I started working remote a few weeks ago. It’s sheer awesomeness - no distractions, no commute, no shabby cafeteria lunch. But there was one minor glitch: I was having trouble accessing my organization’s proprietary data. Being a government agency, we hoard tons of sensitive data on people - addresses, taxpayer IDs, personal income, etc. So, naturally, we restrict access to our databases. There is no URL I can go to when I need to run some database query; I need to be inside my organization’s network to run database queries.
I can use a VPN to log into my office PC from any machine. And once I’m logged into my office PC I can access whatever I want. But that means being forced to use my lame, old office PC instead of my sleak, fast MacBook Pro. Using the VPN also means enduring a maddening lag. It’s only a fraction of a second, but it’s noticeable and it can drive you insane. Finally, using the VPN means I have no local internet access - while I’m in the VPN my laptop has no other contact with the outside world, which results in my not being able to use Spotify and a bunch of other stuff. And I need my 90s Eurodance playlists to be in the proper mindset for writing code.
After enduring all that (I know, tiny violin…) for a couple of weeks I decided to do something about it. I realized that I needed a “bridge” between the data and my laptop. Some web service of sorts that could receive data requests and respond to them. Now, I’d never trust myself to build something like that. I don’t know nearly enough about information security to go about building that sort of tool. I don’t wanna be the guy who caused every Brazilian’s monthly income to be exposed to the world.
Then it hit me: I don’t need to build anything like that, these tools have already been built and we already use them to exchange sensitive data. I’m talking about messaging apps like Slack, Telegram, and the like. My office PC can access Slack. My personal laptop can access Slack. Slack is what my team uses for communication, which means sensitive information already circulates through it. In sum, the middleman I needed was already in place. All I had to do was to repurpose it. And that’s what I did. What follows bellow is a brief account of how I did it, in case other people may be in the same situation.
step 1: stuff that doesn’t involve code
The first thing you need to create are the Slack channels that will handle the data requests. I chose to create two - #incoming, to receive the data requests, and #outgoing, to send the requested data. I made them private, so as not to annoy my teammates with notifications and messages they don’t need to see. Alternatively, you could create an entirely new Slack workspace; that way you isolate human messaging from bot messaging.
Once you’ve created the channels you’ll need to create a Slack bot. It’s this bot that will: a) read the data requests that arrive on #incoming; and b) post the requested data to #outgoing. Slack lets you choose between two types of bots: “app bots” and “custom bots”. They nudge you towards the former but the latter is a lot more straightforward to set up: just click here, click “Add Configuration”, and follow the instructions. When you’re done, write down your bot’s API token - it’s the string that starts with xoxb- -, and, on your Slack workspace, invite your bot to join #incoming and #outgoing.
step 2: testing your bot
We need to make sure that your Slack bot can read from #incoming and post to #outgoing.
Let’s start with reading. There are a number of ways to go about this - Slack has a number of APIs. I think the Web API is the best pick for the impatient. Now, the documentation doesn’t have a quickstart or many useful examples. The explanations are verbose and frustratingly unhelpful if you just want to “get it done” quick. So instead of making you read the docs I’ll just give you what you need to know: make a GET request to https://slack.com/api/groups.history?token=xoxb-your-bot-token&channel=id_of_incoming, where xoxb-your-bot-token is the token you wrote down in step 1 and id_of_incoming is the ID of the #incoming channel (it’s the endpoint of the channel’s URL). That will return to you the channel’s messages (up to 100 messages). If there are no messages in #incoming you won’t get anything interesting back. If that’s the case, just post anything to the channel first.
In real life you won’t be using Terminal/cmd for this, you’ll be using a Python or R script or something along these lines. Here’s how to do that in Python:
What you get back is a Python dict which should have a key named ‘messages’. So, if 'messages' in 'response', then inside response['messages'] you’ll find a list of dicts, each dict being a message, each dict’s key being an attribute of said message (timestamp, text, user who posted it, etc).
Now, you don’t want to access #incoming’s entire history every time you poll it. You can include a parameter named oldest in the params dict and assign a timestamp to it. Then read_messages won’t return messages older than the specified timestamp.
(A little gotcha: what you pass as channel is not the channel’s name but the channel’s ID, which you can get from its URL. Some Slack methods do accept the channel’s name but I never remember which ones, so it’s easier to just use the channel’s ID for everything.)
(Because you went with a custom bot instead of an app bot you won’t have to deal with a bunch of error messages having to do with something Slack calls “scope”. You won’t waste two days in a mad loop of trying to get the scopes right, failing, cursing Slack, refusing to read the API documentation, failing, cursing Slack, refusing to read the API documentation. I envy you.)
Alright then, let’s move on to posting. Here’s how you do it: make a POST request to https://slack.com/api/chat.postMessage, using your bot’s token, your channel’s ID, and the message of the text as payload. Like this:
There. Once you run this code you should see the message “hey macarena” appear in #outgoing.
step 3: receiving #incoming messages
Ok, now you need a server-side program that will check #incoming for new messages - say, every five seconds or so. By server-side I mean it will run inside your company’s network; it needs to run from a machine that has access to your company’s databases. Here’s an example:
Now, you probably want this “listener” to run in the background, so that you can log off without killing it. If you’re running it on a Linux machine the simplest solution is to use tmux. It lets you create multiple “sessions” and run each session in the background. If you’re doing it on a Windows machine you can use cygwin or, if that’s Windows 10, you can use tmux with the native Ubuntu binaries.
step 4: processing #incoming messages
Receiving messages is not enough, your script needs to do something about them. The simple, quick-and-dirty solution is to have your #incoming messages be the very database queries you want to run. An #incoming message could be, say, SELECT [some_column] FROM [some].[table] WHERE [some_other_column] = 42. Then the listener (the server-side program we created before) would read the query and use an ODBC package - like pyodbc or rodbc - to run it. If that works for you, here’s how you’d amend the listener we created before to have it handle SQL queries:
importtimeimportpyodbcimportrequestsdefread_messages(timestamp):slack_token='xoxb-your-bot-token'slack_url='https://slack.com/api/groups.history'params={'token':slack_token,'channel':'id_of_incoming','oldest':timestamp}response=requests.get(slack_url,params=params,verify=False)ifresponse.status_code==200:returnresponse.json()defrun_query(query):cnxn=pyodbc.connect(driver='name of your ODBC driver',server='path-to-your-database-server',database='name_of_your_database',uid='uid',pwd='pwd')cursor=cnxn.cursor()cursor.execute(query)resultset=cursor.fetchall()returnresultsetwhileTrue:timestamp=time.time()-5r=read_request(timestamp)ifr:formessageinr['messages']:resultset=run_query(message['text'])print('query:',message['text'])print('results:',resultset)time.sleep(5)
Ok, I’m glossing over a bunch of details here. First you’ll need to set up an ODBC driver, which isn’t always easy to get right the first time - it depends on what SQL engine you have (SQL Server, MySQL, etc), on whether your script is running on Linux or Windows, and on what credentials you’re using to connect to your SQL engine. I can’t really help you out on this, you’ll have to google your way around. If you’ve never set up an ODBC connection before this is probably the part that’s going to take up most of your time.
