Deepthi's Reading

Systems

030

The roundabout-notes Story

Environmental rounds, dialysis unit

Lovable · Supabase

Edition #030 · 6 chapters · 9 min read

The roundabout-notes Story

Environmental rounds, dialysis unit

Rounding notes and findings captured as structured, filterable data.

An ebook edition of the roundabout-notes build journey: how it started, what actually happened, the conversations that changed the build, what broke, and what I learned. Written in plain English for readers with no technical background. Tools covered: Lovable, Supabase.

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What you'll understand by the end

Not mastery — understanding. By the last page you'll be able to explain these in your own words, even with no technical background.

•what Lovable really is, and what it cannot do
•what Supabase really is, and what it cannot do
•the difference between what the AI does, what the tool does, and what I do
•why permissions and privacy matter once AI can act on your behalf
•where a real person's judgement is still necessary

The concept ladder — the order I actually learned things

I had an idea worth building

↓

I asked an AI to help me shape it

↓

I learned the AI could explain things but could not act outside the chat by itself

↓

I met the tools that actually do the work

↓

I learned how those tools are connected and permitted

↓

I built something real and watched it break

↓

I learned where my own judgement is still required

Chapter 01

Why roundabout-notes had to exist

Track 1 · My journey

The idea started as an irritation rather than an ambition. Rounding notes and findings captured as structured, filterable data. Rounding notes and findings captured as structured, filterable data. The honest test I set myself was simple: if this existed tomorrow, would anyone actually open it twice? Everything in the rest of this book is downstream of that one question. roundabout-notes is what came out the other side.

Learning checkpoint

Before this chapter

I had a problem I could describe in a corridor conversation, but nothing I could hand to anyone.

After this chapter

I still did not know which tools to use, but I knew exactly who I was building for.

What changed my mind

The problem stopped being a complaint and became a specification.

Can you answer these?

?Can you state the problem in one sentence, without naming a solution?
?Do you know who is holding the screen when your product is used?

Chapter 02

The first conversation

Track 1 · My journey

I did not start with code. I started by describing the thing out loud, in ordinary language, the way I would explain it to a colleague. The build tool answered with a working screen, and that changed the conversation from theory to critique — it is much easier to say what is wrong with something in front of you than to specify it from nothing. These were the exchanges that actually moved the build: Me: "Here is the problem in one sentence." → Naming it plainly was what made the first screen obvious. Me: "Show me the smallest version that is still useful." → The scope stopped growing and the build started moving. Me: "Who is holding the phone when this is used?" → That question decided the layout more than any design rule did. Each of those lines looks small. Together they are the design document. I have stopped writing long specifications; the transcript is the specification.

Learning checkpoint

Before this chapter

A blank project and a chat window.

After this chapter

The shape of the product was decided before a single deliberate design decision was made.

What changed my mind

I learned to treat conversation as a design tool, not as preparation for design.

Can you answer these?

?What is the one sentence in your own notes that decided the architecture?
?Are you describing outcomes, or dictating implementation?

Chapter 03

Building it: Lovable, Supabase

Track 1 · My journey

The build ran in short loops: describe, look, react. I would ask for one screen, use it as though I were the person it was meant for, and then correct exactly the thing that annoyed me. The stack was Lovable, Supabase. I did not choose it for elegance; I chose it because it let me get a real, usable thing in front of a real person quickly, and because I could change my mind cheaply when the first idea turned out to be wrong. The rhythm mattered more than the tooling. Ship something small, use it honestly, fix the one thing that hurt most, repeat. Most of the good decisions in roundabout-notes came from that loop rather than from planning.

Track 2 · Learn with Deepthi

Lovable

USED — evidence shows I used it

What did I just discover?: That Lovable was doing more of the work in this build than I gave it credit for.
What I thought it meant: I assumed it was a technical detail I could pick up later.
What I learned: It shaped what was easy and what was expensive, and therefore what the product became.
What is it, really?: Lovable is a web tool where you describe an app in plain language and it writes and runs the real code for you, in a browser.
Think of it like this (analogy): A workshop tool: it does not decide what you make, but it strongly influences what you find yourself making.
How does it actually work?: You write a request. An AI model reads your project files, decides which files to change, writes the change, and the preview reloads with the new version.
A little deeper (optional): Under the hood it is a normal codebase (React front end, a database, server functions). The AI is an editor of that codebase; nothing magical is added at runtime.
Why does this exist?: Building a working web app normally needs setup, hosting and code knowledge. Lovable exists to remove that setup so the idea can be tested first.
Why did it matter to my project?: Because the constraints of your tools quietly become the constraints of your product.
Where I used it: Used throughout roundabout-notes, especially while getting the first working screens in front of people.
Common beginner misunderstanding: Beginners think the AI "understands the whole app perfectly". It only sees the context it is given, which is why clear, specific instructions change the quality of the result.
What this tool cannot do: It cannot know what you did not tell it, it cannot judge whether your product idea is good, and it can confidently make a wrong change that still builds.
What surprised me: How much of the design pressure came from the tool rather than from the problem.

What happens behind the scenes

What the AI is doing

Reads the request and the existing code, then proposes and writes specific file edits.

What the tool is doing

Stores the files, runs the build, serves the live preview and connects the database.

What I am doing

Decides what the product should do, judges whether the result is right, and corrects it when it is not.

What data is moving where?

1.Me: I describe the change
2.AI: reads project files and writes edits
3.Tool: builds and deploys the preview
4.Me: I look at the result and correct it

What is happening right now?

Deepthi writes an instruction

AI model reads the project files

AI writes file changes

Build system rebuilds the app

Preview reloads

Deepthi reviews and corrects

One thing to remember

Remember this

Choose tools by what they make cheap to change, not by what they make possible.

