v1.2.1

Docs-only release marking two milestones: Terminal-Bench 2.0 leaderboard acceptance and the end of the Phase 1 benchmark baseline. No CLI, settings, or skill-pack changes — the env-var path for remote inference (LLAMACPP_BASE_URL / OLLAMA_BASE_URL / LMSTUDIO_BASE_URL pointing at a non-loopback host) has worked since v1.1.0 / v1.2.0, but it was undocumented for the LAN-server case until now.

Added

• README "Serving from another machine on your LAN" section under Local model setup → Option C. Covers all three local providers (llama.cpp --host 0.0.0.0, LM Studio's Serve on local network, OLLAMA_HOST=0.0.0.0:11434 ollama serve), the corresponding *_BASE_URL env on the client, a curl /v1/models reachability check, and a note on opening port 1234 / 8888 / 11434 in ufw. Validated against this repo's own benchmark hardware: LLAMACPP_BASE_URL=http://<lan-ip>:8888/v1 against llama-server --host 0.0.0.0 serves Qwen3.6-35B-A3B to a different machine over WiFi at the same per-token throughput as loopback.

Changed

• Benchmark table — Terminal-Bench 2.0 rows. Replaced the "awaiting maintainer merge" status (HuggingFace PRs #158 and #163) with the accepted leaderboard placements published at tbench.ai/leaderboard/terminal-bench/2.0: Qwen3.6-35B-A3B at 24.6 % ± 3.2 (rank 120) and Qwen3.5-9B at 9.2 % ± 2.4 (rank 142). The mean shifted slightly from the originally-submitted point estimates (23.82 % → 24.6 %, 9.21 % → 9.2 %) once the leaderboard recomputed across all five trials with a confidence interval; the underlying runs are unchanged.
• Roadmap reframed. Phase 1 (build a wide benchmark baseline across short coding exercises, interactive shell tasks, and tool-using research) is now marked complete: Aider Polyglot ✓, Terminal-Bench-Core v0.1.1 ✓, Terminal-Bench 2.0 ✓, GAIA validation ✓. Phase 2 opens now: iterative improvement driven by real-world coding tasks, not by the benchmark suite. New benchmarks (ProgramBench, SWE-bench Verified, GAIA test-split) are deferred until Phase 2 produces enough scaffolding signal to be worth re-measuring — re-benchmarking before the next round of changes lands would mostly re-measure the same baseline.

Notes for upgraders

• No CLI flag, settings, or skill-pack breaks. Existing LMSTUDIO_BASE_URL / LLAMACPP_BASE_URL / OLLAMA_BASE_URL users on either loopback or remote hosts keep working with no changes; the only thing that changed is that the remote-host case is now documented.
• No models.json or .pi/settings.json shape change. Per-model profiles (context limit, thinking budget, temperature) continue to apply regardless of where the inference server lives — they're keyed by <provider>/<model-id>, not by host.