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Balakrishnan Ramdoss discusses how to architect production-grade, AI-powered conversational apps at scale. He explains how to overcome model latency, leverage server-driven UI and Backend-for-Frontend patterns to dynamically render multi-modal interfaces, optimize prompts for UI selection, and…
GPT means generative pre-trained transformer. How GPT differs from an LLM and ChatGPT, how it writes and learns, why models ship in tiers, and where GPT-6 fits.
Graphify is an open-source tool designed to convert codebases and unstructured data into queryable knowledge graphs. Launched in April 2026, it addresses challenges of multi-file reasoning for AI coding assistants. Recent updates have enhanced parser features and cross-file resolutions.
Since about 2020, AI has largely focused on training bigger and better models. Large language models (LLMs) ballooned from millions of parameters to trillions.
On 11 August, Anthropic announced that all future Claude models will generate text that contains a watermark that identifies its results as AI generated. The company is not alone. Google has its own text watermark (which Anthropic’s is based on) that it uses on the output of its Gemini models.
A Markov chain predicts the future using only the present. How one 1913 argument about free will produced PageRank, Monte Carlo, and modern language models.
Cross-entropy measures prediction error in bits. Learn how Shannon entropy, data compression, and the training loss of a large language model turn out to be the same idea.
This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles.
The impact of AI on software development has been both profound and ever-evolving. Last year, I wrote about AMD’s plans to use AI not just for generating new lines of code, but also for other steps in the software development lifecycle (SDLC), such as triaging problems, debugging code, and testing…
Follow along in Colab: Building applications with Large Language Models doesn’t have to be complicated. A lot has been going on recently to simplify the development, so you can utilize already pre-trained models and support even complex pipelines with a few lines of code.
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions
GPT-3 was a major breakthrough in natural language processing. With 175 billion parameters, it demonstrated remarkable few-shot learning abilities and showed that scaling large language models could u
What is Qwen AI Artificial intelligence is evolving fast; models are becoming faster and better. It is no news that Artificial intelligence is here to stay. Qwen AI has gained a lot of attention recently, but what exactly is Qwen AI, and why is it a revolution in artificial intelligence?
In this article discover Quanto a powerful quantization technique designed to optimize deep learning models without compromising the performance of the model.
In this tutorial, we look at the TextAttack framework for NLP data augmentation, adversarial training, and adversarial attacks.
Explore the revolutionary capabilities of the H100 GPU, which plays a crucial role in shaping the future of AI.
Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0, FLAN, and OPT-IML.
Imagine all the things around you — your friends, tools in your kitchen, or even the parts of your bike. They are all connected in different ways. In computer science, the term graph is used to describe connections between objects.
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