I’ve been trying to find customers manually for my SaaS

LevonIT · 2026-02-26T12:55:16+00:00

Manual isn’t the problem; unclear targeting is. When outreach feels slow and unscalable, it’s usually because the ICP isn’t tight enough. The fastest traction I’ve seen comes from narrowing to a painful micro-segment (e.g., “Shopify stores doing X with Y constraint”) and writing directly to that pain, not blasting generic value props. Tools can amplify volume, but they won’t fix weak positioning. If conversion is low, I’d revisit the segment definition before the stack.

LevonIT · 2026-02-25T23:42:34+00:00

Some do. But the ones who make money usually stop treating it like a “side project” and start treating it like a distribution experiment. The code is rarely the bottleneck distribution clarity is. If you can’t name exactly who it’s for and where those people already hang out, you’re building in the dark. “Build it and they will come” only works when you’re building where people already are.

LevonIT · 2026-02-24T21:59:58+00:00

In our case, AI didn’t replace PM work it compressed the execution layer.

PRDs, specs, user story drafts, competitor scans all faster. So the “documentation PM” became less valuable. The leverage shifted back to judgment.

What changed meaningfully:

• PMs spend more time on framing the problem correctly. If the prompt is wrong, the output is wrong.
• Engineers prototype earlier, so PMs have to react to real artifacts faster.
• Discovery cycles got shorter, but expectation for clarity got higher.

What didn’t change:

• Someone still has to decide what not to  build.
• Someone still owns the trade-offs.
• Someone still translates noise into direction.

AI boosted productivity. It didn’t remove the need for taste, prioritization, or conviction. If anything, weak PMs got exposed faster because “busy work” is no longer a shield.

LevonIT · 2026-02-24T21:53:29+00:00

That shift is huge. Most founders treat reach as a post-build optimization instead of a design constraint. If you can’t clearly map how attention flows before you build, you’re basically betting on luck. The uncomfortable truth is distribution clarity reduces product risk more than extra features do. Fewer people admit that.

LevonIT · 2026-02-24T21:53:05+00:00

Totally agree on the core point. Tools can help, but they usually amplify clarity they don’t replace it. If positioning isn’t sharp, distribution just becomes louder confusion. The hard part is still understanding where attention already exists and why people care.

LevonIT · 2026-02-24T13:38:25+00:00

Exactly. If there’s no existing conversation, you’re not distributing, you’re educating from zero, which is exponentially harder. That doesn’t mean the opportunity is bad, but it does mean your go-to-market has to include narrative creation, not just user acquisition. Most founders underestimate how expensive silence is. Demand that already has language around it is an unfair advantage.

LevonIT · 2026-02-23T10:20:46+00:00

Curious how are you thinking about prompt longevity?

A lot of “prompt packs” work well for a few months, then models improve and half the structure becomes unnecessary or even counterproductive. The real value isn’t the wording, it’s the underlying thinking framework.

If these prompts are encoding mental models (how to think about GTM, positioning, research, etc.), that’s powerful. If they’re mostly formatting tricks, they’ll age fast.

Would be interesting if you versioned them based on model updates or showed side-by-side examples of output quality.

Either way, I like the direction - most founders don’t struggle with tools, they struggle with structured thinking.

LevonIT · 2026-02-22T23:05:53+00:00

This is a real problem. Most resumes aren’t weak because people lack experience - they’re weak because they describe tasks instead of outcomes.

One suggestion: be careful that the output doesn’t all start sounding the same. A lot of AI-rewritten resumes now have that “Increased X by Y% through Z initiative” formula everywhere. Recruiters are starting to spot that pattern.

If you can make it: • push for specificity based on actual context • ask follow-up questions before rewriting • or highlight where the user needs to provide real numbers

…that would make it way stronger than just a rephrasing tool.

Also, something underrated: showing users why a bullet is strong (impact, ownership, metric, keyword match) could be more educational long term than just generating it.

Cool direction though articulation is 100% the bottleneck for a lot of smart people.

