Marie Whittaker - @mariecwhittaker
I made a thing! The thing is the Techlady Hackathon Visual Collateral for this year. The way I made the thing is with Adobe Illustrator and my creative juices. I’ll talk about those things.
4th TechLady Hackathon – day of learning and hacking in late October
Marie created visual materials to help brand the hackathon as its own standalone event
Logo was used for stickers and selfie machine
Identifont: which font did someone use in their materials? This helps designers narrow down which font was used when you don’t know or can’t ask the designer who made it.
Used identifont and Adobe Creative Cloud.
Adobe Illustrator: traced over an similar tattoed-knuckles photo, then made it cartoony by tracing over it with the pen tool; adding shading
Result was an SVG – specifically so it would be vector (scalable up or down), which is better when working with text and when working with lines. The edges are crisper, and it’s scalable without losing any image quality.
Inspiration came from a Data Viz leather jacket and the prompt to make something “badass”
Good logo design conveys emotion
Kate Rabinowitz - @datalensdc
Gender diversity of major tech + data meetups plus a way to help!
How many speakers of DC Tech Meetups were female?
Used Python & the Meetup API to collect the data; used pronouns to code the gender.
Focused on Meetup with 1000+ members, and narrowed it to Meetups that focused on
In every case, there were zero female speakers at single-speaker events, across all DC Tech Meetups.
Multi-speaker events weren’t much better
Diversity in meetups is a good thing!
So Kate built WeSpeakToo.org to build a bridge: this is a directory of talented, creative women who should be speaking at your Meetups!
Having an enforceable Code of Conduct also helps to make it so that Meetups can be a safer, better space for all attendees
Psst: DC Hack And Tell’s Code of Conduct is here: https://github.com/dchackandtell/code-of-conduct
Metro pricing comparison map
This compares how long it will take and how much it will cost to go from one Metro Station to another.
Select one station and hover over the second station in order to get the off-peak and peak fares.
Pulled all of the (very messy) data initially from the WMATA API; added unique IDs to each metro station; added columns to create every possible combination of stations and then added in the fares
Structure is all there so it wouldn’t be too difficult to repurpose for another city.
Matthew Coates - @PeaceTechLab
OSRx is designed as a portal for peace builders and other actors to monitor and visualize various conflict data. Currently, two large event databases are represented. Various social media monitors powered by Crimson Hexagon are also shown. In the future, specific topic areas will be featured (for now, see the South Sudan page).
Open Situation Room Exchange – Mapping and Visualizing Conflict Data
Producing data for peacebuilders
Real-time data (social media, news reports)
Periodic indices (World bank, economic stability, etc)
Local data & insights (crowdsourced data sets, etc)
Overview by country; two different databases.
Social media analysis (Crimson Hexagon) monitors social media and visualizes conversations by topic
GDELT Instability comparison: normalizes conflict news vs. all other news to give a % of how much news about conflict there is to give a rough metric of how much conflict there is
Chris Nguyen - @uncompiled
Alexa + Metro = :emoji_1f525:
Most Alexa flash briefings are Customized weather reports or News alerts
Expected response format is XML or JSON
Chris set up a flash briefing to pull from the WMATA API and uses Alexa to speak the
developer.wmata.com – Fetch rail incidents
Uses Scala microservice to process the data (roughly 5 lines)
WMATA uses shorthand notation in their incident API, which doesn’t sound natural if sent through text to speech so it needs to be normalized.
When latency is a constraint, avoid Java (the JVM is super slow to start).
Output the results of a lambda function to an S3 bucket
Jessica Garson - @jessicagarson
Tech Lady Hackathon
Started in 2013
Full of training, workshops, and projects
Second Tech Lady Hackathon in 2014 was where Buscando was created
About 160 women at Techlady Hackathon #4
Featured guest: DC CTO Archana Vemulapalli
Workshops on everything from intersectional feminism and user experience to front-end development and security
Trended on Twitter!
Usually happens once a year; might happen more frequently as well!
What if it happened in multiple cities?
People of all skill levels coming together – for most attendees, this was their first hackathon!
Travis Hoppe - @metasemantic
Trained tiny neural nets to emulate authors, and then have them reproduce text from another genre. Quantifiable author-to-author measures!
The data we feed a model shapes its personality
If each bot (a deep learning model) is a book: Wizard of Oz bot, Wuthering Heights bot, etc.
But what if the Edgar Allen Poe bot reads Wuthering Heights?
Top 100 books pulled from project Gutenbergs. Manually cleaned headers and footers
Each book is used to train one bot Long short term memory Recurrent neural network. Each book took about 1.5 hours to train. Did 80 books
Bots get confused when they see new text (a different book); we can measure that confusion to see author similarity
The Pride and Prejudice bot is least confused by other Jane Austen books. The King James Bible bot is not similar to anything else
Created a similarity map. Comparing two bots takes several seconds, and there are N^2 comparisons
Could a lot of similarity be attributable to say books written in the same tense/ perspective?
…Maybe you do that hack!