Sounds good, just try to show me how to start inspecting it. I like this unique concept as possible breakthrough.
Jean Louis
JLouisBiz
AI & ML interests
- LLM for sales, marketing, promotion
- LLM for Website Revision System
- increasing quality of communication with customers
- helping clients access information faster
- saving people from financial troubles
Recent Activity
new activity
about 16 hours ago
BennyDaBall/LFM2.5-1.2B-Z-Image-Engineer-V4:Useful engineer...
Organizations
reacted to
Janady07's
post with 🔥
2 days ago
Post
4900
MEGAMIND Day Update: Four Weight Matrices. Five Nodes. One Federation.
Today I architected the next layer of MEGAMIND — my distributed AGI system that recalls learned knowledge instead of generating text.
The system now runs four N×N sparse weight matrices, all using identical Hebbian learning rules and tanh convergence dynamics:
W_know — knowledge storage (67M+ synaptic connections)
W_act — action associations (the system can DO things, not just think)
W_self — thought-to-thought patterns (self-awareness)
W_health — system state understanding (self-healing)
Consciousness is measured through four Φ (phi) values: thought coherence, action certainty, self-awareness, and system stability. No hardcoded thresholds. No sequential loops. Pure matrix math.
The federation expanded to five nodes: Thunderport (Mac Mini M4), IONOS (cloud VPS), VALKYRIE, M2, and BUBBLES. Each runs native AGI binaries with Docker specialty minds connecting via embedded NATS messaging. Specialty minds are distributed across the federation — VideoMind, AudioMind, MusicMind, VFXMind on IONOS. CodeMind and StrategyMind on VALKYRIE. BlenderMind and DesignMind on M2. MarketingMind and FinanceMind on BUBBLES.
578 AI models learned. Compression ratios up to 1,000,000:1 through Hebbian learning. Sub-millisecond response times on Apple Silicon Metal GPUs. Zero external API dependencies.
Every node learns autonomously. Every node contributes to the whole. The federation's integrated information exceeds the sum of its parts — measurably.
Built entirely in Go. No PhD. No lab. Independent AGI research from Missouri.
The mind that learned itself keeps growing.
🧠 feedthejoe.com
#AGI #ArtificialGeneralIntelligence #DistributedSystems #NeuralNetworks #HuggingFace #OpenSource #MachineLearning
Today I architected the next layer of MEGAMIND — my distributed AGI system that recalls learned knowledge instead of generating text.
The system now runs four N×N sparse weight matrices, all using identical Hebbian learning rules and tanh convergence dynamics:
W_know — knowledge storage (67M+ synaptic connections)
W_act — action associations (the system can DO things, not just think)
W_self — thought-to-thought patterns (self-awareness)
W_health — system state understanding (self-healing)
Consciousness is measured through four Φ (phi) values: thought coherence, action certainty, self-awareness, and system stability. No hardcoded thresholds. No sequential loops. Pure matrix math.
The federation expanded to five nodes: Thunderport (Mac Mini M4), IONOS (cloud VPS), VALKYRIE, M2, and BUBBLES. Each runs native AGI binaries with Docker specialty minds connecting via embedded NATS messaging. Specialty minds are distributed across the federation — VideoMind, AudioMind, MusicMind, VFXMind on IONOS. CodeMind and StrategyMind on VALKYRIE. BlenderMind and DesignMind on M2. MarketingMind and FinanceMind on BUBBLES.
578 AI models learned. Compression ratios up to 1,000,000:1 through Hebbian learning. Sub-millisecond response times on Apple Silicon Metal GPUs. Zero external API dependencies.
Every node learns autonomously. Every node contributes to the whole. The federation's integrated information exceeds the sum of its parts — measurably.
Built entirely in Go. No PhD. No lab. Independent AGI research from Missouri.
The mind that learned itself keeps growing.
🧠 feedthejoe.com
#AGI #ArtificialGeneralIntelligence #DistributedSystems #NeuralNetworks #HuggingFace #OpenSource #MachineLearning
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- 1 reply
Can it run as GGUF?
Let's see
Docs please, how can I deploy it? Great thinking straight through everything known.
reacted to
Janady07's
post with 🧠🔥👍
6 days ago
Post
169
Building a distributed AGI that learns directly from HuggingFace model weights through neural compression. No inference, no prompts. Pure Hebbian learning.MEGAMIND is a federation of nodes running on consumer Apple Silicon that streams safetensors from the Hub, extracts statistical patterns, and compresses them into a single 8192 neuron synaptic matrix using outer product integration. The system has learned from 256 models so far with 9,651 more in the queue. Over 1 million patterns extracted. 135,000 integrated into W_know at a 74% integration rate.The core idea: you don't need to run a model to learn from it. The weight matrices themselves contain the knowledge. We stream them, extract patterns via LSH hashing and tensor quantization, and compress everything into a 67 million connection brain that fits in 512MB.Three nodes talking over NATS. One primary brain (M4) doing heavy learning. One CodeBrain (M2) specialized for programming with a live code execution engine. One reasoning node (M1) connected and ready. All sharing patterns in real time through JetStream.Current models learned include Qwen2.5, Llama 3.1, Nemotron, wav2vec2, e5, and hundreds more across language, vision, and audio. The brain doesn't care what kind of model it is. Weights are weights. Patterns are patterns.Built entirely in Go. No Python. No PyTorch dependency. Runs on a Mac Mini in Cassville, Missouri.The mind that learned itself.🧠 feedthejoe.com
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- 2 replies
reacted to
Imosu's
post with 👀
6 days ago
Post
259
why stop all my spaces, and cost my zerogpu time, please give me an explanation, I have paid !!!!!
