I'm storing small items in numbered bags. I take a photo and have a web UI for easy searching. It also tracks which items are used frequently vs not used at all in years.
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.
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.
On the other hand, insurance costs for robotaxis should be lower if they are able to drive significantly safer.
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.
Funny coincidence on naming. I called the project Bonsai because I tended to it in a similar way that someone tends a bonsai tree; carefully, over years. Not sure of the origin of the Bonxai name
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.
I'm hoping for a self driving taxi + trains combo to maybe solve the problem.
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.
Doesn't the mechanical part need to position the optics with a fair amount of precision to get a good view? Id imagine very slight movements or misadjustments could cause issues.
You need smooth movement on altitude and azimuth axes, then the telescope must be still and vibration-free to enjoy the view. The other constraint is structural integrity to maintain alignment of the optics. Those two things are the basis of all builds (except for optics!).
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?
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.
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