Insights into ChatGPT's Versatility

The key insights from these article sections are: - OpenAI has made changes to large language models (LLMs) like GPT3 to make them more responsive to human interaction, allowing them to be used as powerful, general-purpose information-processing tools. - These changes enable AI models like GPT3.5 and GPT4 to be used in a programming manner, using new data instead of training, making AI more of a general-purpose tool. - Generative AI, like GPT4, is probabilistic and predicts the probability of words and phrases based on input, making it flexible but also unpredictable. - Training LLMs involves using gradient descent to adjust the neural network’s parameters and make incremental improvements to produce coherent output.
Original article: How ChatGPT turned generative AI into an anything tool
Featured writing
Why customer tools are organized wrong
This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.
Infrastructure shapes thought
The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.
Server-Side Dashboard Architecture: Why Moving Data Fetching Off the Browser Changes Everything
How choosing server-side rendering solved security, CORS, and credential management problems I didn't know I had.
Books
The Work of Being (in progress)
A book on AI, judgment, and staying human at work.
The Practice of Work (in progress)
Practical essays on how work actually gets done.
Recent writing
We always panic about new tools (and we're always wrong)
Every time a new tool emerges for making or manipulating symbols, we panic. The pattern is so consistent it's almost embarrassing. Here's what happened each time.
Dev reflection - February 03, 2026
I've been thinking about constraints today. Not the kind that block you—the kind that clarify. There's a difference, and most people miss it.
When execution becomes cheap, ideas become expensive
This article reveals a fundamental shift in how organizations operate: as AI makes execution nearly instantaneous, the bottleneck has moved from implementation to decision-making. Understanding this transition is critical for anyone leading teams or making strategic choices in an AI-enabled world.
Notes and related thinking
It's going to take a century for artifical intelligence to be able to perform most human jobs. But there are going to be some key developments during the next decade.
Explore how AI will transform jobs in the next decade, from enhancing security to automating coding, reshaping the future of work.
Many businesses are not yet prepared to fully reap the benefits of AI.
Unlock AI's true potential for your business by integrating it into your strategy, boosting productivity, and enhancing customer experiences.
Rose-tinted predictions for artificial intelligence’s grand achievements will be swept aside by underwhelming performance and dangerous results.
Explore the reality of generative AI in 2024 as hype fades, revealing limitations, job displacement, and the need for regulation.