... that helps you recover Auth and validation failures map to stable errors with clear next steps (paybond login, paybond doctor). Optional --debug when you need a stack trace. 0.12.14 hosted MCP over Streamable HTTP https://mcp.paybond.ai/mcp with Authorization: Bearer <paybond_sk_...> same contract as local MCP for hosts that can t spawn stdio. GET https://mcp.paybond.ai/healthz is unauthenticated for liveness. Or self-host: paybond mcp serve --transport http Docs : https://paybond.ai/docs/kit/mcp-... Releases : https://github.com/nonameuserd/p... ... ... github.com/nonameuserd/paybond-kit-python/releases Comment from Raffay Sajjad(@raffay_sajjad): Since this is positioned as spend controls for agents, curious how that maps onto the bearer

... nothing, the reviewer's normal experience becomes an empty screen, and empty screens train people to expect empty. Whether the one that is not empty gets read properly on day forty is an open question. It still beats the alternative, because a difference against a background of silence is about as salient as anything gets. And yes to the small pile. Most of what gets called judgement is work nobody has bothered to make mechanical yet. Comment from Aadil Ghani ... ... /aadilghani/coffee Comment from Aadil Ghani(@aadilghani): @mohsen_bashirzadeh   Paging is the right frame, and yours is the first comment here that names the thing our routing was actually built for. You are right that the link is a bearer

... boring half of that as a service (monet.gg). Instead of a paste-your-key field, the user hits a hosted consent page and clicks Connect on their OpenAI account, like a normal OAuth flow. Your app receives a bearer token and never the key itself, and you call one OpenAI-compatible endpoint. The economics stay exactly what you designed, the setup step stops looking like homework. On your third question, my read is that "the code lives on your ...

... rotation actually finishes" matters, since the old passphrase is still valid the whole time it's running Comment from scritty(@scritty_dev): @gaspard_dupuich  there's no per connection cached grant so nothing outlives the token. auth hashes bearer (sha256) and checks it against live in memory registry on every request. revoking removes hash durably, under a cross-process file lock, persisted to disk. two cases: revoke from running server itself (`revoke_token` tool): it mutates shared registry ...

... Alert counts by type Off by default, yours to expose - Off by default nothing listens until you opt in - Localhost-only, unless you flip it to your LAN (so a Prometheus on your NAS can scrape it) - Optional bearer token - Live whenever Netfox is running it stays in the menu bar, serving with the window closed Setup, the full metric list, and a sample scrape config: https://netfox.app/docs/integrat... Download / update: https://github.com/gfazioli/netf... (or Netfox Check for Updates ...

... from groups You can give a group its own AI context, and it now flows into chat, chart-editing, and automation agents whenever a member of that group is interacting. If different teams think about your data differently engineering versus sales versus

... apps to: Optimise routes based on live traffic Adjust flows based on behaviour Trigger actions when engagement drops Feed real analytics into your AI agents No dashboards. No digging. Just answers and actions. Connect via /mcp/v1 with a Bearer token. Docs: https://ghostlyx.com/features/mc... ...

... core. We only require your AgentReady key . Your LLM API key stays yours we never see it: import requests, os from openai import OpenAI # Step 1: Compress messages with AgentReady res = requests.post(" https://agentready.cloud/v1/comp... ", headers={"Authorization": "Bearer ak_live_116e......"}, json={"messages": [{"role": "user", "content": your_text}]}) compressed = res.json()["messages"] # Step 2: Send to YOUR LLM with YOUR key client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) response = client.chat.completions.create(model="gpt-4o", messages=compressed) Here's everything else ...

... time': { 'type': 'string', 'description': 'Time (HH:MM)' }, 'location': { 'type': 'string', 'description': 'Location or address' }, 'description': { 'type': 'string', 'description': 'Additional details' }, }, display_name='Events', description='Calendar events and appointments') Or via the REST API directly: POST /api/v1/records/schemas Authorization: Bearer mnx_... { "type_name": "events", "display_name": "Events", "description": "Calendar events and appointments", "fields": { "title": { "type": "string", "required": true }, "date": { "type": "string", "required": true }, "time": { "type": "string" }, "location": { "type": "string" }, "description": { "type": "string" } } } 2. Create and Update Records Each record gets a unique ...

... system for HTTP requests. Why it matters When a bug only happens in production, try to reproduce it becomes the bottleneck. With Replay, you can record real requests , replay them locally or in staging , and diff the new response vs the recorded one instantly. What s included in v0.1.300 ReplayLayer middleware for automatic request/response recording Stores: InMemoryReplayStore + FileSystemReplayStore Admin HTTP routes to list, replay, and diff entries ReplayClient for programmatic replay testing Security: disabled by default , bearer

... with Claude. Same user. Same memories. Any model. How it works: Just use the native Google SDK and point it to Mnexium: import { GoogleGenAI } from "@google/genai"; const client = new GoogleGenAI({ apiKey: GEMINI_KEY, httpOptions: { baseUrl: " https://mnexium.com ", headers: { "Authorization": `Bearer ${MNX_KEY}` }, }, }); const response = await client.models.generateContent({ model: "gemini-2.0-flash-lite", contents: "What's my favorite color?", }); // Gemini knows - because GPT-4 learned it yesterday What's supported: Streaming & non-streaming Tool/function calling System instructions Thinking mode Memory learn ...