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Jon Saad-FalconandClaude Opus 4.6
2aebcd7d77
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>
26 lines
616 B
TOML
26 lines
616 B
TOML
[recipe]
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name = "coding_assistant"
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description = "Code generation, review, and debugging with ReAct agent"
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version = "1.0.0"
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[intelligence]
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model = "qwen3:8b"
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quantization = "q4_K_M"
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[engine]
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key = "ollama"
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[agent]
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type = "native_react"
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max_turns = 10
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temperature = 0.3
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tools = ["file_read", "file_write", "code_interpreter", "think", "shell_exec"]
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system_prompt = "You are a coding assistant. Help the user write, review, and debug code. Use the available tools to read files, write code, and execute commands as needed."
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[learning]
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routing = "heuristic"
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agent = "none"
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[eval]
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suites = ["coding", "reasoning"]
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