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>
nisse-dev
The Dev Agent: plans and implements software projects, produces Gitea PRs for human
review, and reacts to review comments and commits. A thin application over the
shared nisse-core library.
Part B of the specification at ../docs/Architecture-Build-Spec.md
covers this agent; Part A covers the library it sits on.