Try it yourself

Rebuild one screen of your own project with Lovable and time it honestly.

Accuracy note: product behaviour checked on 2026-08-15. General concepts are stable; product-specific behaviour can change.

Track 2 · Learn with Deepthi

Supabase

USED — evidence shows I used it

What did I just discover?: That Supabase was doing more of the work in this build than I gave it credit for.
What I thought it meant: I assumed it was a technical detail I could pick up later.
What I learned: It shaped what was easy and what was expensive, and therefore what the product became.
What is it, really?: Supabase is the backend of the app: the database where information is stored, plus sign-in and file storage.
Think of it like this (analogy): A workshop tool: it does not decide what you make, but it strongly influences what you find yourself making.
How does it actually work?: The app asks the database for rows. Before answering, the database checks a rule that says whether this particular user is allowed to see them.
A little deeper (optional): Those rules are Row Level Security policies evaluated inside Postgres per query, using the signed-in user's identity from their token.
Why does this exist?: Every real app needs somewhere safe to keep data and a way to know who the user is. Supabase exists so you do not have to build that from scratch.
Why did it matter to my project?: Because the constraints of your tools quietly become the constraints of your product.
Where I used it: Used throughout roundabout-notes, especially while getting the first working screens in front of people.
Common beginner misunderstanding: People think "the app checks permissions". The safest check happens in the database itself, not in the page.
What this tool cannot do: It will not guess your security rules. A table with no policy simply refuses everyone, and a careless policy exposes everyone.
What surprised me: How much of the design pressure came from the tool rather than from the problem.

What happens behind the scenes

What the AI is doing

Writes the queries and the access rules.

What the tool is doing

Stores the data, verifies the user's identity and enforces the rules on every request.

What I am doing

Decides what is public, what is private and who counts as an admin.

What data is moving where?

1.Me → app: I open a page
2.App → database: request with my identity
3.Database: checks the access rule
4.Database → app → me: only rows I am allowed to see

What is happening right now?

Reader opens a page

App sends a request with the reader's token

Database checks the security policy

Allowed rows are returned

Page renders

One thing to remember

Remember this

Choose tools by what they make cheap to change, not by what they make possible.

Try it yourself

Rebuild one screen of your own project with Supabase and time it honestly.

Accuracy note: product behaviour checked on 2026-08-15. General concepts are stable; product-specific behaviour can change.

Learning checkpoint

Before this chapter

I had screens in my head and a stack I had not yet justified.

After this chapter

A working product, and a much shorter list of things I believed without evidence.

What changed my mind

The stack became a means, not an identity.

Can you answer these?

?Could you rebuild your first screen in an afternoon if you had to?
?Which tool are you using out of habit rather than fit?

Chapter 04

What broke

Track 1 · My journey

The first version asked too much of the person using it. It was complete, and it was ignored — which is the same as being broken. Breakage is the most useful part of the process because it is unambiguous. A feature nobody uses is a feature that failed, no matter how well it is built. So I cut, simplified, and made the default path shorter until the product stopped resisting the person using it. I keep this chapter in every book deliberately. The polished version of a build story is not useful to anyone; the failure is where the transferable lesson lives.

Learning checkpoint

Before this chapter

Something that worked in the demo and failed in real life.

After this chapter

A smaller, blunter, considerably more useful product.

What changed my mind

I stopped defending the first version and started measuring it.

Can you answer these?

?What in your build is complete but unused?
?What would you cut if you had to halve the time it takes to use?

Chapter 05

What actually changed

Track 1 · My journey

The point of roundabout-notes was never the software. It was the change in behaviour around it: fewer things forgotten, less guessing, a clearer handover between the people involved. Environmental rounds, dialysis unit What I watch for now is whether the thing gets opened a second time, without being asked. Second use is the only honest metric I have found at this scale. Everything else is a story I tell myself. The status of this build: live. That is stated plainly on purpose — unfinished work teaches as much as shipped work, and pretending otherwise makes these books useless.

Learning checkpoint

Before this chapter

A product that worked, and no clear evidence that it mattered.

After this chapter

A clear-eyed view of what the product does and does not fix.

What changed my mind

Success stopped meaning "it is built" and started meaning "it is used".

Can you answer these?

?What behaviour changed because your product exists?
?Would anyone notice if it disappeared tomorrow?

Chapter 06

What I would tell you before you start

Track 1 · My journey

Build for the worst day, not the demo. The version that gets used beats the version that is complete. Three things I would repeat on the next build: 1. Describe the problem before naming a tool. The tool is the last decision, not the first. 2. Get something usable in front of a real person within a day. Opinions about a screenshot are worth more than opinions about a plan. 3. Keep a record of the conversation. The transcript is the design history, the documentation and — as this book proves — the product. If you take one thing from roundabout-notes: the difference between an idea and a product is a short loop, run honestly, many times.

Learning checkpoint

Before this chapter

You have read what happened. Here is what to take with you.

After this chapter

A method you can reuse on a completely different problem.

What changed my mind

The build became repeatable rather than lucky.

Can you answer these?

?What is your version of the short loop?
?What will you ship this week, even if it is embarrassing?

Appendix

Deepthi's Dictionary

Short loop: Describe, build, use, correct — repeated in hours rather than weeks. The core working method behind this build.
Second use: Whether someone opens the product a second time without being asked. The simplest honest measure of whether it works.
Conversation beat: A single line in a build conversation that changed a decision. Collected, these form the real design document.
Worst day design: Designing for the tired, rushed, distracted user rather than the ideal one in a demo.
Lovable: Lovable — part of the stack used to build roundabout-notes.
Supabase: Supabase — part of the stack used to build roundabout-notes.