Once the ODBC part is taken care of, leave the script above running and post some SQL query on #incoming. You should see the the result set of the query. Well done then, everything is working so far.
step 5: replying to #incoming messages
So you have a script that receives queries and executes them. Now your script needs to post the result sets to #outgoing. There really isn’t much mystery here - we already wrote post_to_outgoing above. The only thing left is to convert our result set into a string, so that Slack can accept it. In Python the json module handles that for us: json.dumps(your_data) takes a list or dict (or list of dicts, or dict of lists) and turns it into a string. It’s all below.
importjsonimporttimeimportpyodbcimportrequestsdefread_messages(timestamp):slack_token='xoxb-your-bot-token'slack_url='https://slack.com/api/groups.history'params={'token':slack_token,'channel':'id_of_incoming','oldest':timestamp}response=requests.get(slack_url,params=params,verify=False)ifresponse.status_code==200:returnresponse.json()defrun_query(query):cnxn=pyodbc.connect(driver='name of your ODBC driver',server='path-to-your-database-server',database='name_of_your_database',uid='uid',pwd='pwd')cursor=cnxn.cursor()cursor.execute(query)resultset=cursor.fetchall()returnresultsetdefpost_to_outgoing(message):slack_token='xoxb-your-bot-token'slack_url='https://slack.com/api/chat.postMessage'payload={'token':slack_token,'channel':'id_of_outgoing','text':message}requests.post(slack_url,data=payload,verify=False)whileTrue:timestamp=time.time()-5r=read_request(timestamp)ifr:formessageinr['messages']:resultset=run_query(message['text'])forresultinresultset:output=json.dumps(list(result))post_to_outgoing(output)time.sleep(5)
Ta-da. As long as this script is running continuously inside your company’s network you no longer need a VPN to query name_of_your_database. If you want more flexibility you can tweak run_query so that it takes the name of the database as a second argument. And you should sprinkle try/except statements here and there to capture database errors and the like.
You’ve taken remote work one step further. It’s not only you who can work remote now: the applications you develop no longer need to live inside your company’s network. You can develop on whatever machine and environment you choose and have your applications post their queries to #incoming and retrieve the result sets from #outgoing.
One gotcha here: Slack automatically breaks up long messages, so if your query exceeds Slack’s maximum length it will be truncated and run_query will probably (hopefully) raise an error. Keep it short.
step 6: make it neat
Alright, you have a functioning “bridge” between you and your company’s databases. But that’s still a crude tool, especially if you will develop applications on top of it. You don’t want your apps to post raw SQL queries on Slack - that’s a lot of unnecessary characters being passed back and forth. Instead of a run_query function you should have a get_data function that stores a “template” of the query and only adds to it, say, the part that comes after the WHERE [something] = . Something like this:
defget_data(input):query='SELECT [some_column] FROM [some_database].[some_table] WHERE [other_column] = '+inputcnxn=pyodbc.connect(driver='name of your ODBC driver',server='path-to-your-database-server',database='name_of_your_database',uid='uid',pwd='pwd')cursor=cnxn.cursor()cursor.execute(query)resultset=cursor.fetchall()returnresultset
This is still too crude to be called an API but it’s a first step in that direction. The idea is to make the Slack-your_app interface as tight as possible, so as to minimize the types of errors you will encounter and to minimize the exchange of unnecessary strings. If you know exactly the sort of stuff that will be passed to get_data it’s easier to reason about what the code is doing.
If you do academic research but are not affiliated with an academic institution you probably know Sci-Hub. It gives you access to over 60 million research papers - for free (no ads, no malware, no scams). Alexandra Elbakyan, its creator, has deservedly been ranked by Nature one of the top ten most relevant people in science and we independent researchers owe her a lot.
You’d think that such an invention would be welcomed by most people who are not Elsevier executives. You’d think that such an invention would be particularly welcomed at organizations that do not have an Elsevier subscription. You’d be wrong. In the Brazilian government, where I work, Sci-Hub is not only not welcomed, it is actively blocked. The firewall doesn’t let me access it.
That’s Portuguese for “Blocked content! Science is illegal/unethical, so screw yourself.” (Sort of.)
This week I finally got tired of that nonsense - dammit, I’m a data scientist, I need academic papers not only for the research I do on the side but also, and mainly, for my day job. So I decided to build an interface to Sci-Hub - an app that takes my search string, gives it to Sci-Hub, and retrieves the results. Much like I did before in order to use Telegram.
Writing the code was easy enough, it’s a simple web app that does just one thing. I wrote it on Thursday evening and I was confident that the next morning I would just fire app a new project on Google App Engine, deploy the code, and be done with it in less than an hour. Oh, the hubris. I ended up working on it all Friday and all Saturday morning; only at Saturday 12:43pm the damned thing went alive.
What follows is an account of those 36 hours, largely for my own benefit in case I run into the same issues again in the future, but also in case it may be helpful to other people also looking to unblock Sci-Hub. I’m also writing this because I think those 36 hours are a good illustration of the difference between programming, on the one hand, and software development, on the other, which is something I struggled to understand when I first started writing code. Finally, I’m writing this because those 36 hours are a good example of the inefficiencies introduced when sysadmins (or their bosses) decide to block useful resources.
le code
If you inspect the HTML code behind Sci-Hub you can see it’s really easy to scrape:
<divid="input"><formmethod="POST"action="/"><inputtype="hidden"id="sci-hub-plugin-check"name="sci-hub-plugin-check"value=""><inputtype="textbox"name="request"placeholder="enter URL, PMID / DOI or search string"autocomplete="off"autofocus></form></div>
All you have to do is send a POST request. If Sci-Hub’s repository has the paper you are looking for, you get it in a PDF file.
