A9 Arcade: Microservices, GPTs, Agents, SPAs, Automations, One-offs, One-liners and more.

After a quick win building a general purpose Streamlit app to tackle a data translation challenge, I went for a few games of Pinball.

$5 gets you something like 7 credits, as opposed to the standard of $1 per credit. (Pinball, like Costco hotdogs is an inflation beater, BTW) and after a Match, I had played 8 games of the Rush pinball game (in various modes).

I already have a collection of services and automations that make it possible to streamline processes, generate and optimize code (see above). We can now brainstorm with the (licensed and unlicensed) embodiments of thought leaders, famous marketers, musicians, video game developers and more through various LLMs and Models available today.

We live in a world of Micro Service Architecture. "Order to Cash" is a handshake of services and integrations. I've built and worked in similar architectures, and you can likely appreciate Ebay as an example of an app and service that isn't delivered with a big monolith (see my technical note at the bottom). There is a network of services working together to render the necessary content in the app or browser, list the item, show you the item, take your order, bill you for it, ship it to you, etc.. Many of your favorite services and monthly subscriptions are powered by similar networks of transactions and services.

The teams that build, test, run and improve these things have their own networks of transactions, sometimes more physically tangible and sometimes less (npm, anyone?).

Don't they want to blow some tokens and find inspiration behind a glowing screen?

I think so. If you look at any of the "Awesome" resources on GIthub and bring these to light as agents, there is a an entire corner of the arcade populated with something as delightful as Skee-ball.

Sure, here's a table that outlines the estimated level of effort and value associated with making GPT Agents from each of the listed "Awesome" resources. The level of effort is categorized as Low, Medium, or High, and the value is categorized as Low, Medium, or High.

Notes:

1. Level of Effort: This is based on the complexity and the amount of information required to build a useful GPT Agent. Low effort means straightforward information extraction and question answering, Medium involves more complex tasks or specialized knowledge, and High requires significant expertise and potentially extensive data processing.
2. Value: This represents the potential utility and impact of the GPT Agent. High value indicates that the agent can provide significant insights or assistance, Medium value indicates useful but not critical assistance, and Low value indicates niche or specialized use cases.

Technical Note

Not long ago, I wrote a GPT to interact with Khoros Community forums to derive product insights from Public sources. One of these is the A9 Community Bot - Auction Site Edition, which was used to identify posts and synthesize this definition:

These posts collectively indicate that rlogid is an internal identifier used by eBay's systems, often appearing in API responses and error messages, which helps in tracking and debugging specific actions or events on the platform. If you need more detailed explanations or have specific questions about its usage, checking out the full discussions on the linked posts might be helpful.

https://chatgpt.com/share/1853e82e-f6d6-40a3-8aee-0f3065494081