Over the past few years, I have built web applications, experimented with AI models, written more prompts than I would like to admit, and learned one important lesson:
Building an AI demo is easy. Building an AI product people actually use is much harder.
Hi DEV Community. I’m VastPace, a full-stack developer and independent product builder.
This is my first post here, so I want to briefly introduce myself, explain what I’m working on, and share the topics I plan to write about.
What I Build
My main focus is building practical web products with AI.
Not another chatbot wrapper.
Not a landing page connected to an API.
I’m interested in products where AI solves a specific problem inside a complete workflow.
That usually includes:
- collecting and cleaning data
- designing reliable prompts
- choosing the right model for the task
- handling retries and failures
- controlling token costs
- building a usable frontend
- measuring whether the feature is actually useful
The AI model is only one part of the product.
The rest is still traditional software engineering: databases, queues, authentication, caching, monitoring, deployment, user experience, and a surprising number of edge cases.
The Gap Between a Demo and a Product
A demo only needs to work once.
A product needs to work repeatedly.
When building AI-powered applications, I often run into questions such as:
- What happens when the model returns invalid JSON?
- How should long-running tasks be retried?
- Should this feature use an LLM at all?
- How can I reduce API costs without reducing quality?
- Which parts should be deterministic?
- How do I evaluate an answer that has no single correct result?
- How do I stop users from abusing an expensive generation feature?
These problems are less exciting than posting a ten-second AI demo on social media, but they are the problems that determine whether a product survives.
I want to write more about this less glamorous part of AI development.
What I’ll Share Here
My future posts will mainly cover four areas.
1. Building AI-Powered Web Applications
I’ll share practical patterns for integrating language models into real applications, including prompt design, structured output, streaming, background jobs, retries, caching, and cost control.
2. Full-Stack Engineering
I work across the stack, so I’ll also write about APIs, databases, queues, deployment, authentication, debugging, and the infrastructure behind small SaaS products.
3. Product Experiments
As an independent developer, I’m constantly testing ideas.
Some experiments work. Many do not.
I want to document both sides: how I choose an idea, build an MVP, collect feedback, and decide whether to continue or stop.
4. Open Source and Developer Tools
I enjoy exploring open-source projects and developer tools, especially tools related to automation, data collection, AI agents, and developer productivity.
When I find something useful, I’ll try to explain not only what it does, but where it fits in a real workflow.
My Current Principle
The principle guiding most of my work is simple:
Use AI where uncertainty is useful. Use code where consistency is required.
LLMs are good at interpreting messy input, generating alternatives, summarizing information, and helping users explore unclear problems.
Traditional code is better for calculations, permissions, billing, validation, and business rules that must behave consistently.
Trying to make an LLM handle everything usually creates an unreliable and expensive system.
The best AI products are often hybrid systems: part model, part deterministic software, with clear boundaries between them.
Why I Joined DEV
There is already an overwhelming amount of AI content online.
Much of it focuses on announcements, model benchmarks, and impressive demos.
I’m more interested in the engineering decisions behind working products:
- what broke
- what cost too much
- what users misunderstood
- what looked useful but was not
- what finally made the product reliable
That is what I hope to contribute here.
I’m still learning, building, and changing my mind regularly. I’ll share what works, what fails, and what I would do differently next time.
Thanks for reading my first post.
See you in the next one.
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