Ajinkya Thakare

Personal Experience on Using BharatGen's PARAM 1 – A Step Toward Truly Inclusive AI Over the past few days, I've had the opportunity to explore PARAM 1, BharatGen's Indic-scale bilingual foundation model and I have to say, it's one of the most refreshing AI experiences I've had in a while. What stood out to me? -> True bilingual understanding: I could interact naturally in both English and Hindi sometimes even blending the two. Unlike many global LLMs, Param 1 didn't flinch. It understood the context, the tone and the nuance. -> India-centric training data: You can feel the difference. The responses feel grounded, culturally relevant, and more aligned with everyday Indian realities. -> Open and accessible:As a developer and AI enthusiast, having free access to a model trained on 5 trillion+ tokens with 2.9 billion parameters - that's a big deal. Whether it’s education, health, governance, or content creation the applications are massive. For us Indians, this isn't just a model - it's a foundation for inclusive digital transformation in India. If you're building in the Indic AI space, I highly recommend checking it out. Explore the model here: https://lnkd.in/dJVcTBCF #AIforBharat #BharatGen #Param1 #MultilingualAI #DigitalIndia #OpenSourceAI #InclusiveTech #LLMs #HindiLLM #AIExperience #IndianAI

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Stay tuned for vikramvetal.ai! 🚀 Last week, I dived into the world of LLMs, Streamlit, and FastAPI to create an interactive chat-based riddle game inspired by the classic tales of Vikram and Vetal. Think Gandalf.lakera.ai, but with a rich narrative twist! For members who have not heard the story of Vikram and Vetal, they can refer to this link: https://lnkd.in/g7Wqgs3g" The structure of Vikram and Vetal is perfect for this: A story unfolds, the cunning Vetal poses a riddle, and the player (as Vikram) must answer correctly... or face the consequences (Vikram dies! 😱 No pressure!). But here's the cool part: In this modern adaptation, Vikram can *chat* with Vetal! Brainstorm, ask for hints, discuss the riddle – the possibilities are endless. The stories and visuals are all configuration-driven, making it easy to create new adventures. Could this be the future of engaging children's educational media? Fun quizzes for all ages? I'm planning to launch this as an AI experience at vikramvetal.ai. Sneak peek of the code is available here, so you can set up the game locally: https://lnkd.in/gHb2xCGJ Attached a video of the initial gameplay! What are your thoughts on using LLMs to create interactive and engaging stories? Share your ideas in the comments! 👇 #LLM #AI #Streamlit #FastAPI #GameDevelopment #EducationalGames #VikramVetal #GenerativeAI #MachineLearning Sharing for broader reach: Swaroop S Viswanath S Sameet Shetty Ramkumar Krishnan MBA, B.Eng John Maschke divya kodukula Arijit Roy Kanika Seth Aravind Kumar Suhas Ramesh Amrit Narayan Nayak Utsav Harsh Esha Dhing Nitya Keshari Jahnavi . Salla Tony Preeth Vishwank Vikram Singh Rahul Agarwal Ankit Gupta Abhishek Sagar Sudeep Kumar Datta Monojeet Nayak, CISSP Venu A.

