I have been watching Traversy Media since the day I decided to learn web development. Brad's tutorials were part of the reason I stuck with it long enough for things to start clicking. So when he released a full course on building a production SaaS app with Claude Code, I bought it the same day. It did not disappoint - it is dense, it is honest, and it is the single best framing I have found for turning AI-assisted coding from a chat habit into an actual engineering workflow.
Coding With AI: Planning To Production is a long-form course by Brad Traversy on building a real SaaS app end-to-end with Claude Code as the main coding partner. It is split into fifteen sections - from 'what is AI' and vibe coding for prototyping, through planning and context files, all the way to a deployed Next.js app with auth, a Postgres database, file storage, Stripe billing, and OpenAI-powered features. The main project is DevStash, a developer knowledge dashboard with collections, items, search, favorites, a Monaco code editor, a markdown editor, Cloudflare R2 uploads, and a paid tier. It is not a toy app. By the end you have something you could ship.
I already use Claude Code daily - this portfolio was partly built with it, and I had just finished Anthropic's own Claude Code in Action course. What I wanted from Brad was the other half of that picture: not just the tool, but the *practice*. How do you actually plan a real app so that Claude stays on the rails? Where do context files live, what goes in them, when do you update them, and how do you structure a multi-week build so the AI does not drift into a mess? Brad's courses have a reputation for being practical to a fault, and this one delivered on exactly that angle.
I have been watching Traversy Media tutorials since I started learning web development. Brad's format has not really changed, and that is a compliment. He types every line, explains *why* before *what*, and never hides behind finished code. When something breaks during a lesson, he leaves the mistake in and debugs it on camera - which is worth more than any polished take. His pacing is unhurried without being slow, the audio is clean, the screen is readable, and the project scope is always something a single developer can actually ship. That combination is rare, and it is why I trust his courses more than most.
DevStash is a dashboard for developers to stash snippets, notes, links, and files organized into collections. It has auth with GitHub and credentials, email verification via Resend, rate limiting via Upstash Redis, a Monaco code editor for snippets, a markdown editor for notes, file and image upload to Cloudflare R2, a command palette for search, pagination, favorites, pinned items, and a paid tier gated by Stripe. On top of that, the paid tier unlocks AI features powered by OpenAI - auto-tagging, AI-generated descriptions, an explain-this-snippet feature, and a prompt optimizer. Building it is the vehicle - but every single piece is something you will reach for again on the next project.
The course opens with a section called 'Vibe Coding For Prototyping' and then immediately follows it with a 'Reality Check - Prototype vs Production' lesson. That framing is the whole thesis of the course: AI is incredible for producing something that *works* in an afternoon, and terrible for producing something you can maintain six months later unless you steer it. Vibe coding is a legitimate tool - for exploring ideas, for sketching UI, for answering 'would this feel right.' But when the prototype is validated, you throw it away and plan the real thing. Brad is explicit about this, and it took the guilt out of throwing away working-but-messy code.
This was the section I expected to be filler and turned out to be the most valuable. Brad's approach is to spend the first hour of any new project *not* coding. You bootstrap the files manually - README, CLAUDE.md, a context/ folder with project-overview.md, coding-standards.md, ai-interaction.md, and a current-feature.md that tracks what you are working on *right now*. Claude reads all of these on every session, which means the model does not need to rediscover your conventions each time. It is a pattern I had been doing half-heartedly; after this course I formalized it across every repo I touch - including this one. If you look at the CLAUDE.md on my portfolio, it is directly shaped by this section.
Brad covers plan mode, slash commands, config and settings, and the memory file - but unlike a pure tool tour, every concept is introduced at the moment you actually need it. Plan mode shows up the first time a change spans more than one file. Slash commands appear when you notice yourself pasting the same prompt twice. The memory file (#) gets introduced when you catch Claude making the same mistake across sessions. That just-in-time introduction is why the lessons stick. I stopped thinking of these as features to remember and started thinking of them as tools I reach for when a specific friction appears.
The database section is where the course leaves tutorial-land. Brad uses Neon for managed Postgres, Prisma 7 as the ORM, writes a seed script, builds a proper migration workflow with a clear separation between dev and prod, and deploys to Vercel with preview branches hitting a preview database. The initial deploy and prod migration lesson alone is worth the price - it is the thing most tutorials skip, and it is the thing that bites you in week two of a real project. I now treat the migration workflow as something to set up on day one, not day thirty.
