Hitarth Kanakia

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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An excerpt from the Statement of Purpose in my PhD application to IIT Bombay (Nov 2024). My application was accepted but I could not clear the technical interview. <start> 1) Why PhD? I come with varied career experiences. 3 years of Industry experience as software engineer, 2 years of IIT (M.Tech) experience, ~1 year of teaching experience (as an ad hoc faculty in an engineering college), ~1 year of freelance (assisting/training faculties in research/coding) experience. Two insights I obtained from these career transitions are these. * Industry-Academia gap is really wide and need to be addressed seriously. * With AI making progress quickly, life (market, job, teaching, learning) is going to be dynamic. Nature of jobs (everything) is going to keep changing/evolving quicker than ever. Almost all leaders/policy makers/work force must pass through colleges (as students). Any solution/reform to address the above points has to begin from colleges specifically from teachers. We cannot expect students to change/prepare by themselves just with new syllabus, new courses, etc. Experimental/research-oriented/adaptive thinking can be imparted effectively only through teachers possessing these qualities (which will be essential to face the changing times). We saw entrance coaching revolution, edtech revolution, coding (even for kids) revolution. Everything was focused on students and on solving well-defined problems (exams). Unlike in Industry, in Academia people cannot be hired and fired as and when required (as of existing system in India). Retraining existing faculties across the colleges is going to be the essence of future educational policies. The next revolution is going to be faculty development. (industrial trainings, foreign university trainings, other (new) experimental kinds of trainings). IIT's are undoubtedly going to play a major role. I wish to be in Academia, benefitting from, contributing to and leading this future revolution. Doing PhD at IIT Bombay will be a big step towards that direction. </end> Why am I sharing this now? My belief in the above vision has only grown stronger. But my understanding about limitations of one as an individual has also grown. But my realisation of the collective capabilities of human beings has also grown. System is stronger than any individual entity in it. But every entity contributes towards the state of the system at least in a small way. Also, I feel I have turned softer than what I was then. Sharing this now, before I get even softer. #Education #Teaching #Students #PhD #IIT #Failure #Experience #Learning #Future #Vision #Mission

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🔍 Software Defect Prediction with Machine Learning 🖥️🐞 Just wrapped up an interesting assignment as part of my M.Tech in AI/ML at BITS Pilani — building predictive models to identify software modules likely to contain defects. 📂 Dataset: Real-world defect dataset (defect_dataset-ant-1.3.csv) ⚙ Approach: Data Exploration & Visualization — understanding feature relationships through correlation analysis Preprocessing — handling missing values, outliers, skewness, and applying standardization/normalization Feature Engineering — identifying important predictors for defect classification Model Building — implemented and tuned: ✅ Logistic Regression ✅ Decision Tree ✅ K-Nearest Neighbour ✅ Ensemble Method (Random Forest) Evaluation Metrics — Precision, Recall, F1-Score, AUC-ROC 📊 Outcome: Compared all models and identified the best-performing classifier for predicting software defects, which can help improve software quality and reduce testing costs. 📌 GitHub Repository: https://lnkd.in/g7VV93Re This exercise reinforced the value of classical ML models in real-world software engineering problems, especially in scenarios where explainability and interpretability are key. #MachineLearning #MTech #BITS #AIML #SoftwareEngineering #DataScience #PredictiveModeling #ML #AI #QualityAssurance #BugPrediction

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A Thought Exercise : Context Engineering with LLMs is analogous to Feature Engineering in Classical ML What are the similatieties and differences ? Share in the thread #AImusings courtesy: Jim Hong :)

