What is Muse AI?

 Muse has more guidance for users compared to ChatGPT or Gemini, which can feel like a blank canvas.

The Muse app has five tabs: the main chat for communicating with the agent, a customizable feed of your interests and priorities, an Ideas tab with prompt examples, a list of goals to track based on previous conversations, and a section for files and media.

For my upcoming move to a new apartment, I told Muse what I still needed to pack, how much time I had before moving and that I wanted to pack in short spurts.

The AI agent made a list of what to pack each day this week, along with how much time I should spend on packing each day. It also gave general tips like setting aside a specific bag for the first night in my new apartment with all my essentials.

Like other AI agents, it’s good at planning-oriented tasks that usually require some combination of Googling, communicating with friends or family and booking reservations.

After connecting my Google apps to Muse, I instructed the agent to email a friend about what she had in mind for our trip to Cape May, New Jersey, next month. When she responded, I had Muse package her restaurant recommendations into a polished Google Doc, along with similar alternative options.

Muse uses a virtual computer in the cloud for tasks like these, meaning it’s actually browsing the web to accomplish its assigned job. You can see it navigating websites and jump in at any time.

Muse also served as a reminder that AI isn’t ready to handle everything. When I asked for a list of places to go for date night, it pulled up two options in my neighborhood that had been closed for years.

In its list of prompt ideas, Muse said it could haggle on Facebook Marketplace: “When the right one lands I message the seller, talk the price down against comparable listings, and lock in a time and place,” the app’s Ideas tab said.

But when I tried this, Muse said it couldn’t message sellers directly. It would instead vet the listings and draft a message for me to send to negotiate a lower price.

A Meta spokesperson said the description in the prompt ideas list is accurate and that Muse can negotiate with sellers under parameters set by the user.

[Meta Muse](https://ai.meta.com/muse/) is a personal artificial intelligence agent launched by Meta in September 2026 that executes multi-step tasks for you instead of just answering questions. [1, 2] 

Unlike regular chat assistants, Muse works in the background to handle real-world actions across the web and your favorite apps. [1, 3] 

## Key Capabilities

* Task Execution: It can book travel, send emails, fill out online forms, and complete purchases.

* Autonomous Work: It runs routine tasks 24/7 in the background even after you close the app.

* User Control: It asks for your direct approval before doing sensitive actions like spending money or sending personal messages. [1, 2, 4, 5] 

## How It Works and Security

* Virtual Machine: Each user's agent runs inside a private, secure virtual machine in Meta's cloud.

* Privacy: It uses advanced encryption so that even Meta cannot read your private data or conversations.

* Built-in Skills: It automatically turns on tools for image creation, web browsing, or document editing when you give it a goal. [1, 4, 6, 7] 

## Availability and Cost

* Where to Find It: It is available in the United States on iOS, Android, the web at muse.ai, and via WhatsApp for users 18 and older.

* Pricing: It offers a free tier with usage limits, alongside paid power plans for heavier workloads. [1, 2, 3, 6] 

If you'd like, I can share more details about:

* The pricing tiers and token limits

* How its privacy and security compare to other AI tools

[1] [https://gingerlabs.ai](https://gingerlabs.ai/blog/meta-muse-agent-capabilities-and-how-to-use-it)

[2] [https://www.layer3labs.io](https://www.layer3labs.io/guides/meta-muse-explained)

[3] [https://ai.meta.com](https://ai.meta.com/muse/)

[4] [https://www.facebook.com](https://www.facebook.com/zuck/videos/meet-muse-the-personal-agent-that-helps-you-get-things-done/2281597032594351/)

[5] [https://timesofindia.indiatimes.com](https://timesofindia.indiatimes.com/technology/tech-news/meta-launches-muse-a-personal-ai-agent-that-can-do-your-everyday-tasks-company-makes-a-promise-after-muse-ai-image-fiasco-that-allowed-anyone-to-create-ai-images-with-users-instagram-photos/articleshow/133951207.cms)

[6] [https://about.fb.com](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)

[7] [https://www.edapt.me](https://www.edapt.me/blogs/meta-muse-personal-ai-agent-explained)

The Agent harness concept

"Agent harness" is a concept that really only caught on this year, but the work behind it isn’t new at all. If you were building agents last year, you were already doing it: gluing tools together, hand-tuning prompts, bolting on retries and logging, and patching each failure the morning after it woke someone up. What is new is having a name for this process. The name turns out to be surprisingly useful. It lets you treat all of that capability as one thing you design on purpose, instead of a drawer full of one-off fixes.

Harness As a Service vs Self managed 

 The model is the engine. It is where the power comes from. But nobody ships a bare engine bolted to a pallet to a customer. The chassis, brakes, dashboard, and seatbelts are what make it something you’d put your family in. The model is only the engine; it is everything that makes the product safe, reliable, and pleasant to use is the harness you build around it.