This section is where Claude Code stops feeling like a chat tool and starts feeling like a configurable system. Brad walks through custom slash commands (I had played with these), then skills (I had not), then subagents (totally new to me). The flow he teaches is: notice a repetitive task → write a skill or slash command that captures it → add it to the repo so future-you and teammates inherit it. The feature skill he builds - a single /feature command that spans spec, branch, implement, test, commit - is essentially the workflow I now use. The code scanner subagent was eye-opening too: a specialized agent with a narrow prompt and limited tools, spawned on demand. I added one to my own workflow the same day.
Three MCP servers, three entirely different use cases. Neon MCP lets Claude talk to your database directly - introspect schema, run queries, create branches. Context7 fetches up-to-date library docs at query time, which is the single biggest fix for the 'LLM training data is from last year' problem I have. Playwright MCP gives Claude a real browser for end-to-end testing - and, as I am using right now to read the course curriculum for this post, for general web automation. The course shows all three wired into the project, not just demoed in isolation.
The authentication section is a full mini-course on its own. NextAuth in three phases, GitHub OAuth with separate dev and prod keys, credentials auth, email verification via Resend with a verification flag on the user, forgot-password flow, a user profile page, an Auth Code Audit subagent that reviews auth-related files, Upstash Redis for rate limiting, and a bugfix lesson for a real GitHub redirect issue. No hand-waving, no 'we will skip auth for this demo' - you ship with real security primitives in place.
Monaco editor integration (the same editor that powers VS Code), a markdown editor with preview, and Cloudflare R2 for file and image uploads. The R2 setup is a single lesson but it covers bucket creation, credentials, presigned URLs, and a working upload flow - again, the full pipeline, not a fragment. The image gallery and file display lessons tie it all together and cap it with a code audit using a subagent. I had been meaning to try R2 for months; this was the push.
This is the polish section - the stuff that makes a dashboard feel like a real product instead of a CRUD demo. Cmd/Ctrl+K search palette, collection pages, pagination for items and collections, an editor settings panel, a favorites page with toggle and sort, and pinned items. These are small features individually but the lesson on each is tight, and by the end the app feels like something you would actually use. Brad also pulls account actions into a settings page instead of stuffing everything into the header - a small UX call, but one I have seen him make consistently across courses.
Stripe is the section I was dreading and turned out to be the smoothest. Sandbox setup, a written integration plan, two implementation phases, webhook handling with the Stripe CLI for local testing, feature-gate testing, and a clean separation between test-mode and production keys. The Upgrade page ties the subscription state back to the UI. This section alone would sell the course - it is the part every indie SaaS tutorial skips, and it is the part you cannot afford to get wrong.
The final build section adds OpenAI-powered features to DevStash itself, which is a pleasing kind of recursion. Auto-tagging on new items, AI-generated descriptions, an 'explain this snippet' feature, and a prompt optimizer that rewrites a user's prompt into something sharper. The implementation pattern is the same each time - write a server action, call the OpenAI API, stream the result back - and after three of them you internalize the shape of it. The language dropdown lesson earlier in the section is a small thing that matters: every AI feature respects a selected language, which is a nice detail most tutorials skip.
The final section is what a healthy AI workflow looks like: a quick UI review, a refactor scanner subagent pass, connecting a custom domain, wiring the domain into Resend for email deliverability, and a wrap-up. The refactor scanner subagent is the perfect ending - instead of 'we are done, ship it,' the course hands you a tool for keeping the codebase clean as it grows. That mindset - AI is not just for writing code, it is for *maintaining* it - is the one I am taking forward.
Before this course my AI workflow was reactive: open Claude, describe the task, see what happens, correct course as I go. After the course it is deliberate. I plan first. I write a feature spec before I prompt. I keep current-feature.md updated as a handshake between me and the model. I split context into topical files instead of one giant CLAUDE.md. I have a /feature skill that owns the whole loop from spec to commit. I use subagents for narrow, repeated tasks - code scans, security review, UI review. And I trust the tool to do more of the execution because the inputs it receives are now structured. That is the shift the course caused.
This is the best AI-assisted development course I have taken. It is long - not filler-long, build-a-real-thing long - and by the end you have a deployed, billed, secured, AI-enhanced SaaS app and, more importantly, a workflow you can reuse on the next one. Brad's teaching is the same steady, honest delivery it has been for a decade, and pairing that with Claude Code, MCP, Prisma, Neon, Stripe, Resend, R2, and OpenAI makes it the most up-to-date full-stack course I know of. If you are a developer who uses AI every day but suspects you are getting a fraction of the value out of it, this is the course that closes that gap.
