Looking at debugging patterns from our Cline power users, an insight has emerged: They're using DeepSeek R1 ($0.55/M tokens) as a 'code archaeologist' before touching anything.
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1/ First, they have R1 analyze the codebase architecture and create sequence diagrams. Our most successful users report this step alone catches potential issues before they become problems.
"R1's reasoning is top tier for planning. The more sequence diagrams I fill my context with, the higher the accuracy."
2/ Then they use Plan mode to:
- Map dependencies
- Identify potential edge cases
- Create with [x][] format
- Generate minimal test cases
"Ask it to create test files that ONLY figure out one issue with minimal logs. You can only output 200 lines to analyze."hypothesis.md
3/ Only THEN do they switch to 3.5-Sonnet for implementation. The key insight? 90% of debugging time is spent understanding, not fixing.
"Just having it reflect on what it's done and sanity check its work every 2-3 actions has significantly reduced our error rate."
4/ Cost breakdown:
- R1 investigation: $0.55/M tokens
- Sonnet implementation: Standard rates
- Time saved: Users report 50-70% faster resolution
The ROI math is obvious when you're catching architectural issues at R1 prices instead of o1.
5/ Pro tip from our power users: Always ask for confidence scores.
"Have it rate confidence 1-10 with each message. Don't proceed until you get 9+/10."
Save this thread for when you're debugging your next complex issue🚀
1/7 Most AI coding assistants are like having a brilliant developer who can only type code. Cline is like having a senior dev who actually understands your entire project.
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2/7 Here's what we're seeing: Tools like Cursor excel at quick edits and rapid suggestions. That's valuable. But we wondered -- what if AI could do more than just write code?
3/7 That's where Cline's philosophy differs. Instead of limiting context to save tokens, we let Cline read entire codebases, understand documentation, and maintain deep project context throughout your session.
1/ The Model Context Protocol isn't just another dev tool - it's letting AI assistants break free from chat windows to directly manage your Git repos, run tests, and maintain project memory. Here's why MCP is a game-changer
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2/ 🏗️ MCP servers act as intermediaries between LLMs and external tools. They're essentially APIs that let AI assistants interact with the outside world, from Git to testing to documentation.
3/ The community has already built a ton of innovative MCP servers including:
- GitHub issue automation
- Knowledge graph integration
- API testing with Postman
- Browser automation with Playwright
- Database analysis tools
1/ Plan/Act mode is changing how we code. One dev's workflow:
'Right now my SOP is to respond "are you sure? anything u need to double check?" each time until it says "Yes I'm 100% sure" then click act'
Increases accuracy and automation in one click. Simple but game-changing.
2/ Community's already finding creative uses:
- Using R1 for planning, V3 for execution
- Switching modes mid-session when stuck
- Pre-planning complex architectural changes
- Automated documentation updates during planning phase
3/ The ROI is clear: Less token waste, higher accuracy, fewer iterations. One user reports: 'What would've taken 10 hours to figure out, plan/act solved immediately'