"Forget 10x programmers. 1,000x programmers really exist, we just don't fully acknowledge it." Naval Ravikant
He's right. The difference isn't speed or talent. It's direction.
I have spent weeks building features that nobody used.
Asking "should we build this?" would have created more value.
Yup, selection by the procurement department, not the engineers.
GitHub Copilot will beat Claude Code. Not because it's better.
Copilot is the new IBM. Checks the enterprise box. Microsoft is already on the PLS. If it under-delivers, everyone else bought it too.
"You won't get fired for buying X" really means "you won't get blamed for buying X."
Big difference
An OpenAI forward deployed engineer is coming on my podcast tomorrow.
FDEs sit between the models and the companies using them. They see what works, what breaks, and what surprises everyone.
I've got my questions ready, but what would you ask?
Every programmer should learn C.
Implement a linked list, hash table, and binary tree. Then build a simple CLI program and a basic network server.
Not because you'll use it daily, but because it strips away the abstractions and shows you what's really beneath whatever language you use daily.
What AI newsletters are you actually reading?
Not the ones covering the latest model.
The ones writing about AI in healthcare, manufacturing, logistics, defense, energy. Drones, autonomous vehicles, robotics.
The applied stuff. The "boring" stuff. The stuff that's actually changing industries.
Who are the people writing about applying AI to real-world problems?
Not the next coding agent, not the latest LLM wrapper. What about AI for robotics, logistics, healthcare, manufacturing? The other 99% of AI.
Drop names below. I want to follow them.
In any new role you should always seek to understand before you act.
Make sure you understand not just the what, but also the why.
Only then should you start changing the what, if the why is no longer valid.
Exception: if you've been hired to put out a raging fire.
The software engineers who understand the full system, from data flow to failure modes, don't just use AI. They direct it. That's the difference between generating MVPs and shipping products.
What changes is that the finer details of syntax, compilers, module systems, and type systems won't matter as much to everyone.
But you should absolutely understand how the pieces fit together. From syscalls to pixels.
AI amplifies the skills you already have. It lets people with less experience write code and it’s great that more people can build software. But it also means experienced engineers ship even more, even faster.
There's a take going around that not knowing how to code is an advantage when using AI.
It’s wrong.
The more you understand coding and software engineering, the better your prompts, the better you manage the context, the better your feedback, and the better the product you ship.
Joined @johncrickett.bsky.social's Coding Chats podcast, if you like podcasts
Youtube: www.youtube.com/watch?v=z9Rr...
Spotify: lnkd.in/eNJw955r
Apple Podcasts: lnkd.in/epZnMjZz
Overcast: lnkd.in/eFNPGVvS
Tips for AI-assisted software development:
Boring tech gives AI superpowers.
AI coding agents performs best with tools, languages, and frameworks that have been around long enough to show up in its training data.
When use the bleeding edge, it hallucinates.
Tips for AI-Assisted software development:
Work in small batch sizes.
Humans and AI have limited short term memory. Ensure the task you're working on fits within your and AI's short-term memory.
When switching tasks start a new session, clear the context of you and the AI.
“AI is incapable of programming well thought out and complex code”
This is both true and irrelevant.
The goal is to write well thought out and simple code (which may solve a complex problem).
Is that working?
Did they take that approach to Scrum / Agile / Kubernetes / Programming Language / Interviewing Skills ?
I wonder if any other profession just has the "give it a go" approach to new tools :)
Did either company have a push for AI adoption?
If you use AI to help you build software professionally, has your organisation provided any training?
If so, what did it cover?
What was missing?
If not, why not?
That will depend on the scope of the user stories.
I tend towards stories that are as small as possible and will break those into subtasks.
I never have large commits or PRs. A lot of my PRs are single or double digit numbers of lines.
Software Engineers - need a project for the weekend?
How about building your own LLM powered chatbot?
codingchallenges.fyi/challenges/c...
Or one of the 80+ other real-world projects you can build to level up your coding skills:
codingchallenges.fyi/challenges/i...
There's that and kiro.dev
I have tried neither. I would only expect a developer to work from a spec for a user story or part of one.
If you code with AI, and don't do Test-Driven Development, have you tried or are you going to try doing Spec-Driven Development?
What is the attraction?
What do you see as being a blocker to doing it?
Why do people feel the need to post stuff like this:
"So far only the uninformed and b-players are using LLM"
LLMs are way over-hyped, but attacking people is a weak argument.
Yes, I fear there's that real danger of making yourself ignorant.
I certainly agree building a project is the best way.
I'm still on the fence about using AI for it. I think it can work and I fear too many would lack the discipline to then read the code and try to understand it.
So it would end up just like watching a tutorial and copy and pasting the code.
If you use AI to develop software, do you vibe code or do you do AI assisted engineering?
In today's Coding Challenges I used Augment Code to develop a project in Gleam: codingchallenges.substack.com/p/using-ai-t...
If this doesn't look like an AI bubble, what does?