The Two Halves of a Harness

The way I think about it, everything in the harness falls into two halves. Development is what extends the model’s reach: memory across sessions, tools and MCP, retrieval, prompts, and orchestration. Operations is what keeps the thing running reliably once real users show up: observability, evaluation, guardrails, routing, monitoring for drift and cost, deployment, and scaling.

Figure 3. The two halves of a harness (Source: Image created by author).

I want to be careful here, because it’s easy to look at a lopsided diagram like this and conclude the model barely matters. That’s not at all true. The model does the genuinely hard cognitive work. A stronger model lifts everything above it. The point is subtler: An AI agent isn’t a model, it’s a product. Building a good product takes far more than wrapping a model in an API, and most of that "far more" is the harness. A lot of it will feel familiar, too, if you’ve run services before: The operations half is essentially DevOps, wearing a new hat.

references

https://www.infoq.com/articles/agent-harness-build-one/?shem=dsdf,sharefoc,agadiscoversdl,,sh/x/discover/m1/4

What is Radar and why Radar for Kunernetes

Why Radar?

Zero install on your cluster — runs on your laptop, talks to the K8s API directly

Single binary — no dependencies, no agents, no CRDs

Fast on big clusters — tested on tens of thousands of pods, with responsive views and live updates under real cluster churn

Private by design — your cluster data stays on your machine. No account, no agents, no cloud sync, no cluster telemetry

Airgapped-friendly — runs as a single binary against the Kubernetes API and works in locked-down environments with outbound egress blocked

Real-time — watches your cluster via informers, pushes updates to the browser via SSE

Works everywhere — GKE, EKS, AKS, minikube, kind, k3s, or any conformant cluster

AI-ready — built-in MCP server lets AI agents inspect, investigate, and operate your cluster through Radar

In-cluster option — deploy with Helm for shared team access with RBAC-scoped permissions

"Have Radar deployed at work. As far as Kubernetes dashboards go, this is one of the best." — u/TheRealNetroxen

What is Thin MCP

 A Thin MCP (Model Context Protocol) is a minimalist server architecture that exposes only a tightly scoped, task-specific subset of tools or data to an AI model, acting purely as a secure gateway rather than a heavy processing brain. [1, 2] 

Instead of a "Thick MCP" approach—which dumps an entire broad API surface and dozens of irrelevant tools into an LLM's context window—a Thin MCP limits available functions strictly to what is required for the immediate task. [2] 

## Key Characteristics

* Task-Scoped & Atomic: Exposes only the specific tools, resources, or prompts required for a singular workflow or microservice. [2, 3] 

* Stateless and Portable: The tool directory travels easily across agents, projects, or interfaces without bloating or requiring a rebuild of underlying logic. [4] 

* Separation of Concerns: The MCP server acts as a gatekeeper holding specific credentials or mappings, while the AI agent retains the reasoning power (the "judgment")—neither component holds both. [1] 

## Why Use a Thin MCP?

* High Token Efficiency: Benchmarks (such as those by Cyclr) show that task-scoped Thin MCP implementations can cut token consumption by roughly 75% compared to broad Thick MCP approaches while preserving first-answer accuracy. [2] 

* Reduced Prompt Confusion: Limiting the tool definitions prevents the LLM from getting overwhelmed or confused by irrelevant parameters or massive tool catalogs. [3] 

* Easier Maintenance: Smaller, microservice-style wrappers are simpler to maintain when underlying upstream APIs change, though managing a fleet of them can introduce infrastructure and orchestration challenges. [3] 

Would you like to explore:

* How to build or structure a basic Thin MCP server?

* Strategies for managing infrastructure when running multiple microservice-style MCP servers?

[1] [https://wedoworldwide.com](https://wedoworldwide.com/blog/thin-mcp-agent-server-access)

[2] [https://uk.finance.yahoo.com](https://uk.finance.yahoo.com/news/cyclr-benchmark-finds-mcp-server-140000716.html)

[3] [https://www.reddit.com](https://www.reddit.com/r/mcp/comments/1to5c9c/how_thin_are_you_keeping_your_mcp_servers/)

[4] [https://github.com](https://github.com/srhall2314/thin-mcp)

What is WorkIQ?

