Per-token AI charges come to GitHub Copilot

A few years ago, most AI models ran out of context after a short conversation. Today, leading models hold one million tokens or more. This guide breaks down context length in LLMs, how tokens work, what the lost in the middle effect means for output quality, and when RAG outperforms long context.

Caching, lazy-loading, routing, compaction, and more The post Agentic AI: How to Save on Tokens appeared first on Towards Data Science.

The dominant recipe for building better language models has not changed much since the Chinchilla era: spend more FLOPs, add more parameters, train on more tokens. But as inference deployments consume an ever-growing share of compute and model deployments push toward the edge, researchers are increasingly asking a harder question — can you scale quality […] The post UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size appeared first on MarkTechPost.

Why bother paying for your own generative AI (genAI) tokens when you can have the computations done for free using a competitor’s AI-powered customer service bot? That question is at the heart of a CIO.com report that explores the trend and various ways to block it. It’s possible the best response to this kind of computational chicanery is to ignore the thieves and stay focused on delivering the best service for customers — hopefully boosting revenue by doing so.  The CIO.com story offers a detailed look at how to combat the problem  — options that include limiting the number of tokens that can be used for a single answer and layering on AI to validate that questions are legitimate.  But all the proposed approaches have major downsides. For one, the frequency of these inappropriate “queries” might be limited — and the costs of tokens used to handle them might not break the bank.  My argument — to ignore the issue — includes both good and bad facets. On the positive side, genAI-based ch

Generative AI has revolutionized the space of software development in such a way that developers can now write code at an unprecedented speed. Various tools such as GitHub Copilot, Amazon CodeWhisperer and ChatGPT have become a normal part of how engineers carry out their work nowadays. I have experienced this firsthand, in my roles from leading engineering teams at Amazon to working on large-scale platforms for invoicing and compliance, both the huge boosts in productivity and the equally great risks that come with GenAI-assisted development. With GenAI, the promise of productivity is very compelling. Developers who use AI coding assistants talk about their productivity going up by 15% to 55%. But most of the time, this speed comes with hidden dangers. To name a few, AI-generated software without good guardrails could open up security issues, lead to technical debt and introduce bugs that are difficult to detect through traditional code reviews. According to McKinsey research, while G

Microsoft’s ASP.NET Core 2.3, a version of the company’s open source web development framework for .NET and C#, will reach end of life support on April 7, 2027. Following that date, Microsoft will no longer provide bug fixes, technical support, or security patches for ASP.NET Core 2.3, the company announced on April 7, exactly a year before the cessation date. ASP.NET Core 2.3 packages—the latest patched versions only—are supported currently on .NET Framework, following the support cycle for those .NET Framework versions. After April 7, 2027, this support will end regardless of the .NET Framework version in use, according to Microsoft. Support for ASP.NET Core 2.3 packages including the Entity Framework 2.3 packages will end on the same date. Microsoft recommends upgrading to a currently supported version of .NET, such as .NET 10 LTS. To help with the upgrade process, Microsoft recommends using GitHub Copilot modernization, which provides AI-powered assistance in planning and executin