481e83cdbf
With tym landed the DP finally runs on real leaf-sharing searches, so v4s asked for the search-level A/B. No A/B was run: two structural facts make the payoff zero before any seed is drawn, and measuring a no-op would have produced a null that reads like a measurement rather than a fact. Reach. driver._evaluate gates the warm-start on `x0 is None`, and every child gets x0 = innerloop.warm_x0(...) from its parent (driver.py:764), so it fires only for bootstrap-population individuals. Instrumented over a 4000-eval run: 8 DP solves, exactly pop_size. At the corpus baseline's 500k budget that is 8 evaluations out of 500,000. Applicability. Constructed-seed feasibility over 6 seeds: harbor-house 0/6 with leaf_sharing on AND off, programme-house 0/6, harbor-house-l0 4-5/6 (75% over a real search). Even those 8 bootstrap individuals get no warm start on the real programmes; only l0, the reduced programme the DP was validated on, is feasible. The infeasibility is correct, checked because it is the dangerous direction: on 4 harbor topologies the DP rejects, an NM polish minimising the shape-fail family reaches 14/16/17/16 fails, never 0 -- 0/4 false negatives. Full harbor is genuinely shape-infeasible per topology where l0 is not. Per topology only: programme-house reaches 0 hard / 1 soft in the 500k baseline while its constructed seeds are DP-infeasible. shapecurve_prune is separately inert -- it acts only inside the feasibility_max_shape_fails branch and its exact arm needs best_n_fails<=0. tym was still worth doing: the DP now models leaf-sharing exactly (38.23) and fires on real runs, which is what turned an untestable question into a structural answer. Filed homemaker-py-ldj for what would have to change: give the DP reach beyond the bootstrap, and/or use infeasibility as a ranking signal (an infeasible topology still has a shape-fail floor, which is what a pre-filter wants) rather than a precondition that discards it. Closes homemaker-py-v4s. Lint at parity (46). 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 ⚡