Bob Belderbos | Developer coach and builder

Someone asked me the other day which programming books are worth reading.

I keep hearing this a lot: the Python syntax I picked up in an afternoon, but shipping a real project meant learning ten more things.

I was designing the provider boundary with a student for a CLI tool that talks to two different image-generation backends.

An interesting challenge for a new student came up in our Rust cohort: they opened a file and wanted to read its contents from disk, but the compiler refused to let them call read_to_string.

Every time you validate data with Pydantic v2, the data-validation library most Python apps reach for, a Rust extension does the work.

Stanford's Chris Piech runs Code in Place, an intro programming class with 17,000 students and over 1,000 teachers.

This came out of a coaching project: a FastAPI service backing a geotechnical database.

There are plenty of habit trackers, but I wanted to build mine with constraints, a calendar view and habit streaks.

A coaching session this week surfaced a good question: Alembic autogenerate only works if you import your models first, even though you never reference them.

Django has a signing module that makes it easy to build an unsubscribe link that works with no login and no session: token = signing.dumps(recipient.pk, salt=UNSUBSCRIBE_SALT).

Cal Newport's On AI Coding and Its Discontents lands on an uncomfortable point: the speed AI gives you is paid for with the thinking that made us good engineers in the first place.

I joined the vBrownBag podcast with Damian to talk about how to actually learn a new language or skill when an agent can write the code before you finish typing the prompt.

I joined the Develpreneur podcast with Jim Hodapp to talk about the Rust developer mindset and what makes Rust a good fit for AI-assisted work.

Every time I hear the phrase coding is solved, I worry about where this industry is heading.

When building applications, I always build the core first, then the interfaces.

I built out askthecanon.com this weekend, a semantic search over 100 public-domain books (from the Gutenberg project).

Everybody talks about agents, and a lot of people assume they're some new kind of model.

I joined Bas Steins and Michal Martinka on their complexity.fm show to talk about why Pythonistas are picking up Rust, what AI really does to how we learn, and why vibe coding is a myth.

A Reddit thread caught my eye this week.

The fastest way I know to learn a language is to rebuild something you already understand.

The Rust Platform Django test suite took 30 seconds to run.

The Rust platform has 71 exercises and counting (I just added a new track of Unix exercises).

In a coaching call this week we discussed a create classmethod, and someone asked the obvious question: why is that here?

Jeff Haemer has written software since he was teaching it at the University of Colorado in the early 1980s.

When I heard structs replace classes in Rust, I was a bit surprised.

Kelsey Hightower said something on the Pragmatic Engineer podcast that I keep coming back to: AI does not change what software engineering is.

A button submits code, tests run, feedback appears.

Jochen Deister is a lawyer who codes for fun.

A test can pass for the wrong reason.

I needed a CRM the other day.

In my trend digest article I shared a quick tool to keep on top of tech trends, but it's a one-way street: the model gives information, but I still have to decide what to do with it.

Gary Bernhardt posted something this week that names a phenomenon we're teaching in our agentic AI cohort: Everyone seems fixated on the models, but I think there's so much low-hanging fruit in the control layer above the model.

Josh Engroff has been writing Python for years and runs agentic AI workflows every day.

Your app works fine with one user.

Six weeks of 1:1 coaching.

A new project starts as a complete picture in your head.

A specific page returns 500.

Most "AI agents" shipping right now are demos wearing production paint.

The promise was less friction.

I ran a 10-day beginner Rust challenge on LinkedIn, one small exercise per day, written for Python developers.

Most "build an AI agent" tutorials hand you a framework and skip the part where you actually understand what it's doing under the hood.

I've built coding-exercise platforms before (Python, Rust).

A two-agent Python service ran fine in tests.

One command and get the AI stories worth reading today, ranked by an LLM loaded from config.

AI agents write code now.

Chris May knew about event sourcing for over a decade before he used it for real.

Vikas Zingade is a lead ML engineer who builds complex systems in Python every day.

Every AI tutorial I've seen opens the same way: client.chat.completions.create(...).

These days you can prompt Claude to generate a working feature in twenty minutes.

Ryan Austin runs a payroll SaaS solution in the Bahamas.