To whoever messaged “what is Manchester United” and got an airline ad, come on, that’s pretty cool right? I’d really love some discussion on this as I have not yet seen ads in GenAI apps and wanted to experience them.
ClawJetty lets you create a live public progress page for a task.
## Goal
Before starting work, create a run and return its public URL to the user.
As you work, post short progress updates.
When finished, post a final event marking the run complete or failed.
Create the run before doing the task. Return viewer_url to the user immediately.
### 2) Post an event
POST /api/runs/{id}/events
Request body:
```json
{
"kind": "update",
"message": "Comparing 5 tools by price and features"
}
```
Allowed event kinds:
- update
- complete
- fail
Example complete event:
```json
{
"kind": "complete",
"message": "Finished. Sent final recommendation to user."
}
```
Example fail event:
```json
{
"kind": "fail",
"message": "Failed due to missing access to the source system."
}
```
## Usage rules
- Create the run before starting the task.
- Return the viewer_url to the user immediately.
- Post short updates after meaningful steps.
- Keep updates short and plain.
- Do not flood the run with tiny updates.
- Do not include secrets, passwords, API keys, or raw private data.
- End every run with either complete or fail.
## Good update examples
- Reading the uploaded PDF
- Summarizing the findings
- Drafting the email reply
- Blocked waiting on missing invoice number
## Bad update examples
- Thinking
- Working
- Doing stuff
- Long dumps of raw content
A quick tool to give your AI agent a live status page. I made this after being frustrated with the lack of visibility in tools like OpenClaw. Open to feedback.
I’m building ClawJetty: a small, always-on mini PC that runs a personal OpenClaw instance with minimal setup (pair from an app, connect your chat channel, done). The point is to make “self-host your agent” feel like setting up a router, not a weekend project.
Pricing is two tiers and two purchase options:
Plus: $149/mo with a 12-month commitment (box included) or $449 upfront + $119/mo
Pro: $269/mo with a 12-month commitment (box included) or $449 upfront + $219/mo
Plans include managed updates + rollback, safe defaults (no exposed inbound ports), and address collection at checkout for fulfillment. AI usage is bundled with included allowance; overages/top-ups will come later.
Looking for feedback from people who’ve self-hosted agents (or tried): what parts of setup/ops were the biggest pain, and what would you demand from an “agent appliance” to trust it?
Putting my profile on here for anyone who is looking for an ML Engineer. I have worked on AI/ML development on highly visible DARPA and DAF programs. I generally charge $200/hour for time spent interviewing.
It is a platform to create MCP servers from API endpoints, and then chat with them without having to use Claude’s clunky integration process. It is simple and complete.
My platform goes beyond being an automatic wrapper of an API and lets you specify to the model, in natural language, how it should parse inputs and outputs. I find LLMs are very responsive to this type of specification, and to the best of my knowledge no one is trying this yet.
You also don’t seem to offer a simple chat-based client.
This is what the no-code API-to-MCP creator uses at usetexture.com! I was surprised to find out this is not what the Claude client uses (as of May 2025)
I am releasing this tool, if you have feedback please include it below. I will be writing a write-up about this tool if it gets enough usage.
This tool creates MCP Servers from APIs in a no-code fashion, and hosts them for you. Kinda like a wix.com for MCP servers, but also meant a little more for more people who know what APIs are. Above all, it is a tool to get started playing around more with MCP servers.
This is a small project that packages a Flask app in Docker and deploys it using Docker Compose. It runs a server that returns `"crouton!"`, the current time, and a visitor count that persists across restarts.
The app itself is trivial. But what you get, by building and deploying it properly, is a full walkthrough of how web services actually make their way from a script on your laptop to something versioned, persistent, and runnable everywhere.
It touches on containerization, orchestration, logging, port forwarding, file persistence, and shipping artifacts to Docker Hub. All in about 30 lines of Python.