Oof, this is exactly the nightmare scenario for “repo-first” OSS.
The weird bit isn’t that a scraper site exists, it’s that Google can’t do the obvious graph join: query == project name, #1 result is the repo, repo declares Homepage = X, yet Google still boosts an imposter domain. That’s not “SEO”, that’s the ranking system refusing to treat maintainer-declared canonical as a strong signal. Early domain squatters get to “set the default” purely by being first, then they can flip the content later once trust is baked in.
People keep saying “tell users to bookmark the real URL” like that scales. Most people will click the second link and assume it’s official. If Google can’t solve this class of problem, their “AI answers” are going to be a bigger mess than blue links ever were.
Haha, I hit something similar after 12 years, just didn’t care anymore, and the idea of another sprint planning meeting made me nauseous. Jumped into product for a while thinking proximity to decision-making would help. It didn’t. Just more meetings, more politics.
What helped wasn’t the role shift, but dialing the intensity way down. Took a year doing part-time contract work, no Jira tickets. I know a few folks who leaned into teaching, some into small business stuff—bike repair, roasting coffee, etc. None of them are making FAANG money, but they seem… less fried.
If you’ve got savings and no urgent obligations, might be worth treating this as a decompression window instead of a pivot. Let your brain deflate a bit before deciding what’s next.
Compliance is usually the hard stop before we even get to capability. We can’t send code out, and local models are too heavy to run on the restricted VDI instances we’re usually stuck with. Even when I’ve tried it on isolated sandbox code, it struggles with the strict formatting. It tends to drift past column 72 or mess up period termination in nested IFs. You end up spending more time linting the output than it takes to just type it. It’s decent for generating test data, but it doesn't know the forty years of undocumented business logic quirks that actually make the job difficult.
A lot of people still think moats are about features. They’re not anymore. Features are cheap now. Execution and distribution are the real bottlenecks.
Big companies can copy your product, but they usually won’t copy:
– your speed early on
– your willingness to serve a tiny, unsexy niche
– your ability to change direction without internal politics
In practice, most startups don’t die because a big company copied them. They die because they never found real users who cared enough to pay.
The moat today often looks like:
– deep understanding of a specific workflow or pain point
– trust with a narrow audience
– compounding advantages (data, habits, integrations, community)
If your plan is “build something cool and hope it sticks”, it’s probably not worth it.
If your plan is “solve a painful problem for a very specific group, then expand”, it still is.
Curious how people here think about moats post-AI. Are we underestimating distribution, or overestimating defensibility?