One of the most repeated pieces of startup advice is:

Pick one problem, build one product and spend years making it successful.

This makes sense when you are building a venture-backed company and trying to create one huge outcome. But I am not sure it is the best strategy for indie hackers. The alternative is what I call the many-donkeys thesis. Instead of betting your entire career on one product becoming a unicorn, you build a portfolio.

I was thinking about how stressful modern interviews are right now. Whether you are an engineer, a PM, or in Sales, you are expected to have the perfect, charismatic answer to every behavioral and technical question instantly on a live Zoom call. If you freeze up for 5 seconds, you lose the job.

With local AI models getting faster and faster, we are quickly entering an era where candidates can (and will) use real-time AI teleprompters to feed them perfect answers on screen while they talk.

If candidates have access to tools that give them perfect charisma and technical answers in real-time, how are companies going to adapt? Will the traditional remote Zoom interview completely die out and be replaced by in-person whiteboarding again? Curious to hear what founders and recruiters here think about this shift.

The speed at which AI generates code has officially surpassed our capacity to review it. For decades, the bottleneck in software engineering was writing the code. Today, the bottleneck has shifted entirely to trusting that the code can run effectively in production and actually solve the business problem.
But let's be honest: humans never truly scaled code review anyway.
Throughout my career, I have observed developers approving PRs or MRs that contain critical bugs. When they do engage in the review process, they often focus on stylistic issues. Why does this happen? Because reviewers are overloaded with their own backlogs to manage, and often choose the path of least resistance by saying, "Looks good to me."
Now, we are trying to solve this by throwing AI at the problem!
The Missed Reality: Code Review Wasn't Built for the AI Era
Code review - whether conducted by a human or an LLM - was not designed for this era of autonomous coding. Code review does not actually verify whether the code accomplishes what was prompted, nor do they confirm that the code aligns with its intended specification or that an agent s "task completed" claim is deterministically true.
Even a well-conducted review adhering to the 5-axes best practices (correctness, architecture, security, readability, and performance) falls short. Rarely does a code review verify that the original acceptance criteria were met or that the AI agent didn't deviate from the scope just to finish the PR. LLMs notoriously defer hard, multi-step, or dependent tasks, which means that the intent itself has diverged. Furthermore, reviewers rarely verify that test coverage was maintained or check if the AI agent quietly weakened the tests along the way to secure a passing PR.
This leads to a critical question: Even if a human reviewer or an LLM could effectively manage all these new checks, can they realistically and deterministically do so given the massively increased speed of AI code generation?
The solution? We have to move from opinion-based review to evidence-based verification.
This means building guardrails (what I call the claim checker) that bind the original intent to deterministic evidence: ACs (intents) checks, the builds, the tests, the five-axis code review, the scans, the blast radius of the code change, the docs coverage, requirements are complete and don't contradict each other, and the monitoring coverage in the code.
AI can advise. But only evidence should decide whether to merge or not!
It s a shift we probably should have made years ago. But with agent loops writing code at machine speed, it is now the only way forward.

Been thinking about this a lot lately. The more agents get used for real work instead of demos, the more I keep running into (and hearing about) the same issue: an agent completes a step, reports success, and the output is actually wrong or incomplete. You don't find out until later, and by then it's harder to trace back where things broke.

Feels like this is going to become a bigger problem as more B2B tools ship agents into actual workflows instead of just chat interfaces.

Curious how others here are handling it:

• Do you manually spot-check agent output, or trust it by default?

• Has anyone built their own verification step for this?

• Or is a certain error rate just the accepted cost of automation right now?

Hello everyone.

I hope I can get some advices here. I am developer, a solo developer. I make my project, snapencode.com. It is a self-host video platform. The idea is you buy license one time, for lifetime. No more monthly pay. You own it.

The building part, is fun for me. I love code. The logic.

I originally wanted to build this new product to solve a problem I faced in my daily life. But as a solo developer, the massive workload has completely eaten into my rest time, all while I'm still working my day job. On top of that, a recent health issue in my family has left me completely drained and anxious.

Sometimes I wonder if I should just let this project go. Even though I've poured so much into it, the truth is, my life wouldn't fall apart without it. I'm just feeling really lost right now.