152 karma · joined July 4, 2019
Upwork action item: Robin needs to add a new payment instrument to cover the $12000 debt. Freeze Robin's account until then. Any fraud being perpetrated requires Robins involvement, so lean in there.
Upwork action item: tell author that if author hires a lawyer they'll share information necessary for author to pursue a case against Robin.
Author action item: have a lawyer take a look at case vs Upwork and Robin as joint defendants. Make the defendants sort it out.
Kind of am aside, but if Upwork takes zero responsibility and provides no value for the manual payments, seems like all manually tracked work ought to be billed directly between freelancer and buyer. They won't even pursue Robin, the holder of the wrong credit card it seems even when Upwork knows who Robin is.
I'm invested in their success.
I haven't dug into the data on this across the platform but you've given me the idea to go see if I can find evidence of this, and see if I can improve somehow. There's only low hundreds of projects, so I might be able to find some that have this problem.
I know Stanford's research teams all use a common interface to mturk that keeps profiles of turkers on their side so they know who to solicit for upcoming surveys, conduct longitudinal studies, etc. I've always wondered why more universities didn't follow suit.
I built a side project called cogmint based on the insight that simple scoring and ranking of workers was valuable. I ended up building my own worker interface instead of using mturk because it wasn't much additional effort on top of the scoring logic I was building anyway. Perhaps other serious companies came to the same conclusion I did with my hobby project.
I created Cogmint.com ("cognition minting") to solve this problem for myself.
You can submit known correct answers for questions, and those questions are then used as ground truth to score worker accuracy. Workers are then scored on their similarity to known correct answers and other workers that have accurately answered questions. It works surprisingly well for how simple it is. It's been a fun challenge to create simple methods of scoring similarity across different task types.
It's a side project, so don't rely on it for mission critical things, but I rely on it for some production tasks, so it's stable.
It currently supports classification (choose from a set of possible answers) and has beta support for bounding box task types. String input task types are coming very soon.
I'd love to see if it can help you out, I'll waive the fees: I'm not in it for the money I just like making things useful and reliable. Reach out and say hi!
I agree that a drone for cleaning gutters would be incredibly hard due to the torque necessary. Less of a project, but a drone that picks up the Roomba Looj and can place in the gutter would be fun but not that interesting of a challenge.
Drone that can clean your gutters while avoiding obstacles like trees and chimneys. This might be very hard, but would be a challenge.
A series of smart heating/AC vents that open/close based on whether people are in the room. Make them solar powered and "sleep" to save power. I think this might be possible without a microcontroller, but maybe easiest with a very energy efficient one.
Window that opens when the outside temperature is closer to your target temp than the inside temp. E.g. if your home is set to cool but it's cooler outside, open the window. Need a rain sensor.
A very large Roomba. Traditional roombas are small, and not super effective as a result. Tolerating a bigger one might be work it. Get one of those battery powered vacuums that work off of power tool battery packs, perhaps.
I think a few companies use Elixir to power their web crawling/scraping tools. This makes intuitive sense as a good candidate for the process supervisor and parallel work architecture OTP encourages.
Nerves (embeddable Elixir) has come a long way. I switched to Nerves for some Raspberry Pi projects and the amount of time I waste dealing with hardware/config has gone to nearly zero. I am a hardware novice and was able to setup flashing firmware over-the-air updates to the Pi with very little effort. I'm sure the companies that use Nerves in production have more to say about it.
I'm not very tuned into the updates to Scenic, a project for display/UI on embedded screens, but it looks like they've hit some big release/stability milestones.
Phoenix is the way to go for web interfaces, and is an excellent toolset, so alternatives haven't been demanded. For more lightweight http people usually reach for Plug, a key building block of Phoenix, if you won't need the full bird.
Do clothes substantially reduce the effectiveness? Is true line of sight required - visible or near visible spectrum?
Secondly, can you expand a bit about the humidity/condensation aspect? I get the impression that condensation represents inefficiency, and that this somehow avoids having to cool air as a middle layer to cooling a person.
Nerves is also the first Pi software that worked the very first time I plugged in the flashed memory card, which was a really good start.
I'm currently adding Nerves/Pi to a small home autonomous robot and it's been very easy for a guy who definitely isn't a hardware expert. I'm looking for other fun ideas, if anyone has a good idea. I live in an apartment, so indoors ideas only for now.