dongarandClaude Opus 5.5
50b46821d7
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Choosing a local executor model, and tuning its knobs, needs measured success rates across repeated runs, comparable with every model measured before. - Four canned milestones under nisse_dev/bench/tasks: write a function, fix a bug a failing test exposes, thread an option through several files, and find one failure in output far past max_output_chars. Each is an approved plan over a seed project, judged by a hidden check the model never sees; a reference solution proves each is solvable and that its check tells the difference - Runs use the agent's own client wiring, now shared as make_llm_clients, with delegation and ask_expert off: an escalation is a failure of the model under test - Each run is stored as one JSON line in bench/results/, committed, with the model's backing name and declared facts, a note on how it was served, the task's content hash, the agent's commit and tuning, the outcome, turns, misbehaviour counts, context trims, tokens, time and LiteLLM's cost; a fallback answer can never count as a success - The report rebuilds the comparison from every stored run, with Wilson 95% intervals, marking runs against older task versions - scripts/benchmark.py runs and reports; scripts/evaluate_model.sh takes a new model through the context probe, the capability matrix and the benchmark in one command - Clients keep LiteLLM's per-call cost, and BudgetedClient counts the requests it had to trim - The tuning guide gains "Benchmarking models" Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
13 lines
454 B
YAML
13 lines
454 B
YAML
title: Fix a bug a failing test exposes
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check: python -m pytest -q .bench_check
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plan: |
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Milestone 1: fix the failing inventory test.
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1. Run `python -m pytest -q` and read the failure.
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2. The failing test is correct; the bug is in `inventory.py`. Do not change
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any test file.
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3. Fix `total_value` so that each item contributes its unit price times its
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quantity.
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4. Run `python -m pytest -q` until every test passes.
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5. Call finish.
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