Observed Signal · May 14, 2026 · Technical Release · Source: DEV Community · Impact: 4/5 · Sentiment: Positive
Azure Local Foundry CLI with PowerShell
A developer tutorial (published May 14, 2026) showing how to use Microsoft Local Foundry’s CLI from PowerShell to run and automate local LLM inferences. The author explains why local models matter for cost and privacy, notes that Local Foundry ships in preview as an SDK (Windows, Linux, macOS) and CLI (Windows, macOS), and provides install commands (winget/brew). The post demonstrates managing models (list, download, load, run), how to obtain the Foundry REST API URI, and supplies PowerShell helper functions: a service starter/status checker, an Invoke-FoundryRequest wrapper for REST calls, and New-FoundryChatCompletion for OpenAI-chat-compatible POSTs to /v1/chat/completions. Example model names (e.g., phi-3-mini-128k-instruct-qnn-npu:3) and notes on temperature/top_p parameters are included. The author signals a follow-up on the .NET SDK to access the full feature surface.
A major platform (Microsoft) shipping a preview local inference SDK/CLI expands on‑prem and privacy-preserving LLM deployment options and enables automation (PowerShell) that can reduce cloud inference costs and regulatory exposure for enterprises.
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Key Takeaways & Evidence Grounding
- Microsoft Local Foundry is available in preview as an SDK (Windows, Linux, macOS) and as a CLI (Windows and macOS).
- Installation examples: 'winget install Microsoft.FoundryLocal' (Windows) and 'brew install microsoft/foundrylocal/foundrylocal' (macOS).
- Foundry CLI supports model management commands: 'foundry model list', 'foundry model download', 'foundry model load', and 'foundry model run'.
- The Foundry CLI exposes a REST API compatible with OpenAI Chat Completions; PowerShell can call it by obtaining the service URI and POSTing to /v1/chat/completions.
- The article provides PowerShell helper functions (service status/start, Invoke-FoundryRequest, New-FoundryChatCompletion) and example usage with models like 'phi-3-mini-128k-instruct-qnn-npu:3'.
Connected Companies & Entities
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Run AI Locally on Private Files Offline
The briefing explains how organizations and individuals can use local or fine-tuned language models to process sensitive files without sending them to external model providers. It cites Bayer, which fine-tuned a small Microsoft Phi model on proprietary product-label and regulatory data to answer complex crop-protection questions in under thirty seconds, and Discovery Bank, which fine-tuned five variants across two Azure OpenAI models (4o-mini and 4.1-mini) to speed structured workflow outputs from ~5–6s to ~1.5–2s. Microsoft states customers’ prompts, training files, outputs, and fine-tuned models are not used to improve its general foundation models without permission and that fine-tuned models remain exclusive to customers. The piece also covers running models entirely offline on a laptop (LM Studio walkthrough), the limits of local setups versus enterprise systems, and lock-in considerations when a company’s corrections become tied to a specific model or provider.
Run GitHub Copilot CLI with Local LM Studio Models
This technical guide explains how to configure GitHub Copilot CLI to use local LLMs served by LM Studio instead of GitHub-managed cloud models. It describes LM Studio’s OpenAI-compatible local API endpoint (e.g., http://localhost:1234/v1), required environment variables (COPILOT_PROVIDER_BASE_URL, COPILOT_MODEL, COPILOT_OFFLINE), and a simple test workflow via the copilot CLI. The article outlines hardware and model-size trade-offs (smaller models run on laptops but have weaker reasoning; larger models need GPUs) and recommends use cases—privacy-sensitive development, offline work, and learning—while warning the integration is not a first-class, production-grade integration and may fall back to cloud models if COPILOT_OFFLINE is not set. A small VS Code extension (Copilot Insights) for Copilot quota visibility is also mentioned.
Build a Local Terminal AI Agent (v9)
A Dev.to tutorial (published 2026-05-24) shows how to build a terminal-based AI agent using local LLMs. The guide surveys the CLI agent ecosystem, highlights limitations of cloud-dependent tools, and provides hands-on steps: install LM Studio, run a local model (example: Nous-Hermes-2-Mistral-7B-DPO.Q4_K_M.gguf), set up an API server with Ollama, and run a Python CLI agent that calls a local OpenAI-compatible HTTP endpoint. Examples include a TerminalAIAgent implementation, tmux integration scripts, and developer tools (code search, Git helpers). The post also covers basic context-window management and points readers to a paid full guide on Gumroad for extended content.
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