Rushabh Gosar

Running LLMs on mobile phones—completely offline and locally—has become a reality. I recently tried the Google AI Edge Gallery app and was surprised by how effortlessly it brings generative AI to your device, no internet connection required after the initial setup. The app lets you chat, experiment with prompts, and even analyze images, all on your phone. If you're curious to try on-device GenAI for yourself, here’s the link: https://lnkd.in/dFJ9MXWV #GenAI #EdgeAI #LLM #MobileAI

35

2 Comments

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

49

4 Comments

Ruokai Yin, our summer intern from Yale to Microsoft Azure, worked with Xuan Zuo, @Sattwik Deb Mishra, Hokchhay T. and Preyas Shah and me. We published a paper based on his project: https://lnkd.in/eHuG3BSt Summary of paper: Finding optimal sharding is hard especially for large LLMs. A large number of devices have to be divided into TP, PP, EP dimensions. For TP, we have to decide the tensor dimension e.g. head, hidden size to shard for every op. Additionally, TP can be one-dimensional/two-dimensional etc. This makes for a large sharding space that is hard to cover exhaustively. SoTA methods use heuristics to select sharding e.g. GSPMD, Sequence Parallelism, Helix etc. There are significant time gaps, months or years, between the discovery of each of these methods. We want to fast-forward the process of discovery by 1) using a formal representation of the sharding space, and, 2) using reinforcement learning to search the sharding space.    This paper demonstrates that our method is 3.5x better than random search, and on par with hand-tuned heuristics when looking only at 1D sharding and without allowing sequence parallelism. We aim to extend the work to multi-dimensional sharding as well as sequence parallelism. Beyond that, we wish to combine the problem of sharding with fusion, tiling and collectives as well.

58

🌟 From curiosity to stage: my journey with DSPy at The Fifth Elephant A week ago, I had the incredible opportunity to speak at Hasgeek’s Fifth Elephant Conference — one of India’s most vibrant gatherings for data and AI enthusiasts. When I first started exploring DSPy (Community), I was fascinated by how it simplifies building structured, reliable AI systems — turning messy prompt engineering into a declarative, modular process. Over time, that curiosity turned into something bigger — a belief that DSPy represents the next step in how we design AI systems. In my talk, I shared how this framework can help us: 🔹 Build reasoning-centered LLM pipelines 🔹 Reduce prompt fragility through programmatic supervision 🔹 Apply these ideas to real-world problems 🎥 If you missed the session, the full recording is now available — and I would love for you to check it out: 👉 https://lnkd.in/dRBSFRF5 Whether you are building AI agents, working with LLMs, or just exploring what’s next in applied AI — I hope this talk sparks a few new ideas. #DSPy #AI #LLM #MachineLearning #fifthel #Hasgeek #AIEngineering #LLMAgents #ShiftScheduling #OpenSource Hasgeek

73

Snapped this during Nimisha Asthagiri’s talk on Scaling responsible multi-agent architectures at #InfoQDevSummit. “What makes a micro-service different than an AI agent? An agent can make decisions and has self-learning abilities.” This gave me more clarity on how to see AI agents in software developers lens.

12

Generative AI and Character Design: An overview of Zuraverse's Approach I had a fascinating conversation with my best buddy, Abhishek Sagar , the founder of Zuraverse.xyz, about how his team leverages generative AI for character design. It's a complex challenge, especially for a gaming platform with thousands of unique, NFT-based characters that need to maintain consistency while remaining true to their core identity. Abhishek shared some great insights, particularly their method of creating highly descriptive character prompts. Instead of generic descriptors, they use multi-layered strategy for character design consistency: • Prompt Engineering: They use a "recipe" of descriptive elements for each character. This goes beyond just physical appearance and includes: • Core Visuals: Specific details on body shape, color palette, unique markings (scars, tattoos), and accessories (a glowing sword, a specific type of armor). • Personality & Emotional Cues: Describing a "mischievous rogue with a sly grin" or a "somber sorceress with a furrowed brow" helps the AI generate consistent facial expressions and body language. • Style Cues: They include instructions to maintain a specific art style, whether it's "hyper-realistic," "anime-style," or "dark fantasy," to ensure all characters in a specific game universe look cohesive. • Reference Images: In addition to text, Zuraverse uses a core set of reference images for each character. This allows the AI to learn and replicate a character's "look and feel" with much greater accuracy, even when generating the character in new situations, poses, or wearing different outfits. • Agentic AI for Iteration: Abhishek also gave me a sneak peek into his journey with agentic AI. They are not just using a simple text-to-image generator. They're building AI agents that can take the initial character prompt and a set of rules, and then autonomously iterate on the design. This would allow the system to generate hundreds of variations—different outfits, poses, and expressions—while ensuring each one adheres to the character's core design principles. This isn't just theory—it's a practical application of AI by industry experts to tackle a real-world problem. Abhishek also promised that he will be sharing more details in the form of hands-on examples and podcasts in the coming weeks for the benefit of new explorers in his field. Feel free to checkout what zuraverse is Upto at: Zuraverse Story: https://lnkd.in/gz6Wuc4V Zuraverse: https://zuraverse.xyz Review: https://lnkd.in/gqkTSNxK #GenerativeAI #AIinGaming #Zuraverse #CharacterDesign #NFT #Gaming #AIagents #Innovation

