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Frontier vision models have gotten really good at understanding images — but they’ve also had a consistent weakness: They still often treat an image like a single static glance. So if the answer depends on something tiny (a serial number, a distant street sign, a gauge reading, a small UI label), the model might miss it… and then it has to guess. Google’s new capability called Agentic Vision, launched with Gemini 3 Flash, is a major step toward fixing that. ...

Large language models (LLMs) like GPT-4, Llama, or Grok generate text by running inference — the phase where a trained model produces outputs from a given input prompt. While training is resource-intensive and done once, inference happens every time a user sends a query. Understanding the mechanics of inference is key to grasping why some models feel “fast” while others lag, and why certain optimizations matter. At a high level, modern LLM inference (for autoregressive transformer-based models) splits into two distinct phases: prefill and decode. These phases behave very differently in terms of computation and directly affect two critical user-facing metrics: Time to First Token (TTFT) and Inter-Token Latency (ITL). ...

Recently, someone shared a screenshot on x.com, how to download OpenAI Home Directories. I tried it, and it works. In this blog, we will now try to understand exactly what the contents of this home directory are. working with GPT-5.2 thinking with gpt 5.2, i got error zip file not found. https://t.co/c1zTfBlWb9 pic.twitter.com/85tEv28MuJ — Nitin Kalra (@nkalra0123) <a href="https://twitter.com/nkalra0123/status/1999771366397231386?ref_src=twsrc%5Etfw">December 13, 2025</a> Let’s analyse the contents Inside the open ai home directory oai/ Folder: Slides, Docs, PDFs, and Spreadsheets Tooling This folder is a small toolkit for working with common “office” artifacts – PowerPoint decks, DOCX files, PDFs, and spreadsheets. It combines a few Python utilities with a set of practical guides that describe the preferred tools and a quality-check workflow (render → visually inspect → iterate). ...

Introduction As developers, we often find ourselves juggling multiple projects, each requiring different versions of Python or Java. Maybe you’re maintaining a legacy application that runs on Python 3.8 while building a new microservice on Python 3.12. Or perhaps you’re working with Java 11 for one client and Java 21 for another. Manually managing these versions can quickly become a nightmare of PATH variables, symlinks, and “it works on my machine” debugging sessions. ...

Introduction If you’ve ever stared at a cryptic error message from a CLI tool wondering “What HTTP requests is this thing actually making?”, you’re not alone. Whether it’s a failed git clone, a mysterious npm install error, or tracking claude code for finding prompts, finding out what data your application sends to third-party services, understanding HTTP traffic is crucial for modern development. Enter HTTP Toolkit – an open-source powerhouse that makes intercepting and debugging HTTP traffic almost effortless. ...

Hallucinations in RAG (Retrieval-Augmented Generation) chatbots can undermine user trust and lead to misinformation. In this comprehensive guide, we’ll explore proven strategies to minimize these AI-generated inaccuracies and build more reliable chatbot systems. If you’re building a RAG chatbot, you’ve likely encountered the frustrating problem of hallucinations—when your AI confidently provides incorrect or fabricated information. The good news? There are effective, battle-tested solutions to dramatically reduce these errors. Let’s dive into the multi-layered approach that actually works. ...

Large language models are getting smarter—but the real superpower may be how we feed them context. Instead of constantly fine-tuning weights, a growing family of techniques improves models by upgrading the inputs they see: richer instructions, reusable strategies, domain heuristics, and concrete evidence. The paper “Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models” proposes ACE, a practical framework that treats context like an evolving playbook—something you grow, refine, and curate over time to make agents and reasoning systems measurably better. ...

Introduction When training large language models (LLMs) the most important question is simple: how do we measure whether the model is doing well? For regression you use mean squared error, for classification you might use cross-entropy or hinge loss. But for LLMs — which predict sequences of discrete tokens — the right way to turn “this output feels wrong” into a number you can optimize is a specific kind of probability loss: categorical cross-entropy / negative log likelihood, and the closely related, more interpretable metric perplexity. ...

Introduction In the attention mechanism used by Large Language Models (LLMs) like transformers (e.g., GPT), the core idea is to allow the model to dynamically focus on relevant parts of the input sequence when generating or understanding text. This is achieved through a process called scaled dot-product attention, where input tokens (e.g., words or subwords) are transformed into three types of vectors: Q K V, Query (Q), Key (K), and Value (V). These are not arbitrary; they’re learned projections of the input embeddings via linear transformations matrices ...

Introduction Token embeddings (aka vector embeddings) turn tokens — words, subwords, or characters — into numeric vectors that encode meaning. They’re the essential bridge between raw text and a neural network. In this post, below we will run a small demos (Word2Vec-style analogies, similarity checks), and provide concrete PyTorch code that demonstrates how an embedding layer works, I also include a tiny toy training loop so you see embeddings updated by backprop. ...