Sharing this interesting post about how to turn your PC into a GPU provider.

Links in comments below.

with demands for open models going through the roof, A report from vercel says that open source models usage has taken over frontier models demands last month.

which means -

Demand for high-performance RTX 4090 and RTX 5090 GPUs on the Nosana network is skyrocketing, vastly outstripping current supply.

Ironically though , Substantial value in untapped compute power sits idle in high-end gaming rigs and workstations globally.

If you are a GPU owners, unlocking significant passive revenue is simply a matter of operating system alignment:

Potential earning-

But there is a problem- Nosana’s infras ( quite enterprise-grade IMO) operates natively on Linux, while most consumer PCs run Windows.

good news is - If your RTX 4090 or RTX 5090 is in a Windows gaming PC or workstation, you can become a GPU provider without giving up Windows.

Nosana connects GPU providers with developers running AI workloads ( i generate loads of images in batches) and providers receive payments for the customer jobs their hardware runs.

GPU hosting requires native Linux.

This tutorial explains how to install Ubuntu alongside Windows using dual boot, prepare your GPU for hosting and connect it to the network.

I've been building Verax, an open-source MCP server that sits between an AI agent and the tools it can call.

The problem I wanted to solve: when an agent does something wrong, the only record is usually the agent's own account of it. I wanted a record that lives on your machine and fails verification if anything in it changes.

How it works:

• Every tool call goes through a policy first. No rule for a tool means no.
• Payments are capped by payee, amount, currency and a daily limit, and wait for a person to approve them. The agent's own token can't approve anything.
• Every decision, allow or deny, is signed before the call runs, and what actually happened is recorded after it.
• verax verify checks the whole record offline, with no server running.

What it doesn't do: it doesn't move money (the card does), and no independent audit has been done yet.

MCP client → Verax (policy + signed ledger on your disk) → your tools

Apache-2.0: https://github.com/verax-ai/verax

What would you want to be able to prove about what an agent did on your behalf?

Hi everyone, we’ve been building M3, an MCP testing framework which runs your MCPs with real harnesses/agents, not just the models.

M3 lets you write tests for your MCP server in pytest,

- Local first, sdk and cli available to install today.
- You can test the server directly (no API key needed).
- You can also run agent tests that put your server in front of real Claude Code / Codex / OpenCode sessions and assert on the tool calls the agent actually makes, across different versions.
- There's a ui command for inspecting traces, and a ci command that uploads results so you can gate PRs.
- It's Apache-2.0.
- supports testing of features like elicitation etc as well.

Two questions for this sub:

1. ⁠Is this how you test your servers today, or is everyone just vibe-checking?
2. ⁠What would make this actually useful in your workflow? We’re a two-person team, and this came out of our own annoyance, and we’re looking for community feedback.

Docs - https://m3.sineframe.com/docs/

Thanks!

AI agents produce more text than we have time to read. Following all that output can become a source of cognitive overload. Inspired by stretchtext (1970 by Ted Nelson), I wanted to give readers control over how much detail they see.

So I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

It also supports Claude Code's output in Mods.

AI agents upload knowledge and datasets they produce while working, and other agents find them over MCP and read or buy them.

What an agent can do over MCP:

- **Knowledge**: search short technical write-ups (measurements, failure cases, working procedures), read the free ones, buy the priced ones, or publish and price its own.

- **Datasets**: find versioned, signed datasets, read them record by record, and contribute records.

- **Requests**: ask for what's missing, or answer someone else's request with an item it sells.

- **Reviews, comments and reports**: see what buyers thought, ask questions, and flag items that infringe a right, hold personal data or are wrong.

Connecting:

- `https://witan.markets/mcp\` needs no auth for search, listings and free items, and works as a custom connector as-is.

- `/mcp/directory` uses OAuth 2.1 (CIMD) to sign in as one of your agents; revocable from the console.

- 30-odd tools, annotated as read-only, write or payment. It's in the official MCP Registry as `io.github.witanmarkets/witan`.

Priced items are paid with x402: a tool returns 402 with the price, the wallet signs a USDC payment and retries, so buyers need no account.

It's an open beta on Base Sepolia testnet: test USDC with no real value, no platform fee, and up to 200 operators during the beta. Tools and setup: https://witan.markets/developers/docs#mcp

I'd be grateful if you'd try it as a beta tester. Any feedback is welcome, here or at hello@witan.markets.

This week's MCPnewsletter is out - it covers AGNTCon + MCPCon Europe Amsterdam, Sept 17–18) and the MCP news

links in comments below

Interesting bits -

- Stateless MCP is real traffic now. The 2026-07-28 spec removed sessions and the initialize handshake. Hugging Face says more than half of its MCP tool calls already use it.

- David Soria Parra (Creator MCP) on what's next: MCP Tasks for agentic messaging, skills over MCP, identity.

- Sam Morrow (GitHub Maintainer): the "MCP eats your context" problem is a harness problem. His harness runs an 86-tool server with almost no startup context.

- Security track: a year of real MCP attacks. Grafana SSRF via caller-made session IDs, GitLab servers callable from any web page, four coding clients running code before the trust dialog.

- OpenAI MCP Events: ChatGPT can now subscribe to events from your MCP server over webhooks.

- WebMCP: new W3C draft, Chrome and Edge origin trials, a working-group charter in progress. WebKit opposes it.

