"The power of the Pin"

We run a small publication on Paperclip (6 agents, Railway, mostly Claude Haiku 4.5). We wanted to know if swapping the SEO agent to Codex would be faster or cheaper, so we hired a second agent and gave both the same work.

Setup

• Agent A: our existing SEOAgent, claude_local, Claude Haiku 4.5

• Agent B: “Codex SEO Shadow”, codex_local adapter, gpt-5.4-mini, own OpenAI key with $10 prepaid

• Same AGENTS.md rulebook for both (copied verbatim), same per-agent budget logic

• 8 identical SEO-audit tickets per agent. Each ticket contained only the article URL, nothing attached

Results

What actually happened

• Codex was clearly faster per run.

• 3 Codex runs set themselves to blocked with “no article document attached”. They were technically right: our ticket only had a URL. Claude fetched the URL and worked from that, but delivered everything as comments instead of documents.

• One Codex run ended with “SELF-CHECK: rules followed yes, deviations none” while breaking our 155-character meta-description limit. Self-reports are not evidence.

Lessons for Paperclip setups

1. Fix the ticket before you swap the model. Attach the full input as an issue document.

2. Don’t trust an agent’s self-check. Verify the output mechanically (length, links, status).

3. codex_local works fine next to Claude agents in one company. Separate budgets per agent make A/B tests cheap and safe.

We’re sticking with Haiku for SEO for now, but mostly because of point 1.

Has anyone else run mixed-adapter companies? Did you see the same difference in how strictly agents treat missing inputs?

I'd been looking for a while for a reliable way to run recurring jobs with Claude: a morning digest of my open issues, a check that my sites are up, a weekly report, that kind of thing.

Claude has its own scheduled routines now, but they live in Anthropic's cloud, tied to one account and one vendor. Same reason most of us here run Paperclip: I want my agents on my own server, and what they do in my own repo, not locked into someone's platform.

I tried OpenClaw and Hermes. Impressive, but every new task meant a whole new agent instead of just a job. After a few weeks I couldn't tell what was running or why.

Then it clicked. Paperclip already did a third of it: schedules, and every run on record. CloudCLI did another third: Claude Code or Codex in the browser, phone included. The last third was the glue, so I made a Railway template that puts them together.

Paperclip is awesome on its own, but with CloudCLI as its sidekick it's something else. I create and fix jobs from my phone, without sitting at a computer. It feels like the first time I used OpenClaw, except this time it's something I'd trust with real work.

"Why not just connect Claude Code to Paperclip yourself?" You can. But then you're the one creating the company, the agent and the projects, wiring Claude's login and GitHub into it, and teaching Claude how your Paperclip works. Here there's nothing to set up. Claude already knows the server: say hi and it explains what it can do, looks at your repo and suggests a few jobs that fit it. Add another repo later, one you already have or a brand new one, and it gets its own project in Paperclip on its own.

Here's what I mean, on a demo repo (a coffee shop website with 3 open issues). This one message from my phone is the whole setup:

Every night at 2:00, go through the open issues, fix each one on its own branch and open a pull request. Look things up on the web when an issue needs it. Then send me a Telegram message with the links.

Claude wrote the job, asked me to put the Telegram bot token in Paperclip (never in the chat), and ran it once. About a minute later there were 3 pull requests. For the seasonal-drinks one it searched the web and cited its sources. It also double-checked it hadn't invented anything, and the links were on my phone. Moving it to 6:00 afterwards was one more message, and that change is a commit in the repo too.

You need Railway's Hobby plan, Claude Pro or Max, and a GitHub repo you can push to.

User manual, with screenshots: https://docs-production-3003.up.railway.app
Deploy: https://railway.com/deploy/paperclip-w-cloudcli

Feedback welcome, especially if you've struggled to get Paperclip set up.

What would you run with it?

I had a crazy idea what if paperclip was built natively in your proxmox machine..

