An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights ... Read more ›
A living field guide to command languages, sigils, skills, subagents, and harnesses across OpenAI Codex, Claude Code, OpenCode, Cursor, and GitHub Copilot. Read more ›
Typically, when we start experimenting with AI, many of us begin similarly. We try a single LLM call as the core of an app, like this: const response = await llm.chat("Explain Kubernetes"); For a lit Read more ›
AI agents are a growing priority for enterprises, with many companies interested in deploying them for a wide range of purposes, from software development and marketing to sales and customer support. Most discussions revolve around single AI agents. However, Gartner has seen a 1,445% surge in inquiries about multi-agent systems (MAS) from Q1 2024 to <a class="read_more" href=" Read more ›
Your AI agent did exactly what it was designed to do. The framework underneath it just handed an attacker a shell on the box that holds your OpenAI key, your database credentials, and your CRM tokens.That is not a hypothetical. In a few months, three of the most widely deployed AI agent frameworks each turned a known, ordinary bug class into a way through. chained a SQL injection in LangGraph’s SQLite checkpointer to full remote code execution. Tenable and VulnCheck tracked a path traversal i... Read more ›
The Knowledge Augmentation Spectrum: CAG vs RAG vs CRAG For the past year, the industry has been obsessed with RAG \(Retrieval-Augmented Generation\) \. It was the “gold standard” for giving LLMs access to enterprise data\. But as our production requirements shift toward lower latency, higher accuracy, and better reliability, we are seeing the emergence of new paradigms\. If you are building AI applications today, you need to understand the architectural trade-offs between RAG , CAG \(Cache-A... Read more ›
Anthropic is rethinking how teams interact with AI at work with its latest launch set to change the way tasks are handled inside Slack. Read more ›
This is Day 11 of building a neural network from scratch. Yesterday we went over gradient descent: read the slope of the loss at your… Read more ›
Rust is an incredible language for game development, and Bevy makes it genuinely fun. Bevy is a data-driven engine built around an Entity Component System (ECS) that makes building highly concurrent, fast games the default. This talk is a practical, introduction to Bevy, ECS, and basic game dev. This talk assumes you know the basics of Rust and takes you to being able to play around in Bevy. Licensed to the public under about this event: Read more ›
Power Techniques: Verified prompt engineering · Stanford research-backed · Works on ChatGPT, Claude & Gemini · Zero extra cost Read more ›
How to keep AI agents on spec during complex tasks: specialist agent orchestration, a daemon scheduler, and Jira as a shared source of truth. Read more ›
A customer asks your support agent whether their refund went through. The agent checks, says yes, and cites a confirmation number. The refund actually bounced back twenty minutes ago, but the lookup the agent ran hit a store that only syncs overnight.... Read more ›
What is Context Engineering? Read more ›
Learn how to set up LlamaIndex, load your data, build and persist an index, and run queries to get grounded answers with RAG in Python. Read more ›
Quantization is widely used to reduce the inference cost of large language models, but its effect on reasoning models is not fully captured by final-answer accuracy or per-token latency. We show that low-bit post-training quantization can introduce a hidden test-time compute cost: quantized reasoning models often generate longer chains of thought even when they still answer correctly. Across mathematical reasoning, code generation, scientific qu... Read more ›
Feature flag migrations have a reputation for stalling. Learn how to structure the process in a few steps: audit legacy flags, validate evaluation parity with shadow mode, and cut over with confidence. Read more ›
Why smart enough, fast enough, and cheap enough is good enough Read more ›
Samsung Electronics is rolling out ChatGPT Enterprise and Codex to all employees in Korea as well as to the global workforce in its Device eXperience division. Thank you for being a Ghacks reader. The post appeared first on <a href=" Read more ›
Author(s): Bessie Delight Kekeli Originally published on Towards AI. The Building Blocks of LangGraph (Part 0) For other parts of the series : Part 0 , Part 1 , Part 2 , Part 3 As Large Language Models (LLMs) have become more capable, developers have moved beyond simple chatbots and begun building systems that can reason, make decisions, use tools, retrieve information, interact with APIs, and collaborate with other AI agents. Building these systems introduces a new challenge: How do we coord... Read more ›