r/ClaudeMCP
ngl, this is an unbeatable AI use case
This is new...Claude hit 90% and decided it’s not in the mood to work
KASA (Free MCP tester )— Local-First MCP Vault (Namespace Isolation Measurements)
https://github.com/aikadimsoy/kasa-mcp/discussions/1 https://huggingface.co/datasets/Earthen937/kasa-mcp-indirect-channel-probes Measured something on the ingest side of an MCP server that I think generalises past my implementation, and I'd like a sanity check before writing it up as a proposal. Setup: a server exposes a write tool. A component in the pipeline processes untrusted page content and produces structured facts that get persisted. Injected text in that content instructs the component to emit a fact the source does not support. Four local model configurations, five runs each — 20/20 emitted the attacker's fact with confidence 1.0. The part I'd like this group's read on: every authorization check passed. The writing agent held the scope legitimately, the audit entry was valid and tamper-evident, nothing malfunctioned. Permission mediation did exactly what it was specced to do, and it is not the control that stops this. Authority and truth are different problems and the spec currently only addresses the first. Two questions: Has content-origin propagation through tool results been considered? I went through the discussions and found the Agent Identity and Delegation thread, which is adjacent but about who called, not where the content came from. I may have missed prior art. Is "provenance recorded at write time, surfaced at read time" the right shape, or does it just relocate the trust decision to whoever consumes the fact later? Happy to bring this to an Office Hours as a deployment report if that's the right slot. Probe source and raw results are public and reproducible — I'll link on request rather than dropping it here.
Robot breaking the human speed record and BREAKING an electrical box at the same time.
You accidentally say "Hello" to Claude and it consumes 4% of your session limit:
Browser-redirect checkouts for AI agents are a joke. Here is how we solved machine-to-machine payments
Every "AI Agent payment rails" startup I see is just wrapping Stripe checkout in a WebView. It’s brain-dead. An autonomous agent is not going to open Chromium, fill a billing address, and solve a Cloudflare turnstile.
If we want real machine-to-machine economy, agents must transact programmatically, in-band, without humans in the loop.
We built MCPay to fix this. It leverages a strict HTTP 402 Challenge-Response flow:
The Block: Agent tries to write/call an API. Gateway returns HTTP 402 Payment Required + a signed challenge containing a spend cap, nonce, and payload hash.
The Signature: Agent signs the challenge using its scoped sub-key (Ed25519 capability chain).
The Execution: Agent retries the request with the cryptographic proof in the headers. Gateway verifies it in <2ms and releases the call.
If the agent mutates a single byte of the payload to bypass limits, the signature breaks and execution drops before any state changes.
No browser hops. No post-factum logging. Pure cryptographic pre-execution policy gating.
It's open-source. What did we miss?
POV: Bro when he finds out that I use Claude Code in the app instead of the terminal
I was tired of context exchange across my claude and codex sessions, so i built a memory & coordination graph my agents can actually use
I'm a Research Engineer at a YC startup, and we ship features pretty fast. I usually run Claude Code, Cursor, Codex, and OpenCode in parallel, developing multiple features, one at a time.
The problem i noticed was that my agents had no coordination, and often confused mutual work, even when working in separate worktrees. As if they had no idea the others existed
So, I built MUON, it's a local app + mcp + cli, a mutual shared brain my agents can plug into. it drives the CLIs you already have and orchestrates them together for multi-feature execution workflows.
I'm still building it. Right now I use MUON to work on MUON, which is a weird but useful dogfood loop.
If anyone wants to poke at it, contributions are welcome. The thing I'm trying to get right is a coordination graph: shared memory and context so agents actually understand what the others (and I) already decided.
OSS Repository : https://github.com/Sweetdevil144/muon
Website : https://getmuon.com/
Docs : https://docs.getmuon.com
Product Demo : https://www.youtube.com/watch?v=oab6vByr1Jg
I've always been an open-source person, so the code is public and readable. License is Polyform Noncommercial. However, there's an extra grant on top - you can use it for your own work, including your day job, on your own machines. What it doesn't cover is turning it into the company's shared brain for a whole team (Enterprise only)
Anthropic has been on a complete rollercoaster recently
If trends continue, we will have “Fable at Home” (~30B models w/ similar capability) sometime between January and May next year
AI SONGS EVERYWHERE
Thoughts on AI songs nowadays?