I am working with full stack solutions consisting of node.js and React among other things. Different app solutions both for mobile and web. I most often can’t see any use case for AI/ML in our products but I suspect that it is easier to see opportunities when you have some experience with the tools. Any ideas on how I can keep up my learning in these areas so that I stay relevant as a software engineer in the long run? I know that it is a general topic but I think that it is important to stay up to date on the subject.
How to keep up with AI/ML as a full stack dev?
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Re: How to keep up with AI/ML as a full stack dev?
#2"Generative" AI/ML is moving so fast in so many directions that keeping up is a challenge even if you're trying really hard to stay current!
I'm part of a team building developer tools for real-time AI use cases (voice and video). I feel like I have three overlapping perspectives and goals re this new stuff:
1. To figure out what we should build I need to have a good understanding of what's possible and useful right now.
2. I talk to our customers a lot. Helping them understand what's possible and useful today (and what that might look like six months or a year from now) is part of my job.
3. I think this is a step-function change in what computers are good at, and that's really exciting and intellectually interesting.
My AI information diet right now is a few podcasts, twitter, and email newsletters. A few links:
- Latent space podcast and newsletter: https://www.latent.space/podcast
- Ben's Bites newsletter: https://news.bensbites.com/
- Ethan Mollick newsletter: https://www.oneusefulthing.org/
- Zvi Mowshowitz newsletter: https://thezvi.substack.com/
- Rohan Paul twitter: https://x.com/rohanpaul_aiRe: How to keep up with AI/ML as a full stack dev?
#3"Generative" AI/ML is moving so fast in so many directions that keeping up is a challenge even if you're trying really hard to stay current! I'm part of a team building developer tools for real-time AI use cases (voice and video). I feel like I have three overlapping perspectives and goals re this new stuff: 1. To figure out what we should build I need to have a good understanding of what's possible and useful right…
(got pinged here from f5bot) thanks so much kwindla :)
always looking for ideas on how to serve this audience better. feel like there could be more I should be doing.
Re: How to keep up with AI/ML as a full stack dev?
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Re: How to keep up with AI/ML as a full stack dev?
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Re: How to keep up with AI/ML as a full stack dev?
#6I'm going to answer more from an applied perspective than 'real' ML.
Hacker News is a good source for news.
As far as learning, you have to build something.
I suggest you just start with example code from the OpenAI or Anthropic website for using the chat completion API. They have Node.js code.
r/locallama on reddit is interesting.
On Youtube, see Matt Wolfe, Matthew Berman, David Shapiro. Not really developer-focused but will mention developments.
You can search for terms like 'AI Engineer' or "agentic" or "LangChain" on youtube also.
To get motivated, maybe play around with the replicate.com API. It has cut and paste examples and many interesting models.
More ideas: search for "Crew AI" on X/Twitter.
Re: How to keep up with AI/ML as a full stack dev?
#7As an ML researcher my advice for you is: don't
ML moves fast, but not as fast as you probably think. There's a difference between innovations in architectures and demonstrations of them in domains (both are useful, both are necessary research, but they are different).
Instead, keep up with what tools are relevant to you. If things are moving fast and aren't sticking, then in a way they aren't moving fast, are they? You're just chasing hype and you'll never keep up.
On the production side, I also see a common mistake of relying on benchmarks too heavily. I understand why this happens, but the truth is more nuanced than this. Just because something works well on a benchmark does not mean it will work well (or better than others) on your application. ResNet is still commonly used and still a great option for many applications. Not everything needs a 1B+ transformer. Consider your constraints: performance, compute, resource costs, inference time, and all that jazz. Right now if you have familiarity (no need for expertise) in FFNs (feed forward/linear), CNNs, ResNets, and Transformers, you're going to be fine. Though I'd encourage you to learn further about training procedures like GANs (commonly mistaken as an architecture), unsupervised pretraining (DINO), and tuning. It may be helpful to learn a high level of diffusion and LLMs, but it depends on your use cases. (And learn whatever you're interested in and you find passion in! Don't let need stop you, but if you don't find interest in this stuff, don't worry either. You won't be left behind)
If you aren't just integrating tools and need to tune models, then do spend time learning this and focusing on generalization. The major lessons learned here have not drastically changed for decades and it is likely to be that way. We do continue to learn and get better, but this doesn't happen in leaps and bounds. So it is okay if you periodically revisit instead of trying to keep up in real time. Because in real time, gamechangers are infrequent (of course everyone wants to advertise being a gamechanger, but we're not chasing every new programing language right?). Let the test of time reduce the noise for you.
> I most often can’t see any use case for AI/ML in our products
Look to solve problems and then right tool for the problem, don't look for problems to justify a tool (fine for educational purposes).
Re: How to keep up with AI/ML as a full stack dev?
#8Here’s the plan:
Run the following models:
- Speech-to-text - Text-to-text - Text-to-speech - Text-to-image - Image-to-text - Text-to-video - Video-to-text
Start by integrating third-party APIs, and later switch to open-source models.
Implement everything using your preferred backend language. After that, connect it to a frontend framework of your choice to create interactive interfaces.
You want use your own data? Put it in a database and connect it to your backend, and run these models on your database.
Once you’ve done this, you’ll have completed your full stack development training.
Re: How to keep up with AI/ML as a full stack dev?
#9> I most often can’t see any use case for AI/ML
I'm admittedly a skeptic on all this so take what I am about to say with a grain of salt: You should trust that voice. We're in a hype cycle. It was VR before and crypto before that. Big tech is trying _very_ hard to convince you that you need this. They need you to need this tech because they are lighting billions on fire right now trying to make it smart enough to do anything useful. Short of them coming up with a truly miraculous breakthrough in the next 12 to 24 months (very unlikely but theres always a chance) investors are gonna get fed up and turn off the money fountain.
It's always a good idea to learn and grow your skillset. I am just not sure this is an investment that will pay off.
Re: How to keep up with AI/ML as a full stack dev?
#10> I most often can’t see any use case for AI/ML I'm admittedly a skeptic on all this so take what I am about to say with a grain of salt: You should trust that voice. We're in a hype cycle. It was VR before and crypto before that. Big tech is trying _very_ hard to convince you that you need this. They need you to need this tech because they are lighting billions on fire right now trying to make it smart enough to do…
ML researcher here.
I will second this. Even if you think localghost is wrong about AI, it is important to always trust that voice of skepticism (to a limit).
But I will say that we are in a hype cycle and as a researcher I'm specifically worried about this. I get that we have to bootstrap because you can't say "we want to spend money on research" (why?), but if you make a bubble the goal is to fill that bubble before it pops. The more hype you make, the more money you get, but the quicker that bubble pops. My concern here is that too much hype makes it difficult to distinguish charlatans form honest people. Charlatans will jump from cool topic to the next (don't trust someone who was a VR founder, then a crypto founder, and now a ML founder. Trust people who have experience and can stick with a topic for longer than a hype cycle).
The big danger, is if charlatans dominate the space, the hype disappears, and then there is no money for everyone. So if you do believe in the possibility of AGI and that AI/ML can make the world better (I truly do), make sure that we don't over hype. There's already growing discontent for products pushed too early with too big promises. If you really believe (like I do), you have to get rid of the bad apples before they spoil the whole barrel.