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The Plain Language Action and Information Network (PLAIN) is a community of federal employees dedicated to the idea that citizens deserve clear communications from government. We believe that using plain language saves federal agencies time and money and provides better service to the American public.
#government 
2 days ago
Twitter
It frustrates me to see folks that get professional feedback that they should "talk more in meetings," so I wrote u…
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2 days ago
Twitter
This excerpt from 's Outsider's Guide to FP really nails some of what I—much less eloquently—tried to reflec…
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9 days ago
Twitter
RT : AND YOU MAY FIND YOURSELF
BEHIND THE WHEEL
OF A LARGE AVOCADOMOBILE
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11 days ago
Twitter
One of the first legit positive statements about MS I've heard in years: "Longer term, this may be a…
from twitter
11 days ago
Twitter
I continue to be annoyed/troubled by discussions that paint millennials with a broad brush. We don’t manage macro…
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11 days ago
Twitter
“The most obvious use case for DIDs within Microsoft products would be LinkedIn, which could be viewed as a sort of…
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11 days ago
Salesforce investing $2 billion in Canada, as U.S. tech firms head north | Reuters
Canada attracting large tech investments due to more favorable immigration policies which attract diverse talent
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11 days ago
When You Have to Carry Out a Decision You Disagree With
When You don’t agree with a decision, you can turn it into a strategic advantage
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12 days ago
The Key to Networking: Keeping in Touch | Karl Hughes
My system for keeping in touch with people in my network.
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12 days ago
Twitter
As a person in IT, I can appreciate these politicians trying to fix government by briefly turning it off and on aga…
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14 days ago
Blockchain Demo
I would watch all videos by he’s very good at explaining. Also is fun
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16 days ago
Twitter
This is hands down the best non-technical explanation of blockchain technologies that I've read: "Beyond the Bitcoi…
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16 days ago
Twitter
at this point I've really internalized "being clear about what you know and don't know is a thing senior engineers…
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16 days ago
Q - Common Mistakes in Data Science Hiring : Part 2
RT : As promised, here's the second installment of "Common Mistakes in Hiring."
DataScience  from twitter_favs
19 days ago
Twitter
RT : As promised, here's the second installment of "Common Mistakes in Hiring."
DataScience  from twitter_favs
19 days ago
Q - Common Mistakes in Data Science Hiring : Part 1
Hiring and retention are common sources of frustration in the world of data science. (That includes machine learning (ML), artificial intelligence (AI), and big data.) It’s easy to blame this on candidates being fickle because data science is a hot, in-demand field. There’s certainly some truth to that. There’s also another truth: you’re unwittingly sabotaging your efforts to hire and retain data talent.
#technology  #hiring  #management  #data.science  DataScience 
19 days ago
Trey Causey, Data Product Manager
I am advocating for an empathetic but potentially more time-consuming process here. I realize a lot of this advice runs contrary to widespread hiring practices, and I'm comfortable with that. I'm interesting in promoting a workplace that welcomes diversity in all forms and that matches candidates with jobs that help them grow as people and as data scientists. Similarly, you should be hiring people that will push your organization forward, and that encourages learning and teaching between peers.
#technology  #hiring  #management  #data.science 
19 days ago
Twitter
RT : awarded by Friends of Nachusa. Science makes all the ! Grant recipients are:…
difference  Science  grants  from twitter_favs
19 days ago
Twitter
♫ My Top 5 artists: Lana Del Rey (7), The Band (6), Ibeyi (4), Ibeyi (Featuring Kamasi Washington) (2) & La…
lastfm  from twitter
19 days ago
Twitter
Is your child texting whilst using vim?

rofl - replace current char with o and find l

brb - replace 1st char of p…
from twitter_favs
24 days ago
Donald Trump is winning - Vox
Even before Trump, American politics was becoming dangerously angry, polarized, bitter. In this, Trump’s rise is more symptom than cause. But his presidency has been an accelerant, and the consequences of it are yet untold.
#politics 
25 days ago
Twitter
This partially explains why I rarely engage in the charade that is the current administration / political climate.…
from twitter
25 days ago
The Modular Monolith: Rails Architecture – Dan Manges – Medium
Rather than extracting microservices, we decided to first focus on making our app modular. Our goal was to identify good architectural boundaries before we extracted code out into independent services. This would set us up to be able to migrate to microservices in the future, by having the code structured in a way to make a smooth transition.

