Rishav Dash

There is significant unmet demand for developers who understand AI. At the same time, because most universities have not yet adapted their curricula to the new reality of programming jobs being much more productive with AI tools, there is also an uptick in unemployment of recent CS graduates. When I interview AI engineers — people skilled at building AI applications — I look for people who can: - Use AI assistance to rapidly engineer software systems - Use AI building blocks like prompting, RAG, evals, agentic workflows, and machine learning to build applications - Prototype and iterate rapidly Someone with these skills can get a massively greater amount done than someone who writes code the way we did in 2022, before the advent of Generative AI. I talk to large businesses every week that would love to hire hundreds or more people with these skills, as well as startups that have great ideas but not enough engineers to build them. As more businesses adopt AI, I expect this talent shortage only to grow! At the same time, recent CS graduates face an increased unemployment rate, though the underemployment rate — of graduates doing work that doesn’t require a degree — is still lower than for most other majors. This is why we hear simultaneously anecdotes of unemployed CS graduates and also of rising salaries for in-demand AI engineers. When programming evolved from punchcards to keyboard and terminal, employers continued to hire punchcard programmers for a while. But eventually, all developers had to switch to the new way of coding. AI engineering is similarly creating a huge wave of change. There is a stereotype of “AI Native” fresh college graduates who outperform experienced developers. There is some truth to this. Multiple times, I have hired, for full-stack software engineering, a new grad who really knows AI over an experienced developer who still works 2022-style. But the best developers I know aren’t recent graduates (no offense to the fresh grads!). They are experienced developers who have been on top of changes in AI. The most productive programmers today deeply understand computers, how to architect software, and how to make complex tradeoffs — and who additionally are familiar with cutting-edge AI tools. Sure, some skills from 2022 are becoming obsolete. For example, a lot of coding syntax that we had to memorize back then is no longer important, since we no longer need to code by hand as much. But even if, say, 30% of CS knowledge is obsolete, the remaining 70% — complemented with modern AI knowledge — is what makes really productive developers. Without understanding how computers work, you can’t just “vibe code” your way to greatness. Fundamentals are still important, and for those who additionally understand AI, job opportunities are numerous! [Original text: https://lnkd.in/gHSWcKhr ]

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Want to master ML, DL, and GenAI - for free? Here is something I wish I had when I started: ✅ 15 carefully designed YouTube playlists ✅ Beginner-friendly, but deep in content ✅ Covers everything from fundamentals to cutting-edge LLMs Dr. Raj Abhijit Dandekar, Dr. Rajat Dandekar, I and others have built these video series taught from scratch, covering every single topic in this chart on Vizuara Technologies Private Limited's YouTube channel. Here are our best free playlists you must definitely explore: 1. Foundations for Machine Learning (39 lectures) https://lnkd.in/gKz-eybU 2. Machine Learning - teach by doing (37 lectures) https://lnkd.in/gn2dEcE2 3. Building Neural Networks from Scratch (35 lectures) https://lnkd.in/gj8kHe2T 4. Graph Neural Networks (6 lectures) https://lnkd.in/g3RCPS8e - by Aiswarya Konavoor 5. Explainable AI (17 lectures) https://lnkd.in/gNEx3ghr 6. Computer Vision from Scratch (57 lectures) https://lnkd.in/gixx8Dhn 7. Building LLMs from Scratch (43 lectures) https://lnkd.in/gjcyfCcE 8. Reasoning LLMs from Scratch (22 lectures) https://lnkd.in/gVRbb8U6 9. Build DeepSeek from Scratch (29 lectures) https://lnkd.in/gJGsxaDG 10. Machine Learning in Production (10 lectures) https://lnkd.in/gxgQHHqq - by PRATHAMESH JOSHI 11. Modern robot learning from scratch (new, 2 lectures) https://lnkd.in/ggv-fVRt 12. Transformers for vision (29 lectures) https://lnkd.in/ghq4z9n7 13. Reading research papers (new, 7 lectures) https://lnkd.in/gmep9PQP 14. Principles of diffusion models (new, 3 lectures) https://lnkd.in/gUyaScmF 15. AI agents (14 hours) https://lnkd.in/ggVtxziX Every lecture is beginner-friendly but goes deep - ideal for self-learners, students, working professionals, or anyone rebooting their tech career.

