capsul builds a minimal context for your task, under a token budget you set, and measures what it saves against a naive agent that dumps whole files. Fewer tokens, same answer.
Hi Product Hunt 👋
I'm Adrien and we built capsul .
It started with a boring problem: I kept running out of my Claude plan
mid-task, on a Wednesday, every week. Not because I was working more, but
because every turn of a long session re-sends everything that came before it.
The question is a rounding error. The context around it is the bill.
capsul sits between you and the agent CLI you already use, and sends it only
what the task needs. Your existing Claude or ChatGPT subscription is what
answers, so there is no API key to paste and no second model bill.
The part I care about most is the benchmark. Every tool in this space claims
10x. So I ran 9 Claude models, 5 tasks each, 2 arms, 180 cells, discarded none,
and published every interval, including the three models that lose an answer
check with capsul. Median 58% less input, range 32% to 80%. When a row
establishes nothing, it stays in the table anyway.
Raw data and protocol: https://www.capsul.chat/benchmark
Free for 10 prompts on your own repo, so you can measure it on your code
instead of trusting mine.
Happy to answer anything, especially on the methodology. What would you want
measured that I have not?