Tuesday done here in Seoul, and I enjoyed talked to a couple of customers today. Big focus on how to get quality results from LLMs. No single solution to that problem, but a mix of model, smart context, and grounding can help.
[blog] Memory Allocation in Go. Very few people need to know all of this. Probably like twelve. But I admire the depth of content, and learned a few things.
[blog] How to Choose Your AI Agent Framework. Great post. The author looks at different types of frameworks, and offers up a decision tree for choosing one.
[report] Anthropic Economic Index report: Uneven geographic and enterprise AI adoption. Read this online report from Anthropic to get a sense of where there are concentrations of AI usage, and what those leading populations are doing.
[blog] If you are good at code review, you will be good at using AI agents. What an interesting point. Sean makes a reasonable argument here, and his experience mirrors what I’ve seen (regarding the agents) as well.
[blog] How we built it: Real-time analytics for Stripe Billing. Ideally, your systems would tell you immediately what the cost was for the operation that just happened. Easier said then done. But Stripe rebuilt their billing pipeline and got latency down to 15 minutes. Impressive!
[blog] How 18 companies measure AI’s impact in engineering. Read the newsletter this came from, but this post gives an overview and a visual that you’ll find useful.
[blog] Enterprise AI Looks Bleak, But Employee AI Looks Bright. People use LLMs where it helps them get something done. It’s interesting to see the disconnect (again) between executives and employees.
[article] Identify Your Core Values to Make Better Leadership Decisions. Even if you don’t think you have any “core values” that drive your behavior, you do. There’s SOME heuristic you’re applying to decision making.
[blog] We’re Optimizing Ourselves Into Oblivion. The summary Cote provides here spoke to me. Everything doesn’t have to be efficient.
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