Real insights from building actual products
Everyone's talking about AI. Most of it is noise. Here's what we've learned from actually using it to build real products that solve real problems.
This isn't about replacing developers or revolutionary breakthroughs. It's about the practical reality of using AI as a development multiplier. The messy, imperfect, surprisingly effective ways it changes how we work.
AI is a sophisticated hammer. Great for hitting nails, terrible for brain surgery. We use it where it makes sense, ignore it where it doesn't.
AI lets us prototype faster, test more ideas, and iterate quicker. Perfect code can wait - working solutions can't.
We start with the problem, not the AI. If traditional methods work better, we use those. AI is just another option in the toolbox.
Theory is nice. Practice is better. We learn what works by building real products with real users.
The Problem: PitchGrid users were losing saved layouts due to SQLite corruption. Traditional debugging was taking days.
AI Solution: Fed error logs and database schemas to AI, got targeted analysis in minutes. Found the root cause in concurrent write operations.
Result: Fixed in v1.8. Zero data loss since then.
The Problem: Solo developer means no second pair of eyes on code quality.
AI Solution: Custom prompts for reviewing Android code, checking for memory leaks, performance issues, and edge cases.
Result: Caught 3 major performance issues before they hit users. Saved weeks of debugging.
The Problem: Good docs take forever to write. Bad docs help nobody.
AI Solution: Generate first drafts from code comments, then human review and refinement.
Result: 10x faster documentation process. Actually useful docs that stay updated.
AI can suggest patterns, but it can't understand your specific constraints, user needs, or technical debt. These decisions still need human judgment.
Sports video analysis has nuances AI doesn't grasp. Frame timing, motion detection, user expectations - this stuff requires deep domain knowledge.
AI can spot obvious inefficiencies, but real performance work requires profiling, measurement, and understanding the full system context.
Don't try to AI-ify your entire workflow. Pick one specific task, get good at it, then expand. We started with code comments, now use it for debugging, docs, and testing.
Generic prompts get generic results. The more specific context you provide, the better the output. Include error messages, code snippets, and exact requirements.
AI is confidently wrong more often than you'd think. Always test, always verify, always have a human check the important stuff.
Good prompts are like good code - they get better with iteration. Save what works, refine what doesn't, and build a library of proven approaches.
How we measure AI's impact on development speed, code quality, and bug reduction. Real numbers, not marketing fluff.
Honest assessments of AI development tools. What we use, what we've tried, what's worth your time.
How AI helps us go from "this is a problem" to "here's a solution" faster. Our actual workflow, with examples.
Got questions about AI in development? Want to share your own experiences? We're always learning and happy to discuss what's working (and what isn't).