BusyinPassingtime @gauravGRV
Data Scientist, Machine learning , python , d3js etc. New Delhi, India Joined December 2009-
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official š @NanoBanana global hackathon! build a product, app or demo that shows off the "wow" factor of gemini-2.5-flash-image, our SOTA image model weekend hackathon starts Sep 5 5pm PT in collab with @elevenlabsio and @FAL details ā¬ļø
Claude + MCP + n8n = AI Content Army that replaces $10,500+/month content teams... (the exact system I've deployed for 7-figure influencers) ā No more 2-3 week turnarounds for basic posts ā No more generic copy that sounds like everyone else ā No more burning budgets onā¦
Always do these 12 exercises every day
I've created a step-by-step guide to help you self-host n8n This is the exact setup we use to build unlimited AI agents without paying $20K/year on tools like Zapier Want this premium guide for FREE? š RT + Like & Comment āfreeā and Iāll DM it to you No sign-in, no BS (Mustā¦
How Uber built their Agentic-RAG workflow? Weāre going live this weekend to build a prototype replicating @Uberās EAg-RAG (Enhanced Agentic RAG). What can you expect? > Offline document processing with custom metadata for filtering > @LangChainAI - LangGraph to orchestrate theā¦
Remember Sora? It made the internet stop. And yet, only a year later, the hype, the marketing, all fade quickly. But whatās inside the black box stays relevant, the combination of two powerful ideas: Diffusion + Transformer ā¬ļø Download PDF: byhand.ai/DiT
Transformer vs Mixture of Experts, visually explained:
Fundamentals of a š©š²š°šš¼šæ šš®šš®šÆš®šš². With the rise of GenAI, Vector Databases skyrocketed in popularity. The truth - Vector Databases are also useful outside of a Large Language Model context. When it comes to Machine Learning, we often deal with Vector Embeddings.ā¦
After MCP, A2A, & AG-UI, there's another Agent protocol. It's fully open-source and launched by IBM Research. Here's a complete breakdown (with code):
I just finished a 31-page doc on how to build an AI agent that responds to your chat requests (with n8n) - AI agent + automation workflows - Step by step guide - Secret Sauce š RT + Like & Comment "AI agent" and I'll dm it to you FREE (must be following, 24h)
Recent LLM's architecture. Previously I mentioned the same as recent LLM's does not have big architectural changes, they have just added a few more experts of app layer on top of it. Here is the proof. Check it out. Credits: @rasbt š¤
The growth of LLM context length with time: - GPT-3.5-turbo ā 4k tokens - OpenAI GPT4 ā 8k tokens - Claude 2 ā 100k tokens - Llama 3 ā 128k tokens - Gemini ā 1M tokens Let's understand how they extend the context length of LLMs:
Let's build an MCP server (100% local):
š§āāļø Multi-Agent Legal Assistant ā blazing fast RAG. Powered by CrewAI, this demo queries 50M+ vectors in <30ms using Binary Quantization to deliver cited legal answers. Built by @akshay_pachaar.
A simple technique makes RAG up to 40x faster & 32x memory efficient! - Perplexity uses it in its search index - Google uses it in Vertex RAG engine - Azure uses it in its search pipeline Let's understand how to use it in a RAG system (with code):
First Principles Approach to MLOps! MadeWithML is the best place to learn how to combine AI/ML with software engineering to build production-grade solutions. These fundamentals are directly applicable to LLMOps as well. 100% free and open-source.
Adding š šš£ to šš“š²š»šš¶š° š„šš Systems. If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data source selection for retrieval stage. This is how MCP enriches the evolution of yourā¦
šš šš»š“š¶š»š²š²šæš¶š»š“ šš²š®šæš»š¶š»š“ š„š¼š®š±šŗš®š½. š It is created with beginners in mind but can be easily adapted if you are proficient in some of the areas already. ššÆ š¢ š©šŖšØš© šš¦š·š¦š: šš¼š°šš š¼š» ššš»š±š®šŗš²š»šš®š¹š throughout the journey, butā¦

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Insider Tracker @TrackInsiders_
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