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7 Commits
Author SHA1 Message Date
Jon Saad-FalconandClaude Opus 4.6 2aebcd7d77 feat: Phase 23 — Differentiated functionalities 
Trace-driven learning pipeline:
- TrainingDataMiner: extract SFT/routing/agent pairs from traces
- LoRATrainer: fine-tune local models from trace-derived data
- AgentConfigEvolver: rewrite agent configs from trace analysis
- LearningOrchestrator: coordinate mine→train→evolve cycle, wired into SystemBuilder

Eval framework (15 real IPW benchmarks):
- Datasets: SuperGPQA, GPQA, MMLU-Pro, MATH-500, Natural Reasoning, HLE,
  SimpleQA, WildChat, IPW, GAIA, FRAMES, SWE-bench, SWEfficiency,
  TerminalBench, TerminalBench Native
- Scorers: MCQ extraction, LLM-judge, exact match, structural validation
- CLI: jarvis eval list|run|compare|report

Composable abstractions:
- Recipe system: TOML composition of all 5 pillars (3 built-in recipes)
- Agent templates: 15 pre-configured TOML manifests with system prompts
- Bundled skills: 20 ready-to-use TOML skill manifests
- Operator recipes: researcher (4h), correspondent (5min), sentinel (2h)

102 files changed, ~11,500 lines added. 3241 tests pass (44 skipped).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-02 05:34:46 +00:00
Jon Saad-FalconandClaude Opus 4.6 a4c4081ff4 Add desktop distribution pipeline: rolling releases, auto-updates, code signing 
- Rewrite .github/workflows/desktop.yml: 2-job pipeline (validate + build-and-release)
  with rolling desktop-latest pre-release on push to main and stable desktop-v* releases
- Add UpdateChecker component: checks for updates on startup + every 30 min,
  background download with progress bar, one-click relaunch
- Configure Tauri updater: endpoints pointing to desktop-latest release, pubkey placeholder
- Add tauri-plugin-process for relaunch support (Cargo.toml, lib.rs, package.json)
- Add macOS Entitlements.plist for notarization (network + file access, no sandbox)
- Add scripts/bump-desktop-version.sh for atomic version bumps across 3 config files
- Add desktop/README.md with dev setup, auto-update architecture, signing docs
- Update .gitignore for desktop/node_modules, dist, target
- Configure macOS minimumSystemVersion, Windows timestampUrl
- Include all Phase 14-21 work: agent hardening, RBAC, taint tracking, workflows,
  skills, knowledge graph, sessions, A2A, MCP templates, WASM sandbox, TUI dashboard,
  production tools, CLI expansion, API expansion, learning productionization,
  Tauri desktop app, and 10 new channels

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-27 19:14:05 +00:00
Jon Saad-FalconandClaude Opus 4.6 24972e3e52 Add Phase 12+13: energy measurement, install polish, PWA, cross-hardware 
Phase 12 — Energy Measurement Upgrade:
- EnergyMonitor ABC with multi-vendor support (NVIDIA hw counters,
  AMD amdsmi, Apple zeus-ml, CPU RAPL sysfs)
- EnergyBatch batch-level energy-per-token accounting
- SteadyStateDetector CV-based thermal equilibrium detection
- EnergyBenchmark with warmup phase
- InstrumentedEngine prefers EnergyMonitor over legacy GpuMonitor
- Telemetry store/aggregator extended with energy fields

Phase 13 — Install, Hosting, Cross-Hardware:
- jarvis doctor diagnostic command (8 checks, --json output)
- jarvis init post-setup guidance with engine-specific next steps
- README Quick Start section
- MLX engine backend (Apple Silicon → mlx recommendation)
- AMD VRAM/multi-GPU detection via rocm-smi
- PyTorch MPS device selection in orchestrator trainers
- PWA support (vite-plugin-pwa, service worker, manifest, icons)
- Server static file serving fix for PWA files
- Dockerfile.gpu.rocm + docker-compose.gpu.rocm.yml for ROCm
- Eval framework display module and efficiency metrics

2244 tests pass, 37 skipped.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-26 20:09:07 +00:00
Jon Saad-FalconandClaude Opus 4.6 9f5e97eed9 Add eval framework efficiency metrics: MFU, MBU, IPW, IPJ 
Extend the eval framework to compute per-sample hardware efficiency
metrics when model hardware parameters are provided in TOML config:

- ModelConfig gains param_count_b, active_params_b, gpu_peak_tflops,
  gpu_peak_bandwidth_gb_s, num_gpus fields
- RunConfig gains metadata dict, populated by expand_suite() from
  model hardware params
- EvalRunner._process_one() computes IPW (Intelligence Per Watt),
  IPJ (Intelligence Per Joule), MFU, and MBU per sample
- All telemetry fields (energy, power, GPU util, throughput, MFU, MBU,
  IPW, IPJ) written to JSONL output and summary JSON
- RunSummary includes MetricStats (mean/median/min/max/std) for all
  telemetry metrics plus total_energy_joules
- GLM-4.7-Flash eval config enriched with A100 SXM hardware params
- 146 eval tests pass (26 new tests for telemetry, efficiency metrics,
  metadata flow, MetricStats helpers)
- User guide and API docs updated with new fields and output format

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-25 21:17:53 +00:00
Jon Saad-FalconandClaude Opus 4.6 9ec402ab4a Enrich agent tool awareness, normalize engine tool_calls, add telemetry and eval config 
- Add build_tool_descriptions() shared builder for enriched agent system
  prompts (NativeReAct, NativeOpenHands, RLM, Orchestrator structured mode)
- Normalize tool_calls to flat {id, name, arguments} across CloudEngine
  (OpenAI/Anthropic/Google), LiteLLM, and Ollama
- Add Anthropic tool_use extraction and input_schema conversion
- Add Google function_call extraction
- Make ReAct/OpenHands parsing case-insensitive
- Add telemetry efficiency, GPU monitor, and vLLM metrics modules
- Add TOML-based eval suite config system
- Update documentation and changelog

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-25 01:58:18 +00:00
Jon Saad-FalconandClaude Opus 4.6 323d7ff032 Add TOML config system for eval suites, pillar-aligned config, and documentation 
- Eval config: TOML-based suite configs defining models x benchmarks matrix,
  loaded via --config flag. Includes load_eval_config(), expand_suite(),
  7 config dataclasses, 3 example configs, and 61 new tests.
- Pillar-aligned config: generation params in IntelligenceConfig, nested
  engine/learning configs, agent objective/system_prompt/context_from_memory,
  structured learning sub-policies, TOML migration layer.
- Documentation: evaluations user guide, evals API reference, updated
  mkdocs.yml navigation, updated architecture docs.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-24 03:34:05 +00:00
Jon Saad-FalconandClaude Opus 4.6 bd49383201 Add evaluation framework and center README logo SVGs 
Evaluation framework (evals/): benchmarking system for measuring accuracy
across four categories — Chat (WildChat), Reasoning (SuperGPQA), RAG (FRAMES),
and Agentic (GAIA). Two backends: jarvis-direct (engine-level) and jarvis-agent
(agent-level with tool calling), both supporting local and cloud models.
Datasets adapted from IPW, scorers include exact match, LLM letter extraction,
and LLM-as-judge. Parallel execution via ThreadPoolExecutor with incremental
JSONL output. CLI: python -m evals {run,run-all,summarize,list}. 57 tests pass.

SVG fix: center logo content within viewBox by wrapping icon+text in a
translate(90,0) group, eliminating the left-shift visible in the README.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-22 23:48:43 +00:00