 Work IQ is an AI-powered workplace intelligence layer from Microsoft that acts as the "brain" behind Microsoft 365 Copilot and custom AI agents. [1, 2] 

It continuously analyzes and semantically indexes your organizational data—connecting unstructured files (SharePoint documents, Outlook emails, Teams chats and meetings) with structured business data (Dynamics 365 and Power Apps)—so AI tools instantly understand your job, projects, and work patterns without requiring detailed manual prompts. [2, 3, 4] 

## Core Components

* Data & Context: Safely connects to enterprise data to map out relationships, timelines, and communications. [3, 4] 

* Memory & Skills: Retains implicit learning from work habits and utilizes built-in tools for actions like scheduling, file generation, and workflow automation. [4, 5] 

* Security & Governance: Automatically applies your existing enterprise security permissions, sensitivity labels, and Data Loss Prevention (DLP) policies without training foundation models on your private corporate data. [6, 7] 

* APIs: Accessible to developers and applications via [Work IQ API](https://www.microsoft.com/en-us/work-iq) endpoints like REST, Model Context Protocol (MCP), and Agent-to-Agent (A2A) protocols. [1, 7] 

If you'd like, let me know:

* Are you looking to build a custom AI agent using Work IQ?

* Do you need help with admin settings and governance in Microsoft 365?

[1] [https://learn.microsoft.com](https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/work-iq/)

[2] [https://m.youtube.com](https://m.youtube.com/shorts/B9jID_ycpBE)

[3] [https://blogs.perficient.com](https://blogs.perficient.com/introducing-microsoft-work-iq-the-intelligence-layer-for-agents/)

[4] [https://www.youtube.com](https://www.youtube.com/watch?v=9JQCYDi_YUE&vl=en)

[5] [https://www.youtube.com](https://www.youtube.com/shorts/LPAWVi32WRw?vl=en)

[6] [https://www.reddit.com](https://www.reddit.com/r/microsoft_365_copilot/comments/1rg1h97/what_is_work_iq_in_microsoft_365_copilot_and_why/)

[7] [https://www.microsoft.com](https://www.microsoft.com/en-us/microsoft-365/work-iq)

How to setup n8n docker instance with tunnel

 there are two options, with ngrok and cloudflare. Important screenshots below 

reference: https://www.youtube.com/watch?v=O9dpO81dEQ0

What is BaseTen LLM inferencing

[Baseten](https://www.baseten.co/solutions/llms/) is a production-grade machine learning infrastructure platform that specializes in high-throughput, low-latency AI model inference, specifically for large language models (LLMs) and generative AI. [1, 2, 3] 

Rather than training or fine-tuning models, Baseten provides the backend serving layer that turns open-source or custom models (like Llama, DeepSeek, and Gemma) into scalable, production-ready APIs. [4, 5, 6, 7] 

## Core Features and Architecture

* High-Performance Inference Engines: Uses optimized inference frameworks like TensorRT-LLM, vLLM, and SGLang alongside the proprietary Baseten Inference Stack (BIS) to maximize GPU efficiency and minimize time-to-first-token (TTFT). [3, 6, 8] 

* Multi-Cloud Capacity Management: Provisions and dynamically scales GPU resources (including high-end NVIDIA hardware like H100s and B200s) across multiple cloud providers and geographic regions. [4, 9, 10] 

* OpenAI-Compatible APIs: Allows developers to call hosted or deployed models seamlessly using standard OpenAI-compatible API endpoints. [7, 11] 

* Advanced Scaling: Features token-based autoscaling, KV-aware request routing, and active-active multi-node high availability to handle intense production workloads and traffic spikes. [8, 12] 

* Truss Integration: Uses [Truss](https://github.com/baseten-inference), an open-source model packaging framework, enabling developers to deploy complex models using simple YAML configuration files without managing raw Docker containers. [7, 9, 10] 

If you're working on a project, let me know:

* Which model you are looking to deploy or run

* Whether you need help with infrastructure scaling or API integration

I can provide specific configuration tips or examples!

[1] [https://www.baseten.co](https://www.baseten.co/solutions/llms/)

[2] [https://www.zenml.io](https://www.zenml.io/llmops-database/mission-critical-llm-inference-platform-architecture)

[3] [https://aws.amazon.com](https://aws.amazon.com/partners/success/baseten-nvidia/)

[4] [https://www.baseten.co](https://www.baseten.co/blog/mercury-2-is-now-available-on-baseten/)

[5] [https://www.youtube.com](https://www.youtube.com/watch?v=Gig51sj4cL0&t=9)

[6] [https://cloud.google.com](https://cloud.google.com/blog/products/ai-machine-learning/how-baseten-achieves-better-cost-performance-for-ai-inference)

[7] [https://docs.baseten.co](https://docs.baseten.co/examples/deploy-your-first-model)

[8] [https://docs.baseten.co](https://docs.baseten.co/engines/bis-llm/overview)

[9] [https://docs.baseten.co](https://docs.baseten.co/concepts/howbasetenworks)

[10] [https://ai.engineer](https://ai.engineer/orgs/baseten)

[11] [https://docs.baseten.co](https://docs.baseten.co/overview)

[12] [https://www.baseten.co](https://www.baseten.co/resources/guide/the-baseten-inference-stack/)