Tag: AI

Using MiMo V2.5 with GitHub Copilot BYOK and OpenCode Zen (Promo)

MiMo V2.5 is the latest reasoning and coding model from Xiaomi’s AI lab. Xiaomi positions MiMo as a general-purpose reasoning model with particular strength in code generation, structured output and tool calling. In May 2026 the model was made available through OpenCode Zen as part of a promotional offer, meaning it can be used at no cost during the promotion period.

Using OpenAI Models in VS Code with GitHub Copilot BYOK

GitHub Copilot’s pricing shift from flat premium request units to token-based GitHub AI Credits, scheduled for June 1, 2026, changes the economics of heavy AI usage in VS Code meaningfully. Models with large context windows, high output verbosity or intensive agent loops now have a more direct relationship between compute and cost. That makes model selection a more relevant engineering decision than it was under the older quota model.

Using Kimi K2.5 and DeepSeek V4 in VS Code with GitHub Copilot BYOK and OpenCode

GitHub Copilot is moving into a different cost model. GitHub announced that Copilot plans will transition from premium request units to GitHub AI Credits on June 1, 2026. Usage will be calculated from token consumption, including input, output and cached tokens, using the listed API rates for each model. This is a more direct mapping between compute usage and billing, but it also changes the economics for teams that have started to use agentic workflows heavily.

Building Custom AI Tooling with the GitHub Copilot SDK for .NET

The landscape of AI development is shifting rapidly from simple chat interfaces to fully integrated, agentic workflows like the Ralph Loop . GitHub has recently introduced the GitHub Copilot SDK , a powerful toolkit that allows developers to programmatically harness the engine behind the GitHub Copilot CLI.

TOON Format: Token-Oriented Object Notation for LLM-Friendly Data Exchange

Introduction

If you build systems that pass structured data through large language models (LLMs), you eventually hit the same set of issues:

Standardizing AI Context: the Model Context Protocol

The Model Context Protocol (MCP) server acts as a centralized context management backend for advanced AI applications.

Unlike traditional session or prompt-based approaches, the MCP server manages a persistent, structured context that can be queried, updated and synchronized across distributed AI components and user sessions. By providing a standard API for serializing, retrieving and sharing context, the MCP server enables seamless interoperability between different models, services and clients. This is particularly important in multi-agent systems, long-running workflows or environments where state continuity and context portability are required.