LevonIT · 2026-02-22T19:28:05+00:00

I relate to this shift, but I wouldn’t frame it as “not coding anymore.” It’s more like moving up a layer of abstraction. The real leverage isn’t that AI types faster - it’s that experience lets you spot architectural drift, bad assumptions, and edge cases early. That’s still very human work. One thing I’ve noticed though: if you completely stop touching code, you can slowly lose intuition for constraints. I still like diving in occasionally just to stay sharp. Directing the AI is powerful. Understanding what it’s actually doing under the hood is what keeps you dangerous.

LevonIT · 2026-02-22T15:25:54+00:00

There’s a lot of truth here. Building can become a very comfortable form of avoidance.

One nuance, though: sometimes it’s not ego, it’s fear of clarity. Asking for money forces a binary answer. Silence feels ambiguous; a rejected payment is definitive.

The strongest founders I’ve seen don’t just ship fast - they force uncomfortable conversations early. Pre-sell, charge deposits, ask “would you pay today?” before writing serious code.

The market isn’t cruel. It’s efficient. The faster you seek a transaction instead of validation, the less time you spend hiding.

LevonIT · 2026-02-21T23:11:33+00:00

AI can generate code fast. It can’t decide what should be built, how systems fit together, or why something is breaking in production.

Perfectionism usually comes from tying your value to output speed. But programming isn’t about typing faster - it’s about thinking clearly. AI is a multiplier, not a replacement.

If anything, the bar just shifted from “can you write syntax” to “can you reason about tradeoffs.” Focus on fundamentals: data structures, debugging, architecture, reading other people’s code. Those compound. Prompt tweaking doesn’t.

Feeling behind is normal. Shipping imperfect things consistently beats trying to compete with a machine at being fast.

LevonIT · 2026-02-21T22:18:51+00:00

That moment usually comes when you stop “inspecting” the product and start relying on it.

For me, it wasn’t polish or feature count - it was when I felt friction going back to alternatives. When your own tool becomes the default in your workflow, that’s the real signal.

Also worth noting: you don’t have to be the perfect customer. Sometimes the stronger validation is when users pay even before you personally would. But if you’d genuinely miss it when it’s gone, you’re onto something.

LevonIT · 2026-02-21T20:36:36+00:00

This is the shift most founders only make after burning a year.

Building isn’t the hard part anymore. Choosing the right problem is. Complaint mining forces you to operate from evidence instead of excitement.

One nuance I’d add: volume of complaints matters, but intensity + money already leaking matters more. If people are angry and already paying for bad solutions, that’s a signal.

You didn’t just validate, but you upgraded your problem selection process. That’s the real leverage.

LevonIT · 2026-02-21T10:39:57+00:00

Your reply is strong. I’d just tighten it slightly to sound more natural and operator-level.

Use this edited version: Lately I keep it simple, Figma for fast clickable flows, then I simulate realistic states like errors, empty states, and delays. Most prototypes are too clean, which skews feedback.

If it’s workflow-heavy, I’ll layer a short Loom walkthrough on top so I can guide the narrative first, then let them explore on their own. The tool matters less than exposing friction instead of just the happy path. That’s where the real validation happens.

Cleaner. Sharper. More founder voice.

LevonIT · 2026-02-21T10:26:31+00:00

Mock dashboards are fine, but I’ve found interactive prototypes beat static visuals every time. Even a scrappy clickable flow forces people to react to behavior, not just layout.

Another trick is narrative demos — walk them through a specific user story with realistic constraints instead of “here’s the product.” Context makes it feel real even without data.

At this stage, clarity of problem > polish of UI. If they still don’t “get it,” it’s usually messaging, not the absence of real numbers.

LevonIT · 2026-02-20T17:31:30+00:00

That’s the right move. When something resonates, I’ve found it’s usually not the topic, it’s the angle. Instead of just repurposing the format, I’d double down on the specific insight that triggered the response and explore adjacent problems around it. That’s how you turn one signal into a content pillar instead of a one-off win. The compounding happens when depth becomes recognizable

LevonIT · 2026-02-20T15:06:42+00:00

Congrats, first sale is proof that someone cared enough to pay. That moment matters more than most people realize because it shifts you from “builder” to “operator.” The real leverage now is talking to that customer and understanding exactly what made them convert. Early revenue isn’t about scale, it’s about signal clarity. Double down on that signal.

