So if you design call it loop, call it graph, call it workflow, it's kind of all the same. If you design something that gets a trigger or gets an input and does something for you and maybe there's a decision in the way, boom, there's your graph.
You know features are the fun part. A new feature is is just a prompt away. The real cost comes after every feature we shipped of course with a configuration option because we didn't want to break everyone's setup.
for people that aren't building agentic products, but you're using Claude code, every 6 months delete your Claude MD. Delete your skills. Delete your hooks.
Like this is actually like a new form of test time compute. Like when we talk about the scaling laws and kind of we talk about the model getting more intelligent over time, historically, it's been a function of the size of the neural net, the amount of training data, and the number of flops that you put in to the training. And then recently, we also added test time compute. So this is essentially a fancy way a researcher way of saying how many tokens does it generate. And now dynamic workflows are essentially a new way to orchestrate test time compute.
And then there were people who used it as a moral cudgel. Like you should be if you're not using TDD, you're not professional. And that's just such People can write very good software with a wide variety of workflows.
I truly believe that following this kind of approach to AI in the classroom would, in the aggregate, produce better learning outcomes than the research and writing workflows available to students pre-AI.
As reasoning models and agent workflows keep more tokens around (for longer), KV-cache size, memory traffic, and attention cost quickly become the main constraints, and LLM developers are adding a growing number of architecture tricks to reduce those costs.
And so everything is lazy and deferred and functional and reusable inside the compiler. And it's a very different way of writing compilers than than what the textbooks will traditionally teach you.
So, I I think there's a lot of things we still try to imagine. What would be an AI-driven world look like, right? I think people are trying to like retrofit what already exists to fit what they think is a new workflow.
My daily drivers now include Amp, Cursor, and Claude Code. I still enjoy notebooks, but only for data analysis, machine learning, or other exploratory workflows where the iterative, visual nature of notebooks shines.
My daily drivers now include Amp, Cursor, and Claude Code. I still enjoy notebooks, but only for data analysis, machine learning, or other exploratory workflows where the iterative, visual nature of notebooks shines.
My daily drivers now include Amp, Cursor, and Claude Code. I still enjoy notebooks, but only for data analysis, machine learning, or other exploratory workflows where the iterative, visual nature of notebooks shines.
The panel had a generally positive view of Braintrust, highlighting its clean UI and structured approach to evaluations. The tool’s emphasis on human-in-the-loop workflows was a significant strength.
Finally: tmux, which I had been happily using for many years, but slowly collecting configuration for. I decided to give Zellij a try, and haven’t looked back.
An eye-opening view on considerations going into building a widely used public API or reusable library. While the book focuses on the .NET framework, many of the conventions apply to maintainable and reusable components, in general. This book had an outsized impact on me as I read it when I was a mid-level .NET developer.
Just giving more support to agencies in their current configuration is not going to help. To the contrary, it only confirms that profligacy gets rewarded.
Vagrant: I love this little broker tool that helps me configure my Virtual Machines with the intended development environment and also scale the configuration on other similar machines and devices used by my co-workers, clients, and mentees.
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