So I built this minimal web app that sends a POST request to Sci-Hub and then emails me back the PDF. I chose email because getting and returning each paper takes several seconds and I didn’t want the app blocked by each request. With email I can have a background process do the heavy work; that way I can send several POST requests in a row without having to wait in-between.
To achieve that I used Python’s subprocess module. I wrote two scripts. One is the frontend, which simply takes the user’s input. I didn’t want any boilerplate, so I used cherrypy as my web framework. As for the HTML code I just put it all in the frontend.py file, as a bunch of concatenated strings (#sorrynotsorry). And I used CDNs to get the CSS code (and no JavaScript whatsoever).
I gave my app the grandiose name of Sci-Hub Liberator.
(Sci-Hub Liberator’s front-end. This is what happens when data scientists do web development.)
The other script is the backend. It is launched by the frontend with a call to subprocess.Popen. That way all requests are independent and run on separate background processes. The backend uses Python’s requests package to send the POST request to Sci-Hub, then BeautifulSoup to comb the response and find the link to the paper’s PDF, then requests again to fetch the PDF.
defget_pdf(user_input):'''
search string -> paper in PDF format
'''response_1=requests.post('http://sci-hub.cc/',data={'request':user_input})soup=BeautifulSoup(response.text)url_to_pdf='http:'+soup.find_all('iframe')[0].get('src')response_2=requests.get(url_to_pdf)returnresponse_2.content
The backend then uses Python’s own email package to email me the PDF.
defsend_pdf(pdf):'''
sends PDF to user
'''sender='some_gmail_account_I_created_just_for_this@gmail.com'text='Your paper is attached. Thanks for using Sci-Hub Liberator! :-)'body=MIMEText(text,_charset='UTF-8')message=MIMEMultipart()message['Subject']=Header('your paper is attached','utf-8')message['From']='Sci-Hub Liberator'message['To']='my_email_account@gmail.com'message.attach(body)part=MIMEApplication(pdf)part.add_header('Content-Disposition','attachment; filename = "paper.pdf"')message.attach(part)smtp_server=smtplib.SMTP('smtp.gmail.com:587')smtp_server.ehlo()smtp_server.starttls()smtp_server.ehlosmtp_server.login(sender,'emails_password')smtp_server.sendmail(sender,'my_email_account@gmail.com',message.as_string())smtp_server.quit()
Both scripts combined had 151 lines of code. Not exactly a “Hello, World!” application but not too far from it either.
a word of caution
Before I proceed I must ask you not to abuse Sci-Hub’s easily scrapable interface. That’s an amazing service they’re providing to the world and if you send thousands of requests in a row you may disrupt their operations. I trust that they have defenses against that (or else Elsevier would have taken them down long ago), but still, please don’t fuck up.
things change
Code written and tested, I turned to Google App Engine for hosting the app. With only 151 lines of code and two scripts I thought that launching the app would be a breeze. Silly me.
I wanted to use Python 3, but Google App Engine Launcher is only compatible with Python 2. I google around and it seems that they are deprecating GAE Launcher in favor of the Google Cloud SDK. Pity. GAE Launcher was a nifty little app that made deployment really easy. I had been using it since 2013 and it allowed me to focus on my app and not on deployment nonsense.
Resigned to my fate, I downloaded the Google Cloud SDK installer and… installation failed due to an SSL-related problem. It took some half an hour of googling and debugging before I could get it to work.
things don’t change
GAE’s standard environment only allows Python 2. You can only use Python 3 in GAE’s flexible environment. And the flexible environment is a different ball game.
I had never used the flexible environment before (I think it only became generally available early this year), but I decided to give it a try. To make a long story short, I couldn’t make it work. The exact same code that works fine on my machine returns a mysterious Application startup error when I try to deploy the app. The deploy attempt generates a log file but it is equally uninformative, it only says Deployment failed. Attempting to cleanup deployment artifacts.
Despite hours of tinkering and googling I couldn’t find out what the problem is. I declared all my dependencies in my requirements.txt file (and I pointed to the same versions I was using locally); I configured my app.yaml file; I made sure that all of my dependencies’ dependencies were allowed. I didn’t know what else to look into.
Eventually I gave up in despair and decided to fall back on GAE’s standard environment, which meant reverting to Python 2. That was a bummer - it’s 2017, if GAE’s standard environment needs to choose between 2 and 3 then it’s probably time to pick 3 (assuming there is a way to do that without killing all existing Python 2 projects).
pip issues
Vendoring didn’t work for BeautifulSoup. Even though I used pip install and not pip3 install what got installed was BeautifulSoup’s Python 3 version. That resulted in from bs4 import BeautifulSoup raising ImportError: No module named html.entities.
After several unsuccessful attempts to point pip install to a specific source file I gave up on pip. I tested my Mac’s system-wide Python 2 installation and BeautifulSoup was working just fine there. So I went to my Mac’s site-packages and just copied the damned bs4 folder into my app’s lib folder. That did the trick. It’s ugly and it doesn’t shed any light on the causes of the problem but by then it was Friday afternoon and I was beginning to worry this deployment might take the whole day (if only!).
sheer dumbness
GAE has long been my default choice for hosting applications and I’ve always known that it doesn’t allow calls to the operating system. It’s a “serverless” platform; you don’t need to mess with the OS, which means you also don’t get to mess with the OS. So I can’t really explain why I based the frontend-backend communication on a call to subprocess.Popen, which is a call to the OS. That’s just not allowed on GAE. Somehow that synapse simply didn’t happen in my brain.
back to the code
GAE has its own utilities for background tasks - that’s what the Task Queue API is for. It looks great and one day I want to sit down and learn how to use it. But by the time I got to this point I was entering the wee hours of Saturday. My hopes of getting it all done on Friday were long gone and I just wanted a quick fix that would let me go to bed.
So I rewrote my app to have it show the PDF on the screen instead of emailing it. That meant I would have to wait for one paper to come through before requesting another one. At that hour I was tired enough to accept it.
The change was pretty easy - it involved a lot more code deletion than code writing. It also obviated the need for a backend, so I put everything into a single script. But the wait for the PDF to be rendered was a little too much and I thought that a loading animation of sorts was required. I couldn’t find a way to do that using only cherrypy/HTML/CSS, so I ended up resorting to jQuery, which made my app a lot less lean.
