AI coding tools have made building trivial, shifting the true bottleneck to finding validated, high-intent consumer or workflow problems that aren't dominated by massive incumbents or easily replaced by a raw ChatGPT prompt.
Aspiring and early-stage software builders struggle to identify validated consumer problems, target the right niche, and effectively market their tools in an ecosystem increasingly saturated by AI.
Looking for advice on selling small software tools
Building is honestly the easy part now. Knowing exactly who you're building for, making sure the pain is real... is what actually makes money.
commentGreat question. I want to share my experience because I feel like we're experiencing the same curiosity and questions I had almost 2 years ago. I'm based in Toronto. Years ago I studied Computer Programming at George Brown College, but my career has always been in sales and I'm actively selling homes. About 2 years ago I decided to start building apps to see if I could solve real problems and make products people would actually pay for. I wouldn't even call myself a developer. Everything I've built has been with AI. I was mostly supervising it, and whenever it got things wrong, I used the knowledge I had to fix it and keep moving. Unlike your situation, I already knew the problems because I was living them every day as a Realtor. I was paying for HubSpot, REDX, AgentBoss, BoomBoom, Dub, social media schedulers, and a bunch of other subscriptions. My biggest pain was expired listings. Around 95% of my business came from expireds, and after months of door knocking and follow-ups I had hundreds of leads with no easy way to organize them or see which houses I had already visited. No CRM really solved that for me, so I built my own. It took months, but it replaced all those subscriptions and honestly did more than they did. Every agent I showed it to was shocked, and I had 10 people sign up in the first week. Then I built VirtuallyStage because I was tired of paying for virtual staging services where credits expired, subscriptions were expensive, and the image quality wasn't even that good. So I built what I wanted to use myself. The reason I'm telling you all this is because over the last two years I've built multiple apps that solve real problems, and people actually use them and love them. The biggest thing I've learned is that building isn't really the hard part anymore. With AI, after you've done a couple of projects, you realize you can build almost anything. The hard part is getting people to find it. For example, if you ask ChatGPT something like "virtual staging with no subscription" or "high quality virtual staging without credits expiring," my website is ranked number one and that's where most of my sales come from. That wasn't luck. It took months of learning SEO, AI search, indexing, crawling, backlinks, structured data, and understanding how AI actually finds and recommends websites. If you search something broad like "best virtual staging," you won't see me because huge companies own those searches. That's why niching down is so important. If I wish someone had told me anything when I started, it would've been to spend way more time learning marketing than building. Building is honestly the easy part now. Knowing exactly who you're building for, making sure the pain is real, learning how people search, creating content, and getting your product in front of them is what actually makes money. I also learned a lot from mistakes. I built a gym app and charged $45 a month, but I realized if you're paying for ads it's much easier to acquire customers when your product has a higher value. I also built 4 or 5 apps just because I wanted to ship fast. Looking back, I should've spent more time researching before building, but I don't regret it because every project taught me something. If I was starting over today, I'd spend way more time researching the problem, talking to people, validating the idea, and learning marketing before writing any code. You can use Claude or ChatGPT to help with research, but if you're already in an industry where you know the problems firsthand, or you have friends and family that do, that's a huge advantage. You can build the best app in the world, but if nobody knows it exists, it won't matter. That's probably the biggest lesson I've learned over the last two years. Building is maybe 10% now. Understanding the pain, niching down, and learning how to market is the other 90%. That's what I'd focus on if I had to start all over again.
If I was starting over today, I'd spend way more time researching the problem, talking to people, validating the idea, and learning marketing before writing any code.
