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Sumanth

@Sumanth_077 • 76,768 subscribers

Simplifying LLMs, Machine Learning & AI Agents for you! • Building https://t.co/9bR9Ad0j4M • Shipping Open Source AI Apps

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Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

16,653 просмотров

Pandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes🔥 All you have to add is just a couple of lines of code: %load_ext cudf.pandas import pandas as pd

Pandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes🔥 All you have to add is just a couple of lines of code: %load_ext cudf.pandas import pandas as pd

180,800 просмотров

How LLMs work, clearly explained with visuals:

How LLMs work, clearly explained with visuals:

174,922 просмотров

Learn how LLMs work under the hood! This is the best interactive website to learn how LLMs work. It combines clear, step-by-step explanations with dynamic 3D visualizations for an intuitive learning experience.

Learn how LLMs work under the hood! This is the best interactive website to learn how LLMs work. It combines clear, step-by-step explanations with dynamic 3D visualizations for an intuitive learning experience.

113,180 просмотров

Pandas is one of the most important libraries for Data Science. But when working with larger datasets it becomes really slow and runs out of memory Introducing Modin a python library which is 10x faster than Pandas🔥 Thread🧵👇

Pandas is one of the most important libraries for Data Science. But when working with larger datasets it becomes really slow and runs out of memory Introducing Modin a python library which is 10x faster than Pandas🔥 Thread🧵👇

52,793 просмотров

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DeepSeek released R1, the best open-source reasoning model that is on par with OpenAI's o1! I have built a RAG app to chat with your PDF files using the DeepSeek R1 model, running locally on your computer. 100% Open Source

DeepSeek released R1, the best open-source reasoning model that is on par with OpenAI's o1! I have built a RAG app to chat with your PDF files using the DeepSeek R1 model, running locally on your computer. 100% Open Source

678,727 просмотров • 1 год назад

MetaAI's SAM 2 struggles when things move fast or when there are crowded, fast-moving objects! Introducing SAMURAI: An adaptation of the Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory. 100% Open Source

MetaAI's SAM 2 struggles when things move fast or when there are crowded, fast-moving objects! Introducing SAMURAI: An adaptation of the Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory. 100% Open Source

324,662 просмотров • 1 год назад

Transformers visually explained:

Transformers visually explained:

212,302 просмотров • 1 год назад

How LLMs work, clearly explained with visuals:

How LLMs work, clearly explained with visuals:

204,581 просмотров • 2 лет назад

Run AI Agents locally in your terminal using Llama 3.2! gptme: Run Code, edit files, browse the web, and add context with RAG - all from your terminal. 100% Open Source.

Run AI Agents locally in your terminal using Llama 3.2! gptme: Run Code, edit files, browse the web, and add context with RAG - all from your terminal. 100% Open Source.

78,860 просмотров • 1 год назад

Open AI released Operator, an agent that can use the browser to perform and automate tasks for you! I have built an Open Source version of Operator using Browser Use, running locally on your computer. 100% Open Source

Open AI released Operator, an agent that can use the browser to perform and automate tasks for you! I have built an Open Source version of Operator using Browser Use, running locally on your computer. 100% Open Source

62,485 просмотров • 1 год назад

Apple just released and open-sourced FastVLM! FastVLM is a lightning-fast vision-language model that combines rapid image and text understanding with efficient on-device performance. 100% Open Source

Apple just released and open-sourced FastVLM! FastVLM is a lightning-fast vision-language model that combines rapid image and text understanding with efficient on-device performance. 100% Open Source

43,734 просмотров • 1 год назад

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

10,483 просмотров • 7 месяцев назад

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