... rabbit hole on luxury cigar humidors of all things. Weird category to end up in, but it turned out to be a pretty good case study in a pattern I think shows up everywhere in product design. At the entrylevel of that category, almost every product looks and performs about the same. But once you get into the higher tier, the marketing copy across brands starts converging on the same vague language (premium materials, precision engineering, timeless craftsmanship) while ... ... actual functional differences get harder to spot unless you know exactly what to look for. In that specific case it turned out to be things like active

... always thought having a cheap entrylevel plan was necessary to catch hesitant buyers. But after struggling with flat sales despite copy rewrites removing that budget option was what finally worked for us. Instead of boosting signups the cheapest tier was just acting as a safety net that kept users from choosing the product version that provided real value. Curious if other founders have experienced this did removing your cheapest option helps or hurt your overall revenue ...

... them grow into it over time, review their code, build them up. Now with AI handling a lot of basic implementation well, curious if that math still holds. Is anyone still bringing on interns or junior developers for entrylevel coding work, or has AI quietly replaced that hiring category for a lot of teams already. Genuinely trying to understand if this is still a real starting point for someone entering the field, or if that

... difficult to explain in plain text to AI. But with chrome in claude, it was a dream. - AI confuses some variables and enum s lower and upper cases. Keep an eye on it. - If you can only afford entrylevel developers, AI can do a way better job instead. I still think talented and experienced developers have an edge. - After significant development is done, ask AI to audit the whole repo on certain parameters. It will find out many gaps ...

... where context, judgment and deciding what actually matters is involved. That s harder to replace because it s not just about output. Comment from Andrei Tudor(@andreitudor14): The jobs disappearing are the ones doing the repeatable execution. Junior analysts, entry-level

... taught me this the hard way: an AI that gives a confident-sounding answer when it's actually guessing is more dangerous than one that says it isn't sure yet. We ended up building explicit confidence grading (Verified vs Needs Review vs

... took us 3+ years to reach meaningful traction, and we were making $2k MRR in the first 2 years. Unfortunately, there's no formula to know whether you are on the right track or running a zombie. Sunk cost vs. strategic persistence. You bet with your time. 9. SaaS multiples are often fantasy, especially for smaller businesses. If you are below $1M ARR and/or have no aggressive growth potential, do not expect 7-12x multipliers. 10. Lifetime deal buyers ... ... what actually moved the needle. 14. Technical complexity doesn't guarantee success Building more features, next feature fallacy, reaching feature parity with competitors

... definitely lowered the barrier to entry. Tasks that once needed a full dev team can now be handled by solo builders or even non-tech professionals. But I think it's less about software losing value and more about the definition of value shifting. As the how becomes easier, the what and why become more critical. Domain expertise, UX thinking, system design, scalability, integration, and security will still demand deep understanding, especially for production-level