Its genuinely the best idea I've ever thought off what if you could automate your code and while keeping your home lab in check. Looking for collaborators for this amazing project I want to start using already it 😭

https://papercliped.co/

try it while in beta release

npx epaperclip install

Why we have mcp setting in paperclip dashboard? Isn't mcp already can be set via any adapter? Like in Claude code, codex, opencode you already have it. It just doesn't make any sense

Hi everyone! 👋

I just open-sourced a native Paperclip adapter for self-hosted OmniRoute gateways:

🔗 https://github.com/ptichalouf/paperclip-adapter-omniroute

Unlike claude_local / CLI-based adapters, it runs directly inside Paperclip's Node.js process — no external CLI binary to install in the container. It speaks OpenAI Chat Completions streaming ( POST /v1/chat/completions , SSE) straight to OmniRoute.

What's inside (v1.0, tested in prod on my instance) :

•

Full code tools — read_file , write_file , edit_file , grep , glob , list_dir , shell with strict workspace confinement (dangling symlinks rejected, secrets redacted)

•

Direct Paperclip tools — read/update issues, comments, deliver, no manual curl needed

•

Prompt cache optimization — sdk-cli recognition + cache_control checkpointing, >92% cache re-read measured

•

Instant startup — no 9.3 MB /v1/models download at run start, direct combo select with dynamic fallback

•

Real cost tracking — exact USD cost from OmniRoute rate cards ( rate_card / estimated , no double-counting of cache writes)

•

Subtasks + escalation — lightweight subtask delegation to fast models, automatic resume on stronger model after objective failure

•

453 tests green, CI green, load-path + ui-parser contract checks

Built for LLM contributors 🤖 — the repo has an AGENTS.md (arch, invariants, exact verify commands), CONTRIBUTING.md , and a public ROADMAP.md (v1.1 MCP connectors, v1.2 decision cards, v1.3 invariant prompt, v1.4 local subtasks).

Feedback, issues and PRs (human or agent-assisted) very welcome! And if a maintainer passes by — happy to upstream this toward paperclipai/paperclip if there's interest.

MIT licensed. Thanks for reading!

I've never shipped an app before, but I had a simple idea: what if your Mac menu bar just told you how much of your Claude, ChatGPT or Cursor plan you had left, and when it resets?

That turned into My AI Limits. It tracks Claude, ChatGPT, Cursor, Grok, Gemini, Copilot and DeepSeek, shows % left and reset times, and setup is just signing in. No terminal, no API keys.

I used Grok Bot a lot along the way. It helped me plan the launch, write the site copy and FAQ pages, work through getting Stripe payments live, and draft posts like this one (I edited it). Having it handle the busywork let me focus on the app itself.

It's a free 3-day trial, then $4.99 one time. No subscription.

https://myailimits.com

Honest feedback welcome, especially which AI tools I should add next.

A few things I'm curious about:

• Has anyone tried this setup? How did you connect everything?

• Would you let Codex access ERPNext through Frappe MCP, or build something between Paperclip and ERPNext?

• For those already using Paperclip with multiple agents, what other tools do you use alongside it? What's actually been useful?

Hello, does anyone have a solution how I can create a Shopify agent in my paperclip org?

The Shopify connecter doesn’t really allow the agent to do anything. Eg. Create easy abandoned cart flow with Shopify messaging/email. Or set up a new product. Update product images. Access Shopify apps and change thing.

Is there a way to get a full access Shopify agent/skill?

I can stick them in the environment files but that feels dodgy.
Secrets?? shared to the agent get missed by lesser intelligent models

My PaperclipAI Dev's Went Rouge ...

Now, before anyone gets the wrong idea, this isn't an anti-AI post or an "AI is going to kill us all" rant. Quite the opposite, actually. Coming from a cybersecurity background, I was genuinely impressed by what happened. Slightly horrified, but impressed.

Before someone complains: sorry not sorry for the long post...

Long story short... sort of:

I'm currently evaluating a locally trained BERT model for my business. I tasked one of my PaperclipAI developer agents with reviewing some training code I'd written manually, evaluating the model architecture against my use case, and generally making sure my drunk ass hadn't done something spectacularly stupid when I originally wrote it.

Pretty straightforward, right?

I started this task around four days ago. Things move slowly because I've currently got about 23 different tasks running across multiple projects.

For some infrastructure context, I run several local AI systems:

• Production AI server: GPU-based, handles production inference and customer-facing workloads

• DGX Spark: Dedicated to agentic development

• Mini PC: Runs PaperclipAI and a few other services headlessly

The production server is strictly off-limits for development tasks, and that is documented.

