AI for Software Engineers

I’ve finally gotten around to looking at TypeSafe AI’s new “System One Model” called Jev that made waves this last week.

The current market is in an interesting spot, making it difficult for candidates to find jobs, advance their careers, and understand the skills needed to make it in software engineering.

These are the AI articles and resources from this past week you don’t want to miss: How Claude Watermarks AI-Generated Text by : This is a great overview of Claude’s watermarking and how it works.

With the progress of large language model capabilities, the advancements of agent harnesses, and the introduction of agentic development workflows like using the /goal skill, agents are now capable of completing long-running, complex work tasks.

I’m bringing the weekly format back!

AI needs more software engineers.

Dr.

Things are moving so quickly in AI it can tough to know what topics to learn, what’s important, and what to focus on.

I’ve been thinking a lot recently about the efforts to get data centers into space.

There’s been an interesting push recently for “tokenmaxxing”, or the idea that burning through more tokens means an engineer has been more productive.

Large-scale LLM pretraining is a notoriously complex and resource-intensive process.

Google recently released Gemma 4, their next iteration of their small, open models with multimodal capabilities.

Hi everyone!

Welcome to the weekly reading list!

The most difficult problem in ML isn’t technical.

Enjoy this weekend’s reading list!

Even the most talented engineers I know ask me questions about AI because they’re worried about its impact on their career.

Hey y’all, Here’s your weekend reading list to highlight the important events and information shared this week.

Hey y’all, Here’s your weekend reading list!

I’ve conducted enough entry-level technical interviews to identify the patterns and mistakes most candidates make.

I set up a Machine Learning Roadmap for the AI for Software Engineers community a few years ago to share high-quality, free machine learning learning resources in order of how to learn them.

I’ve seen a lot of articles recently claiming AI has had near zero economic impact despite making up a large portion of economic spending.