Hey hunters

We just gave our product page a proper refresh ahead of our launch on June 16th: new gallery, clearer story. Quick rundown for anyone passing by.

Most AI voice agents give you transcripts.
We give you decisions.

Here s the problem we kept seeing:

Hey Product Hunt

Launching tomorrow and wanted to share a bit about what I've been building.

Hardware checks. Compatibility scans. Model deployment. Copilot routing. Local hosting. Multi-cloud instances. Cloud failover. Used to take a day. Now under 10 minutes.
AI moves fast. Deployment doesn't. 40% of teams take more than a week to get a single model into production. Data scientists spend over a quarter of their working day on setup, not science.
That's not an AI problem. That's an infrastructure problem.
oneinfer-edge fixes it. Not by reinventing the stack. By orchestrating what already exists into one open source control plane.
- Multiple serving libraries. One scan.
Ollama, llama.cpp, vLLM, SGLang, TensorRT-LLM, PyTorch, Dynamo. Instead of manually testing each one against your model and hardware, oneinfer-edge evaluates all five simultaneously and tells you exactly which one to use, for local, cloud, or both. Hours of trial and error eliminated before a single deployment.
- Traffic control panel for agentic harnesses. Zero code changes.
You can now leverage locally deployed models through the existing agentic copilots like codex, kilocode, opencode and openclaw and more upcoming.
-Model, serving library and hardware compatibility. Before you deploy.
Wrong serving library for your hardware. Wrong runtime for your model. These failures usually show up mid-deployment. oneinfer-edge runs a full compatibility scan across your model, your serving libraries, and your local hardware upfront. Complete picture. No surprises.
- Model and hardware resource checks. Local and cloud.
Paste any HuggingFace model ID. oneinfer-edge computes model weights, KV cache, and serving library overhead together and tells you whether it fits your machine or which cloud instance makes sense when local is not enough. No wasted downloads. No failed runs.
- Cloud instances marketplace. One API for everything.
Spin up instances across any cloud provider from the same control plane using a single OpenAI-compatible API. No switching between platforms. No managing separate configurations per provider. One place to create, manage, and monitor, regardless of which cloud you choose.
- Hybrid routing. Local, cloud, or both. Optimised automatically.
Local handles volume. Cloud handles complexity. When local capacity is exceeded, traffic fails over automatically. Routine tasks stay local. Complex reasoning goes to the cloud only when needed. Inference already accounts for 80 to 90% of the lifetime cost of a production AI system. Intelligent routing alone cuts that by 30 to 60%. Local-first hybrid orchestration pushes further.
We are just getting started. More coming in the next few days. Stay tuned!!!
Repo: https://github.com/oneinfer/onei...
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Super easy to set up, very low layout intrusion and not dependent of any core file.

All in one place.

Hey PH
I kept noticing the same thing: agencies pay for powerful AI tools, then use a tiny fraction of them and still burn hours on repetitive work.

So I built The Claude Agency Bible 50 prompt templates made specifically for marketing agencies. It covers the work that actually eats your week:
Client acquisition cold email, proposals, discovery calls
Market & competitor research
Content calendars, captions, blog, video scripts
Advertising FB/Google ad angles, landing pages
Client retention & reporting
Agency operations & SOPs

Each prompt uses a role + context + output-format structure, so you get client-ready drafts instead of generic output.

Launch price is $49 (goes up after the first sales): https://primeworkflo.netlify.app

After reading many of the encouraging founder stories here, I've decided to start preparing for the launch of my app, Iter.

Iter is an AI travel planning app that learns a traveler's interests to build personalized itineraries that can be further customized by the user.

As a first-time founder, I'm excited but also a bit nervous. I'm relatively new to Product Hunt and haven't had much time to build a network within the community yet, which makes me wonder whether the launch will simply go unnoticed.