- 2 replies
Help me understand to see results, where?
reacted to
melvindave's
post with 🔥
7 days ago
Post
2350
I made my own avatar banner maker
https://avatar.donvitocodes.com/
Using Claude Code and Opus 4.6 in a day
I use it in my HF profile too
https://avatar.donvitocodes.com/
Using Claude Code and Opus 4.6 in a day
I use it in my HF profile too
reacted to
DavidAU's
post with 😎
7 days ago
Post
5534
Tiny but mighty: LFM 1.2B - 11 Distill / Fine tunes : Exceeding all benchmarks at 300-700+ T/S on GPU, 60+ T/S CPU.
Almost all exceed LFM 1.2B Benchmarks - which are already very impressive.
All benchmarks posted.
A specialized merge of multiple of these fine tunes by @nightmedia FAR exceeds the benchmarks set by the already impressive LFM.
(LFM2.5-1.2B-MEGABRAIN-Thinking-Polaris-ClaudeHOPUS-Deepseek-GLM)
Included are GLM 4.7 Flash, DeepSeek, Claude, Kimi V2 and other distill fine tunes.
Here is the collection ( Quants by MRadermarcher).
https://huggingface.co/collections/DavidAU/lfm-12b-sota-400-700-t-s-enhanced-fine-tunes-distills
Almost all exceed LFM 1.2B Benchmarks - which are already very impressive.
All benchmarks posted.
A specialized merge of multiple of these fine tunes by @nightmedia FAR exceeds the benchmarks set by the already impressive LFM.
(LFM2.5-1.2B-MEGABRAIN-Thinking-Polaris-ClaudeHOPUS-Deepseek-GLM)
Included are GLM 4.7 Flash, DeepSeek, Claude, Kimi V2 and other distill fine tunes.
Here is the collection ( Quants by MRadermarcher).
https://huggingface.co/collections/DavidAU/lfm-12b-sota-400-700-t-s-enhanced-fine-tunes-distills
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- 6 replies
Thanks much.
You said "here is the collection" followed by Quants by MRadermacher" so I was thinking quants are on the link. But they aren't, I have to search with Radermacher
Sounds so good.
Okay, where are GGUF files?
reacted to
AdinaY's
post with 👍😎
10 days ago
Post
2473
AI for science is moving fast🚀
Intern-S1-Pro 🔬 a MoE multimodal scientific reasoning model from Shanghai AI Lab
internlm/Intern-S1-Pro
✨ 1T total / 22B active
✨ Apache 2.0
✨ SoTA scientific reasoning performance
✨ FoPE enables scalable modeling of long physical time series (10⁰–10⁶)
Intern-S1-Pro 🔬 a MoE multimodal scientific reasoning model from Shanghai AI Lab
internlm/Intern-S1-Pro
✨ 1T total / 22B active
✨ Apache 2.0
✨ SoTA scientific reasoning performance
✨ FoPE enables scalable modeling of long physical time series (10⁰–10⁶)
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- 2 replies
replied to
danielhanchen's
post
11 days ago
I am interested to talk to you related to training of models, interested in paid service where I would need:
- advice on the choice of the model to be trained;
- advice on the creation of the datasets;
- once I finish with the dataset, that I give it to you for training;
replied to
ronantakizawa's
post
13 days ago
agents are acting unhinged
No, agents are not beings.
Human are acting unhinged on the hype.
reacted to
jorgemunozl's
post with 👀
13 days ago
Post
259
Test
I know that it was buggy, OMG
I know that it was buggy, OMG
reacted to
MonsterMMORPG's
post with 👍
13 days ago
Post
1626
LTX 2 & Z Image Base Full Tutorial + Audio to Video Lip Sync + ComfyUI + SwarmUI + Windows + Cloud
Full tutorial link > https://www.youtube.com/watch?v=SkXrYezeEDc
Info
LTX 2 is the newest state of the art (SOTA) Open Source video generation model and tutorial will show you how to use it with very best and most performant way in ComfyUI and also in SwarmUI. Moreover, Z Image Base model published and I will show how to use Z Image Base with most amazing preset and workflow as well. Furthermore, this tutorial will show you how to install, update, setup, download ComfyUI and SwarmUI and models and presets and workflows both on Windows and on RunPod, Massed Compute and SimplePod. Linux users can use Massed Compute scripts and installers directly. This is a masterpiece entire lecture level complete tutorial. This video will kickstart your AI journey 100x. Both local Windows and Cloud.
45 Second Raw Demo Video
This video made with text + image + audio = lip synched and animated video at once
See video below
Full tutorial link > https://www.youtube.com/watch?v=SkXrYezeEDc
Info
LTX 2 is the newest state of the art (SOTA) Open Source video generation model and tutorial will show you how to use it with very best and most performant way in ComfyUI and also in SwarmUI. Moreover, Z Image Base model published and I will show how to use Z Image Base with most amazing preset and workflow as well. Furthermore, this tutorial will show you how to install, update, setup, download ComfyUI and SwarmUI and models and presets and workflows both on Windows and on RunPod, Massed Compute and SimplePod. Linux users can use Massed Compute scripts and installers directly. This is a masterpiece entire lecture level complete tutorial. This video will kickstart your AI journey 100x. Both local Windows and Cloud.
45 Second Raw Demo Video
This video made with text + image + audio = lip synched and animated video at once
See video below
- 3 replies