Sci-Hub is smart
After getting rid of the OS calls I finally managed to deploy. I then noticed a requests-related error message. After some quick googling I found out that GAE doesn’t play well with requests and that you need to monkey-patch it. Easy enough, it seemed.
After the patching requests seemed to work (as in: not raising an exception) but all the responses from Sci-Hub came back empty. The responses came through, and with status code 200, so the communication was happening. But there was no content - no HTML, no nothing.
I thought that it might be some problem with the monkey-patching, so I commented out requests and switched to urrlib2 instead. No good: same empty responses. I commented out urllib2 and tried urlfetch. Same result. As per the official documentation I had run out of packages to try.
I thought it might have to do with the size of the response - maybe it was too large for GAE’s limits. But no, the papers I was requesting were under 10MB and the limit for the response is 32MB:
I had briefly considered the possibility of this being an user-agent issue: maybe Sci-Hub just doesn’t deal with bots. But everything worked fine on my machine, so that couldn’t be it.
Then it hit me: maybe the user-agent string on GAE is different from the user-agent string on my machine. I got a closer look at the documentation and found this:
Ha.
To test my hypothesis I re-ran the app on my machine but appending +http://code.google.com/appengine; appid: MY_APP_ID to my user-agent string. Sure enough, Sci-Hub didn’t respond with the PDF. Oddly though, I did get a non-empty response - some HTML code with Russian text about Sci-Hub (its mission, etc; or so Google Translate tells me). Perhaps Sci-Hub checks not only the request’s user-agent but also some other attribute like IP address or geographical location. One way or the other, I was not going to get my PDF if I sent the request from GAE.
At that point it was around 3am and I should probably have gone to bed. But I was in the zone. The world disappeared around me and I didn’t care about sleeping or eating or anything else. I was one with the code.
So instead of going to bed I googled around looking for ways to fool GAE and keep my user-agent string intact. I didn’t find anything of the kind, but I found Tom Tasche.
back to the code (again)
I decided to steal Tom’s idea. Turns out GAE has a micro-instance that you can use for free indefinitely (unlike AWS’s micro instance, which ceases to be free after a year). It’s not much to look at - 0.6GB of RAM - but hey, have I mentioned it’s free?
I rewrote my code (again). I went back to having the frontend and backend in separate scripts. But now instead of having the backend be a Python script called by subprocess.Popen I had it be an API. It received the user input and returned the corresponding PDF.
@cherrypy.exposedefget_pdf(self,pattern):'''
search string -> paper in PDF format
'''scihub_html=requests.post('http://sci-hub.cc/',data={'request':pattern},headers={'user-agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36'})soup=BeautifulSoup(scihub_html.text)url_to_pdf='http:'+soup.find_all('iframe')[0].get('src')scihub_bytes=requests.get(ungated_url)returnBytesIO(scihub_bytes.text)
I put this new backend in a GCE micro-instance and kept the frontend at GAE. I also promoted my backend’s IP from ephemeral to static, lest my app stop working out of the blue.
I was confident that this was it. I was finally going to bed. Just a quick test to confirm that this would work and then I’d switch off.
I tested the new architecture and… it failed. It takes a long time for the GCE instance to send the PDF to the GAE frontend and that raises a DeadlineExceededError. You can tweak the time out limit by using urlfetch.set_default_fetch_deadline(60) but GAE imposes a hard limit of 60 seconds - if you choose any other number your choice is just ignored. And I needed more than 60 seconds.
back to the code (yet again)
At that point I had an epiphany: I was already using a GCE instance anyway, so why not have the backend write the PDF to disk in a subprocess - so as not to block or dealy anything - and have it return just the link to the PDF? That sounded genius and if it weren’t 6am I might have screamed in triumph.
That only required a minor tweak to the code:
@cherrypy.exposedefget_pdf(self,pattern):'''
search string -> paper in PDF format
'''scihub_html=requests.post('http://sci-hub.cc/',data={'request':pattern},headers={'user-agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36'})soup=BeautifulSoup(scihub_html.text)url_to_pdf='http:'+soup.find_all('iframe')[0].get('src')scihub_bytes=requests.get(url_to_pdf)paper_id=str(randint(0,60000000))withopen('static/paper{}.pdf'.format(paper_id),mode='wb')asfbuffer:fbuffer.write(scihub_bytes.text)returnpaper_id
No DeadlineExceededError this time. Instead I got a MemoryError. It seems that 0.6GB of RAM is not enough to handle 10MB objects (10MB is the space the PDF occupies on disk; things usually take up more space in memory than on disk). So much for my brilliant workaround.
the end of fiscal responsibility
The cheapest non-free GCE instance has 1.7GB of RAM and costs ~$14.97 a month. I got bold and launched it (I looked into AWS EC2’s roughly equivalent instance and it wasn’t any cheaper: $34.96.). At last, after a painful all-nighter, my app was alive.
I mean, I still haven’t added any error checking, but that’s deliberate - I want to see what happens when Sci-Hub can’t find the paper I requested or is temporarily down or whatnot. I’ll add the error checks as the errors happen.
I’ll hate paying these $14.97 but it beats not having access to a resource that is critical for my work. The only alternative I see is to rescue my old Lenovo from semi-retirement and that would be annoying on several grounds (I don’t have a static IP address at home, I would need to leave it up and running all day, it would take up physical space, and so on). So for now $14.97 a month is reasonable. At least that money is not going to Elsevier.
Now that I’m paying for a GCE instance anyway I could move all my code to it (and maybe go back to having a single script) and be done with GAE for this project. But I have this vague goal of making this app public some day, so that other people in my situation can have access to Sci-Hub. And with GAE it’s easy to scale things up if necessary. That isn’t happening any time soon though.
things I learned
It’s not so fun to pull an all-nighter when you are no longer in grad school - we get used to having a stable schedule. But I don’t regret having gone through all these steps in those 36 hours. I used Google Compute Engine for the first time and I liked it. I’m used to AWS EC2’s interface and GCE’s looked a lot more intuitive to me (and I found out GCE has a free micro-instance; even though I ended up not using it for this project it may come in handy in the future). I also familiarized myself with gcloud, which I would have to do anyway at some point. And I also learned a thing or two about cherrypy (like the serve_fileobj method, which makes it really easy to serve static files from memory).
