{
  "id": 109946,
  "title": "Hello DEV: I Build Practical AI Tools, Not Just AI Demos",
  "url": "https://urgent.news/2026/08/04/hello-dev-i-build-practical-ai-tools-not-just-ai-demos",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-04T01:38:21.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/vastpace/hello-dev-i-build-practical-ai-tools-not-just-ai-demos-4kbc"
  },
  "original_language": "en",
  "account": "I am VastPace, a full-stack developer and independent product builder. In this post, I aim to introduce myself, share what I am currently working on, and discuss the topics I plan to explore. My main area of expertise is building practical web products that incorporate AI, rather than creating simple demos or mere wrappers. My focus is on solutions where AI improves a particular workflow, involving aspects such as data collection and cleaning, reliable prompt design, model selection, handling retries and failures, controlling token costs, building user-friendly interfaces, and evaluating the feature's utility.\n\nWhile the AI model is a crucial component of my products, it is merely one piece of the puzzle. Traditional software engineering practices like databases, queues, authentication, caching, monitoring, deployment, UX, and dealing with various edge cases are equally essential. A demo only needs to function once, whereas a product must perform repeatedly. When crafting AI-powered applications, I frequently encounter questions such as: handling invalid JSON, retrying long-running tasks, deciding whether to use an LLM, reducing API costs without compromising quality, determining deterministic aspects, evaluating answers with no single correct result, and preventing users from abusing costly generation features.\n\nThese issues may not be as engaging as posting a brief AI demo on social media, but they hold significant importance in determining the product's long-term success. In my upcoming posts, I will primarily focus on four key areas:\n\n1. Building AI-powered web applications: I'll share practical strategies for integrating language models into real-world applications, covering prompt design, structured output, streaming, background jobs, retries, caching, and cost management.\n2. Full-stack engineering: As someone who works across the stack, I'll also write about APIs, databases, queues, deployment, authentication, debugging, and the infrastructure supporting small SaaS products.\n3. Product experiments: Being an independent developer, I constantly test ideas. I want to document both successful and unsuccessful experiments, including how I choose an idea, create an MVP, gather feedback, and decide whether to proceed or abandon the project.\n4. Open-source and developer tools: I'm keen on exploring open-source projects and developer tools, especially those related to automation, data collection, AI agents, and developer productivity. When I discover something useful, I'll explain not only its functionality but also its relevance within a real workflow.\n\nMy guiding principle is straightforward: use AI when uncertainty is beneficial and code when consistency is vital. LLMs excel at interpreting messy inputs, generating alternatives, summarizing information, and helping users explore unclear problems. Conversely, traditional code is more suitable for calculations, permissions, billing, validation, and business rules requiring consistent behavior. Relying solely on an LLM to handle everything typically leads to an unreliable and expensive system. The most effective AI products often combine model-driven and deterministic software components, with distinct boundaries between them.\n\nI joined DEV because the online AI landscape is saturated with announcements, model benchmarks, and impressive demonstrations. Instead, I aim to contribute to the discourse by discussing the engineering decisions behind functional products - the challenges faced, the costs incurred, user misunderstandings, impractical ideas, and what ultimately made these products reliable. Please stay tuned for my future posts as I continue learning, building, and refining my perspective. Thank you for reading my first post.",
  "summary": "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…",
  "key_points": [],
  "editors_take": "By focusing on practical applications and engineering decisions behind functional AI products, VastPace aims to contribute a more nuanced and realistic perspective to the AI development discourse.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}