Artificial Intelligence· 29 posts
AI Agent Protocols Explained: MCP vs A2A vs ACP and the Agent Interoperability Stack
MCP and A2A are not rivals — they are complementary layers of the same stack: MCP connects an agent to tools and data, A2A connects agents to each other. Here is the whole interoperability landscape, with ACP, ANP, and AGNTCY put in their place.
Clustering Machines for Local AI: Running Big Models Across Your Network
When no single machine can hold the model — or you just have spare hardware lying around — you can cluster. Here's how distributed inference works with tools like exo and llama.cpp RPC, and where it helps versus where it doesn't.
Edge Caching for LLM Requests: Stop Paying to Answer the Same Question Twice
A surprising share of LLM traffic is repeats — identical prompts re-run from scratch. Caching responses at the edge serves those instantly for near-zero cost. Here's how LLM caching works, what to cache, and the pitfalls.
How Much VRAM Do You Need to Run an LLM? (The Memory Math, Explained)
The formula that tells you whether a model will fit on your GPU: parameters × quantization, plus the KV cache for your context, plus overhead. Worked examples for 8B, 13B, and 70B models — and the GPUs they fit on.
How to Run an LLM Locally: A Step-by-Step Guide for Beginners
Run a large language model on your own computer in about ten minutes — no cloud, no API keys, no per-token fees. Pick a runtime, download a model, and chat privately on hardware you own.
llama.cpp Speculative Decoding: Does It Work on Cheap GPUs?
We tested speculative decoding in llama.cpp on an RTX 5060 Ti, a GTX 1080 Ti, and a bare CPU. Real benchmarks: where the draft-model trick helps, and where it backfires.
The Hidden Memory Cost of Long Context: KV Cache and VRAM Explained
On a hosted API, a long context window costs you dollars. On your own GPU, it costs you VRAM — and it grows fast. Here's how the KV cache works, why doubling context can double your memory, and how to tame it.
LLM Quantization Explained: How to Shrink Models Without Wrecking Quality
Quantization is the dial that lets a 70B model fit on a consumer GPU. Here's what FP16, INT8, and 4-bit actually mean, what you lose at each level, and how to decode those cryptic Q4_K_M filenames.
Local LLM Performance: What Tokens-Per-Second to Expect From Your Hardware
Why local inference is memory-bandwidth bound, what tokens/sec you'll realistically get from a 4090, a 5090, an H100, or an M-series Mac, and how model size, quantization, and context change the numbers.
MCP Security Risks: A Practical Threat Model for Teams Connecting AI Agents to Tools
MCP isn't uniquely unsafe, but every server you connect widens your attack surface. A risk catalogue, the trust model you're actually accepting, and the governance controls MSPs and security teams should put in place.
What Is an MCP Server? How Model Context Protocol Servers Work (and How to Use One)
An MCP server is a small program that exposes tools, resources, and prompts to an AI app over a standard protocol. Here is what it actually does, local vs remote transports, a working config block, and how to add one to your AI coding CLI.
On-Prem AI for Regulated Industries: Keeping LLMs Inside Your Walls
For healthcare, finance, legal, and government, sending prompts to a third-party API is often a non-starter. Here's how to run capable AI on infrastructure you control — and meet HIPAA, data-residency, and audit requirements.
Giving Your Local LLM an OpenAI-Compatible Endpoint (So Your Apps Just Work)
Every major local runtime can expose an OpenAI-compatible API — which means your existing apps and SDKs can point at your own hardware with a one-line change. Here's how, and how to add failover so you're never stuck.
Run DeepSeek Locally: Hardware Requirements and Step-by-Step Setup
How to self-host DeepSeek models on your own hardware — which variant and quantization to pick for your VRAM, how to run it with Ollama or llama.cpp, and what performance to expect.
Running LLMs on Apple Silicon: MLX vs GGUF and Why Macs Punch Above Their Weight
Apple Silicon's unified memory lets a Mac run models that would need a much pricier GPU. Here's how MLX compares to GGUF, what unified memory means for model size, and the fastest way to run LLMs on M-series chips.
Running Local AI: The Complete Guide to Self-Hosting LLMs on Your Own Hardware
Everything you need to run large language models on hardware you own — runtimes, model formats, quantization, VRAM math, multi-GPU, Apple Silicon, and how to serve it all behind one endpoint. The hub for our local-AI series.
Splitting Models Across Multiple GPUs — and Why Image and Video Models Can't Do It the Same Way
Text models shard across GPUs cleanly; diffusion image and video models fight you every step. Here's how tensor and pipeline parallelism work, why transformers split so well, and why a U-Net/DiT doesn't.
What Is GGUF? Local AI Model Formats Explained (GGUF, safetensors, MLX, GPTQ, AWQ)
GGUF, safetensors, MLX, GPTQ, AWQ, EXL2 — what each model format is, which runtime uses which, and how to pick the right file to download for your hardware.
What Is the Model Context Protocol (MCP)? The USB-C Port for AI, Explained
Model Context Protocol (MCP) is the open standard — created by Anthropic in late 2024 — that lets any AI application connect to tools, data, and prompts through one uniform wire protocol. Here's how the host/client/server architecture, primitives, and transports actually work.
Claude Cowork: Anthropic's Autonomous Desktop Agent (What MSPs Need to Know)
Claude Cowork is an agentic mode in the Claude Desktop app that reads, edits, and organizes files on your computer and runs multi-step tasks on its own. Here's how it works, who can use it, and the security and governance controls IT teams should put in place first.
Claude's "Dreaming" Explained: Self-Improving Memory for Managed Agents
Anthropic's Dreaming feature lets Claude Managed Agents consolidate their own memory between sessions, the way a brain replays the day during sleep. Here's what it does, who can use it, and where it helps.
Claude's Outcomes Feature: Rubric Grading That Knows When an Agent Is Done
Claude Managed Agents now ship with Outcomes, a rubric-driven grading loop where a separate agent scores the work against your definition of done. Here's how it works, who can use it, and how to write a rubric that actually finishes the job.
Claude Computer Use in 2026: What It Does, Where to Run It, and Why MSPs Should Sandbox It
Claude Computer Use lets the model see a screen and drive the cursor, keyboard, and apps to automate UIs that have no API. Here is its 2026 status, supported models, the agent loop, and the security guardrails that matter for an MSP.
Claude's Microsoft 365 Add-Ins: What IT Admins Need to Know
Claude now runs inside Excel, PowerPoint, Word, and Outlook. Here's how the add-ins work, which plans and platforms get them, how to deploy them across your tenant, and what they mean for data governance.
Ollama vs LM Studio vs llama.cpp: Which Local LLM Runner Should You Use?
Ollama, LM Studio, llama.cpp, vLLM, Jan, GPT4All — every local LLM tool compared. What each one actually is, who it's for, real performance differences, and a decision framework that ends the analysis paralysis.
Train a Neural Network in Your Browser (No Code Required)
Learn how neural networks actually work by training one yourself — right in your browser. No Python, no installs, no math degree. Watch backpropagation and gradient descent happen live, then quiz your trained model.
NLP Stop Words Guide | Text Processing Optimization
Master stop words in NLP to improve processing efficiency while preserving meaning in your natural language processing projects.
Machine Learning Guide | AI Fundamentals Explained
Complete Guide to Understanding AI’s Most Powerful Technology
What is Machine Learning? | AI Guide for Beginners
Discover how machines learn to think, from basic concepts to real-world AI applications transforming industries