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AI research in 2025 was defined by major shifts. The industry moved beyond chatbots and into reasoning systems, autonomous agent and multimodal systems. Last year, companies like Google DeepMind, OpenAI, Anthropic, Meta, DeepSeek, and NVIDIA pushed AI research into new territory with papers focused on reasoning, coding agents, reinforcement learning, and scalable safety systems. Here are the top AI research papers of 2025 that every AI researcher, ML engineer, and GenAI builder should know. Top 10 AI Research Papers The papers below were selected based on technical novelty, industry influence and impact within the global AI community throughout 2025. 1.…

  # Introduction  The first time you run ollama run llama3.2 in a terminal and watch a 7-billion-parameter model load onto your own machine — no API key, no billing dashboard, no data leaving your computer — something shifts. Not because it is technically impressive, though it is. But because it is fast, it is capable, and it is entirely yours. You own the conversation. Nobody is logging it. Nobody is charging you per token. The model does not know or care that you are offline. I have been running local models as part of my daily workflow for a while…

You walk into the interview room. The whiteboard displays the following prompt: “A major retailer wants to deploy a GenAI chatbot for customer support. How would you approach this?” You have 35 minutes. Your palms are sweating.  Sound familiar? GenAI case studies currently serve as the primary challenge which interviewers use to test candidates in product management, consulting and AI engineering positions. Most candidates fail this challenge because they lack the ability to establish a standard process for solving these problems.  This guide gives you that framework. We’ll break it apart, then pressure-test it across 2 real-world scenarios you’re likely…

  # Introduction  TurboQuant is a novel algorithmic suite and library recently launched by Google. Its goal is to apply advanced quantization and compression to large language models (LLMs) and vector search engines — indispensable elements of retrieval-augmented generation (RAG) systems — to improve their efficiency drastically. TurboQuant has been shown to successfully reduce cache memory consumption down to just 3 bits, without requiring model retraining or sacrificing accuracy. How does it do that, and is it really worth the hype? This article aims to answer these questions through a description and practical example of its use.   # TurboQuant in a…

Want to add a safety layer in your chatbot, image analyzer or any another LLM-based system? I would strongly suggest you try OpenAI’s moderation model: omni-moderation-latest, this can help your system identify if the input is potentially harmful or not, that too free of cost. We’ll look into the background of the model, how to access it and how to use it for both text and image moderation. Without any further ado, let’s get started.  OpenAI’s Omni Moderation Models OpenAI offers two models specifically for moderation: ‘text-moderation-latest’ (legacy) and ‘omni-moderation-latest’, with the latter one being the latest. The Omni Moderation…

  # Introduction  Why do you use Python? For a lot of people it comes down to “just because,” but it really shouldn’t. Python is a powerful, general-purpose programming language with a simple syntax highlighted by the Pythonic approaches to managing logic and data, that just happens to have found itself the go-to languages of data science, machine learning and AI precisely for these reasons. It’s easy to pick up Python, but you can spend many years working to improve your skills and master the core mechanisms of the language, working to transition from a beginner to a professional who is…

You are a product of your environment, so choose to be with the best. In the age of AI, this proverb is just as true as on the day it was said. If you are to compete in this ultra-fast AI environment with innovations around every corner, being around industry leaders will do you heaps of good. And for this, DataHack Summit 2026 is your best shot. For six years, DataHack Summit has led India’s most active AI conversations. It needs no introduction in AI, machine learning, and data science. Now in its 7th edition, DataHack Summit 2026 returns with…

  # Introduction  Time series feature engineering doesn’t follow the same rules as tabular data. Observations aren’t independent, row order isn’t incidental, and the most useful features are rarely individual readings. You’ll have to identify patterns across time like rates of change, lag comparisons, deviations from a rolling baseline, and more. Building lags, sliding windows, and grouping across resolutions are all, at their core, iteration problems over ordered sequences. Python’s itertools module is a natural fit for this kind of work. It doesn’t replace high-level pandas abstractions like .rolling(), but it gives you lower-level building blocks to construct exactly the features…

Understanding modern AI architectures is harder than ever. Open any Hugging Face repository and you’ll usually find massive config files, layer definitions, parameter counts, and model cards that explain what the model does but rarely help you understand how it is structured internally. That becomes a problem as most developers end up mentally reconstructing architectures which is error prone and tedious. But there’s a much easier way to explore AI models visually as you’ll soon find out. The Simple URL Trick Take any Hugging Face model URL. I’d be using the URL for DeepSeek-V4-Pro model which is: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro Replace: huggingface.co…

  # Introduction  Agentic AI systems depend on a model’s ability to reliably call tools, selecting the right function, formatting arguments correctly, and integrating results into multi-step workflows. Large frontier models such as ChatGPT, Claude, and Gemini handle this well, but they come with tradeoffs in cost, latency, and hardware requirements that make them impractical for many real-world deployments. Small language models have done well to close that gap, and several compact, open-weight options now offer first-class tool-calling support without the need for a data center to run them. And now, in no particular order, here are 5 small language models…