Rahul Shah

Enjoyed collaborating with Josephine Joyce on this DZone article: https://lnkd.in/gAhNzXd9 . In Performance-Focused Platform Engineering, we explore why platform teams must evolve beyond enablement and start engineering for measurable performance outcomes. Curious to hear how others are thinking about this. #PlatformEngineering #DevOps #SRE #CloudNative #IDP

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https://lnkd.in/gqJDGhv6 "In the realm of higher edu­ca­tion, India has a lot to learn from the U.S., espe­cially in terms of pro­mot­ing lib­eral edu­ca­tion, crit­ical think­ing, inter­dis­cip­lin­ar­ity, indi­vidu­al­ised learn­ing, and hol­istic and trans­form­at­ive edu­ca­tion" Prof. V Ramgopal Rao

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Thanks NDTV to bring forward vision of Dr. Jean Shahdadpuri about empowering MSMEs of India with AI with no learning curve, no jargons, just few clicks to growth, productivity and efficiency. Great work team allyra.ai We are live now - start your AI journey at https://portal.allyra.ai India’s First Agentic Business Operating System for MSME live in action

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Sharing two valuable tips I've learned to enhance your experience using Gemini CLI: 1. Start by creating a "GEMINI.md" file within your project. In this file, include tailored instructions relevant to your project and local setup for seamless navigation. 2. For effective communication with Gemini or while working on UI development or troubleshooting, establish a dedicated local folder for images within your project. Remember to update your .gitignore file accordingly to exclude that image folder from git. Capture essential screenshots, store them in this local images folder, and specify the folder's location in your "GEMINI.md" file as the designated spot for screenshots. This way, you can easily reference the appropriate screenshot in your prompts based on the scenario at hand. Utilizing screenshots can often convey information more effectively. With a multimodal LLM at your disposal, showcasing visuals can significantly aid in communication. I welcome input from the community on any additional tips they might have to enrich this collaborative experience!

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Dashboards don’t create accountability. They create meetings. Charts get shared. Everyone nods. Action items get “noted”. Same problems. Next quarter. Accountability doesn’t come from visibility. It comes from systems — where ownership is clear and decisions can’t be ignored. I wrote a short note on why dashboards feel productive but rarely change behaviour — and what actually does. If your org has great dashboards but the same issues every review… this will sound familiar. Dashboards Don’t Create Accountability — Systems Do: https://lnkd.in/gCGpm-VU

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I was leading a LLM Customization session today when someone jumped up and asked a question! Real musings from today's session on LLM customization: Someone jumped up mid-session and said — "Fine-tuning is just a few clicks now. But no one talks about the data prep that goes behind the scenes. THAT's the real slog." 1000% agree. And it's the thing that catches enterprises off guard every single time. Here's the uncomfortable truth: Customizing an LLM has absolutely gotten easier. The tooling is better. The UIs are friendlier. But data prep? Still a slow, expensive, enterprise-grade headache. AND NO AI LABS TALK ABOUT IT! Data scientists already spend ~80% of their time just cleaning and preparing data — before a single training run starts. For LLMs, add to that: collecting labeled examples, removing noise and inconsistencies, formatting for the model's input schema, splitting train/val/test sets, and ensuring domain relevance and diversity. And that's before you hit the real risks of fine-tuning: - Safety guardrail degradation (researchers at Princeton, Stanford, Virginia Tech and IBM found fine-tuned models could be induced to assist with harmful activities) - Catastrophic forgetting of general capabilities - Model version lock-in - Ongoing retraining costs every time your data distribution shifts So the first question I always ask a team that wants to fine-tune: Have you actually maxed out prompting? 9 times out of 10 — the answer is no. Before you go near a training pipeline, exhaust these first: → Few-shot prompting — 3–5 in-context examples can dramatically shift output quality → A real system prompt — role, constraints, output format, tone. Not a two-liner. → Chain-of-thought instructions — especially for reasoning tasks → RAG — if the gap is knowledge, not behavior, this is almost always the right answer Fine-tuning earns its place when: ✓ The task is genuinely outside the model's training distribution ✓ You need consistent behavior changes at production scale ✓ You have clean, high-quality, labeled data actually ready to go ✓ Prompting has plateaued after serious, structured iteration The tooling will keep getting easier. Data prep won't magically disappear. The teams moving fastest aren't the ones with the most custom models. They're the ones who know when NOT to fine-tune. --- Have you seen this pattern in your org? Where did data prep surprise you most? #GenerativeAI #LLM #PromptEngineering #FineTuning #DataPrep #MLOps #EnterpriseAI #AWS #AmazonBedrock #ArtificialIntelligence #AIEngineering #MachineLearning

