Tag: llm

A step-by-step checklist for moving a .NET app off the raw OpenAI 2.12 SDK onto the provider-neutral Microsoft.Extensions.AI 10.7 IChatClient. Covers the AsIChatClient bridge, the CompleteChatAsync-to-GetResponseAsync rewrite, streaming, tool calling, DI registration, and the gotchas that bite mid-cutover.

VS Code 1.128 (July 8, 2026) lets one Claude agent-host session hold several parallel chats, each with its own history, title, and model. Here is what chat.agentHost.enabled actually unlocks and how the quick-chat and BYOK pieces fit.

Claude Sonnet 5 (claude-sonnet-5) shipped June 30, 2026 and now backs the 'sonnet' alias in Claude Code. Its new tokenizer emits about 30% more tokens for the same text, so cost estimates and max_tokens limits tuned for Sonnet 4.6 need a recount.

A step-by-step checklist for moving an existing Semantic Kernel 1.77 .NET app to Microsoft Agent Framework 1.13. Covers the Kernel-to-AIAgent rewrite, plugins-to-tools, thread-to-session, the KernelFunction compatibility bridge, DI changes, and the gotchas that bite mid-cutover.

Both let one agent spawn isolated workers from a Markdown file, but Claude Code nests five levels deep with per-agent tool allowlists and worktree isolation, while Cursor keeps it to two levels with a single readonly switch and reads Claude's format for free. Pick by where your agents run, not by brand.

Use CodeAct (the agent writes executable code as its action) when your tasks chain many tools, loop, or move large data, and you can afford a sandbox. Use the JSON tool-calling loop for a handful of discrete, high-stakes actions where a code interpreter is overkill or unsafe. CodeAct wins on token cost and multi-step success rate; tool calling wins on safety and simplicity.

Use Quartz.NET when an LLM job must run on a real cron and never overlap itself, Hangfire when each run must survive a restart and retry on rate limits, and a plain BackgroundService only for a loose in-process loop. A decision matrix with the cron and concurrency gotchas that pick for you.

Rule-based checks are your floor and they are non-negotiable; LLM-as-judge is the ceiling you add when code quality, not just correctness, is what you ship. Here is the decision, with cost, latency, and the SWE-bench gap that proves why.

Claude subagents give you orchestration-level parallelism out of the box: one agent spawns isolated sub-contexts that run concurrently. OpenAI Assistants never had that, and it shuts down August 26, 2026, so new parallel work belongs on the Responses API plus your own fan-out. Here is how the two models actually differ and which to reach for.

The cache read and write multipliers are identical on both models, so the break-even point is the same. What differs is the minimum cacheable prefix (1,024 vs 4,096 tokens), the per-token dollar savings, and a new Opus 4.7 tokenizer that counts up to 35% more tokens. With claude-sonnet-4-6 and claude-opus-4-7 pricing math.