I’ve written more prompts than I care to admit. 🙂

During my PhD at the University of Copenhagen, I spent embarrassing amounts of time tweaking system prompts, adjusting few-shot examples, and praying that my carefully crafted instructions would survive the next model update. Spoiler: they rarely did. Then recently I discovered DSPy, and I realized I’d been doing it all wrong.

… DSPy (Declarative Self-improving Python) from Stanford NLP flips the entire paradigm. Instead of writing brittle prompt strings, you write structured Python code. Instead of manually optimizing prompts, you let the framework compile them for you. — Read More

“openclaw is the wordpress moment for agents. the shopifys and substacks are coming!”

this might be the typical take of “if you’re in crypto pivot to ai”, reality is there’s lots a real software developer will find useful with openclaw.

what really changes? when normal people realise they can use it usefully. — Read More

Whether you like it or not, AI is finding its way into all of our devices and the apps we use everyday. From chatbots to image generators, you can’t blink without seeing AI somewhere now. However, I never expected to try and enjoy using an AI-powered browser as much as I have over the past week while testing Norton Neo.

After going hands-on with OpenAI’s ChatGPT Atlas browser when it first released last year, I have to admit the bar was quite low. Although both it and Neo are Chromium-based browsers, they do things quite differently, especially when compared to my go-to browser, Google Chrome.

While ChatGPT Atlas tries to turn the traditional web browser on its head, Neo follows in the footsteps of Opera Air and its more mindful approach to how you use the web. Instead of taking the actual browsing out of your hands like ChatGPT Atlas does with its agents, Neo focuses more on refining the browsing experience by making it calmer and smarter at the same time. — Read More

Here are 5 major upgrades to help you create, iterate and collaborate:

AI-Native Canvas
Smarter Design Agent
Voice
Instant Prototypes
Design Systems and DESIGN.md

— Read More

The growing landscape of AI agent development is overloaded with acronyms: MCP, A2A, UCP, AP2, A2UI, and AG-UI, just to name a few. If you’ve ever looked at this list of protocols and felt like you were staring at a wall of competing standards, you are not alone. To help you understand their value, we are going to demonstrate what each one does to save you from writing and maintaining custom integration code for every single tool, API, and frontend component your agent touches.

We will put these protocols into practice by using Agent Development Kit (ADK) to build a multi-step supply chain agent for a restaurant. This scenario works well as a test case because ordering wholesale ingredients requires checking inventory databases, communicating with remote supplier agents, executing secure transactions, and rendering interactive dashboards. — Read More

As with any tool, understanding how coding agents work under the hood can help you make better decisions about how to apply them.

A coding agent is a piece of software that acts as a harness for an LLM, extending that LLM with additional capabilities that are powered by invisible prompts and implemented as callable tools. — Read More

AI and ML have gained a lot of popularity. Every company wants to stay ahead of the curve and introduce AI in its daily operations. Although we have multiple models from ChatGPT, Claude, Cursor, DeepSeek, and other models available in the market today, which amaze the world with their knowledge and data that they share.

However, to learn and grow, we need resources that can help us understand the basics, the technicalities, and most importantly, how to apply these concepts in real-world scenarios.

Below are multiple free resources I’ve gathered to help you master AI/ML concepts effortlessly. — Read More

Most “autonomous AI research” demos look impressive for the same reason magic tricks do: you only see the interesting part. An agent edits some code, runs an experiment, and shows a better result. What you usually do not see is the part that actually determines whether the system is useful: what is the harness optimizing for, how stable is the evaluation, and what happens when the agent fails?

That is why Karpathy’s Autoresearch is worth paying attention to.X

Autoresearch is not trying to be a general-purpose AI scientist. It is a small, tightly constrained system for one specific job: let an agent modify a training script, run a bounded experiment, measure the result, keep the change if it helps, and discard it if it does not. The repo is tiny, but the design behind it is one of the cleanest examples I have seen of how to build a useful autonomous improvement harness. — Read More

Since December, 2025, I’ve been integrating AI agents into my coding workflow.

Previous attempts at agentic workflows have left me exhausted, overwhelmed, and feeling out of touch with the systems I was building. They also degraded quality too much.

My current agentic workflow is about 5x faster, better quality, I understand the system better, and I’m having fun again.

I call this the Night Shift workflow. — Read More

There is currently a social media and industry zeitgeist dialed-in on CLIs…just as there was a moment for MCP but just a few short months ago

While it is true that there are token savings to be had by using a CLI, many folks have not considered how agents using custom CLIs run into the same context problem as MCP, except now without structure and many other sacrifices

In much of the discourse, there is a lack of distinction between local MCP over stdio versus server MCP over HTTP; the latter is a very different use case

… The oversight made by many is that individual usage of coding agents looks very different from organizational adoption of coding agents where there is an emphasis on visibility, telemetry, security, quality, and being able to operationalize and maintain agent-coded systems by a team of varying degrees of skill and experience.

For enterprise and org-level use cases, MCP is the present and future and teams need to be able to cut through the hype of the moment. — Read More