39 posts tagged with "Guide"

A good Scrimba Explain prompt names the topic, the audience, and the slides you want. I ran the same RAG prompt through three teaching styles, attached a Markdown file, and built a three-lesson playlist, seven explainers in total. Professor was not noticeably deeper than Standard, which suggests the prompt text, not the picker, sets the depth.

If you do not know what Explain is yet, the reference page covers entry points and quotas, and the full review covers whether the output is any good. This post is only about the web composer at scrimba.com/explain↗ (opens in a new tab): what to type, which style to pick, and which clicks cost you an explainer.

Scrimba Explain exposes two MCP endpoints. Claude Code connects to either with one command or one JSON file, reads your repository, and streams a narrated video lesson into Scrimba. Drafts cost nothing until someone opens and claims the link. I ran both endpoints against a public Anthropic repo and a merged pull request, and this post is what came back, including the parts that went wrong.

Every Scrimba lesson you can open has an EXPLAIN button in its header. Press it while paused, type a question, and Scrimba builds a short narrated video from that exact lesson: its files, the code you highlighted and what the teacher has said so far. It costs one explainer from your allowance (10 for life on a free account, 100 a month on Pro), and the result stays attached to the lesson.

I pressed it once, on the "Chunking text from documents" lesson in Learn RAG, with a deliberately picky prompt. This post is what got sent, what came back, and the three moments in a course where I think it earns its cost.

Learn DevOps in this order: Linux, Git, Docker, a real deployment, environment variables and secrets, then continuous integration. Kubernetes comes last, and only if you want the DevOps job title rather than the ability to ship your own work. Before you start, spend ten minutes with the free roadmap at roadmap.sh/devops so you can see how those pieces depend on each other. Every stage below has a good free resource and a good paid one: the free route costs you time and assembly, the paid route costs money and buys both back.

The best CSS courses in 2026 are the ones that teach modern CSS, :has(), container queries, and native nesting, rather than float clearfixes and pixel-width media queries. My honest shortlist: start free and interactive with Scrimba's Learn HTML and CSS, go deep with Josh Comeau's CSS for JavaScript Developers, and fill the gaps with Kevin Powell's free videos. The single filter that matters most now is recency, because CSS changed more in the last three years than in the decade before it.

The best interactive coding platforms in 2026 are Scrimba, freeCodeCamp, Codecademy, Boot.dev, Exercism, and Codewars, because each one makes you write and run real code rather than watch someone else do it. That distinction matters more this year than it used to. When an AI will produce a working answer on demand, the platforms worth your time are the ones that keep your own hands on the keyboard, and my honest take is that the right pick depends less on the brand than on how hard it stops you from copy-pasting your way through.

Becoming a developer in 2026 costs anywhere from almost nothing to more than $160,000, depending on the route: the self-taught path runs $0 to a few hundred dollars a year in tools, a full-time coding bootcamp averages about $14,000, and a four-year computer science degree lands between roughly $40,000 and $160,000 or more. That spread is real, but it hides the number that decides which route is actually cheapest. The sticker price is the smallest part of what you pay, and in 2026 two once-invisible costs, the salary you give up while studying and a monthly AI-tool bill, have grown big enough to change the ranking. Here is the honest math, path by path.

The fastest way to get better at reading code is to read more of it on purpose: in small pieces, out loud, and forcing yourself to predict what each line does before you run it. In 2026 that has quietly become the highest-leverage skill a self-taught developer can build, because AI writes the first draft now and your real job is to verify it. My honest take, as an engineer who learned the hard way through 42 Network rather than by prompting: most beginners are drilling the half of the job the machine already does, and skipping the half interviewers now test for.

You build coding experience without a job by manufacturing the proof a job would have given you: shipped projects real people use, open source contributions with your name on the merge, small freelance or volunteer work, and public build logs that show how you think. In 2026 that is not a clever workaround. It is the main road, because the entry-level rung that used to hand beginners their first experience has largely collapsed. I learned to code through the 42 Network rather than a CS degree or a bootcamp, so I built every hour of my early experience without an employer, and the tactics below are the ones that actually move a resume from ignored to interview.

Context engineering is the practice of deciding what information an AI model sees before it writes a single line of code: the files, docs, examples, rules, and tools you load into its context window. It is the skill that quietly absorbed prompt engineering over the past year. My honest take, after using these tools daily as an engineer who learned to code the self-taught route through 42 Network: the name is new and a little buzzy, but the job underneath it is real, and it happens to be good news for self-taught developers, because it rewards understanding a codebase over memorizing clever phrasing.