commentGreat question. I want to share my experience because I feel like we're experiencing the same curiosity and questions I had almost 2 years ago. I'm based in Toronto. Years ago I studied Computer Programming at George Brown College, but my career has always been in sales and I'm actively selling homes. About 2 years ago I decided to start building apps to see if I could solve real problems and make products people would actually pay for. I wouldn't even call myself a developer. Everything I've built has been with AI. I was mostly supervising it, and whenever it got things wrong, I used the knowledge I had to fix it and keep moving. Unlike your situation, I already knew the problems because I was living them every day as a Realtor. I was paying for HubSpot, REDX, AgentBoss, BoomBoom, Dub, social media schedulers, and a bunch of other subscriptions. My biggest pain was expired listings. Around 95% of my business came from expireds, and after months of door knocking and follow-ups I had hundreds of leads with no easy way to organize them or see which houses I had already visited. No CRM really solved that for me, so I built my own. It took months, but it replaced all those subscriptions and honestly did more than they did. Every agent I showed it to was shocked, and I had 10 people sign up in the first week. Then I built VirtuallyStage because I was tired of paying for virtual staging services where credits expired, subscriptions were expensive, and the image quality wasn't even that good. So I built what I wanted to use myself. The reason I'm telling you all this is because over the last two years I've built multiple apps that solve real problems, and people actually use them and love them. The biggest thing I've learned is that building isn't really the hard part anymore. With AI, after you've done a couple of projects, you realize you can build almost anything. The hard part is getting people to find it. For example, if you ask ChatGPT something like "virtual staging with no subscription" or "high quality virtual staging without credits expiring," my website is ranked number one and that's where most of my sales come from. That wasn't luck. It took months of learning SEO, AI search, indexing, crawling, backlinks, structured data, and understanding how AI actually finds and recommends websites. If you search something broad like "best virtual staging," you won't see me because huge companies own those searches. That's why niching down is so important. If I wish someone had told me anything when I started, it would've been to spend way more time learning marketing than building. Building is honestly the easy part now. Knowing exactly who you're building for, making sure the pain is real, learning how people search, creating content, and getting your product in front of them is what actually makes money. I also learned a lot from mistakes. I built a gym app and charged $45 a month, but I realized if you're paying for ads it's much easier to acquire customers when your product has a higher value. I also built 4 or 5 apps just because I wanted to ship fast. Looking back, I should've spent more time researching before building, but I don't regret it because every project taught me something. If I was starting over today, I'd spend way more time researching the problem, talking to people, validating the idea, and learning marketing before writing any code. You can use Claude or ChatGPT to help with research, but if you're already in an industry where you know the problems firsthand, or you have friends and family that do, that's a huge advantage. You can build the best app in the world, but if nobody knows it exists, it won't matter. That's probably the biggest lesson I've learned over the last two years. Building is maybe 10% now. Understanding the pain, niching down, and learning how to market is the other 90%. That's what I'd focus on if I had to start all over again.
Software developers and AI builders looking to identify highly specific, unserved workflow gaps with real payment intent.
Repeated complaints focus strongly on the shift of product difficulty from engineering to marketing, emphasizing that finding a validated, high-intent problem is now the single biggest bottleneck to building software that generates revenue.
Unlike generic idea generators or broad SEO tools (Ahrefs), NichePulse focuses exclusively on unstructured conversational data to discover 'un-googled' hyper-specific pain points before they become competitive keywords.
A data-driven market validation platform that surfaces high-frequency, highly specific workflow pain points from Reddit, X, and industry forums, analyzing search intent data alongside complaint volume to isolate underserved micro-niches.
Users explicitly state they would spend way more time researching the problem and validating the idea before writing any code, identifying marketing and validation as their true financial and operational bottleneck.
“Find validated, high-intent micro-niches to build for in 15 minutes.”
A data-driven market validation platform that surfaces high-frequency, highly specific workflow pain points from Reddit, X, and industry forums, analyzing search intent data alongside complaint volume to isolate underserved micro-niches.
Target online startup communities and subreddits (r/indiehackers, r/micro-saas, Indie Hackers, X/BuildInPublic) by sharing real, breakdown reports of validated micro-niches discovered by the platform.
Building an LLM-based parsing engine that successfully separates low-value user venting from profitable B2B/B2C workflow constraints.
Builders may subscribe for only one month, find 2-3 viable ideas, and immediately cancel until their next build cycle.
Relying on external platform APIs for data ingestion introduces risks around data access stability and platform structural changes.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 9/10 against 3 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "ai-powered", "analytics", "devtools", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