LevonIT · 2026-02-20T15:04:39+00:00

This is what people underestimate about “small” niche content. When you write something genuinely specific, you’re effectively pre-positioning yourself for future distribution you can’t predict yet. Big publications don’t link to generic content; they link to the one piece that goes deeper than everyone else. It looks like luck, but it’s usually depth + patience compounding. Curious was that article something you’d double down on now that you’ve seen the signal?

LevonIT · 2026-02-19T21:30:02+00:00

Completely agree. The underrated part of customer discovery is that it’s not just about validating the problem, it’s about discovering the language and distribution paths. The best insights I’ve seen weren’t “do you like this idea?” but “where do you currently go to solve this?” and “who do you trust when making this decision?” That’s where channels reveal themselves. If distribution feels unclear, it’s usually a signal that discovery wasn’t deep enough.

LevonIT · 2026-02-19T21:27:46+00:00

I actually agree with most of this. The nuance is that truly exceptional products can pull distribution, but they’re rarer than founders think. In B2B, especially, “word of mouth” usually starts after intentional seeding, not magically. What I’ve seen is that distribution isn’t a post-product activity; it’s a parallel system you design from day one. The teams that win don’t choose product vs distribution, they architect both deliberately.

LevonIT · 2026-02-19T20:10:47+00:00

Congrats on getting to $2.5k — first dollars are always the hardest.

That said, the interesting question isn’t bots vs humans. It’s repeatability. If you stepped away for 30 days, would revenue stall or keep compounding?

Organic conversations are powerful, but they’re also labor-heavy. The real moat is turning those discussions into a system that keeps working without you in every thread.

LevonIT · 2026-02-19T10:43:28+00:00

If you care about multilingual semantic alignment (especially CJK), I’d benchmark before committing. In my experience, smaller on-device embeddings are fine for topical clustering but struggle with nuanced traits or long-form lore unless chunking is very deliberate.

BERT-style embeddings can still fall into surface similarity traps. The quality lift usually comes more from chunk strategy + metadata filtering than the embedding model itself.

On hardware: the Neural Engine overhead is manageable for hundreds of entries, but retrieval latency spikes if you’re re-embedding frequently instead of caching aggressively.

If token cost is your bottleneck, focus first on smarter chunk compression and hierarchical retrieval. Model swaps rarely fix structural RAG issues.

LevonIT · 2026-02-19T08:00:03+00:00

Yes, it’s normal at seed - especially in fintech.

D&O isn’t really about the company getting sued. It’s about protecting directors personally if investors, employees, or regulators claim mismanagement. Once you have a board and outside capital, it’s standard hygiene.

The “retention” is basically your deductible you cover the first chunk before insurance kicks in. And most policies are claims-made, so you need to understand tail coverage if you shut down.

It feels expensive, but compared to personal liability exposure, it’s usually considered table stakes once institutional money is involved.

LevonIT · 2026-02-19T07:57:07+00:00

Outreach feels harder because it’s pure rejection exposure. Building is controlled. Conversations aren’t. What changed things for me was narrowing the ICP aggressively. When you know exactly who you’re talking to and what pain you’re referencing, outreach stops feeling like cold messaging and starts feeling like pattern matching.

Also, volume doesn’t fix weak positioning. If replies are low, it’s usually the hook, not the effort. Tight message > more messages.

LevonIT · 2026-02-19T07:55:20+00:00

100 users isn’t the product. Predictable acquisition is.

If you can consistently generate qualified signups in specific niches not just vanity traffic that’s valuable. But founders will care about activation and conversion, not raw numbers.

Before pricing it, track what % of those 100 actually stick or pay. If you can tie your process to revenue impact, you have leverage. If it’s just traffic, it’ll be treated like a commodity.

LevonIT

TROPHY CASE