This paper was published 154 days ago, probably a year since the authors did the experiment. Sooo much has happened since then! This showed already that GPT4 is pretty darn good analyst.
All this real-world complexity can be tamed by stuffing the prompt with a ton of relevant context and an amazing prompt engine. We'll have bots that autonomously query the database hundreds of times building a 5 page "deep-dive" analytics report in minutes.
At least that's what we're trying at patterns.app.
We're taking a proactive approach to our "chat with your database" product. We begin by inspecting your db, building a knowledge base, then AI will recommend relevant insights to you depending on your query history... tinder for data if you will!
To follow up just create a new request and it'll kickoff a chat
If there's already very little margin left in interchange for fintechs, what's to stop further compression in pursuit of customer acquisition for higher margin products? End state... is interchange going to zero?
Hey HN! A colleague on our team wanted to dogfood Patterns to see what might take to build a Slack bot that summarizes the activity of the day with GPT3.
He wrote down his learnings which go into interaction design, prompt design, and how to work with data from Slack.
"When I was a Salesforce developer, I frequently worked with small internal teams within larger organizations. Since traditional AI solutions require large amounts of data and specialized infrastructure to function properly, utilizing AI capabilities was something that a small compliance department or customer service team wouldn’t even consider.
Now with services such as Cohere, you can fine-tune pre-trained Large Language Models (LLMs) and use Patterns to provide easy-to-configure data orchestration with no infrastructure required. No matter what budget or available resources, small teams no longer need to be left behind in the AI revolution and can take advantage of these powerful tools to improve their operations and decision-making.
In this article, I’ll explain how to quickly set up a Salesforce Case classifier using an LLM such as GPT-3. While this is a common use case for AI within Salesforce, this example can be applied to other AI solutions as well."
Spot on analysis of the market — we also think the next frontier for data eng/science is towards further automation. After all, dashboards are input to a person that makes a decision and takes an action; and to your point, tools on the market don’t really serve this use case.
To this end, we plan to support more human-in-the-loop workflows by expanding on our dashboard feature and enabling stateful user input interfaces.
One small note to point #4, while we provide out-of-box infrastructure for easy setup, Patterns can run on your existing data warehouse — so it plays nicely with big teams that have existing tools.
Our target audience are data engineers and scientists who are stitching together business apps like CRMs/email marketing tools etc, and building proprietary automations and analytics in between them like generating a customer health score and actioning on it.
One of the problems we’ve intended to solve is the separation of analytical systems (like all your pipelines for counting customers and revenue) and your automations (like do something on customer signup event).
I think to think we threaded the needle quite well at patterns.app --- letting the user be fully expressive with code, whilst giving the visual flow programming guide
Cool idea, but a lot cooler to be able to attract talent with QSBS stock