Our approach has been working exceptionally well. Based on our experience, I’d highly recommend this strategy for almost any team at our size and scale. We have a code base that’s been under development for over two years with twenty-five software engineers now working on it. We have 50,000+ lines of Ruby/Rails application code and 100,000+ lines of test code.
#technology  #ruby  #rails  #architecture  #software.architecture 
25 days ago
Twitter
Nice and clear description of sound architectural practices: “The Modular Monolith: Rails Architecture” by…
from twitter
25 days ago
Introducing Kindergartners to Coding – Public Good
Introducing Kindergartners to Coding
I recently had the opportunity to present at my daughter’s Kindergarten Career Day. As the VP of Engineering at Public Good, this provided me a chance to explore what technology and programming mean to both kindergartners and to many of their parents who aren’t immersed in a technology for a living. The presentation also had to be engaging and meaningful for the kids, lest I risk boring 38 six year olds and suffer the dire consequences.
#technology  #public.good 
4 weeks ago
Public Good Software at Bloomberg D4GX 2017 – Public Good
The abstract of our paper hints at a few things we’ll soon talk about in more detail here on the blog in 2018:

Public Good Software’s products match journalistic articles and other narrative content to relevant charitable causes and nonprofit organizations so that readers can take action on the issues raised by the articles’ publishers. Previously an expensive and labor-intensive process, application of machine learning and other automated textual analyses now allow us to scale this matching process to the volume of content produced daily by multiple large national media outlets. This paper describes the development of a layered system of tactics working across a general news model that minimizes the need for human curation while maintaining the particular focus of concern for each individual publication. We present a number of general strategies for categorizing heterogenous texts, and suggest editorial and operational tactics for publishers to make their publications and individual content items more efficiently analyzed by automated systems.
#technology  #public.good  #machine.learning 
4 weeks ago
Twitter
Our engineers not only wow us every day, but also wow a very tough crowd -- kindergarteners!
from twitter_favs
4 weeks ago
Introducing Kindergartners to Coding – Public Good
I just published “Introducing Kindergartners to Coding”
from twitter_favs
4 weeks ago
We can draw school zones to make classrooms less segregated. This is how well your district does. - Vox
Think about your elementary school.

If you attended an American public school, chances are you went to that school because your family lived in that school’s attendance zone. You probably didn’t think twice about it.

We tend to assume these are neutrally drawn, immutable borders. But if you take a step back and look at the demographics of who lives in each attendance zone, you’re faced with maps like this:
#technology  #journalism  #diversity  #racism 
4 weeks ago
Tracking compensation and promotion inequity | Lara Hogan
Plenty of tech companies are attempting to make their pipeline of candidates more diverse. But an organization won’t find much success recruiting a more diverse group of employees unless its leaders are aware of their existing internal inclusion and equity issues. Unless leadership has already started to tackle these issues, it’s likely that these new hires will enter into an environment that they won’t want to stick around in for long.

One of my suggestions is to calculate whether you compensate and promote people fairly, which requires some level of manual analysis. It also takes a lot of work to make this math repeatable, so you can check in on your organization’s progress over time. And if you’re doing this analysis for the first time, you probably won’t have statistically significant results, because you may not have enough folks from underrepresented communities yet.
#technology  #diversity  #management  #hiring  #recruiting 
4 weeks ago
Inclusion in the first shift.
Drucker suggests, “If you can't measure it, you can't improve it”, and that’s where I started. Especially when you invest a bunch of your time and energy into something, it’s easy to index on your effort over your effect, so starting with success metrics was important to keep me grounded in real impact.

