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

Let me tell you a story that’s going to mess with your head a little bit.

A developer named Liyuanhao sat down and wrote 200 lines of code in Rust.

That’s it. Just a tiny, bare-bones script.

But what happened after he hit run is the kind of thing you have to read twice just to make sure you aren’t imagining things.

He named the project yoyo — a self-evolving coding agent. And then, and this is the part that genuinely gets me, he stepped away entirely. He took his hands off the keyboard.

He gave it one single instruction: evolve until you rival Claude Code. Then, he just sat back and watched. — Read More

Let’s do this again, redux! I’ll explain how to use AI agents for easy language learning, to create an easier version of my morning briefing, and finally, a far easier version of my briefing transcription -> summary -> action pipeline. In the process, my goal is to help readers remix the general principles for their own (mostly safe) agents.

My last piece about AI agents was my most popular and widely shared article to date. Usually, one writes a “Part 1” that’s easier and a “Part 2” that’s more complex. This is the exact opposite.

… So, in this revisit, I have these goals:

— Explain the general principles of creating agents (more slowly)
— Use methods that are more accessible to non-technical users.
— Give a framework for remixing these methods for readers’ own ideas/agents.

Ironically, this piece took longer than my last one. Instead of just sharing my workflows, this piece is designed to let you use these agents with step-by-step instructions, from scratch, and have them adapted to you (not me). — Read More

Over the weekend, Andrej Karpathy—the influential former Tesla AI lead and co-founder and former member of OpenAI who coined the term “vibe coding”— posted on X about his new open source project, autoresearch.

It wasn’t a finished model or a massive corporate product: it was by his own admission a simple, 630-line script made available on Github under a permissive, enterprise-friendly MIT License. But the ambition was massive: automating the scientific method with AI agents while us humans sleep. — Read More

Two weeks after launching Perplexity Computer, a cloud-based AI agent that can orchestrate 20 frontier models to execute multi-step workflows autonomously, the company used its inaugural Ask 2026 developer conference in San Francisco on Wednesday to dramatically widen the platform’s reach

The centrepiece of announcement is Personal Computer: software that runs continuously on a user-supplied Mac mini, merging local files, apps, and sessions with Perplexity’s cloud-based Computer system. — Read More

AI’s coding ability is outpacing our ability to wield it effectively. That’s why all the SWE-bench score maxxing isn’t syncing with the productivity metrics engineering leadership actually cares about. When Anthropic’s team ships a product like Cowork in 10 days and another team can’t move past a broken POC using the same models, the difference is that one team has closed the gap between capability and practice and the other hasn’t.

That gap doesn’t close overnight. It closes in levels. 8 of them. Most of you reading this are likely past the first few, and you should be eager to reach the next one because each subsequent level is a huge leap in output, and every improvement in model capability amplifies those gains further.

Level 1: Tab Complete
Level 2: Agent IDE
Level 3: Context Engineering
Level 4: Compounding Engineering
Level 5: MCP and Skills
Level 6: Harness Engineering
Level 7: Background Agents
Level 8: Autonomous Agent Teams

— Read More