I feel that would be handy in all sorts of situations when networks are down.
81 karma · joined September 4, 2020
I feel that would be handy in all sorts of situations when networks are down.
I also track historical movements to see which items are never used.
Recently I've moved towards everything being stored in numbered bags, which are hung in order on a line for O(N) retrieval. For storage it tells you which bag to put it in, for retrieval it tells you where it is.
I'm thinking more and more the optimal system will have a physical as well as digital component.
Also, I feel this system would be great for shared workshops at work places and maker spaces etc. I was just rummaging through our lab at work today, there's so many parts in the lab no one would know about, if it was inventorised with a good integrated (AI?) search function the equipment could be much more useful/available.
Also in comparison the cases of disruption/blocking intersections/emergency-services do seem significant - seems plausible in the right circumstances you could lose lives.
Seems they need to 10x their miles before they can start making confident claims about lives saved.
Maybe that's too much of a statistical stretch.
But would be a good to-the-point number to have on hand for some waymo debates.
"yes they caused some disruption in an intersection in so-and-so scenario, but on the other hand they saved X number of human lives last year"
https://github.com/tim-fan/hordor
I was learning Django when I wrote it, today you'd probably get further quicker vibe coding from scratch.
I have about 100 items in storage today, I intend to add more, would like to optimize the workflow as I scale up.
Going forward I'd like to add:
* more optimized storage/retrieval flow. The overall goal for the project is to minimize this friction, as far as possible
* AI enrichment - generate descriptions, aid with search etc. I'd love to be able to query my storage "how can I connect this thing to this old speaker?" and the storage responds eg "you have this cable, this adaptor, plug that into this cable, etc"
I've seen a few related projects but can't find the links just now. There's some cool projects that store items in little trays each with an LED, when you request the item the LED blinks for rapid retrieval. The numbered bags I used are slower for retrieval but cheaper and easier to set up.I do enjoy thinking about the different options and tradeoffs for cost and storage/retrieval time. Also tradeoffs between time and (physical) space.
edit: formatting
Bags are stored in numerical order for quick storage and retrieval.
With this you do your decluttering from the web interface: search for items that haven't moved in years, flag for removal.
For frequently used items the system doesn't make sense - the storage and retrieval overheads are too high. But it pays off for any item you might forget the location of, or forget if you have it at all.
I feel we're overdue to have these types of digital front ends over our household item storage.
Then the one I'm more interested / excited for: optimizing the fleet for the cargo. If most trips involve single passengers, then most cars can be small electric single seaters. This can further reduce insurance costs as well as fuel, maintenance and depreciation.
I'd hope that's enough to offset the price of the sensors, compute hardware, and engineers to maintain the system.
But yes paying back investors; not sure how long that would lead to elevated costs for riders.
https://github.com/facontidavide/Bonxai
Is there some connection between Voxel grids and Bonsai trees that I'm missing??
I'm running a similar but smaller project (5k MAU), my oldest map is central London in 1561
https://onamap.me/maps/London1561/
I got into it because I was interested in the technical challenge of registering GPS to maps which are very warped compared to reality, like very old maps or illustrated tourist maps.
My home page is here for more: https://onamap.me/
I also came across this similar project a while ago:
https://www.verbeeld.be/2024/11/17/using-gps-in-the-year-156...
Good luck continuing to build out the project!
For one a self driving taxi fleet could take up vastly less space - you'd no longer need one car per person, you'd need far fewer parking spots, most cars could be single or double seaters again taking less space and running more efficiently.
The space savings could be used to boost rail-based public transit options, which would see more adoption as self driving taxis make last-mile transport cheaper and easier. A bunch of positive feedback loops driving public transit adoption and improvements.
Result is cleaner and more efficient transport for all, and vast amounts of space returned from serving cars to serving people.
At least that's the dream!
I don't know much about telescopes :p
https://www.reddit.com/r/plaintextaccounting/s/BKsaLrfy3A
I already have thousands of labeled examples and a list of valid categories. I'm also hoping an llm will do a reasonable job.
At the moment I'm wondering what to do with all the example transaction data, as it's likely larger than the context window. I guess I could take a random downsample, but perhaps there's a more effective way to summarize it.
Have you looked at producing 3D reconstructions over the thrown trajectory? And/or something like a gaussian splat-based representation for viewing the whole trajectory at once?
Built as part of a larger carpet based localisation project [2]
1: https://nbviewer.org/github/tim-fan/carpet_color_classificat...
2: https://github.com/tim-fan/carpet_localisation/wiki/Carpet-L...
Would be good to be able to iterate on images (keep this, change that etc).
The use of the seed looks useful but I'm guessing it has its own limitations.
This looks to be the course playlist: https://youtube.com/playlist?list=PLAwxTw4SYaPkCSYXw6-a_aAoX...
The kalman filter stuff starts at video "Tracking Intro - Artificial Intelligence for Robotics"
Also the free course appears to be available here, although a login is required to access:
https://www.udacity.com/course/intro-to-artificial-intellige...
So the clap is a mini sonic boom between your hands?
Animation: https://raw.githubusercontent.com/tim-fan/media/main/2021092...
Code: https://gist.github.com/tim-fan/5f601c274a30505b1ae6b989a015...
I remember reading that for keto diets it's important to not eat too much protein, otherwise the excess protein may be converted to glucose, replenishing glycogen and knocking you out of ketosis.
I ended up jotting down the tasks/dependencies in dot syntax to quickly get items recorded and out of my mind during the day, then at the end of the day updated rendered the whole depency graph to give the overview.
Here's the scripts I used
I played around with a tool for sorting through my personal photos based on that idea.
It gets interesting when you start thinking of how to optimally choose pairs to compare, to maximise signal and minimize redundant comparisons. This becomes more important as the size of the set of objects you are comparing becomes large.
The carpet was an arrangement of 4 particular colors tiled in squares, so I manually made a carpet map (a few hours in excel!), wrote a carpet color classifier to run from under-robot camera data, then integrated with a particle filter for location tracking. Write up is here:
https://github.com/tim-fan/carpet_localisation/wiki/Carpet-L...
I wrote it only ever expecting usage in this particular office, but if anyone has a similar carpet and a robot that needs localizing, please reach out!
And from the alt-text:
"Friction-driven static electrification is familiar and fundamental in daily life, industry, and technology, but its basics have long been unknown and have continually perplexed scientists from ancient Greece to the high-tech era. [...] To date, no single theory can satisfactorily explain this mysterious but fundamental phenomenon." --Eui-Cheol Shin et. al. (2022)
* conversation length
* participants
* location
So you could search for, say, that long conversation I had in the park with Bob.I'm not sure how easy it is to identify/track different participants in a conversation.
Edit:formatting
I did try the quest 2 on a plane and it worked well, apart from when the plane was turning or in turbulence. I suppose the visual tracking makes an assumption that the world is static, which is usually true but breaks down when the plane is bouncing up and down.
And in general how to implement a camera->projector loop with that low latency / high frame rate?
I've been looking into a similar idea and have bodged together something like: USB camera -> laptop (opencv) -> consumer LED projector (via Hdmi). Probably unsurprisingly, latency is a problem.