ai

Overheard : On constant increase in expectations

Sam Altman’s June 10, 2025 post on achieving singularity captured something I’ve been thinking about lately. There’s a particular passage that perfectly describes how we’re constantly ratcheting up our expectations:

Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes.

This hits at something fundamental about human psychology. We have this remarkable ability to normalize the extraordinary, almost immediately.

I see this everywhere now. My kids casually ask AI to help with homework in ways that would have seemed like science fiction just three years ago. We’ve gone from “can AI write coherent sentences?” to “why can’t it write a perfect screenplay?” in what feels like months.

The progression Altman describes—paragraph to novel, diagnosis to cure, program to company—isn’t just about AI capabilities scaling up. It’s about how our mental models adjust. Each breakthrough becomes the new baseline, not the ceiling.

What struck me most is his phrase: “wonders become routine, and then table stakes.” That’s exactly it. The wonder doesn’t disappear because the technology got worse—it disappears because we got used to it. And then we need something even more impressive to feel that same sense of possibility.

Overheard : AI needs cloud

On The Verge‘s Decoder podcast, Matt Garman, CEO of AWS, explained why AI’s potential is intrinsically tied to the cloud. The scale and complexity of modern AI models demand infrastructure that only major cloud providers can deliver

You’re not going to be able to get a lot of the value that’s promised from AI from a server running in your basement, it’s just not possible. The technology won’t be there, the hardware won’t be there, the models won’t live there, et cetera. And so, in many ways, I think it’s a tailwind to that cloud migration because we see with customers, forget proof of concepts … You can run a proof of concept anywhere. I think the world has proven over the last couple of years you can run lots and lots and lots of proof of concepts, but as soon as you start to think about production, and integrating into your production data, you need that data in the cloud so the models can interact with it and you can have it as part of your system.

Agency for AI Agents

Huggingface just released their agentic library to interact with LLMs. I liked the way they define agents.

AI Agents are programs where LLM outputs control the workflow.

And the way they defined the spectrum of agency for the agents

30 day challenge : create software with AI

I like to do 30 day challenges to explore new areas, or to form habits. Some of my previous ones were

I am starting a new challenge today, to create software by leveraging AI. The recent boom in AI and GenAI specifically has made it very easy and quick to bring your ideas to fruition. It is time to start coding and developing software for ideas that have been swirling in my head for sometime.

I will be publishing them at https://kudithipudi.org/lab . I will expand and write up about some ideas and the experience in bringing them to life.

Inspired by https://tools.simonwillison.net/.

HOW TO : Run Anthropic Computer Use Tool on a Windows Machine

Anthropic released their new Claude Sonnet 3.5 model yesterday that has a new capability to control computers. Computer Use capability allows Claude to directly interact with computer interfaces, enabling tasks like web browsing, data analysis, and file manipulation – all through natural language instructions. Similar to tools, but now you don’t have to define specific tools. I think this opens up a whole new window of opportunities to leverage LLMs for.

Anthropic shared a quick start guide to run the model in a container, but the instructions are for Mac/Linux based workstations. I had to make some tweaks to run them on a windows workstation.

Documenting them for anyone that might be trying to do the same

  • Install Docker Desktop
  • Open a command prompt
  • Run the following command to set your anthropic api key system variable
    • set ANTHROPIC_API_KEY=YOUR-ANTHROPIC-KEY
  • Run the following command to start the docker container
    • docker run -e ANTHROPIC_API_KEY=%ANTHROPIC_API_KEY% -v $HOME/.anthropic:/home/computeruse/.anthropic -p 5900:5900 -p 8501:8501 -p 6080:6080 -p 8080:8080 -it ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest
  • Launch the streamlit app by opening this URL in your browser http://localhost:8080/