Extraction Script Reliability

Concerns that compressing git commits to 107 bytes requires LLM to write perfect extraction scripts upfront, risking information loss when scripts are wrong

While the efficiency of compressing massive datasets into tiny summaries is impressive, critics worry that relying on LLMs to write perfect extraction scripts upfront risks significant information loss and increased hallucinations. These concerns center on "pre-compaction" errors, where flawed logic might discard critical details—such as specific commit messages or niche utility functions—that the model fails to identify as relevant initially. However, some argue that this risk is mitigated by storing the full output in searchable indexes, allowing the agent to retrieve missed specifics if the initial summary proves insufficient. Ultimately, the discussion highlights a tense balance between the immediate speed of aggressive data reduction and the long-term reliability of automated information retrieval.

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The hooks seem too aggressive. Blocking all curl/wget/WebFetch and funneling everything through the sandbox for 56 KB snapshots sounds great, but not for curl api.example.com/health returning 200 bytes.

Compressing 153 git commits to 107 bytes means the LLM has to write the perfect extraction script before it can see the data. So if it writes a `git log --oneline | wc -l` when you needed specific commit messages, that information is gone.

The benchmarks assume the model always writes the right summarization code, which in practice it doesn't.

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Not bad, but it sacrifices accuracy and there are risks of causing more hallucinations from having incomplete data or agent writing bad extraction logic. So the whole MCP assumes Claude is smart enough to write good extraction scripts AND formulate good search queries. I'm sure thing could expand in the future to something better, but information preservation is a real issue in my experience.

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Excited to try this. Is this not in effect a kind of "pre-compaction," deciding ahead of time what's relevant? Are there edge cases where it is unaware of, say, a utility function that it coincidentally picks up when it just dumps everything?

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Yeah it's basically pre-compaction, you're right. The key difference is nothing gets thrown away. The full output sits in a searchable FTS5 index, so if the model realizes it needs some detail it missed in the summary, it can search for it. It's less "decide what's relevant upfront" and more "give me the summary now, let me come back for specifics later."

Summarizer