13

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!!!

8

Kanha AI is now live at https://kanhaji.ai Built for families who want a mindful, culturally rooted, and truly screen free AI companion for their children, KanhaJi brings together storytelling, values, curiosity, and safe intelligence into one seamless home experience through the KanhaJi Home Pod. Proud to be a part of the core team behind this journey from idea to reality. Working across product direction, experience design, and system thinking to shape what an AI first childhood environment should feel like has been deeply meaningful. Heartfelt thanks to our incredible core team members who made this possible: Sudhanshu Sharma, Ankush Sabharwal, Lavanya Sharma, Ankur Thakur, Md Ibrahim, Ankit Bhardwaj We are excited to see KanhaJi become a trusted companion in homes that value both tradition and thoughtful technology. Onward.

117

7 Comments

As an AI/ML researcher and technology leader, I notice the world of technology living with a certain sense of angst in the era of AI. Unless we are living under a rock, AI tools, coding agents, and LLMs are no longer technologies of the future. They are already part of the day-to-day workflow of many software engineers. 𝗬𝗲𝘁 𝗮𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀, 𝘄𝗵𝗮𝘁 𝗹𝗲𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘂𝘀𝗲 𝘁𝗼 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀? If our investments must move the bottom line of the business, what guardrails should guide how we employ AI? In my view, every strategic conversation about AI should answer three questions: 𝟭. 𝗗𝗼𝗲𝘀 𝗺𝘆 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝗺𝘆 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀? Not every business problem requires next-generation technology. While there is pressure, even with a fear of missing out to adopt AI quickly, 𝙡𝙚𝙖𝙙𝙚𝙧𝙨 𝙢𝙪𝙨𝙩 𝙚𝙣𝙨𝙪𝙧𝙚 𝙞𝙩 𝙩𝙧𝙪𝙡𝙮 𝙖𝙡𝙞𝙜𝙣𝙨 𝙬𝙞𝙩𝙝 𝙩𝙝𝙚 𝙫𝙖𝙡𝙪𝙚 𝙩𝙝𝙚 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙙𝙚𝙡𝙞𝙫𝙚𝙧𝙨. Building capability purely because it is the buzz of the moment can be worse than being slightly late on the adoption curve. 𝟮. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁-𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗼𝗳 𝗔𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝗱𝗱 𝘃𝗮𝗹𝘂𝗲? Technology can be exciting, but leaders must ask: 𝙝𝙤𝙬 𝙬𝙞𝙡𝙡 𝙩𝙝𝙞𝙨 𝙧𝙚𝙛𝙡𝙚𝙘𝙩 𝙞𝙣 𝙩𝙝𝙚 𝙋&𝙇 𝙞𝙣 𝙩𝙝𝙚 𝙣𝙚𝙖𝙧 𝙖𝙣𝙙 𝙡𝙤𝙣𝙜 𝙩𝙚𝙧𝙢? 𝙒𝙞𝙡𝙡 𝙩𝙝𝙚 𝙞𝙣𝙫𝙚𝙨𝙩𝙢𝙚𝙣𝙩 𝙨𝙩𝙖𝙣𝙙 𝙩𝙝𝙚 𝙩𝙚𝙨𝙩 𝙤𝙛 𝙩𝙞𝙢𝙚 𝙖𝙣𝙙 𝙙𝙚𝙡𝙞𝙫𝙚𝙧 𝙙𝙪𝙧𝙖𝙗𝙡𝙚 𝙫𝙖𝙡𝙪𝙚? 𝟯. 𝗗𝗼 𝘄𝗲 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗼𝗿 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺 𝗵𝗲𝗿𝗲, 𝗼𝗿 𝗰𝗮𝗻 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗱𝗼 𝘁𝗵𝗲 𝗷𝗼𝗯? Today, everyone wants to label their solution with GenAI, agents, or agentic systems to be noticed. That is understandable. 𝘽𝙪𝙩 𝙖𝙨 𝙡𝙚𝙖𝙙𝙚𝙧𝙨, 𝙬𝙚 𝙢𝙪𝙨𝙩 𝙣𝙤𝙩 𝙧𝙚𝙬𝙖𝙧𝙙 𝙩𝙝𝙚 𝙢𝙚𝙧𝙚 𝙥𝙧𝙚𝙨𝙚𝙣𝙘𝙚 𝙤𝙛 𝗚𝗲𝗻𝘼𝙄 𝙞𝙣 𝙖 𝙨𝙤𝙡𝙪𝙩𝙞𝙤𝙣. 