Devin writes actual code instead of just suggesting completions. Built by Cognition Labs with serious VC funding, it spins up its own cloud environment and tries to ship real features. Think GitHub Copilot but it actually commits shit instead of just autocompleting your variable names.
The catch? It costs money every time it thinks, and this thing thinks way too much about trivial bullshit.
How This Thing Actually Works (When It's Not Broken)
Devin doesn't run in your IDE like Cursor or GitHub Copilot. It lives in the cloud with its own setup:
The Cloud IDE (Slow But Functional):
- VS Code clone that feels laggy compared to your local setup
- Terminal that works but has weird PATH issues sometimes
- Browser that's useful for testing but can't access localhost obviously
- File system access that occasionally corrupts binary files
- Git integration that creates PRs you'll spend 20 minutes reviewing
Real talk: The cloud IDE is serviceable but you'll miss your local development environment. Expect to keep VS Code open anyway for serious debugging.
The Planning System (Sometimes Genius, Sometimes Stupid):
Devin breaks down your request into subtasks before coding. When it works, it's genuinely impressive - like having a junior dev who actually reads requirements instead of immediately asking "what do you mean by user authentication?" When it doesn't work, you get 8-step architectural overhauls because you asked it to fix a typo in a comment.
I watched it burn 3 ACUs planning to add a fucking console.log statement. Three ACUs to plan console.log("debug").
Memory That Actually Persists:
Unlike ChatGPT, Devin remembers your codebase between sessions through DeepWiki. It indexes your repo, creates architecture diagrams, and stores project conventions. This actually works well - it won't ask you to explain your database schema every time.
The gotcha: Repo scanning takes forever and crashes halfway through. I lost 2 hours watching it "analyze" a basic React app - it got to 73% and then just... stopped. Plan for 30-60 minutes of "indexing" before Devin becomes useful, assuming it doesn't crash and force you to start over.
The Performance Reality Check
Here's what Devin can actually do, based on benchmarks and my experience burning through ACUs:
SWE-bench Results: 13.86% success rate on real GitHub issues. That sounds terrible until you realize the previous best was 1.96%. Still means Devin face-plants on 6 out of 7 complex issues, but hey - progress.
What I've Actually Seen Work:
- Simple bug fixes: Works great if the bug is obvious and contained
- Boilerplate generation: Excellent at creating CRUD APIs, React components, database schemas
- Code refactoring: Good at applying patterns consistently across files
- Test writing: Generates comprehensive tests that actually catch bugs
- Documentation: Surprisingly good at writing technical docs
What Usually Breaks:
- Complex debugging: Gets lost in large codebases with weird dependency chains
- Performance optimization: Tried to "optimize" our user lookup query by adding 3 JOIN statements that made it 10x slower. Thanks, Devin.
- Legacy code: Completely baffled by "creative" legacy patterns - spent 40 ACUs trying to "modernize" a Python 2.7 script that worked perfectly fine for 6 years
- Integration work: Multiple services = multiple ways to fuck up. Devin once rewrote our entire auth system because I asked it to fix a typo in the login error message. A typo.
The $200 lesson: Start with small, well-defined tasks. Let Devin prove itself before assigning complex features.
Devin 2.0 Updates (The Price Drop That Changed Everything)
When Devin 2.0 launched back in April, it dropped pricing from $500/month to $20 minimum, making it actually affordable for normal developers:
Multiple Devins (Finally): You can run parallel instances now. Useful for having one Devin write tests while another handles the main feature. Just watch your ACU burn rate.
Interactive Planning (Actually Helpful): Devin now shows you its plan before starting work. You can edit the approach, which prevents those "why did you rewrite my entire API?" moments.
Semantic Search (When It Works): The new search actually understands your codebase context. Better than grep, though it sometimes hallucinates function names that don't exist.
Familiar Shortcuts: Cmd+I and Cmd+K work like you'd expect. The IDE feels less alien than the original version.
Reality check: These improvements are solid, but you're still debugging an AI's code. Budget 2x longer than you think for review and fixes.
Integration Reality (Mostly Works, Sometimes Doesn't)
Devin plugs into your existing tools, though setup can be finicky:
Version Control Integration:
- GitHub works flawlessly - PRs, branch management, etc.
- GitLab is supported but occasionally has auth issues
- Custom Git setups require more hand-holding
Project Management (Hit or Miss):
- Jira integration is solid for ticket updates
- Linear works well for small teams
- Notion integration is basic but functional
- Gotcha: Devin doesn't understand your team's workflow conventions
Team Communication:
- Slack integration works but gets noisy fast
- You'll want to set up a dedicated #devin-noise channel
- Progress updates are helpful but can spam your channels
Cloud Deployment (Use With Caution):
- Can deploy to AWS, GCP, Azure
- WARNING: Never give Devin production deploy access unsupervised
- Great for staging environments and development deployments
- Has accidentally nuked test environments - always review deployment scripts
Bottom line: The integrations work but require babysitting. Treat Devin like a junior developer who needs code review, not a senior engineer with root access.