33a76a1ebc
迁移路径: /run/media/airlongdian/EasyU/AirCoding -> /home/airlongdian/DataDevices/AirWorkSpace/AirCoding Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
37 lines
2.3 KiB
Markdown
Executable File
37 lines
2.3 KiB
Markdown
Executable File
# DeepCode CLI Branch
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Date: 2026-05-28
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Status: AirCoding mainline branch, pre-design research phase
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DeepCode CLI is a mainline AirCoding branch (not a downstream simplification like VibeBox). It explores integrating DeepSeek's model architecture innovations and research capabilities into the AirCoding agent runtime.
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## Reference Documents
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- [DeepSeek Research Survey](deepseek-research-survey.md) — Complete inventory of DeepSeek papers, models, architectures, and technical innovations (36 repos, ~395K stars)
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## Key DeepSeek Innovations Relevant to DeepCode CLI
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1. **Multi-head Latent Attention (MLA)** — 93% KV cache reduction for long code contexts
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2. **DeepSeekMoE** — Fine-grained experts + shared experts, 37B activated from 671B total
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3. **GRPO** — RL without critic model, applicable to code/debug agent training
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4. **DeepSeek Sparse Attention** — Efficient long-context processing
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5. **DeepSeek-Coder/V2** — Fill-in-Middle, SWE-bench, Codeforces competitive
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6. **DeepSeek-R1** — Emergent chain-of-thought reasoning via pure RL
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7. **DeepSeek-Prover-V2** — Formal Lean 4 theorem proving
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8. **DeepSeek-Math-V2** — Self-verifiable reasoning, IMO 2025 gold
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9. **Engram** — Conditional memory sparsity as new axis beyond MoE
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10. **DeepSeek-OCR/OCR-2** — Visual understanding for GUI evidence
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11. **3FS/FlashMLA/DeepGEMM/DeepEP/DualPipe** — Full inference infrastructure stack
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## Open Design Questions
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1. How does DeepCode CLI relate to AirCoding mainline — does it add DeepSeek as a provider, or fork architecture?
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2. Which DeepSeek innovations should DeepCode CLI adopt at the runtime level vs. treat as provider capabilities?
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3. Does DeepCode CLI target DeepSeek models as primary, or remain provider-agnostic with DeepSeek optimizations?
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4. Should MLA/MoE-aware context assembly be part of the runtime, or handled by provider adapter?
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5. How should formal proving (Prover-V2) integrate with the existing review/debug workflow?
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6. Should self-verifiable reasoning (Math-V2 style) influence the verification architecture?
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7. Does DeepCode CLI need its own GRPO-trained code agent, or reuse general DeepSeek models?
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8. How does the Engram conditional memory concept map to AirCoding's memory/skills system?
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9. Should infrastructure tools (3FS, smallpond) be optional capabilities for large-codebase workflows?
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