𝙄𝙣𝙨𝙩𝙚𝙖𝙙, 𝙬𝙚 𝙨𝙝𝙤𝙪𝙡𝙙 𝙧𝙚𝙬𝙖𝙧𝙙 𝙨𝙤𝙪𝙣𝙙 𝙩𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙟𝙪𝙙𝙜𝙢𝙚𝙣𝙩. Sometimes the right answer is: • Traditional ML is sufficient. • A deterministic system is better. • A rule-based SaaS solution is the right tool. And that is good engineering leadership. For many businesses, AI is ultimately an enabler, just as technology and data have always been. 𝗕𝘂𝘁 𝗶𝗳 𝗳𝗲𝗮𝗿 (𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁) 𝗱𝗿𝗶𝘃𝗲𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘄𝗲 𝗺𝗮𝘆 𝗲𝗻𝗱 𝘂𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗮𝗸𝗲 𝗼𝗳 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 𝙎𝙤 𝙗𝙮 𝙖𝙡𝙡 𝙢𝙚𝙖𝙣𝙨, 𝙜𝙤 𝙖𝙡𝙡 𝙞𝙣 𝙤𝙣 𝘼𝙄, 𝙗𝙪𝙩 𝙢𝙖𝙠𝙚 𝙨𝙪𝙧𝙚 𝙞𝙩 𝙥𝙖𝙨𝙨𝙚𝙨 𝙩𝙝𝙧𝙤𝙪𝙜𝙝 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙡𝙚𝙣𝙨 𝙛𝙞𝙧𝙨𝙩. Agree or disagree? I’d love to hear how other leaders are thinking about this. #ArtificialIntelligence #GenAI #TechnologyLeadership #EngineeringLeadership #TechStrategy #AIAdoption #SoftwareEngineering #ProductStrategy #LeadershipPerspective #InnovationLeadership #AIinBusiness

18

3 Comments

Is the golden era of pure AI research at Meta officially ending? The departure of VP Jitendra Malik to Amazon Robotics signals a massive shift in the AI landscape. For years, FAIR (Fundamental AI Research) was the gold standard for academic freedom within Big Tech. But as the focus shifts aggressively toward commercial products, the culture is changing. Here is what this exodus tells us about the current state of AI: 📍 The Pivot is Real Meta is moving from "what is possible?" to "what is profitable?" The aggressive push for commercial AI products is reportedly leaving pure researchers feeling sidelined. 📍 The Talent War is Shifting Malik isn't just leaving; he is joining Amazon’s robotics efforts. This suggests the next frontier isn't just LLMs, but embodied AI and physical automation. 📍 The "Brain Drain" Effect With reports citing former employees describing the division as "dying a slow death" and following other high-profile exits, retaining top-tier research talent requires more than just high salaries—it requires a mission they believe in. Think of this like a university lab being turned into a factory floor. While the factory produces immediate value, the explorers who thrive on discovery often pack their bags when the goal becomes pure efficiency. The lesson for tech leaders and developers? We are entering the era of Applied AI. The companies that win won't just be the ones doing the best research; they will be the ones who can successfully transition that research into tangible products without alienating the minds that built it. If you are in AI, now is the time to bridge the gap between theory and deployment. Do you think Big Tech can still sustain pure research labs, or is everything destined for productization? Let me know in the comments. 👇 #AI #TechNews #Robotics #Leadership #Meta #Amazon

3