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Agentic AI role is exponentially increasing in software development lifecycle. Janus is an agentic tool developed by Vayavya Labs that automates part of this lifecycle for code compliance. For more information meet us at nasscom ai

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Parallel AI now supports IMAP/gmail/365 email inboxes for Agents & Sequences. Setup your inbox in minutes to start automating outreach and incoming inquiries. Support for auto-reply and draft mode to meet every businesses needs.

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Excited to share that our journey with DentNode has been featured in Inc42 Media's latest story: “The New Garage: OpenClaw And India’s DIY AI Agent Boom.” 🔗 Article: https://lnkd.in/gQbBHMxH The piece highlights how builders across India are experimenting with AI agents and automation to solve real-world operational problems. OpenClaw and similar frameworks are enabling a new generation of developers to build systems that don’t just chat — they actually do work. Our story in the article starts from a very personal place. I left my engineering role in Bengaluru to work with my father’s dental lab and saw the inefficiencies firsthand — labs juggling multiple scanner portals, downloading patient scans manually, and struggling with fragmented workflows. That experience led to building DentNode — a platform designed to streamline the workflow between dentists, labs, and digital dentistry tools. Being featured alongside this new wave of AI builders is exciting. We’re just getting started. If you’re working in digital dentistry, dental labs, scanners, or aligner manufacturing, I’d love to connect and learn from you. #AI #Startups #DentTech #DigitalDentistry #OpenClaw #DentNode #BuildInPublic #IndianStartups

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Here is a framework for nations to work with revolutions such as #AI and #Quantum. First you have your people learn, then dabble and become early/late adopters (hopefully not laggards!), then become effective technical users and finally value creators. It is only then that the economy benefits from your investment, and you create sustainable job growth. While #India is a key market for #Quantum with millions thirsting to learn and actually do things with this technology, there are still large gaps between: 1. Where Quantum Computing is today Vs where it needs to be for mass adoption [Quantum Sensing on the other hand is ready for prime time and GPS compromising incidents are only increasing] 2. The specialist work being done Vs the ability to hire millions of generalists 3. What it costs to tap into the technology Vs perception of what it should cost as a commodity 4. The talent it takes to produce world class indigenous technology Vs the talent that actually exists on the ground 5... Each of these gaps will close over time. In the lead-up and as a first step, India first needs to learn enough about this technology, ideally from the very best, and spread awareness. The starting point of this journey should ideally not be from dull videos or low quality social media content, but from hands-on practical learning that is delivered through high quality polished content. Such practical training effectively complements all the theoretical content you learn in universities. Q-CTRL's Black Opal product is the world's leading interactive learning tool that gives you just enough fundamentals (think 101) required to program a quantum computer. With over 28,000 users worldwide and success across the UK (Tamil Nadu adopted it last year as part of the naanmudhalvan program). And now, we have expanded our presence in India through a network of local resellers. We encourage Indian companies, governments and universities to work with us either directly or through our contacts. https://lnkd.in/gjJWHYB3 We also look forward to aligning with AICTE standards and to make our content even more relevant to India. Waseem Shiraz, Shobhit Gupta, Nagendra Nagaraja, Reena Dayal, Anil Prabhakar, Kamakoti Veezhinathan, Ajai Chowdhry, Harshan Budke, Sunil Gupta, Arindam Ghosh, Ravi Puvvala Dr M Jayaprakasan, ISDS., National Skill Development Corporation, MindTech, Kquanta Research, RV College Of Engineering, Sanjay Chittore, Uttkrist Innovations Private Limited, Subu Gupta, AICTE QRDLab - Quantum Computing in India, IBM DRDO, Ministry of Defence, Govt. of India, Ashutosh Sharma, Urbasi Sinha, Bloq Quantum LTIMindtree, Tata Consultancy Services Abhay Karandikar, NPTEL, National Quantum Mission, Eltech, Andhra Pradesh Information Technology Academy ITE and C Department Government of AP, Jayesh Ranjan, CDAC Bangalore, Kaushalya The skill University

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