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Physics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics Kamalpreet Singh Kainth, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedat Panat https://lnkd.in/dtRD_ide This paper tackles a critical challenge in using Neural ODEs for modeling stiff biophysical systems, specifically the difficulty in achieving stable and accurate long-term predictions. The core idea is to improve training stability and accuracy by incorporating physics-informed regularization with scale-aware residual normalization within a Neural ODE framework. The authors propose "PI-NODE-SR," which combines two key elements: 1. Physics-Informed Regularization: They leverage the known governing equations (e.g., Hodgkin-Huxley) to define a physics-informed loss term that penalizes deviations from the expected behavior. This is a standard PINN approach, but the key is how they handle the residual. 2. Scale-Aware Residual Normalization: This is the novel contribution. Stiff systems often involve state variables evolving on vastly different timescales. Directly applying a physics-informed loss can lead to one timescale dominating the training process, hindering convergence and accuracy. To address this, they normalize the residual associated with each state variable by a scaling factor. This factor is chosen to balance the contributions of each variable to the overall loss, preventing the faster dynamics from overwhelming the slower ones. They use a low-order explicit solver (Heun method) to calculate the residual. The authors demonstrate the effectiveness of PI-NODE-SR on the Hodgkin-Huxley equations, showing that it can learn from a single oscillation and extrapolate accurately over longer time horizons. Importantly, they show that the method can recover morphological features in the gating variables that are typically only captured by higher-order solvers. ----- This work directly addresses a significant limitation of Neural ODEs in scientific applications: their struggle with stiff systems. By introducing scale-aware residual normalization, the authors provide a principled way to stabilize training and improve the accuracy of long-term predictions. This is particularly relevant for: - Biophysical modeling: Accurately simulating neuronal dynamics, cardiac electrophysiology, and other complex biological processes. - Chemical kinetics: Modeling reaction networks with widely varying reaction rates. - Multi-physics simulations: Situations where different physical processes evolve on different timescales. The ability to use lower-order solvers with neural correction to achieve accuracy comparable to higher-order methods has significant implications for computational efficiency. While the method's sensitivity to initialization is noted, the overall approach offers a promising direction for developing more robust and efficient Physics-Informed Neural ODEs for a wide range of scientific applications.

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RAG vs Fine-Tuning for LLMs (2026): What Actually Works in Production RAG vs fine-tuning in 2026 explained with real tradeoffs, latest trends, and a practical decision framework for production LLM systems. What you'll find inside: - RAG - Fine-Tuning - LLM Engineering Curious how you'd approach this. Drop your take in the comments. Read the full post: https://lnkd.in/gKBrQs2M #RAG #FineTuning #LLMEngineering #AIArchitecture #GenAI2026

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⚡ ML inference continues to fascinate me, and I've been diving deep into LLM inference optimization techniques - something I genuinely enjoy working on! Recently, I've been exploring the intricate challenges of serving large language models efficiently, and it led me to build TorchWeave LLM - an open-source inference compiler that achieves 2-5x throughput improvements through some compelling optimization strategies. What makes ML inference so interesting is how it sits at the intersection of systems engineering and machine learning theory. Every optimization decision has cascading effects on memory, compute, and user experience. 🔄 Continuous Batching: One technique I found particularly rewarding to implement - merging concurrent requests into shared decode steps to maximize GPU utilization. The scheduling algorithms here are genuinely fascinating. ⚡ Per-Request KV-Cache Management: Exploring how attention masks can enable efficient memory management for variable sequence lengths opened up entirely new optimization possibilities. 📡 Real-time SSE Streaming: Building Server-Sent Events with time-to-first-token metrics taught me so much about balancing performance with user experience. The exploration paid off: 98% throughput improvement with 4 concurrent requests Average TTFT of 0.860s Production-ready architecture that scales What I love about working on inference optimization is that every bottleneck you solve reveals three new interesting problems. The rabbit holes of memory management, scheduling algorithms, and distributed systems design never get old. The technical implementation spans everything from PyTorch model interfaces to FastAPI async patterns - each component was a learning opportunity that deepened my appreciation for the complexity of production ML systems. 🔗 Check it out on GitHub: https://lnkd.in/gCZdZ5Pm I'm curious - what aspects of ML inference optimization do you find most compelling? Always eager to learn from others tackling similar challenges! #MachineLearning #LLM #MLInference #PyTorch #Optimization #OpenSource #MLOps #AI #SoftwareEngineering #TechnicalExploration Built with Python 3.12, FastAPI, PyTorch, and genuine curiosity about making models faster ⚡

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