How I Built FeedSquad Without Being a Developer

AI coding assistants for founders are development tools that turn clear specs, review loops, and product judgment into working software for non-technical builders.

At 1:43 in the morning in Levi, the apartment was quiet enough that I could hear the radiator clicking.

Outside the window: snow, darkness, the kind of February night where the whole town feels paused. Inside: a terminal full of red TypeScript errors and a product I had talked myself into believing I could build.

I was not a developer.

Not in the clean professional sense. I could edit HTML. I understood APIs well enough to brief somebody else. I had used WordPress, no-code tools, and enough marketing software to know when a product was held together with tape. But I had not built a multi-tenant SaaS app with authentication, billing, scheduling, databases, OAuth, background jobs, image generation, and production users.

FeedSquad forced me to learn anyway.

The old headline for this post said "a million lines of code." That was emotionally true but technically sloppy. The workspace had crossed seven figures if you counted migrations, generated files, experiments, scripts, docs, and old backup material. The actual application source was smaller. Still far too much code for somebody with my background to pretend he had casually "vibe coded" a product into existence.

The more honest claim is this:

I used AI coding assistants to build something I would not have been able to build otherwise. They changed the work instead of removing it.

What was the first mistake in believing AI coding demos?

AI coding demos make software feel like a vending machine. Describe the feature, press enter, receive app.

That is not how it felt after week three.

The first version of FeedSquad technically worked. Buttons clicked. Records saved. Posts appeared on a calendar. But the structure was bad. Auth logic lived in too many places. Supabase clients were created in too many ways. API routes had inconsistent protection. Some pages were clearly built by a model that understood the local file but not the system.

The app needed taste, constraints, review, deletion, and architecture more than it needed another prompt.

That was the first real lesson. AI can write code quickly. It cannot decide, on its own, what kind of codebase you are trying to become.

What was I actually doing all day?

The work was not "coding" in the traditional sense. It was closer to directing an extremely fast junior engineer who never got tired, never got embarrassed, and regularly made confident mistakes.

A typical session looked like this:

1. I described the smallest useful change I could isolate.
2. The assistant made a patch.
3. I ran the app or the tests.
4. Something broke.
5. I pasted the real error back in.
6. The assistant fixed one problem and sometimes introduced another.
7. I asked it to explain the code until I understood enough to judge it.
8. I rewrote the prompt, narrowed the scope, or deleted the whole attempt.

That loop is slower and messier than the sales pitch. It is also powerful.

The useful skill was specifying the outcome, reading the diff, spotting when the model had invented a shortcut, and keeping the code close to the patterns already in the repo. That same operating model sits behind solo founder AI team.

Why are experienced engineers right to be skeptical?

AI coding tools are real leverage with real failure modes.

The 2025 Stack Overflow Developer Survey found that 84% of respondents were using or planning to use AI tools in their development process. The tools are normal now.

But normal does not mean reliable. Stack Overflow also reported that developers were frustrated by answers that were "almost right." That phrase describes most of the pain.

The METR randomized study on early-2025 AI coding tools is the useful cold shower here. Experienced open-source developers working in repositories they already knew took 19% longer with AI assistance in that experiment, even though they believed the tools had made them faster.

That matched my experience and explained the drag.

For a senior engineer inside a familiar codebase, AI can add review overhead. For a non-engineer founder starting below the threshold of "could build this at all," AI changes the ceiling. It let me start. It did not make me senior.

What did I have to learn the hard way?

The assistant could generate React components. It could not tell me which parts of the product deserved to exist.

It could write an API route. It could not guarantee that route was authorized, rate-limited, validated, idempotent, and consistent with the rest of the app.

It could propose a database schema. It could not feel the cost of a bad schema six months later when every feature had to route around it.

The learning curve moved from syntax to judgment:

Security. Auth is not a checkbox. Every route needs an explicit answer to "who is allowed to do this?" and "what happens if they lie?"

State. A UI that works for the happy path can collapse when data arrives late, permissions change, or the user opens two tabs.

Data ownership. Multi-tenant products punish vague thinking. If a query does not clearly scope to the user or workspace, it is probably wrong.

Deletion. The model likes adding. Products get better when you remove things.

Verification. If I did not run the build, inspect the page, and read the diff, I had not finished the work. I had only generated text that looked like code.

How did building with AI change FeedSquad?

FeedSquad became opinionated about human review because the product was born from human review.

I do not trust content that appears fully formed from a blank prompt. I trust a process where a person brings the raw material: the product truth, the story, the objection, the customer moment, the sharp sentence they would actually say.

The machine can draft. The person has to notice what is false, generic, inflated, or not theirs.

That is true in code. It is true in LinkedIn posts. It is true in marketing.

The best use of AI is giving taste more material to work on. The same line separates useful assistance from AI slop on LinkedIn.

What would I tell another non-technical founder?

Do not start by building the whole product.

Start with one workflow that would still matter if nobody ever saw your pitch deck. Build that. Break it. Fix it. Rewrite it. Then build the next one.

Do not ask AI to "make it production ready." That phrase is too vague. Ask for specific properties: validate inputs, enforce ownership, handle empty states, avoid duplicate clients, add tests for the failure path, follow the pattern in this file.

Do not let the assistant choose the architecture from scratch every morning. The codebase needs memory. The failure mode is visible when a vibe-coded product has no users: momentum replaces evidence.

And do not confuse momentum with progress. A long diff feels productive. A deleted file can be more valuable.

What's the real point of building with AI coding assistants?

Software now has a new middle path. A founder who can think clearly, write precise specs, test patiently, and accept being wrong can build much more than before.

That makes engineering judgment even more valuable. The difference is that people like me can now get far enough into the work to understand why that judgment matters.

FeedSquad exists because the tools were good enough, and because the first dozen versions were not.

That is the part the demos leave out.

Sources:

• Stack Overflow — 2025 Developer Survey: AI
• JetBrains — The State of Developer Ecosystem 2025
• METR — Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

What should non-technical founders know about AI coding assistants?

Can a non-technical founder build a SaaS product with AI coding assistants? A non-technical founder can build much more with AI coding assistants than before, but the work still requires review, testing, and judgment. The assistant can generate code; the founder has to decide whether the product and architecture make sense.

What is the biggest mistake when building with AI coding tools? The biggest mistake is believing the demo version of AI coding. Real products require constraints, deletion, security checks, and repeated fixes after the first generated patch.

Why are engineers skeptical of AI coding assistants? Engineers are skeptical because AI often produces code that is almost right. In familiar repositories, that can add review overhead even when the tool feels fast.

What should founders ask AI coding assistants to do? Founders should ask for specific properties: validate inputs, enforce ownership, handle empty states, avoid duplicate clients, add tests, and follow a named local pattern. "Make it production ready" is too vague to be useful.

What did building FeedSquad with AI change about the product? Building FeedSquad with AI made human review central to the product's philosophy. Code and content both need a person to catch what is false, generic, inflated, or misaligned.

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If you built the product yourself and now need distribution that does not sound like a prompt, FeedSquad is the content layer I built for that exact problem.