blensor

In no order of importance:

  - Refactoring a 13 year old in-house vacation rental booking system ( python/turbogears )
  - Backend development for our VR fitness game ( flask/python )
  - Unity development on our VR fitness game ( C#/Unity )
  - VR game development experiments ( Godot/GDScript )
  - Standalone SLAM localization service ( C++ )
  - Audio analysis ( python/pytorch )
  - Virtual display with Viture display glasses ( C )
  - Reverse engineering a library I am using for another project ( ghidra -> C - no MCP yet, that's something I am looking forward to )
  - Public facing website rebuilding for the booking system above ( PHP/JS )
  - Generative 3D environments for our VR fitness game ( python )
  - Wireless camera/IMU based tracker for the SLAM system ( C )

I'm a model nomad, using whatever solved my last problem the best and where it makes the most sense to start my next work in.

However with the latest models Fable, Kimi K3, 5.6, it's getting to a point where I sometimes forget what model I am on without noticing a difference. And once I realize it because something may not be exactly like I expected it I won't switch for that work either because I don't want to invalidate the cache.

For the next work I will do there is maybe a 50/50 chance to remember to switch the model before I start.

That's not what I would call stickiness towards a certain provider.

It uses polyfill.io which is no longer active and has been taken over by malicious actors.

That's where the sign in request is coming from

Agreed, it was a bit of a pain to get running on my Ubuntu machine because I had old amdgpu-dkms-firmware packages installed without realizing it. But now that it's running it's amazing how well it works

The reason why I was curious is that I am running my stuff on a Strix Halo and I get the feeling that this class of devices ( gmktek, minisforum, lenovo, etc. ) seem to becoming a pretty good alternative

I am curious if you implicitly assumed they are Macs or if that's what you are looking for specifically?

What worries me is that FSD in Tesla does seems to sometimes introduce problems in the driving behavior with new updates. I have not experienced this first hand ( don't own one ) but I am following the r/TeslaFSD and it looks like new versions are sometimes regressing on situations that were handled correctly on older versions.

This leads me to believe that FSD is not yet solved to the level we thought and training to handle a certain new thing correcty can degrade handling of other situations

What exactly do you find particularly ugly on this car?

The only thing that is a bit weird to me are the headlights, otherwise I quite like the look.

And isn't it also mostly a transitioning issue. Those open codebases will be constantly scanned for potential security issues and getting more and more hardened. There are probably a lot of easy wins that are going to be discovered over the next few years but it should taper out after a while.

Tangentially related question. Has anyone analyzed if the content that is being converted could break the model.

So let's say you have a super dull pdf ( or even a scan ) that has the same line over and over again, could this get the model into one of those loops that just keep spewing nonsense.

And thinking that further, could someone prompt inject a model with a handwritten note that only gets "activated" once it's in the context?

This is ( or will be in the future ) a surprisingly relevant issue

Kind of yes. The person who does not spam the messages.

What I noticed for myself is that saying no to something is becoming harder by the day because, why wouldn't you try something if it's so cheap to do.

That makes having the strength to say no almost a quality in and of itself.

That is true both for external pressure and internal pressure.

How do you measure the body regularly without potentially introducing problems just by measuring it?

Maybe the solution is to look out for the most silent engineers. Those that output less despite having the ability to create near infinite output.

After digging around a bit I found an unverified claim from 2024 that GPUs in datacenters have a lifespan of 1-3 years

https://www.tomshardware.com/pc-components/gpus/datacenter-g...

Others say that moderate load means a lifespan of ~5 years

Not sure what that means but I would assume that a datacenter will start replacing a node once the error rate hits a certain threshold without really investigating why it failed, so the practical lifespan may be shorter than 5 years even if it would technically still be usable enough

I also feel that the GPU/NPU value does not lose money as fast anymore.

What I am wondering though is how long can you run such a system at basically full load without interruption before it starts to just physically degrade.

If I have a H100 and I let it run for 4 years at full throttle does it still have the same theoretical value as it had at the start or are the chips just burning out.

I think I remember that back when the cards used for crypto mining were sold en masse on ebay the advice was to stay away from them because they are more likely to fail?

The funny thing is that the page has a broken Google Tag Manager script

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Is the premise of 15-minute cities really that every final destination is walkable within 15 minutes or that you can reach everything you need within 15 minutes?

If I live in a big city with good public transport and have most daily need things walkable within 15 minutes and good public transport connections also within 15 minutes then I can benefit from opportunities that are farther away while also having the locality of the rest of the day to day things.

That's what I personally would consider a 15-minute city

Check the github user profile ;)

I also think in addition to that the increased speed compounds the problems much quicker. And I don't mean bugs. I mean that duplicated code here, that additional state variable to keep everything going there. Not removing things that should be removed because we can work around that, etc.

It's like building a super tall Jenga tower very quickly but laying the bricks much worse than a careful player.

Sorry for forgetting to post that, and thank you for adding it.

I saw your youtube video, and while I am generally a fan of the small guy against big corps, this was a bit much with all the "I am so afraid to say something but I have to" talk.

So I will hold my judgement until this has been disected a bit more

That is the onboard recording feature of the Meta Quest, it does have a way to change the video codec but the audio codec is fixed.

I'd also argue that most off the shelf stuff that records videos in any form uses AAC as a default simply because it's ubiquitous and thus has great cross compatibility.

It's mostly for youtube shorts that don't get a ton of views in the first place, so I am going for a quick and easy fix that keeps the file size mostly the same.

But you are of course right, if I would go for a professional setup I would not recompress it

AAC and mostly recordings of VR games I have to first convert the audio track to MP3 for Davinci Resolve to support it.

kdenlive to me is like gimp. I launch it everytime I want to do something quickly, without really thinking about what tool to use.

With Davinci Resolve I have to intentionally plan on making a video to be willing to use it, because it's much heavier, doesn't support the audio in most of the source videos I am using, so I have to convert that first, and does a lot more than what I usually need.

I've been called worse :)

58% German / 27% Autistic

As an Austrian I am not sure how to feel about that

I still need to do a video on how to use it, if you get stuck just let me know

It may or may not be useful for you but I've been working for a while on converting ORBSLAM3 into a self contained standalone program, without the need for ROS to be useful.

The "UI" for saving/loading the map and calibrating the camera is exposed through a built-in crude webserver. Visualization is done via threejs instead of having a dependency on pangolin.

If your robot can expose the camera feed as anything opencv can ingest ( i.e. mjpeg via http ) you could just point it there and then receive the pose stream via HTTP/SSE

The whole thing is distributed as an AppImage so you just run it and connect to it

https://github.com/mgschwan/ORBSlammer_LocalizationService