Thanks, we had addressed our pain points, happy to see that you resonate with it!

Intelligent people see and understand the world deeper, more than an average person can, They see the world in all its forms, good and bad, understanding too much does not bring happiness but gloom. existence seems to be absurd, the more we understand existence the more one believes that it shouldn't have existed at the first place.

"happiness in intelligent people is the rarest thing I know" is a quote by Hemingway.

Also intelligent people overthink about a particular situation, while its better for survival but it can reduce happniess.

as good as it may sound, but this will never happen. People will never pay for everything, majority don't even buy PC games, they use pirated one's. This is how humans are designed, and this is what which keeps the market afloats.

I cannot make out anything from this. what are you trying to say? AI agents's fundamental task is to implement stuff, or in technical terms tool calling, computer use etc. they take input from user in natural language, and everything is perfectly defined in the prompt. The LLM is instructed to ask whenever not sure, so bootstrapping from hallucinations only happens when the implementation of AI agent is poor.

User: What's the weather in Chicago?

Layer1(LLM) to Layer2(AgentWeather): Can you tell the weather in chicago?

AgentWeather: I have a tool that's called get_weather. Should I use this?

No -> AgentWeather -> LLM: I cannot tell. LLM -> User: I cannot tell.

Yes -> AgentWeather -> LLM: The air temperature is 33°C. LLM -> User: The air temperature in Chicago is 33°C.

One example out of billions why it could go wrong.

Layer1, 2, ..., n, they are all unsupervised language models and they "communicate" via text that's converted into tokens.

What could go wrong?

The entire paper demonstrated the results of the simulation or whatever they did. They did not mention how did they achieve this simulation. running 500-1000 LLMs parallely, will take too much computing resources, neither did they prove the claim they made about their parallel architecture. I remeber there was the paper published about an AI town, in which they mentioned clearly how they implemented it. they also released a recording of the simluation along with the real data of the results. If anyone got how they implemented this paper, please tell me.

Hi guys, I made a completely multiplayer programming game for C++ and python, started it to learn graphics, multithreading and all. ended up creating a small game engine for my game (included a lot of game maths), learnt multithreading and multiprocessing deeply. the game is originally programmed in c++, so had to do everything from scratch. Later ported it to python using pyBind11, wrote the langauge bindings for python. For the multiplayer part, which took the most of the time, ended up creating my own networking protocol( over UDP) according to the game needs. you can see the project here at my website: https://aiplaygrounds.in

I was in 2nd Year of my engineering. as many engineers before going to the college i used to make computer games on C++. Games caught my interest really early, because thats what is really fun and challenging in programming. One day while making a ping pong game, i thought what would happen if both the sides computers will play? who will win? from that i developed a programming game, in which you need to write algorithm for your battleships to destroy other battleships. You could try really good algorithms to test them for eg. min-max, monte-carlo tree search, RL, Deep learning etc.

Since i was in India, people here do programming for the sake of coursework or to get jobs. Really passionate people are difficult to find.

I tried to launch the product in my college, but sadly no one would want to play it. The game was really challenging to grasp at the beginning, I also pitched it to my professors to include it in the course curicullum of AI, they liked the idea, but refused it by saying it will be an overhead for the students to learn first about the algorithm and then about the Game API.

for an year i dejectedly saw that not everyone is as passionate as you are. I found no market for my programming game. If it would have launched somewhere in US, it could have been better, since MIT has such a kind of competition in which students needs to make the bots. it is not that programming games have no market, there are games like : Battlensakes, coderOne etc. but their market share is very less.

I learnt the lesson the easy way i guess, because i had a safety net since i was still in college, and had a job from the following year.

But then i really understood about product market fit, which i used to ignore while they taught in entrepreneurship classes. If anyone wants to see how the game looked : https:\\aiplaygrounds.in . I have revamped by business idea and working on something else.

Current games which are using LLMs only activate the model when the user is talking to the NPC, but in order to create a real dynamic story which is completely random but to the point, the agents need to interact with other as well,so lets say there are around 100 agents in the game they need to interact with each other to generate some emergent behavior. The form of interaction can be questioned here. will it be in natural lang? or just some embeddings or states.

But this thing still has a long way to go.

are you working on that? it needs a stable-diffusion kind of breakthrough to achieve that.

here's a post on HN new right now, there are many papers published if you look for them or know who to follow. I think I follow someone on Twitter, but their algo is promoting rage bait right now, so I see less of the AI content I'd like

https://penghtyx.github.io/Era3D/

not working on it, but there are many papers and projects out there showing it off

Hi, to all the teachers out there, We have created a simulation platform with cautiously curated assignments to teach fundamental AI and beyond to the students. Feedback from teachers is welcome.