Ask HN: What are you working on?
I thought it would be interesting to see what are other people working on. Those projects of course might not be ready to be shown you can only describe them and the main problem though.
Project: I am building a neural network which should be able to generate few frames of the video given the preceding and following frames. Currently I am feeding the network with simple videos I have created where is only a single moving pixel. Since I do not have much experience with neural networks I thought this could be good start.
Problem: Up until now I have not realised how hard is to find simple video datasets.
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https://github.com/wkoszek/ruby_packages
https://github.com/netlify/netlify-cms
E.g. I needed a chart of some rules in the language, so I added it in the app. Needed a way to search substrings in a wordlist, added it to the app. Flashcards etc.
Solution: Collect data on every action that anybody does during a trial, Bucket finished trialers by "Converted" and "Expired". Visualize that data to find out what motivates trialers to convert. Run crazy ML magic on it to predict outcomes for individual trialers. Act on that information.
About half a day into building, it occurred to me that this is not in any way specific to the one product I was building it for, so I moved the API endpoint out to its own domain. There's a general purpose product there in beta now:
https://unwaffle.com
I'm not sure what a participant is, since it only appears in the pricing and nowhere else on the page - but if it is a new user (new signup) then 200 seems a bit large for a hobbyist.
I'd consider a tiny $10 - 20/mo one, sans "Assisted onboarding"
Imagine you have a little $10/month SaaS, with 100 trial signups a month, for which the service in question is outrageously expensive.
Assuming one of your users sticks around for a year, his Lifetime Value (LTV) is $120. And assuming there exists some low-hanging fruit optimization that can get you a tiny 1% uplift in conversions. That translates to (0.01 * 100 * 120) = $120 in extra revenue for your startup each month. So even if you spend the rest of your days just looking at our pretty charts and graphs, you're still doubling your investment.
So yeah, it's priced high because it makes you a lot of money.
http://www.scinder.io
I'm now learning React Native to launch a mobile app and working on segments and routes functionality which is quite touch.
Ideally this would work by setting up a private npm repo and then I can share components between the two.
I think the focus on analysis is more important that the statistics - i mean there is so much training data out their, and hardly any of its used. Ideally I would want the application to highlight the area of the quickest gain - and factor in training sessions off the back of this?
Audience: It is aimed at learners who already know the syntax of a language, but are unsure/unable to start a project of their own.
More info: I have written more about it on Reddit: https://www.reddit.com/r/learnprogramming/comments/62r1wr/i_...
http://howistart.org/
and the aosa-book "500-lines or less":
http://aosabook.org/en/500L/introduction.html
(and maybe with a hint of rosettacode in the mix: http://rosettacode.org/wiki/Rosetta_Code )
I'd love to see some collaborative projects like this - idiomatic/recommended setups of editing/debug/release, as well as approach to coding.
I notice that Python is still absent from the "How I start"-series - something like https://github.com/pypa/sampleproject might form part of a starting point (also the Flask "flaskr" tutorial sets up a bare-bones python package, but needs minor adjustments for windows, as I've noticed using it for a small course on web programming - I'll have to find the time to file an issue and patch).
The great thing about such projects being open to contribution, is that aside from the bike-shedding, one of the best ways to get a correct answer to a problem quickly, is to post the wrong answer on the Internet.
http://www.gpsheatmap.com
http://dspillustrations.com
More info: I am currently building a website to showcase the data, but in the mean time I have a Instagram account where I for example once a weekly update what are the most popular hashtags in the Finnish Instagram community. https://www.instagram.com/iigeesuomi/
When we work with our customers we make sure that their projects get done. Even with the involvement of other partners, we make sure that we take the responsibility of our customers’ project. Sometimes, our customers go on a vacation while we execute the projects with highly qualified professionals that has been vetted by our team.
http://WorkSigma.com is an evolution of that pattern, and we want to formalize it, make it big, and be able to help more customers. We want to be a trusted place to get projects built - be it for the web or connected mobile devices.
P.S. Minimum 20% discount for all HackerNews users for the year 2017. Use code "HackerNews2017".
https://workweek.com
http://www.overt.news
[1] beware, german! also we are reworking the website: https://hackundsoehne.de
The answer is: classification is now for some problem domains a tractable problem where before it required human intervention. Telling fake news from real news requires a body of data (doable), a classification algorithm (mostly a solved problem) and the will, time and other resources to actually do it.
To me it sounds very much as something worth doing and possible, that Google with their vast resources appears to drop the ball here (see Google News, which gives equal billing to ham and spam alike) leaves room for an outsider to go and tackle that problem in a decisive way.
I can see all kinds of possibilities and this to me seems like a very worthwhile task.
That's a very good question, which is why I included it. I like one definition I saw circulating which revolved around "news items intended to deceive". I agree that is too hard to pin down to be useful.
How do you know it's not fake?
There's two way I could read this. The first is distinguishing newly emerging news stories for which the facts don't exist and the second is how to automatically check.
Both are valid questions. For the first, I don't know. For the second there is an entire field of computational fact checking, as well as a range of other features.
Well it's ambitious, so it has that going for it...
Tea?
I'm not sure I get the reference. Yes?
[0] - https://github.com/heartsucker/node-deb
[1] - https://github.com/heartsucker/rust-csrf
[2] - https://github.com/heartsucker/iron-csrf
[3] - https://github.com/freedomofpress/securedrop
Kind of youtube for documents.
https://emailoctopus.com
https://github.com/andy-wood/AutoMesh
Both projects are built with React, with a side goal of staying up to date on the latest developments there.
[1]: https://fman.io
Thanks, Michael
[1]: https://github.com/fman-users/fman/issues/43
(https://github.com/nikitavoloboev/knowledge-map)
I want to make a collaborative mind map where one can see all the knowledge of the world at a glance and be provided with the resources on learning any of the topics there.
I really dislike the black box nature of Google/DuckDuckGo where you first have to know the question before getting an answer. It would be amazing to actually visualise everything and let users explore rather than search and wander around.
PS: damned, I had already starred it on gh :D
The last iteration, an appstore-style application, failed to achieve the grand vision, hence why the remodel.
You can find the new work here:
https://market.secapps.com
All tools are free to use with the caveat that in the future there will be some sort of licensing model while still allowing to take advantage of all tools for free as long as not for commercial use.
I hope that helps.