Consuming information Fast and Slow…. After working extensively with AI agents over the past two weeks, I've noticed two patterns for consuming and dealing with information. The first is “fast”, to just bridge some knowledge gaps; the second is “slow”, to allow my brain to rewire. When I need a specific, real-time answer to clarify a detail, resolve an issue, or gain a high-level overview, I ask AI to summarise and highlight the essentials. That's very efficient and direct. AI is the king here. Conversely, when I want to deeply internalise certain ideas and allow my brain to be rewired, I must interact with the content slowly. This is a fundamentally different approach. Engaging with a book or a long podcast is like inviting another person’s perspective to interact with your neural network. We are the sum of people, books, movies, cities, and environments we encounter; they all shape our “mindscapes”. Therefore, to cultivate taste, authenticity, and your identity, slow consumption is essential. A recent example: I was reading a book where the author briefly referenced the myth of Aphrodite and her dual identity. The author likely assumed his readers are all highly educated and cultured. Not being in this category, I turned to AI to quickly bridge the gap. I didn’t need a deep academic analysis of Greek mythology; I simply needed a targeted piece of information to continue with the text. However, I would never ask AI to summarise the book itself. I am not reading it for mere information, but to allow my brain to encounter and interact with the author’s unique way of thinking and worldview.
ScScrum Master Notes
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Метрики обновлены: 06 июл. 2026 г.
Последние посты
We might soon need a 'gym for the brain' with mathematical problems to solve, just to keep our brains from fully atrophying.
Uncle Bob confirms.
Surprisingly, the Agile principle of 'customer collaboration over contract negotiation' applies perfectly to interacting with AI agents. They may miss details in the initial specification, but the ongoing conversation drives the necessary fixes.
“AGI is the ability to figure things out.” “A human who can figure things out has some baseline knowledge, the ability to reason over that knowledge, and the ability to iterate their way to the answer. An AI that can figure things out has some baseline knowledge (pre-training), the ability to reason over that knowledge (inference-time compute), and the ability to iterate its way to the answer (long-horizon agents). The first ingredient (knowledge / pre-training) is what fueled the original ChatGPT moment in 2022. The second (reasoning / inference-time compute) came with the release of o1 in late 2024. The third (iteration / long-horizon agents) came in the last few weeks with Claude Code and other coding agents crossing a capability threshold.” "If there’s one exponential curve to bet on, it’s the performance of long-horizon agents. METR has been meticulously tracking AI’s ability to complete long-horizon tasks. The rate of progress is exponential, doubling every ~7 months. If we trace out the exponential, agents should be able to work reliably to complete tasks that take human experts a full day by 2028, a full year by 2034, and a full century by 2037. " Source: https://sequoiacap.com/article/2026-this-is-agi/