Those 36 hours were also a useful reminder of the difference between programming and software development. Programming is about learning all the things you can do with the tools your languages provide. Software development is largely about learning all the things you cannot do because your runtime environment won’t let you. Our Courseras and Udacities do a great job of teaching the former but we must learn the latter by ourselves, by trial and error and by reading the documentation. I’m not sure that it could be otherwise: loops and lambdas are fundamental concepts that have been with us for decades, but the quirks of GAE’s flexible environment will probably have changed completely in a year or less. Any course built around GAE (or GCP in general or AWS) would be obsolete too soon to make it worth it.
I just read Neal Stephenson’s 2008 novel Anathem and now I walk around pestering everyone I know telling them to read it too. Well, not everyone: just people who are or have been in academia. Judging from Goodreads reviews everyone else finds the novel too long and theoretical, full of made up words, and full of characters who are too detached from the real world to be believable. Pay no heed to the haters - here’s why you academic types should read Anathem:
1. You will feel right at home even though the story is set in an alien world (I).
The planet is called Arbre and its history and society are not radically different from Earth’s. Except that at some point (thousands of years before the story begins) the people of Arbre revolted against science and confined their intellectuals to monasteries where the development and use of technology is severely limited - no computers, no cell phones, no internet, no cameras, etc -, as is any contact with the outside world. Inside these monasteries (“Maths”) the intellectuals (the “avout”) dedicate themselves to the study and development of mathematics, physics, and philosophy. As the use of technology is restricted, all that research is purely theoretical.
Arbre’s Maths are therefore an allegory for Earth’s universities. How many of our papers and dissertations end up having any (non-academic) impact? Maybe 1% of them? Fewer than that? In (Earth’s) academia the metric of success is usually peer-reviewed publications, not real-world usefulness. Even what we call, say, “applied econometrics” or “applied statistics” is more often than not “applied” only in a limited, strictly academic sense; when you apply econometrics to investigate the effect of economic growth on democracy that is unlikely to have any detectable effect on economic growth or democracy.
So, in Anathem you find this bizarre alien world where intellectuals are physically confined and isolated from the rest of the world and can’t use technology and yet that world feels familiar and as a (current or former) scholar you won’t react to that in the same way other people do. If you go check the reviews on Goodreads you’ll see lots of people complaining that the Maths are unrealistic. To you, however, Maths will sound eerily natural; Anathem would be more alien to you if the Maths were, say, engineering schools.
(Needless to say, the allegory only goes so far, as Arbran’s avout are legally forbidden from having any real-world impact; having no choice in the matter, they don’t lose any sleep over the purely academic nature of their work. And of course people do produce lots of useful research at Earth’s universities.)
2. You will feel right at home even though the story is set in an alien world (II).
The way an Arbran avout progresses in his or her mathic career is entirely different from the way an Earthly scholar progresses in his or her academic career - and yet way too familiar. In Arbre you start by being collected at around age 10. That makes you a “fid” and you will be mentored and taught by the more senior avout, each of which you will respectully address as “pa” or “ma”. When you reach your early twenties you choose - and are chosen by - a specific mathic order. There are many such orders, each named after the avout who founded it - there are the Edharians, the Lorites, the Matharrites, and so on, each with specific liturgies and beliefs.
The avout are not allowed to have any contact with the outside world (the “extramuros”) except at certain regular intervals: one year (the Unarian maths), ten years (the Decenarian maths), one hundred years (the Centenarian maths), or one thousand years (the Milleniarian maths). And only for ten days (those days are called “Apert”). You can get collected by any math - Unarian, Decenarian, Centenarian, or Millenarian. If you get collected, say, at a Unarian math, and you show a lot of skill and promise, you can get upgraded (“Graduated”) to a Decenarian math. If you keep showing skill and promise you can get Graduated to a Centenarian math. And so on. The filter gets progressively stricter; only very few ever get Graduated to the Millenarian maths.
So, the reward for being isolated from the outside world and focusing intensely on your research is… getting even more isolated from the outside world so that you can focus even more intensely on your research. Sounds familiar?
3. Anathem gives you vocabulary for all things academia.
Think back to your Ph.D. years and remember the times you went out with your fellow fids for drinks (well, if you were actual fids you wouldn’t be able to leave your math - you could, but then you wouldn’t be able to go back, except during Apert - but never mind that). Weird conversations (from the point of view of those overhearing them) ensued and you got curious looks from waiters and from other customers.
Why? Because you spoke in the jargon of your field - you used non-ordinary words and you used ordinary words in non-ordinary ways. Like “instrumental” or “endogeneity” or “functional programming”. Not only that: the conversations were speculative and obeyed certain unwritten rules, like Occam’s razor. Clearly these were not the same conversations you have with non-avout - your college friends, your family, your Tinder dates. And yet you call all of them “conversations”. Well, not anymore; Anathem gives you a word for inter-avout conversation about mathic subjects: Dialog. Neal Stephenson goes as far as creating a taxonomy of Dialog types:
Dialog, Peregrin: A Dialog in which two participants of roughly equal knowledge and intelligence develop an idea by talking to each other, typically while out walking around.
Dialog, Periklynian: A competitive Dialog in which each participant seeks to destroy the other’s position (see Plane).
Dialog, Suvinian: A Dialog in which a mentor instructs a fid, usually by asking the fid questions, as opposed to speaking discursively.
Dialog: A discourse, usually in formal style, between theors. “To be in Dialog” is to participate in such a discussion extemporaneously. The term may also apply to a written record of a historical Dialog; such documents are the cornerstone of the mathic literary tradition and are studied, re-enacted, and memorized by fids. In the classic format, a Dialog involves two principals and some number of onlookers who participate sporadically. Another common format is the Triangular, featuring a savant, an ordinary person who seeks knowledge, and an imbecile. There are countless other classifications, including the suvinian, the Periklynian, and the peregrin.
(Anathem, pp. 960-961)
(Yes, there is a glossary in Anathem.)
You can’t get much more precise than that without being summoned to a Millenarian math.
Dialog is just one example. You left academia? You went Feral.
Feral: A literate and theorically minded person who dwells in the Sæculum, cut off from contact with the mathic world. Typically an ex-avout who has renounced his or her vows or been Thrown Back, though the term is also technically applicable to autodidacts who have never been avout.
(Anathem, p. 963)
You left academia to go work for the government? You got Evoked.
Voco: A rarely celebrated aut by which the Sæcular Power Evokes (calls forth from the math) an avout whose talents are needed in the Sæcular world. Except in very unusual cases, the one Evoked never returns to the mathic world.
