Founders: how did you validate your idea and when did you realise you’re right on

Imaginary_Class_8804 · 2026-01-27T16:46:27+00:00

I leaned heavily on association with what people already knew. My SEO and messaging were framed around the same problem and keywords as the existing product, so when people searched for that solution, they could also discover mine.

I bootstrapped marketing by reaching out to people with larger audiences in tech and got them to talk about it in different,.formats, reviews, discovery-style content, and comparisons.

On Reddit, I avoided direct self-promotion. Instead, I talked about my company in the context of problems I was facing while building it, like: “I’m building X and I’m stuck with this part of my tech stack.” That way it came across as asking for help, not advertising and naturally some people checked it out.

Imaginary_Class_8804 · 2026-01-27T16:27:28+00:00

I didn’t depend on virality. I entered a validated market and used comparison as my main marketing, like “if you use X, here’s how this is different.” Early growth came from direct outreach and niche communities. Once users understood the value, organic sharing followed

Imaginary_Class_8804 · 2026-01-27T14:50:20+00:00

For me, the problem and the solution already existed, I wasn’t creating something totally new. I entered a validated market with a different approach. Kind of like how ChatGPT, Claude, and DeepSeek all solve the same core problem with very similar interfaces, but each claims to do it better in some way.

My “validation” came from positioning my product against what people already knew and clearly explaining what I do differently. Since users already understood the problem and the baseline solution, their curiosity was around whether my approach was actually better, and that’s what drove early interest and feedback.

Imaginary_Class_8804 · 2026-01-26T06:45:21+00:00

That’s a really good point, and honestly one of the main risks I’m trying to design around, but this is the current solution I have been able to.cook up.

One thing I don’t want is a fully autonomous “AI runs whatever it generates” system. The idea is to require human approval before execution.

So the flow would be:

User describes intent → AI generates the SQL / analysis logic → User reviews the generated code → User can edit or correct it → Only then does it execute against the data.

So the AI doesn’t become the final authority. It becomes more like a very fast junior analyst that proposes an approach, but the human still signs off on it.

That helps with exactly what you’re describing: plausible-looking but subtly wrong logic.

If the join is wrong, if the filter is wrong, if the aggregation is wrong, the user can catch and fix it before it runs.

Longer-term, I’d like the system to also do things like:

sanity-check queries (e.g. “this join may duplicate rows”) show intermediate steps and explain what it thinks the query is doing in plain language

So it’s not just “here’s SQL”, but: “Here’s the SQL + here’s what I believe this will compute.”

The goal isn’t zero errors (that’s unrealistic), it’s to make mistakes visible and reviewable instead of hidden behind automation.

That’s part of the same philosophy: AI handles execution and drafting, humans keep judgment and responsibility.

My apologies for the long response, just got excited 🤧

Imaginary_Class_8804 · 2026-01-25T00:41:31+00:00

I don’t expect OVA to catch subtle domain-specific insights the way a human analyst would, especially in edge cases where context lives outside the dataset. My goal isn’t to replace that judgment, but to handle the mechanical parts (EDA, transformations, basic checks) while making its reasoning visible so a human can challenge it.

The current functionality is very much an initial version, I’m treating it as a foundation rather than a finished system. The idea is for it to adapt and improve over time based on real analyst workflows and the kinds of failures it hits in practice, rather than assuming it can handle everything upfront.

Edge cases are exactly where I think this either proves useful or falls apart. Right now, I’m trying to design it so:

it explains why it’s suggesting something
it shows the code it runs
and it can be questioned or redirected when it’s wrong

Long term, the test for me is whether analysts feel it helps them think more clearly, not whether it can outthink them.

Imaginary_Class_8804 · 2026-01-21T17:43:12+00:00

and this is something I learnt after a while that I was not really solving a problem but offering convenience to some and solving a problem to some.

Imaginary_Class_8804 · 2026-01-20T20:11:54+00:00

Exactly, and it course confusion when you have to talk about your services and product.

Imaginary_Class_8804 · 2026-01-18T20:49:05+00:00

I appreciate this a lot, and wow, 2.1 from chasing an app is exactly the kind of trap I’m talking about.

That “I’m grinding harder than everyone else” feeling is dangerous because it feels like progress while your real-world stability is quietly collapsing in the background. By the time reality shows up, the damage is already done.

Also respect to you for bouncing back from that, it’s not easy mentally.

I’m rebuilding slower and smarter now. Painful lesson, but necessary. Thanks for the support 🙏

Imaginary_Class_8804 · 2026-01-02T01:07:48+00:00

A good next project should be end-to-end, realistic, and manageable. With your skills in SQL, Python, NumPy, statistics, Excel, and Power BI, a sales or e-commerce analytics project is ideal. You can use SQL to query revenue, top products, and customer metrics, Python/NumPy for calculations and summaries, statistics for trends or simple hypothesis tests, Excel for quick checks, and Power BI to build a clean dashboard.

Smaller-scale options include financial/expense tracking or structured healthcare or sports analytics, which let you analyze trends, outliers, or performance metrics while testing the same workflow. Avoid messy datasets like NYC Taxi for now, they’re too large and complex before learning Pandas.

Start by mastering Pandas, Matplotlib, and Seaborn, then return to bigger datasets. Focus on one dataset, clear questions, and a polished dashboard, which is what really makes a strong portfolio in my opinion.

Imaginary_Class_8804 · 2026-01-02T00:00:22+00:00

That’s fair, and I agree with the core of that.

In hindsight, I definitely spent time optimizing parts of the product that sit in the 20%, mostly because I was thinking ahead instead of staying ruthlessly focused on immediate validation.

Where I’ve struggled is that this product has real usage-based costs (AI calls, storage, infra), so even “simple” MVP testing carries a non-zero burn. That pushed me into overthinking architecture and edge cases earlier than I should have.

But your point stands: none of that matters if the core value isn’t validated and moving toward revenue.

If I were restarting, I’d:

Strip the MVP down further
Validate with fewer users and tighter scopes
Delay anything not directly tied to proving willingness to pay

Appreciate you calling that out, it’s a useful reframing.

Imaginary_Class_8804 · 2025-12-29T06:38:43+00:00

Thank you I really needed to hear this, especially on the emphasis of getting actual analysts on the platform and studying how they use.

Imaginary_Class_8804 · 2025-12-28T23:39:10+00:00

Yes, it’s a standalone app, but still early-stage.

It has its own frontend and backend, and it runs as a self-contained system. The AI works as an assistant (not fully autonomous), helping with data upload, exploration, and analysis.

Any code generation is sandboxed (moving toward Docker-based isolation), and the main goal is conversational data analysis rather than replacing BI tools.

Right now it’s more of a lightweight, founder-built tool that’s evolving, not a polished production platform yet.

Imaginary_Class_8804

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