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teilom

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[untitled]

1 points·by teilom·3개월 전·0 comments

Ask HN: What's your biggest LLM cost multiplier?

7 points·by teilom·5개월 전·6 comments

Show HN: TextPolicy – reinforcement learning for text generation on a MacBook

github.com
4 points·by teilom·11개월 전·0 comments

WikipeQA: An evaluation dataset for both web-browsing agents and RAG systems

huggingface.co
1 points·by teilom·작년·0 comments

[untitled]

1 points·by teilom·2년 전·0 comments

comments

teilom
·5개월 전·discuss
+1 on interaction terms + tails : fanout × retries × context growth is where linear token math dies.

One thing we do in enzu is make “budget as constraint” executable: we clamp `max_output_tokens` from the budget before the call, and in multi-step/RLM runs we adapt output caps downward as the budget depletes (so it naturally gets shorter/cheaper instead of spiraling). When token counting is unavailable we explicitly enter a “budget degraded” mode rather than pretending estimates are exact.

Also agree p90/p95 cost/run matters more than averages; max-output caps are crude but effective.

Docs: https://github.com/teilomillet/enzu/blob/main/docs/PROD_MULT... and https://github.com/teilomillet/enzu/blob/main/docs/BUDGET_CO...
teilom
·5개월 전·discuss
If you’re trying to estimate before prod, logging these 4 things in a pilot gets you 80% there: - tokens/run (in+out) - tool calls/run (and fanout) - retry rate (timeouts/429s) - context length over turns (P50/P95)

Fanout × retries is the classic “bill exploder”, and P95 context growth is the stealth one. The point of “budget as contract” is deciding in advance what happens at limit (degraded mode / fallback / partial answer / hard fail), not discovering it from the invoice.
teilom
·5개월 전·discuss
Background note I wrote (framing + “budget as contract”): https://github.com/teilomillet/enzu/blob/main/docs/BUDGETS_A...