Let someone call themselves whatever they want. If they can do the job they were hired for then... who cares?
Well, most are called "project manager" now. But it would still be a giant red flag, just like the project manager job title or even worse, using PM so you don't know exactly what it means.
I'm only suspicious if they don't simultaneously and eagerly show me their Github so that I can see what they've accomplished.
Sometimes this industry is a lot like the "finance" industry: People struggling for credibility talk about it constantly, everywhere. They flex and bloviate and look for surrogates for accomplishments wherever they can be found. Peacocking on github, writing yet another tutorial on what tokens are and how embeddings work, etc.
That obviously doesn't mean in all cases, and there are loads of stellar talents that have a strong online presence. But by itself it is close to meaningless, and my experience is that it is usually a negative indicator.
Is it just me? Why are people using them? I feel like objectively they look like fake garbage, but obviously that must be my subjective biases, because people keep using them.
Some people can recognize these shortcomings and simply don't care. They are fundamentally nihilists for whom quantity itself is the only important quality.
Either way, these hero images are a convenient cue to stop reading: nothing of value will be found below.
If you don't like such content. But I would say don't judge a book by its cover.
Reminds me of the image attached to Karpathy's (one of the founding members of openAI) twitter post on founding an education AI lab:
https://x.com/karpathy/status/1813263734707790301
I just don't understand how he didn't take 10 seconds to review the image before attaching it. If the image is emblematic of the power of AI, I wouldn't have a lot of faith in the aforementioned company.
If you're going to use GenAI (stable diffusion, flux) to generate an image, at least take the time to learn some basic photobashing skills, inpainting, etc.
Last time I worked on my laptop on a trestle table in the forest at dusk it looked almost exactly like this.
It is like standing in front of a Zara, and wondering why people are in that shop, and not in the Versace shop across town. Surely, if you cannot afford Versace, you rather walk naked?
I have been running the 32B parameters qwen2.5-coder model on my 32G M2 Mac and and it is a huge help with coding.
The llama3.3-vision model does a great job processing screen shots. Small models like smollm2:latest can process a lot of text locally, very fast.
Open source front ends like Open WebUI are improving rapidly.
All the tools are lining up for do it yourself local AI.
The only commercial vendor right now that I think is doing a fairly good job at an integrated AI workflow is Google. Last month I had all my email directed to my gmail account, and the Gemini Advanced web app did a really good job integrating email, calendar, and google docs. Job well done. That said, I am back to using ProtonMail and trying to build local AIs for my workflows.
I am writing a book on the topic of local, personal, and private AIs.
Still, I would really prefer everything running under my own control.
I did a quick and dirty prototype with Claud for this, but it returned everything with an offset and/or scaled.
Would be a killer app to be able to auto-fill any form using OCR.
You truly know how to align yourself with hype cycles?
my barometer for penetration is how often the non-tech people talk about it, e.g. goofball uncle didn't buy a drone, but he went hard on BTC. if he's still holding he probably made money recently, too.
If humans were as bad as LLMs at basic math and logic, we would consider them developmentally challenged. Yet this constant insistence that humans are categorically worse than, or at best no better than, LLMs persists. It's a weird, almost religious belief in the superiority of the machine even in spite of obvious evidence to the contrary.
Ineffective seems harsh.
If a model goes sideways how do you fix that? Could you find and fix flaws in the base model?
There's a lot of tooling out there making this accessible to someone with a solid full-stack engineering background.
Training an LLM from scratch is a different beast, but that knowledge honestly isn't too practical for everyday engineers given even if you had the knowledge you wouldn't necessarily have the resources necessary to train a competitive model. Of course you could command a high salary working for the orgs who do have these resources! One caveat is there are orgs doing serious post-training even with unsupervised techniques to take a base model and reeaaaaaally bake in domain-specific knowledge/context. Honestly I wonder if even that is unaccessible to pull off. You get a lot of wiggle-room and margin for error when post-training a well-built base model because of transfer learning.
So individual apps don't need to do anything to have AI.
Basically what chatgpt did for chatbots, but at app level. There are lots of apps that take a long time to master. But the average joe doesn't need to master them. If I want to lightly edit some photos, I know photoshop can do it, but I have no clue where that specific thing is in the menus, because I haven't used it in 10 years. But it would be cool to type in a chat box "take all the pictures from my sd card, adjust the colors, straighten the ones that need it, and put them in my Pictures folder under "trip to the sea". And then I can go do something else for the 30-60 minutes it would have taken me to google how to do all of that, or script something, etc.
The ideea of an assistant that can work like that isn't that far-fetched today, IMO. The apps need to expose some APIs, and the "os" needs an language -> action model capable enough to handle basic stuff for average joes. I'd bet good money sonnet3.5 + proper APIs + a bit of fine-tuning could do it today for 50%+ of average user cases.
Most of the ML candidates I see now are all "working with LLMs". Most of the ML engineers I know in the industry who are actually shipping valuable models, are not.
Cool, you made a chatbot that annoys your users.
Let me know when you've shipped a fraud model that requires four 9's, 100ms latency, with 50,000 calls an hour, 80% recall and 50% precision.
You might say this is about Helix being small and trying to break into a crowded market, but OpenAI and Google offered similar contests / offers that asked users to submit ideas for LLM applications. Considering how many LLM sample apps are either totally useless ("Walter the Bavarian, a chatbot who gives trivia about Oktoberfest!") or could be better solved by classical programming ("a GPT that automatically converts currencies to USD!), it seems AI developers have struggled to find a single marketable use case of LLMs outside of codegen.
I hope there will still be room for devs in the future.
I could go on and on.
Copy paste is great until you literally dont know where you are copy and pasting
But that's akin to web devs of old that stitched up some cruft in Perl or PHP and got their databases wiped by someone entering a SQL username. Yes, it kind of works under ideal conditions, but can you fix it when it breaks? Can you hedge against all or most relevant risks?
Probably not. Don't put it your toys into production, and don't tell other people you're a professional at it until you know how to fix and hedge and can be transparent about it with the people giving you money.
