Neelam Yadav

Follow me if you like Python, ML, RAG, AI Agents, MLOps, LLMOps & DevOps

[Elon Musk @elonmusk]

The latest 𝕏 algorithm has been published to GitHub
https://t.co/ZCOm51uxmh

[Neelam Yadav @XTechOps]

A practical GenAI lesson:

Do not trust AI because it explains itself well.
Trust it because you built systems around it that make it reliable.

For me, that means:
➡️ retrieval
➡️ grounding
➡️ policy checks
➡️ evals
➡️ human verification where needed

AI trust is a system design problem.

[Neelam Yadav @XTechOps]

A practical GenAI lesson:

Do not trust AI because it explains itself well.
Trust it because you built systems around it that make it reliable.

For me, that means:
➡️ retrieval
➡️ grounding
➡️ policy checks
➡️ evals
➡️ human verification where needed

AI trust is a system design problem.

[Neelam Yadav @XTechOps]

A simple way to think about hallucination:

Sometimes AI does not hallucinate because it wants to make things up. It hallucinates because it thinks it knows the answer when it actually doesn’t. That’s a huge difference.

The fix is not just “prompt better.”
The fix is better grounding, retrieval, and validation.

[Neelam Yadav @XTechOps]

𝗧𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗻𝗲𝗲𝗱𝘀 𝗺𝗼𝗿𝗲 𝗘𝘂𝗴𝗲𝗻𝗲 𝗥𝗼𝘀𝗵𝗮𝗹.

The 𝗺𝗮𝗻 𝗯𝗲𝗵𝗶𝗻𝗱 𝗪𝗶𝗻𝗥𝗔𝗥.
And yes, RAR literally stands for Roshal Archive.

Back in 1993, he created the RAR format.
Then in 1995, he released WinRAR for Windows.

And then he accidentally created one of the greatest distribution hacks in software history:

“𝟰𝟬-𝗱𝗮𝘆 𝗳𝗿𝗲𝗲 𝘁𝗿𝗶𝗮𝗹.”

But here’s the funny part everyone remembers:
The reminder showed up.
People clicked OK.
And kept using it anyway.

For years.
Sometimes decades.

No drama.
No aggressive lockouts.
No “your files are blocked until you pay” nonsense.

Just a product that solved a real problem:

smaller files
easier sharing
cheaper storage
better compression

That’s why WinRAR spread everywhere.

School computers.
Office desktops.
Internet cafes.
Home PCs.

In a world obsessed with growth hacks,
WinRAR won with something simpler:

make useful software
and become impossible to uninstall from human habit.

Absolute legend.

#WinRAR #EugeneRoshal #Software #TechHistory #BuildInPublic

[Neelam Yadav @XTechOps]

𝗚𝗼𝗼𝗴𝗹𝗲 𝗱𝗶𝗱𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵 𝗚𝗲𝗺𝗺𝗮 𝟰.
They launched a 𝗟𝗢𝗖𝗔𝗟 𝗔𝗜 𝗳𝗹𝗲𝘅. 🔥

Everyone talks about benchmarks.

I care about this:
Can I run it on my own machine?

With Gemma 4, the answer is yes.

Google’s official docs explicitly support local/on-device usage, and publish memory guidance for local inference too.

The standout:
26B A4B MoE
26B total params
4B active per token

So this launch is not just about model size.

It’s about making serious AI more practical locally.

#Gemma4 #GoogleAI #MoE #OpenSourceAI #LLM #GenAI

[Neelam Yadav @XTechOps]

One thing I’ve noticed with AI:

Sometimes it gives the right answer
but the explanation it gives for how it got there is not the real one.
That’s why in production, I don’t trust “nice reasoning steps” blindly.

Lesson:
Good output does not always mean faithful reasoning.

Always verify

[Neelam Yadav @XTechOps]

@NehraWorkss Lets connect

[Neelam Yadav @XTechOps]

Its the reality. Every non tech person has now become highly technical person in there opinion

[Neelam Yadav @XTechOps]

@waelnassaf Lets connect. I am into building AI tools

[Neelam Yadav @XTechOps]

@SurajJ326311 Lets connect

[Neelam Yadav @XTechOps]

@ganesh__47 I am building AI tools

[Neelam Yadav @XTechOps]

RAG vs Agentic RAG : Lets simplify it

RAG = User asks a question → system retrieves relevant documents → LLM answers from that context 📚
It is great for:
✅ grounded answers
✅ enterprise knowledge search
✅ reducing hallucinations
✅ fast Q&A over your data

But mostly, it is still a retrieve + respond pattern.

Agentic RAG = The system does not just retrieve once and answer.
It can:
🔎 rewrite the query
🧭 decide which source/tool to use
📂 retrieve from multiple places
🧪 validate what it found
🔁 loop again if context is weak
📝 then generate the final answer

So the shift is:
RAG = fetch context and answer
Agentic RAG = reason, plan, retrieve, verify, then answer

#AI #RAG #AgenticAI #AgenticRAG #LLMOps #GenerativeAI #EnterpriseAI

[Neelam Yadav @XTechOps]

𝗖𝗼𝗺𝗽𝗼𝘀𝗲𝗿 𝟮 𝗶𝘀 𝗻𝗼𝘄 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗶𝗻 𝗖𝘂𝗿𝘀𝗼𝗿.

This feels like another strong signal in the evolution of AI coding. Not just faster autocomplete, but more capable agentic coding inside the developer workflow.

𝗪𝗵𝗮𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁:
⚡ frontier-level coding performance
💸 strong intelligence-to-cost balance
🧠 Cursor’s own model for agentic coding
🚀 available with both standard and fast variants

Cursor says Composer 2 is now live in the product, with pricing starting at $0.50/M input and $2.50/M output tokens, plus a faster default variant.

We are moving from:
𝘈𝘐 𝘵𝘩𝘢𝘵 𝘩𝘦𝘭𝘱𝘴 𝘸𝘳𝘪𝘵𝘦 𝘤𝘰𝘥𝘦
to
𝘈𝘐 𝘵𝘩𝘢𝘵 𝘤𝘢𝘯 𝘱𝘢𝘳𝘵𝘪𝘤𝘪𝘱𝘢𝘵𝘦 𝘮𝘰𝘳𝘦 𝘢𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘪𝘯 ��𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴.

Interesting times ahead for developers, platform teams, and anyone building with AI-native tooling.

#Cursor #Composer2 #AICoding #GenAI #DeveloperTools #SoftwareEngineering #AgenticAI

[Neelam Yadav @XTechOps]

Where do you get more value today - X or LinkedIn? 👀

[Neelam Yadav @XTechOps]

If you are looking for interview calls as a GenAI Developer, don’t just list skills.
Add:
✅ 1 solid RAG project
✅ 1 real end-to-end GenAI project

That alone can increase your chances of getting shortlisted.
Try it. Thank me later.

#GenAI #RAG #AgenticAI #LLMOps #AIJobs

[Neelam Yadav @XTechOps]

@KevinBelfort_30 lets connect

[Neelam Yadav @XTechOps]

@impeculiar1b lets connect

[Neelam Yadav @XTechOps]

@omjain23 lets connect