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Competitive Feature Research\n\nResearch latest features from competitors and identify gaps for this repo.\n\n### Competitors to monitor:\n- Cursor 3 — Multi-agent workspace, parallel execution, local/cloud handoff\n- Claude Code — /powerup, transcript search, MCP elicitation, named subagents\n- OpenAI Codex — Cloud sandboxes, 1000 tok/sec on Cerebras\n- Google Antigravity — 2M context, artifacts, manager view\n- Manus AI — Cloud sandbox, web deployment, speech-to-text\n- OpenClaw — Autonomous loop, multi-model routing, device pairing\n- Roo Code — Skills as slash commands, smart context condensation\n- Windsurf — Cascade multi-file editing, supercomplete\n- SambaNova — Agent inference optimization, SN50 chip\n- Qwen3.6-Plus — Code agent optimization, MCP support\n- StepFun 3.5 Flash — #1 cost-effective model for agents\n\n### What's needed:\n- [ ] Research latest features from top 10 competitors\n- [ ] Identify feature gaps for this repo\n- [ ] Create implementation plan for top 3 gaps\n- [ ] Implement at least 1 new feature\n- [ ] Update competitor scanner with new findings\n\n### Priority: P1\nStaying ahead of competitors is critical.\n\n### Acceptance Criteria:\n- Competitive analysis document created\n- At least 3 feature gaps identified\n- Implementation plan for top gaps\n- At least 1 new feature implemented\n- Competitor scanner updated
Competitive Feature Research\n\nResearch latest features from competitors and identify gaps for this repo.\n\n### Competitors to monitor:\n- Cursor 3 — Multi-agent workspace, parallel execution, local/cloud handoff\n- Claude Code — /powerup, transcript search, MCP elicitation, named subagents\n- OpenAI Codex — Cloud sandboxes, 1000 tok/sec on Cerebras\n- Google Antigravity — 2M context, artifacts, manager view\n- Manus AI — Cloud sandbox, web deployment, speech-to-text\n- OpenClaw — Autonomous loop, multi-model routing, device pairing\n- Roo Code — Skills as slash commands, smart context condensation\n- Windsurf — Cascade multi-file editing, supercomplete\n- SambaNova — Agent inference optimization, SN50 chip\n- Qwen3.6-Plus — Code agent optimization, MCP support\n- StepFun 3.5 Flash — #1 cost-effective model for agents\n\n### What's needed:\n- [ ] Research latest features from top 10 competitors\n- [ ] Identify feature gaps for this repo\n- [ ] Create implementation plan for top 3 gaps\n- [ ] Implement at least 1 new feature\n- [ ] Update competitor scanner with new findings\n\n### Priority: P1\nStaying ahead of competitors is critical.\n\n### Acceptance Criteria:\n- Competitive analysis document created\n- At least 3 feature gaps identified\n- Implementation plan for top gaps\n- At least 1 new feature implemented\n- Competitor scanner updated