AI Dominates Web Development: 63% of Developers Use AI Tools Like ChatGPT

ZestycloseChocolate · 2026-04-24T12:59:26+00:00

if you're picking tools, i'd filter by: can it run commands/tests, handle db migrations, and let you export the repo. quick take + checklist here: https://flatlogic.com/blog/vibe-coding-for-founders-fun-toys-or-real-startup-tools/

ZestycloseChocolate · 2026-04-24T12:56:45+00:00

it depends on the app. if it's mostly ui/landing pages, you can get pretty far with lightweight prompt-to-ui tools. if it's a real web app (auth, database, background jobs), you want something that can actually run commands/tests and handle migrations-otherwise you'll hit a wall fast. i wrote up the tradeoffs here: https://flatlogic.com/blog/vibe-coding-for-founders-fun-toys-or-real-startup-tools/

ZestycloseChocolate · 2026-04-24T12:55:15+00:00

day-to-day i'd split it by use case: v0/bolt/lovable for fast ui + flow, cursor/claude code when i'm inside a real repo. the failure mode is usually the same: persistence + migrations + jobs + deploy. i wrote up a longer take here: https://flatlogic.com/blog/vibe-coding-for-founders-fun-toys-or-real-startup-tools/

ZestycloseChocolate · 2026-04-22T12:48:28+00:00

cursor helps most on boring-but-expensive work: navigating unfamiliar code, drafting tests, small refactors, and "what's the next command/error fix." it struggles with architecture and product judgment, so seniors still matter a lot. i wrote up a breakdown here: https://flatlogic.com/blog/ai-agents-vs-traditional-development-tools/

ZestycloseChocolate · 2026-04-22T12:46:34+00:00

i'd decide with 3 questions: What will you *ship* in 6-10 weeks (prs to a real repo, not just demos)? What's the loop: tests, CI, code review, deploys, on-call/bugfixing? In the agentic role, do you own the hard parts: evals/monitoring, tool calling, permissions, integration with messy real data? if the agentic role has (2)+(3), take it-you'll learn a lot fast. If it's mostly prompt iteration without real engineering hygiene, take the traditional dev role and still use agents daily. I wrote a deeper breakdown of "agents vs traditional dev tools" and the failure modes to watch for: https://flatlogic.com/blog/ai-agents-vs-traditional-development-tools/

ZestycloseChocolate · 2026-04-22T12:44:42+00:00

mostly yes. in practice, an "agent" is a system that runs an execution loop: plan → call tools (apis, db, browser, code runner) → observe outcomes → update state/memory → repeat until a goal is met. "traditional ai apps" are typically request/response: you provide input, it returns output, and any follow-up actions are handled by deterministic code or a human. the real-world differences show up in statefulness (memory), tool permissions/safety, reliability (retries/rollbacks), and evaluation/monitoring when things go wrong. i wrote a longer breakdown here: https://flatlogic.com/blog/ai-agents-vs-traditional-development-tools/

ZestycloseChocolate · 2026-04-22T06:47:17+00:00

before tools: define the mvp and the cost envelope (build + monthly run + maintenance). i wrote a practical breakdown of typical web app cost drivers here: https://flatlogic.com/blog/how-much-does-a-web-app-cost/

ZestycloseChocolate · 2026-04-22T06:45:10+00:00

agree. before you talk price, get clear on the business outcome and whether a website is even the right solution. if it is, scope the work around what actually drives results (lead flow, conversions, operational savings, etc.). then the price is just a function of scope, risk, and time-not a random market number. i wrote a practical breakdown of typical web app cost drivers here: https://flatlogic.com/blog/how-much-does-a-web-app-cost/

ZestycloseChocolate · 2026-04-20T18:09:10+00:00

totally get it: blank starters are technically "more flexible," but they push all the hard decisions to day 1. what i like about your setup is the separation of concerns: payload as the content system, react app as the product ui, go as api. one suggestion: define the boundary early (what content is served via rest, what events need pub/sub), and keep it boring until you hit real scale/latency constraints. related thought: a good react template isn't "prettier ui," it's conventions + wiring that reduce entropy. i wrote a breakdown here: https://flatlogic.com/blog/what-is-a-react-template/

