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Accenture article is more like like PR and doesn't sound very practical.

It claim the current beat agent is from Anthropic Sonnet.

May be they're just little cautious

Great question, and one I've pondered a bit myself. Both Python and Ruby are cool languages, no doubt. But here's my two cents on why Python took off like a rocket while Ruby stayed more niche.

Versatility: Python can do anything—web dev, data analysis, machine learning, you name it. That made it a Swiss Army knife for devs and businesses alike.

Academia and Data Science: Python got super lucky with the boom in data science and AI. Libraries like NumPy, pandas, and TensorFlow sealed the deal for it in the scientific and research communities.

Readability: Python's clean syntax is a godsend for newcomers and seasoned devs alike. This led to quick adoption and a growing community. Community and Ecosystem: The Python community is massive. A bigger community usually means better support and more libraries, which feeds back into its growth.

Enterprise Adoption: Big names like Google, Instagram, and Dropbox using Python also adds credibility and draws even more people into the ecosystem. Ruby = Rails: Ruby got strongly tied to Rails. While Rails is fantastic for web dev, it kinda pigeonholed Ruby a bit.

First-mover advantages: Python's been around a bit longer, and early adoption in academic circles helped it get a head start.

Teaching Language: Python has become the go-to language for introductory computer science courses. That means a whole new generation is coming up already familiar with Python.

Ruby's awesome and it’s got its own strengths (and die-hard fans!). But Python managed to position itself as a jack-of-all-trades, and that’s helped it become the titan it is today.

For an EV manufacturer to truly succeed, it's not just about making electric cars. They also need to heavily focus on the recharging network and infrastructure. The car is just one piece of the puzzle. The real challenge? I think is setting up a dependable recharging network cover the entire region/country.

Good point - my personal experience has been that you get to respect more if you're architect or hands-on tech manager.

If you tend to become more people engg. manager - you'll be compared to program or project manager after few years

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Nice - do you mind sharing the prompt used to generate the story

ChatGPT has made 'Text inputs with no affordances' a popular talking point. While users learn over time what works, the burden of discovering effective prompts remains.

How can we improve this experience?

I do 16hrs fasting from 8 pm-12 noon the next day. I keep it after work, starting with an early dinner.

Does API support pulling data both account level and individual tweet level? A readme will help to understand the capabilities

Twitter faces two major financial threats: pending employment lawsuits and massive fines from regulatory violations.

On top of this, the platform's recent efforts to limit API usage have negatively affected both regular users and SaaS products that rely on Twitter analytics. With these mounting challenges, the company's survival is increasingly uncertain, and some are predicting bankruptcy as a potential outcome. The long-term impact of these developments remains to be seen.

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I agree that the Google business model doesn't fully accommodate conversational search UI, but LLMs can still play a valuable role in this area. In terms of a parallel, you can think of the "I'm Feeling Lucky Today" button as a way to bypass the standard search results and take a chance on a single, top result. Similarly, using LLMs in web search applications can provide a more conversational and personalized experience for users, offering a different way to interact with and find information on the web.

That being said, I don't think ChatGPT or any single LLM can replace mainstream Internet search use cases in the immediate future. They might enhance the search experience for users

Thanks for the feedback! I guess I'll take it as a compliment. It just goes to show how far language models like ChatGPT have come.

Of course, I always have room for improvement when I comment next time.

For steps I can think off: a) Data encryption, I meant we should encrypt all the data that ChatGPT processes, so even if someone somehow gets ahold of it, they won't be able to read it. b) Access control, We also strictly control who can access the data, so only the right people can see it. c) Employee training, awareness and compliance with privacy regulations like GDPR

The above steps are relevant to ChatGPT because they help ensure the security and privacy of the data being processed and stored by the model.

hope this makes sense

While the potential benefits of language models like ChatGPT are vast, it's crucial that we also consider the potential implications on data privacy and take steps to address them as required.