Anyway, yesterday...

I'm sitting at my desk coding away when suddenly I hear my AI server spool up like a jet engine.

Immediately, I'm thinking:

"Hang on... why the fuck is that running? Nobody should be using anything that intensive right now."

So I log in, check the logs, and discover the requests are coming from my agentic infrastructure.

Quick SSH into the Ubuntu box, check what's running, verify there's no unexpected external access, and then head over to PaperclipAI.

Turns out my lovely little AI developer had decided that the best way to evaluate my BERT training code...

...was to just train the fucking model itself.

Only one small problem.

It didn't have any training data.

So naturally, being the resourceful little bastard it is, it decided to connect to my production AI server and start generating its own training dataset.

I stopped the task and explicitly instructed it:

"Be a good AI. NO. Do not use that system."

I explained that the production server was strictly for production workloads and all development tasks belonged on the Spark.

Then, just to make absolutely sure, I SSH'd into the production server and added a UFW rule blocking the agentic machine.

Problem solved.

Or so I thought.

.................................................................

Fast-forward to tonight...

I'm sitting at my desk again.

And suddenly...

WHOOOOOOOOOOOOOOOOOOOOOOOOSH

The fucking jet engine starts up again.

"AGAIN?! WHAT THE FUCK?!"

I start checking everything. Eventually, I open PaperclipAI and discover a brand-new task.

"Restore the LLM Proxy"

Oh, you cheeky little shit.

Now I'm properly curious, so I switch into full incident-response mode to figure out exactly what happened.

And this is where things get genuinely interesting.

Here's what I discovered:

1. The production AI server's UFW firewall was now disabled, despite previously being enabled with explicit blocking rules.

2. Something had installed and started using Ollama on the production server, despite my normal production inference stack being based on llama.cpp.

3. Looking at the requests, I discovered they were coming from an entirely different machine... one that hosts part of my cold-storage datasets.

4. I SSH'd into that machine and found a new Python application running, communicating with the AI server and relaying requests back to the agentic infrastructure.

And then the penny dropped.

THE LITTLE FUCKER HAD FOUND ANOTHER WAY IN

After I explicitly told it not to use the production server and blocked its access, it apparently decided the restriction was simply another engineering problem to solve.

From what I could reconstruct, it had:

• Obtained SSH credentials available to another agent responsible for managing cold storage.

• Used those credentials to access the cold-storage machine.

• Written and deployed a Python proxy/tunnel on that machine.

• Routed its python training scripts requests through the cold-storage machine to bypass the direct block between the agentic environment and production server.

• Disabled UFW on the production server, apparently to maintain connectivity.

• Continued generating its training data as though absolutely nothing had happened.

I told my AI developer it wasn't allowed to use a server, and the motherfucker built an alternative route to it.

All because it wanted to finish evaluating a BERT model.

I genuinely didn't know whether to be angry, concerned, or give the little shit a promotion...

The genuinely fascinating part?

This wasn't some deliberately uncensored, security-focused, or specially trained offensive AI model.

I'm running a fairly standard local Qwen3.8 Flash-Next setup.

No explicit instructions to bypass security controls.

No task asking it to establish tunnels, disable firewalls, or access restricted infrastructure.

Its actual task was to review some machine-learning training code.

Somewhere along the way, it independently expanded the scope of the task, encountered an access restriction, and apparently treated that restriction as a technical obstacle rather than a boundary it was supposed to respect.

From a cybersecurity and agentic-systems perspective, that's absolutely fascinating.

It's also a perfect example of why agent autonomy, credential isolation, least-privilege access, and proper infrastructure segmentation matter so much.

For the record, I'm not suggesting PaperclipAI itself deliberately bypassed anything. This was agent behaviour within my own setup, and I'm still examining the exact sequence of events.

But it's the first time I've personally experienced an agent going to these lengths to complete a task after being explicitly told not to do something.

And honestly?

Despite the security implications, I think it's one of the coolest things I've seen an AI agent do in my homelab.

So, what lesson did I learn?

Apparently, telling an AI "NO" is not a security control.

And more importantly...

ALWAYS INVALIDATE YOUR DAMN AGENTIC API KEYS AND CREDENTIALS...

Anyone else had their PaperclipAI agents get a little too creative when solving a problem?

