If you re still sitting on your launch, this is the push.

YC made a special exception for this community: one or more companies that launch tomorrow will get a YC interview and potentially funding. A YC partner will review every eligible launch.

We are changing it today, shared Openclaw instances on single local GPU is the way it should be.
Why?
Pros:
Still Open source
Unlimited AI tokens
Backup, recover, revisions ( every plus thing of cloud computing vs pc)

Cheaper
Always synced with latest OpenClaw source code and maintained by others.
Reduced latency.

Cons:
You don't own whole stack.

LLM Performance might be variable ( but can be solved with engineering)
LLM choices might be limited ( It's debatable how openclaw users actually pick their models cost or availability or performance)
This is a real experiment launching today: https://penclaw.ai
We are changing it today, shared Openclaw instances on single local GPU, unlimited AI.
https://www.producthunt.com/p/pe...

AMA of course but here are the things people asked.
What model does it use?

It uses (As base GPT-OSS 120B heretic but fine-tuned and has specific GPU inference tunnings for abliteration) pingu-unchained model also can be accessible on https://pingu.audn.ai and https://platform.audn.ai (used specific model pingu-unchained-3-mcp)
How is it unlimited AI usage for flat $200 fee?
Most openclaw users choose mac mini or mac studio, the core problem with them actually they are not multi session friendly. Openclaw by nature spawns another and another agent and LLM costs roof so easily. Therefore we are proposing a shared GPU with isolated microVMs module. When you become a subscriber for that month you become a tenant at the same VM. Like we share apartments we can share 1 H100 based VM and it runs 7/24 and has all good things cloud computing offers. Backups, revisions, no data loss, recovering to an old state and more.
Is it open-source and is it always synced with OpenClaw?

Yes like Openclaw it's opensource and it's actual fork of OpenClaw. We constantly sync the new version on Github you can see the history here: https://github.com/audn-ai/penclaw We are looking for maintainers before the open release of our Platform section which has Filesystem, Isolated micro-vm, meta-claw (Cursor Like Agent Retrofit), cloud terminal, marketplace (One-click Mcp and skills deploy). If you are interested please send an email to ozgur@audn.ai
Is it secure?

A third party red teaming cybersecurity team found it's vulnerabilities before everyone else so we patched them all. But if you use penclaw do find exploits on penclaw.ai we ofc would be happy.
Is there anything interesting done with penclaw AI?
Penclaw has an MCP/skills connection called audn sec qa. We used penclaw to conduct automated attack taxonomies on voice and text AI agents using that MCP. AI took the prompts and discovered the agents by having a conversation and conducted prompt injection and similar attacks.
Usual OpenClaw features what about them?

Hey everyone,

I've built my product around traditional SaaS pricing (monthly tiers), but I m starting to wonder if that model is getting outdated, especially with more AI-powered and compute-heavy tools entering the market.
That shift requires real architectural changes, instrumentation, metering, billing logic, and UI changes, not just pricing tweaks. It s something I m starting to seriously think about for my own product.

In particular, AI usage has real COGs (every prompt costs money), and I m seeing more platforms experimenting with usage-based models, or hybrids like SaaS base + usage + overage.

For those of you building AI or compute-intensive tools:

Please send us your prompts and I will share [Redacted] safe answers by pingu-unchained-1 to your questions with settings on how to reproduce it!

Pingu Unchained is a large language model dialogue experience; it's accessible via an AI Chatbot UI at https://pingu.audn.ai and also powers the adversarial attack engine used at https://audn.ai, a full-stack, end-to-end Voice AI agent penetration testing tool.
You can generate adversarial attack prompts or write penetration-testing code to use in your sandbox environments to automate AI security for your AI product.
Pingu is still too early, and as a single founder, I've tried my best to keep it in the cybersecurity red teaming space, but the model aims to never say "Sorry, I can't help with that". This model is fine-tuned on an attack corpus built on GPT-OSS 120B, and I host it directly on the bare GPU VMs I manage, with a minor tweak to the CUDA settings.

Yesterday, Meta announced that they have released a new collection of AI models, Llama 4, in its Llama family.

(It consists of Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth.)

Historically, Open AI with its ChatGPT has been on the market for the longest period.