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Ribbing aside, the "rethinking" here sounds a lot like an AI-specific spin on the "bring the code to the data, instead of data to the code" paradigm. I imagine that the same multitenancy caveats of allowing arbitrary user-defined queries against a relational database (execution limits, access controls, etc.) will apply here as well.
Earlier quoted context omitted.
Yeah I think this maps onto grep-based code search well. Right now agents grep, read the results, grep again, and every hit lands in context. This architecture turns that into one program: fan out the greps, filter and dedupe in the sandbox, hand back only what matters. The "won't know the codebase" worry flips around too. The first program shouldn't be "find the bug," it should be "build me a map" (grep the file tre…
Claude code already fans out and sandboxes context by calling sub agents so I'm not sure this approach brings much benefit there. A complex search strategy only makes sense if the search is slow and compute intensive.
Perplexity still exists?
[flagged]
(Your earlier comments were, of course, fine.)
Sounds like the goal here is to augment search with a sort of query language . One that's more structured than a mere list of keywords? Ribbing aside, the "rethinking" here sounds a lot like an AI-specific spin on the "bring the code to the data, instead of data to the code" paradigm. I imagine that the same multitenancy caveats of allowing arbitrary user-defined queries against a relational database (execution limit…
Sounds like the goal here is to augment search with a sort of query language . One that's more structured than a mere list of keywords? Ribbing aside, the "rethinking" here sounds a lot like an AI-specific spin on the "bring the code to the data, instead of data to the code" paradigm. I imagine that the same multitenancy caveats of allowing arbitrary user-defined queries against a relational database (execution limit…
I don't understand why you wouldn't simply provide SQL with requisite security precautions. Do you really need more?
I found this approach very interesting and was wondering if it could be applied to grep-based search for coding agents to increase speed and reduce LLM turns, but the part im not quite understanding is how the model will know enough about the codebase to construct a complicated multi-stage search pipeline based just on the prompt. Maybe this is just different from web search, but it seems like the model needs sequent…
Coding agents prefer to do iterative search, I have yet to see them create a complex search script. They try different search cmds in parallel, evaluate their results and then refine or dive deeper. This approach usually works great but I can see many use cases where a smarter search strategy may make sense especially to optimize context.
Normally you'd tell your customer they need to fix their code if they're doing something inefficient, but here, they didn't write the code and are presumably not expected to understand it.