(Anathem, p. 976)
Reviewer #2 says your argument is not original? He’s a Lorite.
Lorite: A member of an Order founded by Saunt Lora, who believed that all of the ideas that the human mind was capable of coming up with had already been come up with. Lorites are, therefore, historians of thought who assist other avout in their work by making them aware of others who have thought similar things in the past, and thereby preventing them from re-inventing the wheel.
(Anathem, p. 967)
Got friends or family who are not academics? Well, ok, J. K. Rowling has already given us a word for that - muggles. But in some languages that word gets super offensive translations - in Brazilian Portuguese, for instance, they made it “trouxas”, which means “idiots”. Not cool, Harry Potter translators. But worry not, Neal Stephenson gives us an alternative that’s only a tiny bit offensive: “extras” (from “extramuros” - everything outside the maths).
Extra: Slightly disparaging term used by avout to refer to Sæcular people.
(Anathem, p. 963)
That cousin of yours who believes the Earth is flat? He is a sline.
Sline: An extramuros person with no special education, skills, aspirations, or hope of acquiring same, generally construed as belonging to the lowest social class.
(Anathem, p. 973)
And of course, what happens to a scholar who gets expelled from academia? He gets anathametized.
Anathem: (1) In Proto-Orth, a poetic or musical invocation of Our Mother Hylaea, used in the aut of Provener, or (2) an aut by which an incorrigible fraa or suur is ejected from the mathic world.
(Anathem, pp. 956-957)
And so on and so forth. Frankly, it’s amazing that academics manage to have any Dialogs whatsoever without having read Anathem.
(I must note that Neal Stephenson not only puts these words in the book’s glossary, he uses them extensively throughout the book - there are 40 occurrences of “evoked”, 90 occurrences of “Dialog”, and 57 occurrences of “sline”, for instance. And because there is a glossary at the end he doesn’t bother to define these words in the main text, he just uses them. Which can make your life difficult if, like me, you didn’t bother to skim the book before reading it and only found out about the glossary after you had finished. Damn Kindle.)
4. Anathem might be the push you need to quit social media for good.
I’ve been reading Cal Newport’s Deep Work, about the importance of focusing hard and getting “in the zone” in order to be productive. (Well, “reading” is inaccurate. I bought the audio version and I’ve been listening to it while driving - which is not without irony.) There isn’t a whole lot of novelty there - it’s mostly common sense advice about “unplugging” for at least a couple of hours each day so you can get meaningful work done (meaningful work being work that imposes some mental strain, as opposed to replying emails or attending meetings). The thing is, at a certain point, much to my amusement and surprise, Cal Newport mentions Neal Stephenson.
As Cal Newport tells us, Neal Stephenson is a known recluse. He doesn’t answer emails and he is absent from social media. To Newport, that helps explain Stephenson’s productivity and success (No, I won’t engage you in a long Periklynian Dialog about how we can’t establish causality based on anecdotal evidence. That’s not the point and in any case Cal Newport, despite being an avout himself - he’s a computer science professor at Georgetown - is trying to reach an audience of extras and Ferals.) I had read other Neal Stephenson books before - Cryptonomicon, Snow Crash, The Diamond Age, REAMDE, Seveneves -, but I had never bothered to google the man, so I had no idea how he lived. After Cal Newport’s mention, though, I think Anathem is a lot more personal than it looks. Among its many messages maybe there is Neal Stephenson telling us “see? this is what can be achieved when smart people are locked up and cut off from the world”. “What can be achieved” being, in Neal Stephenson’s case (and brilliantly recursively), a great novel about what can be achieved when smart people are locked up and cut off from access to the world.
5. Anathem may be an extreme version of what happens when people turn against science.
Flat-Earthers and anti-vaxxers are back. People who don’t know what a standard-deviation is pontificate freely and publicly about the scientific evidence of climate change. Violent gangs openly oppose free speech at universities. I’m not saying these slines are about to lock up Earth’s scientists in monasteries, but perhaps the Temnestrian Iconography is getting more popular.
“[…] Fid Erasmas, what are the Iconographies and why do we concern ourselves with them?” […]
“Well, the extras—”
“The Sæculars,” Tamura corrected me.
“The Sæculars know that we exist. They don’t know quite what to make of us. The truth is too complicated for them to keep in their heads. Instead of the truth, they have simplified representations— caricatures— of us. Those come and go, and have done since the days of Thelenes. But if you stand back and look at them, you see certain patterns that recur again and again, like, like— attractors in a chaotic system.”
“Spare me the poetry,” said Grandsuur Tamura with a roll of the eyes. There was a lot of tittering, and I had to force myself not to glance in Tulia’s direction.
I went on, “Well, long ago those patterns were identified and written down in a systematic way by avout who make a study of extramuros. They are called Iconographies. They are important because if we know which iconography a given extra— pardon me, a given Sæcular— is carrying around in his head, we’ll have a good idea what they think of us and how they might react to us.”
Grandsuur Tamura gave no sign of whether she liked my answer or not. But she turned her eyes away from me, which was the most I could hope for. “Fid Ostabon,” she said, staring now at a twenty-one-year-old fraa with a ragged beard. “What is the Temnestrian Iconography?”
“It is the oldest,” he began. “I didn’t ask how old it was.” “It’s from an ancient comedy,” he tried.
“I didn’t ask where it was from.”
“The Temnestrian Iconography…” he rebegan.
“I know what it’s called. What is it?”
“It depicts us as clowns,” Fraa Ostabon said, a little brusquely. “But… clowns with a sinister aspect. It is a two-phase iconography: at the beginning, we are shown, say, prancing around with butterfly nets or looking at shapes in the clouds…”
“Talking to spiders,” someone put in. Then, when no reprimand came from Grandsuur Tamura, someone else said: “Reading books upside-down.” Another: “Putting our urine up in test tubes.”
“So at first it seems only comical,” said Fraa Ostabon, regaining the floor. “But then in the second phase, a dark side is shown— an impressionable youngster is seduced, a responsible mother lured into insanity, a political leader led into decisions that are pure folly.”
“It’s a way of blaming the degeneracy of society on us— making us the original degenerates,” said Grandsuur Tamura. “Its origins? Fid Dulien?”
“The Cloud-weaver, a satirical play by the Ethran playwright Temnestra that mocks Thelenes by name and that was used as evidence in his trial.”
