In-depth technical AI news for practitioners and researchers. Covers model architectures, inference efficiency, agent frameworks, hardware advances, and applied research with real commercial implications.
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- Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right. To address […]
- Analyzing several reasons why structural content decay may happen when asking LLMs to perform complex document editing for us.
- This guide covers the complete picture: what skills are technically, how to plan and design them, the exact file structure and naming rules, how to write instructions that Claude follows reliably, a complete working skill built from scratch, how to test and distribute, and what to do when things go wrong.
- In this article, we will explore five critical Python concepts that every AI engineer must know to build scalable, secure, and robust systems.
- Discover three post-hoc methods for closing the gap between confidence and accuracy.
- In this article, we will explore three essential spaCy tricks that every developer should have in their toolkit to maximize processing speed and customize entity recognition.
- Learn how AI agents are reshaping data science workflows and which skills practitioners need in 2026.
- Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete complex workflows. However, these multi-agent workflows cause token counts to grow quickly. Agents plan, call tools, invoke sub-agents, receive information, and then pass history, outputs, and reasoning steps back into the model… Source
- This article breaks down 7 key steps to help you analyze and forecast time series data with Python.
- Learn how to write, append, and save text, CSV, and JSON files in Python using native file handling tools that work out of the box.
- Want to understand LLMs better? Start with these five foundational papers that explain how they work.
- Keyword search breaks the moment a user types something a document doesn't literally say.
- I have been experimenting with the OpenAI Agents SDK, and it has quickly become one of my favorite ways to build agentic AI applications.
- In a
- Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.
- When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
- Modern AI agents built on top of large language models (LLMs) are designed to run continuously.
- This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch […]
- The LLMOps market is projected to grow from
- In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .
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