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Show HN: Rudel – Claude Code Session Analytics

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Re: Show HN: Rudel – Claude Code Session Analytics

#101

Post by alvarogar »

swaminarayan wrote: post #98

26% of AI coding sessions are abandoned within 60 seconds .Is this a prompt problem, a tooling problem, or a limitation of current models?

The main reason we've seen is a bad first impression: the agent misunderstands the initial prompt, doesn't load the available tools or starts doing something clearly wrong, and the user ctrl+c's instead of correcting it since it's faster to just start a new one.

The learning is that it is fixable. Better CLAUDE md instructions, clearer initial prompts, and skill configurations that reduce the uncertainty cut abandonment significantly in our team.

Re: Show HN: Rudel – Claude Code Session Analytics

#102

Post by hudtaylor »

longtermemory wrote: post #61
Aurornis wrote: post #49

> 26% of sessions are abandoned, most within the first 60 seconds Starting new sessions frequently and using separate new sessions for small tasks is a good practice. Keeping context clean and focused is a highly effective way to keep the agent on task. Having an up to date AGENTS.md should allow for new sessions to get into simple tasks quickly so you can use single-purpose sessions for small tasks without carrying…

I agree. In my experience: "single-purpose sessions for small tasks" is the key

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Re: Show HN: Rudel – Claude Code Session Analytics

#103

Post by mihir_kanzariya »

duskdozer wrote: post #97
mihir_kanzariya wrote: post #96

Earlier quoted context omitted.

[flagged]

No, LLMs often have an identifiable style that can be unpleasant to read. I came here after identifying your account as a bot, but it looks like that's not true. I would rather read whatever English you are able to write. If it's not enough for you to communicate, it would be better to just use a traditional machine translator, such as https://libretranslate.com/

[flagged]

Re: Show HN: Rudel – Claude Code Session Analytics

#105

Post by jimmySixDOF »

monsterxx03 wrote: post #93

I built something in a similar space: Linko ( https://github.com/monsterxx03/linko ), a transparent MITM proxy with a webui that lets you see what's actually being sent between Claude Code and LLM APIs in real time. It's been really helpful for me to debug my own sessions and understand what the model is seeing (system prompts, tool definitions, tracing tool calls etc.).

Note: Linko currently only supports macOS.

Re: Show HN: Rudel – Claude Code Session Analytics

#106

Post by zippolyon »

Great work on the session analytics. The "error cascade in first 2 minutes predicts abandonment" finding is exactly the kind of signal that causal auditing can act on. We built K9 Audit for the complementary problem: not just when sessions fail, but why — recording every tool call as a CIEU five-tuple (intent vs actual outcome) with a hash chain. The "26% abandoned" stat likely hides silent deviations that looked like success. k9log causal --last traces root cause across steps in seconds. https://github.com/liuhaotian2024-prog/K9Audit

Re: Show HN: Rudel – Claude Code Session Analytics

#107

Post by zippolyon »

ericwebb wrote: post #72

I 100% agree that we need tools to understand and audit these workflows for opportunities. Nice work. TBH, I am very hesitant to upload my CC logs to a third-party service.

The hesitation about log upload is exactly why K9 Audit works differently — local by default, SHA256 hash-chained, zero data leaves your machine unless you explicitly configure a sync endpoint. pip install k9audit-hook and drop one JSON file in .claude/. https://github.com/liuhaotian2024-prog/K9Audit
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