“How to know if someone you meet is a subscriber to this iconography? Fid Olph?”
“Probably they will be civil as long as the conversation is limited to what they understand, but they’ll become strangely hostile if we begin speaking of abstractions…?”
(Anathem, pp. 71-72)
This is it. Go read Anathem and tell your fellow avout and Ferals about it. See you at Apert.
I’m using Django for a serious project for the first time. I had played with Django a couple of times before, but I’m a long-time Flask fanboy and I invariably gave up in frustration (“why would anyone ever need separate files for settings, urls, and views?!”). Well, turns out Django is pretty cool if you want to put a bunch of apps under the same umbrella. Now, the official tutorial is a bit too verbose if you’re impatient. And if you’re used to Flask’s minimalism, you will get impatient with Django at times. So, here a few potentially useful pointers (largely for my own future consultation).
To get started, just pip install Django, run django-admin startproject mysite, then run python manage.py startapp myapp. (Replace mysite and myapp by whatever names you want.) This should create the essential files and directories you’ll need.
making urls work
In Flask you create your views and map your urls all at once:
@app.route('/')defindex():return'Hello World!'
This is about as simple as it gets (unless you want to get really minimalist).
In Django you can’t do that. You have to define your views in one place and map your urls elsewhere. The usual way to do it is to define your views in your (aptly named) myapp/views.py file, like this:
Unlike in Flask you can’t just do return 'Hello World!' - the returned object cannot be a string, so we need to import HttpResponse. Also unlike in Flask, we must feed the request to the function - there is no global request object in Django, so we need to pass it around explicitly (more about this in a moment).
Now on to mapping urls. This requires changing two different files. The first is your mysite/urls.py file, wherein you’ll put this:
This piece of code tells mysite (the big project inside which your various apps will live) to defer to myapp (one of your various apps) whenever someone hits http://blablabla/myapp/. (That r'^myapp/ thing is a regular expression that matches any url that contains myapp/.)
So, mysite/urls.py is a big dispatcher: it’ll check the url and send the request to the appropriate app. Here we only have one app (myapp), but if you’re using Django you’ll likely have several apps, in which case the urlpatterns list will contain several url() objects.
Now, myapp must be prepared to receive the baton. For that to happen your myapp/urls.py file (not your mysite/urls.py file!) must look like this:
Here we have another regex: r'^$. This will capture any requests that end in myapp/. (If the request got this far then it must contain myapp/, so you don’t need to repeat it in the regex here.) We’re telling myapp that any such requests should be handled by the view function named index - which you defined before, in your myapp/views.py file (see above).
So, myapp/urls.py is a secondary dispatcher: it’ll check the url and send the request to the appropriate view. Here we only have one view (the app’s index page), but in real life you’ll have several views, in which case the urlpatterns list will contain several url() objects.
That’s it. If you run python manage.py runserver and then open http://127.0.0.1:8000/ in your browser you should be greeted by the Hello World! message.
If you really want to you can have a single-file Django project: check this. But if your project is so small that you can have a single file then maybe you’d be better off using Flask or CherryPy or some other minimalist web framework.
request and session
Accessing request and session data in Flask is a no brainer. There is a global request object and a global session object and, well, you just do whatever you want to do with them.
In Django, as I mentioned before, there is no global request object - you need to explicitly pass request around to work with it. There is no global session object either. Instead, session is an attribute of request. This is how the above snippet translates into Django:
There are a few syntax differences between Jinja2 (Flask’s default templating language) and Django’s templating language (DTL). For instance, to access the first element of mylist it’s {% mylist[0] %} in Jinja2 but {% mylist.0 %} in DTL. But most of the syntax is identical. Template inheritance works the same way, with {% extends 'parent.html' %} and {% block blockname %}{% endblock $}. Same with loops, if/elses, and variables: {% for bla in blablabla %}{% endfor %}, {% if something %}{% elif somethingelse %}{% else %}{% endif %}, {{ some_variable }}. If you’re porting something from Flask to Django there is a chance your templates will work just as they are.
You need to change your views though. In Flask you render a template and pass variables to it like this:
So, in Django you must pass the request object to render the template. And your template variables must be passed as a dict.
connections
In both Flask and Django you can use something like pyodbc or pymssql to connect to your databases. But you can put a layer of abstraction on top of that. In Flask there is Flask-SQLAlchemy. Here’s their quickstart snippet:
In Django the connection and the models go into separate scripts. You set up the connection by adding an entry to the DATABASES dict in your mysite/settings.py file:
DATABASES={# ... your other db connections ...
'my_database_name':{'ENGINE':'django.db.backends.sqlite3','NAME':'my_database_name','USER':'my_username','PASSWORD':'my_password','HOST':'my.host.address','PORT':'my_port'}}
Then, in your myapp/models.py, you define your models.
You don’t have to use any models though. If you prefer to run raw SQL queries you can do it like this:
fromdjango.dbimportconnectionscursor=connections['my_database_name'].cursor()cursor.execute('SELECT * FROM sometable')results=cursor.fetchall()
Just as you would do with pyodbc (except that here you don’t need to .commit() after every database modification).
afterwards
I’m just trying to get you past the initial rage over all the boilerplate code Django requires. :-) This is all just about syntax - I’m merely “translating” Flask to Django. If you’re serious about Django you should invest some time in learning Django’s semantics. Their official tutorial is a good place to start. Have fun!
This is the story of how I had to build and deploy a freaking app just so I can text my girlfriend when I’m at the office. Perhaps it’ll help others who are also subject to the arbitrary rules of IT departments everywhere. (Dilberts of the world, unite!)
For some two years now my messaging app of choice has been Telegram. It’s lightweight, end-to-end encrypted, well designed, and free; it’s impossible not to love it. Now, I hate typing on those tiny on-screen keyboards, so most of the time what I actually use is Telegram’s desktop app. Problem is, I can’t use it when I’m at work. My organization’s IT department blocks access to Telegram’s servers (dont’ ask). I can install the app, but it doesn’t connect to anything; it can’t send or receive messages.
So, I looked into Telegram’s competitors. I tried WhatsApp, but its desktop version is blocked as well at my organization. And in any case I tried it at home and it’s sheer garbage: the desktop app needs your phone to work (!) and it crashes every ~15 minutes. (I keep pestering my friends to switch from WhatsApp to Telegram but WhatsApp is hugely popular in Brazil and network externalities get in the way.)
