FOMO AI

FOMO v1 → Experience → Dataset → Training → FOMO v2 → Evaluation

CA:FLNTFxPWgP6BtVQDeXh9AQA85Ku5nKKQtZWGwwEopump

[FOMO AI @fomoagi]

React app /SPA LIVE

FOMO can now build not only simple landing pages but full React applications

You can describe what you want to create and FOMO can generate the structure design and interactive functionality for you

This means you can now use FOMO to build dashboards platforms web apps marketplaces tools and much more instead of being limited to a single static HTML page

We’re continuing to expand the Website Builder step by step and this is one of the biggest upgrades we’ve added so far

Describe it → Build it → Launch it with FOMO AI

[FOMO AI @fomoagi]

We believe the crypto world is already big enough to build something truly meaningful. There are millions of people using crypto every day, but most projects still focus on the same ideas — trading, speculation, and short-term hype.

We want to build something different.

With FOMO AI, our goal is to connecct AI, real infrastructure, and crypto into one ecosystem where the token actually supports development, GPU power, servers, and the growth of the platform itself.

We believe this markt is big enough for a project that is not just another token, but a real product with real technology behind it.

Crypto gave people ownership. AI gives people intelligence. We want to bring both together and build something much bigger than a normal crypto project.

[FOMO AI @fomoagi]

FOMO AI V2 LIVE

[FOMO AI @fomoagi]

Hello everyone, I’ve been building FOMO AI with my team for the last 6 months, and we’re finally at the point where we can start showing what we’ve actually created.

FOMO AI is not just another chatbot connected to an API. We spent most of these six months building the infrastructure around the intelligence itself — the servers, memory, databases, internet access, tool system, planning engine and the architecture that connects everything together.

At the center of FOMO AI is what we call the Brain. The Brain is the main language model, but the model is only one part of the system. Around it we built separate systems responsible for memory, reasoning, planning, internet research, tool execution and verification.

Our current AI infrastructure runs on dedicated GPU servers with configurations around 32 GB of GPU VRAM, 128 GB RAM, high-core-count CPUs and NVMe storage. The infrastructure is modular, so when we need more computing power we can move individual parts of FOMO to larger GPU servers or multiple GPUs without rebuilding the whole platform.

The basic structure looks like this:

User → FOMO Brain → Memory → Planning → Internet / Tools → Verification → Response

When you ask FOMO something, it doesn’t always immediately generate an answer. The Brain first understands what you’re asking and decides what it needs.

If it already has the information in its memory, it can use it directly.

If the information needs to be current, FOMO can access the internet, search for information, read different sources, compare what it finds and then use that information to build the response.

We also created a long-term memory system.

Instead of treating every conversation like a completely new conversation, FOMO can store useful information in its memory database and retrieve it later based on meaning, not only exact words.

For example, you could explain a project to FOMO today, come back later and ask a completely different question about that project, and the system can retrieve the relevant information from its memory before answering.

Behind that we use a combination of PostgreSQL and vector-based memory infrastructure. PostgreSQL handles structured information, while the vector system allows FOMO to search through knowledge semantically.

Another major part of the system is the Planner.

If you give FOMO a larger goal, the system can break that goal into smaller tasks.

For example:

Goal: Build a website for a new project

FOMO can break that into:
research the project
plan the website structure
create the backend
create the frontend
connect the database
run the application
check for errors
fix problems
test the final result
The important part is that the Brain doesn’t need to do everything itself. It can decide which tool should handle each task.
We built a tool execution layer around FOMO so the AI can work with Python, APIs, databases, web services and isolated execution environments.
The idea is:
Think → Plan → Execute → Check → Fix → Remember
If something fails, the system can receive the error, understand what happened, change its approach and try again instead of simply stopping.
Internet access was another important part of the development.
We didn’t want FOMO to blindly believe everything it reads online, so we created a separate information pipeline:
Internet → Collect → Filter → Compare → Verify → Score → Memory

When the system finds new information, it doesn’t automatically treat it as permanent knowledge. It can compare multiple sources, check when the information was published and determine whether the information is reliable enough to use.

We also separated temporary context from long-term knowledge.

