# Windsurf Usage Review — 01/13/2026 to 02/11/2026

> **Reviewed:** 2026-02-12
> **Period:** 30 days (Jan 13 – Feb 11, 2026)
> **Projects:** LysnrAI (learning_voice_ai_agent) + MindLyst (learning_multimodal_memory_agents)

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## Key Metrics at a Glance

| Metric | Value | Assessment |
|--------|-------|------------|
| **Lines written by Cascade** | 227,885 (year) / 143,815 (period) | Extremely high output |
| **% new code by Windsurf** | 99% | Near-total AI-assisted development |
| **Cascade conversations** | 21 | ~0.7/day — focused, long sessions |
| **Cascade messages sent** | 1,470 (Write), 71 (Chat) | 95% Write mode — action-oriented |
| **Credits used** | 8,069 | Heavy but productive usage |
| **Terminal messages sent** | 5,893 | Heavy command execution |
| **Workflows used** | 35 | Good use of custom workflows |
| **Memories used** | 20 | Context retention across sessions |
| **Web searches** | 9 | Minimal external lookup needed |
| **Previews** | 45 | Regular visual verification |
| **App deploys** | 1 | |
| **Commands used** | 0 | Slash commands not utilized |
| **Tab acceptances** | 1 (Markdown) | Almost zero autocomplete usage |

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## Model Distribution

| Model | Usage % | Role |
|-------|---------|------|
| **GPT-5.2 Low Reasoning** | 50.52% | Bulk code generation, simple edits |
| **Claude Opus 4.5 (Thinking)** | 22.19% | Complex reasoning, architecture |
| **GPT-5.2 Low Reasoning** (variant) | 16.97% | Additional generation |
| **Claude Opus 4.5** | 8.22% | Targeted complex tasks |
| **SWE-1.5 (Promo)** | 2.09% | Trial/evaluation |

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## Strengths

### 1. Extremely productive output
143,815 lines in 30 days across 21 conversations is exceptional. That's ~6,848 lines per conversation and ~4,794 lines per day. This built out the entire LysnrAI monorepo (6 client apps, 5 backend services, 600+ tests) and the MindLyst KMP foundation.

### 2. Write-heavy workflow (95% Write vs 5% Chat)
1,470 Write messages vs 71 Chat messages shows a highly action-oriented approach — using Cascade primarily for implementation rather than Q&A. This is the most productive usage pattern.

### 3. Heavy terminal integration (5,893 messages)
~4 terminal commands per Cascade message indicates extensive build/test/verify cycles. This is a sign of rigorous development — not just generating code, but continuously validating it.

### 4. Good workflow adoption (35 uses)
Custom workflows for starting services, running tests, building releases, etc. are being used regularly. This reduces repetitive work and ensures consistency.

### 5. Memory utilization (20 memories)
Using persistent memory for project context (architecture decisions, rebranding mappings, service configs) avoids re-explaining context across the 21 conversations.

### 6. Focused activity pattern
The heatmap shows concentrated bursts of activity (Dec–Feb) rather than scattered usage. This aligns with the LysnrAI monorepo buildout and MindLyst kickoff — deep focused sprints.

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## Areas for Improvement

### 1. Tab completions nearly unused (1 acceptance)
**Current:** Only 1 Markdown tab acceptance in the entire period.
**Recommendation:** Enable and use Windsurf's inline tab completions for:
- Boilerplate code (imports, function signatures, test setup)
- Repetitive patterns (Cosmos CRUD, Fastify route scaffolds)
- Variable/method name completion
This alone could save significant keystroke overhead for the ~1% of code you write manually.

### 2. Zero slash commands used
**Current:** 0 commands used despite having 10+ custom workflows defined.
**Recommendation:** Use slash commands (e.g., `/start-all-services`, `/debug-service`, `/test-ios-app`) directly in chat to trigger workflows. Currently, workflows are used (35 times) but commands are at 0 — this suggests workflows are being triggered via other means or the slash command integration isn't configured.

### 3. Chat mode underutilized (5%)
**Current:** 71 Chat messages vs 1,470 Write messages.
**Recommendation:** Use Chat mode for:
- **Architecture discussions** before implementing (e.g., "should I use KMP or native for mobile?")
- **Code review** — paste code and ask for review before committing
- **Debugging strategy** — discuss approach before diving into Write mode
A healthy ratio might be 80/20 Write/Chat rather than 95/5.

### 4. Web search barely used (9 searches)
**Current:** Only 9 web searches in 30 days.
**Recommendation:** Use web search for:
- Checking latest API docs (Azure Speech SDK, Stripe, Fastify 5)
- Verifying deprecation notices before adopting patterns
- Finding community solutions for edge cases
This would reduce reliance on training data which may be outdated.

### 5. MCP integrations not used (0 invocations)
**Current:** No MCP (Model Context Protocol) tool invocations.
**Recommendation:** If you have MCP servers configured (e.g., for Azure, GitHub, Cosmos DB), using them could provide real-time data access during development sessions.

### 6. Model selection could be more strategic
**Current:** 50% GPT-5.2 Low Reasoning + 22% Claude Opus 4.5 Thinking.
**Recommendation:**
- Use **Claude Opus 4.5 (Thinking)** for: architecture decisions, complex debugging, multi-file refactors, security reviews
- Use **GPT-5.2** for: bulk code generation, simple CRUD, test writing, documentation
- The current 50/22 split seems reasonable, but consider bumping Claude usage for critical paths (auth, billing, data integrity) where reasoning depth matters more

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## Usage Efficiency Score

| Category | Score | Notes |
|----------|-------|-------|
| **Output volume** | 10/10 | 143K lines in 30 days is extraordinary |
| **Write/Chat balance** | 7/10 | Could use more Chat for planning |
| **Terminal integration** | 10/10 | 5,893 commands shows rigorous verification |
| **Workflow adoption** | 8/10 | 35 uses is good; slash commands at 0 |
| **Tab completions** | 1/10 | Nearly unused — biggest improvement area |
| **Memory usage** | 7/10 | 20 memories is adequate; could store more |
| **Web search** | 4/10 | 9 searches is very low for this volume |
| **Overall** | **7/10** | Highly productive; optimize completions + search |

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## Recommendations Summary

1. **Enable tab completions** — biggest low-effort win for daily coding speed
2. **Use Chat mode for planning** — 5-10 min of Chat before a big Write session saves rework
3. **Use web search** for API docs and deprecation checks
4. **Try slash commands** to trigger workflows directly from chat
5. **Consider MCP integrations** for real-time Azure/GitHub data access

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## What You Built in This Period

With these 21 conversations and 143K lines, you shipped:
- Complete LysnrAI monorepo (347 commits, 6 apps, 5 services, 600+ tests)
- MindLyst KMP foundation (shared module, 3 platform UIs, design system)
- Full microservices extraction (6 phases)
- 18 user + admin features
- Docker Compose validation
- Mobile apps for iOS + Android
- Public roadmap with voting
- Comprehensive documentation (23+ docs)

That's an impressive output for 30 days of solo development with AI assistance.
