The current "Hermes Agent vs Claude Code" framing is the wrong comparison. The two tools live at different layers of the coding agent stack, and most of the YouTube hot takes treating them as alternatives are reading them as if they competed for the same job. They do

An article circulating this week argues that faculty AI buy-in in higher education is a human factors engineering problem. The framing is correct. The path the piece describes skips the only two steps that matter, and the reason it skips them is structural, not pedagogical. Start with the framework on

Anthropic added the /goal command to Claude Code in v2.1.139. You set a completion condition; the agent keeps working across turns; a second model reads the transcript and decides whether the condition was met. It is the built-in version of the keep-going loops people have been hand-rolling for

A DevOps engineer posted on r/devops this week with what reads like a familiar shadow-IT question dressed in 2026 clothes. Marketing, product, and sales people across his AI startup are shipping internal apps with Cursor and Claude Code. They deploy to Vercel, Cloudflare Pages, Netlify. The data is real.

Anthropic launched Claude for Small Business on May 13. Every read I've seen has focused on the product surface: 15 prebuilt agentic workflows, 7 named SaaS integrations, a trust posture built around in-the-loop approval and no-training-on-data defaults. The product is fine. The strategic signal is somewhere else. Read

Carl Frey's recent NYT piece argues that AI's real impact isn't job replacement — it's labor transfer from worker to consumer. We become our own travel agent, accountant, exterminator, doctor. The work doesn't disappear; it moves out of the labor statistics

In 2025, four companies — Microsoft, Alphabet, Amazon, Meta — spent over $300 billion on AI data centers. The combined 2026 number is forecast at $725 billion, a 77% jump in a single year. For comparison, the entire global SaaS market in 2025 is roughly $295–370 billion depending on whose definition

Someone on r/devops just asked whether to take an AWS Cloud Support Engineer role for 18 months — trading hands-on building for "AWS internals exposure and the brand on the CV." Both replies told them not to. They're right, but for older reasons than they realize.

The first time you trim an eval because it costs too much to run, you have started letting the pricing model design your agent. The problem with token pricing for AI is not magnitude — tokens aren't expensive in aggregate, by knowledge-work standards. The problem is that the unit