v1.0.1

1.0.0 patch

Fixes the PyPI wheel: bundles catalog/ data and resolves it from the installed layout, so python -m synth discover and ade-gui work out of the box after pip install.

Also publishes to PyPI (OIDC trusted publishing).

v1.0.0 — desktop GUI + documentation platform

v1.0.0 — stable. Adds a standard-library desktop GUI, a full MkDocs
documentation site deployed to GitHub Pages, and an expanded CI pipeline.

Desktop GUI (python -m gui)

• Dark-themed Tkinter app — zero third-party runtime dependencies.
• Discover tab: run the synthesizer (smoke/full), per-target verified table, one-click report & solutions access.
• Pattern discovery tab: enter any integer sequence, get ranked hypotheses + next-term predictions.
• Engine tab: check vectors, run pytest, build all tiers, benchmark, docs/GitHub links.
• Headless-testable core (gui/core.py); --selftest mode and dedicated CI job.
• Also reachable via python engine/runner.py gui and the ade-gui console script.

Documentation platform

• MkDocs Material site (docs/) — Home, Engine, Synthesis, Pattern discovery, GUI, Contributing.
• Deployed to https://dsk-dev-ai.github.io/algorithm-discovery-engine/ from main.
• CI enforces a strict (--strict) docs build on every push/PR.

CI

• New jobs: Docs (strict build) and GUI core smoke.
• mypy now type-checks gui (strict) alongside algo_discovery/ads/synth.
• Pages workflow builds the MkDocs site and deploys it.

Packaging

• Version bumped to 1.0.0 (Production/Stable classifier).
• Wheel now ships gui package; new docs uv dependency group.
• 9 jobs × all green on PR #1: Ruff+mypy, Pytest 3.10/3.12/3.13, Java, C++, Rust, vectors-sync, discovery smoke, GUI smoke, docs.

v0.3.0 — algorithm discovery + discoverability revamp

v0.3.0 — the multi-language solving engine gets a verified local algorithm synthesizer, plus a discoverability revamp.

New: local algorithm synthesizer (src/synth)

• Grammar-based scanner search + strategy templates (vote, seen, fib, circular-kadane) rediscovers and verifies algorithms from curated I/O examples.
• Smoke run: 7 targets · 7 verified · 0 rejected — max_subarray rediscovered; best-time-buy-sell, jump game, contains-duplicate, majority-element, climbing-stairs surfaced as new-to-catalog.
• Every candidate is fuzz-verified (60+ inputs) against an independent oracle before reporting.
• uv run python -m synth discover [--smoke] or python engine/runner.py discover; outputs to catalog/discoveries/.

Repertoire (from 0.2.0)

• 10 algorithms + 6 data structures solved, tested, and benchmarked in Java · C++17 · Rust · Python.
• Single catalog/problems.json source of truth; byte-identical vector sets; --check keeps them in sync.
• Pattern-discovery framework (algo_discovery, 10 hypotheses, 65 tests).

Discoverability & packaging

• 18 GitHub topics, keyword-rich description, README with hero banner, mermaid diagram, benchmarks, and dev guide.
• GitHub Pages landing site (docs/) deployed to https://dsk-dev-ai.github.io/algorithm-discovery-engine/
• GitHub Sponsors funding config (NextGenAI Labs) + custom social-preview banner.
• PyPI metadata: 18 keywords, version 0.3.0.

Checks: ruff · mypy (strict) · 118 pytest · runner test/bench/discover — all green on CI (3.10–3.13).