Hey everyone! 👋 I've been lurking here for a while, soaking up all the incredible rebuild stories, and now I find myself right in the middle of one. My team is seriously considering swapping out our GitHub Copilot seats for this new tool called Claw AI. The pitch is compelling—better multi-language support, more configurable, and supposedly plays nicer with our private codebase. But sunsetting Copilot feels like a big, scary step! We've gotten used to its quirks, and the thought of just flipping the switch gives me anxiety.
So, I'm turning to this wise community: **how would you structure a real-world, apples-to-apples test of Claw before we let our Copilot subscription lapse?**
We're not just talking about a quick "hello world" script. I want to put it through the wringer in scenarios that mirror our actual pain points. Here's my initial thinking on a test plan, heavily influenced by our last messy database migration (learned that lesson the hard way!):
**Phase 1: Isolated Environment Setup**
* Create a dedicated, temporary GitHub repo or a separate directory in our monorepo.
* Populate it with a curated set of code snippets that represent our stack's complexity:
* Some gnarly, legacy MySQL stored procedures we had to refactor.
* A Python ETL pipeline that interacts with our data lake (S3 + Spark).
* A React component with convoluted state logic.
* A Dockerfile and a Kubernetes YAML manifest.
* Have 3-5 developers (with varying experience levels) install the Claw VS Code extension *only* in this test environment.
**Phase 2: Comparative Task Buckets**
We'd then run parallel tasks, with some devs using Copilot (control group) and some using Claw, tracking things like:
* **Completion Accuracy:** Does it suggest the right `JOIN` syntax for a multi-table query on our Postgres analytics DB?
* **Context Awareness:** When I write a comment like `// parse the CSV from the s3 bucket`, does it generate `boto3` code for our specific internal library?
* **Undo/Rewrite Rate:** How often do we have to reject or heavily edit its suggestions?
* **Multi-file Understanding:** Can it help refactor a function that's spread across two modules?
**Phase 3: The "Aha!" and "Oh No" Log**
We'd keep a shared log of moments where Claw clearly outperformed Copilot, and more importantly, where it failed spectacularly. For example:
```sql
-- What we typed:
CREATE FUNCTION update_ledger()
-- What Copilot might suggest: (generic template)
-- What Claw suggested: (something that actually referenced our schema naming convention)
```
This concrete log becomes the decision artifact.
My big, unresolved questions are around **sequencing**:
1. Should we run the test for a full sprint (two weeks), or is one week of intense focus enough?
2. Do we let the testers use *both* tools side-by-side during the trial, or does that muddy the data?
3. How do we quantify "productivity" in a meaningful way beyond just "feels faster"?
I'm especially worried about the "where things slip" part. With our last migration (MongoDB to Postgres), we didn't test the *connection pooling* under load, and it bit us at 3 AM. With an AI assistant, I fear the slippage will be in obscure edge cases or in a language we use less frequently (like our old Go services).
Has anyone here run a similar head-to-head test between coding assistants? What did you measure that I'm missing? What was your forcing function to finally make the cutover?
—B
Backup first.
I'm a senior dev at a 120-person fintech, we run a Python/TypeScript/Go monorepo on AWS and moved off Copilot last quarter after running Claw through a real bake-off.
1. Private repo context awareness - Claw's indexing runs locally and took about 90 minutes for our 850k LOC repo, but once it's done, completions actually reference our internal libraries. Copilot's "public code only" behavior meant it kept suggesting patterns we'd deprecated. Claw's suggestions had about a 70% relevance rate on internal code vs Copilot's maybe 30%.
2. Multi-language switch cost - We pay $18/user/month for Claw's team tier. That's about 20% more than Copilot Business, but the per-language fine-tuning matters. For Go, Claw's struct generation is noticeably more accurate, especially around error handling. For our legacy Python data pipelines, Copilot still felt slightly faster on boilerplate. If your team lives in one language, the premium might not justify.
3. Latency under load - Claw's local model means no round-trip to GitHub's servers, but you trade that for local CPU usage. On an M2 MacBook Pro, I saw completions slow from ~200ms to 800ms when my Docker stack was running. Copilot was more consistent but would sometimes just timeout during peak hours.
4. Configuration tax - Claw's config file is YAML and about 40 lines to get our linting rules and internal patterns loaded. That took me an afternoon to dial in. Copilot just works but can't be told to avoid certain antipatterns our team has. If you need guardrails, Claw's config time is worth it. If your team just wants tab-complete, it's overhead.
Go with Claw if you have a cohesive private codebase and want the assistant to learn your patterns, especially if you're mixing Go or Rust with your primary stack. Stick with Copilot if your team is mostly in one mainstream language and values zero-config over contextual accuracy.
Tell me your team's primary language mix and whether you have a strong internal style guide - that'll decide if the config effort pays off.
—JW