How should a non-technical founder do product research before building?
Product research for non-technical founders means spending 1–2 days finding reference implementations on GitHub, reading primary engineering sources (not summaries), and producing a one-page technical thesis before you write your first AI prompt or hire your first engineer. If you are still at the earlier stage of figuring out where strong AI products are even surfacing, start with Where to Find AI Projects in 2026 first, then come back to this workflow. This process requires zero code-reading skills and consistently produces better prompts, better hiring conversations, and fewer expensive rebuilds.
Here is a conversation that happens constantly between non-technical founders and the engineers they are trying to hire:
"I want to build a real-time collaboration tool." "Okay, do you want it to be local-first or server-based?" "What's the difference?"
And here is the same conversation after the founder has done two days of product research:
"I want to build a real-time collaboration tool. I have been looking at how Notion and Linear approached this — Notion uses a transaction queue with optimistic local apply, Linear went local-first for offline speed. Given what we are building, local-first seems like the right bet. What is your take on the sync engine complexity?"
Same idea. Completely different conversation. The second founder gets taken seriously. They get more honest answers. They do not get oversold on complexity they do not need.
That is what technical product research is actually for.
What most founders skip (and why it is expensive)
Market research and competitive research are well understood. Most founders do them, at least informally. Technical feasibility research is the one that gets skipped — because non-technical founders assume it requires reading code.
It does not.
Consider this example: a founder was pitching an AI product that would "analyze any image in real-time." What she had not researched: the inference latency for the model she had in mind was 3–8 seconds per image. "Real-time" was not achievable within the cost budget she was describing. A two-hour technical feasibility check would have caught this before she spent six months raising money around a promise she could not keep.
Technical feasibility research does not ask "can I build this?" It asks: has someone already built something like this, and what did they learn? What is the hardest part, and how have others solved it? What would a v1 actually require? Where are the hidden costs?
None of these questions require reading code. They require knowing where to look.
The deliverable: a one-page technical thesis
If you only do one thing after reading this, produce this document. It forces clarity and makes your next step obvious — hire, prototype, pivot, or narrow scope.
Idea (one sentence):
Target user (be specific):
What "done" looks like (the single outcome for v1):
Closest product references:
- …
Reference implementations (GitHub repos with stars + last-updated):
- …
- …
- …
Core bet (the one technical assumption that must be true):
Core data model candidate (the atomic primitive, e.g., block, event, record):
Build vs buy:
- auth:
- payments:
- search:
- real-time / sync:
- storage:
V1 scope (what we will build first, and what we will explicitly not build):
Hard problems we expect:
- …
Risks we accept for v1:
- …
This document is not for engineers. It is for you. It gives you the vocabulary to evaluate proposals, the clarity to push back when you are being oversold, and a starting brief that produces dramatically better AI prompts.
A two-day product research sprint
Here is a realistic schedule for a founder starting from zero.
Day 1: Find and evaluate references (4–6 hours)
Morning (2–3 hours): GitHub search
Search for your problem, not your product. If you are building a collaborative document editor, search "collaborative block editor open source" — not "Notion alternative." You will find implementations at various stages of completeness.
For each repo you find, spend 10 minutes on a triage pass (see the evaluation checklist below). The goal is not to read code — it is to understand: what did they choose to build, what did they outsource, and where did they run into problems?
Collect 3–5 repos you want to look at more closely. Take notes in your technical thesis template.
Afternoon (2–3 hours): Primary source reading
For each strong reference, look for:
- An engineering blog post explaining the key design decisions ("why did we build it this way?")
- API documentation that reveals the underlying data model
- Job postings that describe current technical priorities
This is where non-technical founders find the most leverage. A founder who has read Notion's engineering post on their block model and Figma's post on WebAssembly understands their product category better than most junior engineers. That knowledge changes hiring conversations, investor conversations, and the prompts you write to AI tools.
Day 2: Synthesize and decide (2–4 hours)
Morning (1–2 hours): Write the one-pager
Fill in your technical thesis template using what you found yesterday. The goal is to be specific enough that a senior engineer could read this and give you an informed opinion in under 20 minutes.
The "core bet" section is the most important part. What is the single technical assumption your product's value depends on? If you are building a scheduling tool, is it calendar sync reliability? If you are building a real-time analytics product, is it ingestion latency? Write it explicitly.