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Salim FEKU not able to digest NoSQL databases are converging to PostgreSQL. Setting: A chai ki dukaan (tea stall) in Hyderabad. Salim Feku is holding forth, gesturing wildly with his chai glass. (Salim Feku sips his chai, then puts the glass down with a flourish, looking around at an imagined audience.) SALIM FEKU: Arre bhai, aaj kal ki duniya mein kya chal ra? Sab bolte, "PostgreSQL, PostgreSQL!" Kya hai re usmein? Apun bolta, woh to puraane zamane ka ghoda hai! (He leans in conspirantly, lowering his voice slightly but still loud enough for everyone to hear.) SALIM FEKU: Suno tum meri baat, agar tumko asli data rakhna hai, jo kabhi nikalta nai, jo tezz bhaagta, toh tumko NoSQL hona bhai! Yeh PostgreSQL, woh to apna aadmi, kaisa... sara din join lagate baithta. Ek table se dusra table, dusre se teesra... Arre baba, jab tak woh join lagake data nikalta, apun chai pi ke doosra dhanda kar leta! (He chuckles, shaking his head.) SALIM FEKU: Apun ke paas kya hai? MongoDB! Arre kya cheez hai woh! Tum feko usmein kuch bhi! Document feko, JSON feko, apun ka data, seedha andar. Koi tension nai. Woh PostgreSQL, usko pehle batao 'column' kya hai, 'row' kya hai, 'schema' kya hai. Arre baba, itna sochne mein hi system slow ho jata! (He snaps his fingers.) SALIM FEKU: Aur suno, jab apun ka traffic aata, haan? Croreon log aate, kya karte? Woh PostgreSQL to ek machine pe baitha rehta. Apun ka Cassandra, DynamoDB... yeh log kya karte? "Arre bhai, thoda data tu le le, thoda data main le leta," bolke baant lete. Shaana log! Sab milke kaam karte, isliye apna system kabhi rukta nai, bhai! Woh PostgreSQL ko to pasina aa jata! (He gestures expansively, like he's spreading data everywhere.) SALIM FEKU: Woh bolte, "Hum consistent hai!" Arre haan, theek hai. Lekin consistency ke chakkar mein kya, apna customer wait karte baithta! Apun ka NoSQL? Woh bolta, "Thoda late aaya to kya hua? Aayega to sahi! Customer khush hona, jaldi result aana!" Zara sa "eventually consistent" hua to kya hua? Duniya to chalte rehti na! (He lowers his voice again, for the grand finale.) SALIM FEKU: Toh bhai, agar tumko naya, tezz, bina tension ka system banana hai... toh woh puraane dimaag ke relational ko choro! Apun ka NoSQL, woh hai asli king! Baaki sab to... chillar hain! (Salim Feku takes another proud sip of his chai, looking very pleased with his pronouncement.Just then, his eyes fall on someone approaching the chai stall. His confident smirk vanishes, replaced by a look of surprise, then forced cordiality.) SALIM FEKU: (Suddenly a soft, almost meek tone) Arre! Kaun? Ismail Bhai... PostgreSQL wale!?! (He rushes forward, abandoning his lecture, ) SALIM FEKU: Aao Miya! Kaisa hai tum? Tumhari hi raah dekh raha tha main! Kaisa, sab khairiyat? Woh, woh main to... ...main to bas logon ko bata raha tha ki purani cheezo ki reliability kitni zaroori hai! Haan! Tumhari hi baat kar raha tha, bhai! Maine suna..tum bhi distributed ho gaye!!!

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Ed Donner, Sachin Chandrashekhar 🇮🇳 Building an Agentic AI Email System: I have been experimenting with Agentic AI and the OpenAI SDK to automate email workflows, and it’s been an amazing hands-on learning experience. Here is what I did: 1. Built agents that can draft emails automatically. 2. Created tools to generate email subjects and convert plain text into HTML emails. 3. Implemented a handoff system so the best draft gets selected and sent automatically. Learned how important tool prompting and agent instructions are to make the workflow smooth. It’s exciting to see AI agents working together, almost like a small team, to get a task done. This project really helped me understand how to orchestrate AI for real-world automation, like email campaigns or notifications. Next, I want to explore connecting this system to LinkedIn posts and automated data pipelines—bringing end-to-end automation to life. #OPENAI Agentic SDK #OPENAI #GPT Models # GEMINI #Python #Automation #DataEngineering #AWS Pipelines. Code Snippet: emailer_agent = Agent( name="Email Manager", instructions=instructions, tools=email_tools, model="gpt-4o-mini", handoff_description="Convert an email to HTML and send it") tools = [hr_tool1, hr_tool2] handoffs = [emailer_agent] hr_instructions = ''' You are an HR agent at Cognizant. You are responsible for recruiting Data Engineers. Steps you must follow: 1. Use the HR tools such hr_tool1 and hr_tool2 to generate candidate email drafts. 2. Evaluate all drafts and choose exactly one best draft. 3. Handoff only this single best draft to the emailer_agent for formatting and sending. Critical Rules: - You must always use the HR tools to generate drafts (never write the drafts yourself). - You must never output more than one draft. - You must always handoff exactly one draft to the emailer_agent. - Do not ask clarifying questions or request more input. - Produce the final result in one step with a single handoff.''' hr_manager = Agent( name='hr manager', instructions=hr_instructions, tools = tools, handoffs= handoffs, model = 'gpt-4o-mini' ) with trace('Hr agent'): try: response = await Runner.run(hr_manager, "Send out a Recruiting email addressed to Dear Applicant from OPENAI") except : response = {"final_output": "Could not complete in 2 turns, please retry with higher max_turns."}

