Why Anthropic’s MCP is a Big Deal
The deep dive into how a request travels across the MCP stack and how a response is transported back is the best damn example I’ve ever read about how MCPs work.
The deep dive into how a request travels across the MCP stack and how a response is transported back is the best damn example I’ve ever read about how MCPs work.
The correlation (or the lack of) between speed/latency and efficiency in the conclusion is confusing. Ideally, more efficient systems should offer better speeds with similar resources. Is the mentioned efficiency expressed purely in terms of scalability? Can someone enlighten me?
Great historical context about a key problem RAG was designed to solve and why there is a better solution now in agentic search - thanks to huge context windows in today’s LLMs. It’s also fascinating to learn how Claude Code cleverly uses decades-old filesystem tools to perform lightning fast navigation and regex searches without needing some sort of indexing.
Validates my long-held belief about the dangers of hero culture our society idealizes. Behind every successful individual is a team - family, friends, mentors. Not only is it unfair to attribute all success to one person, it sets a false expectation about how you can replicate that success.
TIL React is no longer limited to UIs for the web and mobile. Claude Code (and several other agentic CLI tools) use React for command-line UIs, whatever UI means in that context. See https://term.ink/.
One more peril of AI. If you cannot fight it, embrace it!
The age of solopreneurship. My biggest takeaway is the example for identifying a Burning Problem.
“Comfort” and “yes” debts are the most dangerous of the three career debts. They prevent you from doing focus work, learning new marketable skills, and strengthening resume. Despite immensely growing as an engineering leader, I sadly accumulated career debt on the technical hands-on front in the last couple of years. Thankfully, I’ve been making up for it and paying back my debt in the last few months through deliberate learning and practice. If that resonates with you, do it before it’s too late.
An interesting example of software supply chain cyber attacks. Remember CrowdStrike?
Measuring developer productivity was hard enough. Now engineering managers have to contend with measuring the impact of AI on dev productivity. Laura provides a list of key metrics (based on trends across big tech) that should be a good starting point for most orgs. Essentially, establish a baseline and later measure the same metrics again after introducing AI in dev workflows.