Abu Dhabi Digital Authority (ADDA)

Lead Technical Specialist

VuePay

Technical Director cum CTO

Xische & Co

Engineering Technical Lead

Pocket App

Leading development team based in UK and India. My role is to directly manage projects worth of £250k+ (Tier 1). My current focus is scaling Pocket App's business to increase agility, bring improved product to market, and manage a well-diversified technical team. I manage technical quality and staffing for the firm in addition to spotting and seeding business development opportunities to accelerate company growth.

Head of Technology

Managing 40+ developers along with guidance to developers on various projects which is diversified on various platforms like Android, iOS, Hybrid, PHP, AWS etc. Along with this my job role was to handle clients, deliverables for various projects, project quotations and training the existing team on ios, android and html5 apps.

Technical Manager

Hurix Systems

Managing a Team of 10 junior developers assigning them work, managing deliverables and also developing on Android and HTML5 during this tenure.

Android,HTML5 Developer

Quinnox Consultancy services

I had worked on multiple projects for clients Coca- Cola, HNI Corp, BookTrack. I was developing natively on Platforms Android, iOS and was also developing on technology like Sencha touch, PhoneGap.

Android ,iOS and blackberry Developer

With over 14 years of development expertise and 10 years of management experience, I specialize in creating modern, scalable, and resilient products for various domains, such as web, mobile, backend, and blockchain. As a Lead Technical Specialist at Abu Dhabi Digital Authority (ADDA), I oversee the technical architecture and delivery of digital solutions that enhance the lives of citizens and residents in Abu Dhabi.

I am passionate about developing high-performing teams that foster a culture of innovation, collaboration, and learning. I empower my engineers with the necessary business context, a psychologically safe environment, and goals that align their work with the success of the organization and their professional growth. I also leverage my skills and credentials as an AWS Champion, a Certified Scrum Master, and a React & React Native Nanodegree holder to design and implement cutting-edge technologies and best practices.

So RAG systems are slow. Like, really slow.

You hear this always....

And here is the kicker most of that slowness? Completely unnecessary.

I just read through Meta's REFRAG paper, and honestly, it's one of those "why didn't we think of this sooner" moments. You know the ones.

Think about how RAG works right now. You retrieve 80 passages. The LLM dutifully processes every. Single. Token. Even though, and this is the crucial part maybe only 3 of those passages actually matter for your query.

The rest? Noise. Filler. Computational waste.

Here's what people will relate to

When you concatenate a bunch of retrieved passages, they don't really talk to each other. They are diverse (by design, thanks to deduplication). 

The attention patterns look block diagonal, meaning tokens within a passage attend to each other. Still, there is basically zero cross-attention between different passages.

We are doing quadratic-complexity attention calculations on stuff that doesn't need it.

REFRAG's approach is pretty straightforward (the best ideas often are) compress chunks of text into single embeddings. Use a lightweight encoder—like RoBERTa, to create these compressed representations. Feed those to the decoder instead of thousands of individual tokens.

The results are... wild

  - 30.85× faster time-to-first-token
  - No accuracy loss
  - Actually extends context window by 16×
 
Let me repeat that: 30× faster with no performance hit.

They even added an RL policy that learns which chunks are essential enough to expand back to full tokens. So you get selective decompression where it matters. Smart.

Why this matters for practitioners

If you are building RAG systems (and who is not these days?), you are probably hitting latency walls, especially at scale. Users don't wait around for slow responses; it doesn't matter how accurate they are.

The beautiful part? REFRAG does not require changing your LLM architecture. No new decoder parameters. It is a plug-in optimisation.

The broader lesson here

Sometimes optimisation is not about making the algorithm faster it is about recognising what work you don't need to do in the first place.

RAG has a unique structure. 
Block-diagonal attention. 
Sparse relevance. 

We have been treating it like generic long-context inference, but it's not. It needs specialised treatment.

Has anyone else experimented with chunk-level compression for RAG? Or hit similar latency bottlenecks?

Code should be dropping on GitHub soon apparently

#MachineLearning #RAG #LLMs #AIEngineering #NLP
---
P.S. - The paper also shows this works for multi-turn conversations and document summarisation, not just RAG. So if you are dealing with long contexts anywhere... yeah, pay attention to this one.

