235 karma · joined November 13, 2019
You can email prompts directly to your ThreadWise address and get instant AI-powered responses, essentially an always available co-worker. Another great feature is the ability to schedule recurring tasks and since the AI has web access, you can get things like:
Daily mortgage rates or airfare price monitoring
Weather and news summaries
Sport scores, jokes, quote of the day
Pull data from public APIs (and more)
So you can essentially use it as a personal newsletter, crafted to your taste.
The free tier will let you test this out for free! I am looking for some feedback/criticism, testing, and additional ideas and I am open for collaboration if you have experience with sales. Also open to hearing which scheduled tasks people would find most useful.
Why I built it: I noticed a trend online, as well as with family/friends, that people would like to have a quick access to AI in instances where they couldn't always install apps or use browser-based tools (such as in remote/low bandwidth environments). This is when it him me, email clients already have all the features needed to interact with an AI (text + attachments) and I quickly got to work.
Some of the advantages are also that since there are no new apps, or browser tabs needed, the tool is ideal for companies who don't have the bandwidth to setup full fledged AI solutions on their own. The companies can choose either between public LLMs (e.g. OpenAI) or host everything on-premise with locally run models, so no data ever leaves the premises.
Eager to hear what you all think!
I wrote a wrapper around it that works in a web browser (you'll need an OpenAI API key): https://github.com/uhsealevelcenter/IDEA
I'm assuming that each solar panel is 2 by 1 meter, which would mean that it produces about 400 watts (20% efficiency at 1000 watts per sq meter coming from the sun). You can use this calculator to estimate how power you can produce at the given location for a given system size in kilowatts: https://pvwatts.nrel.gov/pvwatts.php
The system above is 1310400 watts or ~1,310 kW, which according to the calculator produces about 2 million kWh/year.
If he makes $20,000 that would mean that he gets paid only $0.01 per kW of power. And even if my assumption above about the size of each individual panel is off by a factor of 2 and they are only 1 sq meter in size (which I think they are not because the article states that the solar farm can power about 300 average households, which require the annual power output to be more than what I estimated above) that would make $0.02 per kW of power. How is it possible that the amount earned per kW is so low when the utility companies in Colorado charge about $0.14 per kW (effective rate)? And who is actually the customer here and where is the money coming from? I'm just curious to learn more.
Imagine how glorious and huge the Earth must look like from the Moon.
Conveying the urgency and the real risk posed to coastal communities due to sea level rise is immensely important, not only to climate researchers but to laypersons and general population directly affected by the effects of climate change. Standard scientific materials and tools for depicting sea level rise projections, such as time series or static maps, often fail to communicate the immediacy and the real threat posed to local communities and infrastructure. So the main goal of the app is to help general public conceptualize the threat imposed by sea level rise.
In future, the method could be scaled to be used at any coastal location (as long as we have the necessary data). If there is interest, I will go into more details on the data workflow of how the app was made. Android version should be live soon (under review).
One of the feature that this is missing is a plain 3D viewer (without the need for camera and augmented reality). And maybe a screenshot option to take a photo of the terrain and the impact. Probably going to be available in the next version.
Feel free to ask any questions.
I searched the App Store for the puzzle but couldn’t find It and that’s when I decided to make a prototype on my phone. Once I built it, it took me a couple of days to solve it. I then showed it to a couple of my friends and they were immediately intrigued as the puzzle looks easily solvable, but they would spend an hour or two playing it on my phone (even though I begged them to return it back to me). This prompted me to go ahead, polish it, add new levels, and make it publicly available.
I need some advice on how I could automatically find am optimal solution for the puzzle. Is the A* my only option? The game was built in Unity. Does anyone know of a way to use Unity ML agents to find a solution to the puzzle? I would like to be able to show users hints as well as maybe even automatically create puzzles (would check if a solution exists and how difficult the solution is). Any other feedback more than welcome.
I admit that building 2D games with unity is a bit of an overkill but the fact that everything just works across different platforms makes it well worth it. I originally developed games using Java and a cross platform framework called libgdx. It was fun till you needed to implement things like cross-platform advertisements and IAPs. With unity you know that the support will be there for the foreseeable future and no other engine comes close in terms of the available free online resources.
Keep up the great work! We use Unity3D for our development
[0]: https://apps.apple.com/us/app/gibberish-game-against-friends...
[1]: https://apps.apple.com/us/app/the-setting-sun-block-puzzle/i...
The goal is to slide the red tile where the yellow ones are. I am trying to develop an algorithm that would be able solve these type of puzzles automatically so I can provide user with hints. Can anyone provide any ideas or resources on how this could be achieved? If you look at the screenshot you'll notice that the board always has two empty cells that you can slide the tiles into. You can also pre-order the game here: https://apps.apple.com/us/app/the-setting-sun-zen-puzzle/id1... I am planning on releasing it by Thanksgiving.
[1]: http://eprints.qut.edu.au/85005/1/__staffhome.qut.edu.au_sta...