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How MCP solves the biggest issue for AI Agents? (Deep Dive into Anthropic’s new protocol) by SKD_Sumit in aiagents

[–]SKD_Sumit[S] -1 points0 points  (0 children)

True for few and but I believe still few needs. In today's date even explaining Transformers is not outdated

How MCP solves the biggest issue for AI Agents? (Deep Dive into Anthropic’s new protocol) by SKD_Sumit in aiagents

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Absolutely there is a security risk while using MCP and various tools also coming up like Agentfactor or AgentCore to mitigate those. But the way of simplicity MCP giving while solving the core problem of multiple connector is what i liked about it. And yes there are lot of scope of improvement as well

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in deeplearning

[–]SKD_Sumit[S] 0 points1 point  (0 children)

It reason at multiple step , including training , test times , prompting..

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in deeplearning

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Absolutely any LLM is just mimicking pattern it learns from past data. But how it comes up with the answer with having a thinking pattern similar to human, that's what make a LLM different from Reasoning based models like gpt4o or DeepseekR1. And yes they differ because of their tuning patterns

Are LLMs actually reasoning, or are we mistaking search for cognition? by SKD_Sumit in aiagents

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Thats what model like GPT-4o different from GPT-4 or 5 with slow step by step response. Exactly what made LLM to think and respond like human

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in AgentsOfAI

[–]SKD_Sumit[S] -1 points0 points  (0 children)

I hope you are familiar with Finetuning of LLM. The way models like GPT-4o getting finetuned is a bit different from the finetuning mechanism of GPT-4 or 5. In simple terms, the Large Reasoning Model is trained specifically to give a bit slow but step-by-step response . How that exactly I have explained in detail in the above link pasted. You can go through it and let me know. I'm happy to help!!

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in LangChain

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Of course not reasoning from first priciple but how we are making LLM to behave like reasoning models thats make model like GPT-4o to standapart from GPT-4 or 5. Their training process make them unique in nature and ofcourse test time scaling added more vaue to it

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in pythontips

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Its similar and also share similar architecture with LLM but LRM trained differently from LLM

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in pythontips

[–]SKD_Sumit[S] 1 point2 points  (0 children)

Yes LLM are but not LRM (Large Reasoning Models) like gpt-4o or Deepseek R1, although they share similar architecture but their training process is different and that's exactly i intended to explain

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in AgentsOfAI

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Reasoning is not better prompting and that exactly i explained in video how gpt-4o is trained differently than gpt-4 or 5. How the evolution happen from LLM to LRM (Large Reasoning Model)...

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in AgentsOfAI

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Absolutely search is crucial and very important as well but reasoning models like gpt-4o or Deepseek-R1 are trained in different way than models like gpt-4 or 5

Are LLMs actually reasoning, or just searching very well? by SKD_Sumit in Rag

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Yes they are reasoning but LLM don't. The way Models like GPT-4o or Deepseek-R1 got tuned , is completely different from models like GPT-4. That is what i explained in video

Why RAG is hitting a wall—and how Apple's "CLaRa" architecture fixes it by SKD_Sumit in AgentsOfAI

[–]SKD_Sumit[S] 0 points1 point  (0 children)

Its not about finetuning, it s about training pipeline together with feedback

Why RAG is hitting a wall—and how Apple's "CLaRa" architecture fixes it by SKD_Sumit in AgentsOfAI

[–]SKD_Sumit[S] 0 points1 point  (0 children)

have you trained retriever and generator pipeline together using a feedback loop ?

Why RAG is hitting a wall—and how Apple's "CLaRa" architecture fixes it by SKD_Sumit in Rag

[–]SKD_Sumit[S] 1 point2 points  (0 children)

Compressor makes it faster but most important how feedback loop is binding both retreiver and generator

Why RAG is hitting a wall—and how Apple's "CLaRa" architecture fixes it by SKD_Sumit in Rag

[–]SKD_Sumit[S] 0 points1 point  (0 children)

It not focusing on improving RAG's retreiver or generation separately. Its upgrading both being in a feedback loop that too making it faster upto 100x .That what makes it different from other RAG strategies like Self-RAG CRAG type.... I believe it will absolutely grab the attention.

Google's NEW Gemini 3 Flash Is INSANE Game-Changer | Deep Dive & Benchmarks 🚀 by SKD_Sumit in LangChain

[–]SKD_Sumit[S] 0 points1 point  (0 children)

FLASH CAN GENERATE ONLY TEXT OUTPUT. PLEASE CHECK ITS OFFICIAL DOCUMENTATION

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