Stack Overflow was a nice experience for me because I was able to hit 2k reputation fairly quickly, in just 30 days of posting and 6 weeks calendar time. That being said, it never had the community feel of places I spent during my formative years, which were more on forums and IRC.
Here's a conference talk I gave on how to gain Stack Overflow reputation from back in 2018, selected out of 5 submitted talks. It's amazing how fast times have changed from before, during, and now after.
1. The code generated should be available to use. Some languages are simple enough there is an obvious way to do it. Many companies have developer programs with staff producing code intended to be used in the form of open source SDKs, example code, and tutorials.
2. If on the off chance, there is code that shouldn't be there, people should use DMCA. Anthropic, GitHub, and others support this.
3. At the macro level, it's hard to know know where this is going, so we should look to bellwether apps with more attention for guidance.
Agree it will be interesting to see how things play out. There's enough permissive open-source licensed code available that using that only could be an option.
As for Mickey, is the difference from Oswald enough today?
I've found that for API integration, developing against a spec, like OpenAPI, and using/building an auto-generated SDK is still very useful for coding agents.
When there is no spec for REST APIs, I built a tool that can convert HAR files, Postman collections and other data to OpenAPI spec from which I build client SDKs for coding agents to use.
Given that Claude is attributed to 19M+ commits on GitHub, it will be interesting to see where this ends up. Specifically on copyright, it will be interesting to see if any DMCA takedown notices are filed, including popular projects such as OpenClaw, GSD, Gas Town, Vibium, and others.
If there's any stolen code generated by AI, it's certainly not intentional and a DMCA notice would be appreciated. It would be interesting to see how prevalent this is in AI generated code - is anyone doing a study?
Stars will likely go up over time, but more than the stars it's the testing and maintenance over time that's valuable. There's little promotion right now, but there are already some stars, PRs, and issues. In fact, I'm working on merging PRs now.
Open source has never been more alive for me. I have been publishing low key for years, and AI has expanded that capability more than 100 fold, in all directions. I had previously published packages in multiple languages but recently started to cut back to just one manually. But now with AI, I started to expand languages again. Instead of feeling constrained by toolchains I feel comfortable with, I feel freedom to publish more and more.
The benefits to publishing AI generated code as open source are immense including code hosting and CI/CD pipelines for build, test, lint, security scans, etc. In additional to CI/CD pipelines, my repos have commits authored by Claude, Dependabot, GitHub Advanced Security Bot, Copilot, etc. All of this makes the code more reliable and maintainable, for both human and AI authored code.
Some thoughts on two recent posts:
1. 90% of Claude-linked output going to GitHub repos w <2 stars (https://news.ycombinator.com/item?id=47521157): I'm generally too busy to publishing code to promote, but at some time it might settle down. Additionally, with how fast AI can generate and refactor code, it can take some time before the code is stable enough to promote.
2. So where are all the AI apps? (https://news.ycombinator.com/item?id=47503006): They are in GitHub with <2 stars! They are there but without promotion it takes a while to get started in popularity. That being said, I'm starting to get some PRs.
This. Maintainability and refactorability are some of the major Go superpowers for me which enables getting into any code base and updating it. These are supported by features like static typing, fast compile times, etc.
Of note, I've found this to be very important with AI generated code, where it's easy to grok and refactor AI code.
I enjoy the Go ecosystem quite a bit and haven't found many issues with documentation. I love how open source modules are documented on pkg.go.dev, including those from major providers, like AWS, Google, etc. Every library has the same references. When examples are useful, such as with charting modules, I've found that the projects do provide them. On the occasion where the README.md code is out of date, it's been easy for me to check pkg.go.dev and update it myself.
I still get substantial value out of Coursera. I started with Andrew Ng's original Machine Learning course way back when, and recently completed classes that I thought were well worth my time on Claude Code, Crew AI, AutoGen, and MCP.
While not revolutionary, a recent improvement is AI-based review, which is much appreciated for it's near instant review.
From a transformative perspective, I like the AWS Skill Builder SimuLearn classes. They say teaching is one of the best ways to learn, and I found the chat-based role play where you are the expert to be very interesting.
Here's a conference talk I gave on how to gain Stack Overflow reputation from back in 2018, selected out of 5 submitted talks. It's amazing how fast times have changed from before, during, and now after.
https://grokify.github.io/stackoverflow-the-hard-way/