Vijayendra Grampurohit

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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🚨 India’s consumption puzzle: Why do two official datasets tell different stories about how we spend? PFCE (top-down; macro-consumption) vs. HCES (bottom-up; micro-consumption) 🔹 Gap has widened from 5% in 1972–73 to ~45% today. 🔹 Free/subsidised services (education, healthcare, meals) inflate PFCE but barely appear in HCES 🔹 Food share in household budgets is falling; non-food (transport, healthcare, rent, education) dominates This divergence isn’t just statistical—it affects how we measure poverty, prosperity, and the impact of policy. 📊 Latest piece by Ashish Kumar and Payal Seth from Pahlé India Foundation (PIF) in businessline unpacks the data divide and calls for urgent reforms (https://lnkd.in/dJWjyCQe)

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