# Learning from Matt

Choose a starting point around a problem you actually need to solve.

## Start with the problem on your desk

You have saved tutorials and tried new tools, yet a real project still leaves you wondering what to do next. These notes offer a starting point for that moment: identify one obstacle and work toward one observable result.

A reading list becomes useful when it changes what you do. Each lesson asks you to make a choice, try something small, and check the outcome.

Figure: From alignment to PR, with review feeding back into standards. ([home-hero](/figures/home-hero.en.svg))

## A teacher who moved into development

Matt Pocock moved from voice teaching into software development. His public work has covered XState, TypeScript education, AI engineering, and agent workflows. Those areas provide the context for these notes.

This site pays tribute to his teaching and public sharing. We study how problems are framed, explanations are built, and feedback informs the work. The lessons and exercises here are independently written by the maintainers.

> I poured my 10 years of teaching experience into a skill.
>
> — Matt Pocock, [source](https://x.com/mattpocockuk/status/2064023481398824993), 2026-06-08

That line describes the author's investment in his work. Whether a teaching approach helps you is a separate question, best tested by completing an unfamiliar task yourself.

## Go directly to the author

Explore his [X account](https://x.com/mattpocockuk), [GitHub](https://github.com/mattpocock),
and [AI Hero](https://aihero.dev/skills). Open the originals when you need context or want to check a date.

These are unofficial, noncommercial study notes. Read [About and Copyright](/en/about-and-copyright) for the distinction between quotations, our commentary, and licensed reuse.

## Choose the obstacle you recognize

Think of a recent task that stalled. Did the agent build the wrong thing? Did a type error stop you? Could you solve the problem but struggle to explain it? Pick the route closest to that experience.

- **AI coding** Use the [workflow overview](/en/ai-coding-workflow) to choose the steps your task needs.
- **TypeScript** Start with [foundations](/en/typescript-foundations) to practice checking data and understanding types.
- **Teaching** Try [writing to teach](/en/writing-to-teach) to turn one idea into a lesson someone can use.

Figure: Choose a learning entry point for your current problem. ([home-routes](/art/home-routes.en.svg))

## Make your first choice testable

### Choose an outcome for today's reading

- Task: name a real obstacle and select one route above.
- Deliverable: write one sentence describing a small task you want to complete afterward.
- Check: name an observable result, such as reproducing a failure and verifying its repair.

Suggested approach: If an agent built the wrong behavior, choose the workflow route. Aim to produce agreed behaviors and a separate list of unresolved questions.

You are ready to begin when you can name a page, an action, and the evidence that will count as success. If that feels vague, narrow the task to one operation or one error.

## Corpus: find the original

The [corpus](/en/corpus) is a continuing index of selected public work. Records carry links, dates, types, tags, engagement figures, and summaries written for this site. Follow a record to read its source.

Figure: Public sources become searchable summaries that link to the originals. ([home-corpus](/diagrams/home-corpus.en.svg))

## Quick reference


## Sources

- [Matt Pocock · 2026-06-08](https://x.com/mattpocockuk/status/2064023481398824993)
- [X](https://x.com/mattpocockuk)
- [GitHub](https://github.com/mattpocock)
- [AI Hero](https://aihero.dev/skills)

Independent, unofficial, noncommercial study notes. This site has no affiliation with or endorsement from Matt Pocock. Referenced works belong to their authors; MIT reuse carries separate copyright and license notices.

The diagram CSS on this page is adapted from Archify (https://github.com/tt-a1i/archify) under the MIT licence; the complete licence follows.

### Full licence: Archify (tt-a1i/archify)

```text
MIT License

Copyright (c) 2026 tt-a1i (Archify)
Copyright (c) 2025 Cocoon AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```