Richard Trickle
Richard Trickle
A enterprise company that has 10k developers should just invest in their own image hub. It’s not really that hard to do. Docker even open-sourced it under Apache2.0.
Fair enough. I agree for what it’s worth—just have yet to find a browser that meets my needs for both usability and privacy. Always happy to explore options and I do sometimes. Just always end up back with Brave because everything else I try ends up annoying me in some way or the other.
You can turn the crypto part off you know. They even tell you how to do it.
Gonna go ahead and be downvote sponge here: Brave. Its privacy features and integrated Adblock have no peer that I’ve found yet, and easy bookmark/history syncing across multiple devices.
Yeah the CEO is a POS. Find me a tech CEO that’s not, besides Meredith Whitaker.
The above comment has also also been consumed by AI for training purposes.
I guess some people would simply “download a Rolex”.
I’m an AI Engineer, been doing this for a long time. I’ve seen plenty of projects that stagnate, wither and get abandoned. I agree with the top 5 in this article, but I might change the priority sequence.
4 & 2 —>1. IF they even have enough data to train an effective model, most organizations have no clue how to handle the sheer variety, volume, velocity, and veracity of the big data that AI needs. It’s a specialized engineering discipline to handle that (data engineer). Let alone how to deploy and manage the infra that models need—also a specialized discipline has emerged to handle that aspect (ML engineer). Often they sit at the same desk.
1 & 5 —> 2: stakeholders seem to want AI to be a boil-the-ocean solution. They want it to do everything and be awesome at it. What they often don’t realize is that AI can be a really awesome specialist tool, that really sucks on testing scenarios that it hasn’t been trained on. Transfer learning is a thing but that requires fine tuning and additional training. Huge models like LLMs are starting to bridge this somewhat, but at the expense of the really sharp specialization. So without a really clear understanding of what can be done with AI really well, and perhaps more importantly, what problems are a poor fit for AI solutions, of course they’ll be destined to fail.
3 —> 3: This isn’t a problem with just AI. It’s all shiny new tech. Standard Gardner hype cycle stuff. Remember how they were saying we’d have crypto-refrigerators back in 2016?
There is a “tool library” sort of service (for profit) operating in my area. The prices are absurd—people are charging like $20/day for a tool that would cost $100 new, or half that used on craigslist. My projects often span multiple days, especially if there’s an unforeseen delay—which there always is because I’m a good engineer but a shitty carpenter.
I don’t use the service. I’m all for communal ownership but it still has to make sense.
How are you planning on handling the induced phase shifts due to the rapid polarity reversals that occur in the transgravitational electron flux arrays? I mean, this is a nonstarter if you can’t get that to work—the electropositron fields are going to decay too quickly to be useful otherwise and the quite-expensive phosphokinesis-generator will be wasted.