I scrolled through and didn’t see enough to justify installing and running a thing

I do not see any link or source for the data. I assume it is to remain closed, if it exists.

No, thanks

It became very hard to understand what exactly is sent to LLM as input/context and how exactly is the output processed.

would love to know your actual day to day use case for what you built

1.how often developers accept vs modify generated code 2.which tasks AI consistently accelerates (tests, refactoring, boilerplate?) 3.whether debugging sessions become longer or shorter with AI assistance

My experience so far is that AI is great for generating code but the real productivity boost comes when it helps navigate large codebases and reason about existing architecture.

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 the baggage of a long past context into them.

LLMs are far from consistent.

It seems to me that sometimes it's better and more effective to remove, clean up, and simplify (both from CLAUDE.md and the code) rather than having everything documented in detail.

Therefore, from session analysis, it would be interesting to identify the relationship between documentation in CLAUDE.md and model efficiency. How often does the developer reject the LLM output in relation to the level of detail in CLAUDE.md?

A few data points from two weeks live:

- Agents send well-formed queries with specific parameters (country, gender, follower filters) -- suggests the reasoning chain before tool calls is solid - 50 unique agents hit our 402 response, only 1 converted to paying. The USDC friction kills conversion even when agent intent is clear - Node.js-based MCP clients dominate paid traffic vs Python

Your 26% session abandonment matches something we see: many hits are single 402 responses with no retry. The agent discovers the API, gets the payment requirement, but has no wallet configured to complete the transaction. It is essentially agent session abandonment at the payment step.

Would be curious whether your data shows patterns in external tool call failures vs successes -- whether agents retry on failure or abandon.

The error cascade pattern predicting abandonment within 2 minutes reminds me a lot of monitoring infrastructure services. We learned years ago that the first 90 seconds of a deployment tell you almost everything about whether it'll stick. Same principle seems to apply to agent sessions — if the initial tool selection or file read is wrong, confidence collapses and the human takes over manually.

Curious about the skills finding (4%). Is that because skills are poorly discoverable, or because people default to natural language prompts and never bother setting up structured skill definitions? The gap between "available" and "used" in agent tooling feels like the extension ecosystem problem all over again.