sbts-aru VS rupy

Unfortunately a Raspberry Pi is a bit ill suited for production environments. Id recommend an RTC module. Otherwise this might be helpful: https://github.com/hcfman/sbts-aru

If you install my sbts-aru project

https://github.com/hcfman/sbts-aru

It will shrink your partitions, add news and install one of these and set up a sub micro second system clock and an audio recorder suitable for sound localization with a single install command.

I recommend primo EM272 microphone capsules for use with https://github.com/hcfman/sbts-aru. They are high quality, very sensitive with high signal to noise ratio, lauded for nature recording use cases. They can be bought assembled for around 65 euros in the Netherlands. However these capsules are often found in much more expensive equipment.

My sound localizing Raspberry Pi installs a resilient base system as part of its install.

https://github.com/hcfman/sbts-aru

https://hackaday.com/2023/12/30/localizing-fireworks-launche...

With one command it for all Pi’s for both Raspbian and bookworm it:

* Shrinks the file system (Gee, how does it do that with just one disk ? ;-) )

I have a sound localization project that can help with that

https://github.com/hcfman/sbts-aru

You need to be able to hear the sound from three or more recorders. And normally localization is better within the polygon of microphones but there’s an area of better localizability extending outside of a vertex.

Interesting history!

And while we are in the subject of sound localizing may I take the liberty of introducing my new raspberry pi sound localizing audio recorder.

https://github.com/hcfman/sbts-aru

It installs with one command on all Raspberry Pi versions and synchronizes the system time to less than 1 microsecond of error with a cheap GPS.

With three of these I’ve been able to sound localize the explosions from illegal fireworks to a specific car park from more than 3km away with lots of houses in between.

When I got to the car park I could smell the sulphur from the fireworks.

This will even run on a Raspberry Pi zero running of a battery with a 6 euro neo 7m gps and a 6 euro usb mic.

Very nice tool!

So grab a spare Raspberry Pi, a GPS, a cheap USB sound card and a mic and get recording with this Pi based Acoustic Recording Unit

https://github.com/hcfman/sbts-aru

And while you are at it, install 3x or more and localize where the birds are.

Sound localizing. I was intensely passionate about it when I saw the possibility to do it well on a Raspberry Pi. There were quite a few more problems than so expected which is why it took five months (of weekends) to complete it well.

But I’m super happy with the result and have a bunch of geeks with recording nodes setup a long distance apart. Localizing large explosions show that it’s possible to localize to a carpets even when some of the nodes are almost 5 away.

For those interested, here is the project:

https://github.com/hcfman/sbts-aru

I have been running a Raspberry 2 cluster for 10 years: http://host.rupy.se

A few weeks back the first SD card to fail got so corrupted it failed to reboot!

My key learning is use oversized cards, because then the bitcycle will wear slower!

I'm going from 32GB to 256/512/1024!

How this article does not mention SSE, comet or chunking escapes me.

What does their definition of event-driven really look like in practice.

Nobody has a clue.

Here is the ideal event driven system, it's async-to-async: https://github.com/tinspin/rupy/wiki/Fuse

The example is not working because I had to shut down the services for multiple reasons, but the high level of it is that you use 4 (potentially different) threads to do one request/response middle man transaction.

That way you have _zero_ io-wait or idling. I'm surprised nobody has copied this approach since I invented it 10 years ago. I understand why though you need your entire chain to be async and that means rewriting everything and that is a big risk when it's hard to debug.

But if you succeed you can build something that is 10x perf/watt than all other implementations. Which is going to be important when interest rates go higher and crash our entire industry.

The hardware is peaking.

So software is where you can make the difference: http://host.rupy.se

I think we're all confused over the definition. Also one might understand what all the proponents are talking about better if they think about this more as a process and not some technological solution:

https://github.com/tinspin/rupy/wiki/Process

All input I have is you want your code to run on many machines, in fact you want it to run the same on all machines you need to deliver and preferably more. Vertically and horizontally at the same time, so your services only call localhost but in many separate places.

This in turn mandates a distributed database. And later you discover it has to be capable of async-to-async = no blocking ever anywhere in the whole solution.

The way I do this is I hot-deploy my applications async. to all servers in the cluster, this is what a cluster node looks like in practice (the name next to Host: is the node): http://host.rupy.se if you click "api & metrics" you'll see the services.

With this not only do you get scalability, but also redundancy and development is maintained at live coding levels.

I have hosted my own web server both physically and codevise since 2014.

It's on a Raspberry 2 cluster:

http://host.rupy.se

Since 2016 i have my own database also coded from scratch:

http://root.rupy.se

We need to implement HTTP/1.1 with less bloat, a C non-blocking web server that can share memory between threads is probably the most interesting project for humans right now, is anyone working on that?

I have one in Java: https://github.com/tinspin/rupy

Here is the 2000 lines of code of the entire database: http://root.rupy.se/code?path=/Root.java

And here you can try it out: http://root.rupy.se

The smallest PaaS you have ever seen is one order of magnitude larger than mine: https://github.com/tinspin/rupy

And I bet you the same goes for performance, if not two!

The data is here: http://fuse.rupy.se/about.html

Under Performance. Per watt the fuse/rupy platform completely crushes all competition because of 2 reasons:

- Event driven protocol design, averages at about 4 messages/player/second (means you cannot do spraying or headshots f.ex. which is another feature in my game design opinion).

- Java's memory model with atomic concurrency which needs a VM and GC (C++ copied that memory model in C++11, but it failed completely because they lack both VM and GC, but that model is still to this day the one C++ uses), you can read more about this here: https://github.com/tinspin/rupy/wiki

You can argue those points are bad arguments, but if you look at performance per watt with some consideration for developer friendlyness, I'm pretty sure in 100 years we will still be coding minimalist JavaSE on the server and vanilla C (compiled with C++ compiler) on the client.