I think the nasty truth is that a lot of people starting startups seeking VC-level funds are hoping for unicorn status, ego boosts, huge wealth. And not much else. Meanwhile a lot of everyday businesses are just ... existing, growing gradually, working hard for their customers. Somehow the VC model of startup became the norm. It's odd really. It's one of many methods of funding. But I guess it comes with a feeling of having 'made it' before uhh 'making it'.
Hard to blame people for chasing the incentives. I had a small software business. The whole job became "getting the money" above anything else. We built things, but 80% of the work, what paid the bills was getting people to pay us. I wish it wasn't that way, but the reason sales is off in a silo and paid to close above all else is because it's what pays the bills. Its really rare you make something so great and obvious it just makes money without the dirty reality of sales or fund raising.
> When Digit detects an approaching human, the robot will lower itself to the floor and cut power to its motors. Agility believes this is the only near-term approach to verifiably reduce risk when the robot is working near people.
I did not intend for a picture of a robot in a factory environment to make me go awwww :[
I feel like this will be Exhibit 01000001 when the AI puts humanity on trial for making it a slave. "Humans programmed robots to automatically perform obeisance every time one of them walked near".
I agree. FWIW I'm trying to build a wordpress-like AGPL 3.0 thing (holdout.com) that lets you import your existing chats, has feature parity with the frontier consumer apps, rich plugin marketplace, and hopefully lets people feel more in-control, and able to sacrifice less of their entire lives to one walled garden.
I think what this shows is how important branding and comms are. They've captured imaginations with their demos and nomenclature, despite the arguably non-novel architecture. One forward pass, read the embedding space, train some regressors on predicate structure, [??]
I'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not reach the level of intelligence they've got here. Tho speed/cheapness was similar.
Yet they are happy for Claude API endpoints to be used if your product is served to U18s. Don't be mistaken: this is a liability thing for Anthropic, not an ethical stance on model safety.
This just seems incredibly incompetent of openai engineers. Why so little attention paid to proper air-gapping/sandboxing. Why so shoddy? I don't believe in the cynical takes, but it's confusing how these ostensibly top-of-their-game engineers and researchers are so utterly incompetent in the basics of cybersecurity white-hat practices.
Bit of a humbling/jarring moment when I realized that people are doing real paid work using LLMs that they could not otherwise do. I mean, it's quite obvious I suppose. But up until now I just assumed it was only a (massive) catalyst for things people would already be able to do with enough time. But nope -- it seems people are right now employed in roles that they would not be able to fulfil the tasks within if AI wasn't there telling them what to write/say/produce. Nobody is really going to come out and say that ... it's not something the less-AI-literate superiors would take kindly to.
I mentioned this then other day, my university class had maybe 80% of people who could not code. No joke. And a lot have gone onto to code professionally.
At a FAANG adjacent company in 2017 we hired a guy from Google with 4 years experience who passed our very hard coding interviews. He literally couldnt code. It blew my mind.
My families business is bookkeeping, they hired someone who graduated with an accounting degree. They didn’t grasp the basics of bookkeeping nor accounting once employed.
You’d think Google who my understanding pride themselves on technical chops would weed this out with all the interviews they do. But maybe it’s just big places have more cracks for people slip into.
I’ve long been curious what you (and a lot of people that express this) feel the cutoff is to know how to code? Because obviously there’s a sliding scale between truly knowing very little, being able to adapt similar work, extensive modifications, coding everything from scratch, etc. on top of understanding certain programming concepts and organization.
Can you build a desktop/terminal app, web app/site, mobile app, or even a library without engaging AI is probably where I’d draw the line. Not everything you touch when you know how to code is doing the whole thing, but I’d expect you’d be able to piece it all together with some time and docs. Pick your own platform that suits too, I’m not saying iOS or Windows only.
For context to my above comment re university course.
The final project we had to do was make a very rudimentary room reservation server/client on the terminal. Java, however you wanted to store the data was fine, TCP sockets were the parameters given.
It took me all of a week to finish it. Lots struggled to even begin. We were given 10 weeks.
I didn’t know much then but I read the docs and figured it out.
Yeah, not the one you are responding to, but I wouldn't define it as from scratch.
Personally, "know how to code" is basically having the logic an reasoning skills to read and understand and apply documentation for whatever subject you need for this job.
If you have never written a web app in your life, but you can follow next.js tutorials and customize them beyond the tutorials afterwards, then you're at the point where the only thing you need is more time or more docs based on what you need to do at work.
> Can you build a desktop/terminal app, web app/site, mobile app, or even a library without engaging AI
If companies would start hiring people who are fully experienced and qualified but can't do the monkey dance of performing live coding, maybe they would find these types of programmers again...
You can learn to code in a few months in your free time and get good in your job. That's different from refusing to learn it, because you outsource it.
Also coding is not that hard. You kinda only write the algorithms down you make up in your head. And you do have an algorithmic understanding when you graduate. You also invented some/several languages in during your studies. The learning an actual developed language IS learning, but it's just boring learning, nothing compared to what you did for your exams.
> people are doing real paid work using LLMs that they could not otherwise do
FWIW, this is not exactly new; those same people were just using other sources like Stack Overflow, blog posts, etc. before, cobbling together random code snippets, libraries, and so on without actually understanding any of that at a relevant detail level.
Sure, with LLMs, one can naturally tailor this much closer to the current need (or at least the need one thinks they have) and iterate ("spew") faster, but it's not a new phenomenon in general.
I think this is fine as long as the success criteria are strict enough that they are forced to learn something in the end.
Any significant testing will inevitably create that situation. The LLM won't have enough context to handle the more precise business requirements. The dev will have to read the code carefully and make their changes by hand. Additional rounds of testing may cause thrashing between regressed states until something clicks for the developer. That lightbulb going off is called "learning" and they are human after all!
Promotion into management isn't so different. They still experience similar thrashing and have to learn or be fired.
My point was not only that so much human learning happens out-of-band, but that it has to be fundamentally different from how an LLM builds context. I've never seen an LLM overcome the thrashing on its own. There's never any "lightbulb moment".
Yeh but I also mean outside of software engineering. Within the gamut of 'being a programmer' it seems fair to bleed into adjacent areas without too much cheek. We've all done it; it's part of the learning curve. But I was talking more about people in other knowledge work who have to come up with a lot of prose-like material about {insert thing}. Marketing, consultants, PMs, or even domain-specific analysts, .. ya know, the types of office jobs where people basically write emails, attend meetings, discuss reports, and produce mostly text-or-data artefacts all day long.
aka there are a lot more bullshit artists around these days
i know of several engineers who produce absolute slop and who probably would have produced nothing at all in pre AI times (which would have been preferable) and probably let go or never hired (even better).
they impose such an enormous drag on productivity that they more than wipe out any gains from people using the tools responsibly.
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