos layer is configurable: you can dial in exactly what failure scenario you want to test and see how each client handles it.

If your knowledge base lives in Confluence, getting it into a RAG pipeline is painful. The API returns XML-based storage format, links are internal IDs, comments are split across two different API versions, and incremental updates are unreliable.

I built confluence2md to solve this. It's a CLI tool that converts Confluence wikis to clean Markdown and outputs a metadata.json with the full page graph (titles, URLs, parent/child relationships, tags).

The output is designed to be easy to chunk and embed:

• One Markdown file per page
• Filenames and internal links rewritten to be human-readable
• Comments included inline
• Attachments downloaded locally
• Incremental updates so you don't re-process the whole wiki on every run

Repo: https://github.com/gaborkoos/confluence2md
Writeup on the technical challenges: https://blog.gaborkoos.com/posts/2026-05-22-How-I-Built-a-Confluence-Crawler/

Happy to answer questions about the chunking strategy or metadata structure if useful for your setup.

I just open-sourced confluence2md, a Go CLI for exporting Confluence spaces to local Markdown files.

What it does:

• Crawls Confluence from seed pages, follows links up to configurable depth
• Writes clean Markdown with stable filenames (includes page ID for tracking renames)
• Rewrites internal links as relative local paths, preserves external URLs
• Downloads attachments and rewrites references
• Supports incremental updates with dual-checkpoint reliability
• Outputs metadata.json with page metadata and bidirectional link graph

Why open source: The Confluence export problem is real and affects a lot of teams. Existing tools are either outdated, locked into specific workflows, or just don't work. I wanted something simple that just exports your docs cleanly so you can:

• Version your knowledge in Git
• Feed docs into RAG/AI pipelines
• Browse offline in your preferred Markdown editor
• Build local full-text search

Tech:

• Pure Go (zero CGO), cross-platform
• Full test suite
• GitHub Actions CI/CD with automated releases and pre-built binaries
• MIT licensed, contributions welcome

Links:

• GitHub: https://github.com/gkoos/confluence2md
• Releases: https://github.com/gkoos/confluence2md/releases

Looking for early feedback, bug reports, and contributions. If you've dealt with Confluence exports before, I'd love to hear what would make this tool more useful for your workflow.

This one took a while, it's probably the longest thing I've written on this blog. I wanted to do a proper end-to-end walkthrough of cmd/compile: real package names, real data structures, diagrams for the AST and SSA CFG, and the flags you actually need (-m, -m=2, GOSSAFUNC, -S) to observe each phase yourself rather than just take my word for it.

Covers the full pipeline: lexer → parser → type checker → IR lowering → SSA construction → optimization passes (inlining, escape analysis, BCE, nil check elimination, register allocation) → architecture-specific code emission.

Hope it's useful — happy to answer questions or push back on anything that looks wrong.