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Daily Edition · Page 2

THE DAILY INDEX

Technology, AI, software, security, crypto and business

PROGRAMMING, DESIGN & DATA SCIENCE

TLDR

TLDR

THE MAGIC OF SOFTWARE; OR, WHAT MAKES A GOOD ENGINEER ALSO MAKES A GOOD ENGINEERING ORGANIZATION

Many of today's best practices were drawn from long-established internet companies like Google. However, the problem with copying its current practices on the basis of its success is that most of those companies found near-invincible business models, and so almost any practice developed or selected at random likely would continue to be successful to some degree. The practices and cultures that have developed at these companies more often reflect optimizations for the benefit of various decision makers rather than optimizations for actually creating something great. The qualities that make a good engineer are often the same ones that create good engineering organizations - both start with deep understanding as the basis for innovation and recognize that vision and engineering are often deeply intertwined and mutually informative rather than linear.

moxie.org · 10 min

TLDR

KNOW YOUR ENEMY: HOW THREE YEARS AT MCKINSEY SHAPED MY SECOND STARTUP

Zac Townsend was an Associate Partner at McKinsey. This post talks about the reasons he joined the company, his time there, the projects he worked on, and the lessons he learned from the experience. After working there for three years, Townsend learned that it is impossible to fix institutions that have gotten too big - there are too many people, too many processes, and too much regulatory overhang. This led him to create his own company.

blog.zactownsend.com · 7 min

TLDR

WORKING WITH LLMS: A FEW LESSONS

Large Language Models (LLMs) are inherently probabilistic, which means you have to add evaluation frameworks, human-in-the-loop processes, and design for graceful failure, using LLMs for probabilistic guidance rather than deterministic answers. Unlike with traditional software, there is no way to get better at using AI than using AI - there is no substitute for trial and error. This makes it hard to predict development and makes building future-proof technology almost impossible. While software is inexpensive to distribute once written, LLMs have continued costs.

strangeloopcanon.com · 7 min

TLDR IndexPage 2