Shibsankar Das

Lately there hasn’t been a dull moment in the R-world when it comes to working with LLMs. And as I said in one of my earlier posts: packages emerge faster than you can type 𝚒𝚗𝚜𝚝𝚊𝚕𝚕.𝚙𝚊𝚌𝚔𝚊𝚐𝚎𝚜() 😬. But the same can be said for updates to those packages! In today’s spotlight: 𝗲𝗹𝗹𝗺𝗲𝗿 version 0.3.0 🐘 If you’re not familiar with 𝗲𝗹𝗹𝗺𝗲𝗿 yet, here’s a short summary: 𝗲𝗹𝗹𝗺𝗲𝗿 helps you to call LLM APIs from R in a consistent way, and of course, this opens up a world of possibilities. It’s proving to be a handy back-end for various other LLM-related packages. You could say it’s the workhorse in R’s “LLMverse”. So what’s new? 🔹a handy new function called 𝚌𝚑𝚊𝚝() which can be used instead of specific provider functions like 𝚌𝚑𝚊𝚝_𝚊𝚗𝚝𝚑𝚛𝚘𝚙𝚑𝚒𝚌() or 𝚌𝚑𝚊𝚝_𝚘𝚙𝚎𝚗𝚊𝚒(). It simplifies the chat interface: you now just specify the provider and model in a single string. 🔹defining tools for function calling is also simplified. A tool is just an extra ability an LLM can use. You’re basically saying: “here’s a function you’re allowed to use, this is what it’s called, what it does, and what kind of input it needs”. Specifying that is more intuitive now. Note that this change is breaking. 🔹we hate big unexpected bills so we like to track costs 💸. The latest version of ellmer contains another pricing data source so there’s info for more providers/models. Simply call the 𝚐𝚎𝚝_𝚌𝚘𝚜𝚝() method. PS: 𝗲𝗹𝗹𝗺𝗲𝗿 and 𝗰𝗵𝗮𝘁𝗹𝗮𝘀 (the Python equivalent of 𝗲𝗹𝗹𝗺𝗲𝗿) are closely aligned. I expect (or hope) 𝗰𝗵𝗮𝘁𝗹𝗮𝘀 will follow the simplified chat interface too. Tool spec is already intuitive there and the cost thing is taken care of in the dev version.

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Introducing DynaRoute - intelligent model routing for efficient LLM usage At Vizuara, we (Pritam Kudale, Pranavodhayam P R, Raj, Rajat) have spent the last few months studying how teams use large language models in production. What we found was simple but eye-opening. Most teams rely on one large and expensive model for every kind of query, whether it is a quick factual question or a complex reasoning task. This works, but it wastes a lot of money and computing power. That is what led us to build DynaRoute (https://lnkd.in/dGSx2KRq), an intelligent routing layer that automatically analyzes your queries and sends them to the most suitable model based on complexity, cost, and performance. You do not have to change your application or rebuild your backend. You can continue using your familiar OpenAI-style API, just by switching the base URL and key. From that point, DynaRoute handles everything intelligently behind the scenes. In our internal benchmarks, DynaRoute delivered remarkable results. Real-world employee questions achieved up to 92 percent cost savings, while code-related queries saw around 74 percent savings. All of this happened while maintaining nearly the same quality and accuracy as top-tier models. For teams that are running customer support systems, coding assistants, agents, or RAG-based pipelines, DynaRoute can reduce your inference bills drastically while preserving the quality your users expect. You can think of it as a smart, adaptive router that continuously learns how to choose between models like GPT-4.1-nano, Gemini 2.5 Flash, and Claude Sonnet, depending on the query. We also provide API access so you can integrate DynaRoute directly into your own platform or agent framework. The API is OpenAI-compatible and can be configured with just a few lines of code. Here are our current plans: Free Tier: ₹0 per month, includes up to 50,000 tokens, OpenAI-compatible endpoint, and access to premium models. Plus Tier: ₹888.30 per month, includes up to 3 million tokens, unlimited requests, access to premium models, and priority support. Enterprise Tier: completely custom implementation for companies that need dedicated support, detailed analytics, and on-premise deployment. We also provide custom routing models for enterprise-level optimization. We are currently offering Enterprise Plan API access to selected partners who wish to integrate DynaRoute into their platforms. If you want to optimize the cost and performance of your LLM-based applications without compromising output quality, give DynaRoute a try. 👉 Learn more and get your API key at https://lnkd.in/gTrs9NWA

