arjunbajaj

Hey, thanks a lot! Really appreciate you reaching out!

I agree. There's so many cool things to do in this space, and so many unique ways to achieve them. We're excited to be here, and would love to chat sometime as well!

The core issue is the user/developer experience of Industrial IoT is nowhere near where it should be. I understand where you're coming from, and I feel the same way too. Building a great developer experience is something we deeply care about. Exactly for that reason, we started building our own Device SDKs.

We also have support for open protocols such as MQTT and HTTP+SSE, but the Device SDKs enable us to provide a richer set of capabilities. Our SDKs actually speak a custom protocol we developed for higher efficiency. We're also going to add many more features such as automatic telemetry collection and tracing support, which is more feasible with a plug-and-play SDK.

Another big issue you pointed out is with documentation, a key part of Developer Experience is always great docs. A compelling model might be standalone open source tooling that works independently, with an integrated platform that ties it all together, creating a strong ecosystem.

I've been using Home Assistant with a bunch of Zigbee and Wifi devices at home, and it's been pretty stable. However, for an industrial context, there are already many other hurdles, having a platform handle a lot of the cloud infra and connectivity & monitoring is really helpful.

Hey, thanks for the feedback. Great point! We're gonna add something like this to the landing page soon. And we're going to write a few blog posts showing quick integrations across different hardware and protocols as well.

This resonates with me a lot, and well-timed too!

I've always been unhappy with the way tasking/todo app (don't) work for me. I just started building a TUI in Zig (with the help of Codex) to manage my daily tasks. And since I'm building it just for me, the scope is mine to determine too.

A truly great piece of software! Been using it for 5+ years.

I think NetNewsWire is a great example of what software should strive for: a useful set of features, while being fast and smooth.

It's a signal vs noise filter, because today, AI can make more mistakes. Your operating system or IDE cannot lead you to make a similar level or amount of mistakes while writing code.

It is of course your responsibility, but the maintainer may also want to change their review approach when dealing with AI generated code. And currently, as the AI Usage Policy also states, because of bad actors sending pull requests without reviewing or taking the responsibility themselves, this acts as a filter to separate your PR which you have taken the responsibility for.

I can see this becoming a pretty generally accepted AI usage policy. Very balanced.

Covers most of the points I'm sure many of us have experienced here while developing with AI. Most importantly, AI generated code does not substitute human thinking, testing, and clean up/rewrite.

On that last point, whenever I've gotten Codex to generate a substantial feature, usually I've had to rewrite a lot of the code to make it more compact even if it is correct. Adding indirection where it does not make sense is a big issue I've noticed LLMs make.

We're currently working on a data explorer which will allow you to choose, filter, and sort datapoints in a table view. We're gonna add time-based aggregations, charts, anomaly detection, and correlation analytics soon after.

Any feedback or suggestions on what you would like to see?