vLLM just got a major upgrade! Originally built for autoregressive text-based LLM serving, vLLM-Omni now lets you serve text, image, video, and audio models – all from a single framework. You can also serve diffusion models for fast parallel generation. 100% open-source.
@akshay_pachaar
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Share AI Insights and Follow for LLM Tutorials
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 20 décembre 2025
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/vwPMkQAl12If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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8 RAG Architectures Every AI Engineer Must Know
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8 RAG architectures all AI Engineers should know:
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Building RAG Applications on AWS: Ingestion and Querying Stages
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Building a RAG app on AWS is simpler than you might think.
— Akshay 🚀 (@akshay_pachaar) 20 décembre 2025
Let me explain how you can achieve this using services you already know:
At its core, RAG follows a two-stage pattern: ingestion and querying.
Here's how you can implement each stage on AWS:
1️⃣ Ingestion: Turning raw… pic.twitter.com/UOTBpoUcpeBuilding a RAG app on AWS is simpler than you might think. Let me explain how you can achieve this using services you already know: At its core, RAG follows a two-stage pattern: ingestion and querying. Here's how you can implement each stage on AWS: Ingestion: Turning raw
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Mini-SGLang: Simplified LLM Serving in 5000 Lines of Code
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vLLM has 100k+ lines of code. Mini-SGLang does the same core job under 5000 lines of code. this repo is designed to simplify modern LLM serving systems, offering a capable inference engine and a clear reference code for researchers and developers. 100% open-source.
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Share AI and Machine Learning Insights with Your Network
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Graphiti GitHub Repository – Star the Project
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Graphiti GitHub repo: https://
github.com/getzep/graphiti (don't forget to star ) -
Memory in AI Agents: The Evolution Beyond RAG
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RAG was never the end goal.
— Akshay 🚀 (@akshay_pachaar) 30 novembre 2025
Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible.
RAG (2020-2023):
– Retrieve info once, generate response
– No decision-making, just fetch and answer
– Problem: Often retrieves… pic.twitter.com/EN8cfP6qflRAG was never the end goal. Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible. RAG (2020-2023): – Retrieve info once, generate response
– No decision-making, just fetch and answer
– Problem: Often retrieves -
Data Leakage in Recommendation Systems from User Interaction Patterns
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Good point. Btw, you also see a similar leakage trap in recommendation datasets. Most recsys data is built around users interacting with items over time, which means the same user appears dozens or hundreds of times. If you do a plain random split, you accidentally leak the
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Reshare Insights on LLMs AI Agents and Machine Learning
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!