I will be at GenAI Zurich conference on April 1st-2nd learning about Applied Generative AI, Agentic Systems, enterprise automation, physical AI, and a lot more!
I’m really excited to learn from the best European experts that are building the future of intelligence, from Google Deepmind, HuggingFace, AlpineAI…
I already made the list of all the talks I will be attending. Have you?
If you are going, DM me and let’s connect there :)
#GenAIZurich #conference #zurich
@SumitM_X Wow indeed that was insightful, thank you
Why not using tries? I mean O(1) lookup is better than O(log(n)) for sure. Also it seems a bit array consumes way less memory.
Yeah ok I answered myself haha
@SumitM_X Each time a new user registers its hardcoded into a frontend variable
O(1) look up
backend untouched
scales efficiently
no cache misses
You are welcome
@KevinNaughtonJr Absolute cinema
Honestly this is encouraging. No flashy “from 0 to $10k/month in 30 days” vibe. You were consistent, smooth and solid growth.
Keep the vibe man 🚀
Today I inferenced AWS Bedrock with Spring AI, connected it to a PostgreSQL DB, created tools to let AI interact with the DB, and dockerized the app.
Spring AI is the new Spring offering that allows you to connect to any model provider, embedding, add tools seamlessly, and even manages memory and trims messages to handle the context!
It also integrates seamlessly with PostgreSQL, allowing you to save all messages inside your DB without too much effort.
This app I did allows the Bedrock model to create personas and query them, as well as get current UTC time statically.
A really nice tool! Like LangChain but for Spring!
100% good things take time
And I understand you. I’ve learnt that first we must build systems that make the journey sustainable and take us closer to our goals.
There is a phrase I like that is “you don’t get to the finish line by burning the engine down by pressing the pedal as hard as you can”.
The ones that “win” are those who can be in the race for longer.
Keep going Kevin! From time to time you appear in my timeline and I’m glad that things go well to you :)
The more I read about physical/embodied AI the more I get hyped about it, and it’s the next frontier. AI is good when we give it steroids (HUGE GPUs), but taking AI to robots it’s way more challenging than people think.
You have to fight against even bigger constraints than traditional AI, you CANT just spawn 3 new EC2 instances or ask for a better GPU at running time.
You want polished behavior? Cool, go and fine tune a small model, otherwise you just get a prototype.
You want to fine tune it? Good luck finding and cleaning real world data AND training the model (this is where tools like NVIDIA Cosmos/Omniverse and Edge Impulse come in…)
You want a big model because they are better and more general? Good luck fiting a big chunk of GPU in a robot AND powering it with batteries 🫠
You want the most generic AI possible? Nope. You will have to choose, otherwise you will be trying to be better than the frontier of embodied AI, side by side with actors like Figure, Optimus or Boston Dynamics.
Do you want to use the Cloud for your robots? Well, for some use cases maybe ok, but latency will completely block you from lot of use cases 😅 also you rely on good internet connection AND bandwidth
My next talk is going to be about Edge AI, and I believe that people in this space must be familiarized at least with the promise, the constraints, tools, techniques, main actors, and real solutions 🚀