4c95505907
shapecurve.leaf_constraints derived each leaf's feasible area from its own type's base (target, sigma). quality_size does not: a leaf holding k same-code rooms is centred on k*target with sigma*k, and a co-typed leaf adds both codes' targets. The DP modelled neither, so eligible() excluded leaf_sharing/max_share/multi_use -- and leaf_sharing defaults True in driver.search, so the guard excluded essentially every real run. The DP was correct and unreachable. Why the guard could not just be dropped, measured before touching it: on 6 harbor constructed seeds, 24 of 24 shared leaves (100%) have a real area outside the unscaled single-room bounds. Relaxing eligible without modelling k would have made the DP call every one of those topologies infeasible -- false negatives that prune feasible topologies and misdirect the NM warm-start. The guard was load-bearing. Fix: mirror quality_size by asking the SAME Fitness object -- k = graph.leaf_share(leaf, fit._max_share) when fit._leaf_sharing, then target*k / sigma*k, else fit._leaf_co_type for the additive case. Same object, same flags, same branch order, deliberately not re-derived: 39.5's cpsat._matches bug was a solver optimising a relation the scorer had moved, and this is the same hazard class. Verified as an exact inversion: for every shared leaf in a real seed, quality_size evaluated at the DP's amin and amax returns FAIL_THRESHOLD to 1e-9 (k=3 n-leaf: bounds [128.50, 231.50], both 0.100000). superpose stays excluded for a different reason than the others: it does not rescale a target, it changes which type the leaf is scored as, and the collapse happens after the DP has read leaf.type. shapecurve_warmstart/shapecurve_prune remain default off, so no current run changes -- including the cold-start baseline in progress. They are now applicable, which unblocks homemaker-py-v4s. Closes homemaker-py-tym. Lint at parity (46); tests 387 passed (3 new, 1 legacy rewritten to the new contract rather than deleted), 0 failed. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB |
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Beads - AI-Native Issue Tracking
Welcome to Beads! This repository uses Beads for issue tracking - a modern, AI-native tool designed to live directly in your codebase alongside your code.
What is Beads?
Beads is issue tracking that lives in your repo, making it perfect for AI coding agents and developers who want their issues close to their code. No web UI required - everything works through the CLI and integrates seamlessly with git.
Learn more: github.com/steveyegge/beads
Quick Start
Essential Commands
# Create new issues
bd create "Add user authentication"
# View all issues
bd list
# View issue details
bd show <issue-id>
# Update issue status
bd update <issue-id> --claim
bd update <issue-id> --status done
# Sync with Dolt remote
bd dolt push
Working with Issues
Issues in Beads are:
- Git-native: Stored in Dolt database with version control and branching
- AI-friendly: CLI-first design works perfectly with AI coding agents
- Branch-aware: Issues can follow your branch workflow
- Always in sync: Auto-syncs with your commits
Why Beads?
✨ AI-Native Design
- Built specifically for AI-assisted development workflows
- CLI-first interface works seamlessly with AI coding agents
- No context switching to web UIs
🚀 Developer Focused
- Issues live in your repo, right next to your code
- Works offline, syncs when you push
- Fast, lightweight, and stays out of your way
🔧 Git Integration
- Automatic sync with git commits
- Branch-aware issue tracking
- Dolt-native three-way merge resolution
Get Started with Beads
Try Beads in your own projects:
# Install Beads
curl -sSL https://raw.githubusercontent.com/steveyegge/beads/main/scripts/install.sh | bash
# Initialize in your repo
bd init
# Create your first issue
bd create "Try out Beads"
Learn More
- Documentation: github.com/steveyegge/beads/docs
- Quick Start Guide: Run
bd quickstart - Examples: github.com/steveyegge/beads/examples
Beads: Issue tracking that moves at the speed of thought ⚡