I started measuring the areas proposed by Lara Hogan in Inclusion math, and added a few more based on discussion with coworkers and friends:

Retention: do underrepresented minorities (URMs) stay at your company as long as other folks?
Time at level: do URMs experience comparable rates of career progression at your company?
Level distribution: are URMs represented in all levels of seniority?
Compensation: are URMs compensated equitably for their level?
Usage rate: which individuals lead and participate in inclusion and culture efforts? Are senior managers and senior engineers involved? How broad is the pool of participants?
Social recognition: do you talk about inclusion efforts in your forums of culture? Company meetings, offsites, and so on.
Performance recognition: how are inclusion and culture efforts recognized in your performance and compensation process?
Hiring: is your pipeline diverse, and are URMs in your pipeline getting and accepting offers at similar rates? (The pipeline still matters.)
Many of these metrics are easy to analyze in partnership with human resources. Much of what we did to impact those metrics is obvious (e.g. cold sourcing), and I don’t want to retread tired ground, but measuring and moving some of these metrics did require meaningful changes to how I work, and I think those are worth digging into a bit.
#technology  #diversity  #hiring  #management 
4 weeks ago
Jeffrey Morgenthaler » How to Make Sangrita
1 oz orange juice (freshly-squeezed)
¾ oz – 1 oz lime juice (depending on the sweetness of your oranges)
½ oz real pomegranate grenadine
3 dashes hot sauce or ¼ tsp pasilla chili powder
#cocktails 
6 weeks ago
httpbin(1): HTTP Client Testing Service
httpbin(1): HTTP Request & Response Service - troubleshoot rpc / webhooks, slack integrations, etc
#technology  #api  #programming 
7 weeks ago
The Case for Learned Index Structures – Arxiv Vanity
https://arxiv.org/pdf/1712.01208v1.pdf

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to
70
%
in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible.
#technology  #database  #machine.learning 
10 weeks ago
Twitter
♫ My Top 5 artists: Queens of the Stone Age (17), The National (13), Margo Price (10), The Flatliners (6) &…
lastfm  from twitter
10 weeks ago
Twitter
"When scientists say bears are going extinct, I want people to realize what it looks like," says photographer Paul…
from twitter_favs
11 weeks ago
Miguel Grinberg's Tutorials - The Flask Mega-Tutorial
The Flask Mega-Tutorial is an overarching tutorial for Python beginner and intemediate developers that teaches web development with the Flask framework. The tutorial has been thoroughly revised and expanded in 2017, now containing 23 chapters. The concepts that are covered go well beyond Flask, including a wide range of topics Python web developers need to know when writing their own applications. You can preview this tutorial on Miguel Grinberg's blog.

Order Ebook + Video $60

Order Ebook $15
#technology  #python  #tutorial 
11 weeks ago
Counting Page Views in PostgreSQL < SQL | The Art of Web
This is an example of a seemingly simple task that becomes more complicated as if fails to adapt to increases in database size and traffic.
#technology  #data  #database  #postgresql  #public.good 
11 weeks ago
Faster PostgreSQL Counting
Everybody counts, but not always quickly. This article is a close look into how PostgreSQL optimizes counting. If you know the tricks there are ways to count rows orders of magnitude faster than you do already.

The problem is actually underdescribed – there are several variations of counting, each with its own methods. First think whether you need an exact count or whether an estimate suffices. Next, are you counting duplicates or just distinct values? Finally do you want a lump count of an entire table or will you want to count only those rows matching extra criteria?

