larsonnn

When you are sharing the calendar it will be synced on every device. And yes as a family we go through all dates on Sunday for the next week.

Which is a nice time with the family we are sitting drinking coffee also with other family members.

When you Lack so much organization skills the AI would never get the data to write you this.

I argue that this kind of tools are fun to play but in the end is it really helpful? I start my day like every day and on work I just check the calendar. My private calendar has all Information i need. Where is the gap where an Assistent makes sense and where we are just complicating our lives?

> Good engineers know how to influence others and the organization to deliver a solution as a team.

This is a Problem, assuming someone is good at talking is also a good engineer.

> Good engineers constantly work on reducing complexity in the codebase to consistently provide high quality.

Complexity for the manager? Who is deciding how complex a system is. This could also be a skill issue with the „team“ by taking skilled engineers and mix them with not so skilled ones.

Overall the article is written for managers which try to add a image of an engineer which is like a manager. Influence people and give up on complex stuff.

Site is blocking Apples private relay :(

From my perspective, hailing from Germany, I don't anticipate a decline in developer compensation. Instead, I foresee a future where companies will be selective, opting for developers who are adept at integrating AI into their workflow.

As we begin to reshape our approach to project construction, the inclusion of AI systems will become a standard component in our calculations. This realization will inevitably lead us to understand that certain tasks no longer require as many developers as they once did. Consequently, there might be a reduction in hiring rates for developers.

Nevertheless, I remain optimistic that while the demand for developers might become more selective, their compensation could potentially increase due to the specialized skills required in an AI-integrated landscape.

Interesting read. Just to add, even HTTPS isn't completely safe from Man-in-the-Middle attacks, especially in corporate environments. A few points to consider:

- Some apps bypass or poorly implement certificate pinning.

- Companies using self-signed certs can be spoofed.

- There's always the risk of compromised Certificate Authorities.

- Corporate tools that inspect HTTPS traffic, while useful for security, can inadvertently become a vulnerability if misconfigured or hacked.

Always good to remember: HTTPS is a protective layer, but not an unbreakable barrier.

Every advancement that simplifies coding potentially displaces a job. If tools like GPT-4 amplify my efficiency by even 1-5x, it's not just code— it implies a broader workload shift across the tech sector.

Pretty simple it’s just like any abstraction. This AI will not work when nobody would deliver the answer beforehand. LLMs are given inputs of existing code. When you abstract that you better hope you have good code in it.

So my question would be, what is the use case?

I guess it’s more like planing software and not implementing it.

You can pretty well plan your software with ChatGPT. But it will just help you not really doing the job.

The demo already shows the problem. It picks an event, the AI picks Earth Day. Then it makes a recipe with an avocado in it.

And writes it is good for the planet. Avocados are exactly the opposite. They have an extremely high water consumption and then have to be imported from all over the world.

Contrary to the author who claims, "Auto-GPT pushes the boundaries of what is possible with AI." I don't find that.

Why can't you just use GPT-4 as it is. It is an insanely tool to simplify many things. But it's still a long way from being ready, and it's not meant to decide anything on its own. And even to reflect reasonably out of own motivation.

I bet they post sensitive data to all platform which may help them.

Also I guess chat with Microsoft teams or slack is ok.

You are enlightened.

ML is a tool for results that are just good enough. But people think they can program with it.

LLMs are a good way to get a glimpse of the right answer you still need to get deeper look in topics for:

A: validate the result of the LLM

B: get a better understanding of your topic

But is this trade off not a little bit to much?

It reminds me of Twitter or other companies which starting to change programming languages for performance reasons.

So true and so annoying.

When you think, it’s not worth it, Try to imagine when your software not run once, but runs a few thousand times or more per second.

So by having some operations exceptional faster you could not only save time also you save energy.

SPOILER ALERT:

did you play it reverse? I bet there is some hidden message…