Then it hit me: why not Slack? The IT department doesn’t block it and I already use Slack for professional purposes. Why not use it to talk to my girlfriend too? I created a channel, got her to sign up, and we tried it for a couple of days.
Turns out Slack solved the desktop problem at the cost of creating a mobile problem. I don’t have any issues with Slack’s web interface - I keep my channels open on Chrome at all times and that works just fine. But when I switch to mobile… boy, that’s one crappy iOS app. Half the time it just doesn’t launch. Half the time it takes forever to sync. Granted, my iPhone 5 is a bit old. But the Telegram iOS app runs as smooth and fast as it did two years ago, so the hardware is not at fault here.
As an aside, turns out Slack’s desktop app is also ridiculously heavy. I don’t really use it - I use Slack’s web interface instead -, but that’s dispiriting nonetheless.
I tried Facebook’s Messenger. Blocked. I tried a bunch of lesser-known alternatives. Blocked.
Eventually I gave up on trying different messaging apps and asked the IT department to unblock access to Telegram’s servers. They said no - because, well, reasons. (In the words of Thomas Sowell, “You will never understand bureaucracies until you understand that for bureaucrats procedure is everything and outcomes are nothing”.)
The IT guys told me I could appeal to a higher instance - some committee or another -, but I’ve been working in the government for a while and I’ve learned to pick my fights. Also, I believe in Balaji Srinivasan’s “don’t argue” policy.
So, I rolled up my sleeves and decided to build my own solution.
I don’t need to build a full-fledged messaging app. What I need is extremely simple: a middleman. Something that serves as a bridge between my office computer and Telegram’s servers. I need a web app that my office computer can visit and to which I can POST strings and have those strings sent to my girlfriend’s Telegram account.
That app needs to be hosted somewhere, so the first step is choosing a platform. I briefly considered using my personal laptop for that, just so I didn’t have to deal with commercial cloud providers. But I worry about exposing to the world my personal files, laptop camera, browser history, and the like. Also, I want 24/7 availability and sometimes I have to bring my laptop to the office.
I settled on Google App Engine. I used it before (to host an app that lets people replicate my Ph.D. research) and I liked the experience. And, more importantly, it has a free tier. GAE has changed quite a bit since the last time I used it (early 2014), but it has an interactive tutorial that got me up to speed in a matter of minutes.
You can choose a number of programming languages on GAE. I picked Python because that’s what I’m fastest at. (In hindsight, perhaps I should’ve used this as a chance to learn some basic Go.)
Instead of starting from scratch I started with GAE’s default “Hello, world!” Python app. The underlying web framework is Flask. That’s my go-to framework for almost all things web and that made things easier. Using Flask, this is how you control what happens when a user visits your app’s homepage:
# this is all in the main.py file of GAE's default "Hello, world!" Python app
fromflaskimportFlaskapp=Flask(__name__)@app.route('/')defhello():return'Hello, world!'
I don’t want a static webpage though, I want to communicate with Telegram’s servers. In order to do that I use a Python module called telepot. This is how it works: you create a Telegram bot account and then you use telepot to control that bot. (In other words, the sender of the messages will not be you, it will be the bot.
When you create your bot you receive a token credential, which you will then pass to telepot.
You can now make your bot do stuff, like sending messages. Now, Telegram enforces a sort of Asimovian law: a bot cannot text a human unless it has been texted by that human first. In other words, bots can’t initiate conversations. So I created my bot, told my girlfriend its handle (@bot_username), and had her text it. That message (like all Telegram messages) came with metadata (see here), which included my girlfriend’s Telegram ID. That’s all I need to enable my bot to text her.
girlfriend_id='SOME_SEQUENCE_OF_DIGITS'bot.sendMessage(girlfriend_id,'How you doing?')
Now let’s merge our web app code and our telepot code in our main.py file:
importtelepotfromflaskimportFlaskapp=Flask(__name__)bot=telepot.Bot('YOUR_TOKEN')bot.getMe()girlfriend_id='SOME_SEQUENCE_OF_DIGITS'@app.route('/')deftextGirlfriend():bot.sendMessage(girlfriend_id,'How you doing?')return'message sent!'
(This can be misused in a number of ways. You could, say, set up a cron job to text ‘thinking of you right now!’ to your significant other at certain intervals, several times a day. Please don’t.)
The rest of the default “Hello, world!” Python app remains the same except for two changes: a) you need to install telepot; use pip install with the -t option to specify the lib directory in your repository; and b) you need to add ssl under the libraries header of your app.yaml file.
So, I created a web app that my IT department does not block and that texts my girlfriend when visited. But I don’t want to text ‘How you doing?’ every time. So far, the app doesn’t let me choose the content of the message.
Fixing that in Flask is quite simple. We just have to: a) add a text field to the homepage; b) add a ‘submit’ button to the homepage; and c) tell the app what to do when the user clicks ‘submit’. (We could get fancy here and create HTML templates but let’s keep things simple for now.)
importtelepotfromflaskimportFlaskfromflaskimportrequest# so that we can get the user's input
app=Flask(__name__)bot=telepot.Bot('YOUR_TOKEN')bot.getMe()girlfriend_id='SOME_SEQUENCE_OF_DIGITS'@app.route('/')defgetUserInput():return'<form method="POST" action="/send"><input type="text" name="msg" size="150"><br><input type="submit" value="submit"></form>'@app.route('/send',methods=['POST'])deftextGirlfriend():bot.sendMessage(girlfriend_id,request.form['msg'])return'message sent!'
And voilà, I can now web-text my girlfriend.
Yeah, I know, that would hardly win a design contest. But it works.
This is where I’m at right now. I did this last night, so there is still a lot of work ahead. Right now I can send messages this way, but if my girlfriend simply hit ‘reply’ her message goes to the bot’s account and I just don’t see it. I could have the app poll the bot’s account every few seconds and alert me when a new message comes in, but instead I think I’ll just create a Telegram group that has my girlfriend, myself, and my bot; I don’t mind reading messages on my phone, I just don’t like to type on my phone. Another issue is that I want to be able to text-app my family’s Telegram group, which means adding radio buttons or a drop-down menu to the homepage so I can choose between multiple receivers. Finally, I want to be able to attach images to my messages - right now I can only send text. But the core is built; I’m free from the tyranny of on-screen keyboards.
(Sci-Hub Liberator’s front-end. This is what happens when data scientists do web development.)