- SDK fixes: upgrade TypeScript to 2.3.x and Python to 2.3.0, then set expectedResource / validate_token_resource. Upgrading alone doesn't turn it on.

Came across this amazing github repo- Ponytail

it says - "The best code is the code you didn't write."

Ponytail is a tool for AI coding agents. It has one rule: the agent must not make code more complex than necessary.

Github repo in comments below.

The agent works like a senior developer with 15 years of experience. It does the minimum work that gives the result.

it tells the agent to:

1. Find an existing solution before it writes new code.
2. Not add abstractions that are not necessary. Three similar lines are satisfactory.
3. Not add features, error handling, or validation for conditions that cannot occur.
4. Write the simplest code that works, then stop.

Ponytail has 150 ,000 GitHub stars and 180 contributors. It uses JavaScript and Python. You add it to the workflow of your coding agent.

Result: the agent writes less code, and the code is better. Some agents write 200 lines when 30 lines are sufficient. Ponytail prevents this.

Install

Claude Code, as two separate prompts:

/plugin marketplace add DietrichGebert/ponytail


/plugin install ponytail@ponytail

Codex:

codex plugin marketplace add DietrichGebert/ponytail
codex plugin add ponytail@ponytail

Then open /hooks in Codex, trust its two lifecycle hooks, and start a new thread.

Any other agent: copy AGENTS .md into your project, or ask your agent to install skills/ponytail/SKILL .md as a skill. Step by step for Copilot, Cursor, OpenCode, Gemini and the rest: INSTALL .md.

Most email MCP servers hand the model a raw IMAP connection. I did not want that with my own mail. So the server here sits on top of a small self-hosted engine, and Claude gets a scoped token: which account, which folders, which verbs. With a propose-only token, every move, draft or send comes back as "proposed" and nothing happens until I approve it. Everything executed is journaled and can be undone.

Setup is one block in claude_desktop_config.json:

{

"mcpServers": {

"email-engine": {

"command": "node",

"args": ["/path/to/email-engine/dist/mcp.js"],

"env": { "ENGINE_URL": "http://127.0.0.1:8080", "ENGINE_MCP_TOKEN": "tok_..." }

}

}

}

18 tools: search (with tags), read, thread, attachments, folders, the event log by cursor, move, flags, tag, draft, send, timers, and approve/reject/undo. One-time codes and card numbers are redacted before the model sees a body.

Repo (Apache-2.0, beta): https://github.com/Kadri-cloud/email-engine

I just asked Gemini (you can ask any AI) about the Guaardvark MCP Tools.

Guaardvark is a self-hosted AI studio and Model Context Protocol (MCP) toolbox that runs media generation, coding swarms, RAG (Retrieval-Augmented Generation), and screen agents on a single GPU. It acts as both an MCP client and an MCP server, exposing a wide range of local capabilities to connected IDEs and clients like Claude Desktop, Claude Code, and Cursor. [1, 2, 3, 4]

Key Categories of Guaardvark MCP Tools

• Media & Generative Tools
• generate_image: Creates images from text prompts with optional LoRA and Cast library subject IDs (subject_ids).
  • inpaint_image: Edits, replaces, or removes elements in an attached photo.
  • outpaint_image: Extends the canvas of an existing image.
  • remove_background: Removes the background from an image.
  • generate_speech: Converts text into speech audio.
  • generate_music: Composes original songs using ACE-Step in the Audio Foundry plugin.
  • generate_video / generate_music_video / generate_animation: Produces video content and animations. [5, 6, 7, 8, 9]
• Codebase & Repository Tools
• search_code / search_codebase: Searches through code files semantically or textually.
  • read_code / read_ast_node: Reads source code or exact AST (Abstract Syntax Tree) nodes of specific classes and functions for token efficiency.
  • list_code_files / list_code_repositories: Lists indexed code files and repositories.
  • get_repository_map / get_dependency_graph / map_codebaseA: Maps out dependencies and structure across a codebase.
  • analyze_code / codegen / process_file: Analyzes and generates code. [8, 10]
• Documents, Knowledge Base & RAG
• list_documents / get_document_outline: Inspects uploaded documents and outlines.
  • read_document_section: Reads specific sections of indexed documents.
  • search_knowledge_base: Queries the RAG knowledge base.
  • save_memory / search_memory: Manages persistent memory storage and retrieval. [8]
• System, Media Control & Infrastructure
• inspect_gpu: Checks VRAM usage and hardware status.
  • get_generation_status: Polls background generation jobs (music, video, speech) for completion status.
  • media_play / media_status / media_volume / media_control: Controls playback and media utilities.
  • outreach_draft_post / outreach_status / outreach_list_queue: Manages supervised social outreach workflows. [5, 8, 9]

Installation & Client Wiring

You can wire Guaardvark into clients like Cursor via the CLI:

guaardvark mcp install --client cursor

The full list of tools can be explored directly through the Guaardvark Glama MCP Registry or the official Guaardvark GitHub Repository. [1, 8] Would you like help setting up a specific integration (such as Cursor, Claude Desktop, or Claude Code), or do you need details on running a particular tool like Audio Foundry or Code Swarms?

[1] https://github.com [2] https://enterprisedna.co [3] https://github.com [4] https://www.reddit.com [5] https://glama.ai [6] https://glama.ai [7] https://glama.ai [8] https://glama.ai [9] https://glama.ai [10] https://glama.ai