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Quix was conceived in Formula 1 🏎️ It was back in 2017 when me and Tomáš Neubauer joined forces to run Project MIRACLE. At the time McLaren Racing were moving from desktop-based technologies towards a centralised data platform. It was early in the journey, but we had an emerging Telemetry Analytics architecture and wanted to see how far we could push it. It started with hackathons (the video) - as most interesting things do - but led to showcasing an unrivalled data platform that gave us the capability to: • Ingest and enrich live telemetry data. • Process complex analytical models in real-time. • Deliver insights immediately to Performance Engineers. • Centralise all data, scripts & insights globally. It was impressive, but what actually kick started Quix was the 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧 we had in developing the applications. We ended up running a six-month project with a team of 10 engineers to build specific Telemetry Analytics use cases. And we came across friction 𝐞𝐯𝐞𝐫𝐲 𝐬𝐢𝐧𝐠𝐥𝐞 𝐝𝐚𝐲, including: ❌ Getting live telemetry data into a format the Engineers could use. Raw telemetry is not analytics ready. Our engineers wanted a simple way to 'just' open the data and analyse it in their favourite tools. We typically sent them .csv files 😅 ❌ Getting access to Cloud resources from IT. Standing up the initial infrastructure took a month, and then more delays everytime we needed new services - like a database - or some more VM's 🤯 ❌ Packaging MathWorks #matlab and #simulink models to work in containers There are at least 3 ways to package a model artifact and run it in the cloud. You also have to wire-up the input parameters to your telemetry data channels and test them before you run a test or go racing. The teams working on the models couldn't do it so it went in the Software Engineers backlog ⏳ ❌ Collaborating across teams There are a lot of moving parts in an R&D team. Software Engineers were building data ingestion and normalisation pipelines, Data Scientists were training and backtesting AI models, Performance Engineers were experimenting with parameterized Matlab & Simulink models. We regularly got into a mess which slowed us down 🐢 This was the inspiration for Quix - how might we remove friction to deliver complex business use cases more quixly. 8 years later, we have built >60 features that let engineers focus 💯 percent of their time building solutions.

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The AI/ML job market is hot again, with tons of openings across companies. 📌ML Design: Combating Counterfeit Fraud youtu.be/YQZBgvLB_EQ 📌ML Design: Detecting Buyer Returns Fraud youtu.be/as4i1tUo0EA 📌Derive & Implement Logistic Regression from Scratch youtu.be/cT_U40djaww  📌PCA Explained Simply with Codes youtu.be/yxz3UpPMycg 📌 Ongoing Interviewing Trends: youtu.be/XrZXojl5Zd8 📌 Essential Mathematics for ML: youtu.be/oMY2uKjx_Zc 📌Categorical Variable Encoding Strategies youtu.be/MKuAQv6ybc8  🎯Complete guide for Linear Regression lnkd.in/gdRsMHbn 🎯Complete playlist for Logistic Regression lnkd.in/gtPfmQUv 📌 Classification Metrics: youtu.be/E2HRSJKU-_4 🎯Data Drift Detection & Model Monitoring youtu.be/tQjRQWfYQ10 📌 Conversational AI: Tech behind Chat-GPT: youtu.be/JKoJ5YIr2O4 📌 Pushing LLM Boundaries Sparse & Flash Attention, Quantisation, Pruning, Distillation, LORA : youtu.be/mF7OM_XU2S4 📌 PySpark Essentials youtu.be/aruptWppgSs #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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I have covered 2 research papers on RAG (Retrieval Augmented Generation) in my recent youtube videos. 🚀 1. Self RAG [published Oct 2023] ✅ 2. Corrective RAG (C-RAG) [published Oct 2024] ✅ In the videos, we implemented the above papers from scratch in LangChain and Python and also did a detailed Code walkthrough. If you're interested to know how to build them, feel free to give them a watch! 💯 These are a part of my "AI Engineering 101" playlist. ❤️ Link to the channel is in the comments. 🔗 #aiengineer #youtube #rag #programming #coding

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Recently, PW (Physics Wallah) launched Aryabhata 1.0, an AI model built using something called an SLM, which stands for Small Language Model. Unlike big models like GPT-4 or Gemini (called LLMs – Large Language Models) that need a lot of data, power, and memory to run, SLMs are smaller, faster, and easier to use. They are made for specific tasks and give quick answers without using too many resources. This makes them a good fit for education, where speed and accuracy are more important than handling every type of question. While LLMs try to do everything, SLMs are focused — perfect for things like doubt-solving, tutoring, and exam help. Note: A lot of news has surfaced claiming that it is just a wrapper on top of the Qwen model. However,this post is solely focused on how LLM and SLM models differ.