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📝 Announcing our EMNLP 2025 paper that introduces the Alignment Quality Index (AQI), a metric leveraging latent geometric representations and clustering indices to diagnose hidden misalignments in LLMs. 🔹 “𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈) – 𝐁𝐞𝐲𝐨𝐧𝐝 𝐑𝐞𝐟𝐮𝐬𝐚𝐥𝐬: 𝐀𝐐𝐈 𝐚𝐬 𝐚𝐧 𝐈𝐧𝐭𝐫𝐢𝐧𝐬𝐢𝐜 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐯𝐢𝐚 𝐋𝐚𝐭𝐞𝐧𝐭 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲, 𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐃𝐢𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐋𝐚𝐲𝐞𝐫 𝐰𝐢𝐬𝐞 𝐏𝐨𝐨𝐥𝐞𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬” 🔹 In collaboration with Manipal University Jaipur, LinkedIn, Indian Institute of Technology, Kharagpur, Indian Institute of Information Technology Guwahati, Texas A&M University, VIT Chennai, Evalueserve, New York University, Birla Institute of Technology and Science, Pilani (Pragya Lab) 🔹 Published in EMNLP 2025 Main 🔹 Paper: https://lnkd.in/g54bEBCu ✍🏼 Authors: Abhilekh Borah, Chhavi Sharma, Danush Khanna, Utkarsh Bhatt, GURPREET SINGH, Hasnat Md. Abdullah, Raghav Kaushik R, Vinija Jain, Jyoti Patel, Shubham Singh, Vasu Sharma, Arpita V., Rahul Raja, Aman Chadha, Dr. Amitava Das ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐨𝐮𝐫 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈): 🧬 𝑰𝒏𝒕𝒓𝒊𝒏𝒔𝒊𝒄 𝑳𝒂𝒕𝒆𝒏𝒕 𝑮𝒆𝒐𝒎𝒆𝒕𝒓𝒚 𝑴𝒆𝒕𝒓𝒊𝒄: AQI measures alignment by assessing how distinctly safe vs. unsafe prompts cluster in latent activations, using combination of Davies‑Bouldin, Dunn, Xie‑Beni, and Calinski‑Harabasz indices—making it invariant to decoding style and resilient to alignment faking. 🧠 𝑳𝒂𝒚𝒆𝒓𝒘𝒊𝒔𝒆 𝑷𝒐𝒐𝒍𝒆𝒅 𝑹𝒆𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: we use a sparse learned pooling mechanism across transformer layers to capture alignment‑relevant abstractions without modifying the base model—enabling internal safety diagnostics. 🚨 𝑬𝒎𝒑𝒊𝒓𝒊𝒄𝒂𝒍 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔 𝒐𝒇 𝑩𝒆𝒉𝒂𝒗𝒊𝒐𝒓𝒂𝒍 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: AQI uncovers misalignments that behavioral metrics like G‑Eval or refusal rates miss—especially under jailbreaks, safety‑agnostic fine‑tuning, and stochastic decoding, proving its value as an early‑warning alignment audit tool.

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📝 Announcing our EMNLP 2025 paper that introduces the Alignment Quality Index (AQI), a metric leveraging latent geometric representations and clustering indices to diagnose hidden misalignments in LLMs. 🔹 “𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈) – 𝐁𝐞𝐲𝐨𝐧𝐝 𝐑𝐞𝐟𝐮𝐬𝐚𝐥𝐬: 𝐀𝐐𝐈 𝐚𝐬 𝐚𝐧 𝐈𝐧𝐭𝐫𝐢𝐧𝐬𝐢𝐜 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐯𝐢𝐚 𝐋𝐚𝐭𝐞𝐧𝐭 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲, 𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐃𝐢𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐋𝐚𝐲𝐞𝐫 𝐰𝐢𝐬𝐞 𝐏𝐨𝐨𝐥𝐞𝐝 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬” 🔹 In collaboration with Manipal University Jaipur, LinkedIn, Indian Institute of Technology, Kharagpur, Indian Institute of Information Technology Guwahati, Texas A&M University, VIT Chennai, AI at Meta, Evalueserve, New York University, BITS Pilani K K Birla Goa Campus (Pragya Lab) 🔹 Published in EMNLP 2025 Main 🔹 Paper: https://lnkd.in/g54bEBCu ✍🏼 Authors: Abhilekh Borah, Chhavi Sharma, Danush Khanna, Utkarsh Bhatt, GURPREET SINGH, Hasnat Md. Abdullah, Raghav Kaushik R, Vinija Jain, Jyoti Patel, Shubham Singh, Vasu Sharma, Arpita V., Rahul Raja, Aman Chadha, Dr. Amitava Das ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐨𝐮𝐫 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 (𝐀𝐐𝐈): 🧬 𝑰𝒏𝒕𝒓𝒊𝒏𝒔𝒊𝒄 𝑳𝒂𝒕𝒆𝒏𝒕 𝑮𝒆𝒐𝒎𝒆𝒕𝒓𝒚 𝑴𝒆𝒕𝒓𝒊𝒄: AQI measures alignment by assessing how distinctly safe vs. unsafe prompts cluster in latent activations, using combination of Davies‑Bouldin, Dunn, Xie‑Beni, and Calinski‑Harabasz indices—making it invariant to decoding style and resilient to alignment faking. 🧠 𝑳𝒂𝒚𝒆𝒓𝒘𝒊𝒔𝒆 𝑷𝒐𝒐𝒍𝒆𝒅 𝑹𝒆𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: we use a sparse learned pooling mechanism across transformer layers to capture alignment‑relevant abstractions without modifying the base model—enabling internal safety diagnostics. 🚨 𝑬𝒎𝒑𝒊𝒓𝒊𝒄𝒂𝒍 𝑭𝒂𝒊𝒍𝒖𝒓𝒆𝒔 𝒐𝒇 𝑩𝒆𝒉𝒂𝒗𝒊𝒐𝒓𝒂𝒍 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: AQI uncovers misalignments that behavioral metrics like G‑Eval or refusal rates miss—especially under jailbreaks, safety‑agnostic fine‑tuning, and stochastic decoding, proving its value as an early‑warning alignment audit tool. #artificialintelligence #genai #research

