Congrats and welcome to the new public status! Forgive the non-substantive comment but that's awesome :-)

I'm Jed, the author. I wrote this guide up because I'm working on an AI search startup using agents, and have been asked by other founders a few times why their content doesn't show up well with AI search engines like ours or Perplexity or ChatGPT with search, or with agent computer-use like the new OpenAI Operator.

We're ingesting around 30m to 50m pages a day so have learned a lot about what blocks AI crawlers and agents. And I wanted to share some guidelines on how to make sure your startup or documentation or other content shows up as well as possible for agent use.

Congrats on the launch. Adding a memory layer to LLMs is a real painpoint. I've been experimenting with mem0 and it solves a real problem that I failed to solve myself, and we're going to use it in production.

One question that I've heard a few times now: will you support the open source version as a first class citizen for the long term? A lot of open source projects with a paid version follow a similar strategy. They use the open source repo to get traction, but then the open source version gets neglected and users are eventually pushed to the paid version. How committed are you to supporting the open source version long term?

Could this be used for doing team visualization dashboards - like metrics displays for a team with KPIs? Or is that outside the ambit? I'm asking because I feel like there is a gap between the easy set up and configurability of something like Geckoboard and the extensibility and power of something like Grafana. That aside, the idea of team spaces/boards with a dashboard feel that is as easy to use as Notion is neat. And the UX looks really nice. Good luck!

We migrated microservices from Heroku to Porter, and also from standalone VMs and K8s running on AWS to Porter. As a coder trying to do both dev and devops on a tiny team, it was life changing for me.

The key benefits for a small startup team are:

1. Effortless CI/CD: Deploying services on K8s clusters across different clouds becomes trivial. Setup a dockerfile in your repo, point Porter at it, deploy. We mostly run APIs behind AWS API Gateway.

2. Startup credits: You can use your existing credits on AWS, Azure etc.

3. Zero lockin: You can deploy in parallel and switch service providers.

4. Devops expertise: The Porter team have given us next-level hands-on support and help to figure out how to run things optimally. A lot of sensible defaults are built in. As a coder, they have knowledge of how to scale services effectively that (to be blunt) I couldn't match no matter how much time I spent trying to learn it as a lay person.

If you're a K8s and devops master, you probably don't need this. If like me you're a programmer with limited devops skills looking for the fastest and easiest way to just solve deployment and scaling, Porter is close to magic. Plus they have one of the most helpful and friendly teams I've worked with anywhere.

(edit for typo)