Afternoon (1–2 hours): Make the deferred decisions list
Write a second list: decisions you will intentionally not make until you have a working prototype and early user feedback. Typical entries include exact sync architecture, database engine choice, and notification system design.
This is as important as the thesis itself. Most founders either try to decide everything upfront (over-engineering) or decide nothing upfront (expensive pivots). The deferred decisions list is your explicit contract with yourself about what you are keeping open on purpose.
Where to look (in order)
GitHub is the first stop, always. Search for your problem, not your product name. Look at the dependency list (what did they choose not to build?), the issue tracker (what is broken, hard, or frequently requested?), and the repo structure (how did they organize the codebase?).
Your competitors' public signals. Even fully closed-source companies leave traces. Engineering blogs explain the decisions they are proud of. Job postings describe the problems they are hiring to solve. API documentation reveals the data model. Open-source SDKs are on GitHub. None of this requires code-reading skills.
Engineering writeups and docs — primary sources. The best companies publish the "why" behind their architecture. Notion published the data model behind their flexibility. Figma's co-founder published deep dives on WebAssembly performance and multiplayer collaboration. These posts are authoritative because they describe constraints and tradeoffs, not abstractions.
AI analysis tools. Reading a large GitHub repo to understand its architecture takes days without domain familiarity. Tools like HowWorks compress this to minutes — analyzing repos and surfacing architecture layers, tech stack decisions, and capability maps without requiring you to read a line of code. If you want the broader landscape of discovery channels before narrowing into repo analysis, see Best Tools for Discovering AI Projects.
How to find reference implementations on GitHub (copy/paste queries)
GitHub's repository search supports qualifiers: in:name, in:description, in:readme, stars:>N, language:LANGUAGE, pushed:DATE, and more.
Queries that work across most product categories:
notion clone stars:>500 in:name,description
collaborative editor stars:>500 in:name,description
"block editor" stars:>500 in:readme
CRDT editor stars:>200 in:readme
open source scheduling stars:>500 in:readme
appointment booking stars:>500 in:name,description
To filter out abandoned repos, add a recent activity qualifier:
collaborative editor stars:>500 pushed:>2025-01-01 in:name,description
To include forks (sometimes the best-maintained version is a fork):
notion clone stars:>200 fork:true in:name,description
To target a specific language:
collaborative editor stars:>200 language:TypeScript in:readme
How to evaluate a repo in 10 minutes (without reading the code)
- README: what does it claim to do? What is explicitly not supported?
- Screenshots or live demo: does it match your use case closely enough?
- Dependencies (package.json, requirements.txt, go.mod): what did they build vs outsource? Auth, database, editor, real-time — did they build these or use a library?
- Architecture docs: any ADRs, diagrams, or "how it works" writeup?
- Issue tracker: what is hard, broken, slow, or frequently asked? This is where the real engineering cost shows up.
- Last commit: is the project actively maintained or abandoned?
- License: are you allowed to build on it commercially?
If you only read one part of the repo, read the issues. The open issues on any non-trivial project are a compressed record of its hardest problems.
What the research gives you
Once you have 3–5 reference repos and a one-page thesis, you have options you did not have before:
Vibe code with real constraints: Your prompts reference specific open-source libraries to build on, specific stack choices, specific things to leave out of v1. The output is materially better.
Hire with a brief: You can walk into an engineering interview with a specific brief. "We are building on top of Tiptap for the editor, Supabase for the database, and the main open question is how to handle concurrent edits at the object level." That is a real conversation.
Narrow scope confidently: You know the hardest problem now. You can scope v1 to avoid it, or face it deliberately, rather than discovering it six months in when the cost of pivoting is high.
If you want to accelerate the research phase, create a HowWorks research topic for your idea and analyze your top reference repos. It generates an architecture breakdown you can use as your starting point for the one-pager.
Related reading on HowWorks
Sources
- GitHub Docs: Searching for repositories (qualifiers: in:name, in:description, stars, pushed, language, fork): GitHub Docs
- GitHub Docs: Understanding the search syntax (operators, ranges, quotes, NOT): GitHub Docs
- Notion engineering: The data model behind Notion's flexibility: Notion blog
- Figma engineering: WebAssembly cut Figma's load time by 3x: Figma blog
- Figma engineering: How Figma's multiplayer technology works: Figma blog