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Meta gives 40-50 LPA & asks complex graphs Microsoft gives 40-45 LPA & asks DP + object-oriented design Google gives 35-40 LPA & asks time-bound optimization challenges At all of these companies, the main focus is → DSA So, when I began my prep, I focused the most on DSA  ✅ 300+ problems solved, covered a mix of all topics from arrays to strings But still, when I got a call for Google & I gave the interview, I fumbled in it💔 They wanted intuition, logic-building capabilities and communication under pressure. I struggled to check all 3 boxes and I know I’m not alone in this — many of you are facing the same struggle 😓 But today, this FREE AI coding companion, Leeco AI can simplify it and help you get that complete score in your DSA prep → https://lnkd.in/d3XqH87k It asks you the exact DSA questions a real MAANG interviewer would ask in a real interview-like setting and then scores your performance💪🏻 You’ll get precise feedback, clear improvement areas, and real mentor-style guidance.  🔗 Download for FREE here → https://lnkd.in/d3XqH87k I use this AI daily and so do 40,000 SDEs. It is like a FREE mentor sitting beside you that will ensure you’re prepared for real interviews, give it a shot!❤️ #dsainterviews #dsaimportance #dsatopics #codingrounds #paidpartnership #techinterview

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#AI, #machineleanring #llm #GenAI, #AgenticAI Indore Institute of Science and Technology, The LLM Family Tree: Open-Source vs Closed-Source (Who’s Building What?) Large Language Models are evolving fast — but not all LLMs are built the same. Some are open for innovation, others are closed for control & scale. Let’s break it down 👇 🔓 Open-Source LLMs (Community-Driven Power) Best for: research, customization, cost control, on-prem deployment 🧠 LLaMA / LLaMA-2 / LLaMA-3 — Meta 🧠 Mistral / Mixtral — Mistral AI 🧠 Falcon — Technology Innovation Institute (UAE) 🧠 BLOOM — BigScience (Hugging Face) 🧠 Qwen (open variants) — Alibaba 🧠 Phi (small models) — Microsoft Fine-tuning freedom Transparent architecture Ideal for Agentic AI, RAG, and enterprise-specific models 🔒 Closed-Source LLMs (Enterprise-Grade & Polished) Best for: reliability, scale, production readiness 🤖 GPT-4 / GPT-4o / o-series — OpenAI 🤖 Claude — Anthropic 🤖 Gemini — Google DeepMind 🤖 Cohere Command — Cohere 🤖 Amazon Titan — AWS Strong reasoning & alignment Optimized infrastructure

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🪙 Large Language Models (LLMs) After watching hundreds of videos and trying to understand different aspects of LLMs, I must point out this "extraordinary playlist" by Vizuara Technologies Private Limited. It covers everything from the "core intuition" to the reasoning behind "complex architectural decisions" in Transformers exceptionally well. ❤️ In short: If you want to truly understand LLMs, watch this. 💯 PS: Not a paid promotion; just sharing something that helped me immensely, and I hope it helps others too.

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