Contribute to forrestchang/andrej-karpathy-skills development by creating an account on GitHub.

Learn how to write effective specifications for AI coding agents to improve clarity, focus, and productivity in your AI-driven development workflows.

Giving agents access to a sandboxed environment with a shell and a filesystem has been the latest hype when it comes to agentic harnesses. Recent examples of this include: Turso’s AgentFS Anthropic’s Agent SDK, which brings Claude Code’s harness to non-coding domains Vercel rebuilding their text-to-SQL agent on top of a sandbox Anthropic’s Agent Skills for filesystem-based progressive disclosure The argument for why this is good goes something like this: The big labs are doing heavy RL for coding tasks in these kinds of environments. Aligning more closely with such a harness brings free gains from the coding domain to other problem spaces.

11 new technologies coming in 2026 may bring life-changing impacts to users. How might they change your life?

Comprehensive Claude Code project configuration example with hooks, skills, agents, commands, and GitHub Actions workflows - ChrisWiles/claude-code-showcase

What you need to know about local model tooling and the steps for setting one up yourself

Open-source platform to build and deploy AI agent workflows. - simstudioai/sim

This article is for vibe coders and developers seeking private, fast, and affordable AI coding solutions.

I’ve been experimenting with a pattern for LLM usage recently that’s working out really well: asynchronous code research tasks. Pick a research question, spin up an asynchronous coding agent and …

They're like riding a bike: easy, and you don't get it until you try.

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Max Mynter - Full Stack Machine Learning Engineer. Explore my blog and portfolio. Multiple years of experience in data science, software engineering, and machine learning.

Contribute to humanlayer/advanced-context-engineering-for-coding-agents development by creating an account on GitHub.

Learn how to create custom chat modes in VS Code for GitHub Copilot to enhance your workflow in large, complex projects with specialized AI configurations.

Developers can use their AI tool of choice for spec-driven development with this open source toolkit.

The GPT-5 launch was uh, rough. A lot went wrong here, and I want to talk about what really happened...Thank you Kilo Code for sponsoring! Check them out at:...

Claude Code is the most delightful AI agent/workflow I have used so far. Not only does it make targeted edits or vibe coding throwaway tools less annoying, ...

My experience creating software with LLM coding agents - Part 2 This post details my experiences creating software with LLM coding agents,...

Lauren Goode convinced her editors at Wired to let her spend a couple of days at a tech company called Notion learning how to vibe-code (i.e. AI-assisted computer programming): Why Did a $10 Billion Startup Let Me Vibe-Code for Them — and Why Did I Love I

We want to make Piccalilli’s position absolutely clear on LLMs and generative AI. It’s important for you, the reader, to understand our position.

AGENTS.md is a simple, open format for guiding coding agents. Think of it as a README for agents.

Personal blog of Stephen Diehl - Software engineer writing about technology, programming, and the future

Explore, build, and refine codebases with Claude Code.

LLMs have unlocked powerful new capabilities, but the dominant pattern for using them, writing increasingly desperate prompts, is fragile, unscalable, and of...

It is wild to think that it has been only a handful of weeks. Claude Code has considerably changed my relationship to writing and maintaining code at scale. I still write code at the same level of quality, but I feel like I have a new freedom of expression which is hard to fully articulate. Claude Code has decoupled myself from writing every line of code, I still consider myself fully responsible for everything I ship to Puzzmo, but the ability to instantly create a whole scene instead of going line by line, word by word is incredibly powerful.

The glamourous AI coding agent for your favourite terminal 💘 - charmbracelet/crush

Some of my attempts to make agents work better that just didn’t work.

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference DSPy: Prompt Optimization for LM Programs Michael Ryan, Stanford It has never been easier to build amazing LLM powered applications. Unfortunately engineering reliable and trustworthy LLMs remains challenging. Instead, practitioners should build LM Programs comprised of several composable calls to LLMs which can be rigorously tested, audited, and optimized like other software systems. In this talk I will introduce the idea of LM Programs in DSPy: The library for Programming — not Prompting LMs. I will demonstrate how the LM Program abstraction allows the creation of automatic optimizers for LM Programs which can optimize both the prompts and weights in an LM Program. I will conclude with an introduction to MIPROv2: our latest and highest performing prompt optimization algorithm for LM Programs. Michael Ryan is a masters student at Stanford University working on optimization for Language Model Programs in DSPy and Personalizing Language Models. His work has been recognized with a Best Social Impact award at ACL 2024, and an honorable mention for outstanding paper at ACL 2023. Michael co-lead the creation of the MIPRO & MIPROv2 optimizers, DSPy’s most performant optimizers for Language Model Programs. His prior work has showcased unintended cultural and global biases expressed in popular LLMs. He is currently a research intern at Snowflake.

We built a browser-based terminal controller in one day using Claude Code, named pipes, and Xterm.js. No SSH needed, just open your browser and start typing. Check and command your agents on the go!