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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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Shantanu Narayen’s 18 year tenure as CEO at Adobe is a reminder of how rare long-term stewardship has become in Silicon Valley. During his time with the company, the company’s stock increased more than 6x and its business model fundamentally changed. The most important decision of his tenure was moving Adobe from selling boxed software like Creative Suite to a subscription platform through Creative Cloud. At the time it was controversial. Today, it is considered one of the most successful SaaS transitions in the history of enterprise software, turning Photoshop, Illustrator, and Acrobat into one of the most profitable recurring software ecosystems ever built. The respect he commands across the industry says a lot about how difficult that transformation actually was. Microsoft CEO Satya Nadella even posted publicly congratulating Narayen on a “legendary run,” praising both his leadership and the way he expanded what creators and businesses could do with software. In an industry that is often intensely competitive, that kind of recognition from peers is not given lightly. There are only a few CEOs in the world who truly get to define one era of software and then help set the stage for the next. Narayen did it by turning Adobe into a SaaS powerhouse. However, the next leader will have to answer a harder question. What does a creative software company look like when AI becomes part of the creative process itself? #Adobe #Leadership #SaaS #AI #Technology

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🚀 Fine-Tuning Small Language Models (SLMs): A Step-by-Step Guide As AI adoption accelerates across industries, Small Language Models (SLMs) are emerging as powerful, cost-effective tools for domain-specific tasks — especially in regulated sectors like healthcare. Here’s a practical guide to fine-tuning SLMs for your enterprise use case: 🧠 Step 1: Define the Use Case Start with a clear objective — e.g., member journey optimization, clinical note summarization, or marketing content classification. 📦 Step 2: Prepare the Dataset Curate high-quality, domain-specific data. Clean and tokenize text. Ensure compliance with data privacy (HIPAA, etc.). 🏗 Step 3: Choose the Right SLM Options: TinyLlama, DistilBERT, BGE-Small, etc. Consider model size, latency, and inference cost. 🛠 Step 4: Set Up the Environment Use Databricks, Hugging Face, or Azure ML. Leverage GPU clusters for faster training. 🔁 Step 5: Fine-Tune the Model Use transfer learning with domain-specific data. Apply techniques like LoRA or QLoRA for efficiency. Monitor training loss and validation accuracy. 🔍 Step 6: Evaluate & Test Use benchmark datasets and real-world samples. Measure precision, recall, F1-score. Perform bias and safety checks. 🚢 Step 7: Deploy Securely Containerize with Docker. Use Azure endpoints or Databricks Model Serving. Implement RBAC and audit logging. 📈 Step 8: Monitor & Improve Track performance in production. Collect feedback and retrain periodically. 💡 SLMs are not just lightweight — they’re agile, interpretable, and ideal for enterprise AI. If you're working in healthcare, MarTech, or regulated domains, now is the time to explore their potential. Let’s connect if you're exploring SLMs for your next AI initiative! #AI #SLM #Databricks #HealthcareAI #MarTech #FineTuning #LLM #AzureML #MLOps #EnterpriseAI #DataScience

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