How to Build an AI Scheduler Solo: From Zero Budget to MWP

A developer from Habr published a case study about how they single-handedly built an AI scheduler with no budget — from concept to working MVP. The product is now moving toward the next stage: MWP, minimal wowable product.

An Idea That Was Waiting

The author dreamed of a smart chat for planning routine tasks long before large language models became mainstream. The idea was waiting for the right balance of price, speed, and model maturity — and at some point, the author decided not to wait any longer. An attempt to bring a collaborator into the project didn't work out: there was plenty of talk, but only one person doing the work.

Without a budget, but with experience: the author had previously used agent-driven development to create their own programming language, and in their day job helped the entire team implement AI in analysis, testing, and coding. But building an entire product solo is a different scale of challenges. No delegation, no role division.

What Solo with Agents Means

When you're piloting everything yourself, your circle of responsibility becomes unusually wide. The author lists what they had to take on:

• UI, UX, and CUI (conversational user interface) design
• Chatbot architecture and various schemes for working with a language model
• User scenarios — from onboarding to edge cases
• Security: authentication, data storage, protection against misuse
• Testing, coding, analysis — also with agent involvement

Key takeaway: AI agents significantly lower the entry threshold for solo product development. What previously required a team of specialists, one person with properly configured tools can handle today — though not quickly. Tools take on part of the cognitive load, not just routine code.

From MVP to MWP

The author has already reached the MVP stage — minimum viable product. But they believe the truly important goal is different: MWP (minimal wowable product) — minimum delightful product. The difference is fundamental. MVP simply works. MWP — delights. For a scheduler, this means a specific user scenario: a person doesn't think about how to phrase the task. The agent clarifies if something is unclear. If the intention is understood correctly — it captures it in plans without extra rituals, tags, and templates.

"If something is unclear — the agent will clarify it, and if it's

clear — it will catch the intention and record it in the plans," — the author describes the target experience.

Most modern schedulers still require users to think for the tool: come up with categories, priorities, phrasings, deadlines. A scheduler that removes this cognitive friction would be a qualitatively different product.

What This Means

The story is valuable not because of the mere fact of creating yet another scheduler — there really are plenty of them. It's valuable for its specificity: an honest breakdown of what it means to make an agent-driven product solo, without embellishment — including the failed collaboration and zero budget. A detailed technical breakdown of architecture, model interaction schemes, and user scenarios — in the full article on Habr.