12591282da
Major rewrite based on deep study of Karpathy's autoresearch repo.
Architecture changes:
- Multi-experiment support: .autoresearch/{domain}/{name}/ structure
- Domain categories: engineering, marketing, content, prompts, custom
- Project-level (git-tracked, shareable) or user-level (~/.autoresearch/) scope
- User chooses scope during setup, not installation
New evaluators (8 ready-to-use):
- Free: benchmark_speed, benchmark_size, test_pass_rate, build_speed, memory_usage
- LLM judge (uses existing subscription): llm_judge_content, llm_judge_prompt, llm_judge_copy
- LLM judges call user's CLI tool (claude/codex/gemini) — no extra API keys needed
Script improvements:
- setup_experiment.py: --domain, --scope, --evaluator, --list, --list-evaluators
- run_experiment.py: --experiment domain/name, --resume, --loop, --single
- log_results.py: --dashboard, --domain, --format csv|markdown|terminal, --output
Results export:
- Terminal (default), CSV, and Markdown formats
- Per-experiment, per-domain, or cross-experiment dashboard view
SKILL.md rewritten:
- Clear activation triggers (when the skill should activate)
- Practical examples for each domain
- Evaluator documentation with cost transparency
- Simplified loop protocol matching Karpathy's original philosophy
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""Measure build/compile time.
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DO NOT MODIFY after experiment starts — this is the fixed evaluator."""
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import subprocess
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import sys
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import time
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# --- CONFIGURE THESE ---
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BUILD_CMD = "npm run build" # or: docker build -t test .
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CLEAN_CMD = "" # optional: npm run clean (run before each build)
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RUNS = 3 # Number of builds to average
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# --- END CONFIG ---
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times = []
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for i in range(RUNS):
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# Clean if configured
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if CLEAN_CMD:
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subprocess.run(CLEAN_CMD, shell=True, capture_output=True, timeout=60)
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t0 = time.perf_counter()
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result = subprocess.run(BUILD_CMD, shell=True, capture_output=True, timeout=600)
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elapsed = time.perf_counter() - t0
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if result.returncode != 0:
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print(f"Build {i+1} failed (exit {result.returncode})", file=sys.stderr)
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print(f"stderr: {result.stderr.decode()[:200]}", file=sys.stderr)
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sys.exit(1)
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times.append(elapsed)
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import statistics
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avg = statistics.mean(times)
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median = statistics.median(times)
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print(f"build_seconds: {median:.2f}")
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print(f"build_avg: {avg:.2f}")
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print(f"runs: {RUNS}")
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