AI-Assisted Development in 2026: Tools, Models and Honest Expectations

A developer is launching a 14-part series demonstrating AI-assisted development by building a clinic booking-and-queue system with Blazor Server, PostgreSQL, and SignalR, using Anthropic's Claude Code. The series will show every prompt, keep mistakes, and count costs, emphasizing a review-driven loop where the developer reads, runs, and tests AI-generated code. The first part outlines the tool landscape, model choices (Claude Opus 5, Sonnet 5, Haiku 4.5), and pricing as of August 2026.

Over the next fourteen parts we're going to build one real application — a clinic booking-and-queue system with Blazor Server, PostgreSQL and SignalR — and an AI is going to write most of the code. We'll show every prompt, keep every mistake in, and count every dollar. This first part is the map: the tools, the models, and what you can honestly expect before we type a single prompt. If you've never used AI tooling, here's the landscape in one paragraph. The first way is chat : you open a chat window, paste some code, ask a question, and copy the answer back into your editor. It works — plenty of developers learned a lot this way — but notice who's doing the manual labour. The model only knows what you paste, it can't run anything, and you are the clipboard, ferrying code back and forth and hoping you pasted enough context. The second way is agentic : a tool that lives in your project folder, reads your files itself, proposes edits as reviewable diffs, runs your build and your tests, and asks permission before doing anything. You stop being the clipboard and become the reviewer — which, as we'll see, is the job that actually matters. This series uses Claude Code , Anthropic's agentic tool. It comes in whichever shape suits you: a command-line tool, a desktop app for Mac and Windows, a web version at claude.ai/code, and IDE extensions for VS Code and JetBrains. Everything in this series works the same in all of them; we'll set it up properly in Part 2 https://www.coder000.com/post/claude-code-setup-for-dotnet . Before tools and prices, the thing that actually decides whether AI development goes well. Everything we build in this series follows one loop: diagram on the original post — the AI development loop: you write a prompt, the AI writes code, you review and test it; problems loop back into a refined prompt, good code gets committed. You describe what you want and why. The AI writes code you didn't type. Then comes the step that separates professionals from headlines: you read it, run it, and test it . Code that isn't right doesn't get argued with — it feeds a sharper, smaller prompt. Code that is right gets committed in a small, reviewable piece. That's the whole method. Every part of this series is this loop, applied to a different problem, and the review step is the one we never skip. "AI" isn't one thing you talk to; you choose a model per task, and the choice is mostly about money and thinking depth. Models are priced per token — roughly three-quarters of a word — and you pay separately for what the model reads input and what it writes output . Here's the current Claude line-up: Claude models, August 2026 — prices per million tokens input / output | Model | Price in / out | What it's for | |---|---|---| | Claude Opus 5 | $5 / $25 | The deep-work default: hard design, debugging, code review | | Claude Sonnet 5 | $3 / $15 | The everyday coding workhorse | | Claude Haiku 4.5 | $1 / $5 | Quick questions and mechanical edits | Each model also takes an effort setting, from low to max, which controls how hard it thinks before answering. High effort on a hard problem buys real quality; high effort on "rename this variable" buys you a more expensive rename. Our rule of thumb, which the whole series follows: The rule of thumb this series lives by | The task | The pick | Because | |---|---|---| | Writing a spec, designing a schema, reviewing code | Opus, high effort | Mistakes here compound for months; thinking is cheap by comparison | | CRUD features, scaffolding, tests | Sonnet | Well-trodden ground; the workhorse handles it | | "Rename this everywhere", formatting, tiny edits | Haiku | Mechanical work deserves mechanical prices | 📝 Note: everything above is true as of August 2026 . Models and prices churn faster than any other fact in this series — this is the one section you should re-check against current pricing before trusting the numbers. The method — match the model to the stakes of the task — outlives any price list. 🧠 Model pick: every part of this series opens its toolbox in a box like this one — which model, what effort, and why. This part is all reading, so nothing was billed to the build yet; the standing picks are the table above, and you'll see us follow it: Opus for the spec and schema in Parts 3–4, Sonnet for the scaffolding in Part 5. Here's the part most AI content skips. These tools are genuinely, sometimes shockingly good at a specific set of things: And there's a set of things it reliably gets wrong, no matter how impressive it looks while doing them: 🤖 What the AI got wrong: nothing yet — we haven't asked it for anything. But this box appears in every part, because keeping the mistakes in is the point of this series. Coming attractions, all real: an SMS feature nobody asked for Part 3 , timestamps without time zones Part 4 , a chat design that fails authentication forever Part 8 , and a test that passes because the code is wrong Part 9 . The human stays the pilot. That's not a disclaimer; it's the method. A real app needs a real problem, so meet ClinicLive : front-desk software for a small clinic. Patients book a 15-minute appointment from their phone — no account, no password, just a short confirmation code. A tablet at the door lets them check in with that code. A waiting-room screen updates by itself the second someone checks in. And behind the login, reception and practitioners see today's schedule, call the next patient, and chat with each other — with presence and typing indicators — instead of shouting down the corridor. diagram on the original post — ClinicLive architecture: the booking page, check-in kiosk and waiting-room board talk to the Blazor Server app; the QueueHub pushes live updates out; EF Core persists everything to PostgreSQL. That little diagram is the whole build: three browser surfaces, one Blazor Server app, one PostgreSQL database, and SignalR pushing changes out so nobody ever presses refresh. It's small enough to finish and real enough to hurt — bookings can race, queues can be ordered wrong, and time zones can lie. Perfect. The finished code lives in the companion repository at github.com/rahulvyas777/clinic-live https://github.com/rahulvyas777/clinic-live , and it has one unusual property: every commit message contains the actual prompt that produced the commit . The repo's history is the series. From Part 3 onward there's a tag per part, so you can check out the code exactly as it stood at the end of any chapter. Alternatives absolutely exist — GitHub Copilot, Cursor, Google's Gemini CLI, and more arriving monthly — and they're good tools; the loop in the figure above applies to all of them. We use Claude Code for this series for an unexciting reason: going deep on one tool teaches you more than a survey of five, and depth is what you can carry to whichever tool your team ends up using. If your instinct is to comparison-shop first, our beginner's guide to AI-assisted coding https://www.coder000.com/post/what-is-ai-assisted-coding surveys the field; this series is the deep end. 💸 The meter: our build so far: $0.00 . Every part reports the running total, honestly. And a spoiler worth having up front: the entire fourteen-part build — spec, schema, features, real-time, tests, debugging, hardening and deployment — lands at about six dollars of model usage. Hold that number next to any hype you've heard, in either direction. ✅ Checkpoint: before Part 2 you should be able to say what separates chat from agentic tools, name which Claude model you'd pick for a schema design versus a mechanical rename, and sketch ClinicLive's three surfaces from memory. No installs yet — but having an Anthropic account ready a paid Claude plan or API billing, either works will make the next part a straight run. The map is drawn. In Part 2 https://www.coder000.com/post/claude-code-setup-for-dotnet , Claude Code goes onto your machine, learns your project's house rules from a file called CLAUDE.md, and makes its first change to real code — reviewed, understood, and committed like you mean it. Originally published at coder000.com https://www.coder000.com/post/ai-assisted-development-with-claude . Part of the series From Prompt to Production https://www.coder000.com/series/from-prompt-to-production — read the whole series there.