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.

Overheard : Good business vs Bad Business

A simple visual of attributes of Good business vs Bad business based on a snippet Codie Sanchez shared in a podcast with Shane Parrish

✅ GOOD BUSINESS

  • 💰 Profitable + Cash flowing
  • 🤝 Get paid upfront
  • 📈 Long history of success
  • 👵 Easy to explain to grandma
  • ♻️ Sustainable model
  • 🎯 Predictable future

❌ BAD BUSINESS

  • 📉 Unprofitable
  • ⏳ Pay comes after service
  • 🌱 New/unproven model
  • 🤔 Complex to explain
  • 🎲 Uncertain future

Codie said

In my definition, good business equals profitable, cash flowing, what I call a cash-flow versus cash-suck business (so you get paid upfront for a service, not after you provide a service), sustainable (it can exist for a long time), historical (it has existed for a long time), understandable (you can explain it to grandma really easily), and you have what’s called the Lindy effect, the likelihood of the future continuing to cash-flow just as it did in the past. Those are my parameters for a good business. A bad business would be a business that is unprofitable, hard to understand, hasn’t been around for very long, and you have to provide the service before you get paid for the service. That is a business that is just much harder. That’s a harder game to win.

Overheard : Worthless friends vs Transactional friends

Codie Sanchez quoting Prof. Arthur Brooks on different types of friendship in a conversation with Shane Parrish.

Worthless friends are the friends that have no transactional value. You don’t want anything from them. They don’t want anything from you. They want to hang out with you. They want to go on a walk with you. They don’t want your email list. They don’t want access to your money. They just want to have a beer on a Friday night. And these friendships end up materially increasing, our happiness, these worthless friends, whereas these transactional friendships actually end up, in many ways, decreasing our happiness

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

Overheard : India 1-2-3

Great discussion between Jim O’Shaughnessy and Sajith Pai on the India as a market in the Infinite Loops Podcast. Sajith did a great job describing India as a combination of three markets and not a monolithic market of 1.5 billion people.

India is not a 1.5-billion-person market that many Westerners believe. Instead, it’s three distinct “countries” hiding in plain sight. There’s India One: 120 million affluent, English-speaking urbanites (think the population of Germany) who love their iPhones and Starbucks. Then comes India Two: 300 million aspiring middle-class citizens who inhabit the digital economy but not yet the consumption economy. Finally, there’s India Three: a massive population with a similar demographic profile to Sub-Saharan Africa, that’s still waiting for its invitation to join India’s bright future.

Highly recommend checking out the podcast and this report (on Indus Valley – a play on words comparing the market in India to the tech market in Silicon valley) that Sajith and team put together.

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/

HOW TO : Troubleshoot Zscaler client

I recently encountered some connectivity issues while working from home and trying to access some corporate resources. Notes for myself on some tips our infosec team shared to troubleshoot the Zscaler client since all the traffic to the interweb gets routed through it.

  • http://speedtest.zscaler.com/perf
    • Gives you an overview of which Zscaler pop you are connecting to and access speed to the Internet via that pop.
  • http://127.0.0.1:9000/?ztest?q=@YOUR-CORPORATE-DOMAIN (ex: google.com)
    • This provides a detailed report, including:
      • DNS Reachability Test: Confirms if DNS is resolving correctly.
      • UDP Connectivity Test: Checks if UDP packets can pass through.
      • TraceRoute to Zscaler: Shows the path your data takes to reach Zscaler.
      • Throttling Test: Identifies any speed drops.
      • Download/Upload Bandwidth: Measures the speed at which data transfers.
  • https://ip.zscaler.com
    • A quick utility to check where and how your traffic is routed through the Zscaler network. Very similar to the perf test data, but doesn’t let you run a performance test.