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What’s common in the tech stack of Netflix, Reddit, and Airbnb? -> Yes , you guessed it right — Flask is one of the common framework used by all and other many tech companies and startup 🧠🔥 At Vizuara, I just recorded a new video lecture for our 'ML in Production' series, explaining Flask with a practical machine learning demonstration. This is Part 1 of our upcoming end-to-end ML pipeline project, where we’ll walk through the entire journey — from building models to deploying them as interactive web applications. 🎥 Watch the video here: https://lnkd.in/g6W7rAqk 📘 What we cover in this session: 🔹 What is Flask and why it’s popular for web + ML apps 🔹 What is difference between flask and Django 🔹 How to integrate a Machine Learning model with Flask ✅ Bonus: A full hands-on implementation using the Iris dataset 🗒️ Lecture Notes: https://lnkd.in/g4diPpNG ✍️ Read our detailed Substack article: https://lnkd.in/gg9_duQ7 We’re just getting started in developing full end to end ML Project — the upcoming parts will dive deeper into building a complete ML pipeline, including data preprocessing, model training, deployment, and monitoring! If you have any Suggestions or Feedback for us , Feel free to contact us. #Flask #MLDeployment #MachineLearning #Python #IrisDataset #ModularCoding #Vizuara #DataScience #MLOps #TechEducation #AI

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Linking Krishna’s timeless wisdom to economics gives us fresh ways to think about choices, balance, and efficiency. 1. Law of Diminishing Utility – “युक्ताहारविहारस्य युक्तचेष्टस्य कर्मसु” (Gita 6.17). Krishna teaches moderation in food, work, and rest. Economics echoes this in the law of diminishing marginal utility: excess leads to declining satisfaction. 2. Opportunity Cost – “कर्मण्येवाधिकारस्ते मा फलेषु कदाचन” (Gita 2.47). Krishna reminds Arjuna that we control only our actions, not results. Economics too stresses focusing on choices and their trade-offs rather than being obsessed with uncertain outcomes. 3. Division of Labour – “चातुर्वर्ण्यं मया सृष्टं गुणकर्मविभागशः” (Gita 4.13). Krishna speaks of society built on work and qualities. This mirrors specialization and comparative advantage, the backbone of productivity. 4. Equilibrium – “समत्वं योग उच्यते” (Gita 2.48). Krishna calls equanimity true yoga. Just as markets balance supply and demand, life too finds stability in balance between gains and losses. 5. Consumption and Savings – “अनन्याश्चिन्तयन्तो मां… योगक्षेमं वहाम्यहम्” (Gita 9.22). Krishna assures preservation of what we have and provision of what we lack. This resonates with intertemporal choice in economics: balancing today’s consumption with tomorrow’s security. 6. Utility Maximization – “उद्धरेदात्मनात्मानं नात्मानमवसादयेत्” (Gita 6.5). Krishna teaches that the self can be friend or enemy. Rational allocation of resources is key to ensuring our choices serve us rather than harm us. In both economics and the Gita, the message is clear: balance, rational action, and right allocation lead to sustainable well-being.

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From concept to implementation to diagnostics — the DiD series is now complete! 🎉 Over the past few weeks, I been sharing a blog series with Banani Mohapatra breaking down causal inference methods - from intuition to methodology. We started with the intuition and assumptions, then moved to a step-by-step implementation guide, followed by a real-world case study on free shipping, and finally wrapped up with diagnostics and robustness checks. If you're a data scientist working with product or growth teams, this is a foundational tool for your causal inference toolkit. Catch up on the full series here and subscribe to the newsletter: https://lnkd.in/ethamg6R If you are curious to learn more from Banani and me, sign up for our upcoming AB Testing workshop: https://lnkd.in/gpxBwvrM

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