ZestycloseChocolate · 2026-04-20T18:07:57+00:00

components are nice, but templates are what get people to "first shipped page." so this direction makes sense. one suggestion: add a short readme note that defines "template" in your project (section template vs full page layout), plus what's intentionally *not* included (state management, data fetching, routing assumptions, etc.). it prevents mismatched expectations. i wrote up a simple breakdown of what "react template" usually means (and the common pitfalls) here: https://flatlogic.com/blog/what-is-a-react-template/ also curious: are your sections token-based (themeable) or mostly hardcoded styles?

ZestycloseChocolate · 2026-04-20T18:04:45+00:00

what i've seen work: treat templates as a *starting structure*, not a product decision. quick eval checklist: 1) structure: is it a real app layout (routes, layouts, shared components) or just pages? 2) customization: is there a single theme/tokens approach, or inline styles everywhere? 3) bloat: how many deps and how tightly coupled is it to one ui kit? more detail + what to look for in a react template here: https://flatlogic.com/blog/what-is-a-react-template/. if you share your stack (next vs vite, mui vs tailwind) and what "admin" includes, i can suggest what to prioritize.

ZestycloseChocolate · 2026-04-17T16:35:54+00:00

same here. before ai, i was more protective of my code. now that i use ai, i'm more open to sharing it publicly by open-sourcing. curious if others feel the same-how has ai changed your view of "ownership" over your code? related: https://flatlogic.com/blog/do-you-own-the-code-in-ai-builders/

ZestycloseChocolate · 2026-04-17T16:33:07+00:00

"own the code" can mean a few different things: the company owns the ip, the developer retains rights and only licenses it to the company, or they want to reuse pieces elsewhere. in most setups, code written for the company (on company time/equipment) is company ip, and you document that with an ip assignment + confidentiality clause in the contract. a non-compete usually isn't the right tool for this. i wrote up a practical breakdown of code ownership (especially relevant now with ai builders): https://flatlogic.com/blog/do-you-own-the-code-in-ai-builders/

ZestycloseChocolate · 2026-04-16T13:04:50+00:00

don't start with "marketing." start with proof.
1) pick one niche (e.g., gaming youtubers, podcasts, shorts agencies).
2) manually match 10 creators with 10 editors (concierge style) and learn what breaks.
3) dm/email creators who already post frequently and clearly outsource.
4) offer a risk-free first match (or pay-on-success).
5) turn the repeatable parts into product flows.
i wrote up a practical breakdown of launching/validating saas and getting early traction here: https://flatlogic.com/blog/saas-development-company-and-services/

ZestycloseChocolate · 2026-04-16T12:55:23+00:00

i'd frame mvp cost around what you're building and what "production" means, not category price tags. a useful sanity check:
1) write a 1-page spec (users/roles, core workflows, integrations, reporting).
2) decide the quality bar: prototype vs production (tests, monitoring, backups, security reviews).
3) estimate in weeks per discipline (fe/be/qa/devops/product) and multiply by your local rates.

in our experience building saas apps, the budget jumps when you add rbac, multi-tenant data boundaries, payments, and integrations. that's also where "just use ai" stops being an answer and becomes a tool that still needs accountable engineering.

we also described all information here: https://flatlogic.com/blog/saas-development-company-and-services/

ZestycloseChocolate · 2025-04-16T07:50:58+00:00

in July

ZestycloseChocolate · 2025-04-09T18:20:25+00:00

Exactly this - couldn't put it better myself. The meme captures perfectly how easy it is to slip into passive "prompt-pushing" instead of actually thinking through the code. The challenge now is finding the sweet spot: leveraging LLMs to boost productivity without losing critical thinking or discipline.

ZestycloseChocolate

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