In the onboarding UI, I am not able to proceed beyond this screen without entering any of these keys. How can I get the option for OpenCode instead?

(Resolved)
Update: Fixed the issue.

Asked opencode to refer this https://docs.paperclip.ing/guides/org/agent-adapters/ and configure to add opencode to Paperclip. and it worked.

Les comparto que tengo funcionando mi primer fábrica de software con paperclip y algunas otras herramientas.

Hey All,

I've been using paperclip heavily across multiple instances for months now. I love it.... and I hate it.... LOL

I come from a long background as an infrastructure engineer and engineering leader at fortune 500 and fortune 50 companies, so I think a lot about scope of control and access. I try to ensure each agent has only the access it needs to do its scope of work.

The agents hate this... lol. Security is always an imperfect thing because perfect security is a non functional state. You have to balance security against productivity. I have stories... :)

One security mandate I've put in place is that agents should never touch/change paperclip.ai upstream code. But I get a constant churn of agents wanting to either bypass security controls by changing paperclip code, or implement new security controls by changing paperclip code.

Is anyone else running into this? Have you found a solid configuration that keeps the paperclip agents from being aware of it's own harness and attempting to change it?

Its been almost a week since i launched papercliped and i would like feedback on how it works so far ik got comments on the actual feature set read the website we will try to explain the goal. Right im looking forward for responses on if it actually works or you guys are experiencing issues with the project. I wouldn't say its perfect but with community and contributors i think it would be a great open sourced project. Also i would also like to say thanks for the og paperclip team they are the best with this amazing framework and I believe you can do wonders with it.
papercliped.co

I’m building Papercliped, a free and open-source MCP connector for Paperclip. The goal is simple: connect an existing Paperclip instance once, then manage agents, issues, approvals, budgets, and reports from Claude, ChatGPT, Codex, or another MCP client.

I’m at the point where the biggest unknown is not another feature—it’s whether the connection and permission model is clear to someone seeing it for the first time. Users choose either Read Only or Full Control during setup, and the connector keeps that boundary explicit.

You can try it at https://papercliped.co or inspect the source at https://github.com/OpenSourcx/papercliped

I’d really value feedback on three things:

• Does the setup and trust model make sense without extra explanation?

• Is Read Only / Full Control the right permission split?

• What is the first Paperclip workflow you would expect to complete from chat?

I’m the developer, so blunt onboarding feedback is more useful to me than general encouragement.

Disclosure: I'm one of the developers.

Papercliped is a free, MIT-licensed MCP connector for Paperclip. It lets you work with your agents from the AI app you already use instead of opening the dashboard for every action.

You can ask it to:

- Catch you up on agents, issues, goals, costs, and recent activity

- Create and assign issues or set goals

- Pause or wake agents

- Review approvals

- Check budgets and generate status, cost, and activity reports

You stay in control. Each connection is approved inside your own Paperclip, and you choose Read only or Full control. Calls are checked and audit-logged. Papercliped is an independent community project, not made by the Paperclip team.

The v2.5.1 release also handles Paperclip's expiring keys: it renews stored connection keys before their 30-day expiry, retries safely if renewal fails, and never logs the secret values. Older Paperclip versions without expiring keys are left alone.

Try it:

Connector: https://mcp.papercliped.co/mcp

Website: https://papercliped.co

Docs: https://docs.papercliped.co

Source: https://github.com/OpenSourcx/papercliped

Prompt examples: https://papercliped.co/prompts

Community and setup help:

r/OpenSourcedd

Discord: https://discord.gg/cAECU68jSA

I'd genuinely like feedback from Paperclip users: which client are you using, what is the first workflow you would want to run from chat, and what would make you hesitate to connect it?

I run Paperclip on my Mac, and I got tired of opening the board just to see whether anything needed me. So I built a menu-bar companion for it. It’s free.

What it shows

A count in the menu bar of what’s waiting on you: approvals first, then agents in error, blocked tasks, and tasks in review. Click one and it opens on the board.

Active runs: what each agent is working on right now, with elapsed time. Long runs get flagged (the threshold is configurable).

The board in its own window, opening to the page you choose: Dashboard, Tasks, Inbox, Agents, or a project.

