weekend ai reads for 2025-07-18

The funny thing is, some of the best things I’ve done, the ones that actually brought meaning, connection, and even some success, came to me from asking a very different question. And it was, “Who will this make me?”

You can have an unreasonable amount of influence by being the person who writes stuff down.

Generative AI is best understood as a satisficing technology.

Satisficing is a portmanteau of “satisfy” and “suffice.” It was coined by Herbert Simon in the 1970s.

In layman’s terms, satisficing is the process of (1) establishing the threshold where your work product is good enough, (2) working until you have reached that threshold and then (3) stopping. Think of satisficing as an alternative to maximizing — expending maximum effort to produce your very best work.

As models grow more powerful, any task that can be turned into data—from spreadsheet analysis to therapy sessions—is increasingly within reach of automation.

“The more you get into it, the more you realize that you need a full engineering team and development team to be able to really leverage that investment, which turns you towards looking at commercial offerings instead because they’re all just turnkey,” said Bockelie.

If you treat AI like an answer generator, it’ll give you a below-average output. Here’s how to coach it to perform like a top-performing human.

NotebookLM users will be able to read the original source material, but also ask questions, explore topics, and get answers that include citations, according to Google.

An unusual part of OpenAI is that everything, and I mean everything, runs on Slack. There is no email. I maybe received ~10 emails in my entire time there.

OpenAI is incredibly bottoms-up, especially in research. Good ideas can come from anywhere, and it’s often not really clear which ideas will prove most fruitful ahead of time. Rather than a grand ‘master plan’, progress is iterative and uncovered as new research bears fruit.

But without the fundamentals of a digital transformation and a data strategy, simply layering AI over the top of your current operating system isn't the solution (especially in the context of free-flowing tasks). It’s critical to understand the relationship between the data underpinning Gen AI before going any further.

Your AI strategy will put you ahead — or make it hard to ever catch up

Your workforce could double — thanks to AI agents

ROI for AI depends on Responsible AI

AI will be a value play — and a boon for sustainability

AI will cut product development lifecycles in half

AI will transform industry-level competitive landscapes

If you don’t have a clean process today, it’s very hard to bring automation to that work, so many companies are using AI as an opportunity to bring more discipline to the workflows.

The changes posed to traditional means of learning and assessing learning by the widespread (essentially, universal) adoption of LLMs in the classroom have made pre-2022 assessment practices obsolete, even misleading.

My kids aren’t yet at the age where they'd be using such tools day-to-day, but I find myself already thinking through what this is going to be like. It’s starting to come into focus...

In this example, we will use mem0, an open-source tool for giving AI agents long-term memory and Gemini 2.5 Flash as the LLM. We will build a simple chatbot that saves what you talk about and uses that history to give you better, more personalized answers.

In this talk, he shares what he’s learning now: why execution speed matters more than ever, how agentic workflows are changing what startups can build, and why concreteness beats vagueness when turning ideas into products. He reflects on the rise of AI coding assistants, the shifting bottlenecks in product development, and why, despite faster software, it’s still human judgment and responsibility that will shape what comes next.

Training instability has been the hidden tax on large language model development, forcing companies to restart expensive training runs, implement costly safety measures, and accept suboptimal performance to avoid crashes. Moonshot’s solution directly addresses exploding attention logits by rescaling weight matrices in query and key projections, essentially solving the problem at its source rather than applying band-aids downstream.

The new dashboard will provide engineering managers with detailed metrics on how their teams use Claude Code, including lines of code accepted, suggestion accept rates, total user activity over time, total spend over time, average daily spend for each user, and average daily lines of code accepted for each user. The feature comes as companies increasingly demand concrete data to justify their AI spending amid a broader enterprise push to measure artificial intelligence’s return on investment.