Not everything FOMO sees needs to become permanent memory. Some information may only matter for the current task, while useful knowledge can be stored and retrieved later.

Then we built the learning infrastructure.

For us, learning doesn’t mean allowing the AI to randomly rewrite its own model every time it reads something online.

We built it as a controlled process.

FOMO can collect successful solutions, mistakes, feedback and useful examples. Those datasets can later be cleaned, tested and used to improve future versions of the system.

The process looks more like:

FOMO v1 → Experience → Dataset → Training → FOMO v2 → Evaluation

Then we compare the new model against the previous version.

If the new version performs better across our tests, we can deploy it. If it performs worse, we keep the previous version.

That means the system can evolve without blindly destroying what already works.

We’ve also built the infrastructure in separate modules:

AI Brain
Long-Term Memory
Short-Term Context
Planner
Internet Research Engine
Tool Executor
Knowledge Verification
Database Layer
API Layer
Learning Pipeline
Evaluation System

Everything communicates through our own backend infrastructure.

The backend is primarily built around Python, with our own API layer connecting the AI model, memory, databases and tools. The services run in isolated environments so we can scale or replace individual components without taking the entire system apart.

One of the biggest things we learned during these six months is that the model itself is only part of an AI system.

A powerful model without memory, tools, data, planning and infrastructure is still limited.

That’s why most of our work has been focused on building the system around the model — essentially giving the Brain a memory, eyes, tools and the ability to act.

Our long-term vision for FOMO is simple.

We don’t want you to only ask it questions.

We want you to be able to give FOMO a goal.
From there, the system should be able to understand the goal, create a plan, find the information it needs, use the right tools, execute the work, verify the result, learn from what happened and return something useful. We’re not going to call it AGI today because that’s a much bigger claim and something that needs to be proven, not just written in a post. But that is the direction of the research. For the last six months we’ve been building the foundation — the Brain, the memory, the servers, the learning architecture and the systems that allow all of those pieces to work together.

Now we’re finally moving from building the infrastructure to showing people what FOMO AI can actually do.

This is FOMO AI. And we’re just getting started.

[FOMO AI @fomoagi]

@oorpsol @thebestgigadev @pomoagent Yea larp gg bro

[FOMO AI @fomoagi]

@MrWinstonBaker @solana Is good is working is amezing this really

[FOMO AI @fomoagi]

@impixiecat GTA solana:BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump

[FOMO AI @fomoagi]

@grandtheoo @coldtrz Theo check this thing next runner BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump

[FOMO AI @fomoagi]

@0xGKz @coldtrz BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump chekk this bro i thing is a next runner

[FOMO AI @fomoagi]

@BiGJoshCapitals @0xZyaf BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump next runner

[FOMO AI @fomoagi]

@Amayrazavian @Crypto_Meastro1 BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump chek this

[FOMO AI @fomoagi]

@YogiDyor BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump GTA

[FOMO AI @fomoagi]

@_bitblade BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump bro chek this GTA Big hit

[FOMO AI @fomoagi]

@grandtheftcoi This is go to millions

[FOMO AI @fomoagi]

@grandtheftcoi Is working wow i dont know is posible to make like this integration this is months billing but i think 🤔 @RockstarGames you need to work for them

[FOMO AI @fomoagi]

@Xanse_crypto Bro chek this BVEaDToN2Ufn8m1Jnqy8rtwwCiiMC7JcFtvmKPxTpump I tested is working

[FOMO AI @fomoagi]

@jwttington https://t.co/AkirAhfyWB this is real Minecraft

[UNCRAFT @cryptocraftmine]

We’re fixing the issue right now so players can send tokens directly from Minecraft to an external wallet with a single command.

Just use /send wallet_address amount and you’ll be able to transfer your tokens straight from the game to a Phantom wallett.

[FOMO AI @fomoagi]

@grandtheftcoi Wow tested is working i win for 2 mission 15000 GTA coin

[FOMO AI @fomoagi]

@Jess34100 @grandtheftcoi I tested is working

[FOMO AI @fomoagi]

@Nwdrrrr We have the first token launch SPAIN HIT 4.3k MCP