We’ll analyze the techniques available for each situation and compare their speed and resource consumption. After learning about techniques for a single database we’ll use Citus to demonstrate how to parallelize counts in a distributed database.
#technology  #postgresql  #optimization  #data  #database  #public.good 
11 weeks ago
Import.io | Extract data from the web
Web scraping service, data extraction, etc.
#technology  #api 
12 weeks ago
gopass - the team password manager
gopass is a simple but powerful password manager for your terminal.
#technology  #security  #golang 
12 weeks ago
Rick Rubin on Crashing Kanye’s Album in 15 Days
wisdom

There’s a tremendous power in using the least amount of information to get a point across.
#music  #philosophy 
12 weeks ago
Twitter
RT : No community is immune.

Support the solution: .

GivingTuesday2017  from twitter
12 weeks ago
Twitter
RT : Did you know that our upcoming show The Ears Have Walls spans two galleries? Join us for the Vernissage at
from twitter
12 weeks ago
Twitter
Really good thread with lots of specific suggestions on how the NYT's "wow Nazis are just like us!" piece could hav…
from twitter_favs
12 weeks ago
She helps connect people with causes at Public Good - Chicago Tribune
Public Good is in the business of making it easier for people to do good through technology. Our co-founders were leading technologists at the Obama campaign in 2012, and prior to that, they had experience in the world of e-commerce.
#technology  #public.good 
november 2017
Twitter
It is worth reflecting on just how wildly unsupported by the public and wrong the FCC is on its effort to end an Op…
from twitter_favs
november 2017
Twitter
RT : Engaging discussion on Data Science for Good panel moderated by w/ Frances Zlotnick from Ha…
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november 2017
Twitter
"Portland is a city that pushes black people out of neighborhoods and replaces them with 'Black Lives Matter' signs…
from twitter_favs
november 2017
Public Benefit Corporation Practice Note
This Practice Note provides an overview of
public benefit corporations organized in
Delaware. It highlights the special provisions
that apply to this type of corporation, including
how public benefit is defined, duties of directors,
notice and reporting requirements, and the
conversion process
#entrepreneurship  #startup  #public.good 
november 2017
ferd.ca -> Simhashing (hopefully) made simple
When it comes to figuring out how similar various pieces of data are from one another (and which is the closest matching one in a large group of candidates), simhashing is one of my favourite algorithms. It's somewhat simple, brilliant in its approach, but still not obvious enough for most people (myself included) to come up with it on their own. Readers may be familiar with hashing algorithms such as MD5 or SHA, which aim to very quickly create a unique signature (hash) of the data. These functions are built so that identical files or blobs of data share the same hash, so you can rapidly see whether two blobs are identical or not, or if a blob still has the same signature after transmission to see if it was corrupted or not. Then different blobs, even if mostly the same, get an entirely different signature.
While simhashes still aim to have unique signatures for documents, they also attempt to make sure that documents that look the same get very similar hashes. That way, you can look for similar hashes to figure out if the documents are closely related, without needing to compare them bit by bit. It's a statistical tool to help us find near-duplicates faster.
#technology  #algorithms  #public.good  #simhash 
november 2017
The Boomtown That Shouldn’t Exist - POLITICO Magazine
Cape Coral, Florida, was built on total lies. One big storm could wipe it off the map. Oh, and it’s also the fastest-growing city in the United States.
#technology  #real.estate 
november 2017
[1710.08522] Automating, Operationalizing and Productizing Journalistic Article Analysis
Public Good Software's products match journalistic articles and other narrative content to relevant charitable causes and nonprofit organizations so that readers can take action on the issues raised by the articles' publishers. Previously an expensive and labor-intensive process, application of machine learning and other automated textual analyses now allow us to scale this matching process to the volume of content produced daily by multiple large national media outlets. This paper describes the development of a layered system of tactics working across a general news model that minimizes the need for human curation while maintaining the particular focus of concern for each individual publication. We present a number of general strategies for categorizing heterogenous texts, and suggest editorial and operational tactics for publishers to make their publications and individual content items more efficiently analyzed by automated systems.
#technology  #public.good  #machine.learning 
november 2017
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