Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models
What changed
I’ve been building Echo ( https://echo.tracerml.ai/), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined.
Why it matters
A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.
How it compares
Related prior coverage to compare against:
- Nous Research's NousCoder-14B is an open-source coding model landing right in the Claude Code moment
- The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway
- Claude Code costs up to $200 a month. Goose does the same thing for free.
Sources
- Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models (hn-frontpage)primary