How I Shipped an LMS MVP in 5 Days (What Actually Worked)
How I Shipped an LMS MVP in 5 Days
I built a working learning management system (LMS) MVP in 5 days, using AI-driven development, and handed it over on day 6. The startup that asked for it needed something real enough to test its idea with users, not a slide deck. This post covers the plan, the stack, what I cut and what I would do again.
If you are a founder deciding how to get a first version built, the short answer is this: pick the one question your MVP must answer, build only what answers it, and keep every decision reviewable by a human.
What Did the Startup Actually Need?
The startup wanted to validate a marketplace idea. Institutes and individual teachers would register, upload courses, and sell them either on a subscription or as standalone courses. Learners would watch the videos and find courses through a recommendation bot.
That is a large product. A full version has payments, subscriptions, admin tooling, analytics and much more. The founder did not need any of that yet. They needed to answer one question: will teachers upload real courses, and will people want to watch them on this platform?
Everything in the 5 days was measured against that question. This is the same "WHY before WHAT before HOW" thinking I wrote about in Developers Are Great at HOW. But What About WHY and WHAT?.
What Did I Build in 5 Days?
The MVP covered the parts that make the marketplace real:
- Institute and teacher registration, so both kinds of seller could join.
- Course upload, including video, for institutes and individual teachers.
- Video processing and streaming, so uploaded lessons play smoothly in the browser.
- Course listing and discovery, including a small bot that recommends courses to learners.
- Selling model in the data, with subscription-based and standalone courses represented, so the founder could see how the marketplace would look.
That is a whole platform end to end, but a thin one. Every feature does the minimum needed to be used by a real person.
What Did I Leave Out on Purpose?
I did not build the payment gateway, the subscription module or the full set of administration rules.
This is the part founders find hardest to accept, because payments and subscriptions feel like the "real" product. But they are expensive to build and they do not tell you whether the idea works. If teachers never upload courses, a perfect billing system is wasted money.
A useful rule: if a feature does not help you answer your validation question, it belongs after validation. Cut it now, and write it down so it is not forgotten.
This trade-off is the same one I describe in What a 5-Day AI MVP Actually Looks Like: scope is your strongest tool, and what you leave out matters as much as what you build.
What Did Each Day Look Like?
Day 1: Document the features and scaffold the services
I started by writing the features down in plain language and agreeing on what was in and out. Then I scaffolded the services: the Next.js front end, the Node.js and TypeScript back end and the PostgreSQL database. A clear written scope on day 1 is what stops the week from drifting.
Day 2: Core video upload and authentication
The video upload pipeline is the riskiest part of an LMS, so I built it first. Auth came alongside it. Videos are processed with FFmpeg and stored as chunks in S3 on AWS, which allows streaming without downloading the whole file.
Day 3: Features around the video
With upload and playback working, I built the features that make a video useful: course structure, listing, teacher and institute profiles, and the course recommendation bot running on Gemma 4.
Day 4: Front-end design and branding
Only on day 4 did I focus on how it looked. A validation MVP still has to look credible, because users judge a product in seconds. I applied the startup's branding and cleaned up the main flows.
Day 5: Polish, testing and feedback
I spent day 5 polishing the code, running testing rounds and collecting feedback. Bugs found on day 5 are cheap. The same bugs found by a paying user are not.
Day 6: Handover
I handed the platform over with the code and what they needed to run it. Building took 5 days; handing it over properly took a sixth.
What Stack Did I Use, and Why?
| Layer | Choice | Why |
|---|---|---|
| Front end | Next.js | Fast to build, good routing and a clear path to production |
| Back end | Node.js with TypeScript | One language across the stack; types catch mistakes early |
| Database | PostgreSQL | Relational data suits users, courses, institutes and sales |
| Video | FFmpeg | Reliable video parsing and processing |
| Storage and hosting | AWS with S3 | Video chunks in S3 allow streaming at low cost |
| Recommendations | Gemma 4 bot | A small model is enough to suggest courses from the catalogue |
None of these are exotic. That is deliberate. A boring stack means I spend the week on the product and not on the tools. If you are unsure how to pick one, the rule I follow is: choose what one engineer can build with fastest and what can grow into the full product.
How Did AI-Driven Development Change the Week?
The whole MVP was built with AI-driven development. AI tools wrote first drafts of scaffolding, services, screens and tests far faster than I could type them. That is the reason a platform this size fit into 5 days.
But AI did not decide what to build. I set the scope, chose the architecture, wrote clear requirements for each piece and reviewed the output. AI shortens the time it takes to build. It does not shorten the time it takes to think. A fast build of the wrong product is still the wrong product.
For teams thinking about where AI helps a product, Why AI Features Fail Without Product Thinking covers the other side of this.
What Happened After the MVP?
The startup used the MVP for prototyping and validation. About two weeks after delivery they had their answer: the idea held up. After several discussions about scope and priorities, full development began, including the parts I had deliberately left out: payments, subscriptions and administration.
That is what an MVP is for. It turned an expensive guess into a decision the founder could make with evidence.
What Would I Do Again?
- Write the validation question first. Every scope decision came back to it.
- Build the riskiest part first. Video upload and streaming on day 2 meant no nasty surprise on day 5.
- Cut whole modules, not corners. Leaving out payments entirely is safer than building a rushed version of them.
- Put design late but not last. Day 4 was enough for credibility without slowing the core work.
- Keep a human in charge of the decisions. AI wrote a lot of the code. I owned the scope, the structure and the review.
If you have an idea and want to know what a first version could look like, see how I work on Build Your Product.
Frequently Asked Questions
Can you really build an LMS MVP in 5 days? Yes, if the scope is a validation prototype and not a finished product. In 5 days I built course and institute upload, teacher registration, video upload and streaming, and a course recommendation bot. Payments, subscriptions and the full admin rules were left out on purpose, because they do not help a founder learn whether the idea works.
What did you leave out of the MVP, and why? The payment gateway, the subscription module and the full set of administration rules. Each of these takes real time and none of them answers the core question: will teachers upload courses and will learners watch them? Those parts were built after the startup validated the MVP.
How does AI-driven development speed up an MVP? AI tools produce first drafts of scaffolding, services, UI and tests far faster than typing them by hand. The speed-up only works when a senior engineer decides the architecture, writes clear requirements, and reviews every result. AI shortens the build time, not the thinking time.
What happens after the 5-day MVP is delivered? The founder puts it in front of real users, collects feedback and decides whether to continue, change direction or stop. In this project the startup validated the idea about two weeks after delivery, and full development started after several discussions about scope.
What tech stack works for a video-based MVP? For this LMS I used Next.js on the front end, Node.js with TypeScript on the back end, PostgreSQL for data, FFmpeg to process video, and AWS with S3 to store video chunks. The stack is not exotic. It is chosen so one engineer can move quickly and the code can grow into the full product.