Tests Are The New Moat | Daniel Saewitz

As AI becomes better at cloning people's open source work, what ends up becoming most valuable are software contracts, tests, and API surface area. This clashes the incentives of clearly defining your commercialized open source software with protecting it.

Agents Done Right: A Framework Vision for 2026

Agents choke on context, loop on failures, and dump walls of code for review. It's time to rethink the architecture.

Code Was Never the Asset - The Phoenix Architecture

Why AI makes the hidden economics of software unavoidable

Where Should the Agent(s) Live? – OpenComputer

Isolation models, agent placement tradeoffs, credential design, and sandbox lifecycle patterns for agentic systems.

Computers for agents

Sandboxes aren't enough. Give your agent a real computer and get back to building.

Infrastructure in the Age of AI Gatekeepers

What happens when AI agents choose your stack

Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI

Organizations rolling out agentic command line tools like Anthropic's Claude Code and GitHub's Copilot CLI need to know who will try them, who will keep using them, and whether the tools produce enough output to justify their cost. At organizational scale, token spend can run into millions of dollars annually, so misreading adoption, retention, or impact can make a rollout expensive without changing engineering velocity. Studying tens of thousands of engineers at Microsoft over its early-2026 rollout, we find that first use spread primarily through social networks, retention was associated more with engineers' coding activity than with demographics, and adopters merged roughly 24% more pull requests than they would have otherwise. We use merged pull requests as our proxy for output -- acknowledging that a merged PR is not the same as the value it delivers -- and the lift persists across our four-month window. These results suggest that CLI coding agents are neither uniformly adopted nor mere novelty effects and that organizations should treat visible peer use as central to rollout strategy.

Why AI agent startup /dev/agents commanded a massive $56M seed round at a $500M valuation | TechCrunch

/dev/agents, a new company by the developers of Android, believes a new operating system is needed to fully realize the potential of AI agents.

dagger/container-use

Development environments for coding agents. Enable multiple agents to work safely and independently with your preferred stack.

agentOS — A faster, lighter, cheaper alternative to sandboxes

A portable open-source operating system for agents. Stateful runtime, universal agent interface, and secure code execution. One SDK, deploy anywhere.

luca. ∆ИƉЯƐΛ on Twitter / X

People keep saying AI coding agents can only build basic, cookie-cutter apps. I decided to prove them wrong.For my first major public demo, I spent some time pushing @Replit 's AI agent to its absolute limit. The result? I rebuilt macOS entirely on the web. No templates. No… pic.twitter.com/0DNTDpp0aA— luca. ∆ИƉЯƐΛ (@agi2asi) March 4, 2026

How we built our multi-agent research system

On the the engineering challenges and lessons learned from building Claude's Research system

shepherd-agents/shepherd

A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents

AI Cybersecurity After Mythos: The Jagged Frontier

Why the moat is the system, not the model