Sangam Pandey

 for comparison, here's the REFRAG approach, showcasing the compression and selective expansion for a much faster process

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Microsoft’s New Agent Framework 

In Microsoft world we all are experimenting with multi-agent systems using Semantic Kernel for orchestration (battle tested in production) and AutoGen for fast prototyping (was about to reach production)?

Well, Microsoft just decided to combine both worlds into one unified open source SDK and it is called the Agent Framework.

Why 👇

🧩 1. Cleaner Architecture
No more kernel headaches. You can now spin up a single chat agent, plug in your preferred chat client, and attach tools at the agent or workflow level. It is minimal, elegant, and fast to build.

⚙️ 2. Orchestration that Scales
It supports both multi agent collaboration (for dynamic reasoning and concurrency) and workflow orchestration (for deterministic business processes).
Think of it as the bridge between research style agents and enterprise grade automation.

🧠 3. Memory with Purpose
Agent threads now handle short and long term memory seamlessly Redis, Cosmos DB, MemZero, and soon Azure Foundry for managed agent memory.

🛡️ 4. Enterprise Ready by Design
PII masking, audit trails, Entra ID guardrails, and human in loop approval flows make it fit for real world enterprise deployment.

💻 5. Developer Experience That Feels Right
Supports both Python and .NET, comes with a visual DevUI (they almost covered whatever required for a UI development saving 6 - 8 hours) for prototyping, CLI tools for organizing agents and workflows, and full tracing support.

This is Microsoft saying: “We are not just building agents, we are building the ecosystem they will live in.”

If you have been working with Semantic Kernel or AutoGen, this is the signal to start migrating and experimenting.

Talks about #career, #leadership, #cloudcomputing, #softwareengineering, and #digitaltransformation

Sharing Insights on AI, Machine Learning, Software Engineering, Cloud Technology, Space Development, Critical Software Design

If you want to learn AI from the ground up, check out “𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻” by 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 (11 hr 50 min), available 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 on YouTube.

Link: https://lnkd.in/gEqy55q9

Quote from Brian Yu, the instructor
```
Anytime you see a computer do something that appears to be intelligent or rational, like recognizing someone's face in a photo, playing a game better than people can, or understanding human language, these are all examples of AI. In this class we'll explore the ideas that make that AI possible.
```

🔴 Lecture 0: 𝗦𝗲𝗮𝗿𝗰𝗵 (1 hr 50 min)
Finding a solution to a problem, like a navigator app finding the best route or an AI playing a game.

🔴 Lecture 1: 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 (1 hr 50 min)
Representing information and drawing inferences from it.

🔴 Lecture 2: 𝗨𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 (1 hr 55 min)
Dealing with uncertain events using probability.

🔴 Lecture 3: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (1 hr 45 min)
Finding not only a correct way to solve a problem, but a better (or the best) way to solve it.

🔴 Lecture 4: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (1 hr 45 min)
Improving performance based on access to data and experience.

🔴 Lecture 5: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (1 hr 40 min)
A program structure inspired by the human brain that is able to perform tasks effectively.

🔴 Lecture 6: 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 (1 hr 5 min)
Processing natural language, which is produced and understood by humans.

The lectures are recorded specifically for online students with excellent production quality. Brian Yu explains complex concepts clearly, making this one of the highest-quality free AI courses available.

#AI #MachineLearning #Python #OnlineLearning #FreeCourse

Anything coming from Andrew Ng becomes a sensation in AI. Best part this is not attached to any vendor and talks a lot about error analysis and Evaluation.

Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of LandingAI

Announcing my new course: Agentic AI!

Building AI agents is one of the most in-demand skills in the job market. This course, available now at deeplearning.ai, teaches you how.

You'll learn how to implement four key agentic design patterns:
- Reflection, in which an agent examines its own output and figures out how to improve it
- Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc.
- Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and
- Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task

You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns.

But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents.

Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices.

This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps.

Come join me, and let's build some agentic AI systems!

Sign up to get started: https://lnkd.in/g6yuG5Rh

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