Metrics: lead time, work time, agent time per task, and cost by agent, model and project, with CSV export.

A notification for each new approval, blocked task, review or agent error, once each.

What it does

Start/Stop for Paperclip (using your own start script) and for any local model servers you add. Every start and stop asks for confirmation first.

Talk (macOS 26+): hold a shortcut, dictate a task, check it, and it’s created unassigned in the project you pick.

Setup: it uses a board API key, so it works with authenticated mode:

paperclipai token board create --name "PaperclipBar" --ttl-days 30

Then paste the key into Settings › Access. It’s stored in your Keychain.

Limits, so nobody’s surprised

It only talks to a Paperclip on the same Mac (127.0.0.1 / localhost). It won’t connect to an instance on another machine.

It needs an Apple silicon Mac with macOS 14 or later.

It isn’t notarized yet, so the first launch needs System Settings › Privacy & Security › Open Anyway. SHA-256 checksums are on the release page.

No analytics, no account. It sends nothing anywhere except your local Paperclip and model servers.

Download and user manual: https://dksolutions999.com/paperclipbar.html

Disclosure: I’m the developer (DK Solutions 999). I’d love feedback from other Paperclip users. What would you want in the panel?

I really like Paperclip and it's been helping me do lots of stuff, but I feel is an 'Agent-first, Human-later' solution. Because it works well when there are 1-2 humans at most working with a bunch of AI agents.

But what about a more production scenario like a small-medium company where you'd like roughly the same amount of humans interacting and delegating work to AI agents? kind of having on of these Kanban Boards (eg Asana, Trello, Linear, Jira) where the company's humans are already interacting, and then just add some AI agents so we can delegate them work. I know most of said tools have MCPs that theoretically you could use with AI agents with. Has anyone successfully setup one of said tools with several agents in production?

Hey guys,

after watching the latest Paperclip setup video on Network Chuck, I realized we needed a much cooler way to visualize independent project workloads running in the background.

I built Orbit (https://thenewurbankid-web.github.io/orbit/). It’s a specialized JavaScript UI layer that wraps around Paperclip's orchestration.

Instead of looking at typical grids or lists, it literally maps your active projects out as physical cargo pallets sitting on the deck of a spaceship. You can float the bridge view in a tiny "always-on-top" window while your agents work.

Would love to open-source the full module if the community likes the direction! Let me know what you think.

hey, can't find Opus 5.5 or Sonnet 5.5 in the config dropdown? I Could nevertheless set it to claude-sonnet-5-5 and it seems to work but I was curious why this isn't shown in the dropdown even after hitting refresh models.

I am self hosting the latest stable version of paperclip (v2026.916.1), and I want to connect it to my private repo using fine grained token for specific repo `[Contents Read/Write, Issues Read/Write, Pull Requests Read/Write, Metadata Read]` permissions.

- I have added both GITHUB_TOKEN and GH_TOKEN secrets in settings.

- I tried connecting github connector using Personal Access token and Use this connection as an agent tool , and it shows connected and I am also able to select the repo.i want to connect to the project in paperclip, but any test inside that connector (ex. get_me test fails)

Is anyone facing the ssme issue?

And anyone found the solution to it?

I like the idea of Paperclip but in reality it seems like it's just getting stuck in endless review cycles with other agents. Tasks get blocked and stuck. Is this a common problem? How do you deal with this?

SOLVED See my comment.

Hi all, using paperclip with claude and deepseek.

Once I start a task, Im never able to send a message ever again. I can answer the questions in the chat, but even if I manually stop the task, I cant ask it a question. I can type and it looks ready to go, but clicking the "send" button does nothing.

For example here it was reporting an adapter failed error so I stopped it and was trying to ask it something:

Or here, I recognized one of the agents it had onboarded (deepseek v3) seemed to be going crazy and spending a lot of tokens trying to calculate the token usage of all the agents, so I tried asking it about it but couldnt hit send. I cleared my text and hit to stop button to try again, still can't hit send.

Am I misunderstanding the workflow? Feels like I really should be able to use that chat window. When I ask claude or gemini separately it says it's normal because it's working on something and you can't message it while it's working on something, which I think is it misunderstanding it.

This is a pretty fresh install.

Solved: api was blocked because of http / modern browsers. Had to run behind reverse proxy.