Decant – Understand how you spend tokens
Hey HN - there are lots of tools to understand how many tokens you use and how much it costs, but we haven't found any that tell you where those tokens are going! Decant helps you understand what you are spending tokens on (context gathering, planning, code, chat, etc), so you can optimize it.
In plain words
Decant is a local-first analysis tool that breaks down token spending across Claude Code and Codex sessions, showing developers exactly where tokens go—whether to context gathering, planning, code generation, or chat. It tracks token spend, context windows, files touched, and associated costs. The tool is designed for developers who want to understand and optimize their token usage beyond just monitoring totals.
written from the facts on this page · September 2026
From the sources
Local-first analysis of Claude Code and Codex sessions: token spend, context windows, files touched, and cost. - dosu-ai/decant
More dev tools this month
the category →
Open-source GTM skills for technical founders
Dev tools · 29d ago · gtmcofounder.com
OpenTrailPaper is open-source bike computer firmware for the LilyGO T5S3 4.7" E-Paper PRO. It supports offline maps, GPX routes, FIT recording and Bluetooth sensors.
Dev tools · 1d ago · opentrailpaper.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com