ekingery + #technology   1632

Ethics + Data Science – dj patil – Medium
How much has data changed our lives over the past decade? Just over 10 years ago the iphone was launched. Back then, our phones took grainy photos and video was just wishful thinking. It was still…
#technology  #data.science  #data  #philosophy 
13 days ago by ekingery
Gobo
Algorithms control what we see on our feeds,
but we can't control the algorithms.
Do you get most of your news from social media? Us too. Do you know anything about how they pick what to show you? No? Neither do we. Facebook, Twitter, and others sites use complicated computerized rules to decide which posts you see at the top of your feed and which you don't. These algorithms have reinforced our echo chamber - showing us content like what we share - and made hard to burst our filter ...
#technology  #public.good 
16 days ago by ekingery
Ethan Zuckerman, Inventor of Pop-up Ad, Interview
‘We’ve Lost 10 Years of Innovation. This Decade Has Been Boring for the Web.’
A conversation with Ethan Zuckerman, media scholar and inventor of the pop-up ad, on the original sin of advertising and the web’s lost decade.
#technology  #philosophy 
16 days ago by ekingery
Antonio Garcia Martinez, former Facebook Employee Interview
‘The Organic Side, to Me, Is Scarier Than the Ad Side’
A conversation with former Facebook product manager Antonio Garcia Martinez on Mark Zuckerberg’s “disingenuous and strange” reaction to the election.
#technology  #philosophy 
16 days ago by ekingery
An Apology for the Internet — From the People Who Built It
Even those who designed our digital world are aghast at what they created. A breakdown of what went wrong — from the architects who built it.
#technology  #philosophy 
16 days ago by ekingery
Jaron Lanier Interview on What Went Wrong With the Internet
A conversation with VR pioneer Jaron Lanier on Silicon Valley’s politics, being quoted by Mark Zuckerberg, and what went wrong with the internet.

#technology  #artificial.intelligence 
16 days ago by ekingery
If you can quit social media, but don’t, then you’re part of the problem, Jaron Lanier says - Recode
On this week’s new episode of Too Embarrassed to Ask, Kara Swisher talks with Jaron Lanier, a VR pioneer and longtime technology critic who currently works at Microsoft Research. He’s the author of a new book, “10 Arguments for Deleting Your Social Media Accounts Right Now” and explains why those who have the opportunity to quit platforms like Facebook and Twitter should do so. He compares the problem to past crusades against “mass addictions” like smoking or drunk driving, arguing that hearing ...
#technology  #artificial.intelligence 
16 days ago by ekingery
Advice for a new executive | Lara Hogan
When I was getting ready to join Kickstarter as VP of Engineering, Chad Dickerson (who was the CEO of Etsy when I worked there) offered to send me a bunch of advice. Chad had been a CTO multiple times before being CEO; he knew that this executive-level role was brand new to me, so he offered to help give me a steer and a foundation as I walked into this totally new territory.

#technology  #management 
20 days ago by ekingery
An Introduction to HyperLedger Technologies
This paper provides a high-level overview of Hyperledger: Why it was created,
how it is governed, and what it hopes to achieve. The core of this paper presents
five compelling uses for enterprise blockchain in different industries. It also
describes the open source frameworks and tools that Hyperledger is developing
to help enterprises around the world deliver on the promise of blockchain for
more secure, more reliable, and more streamlined interactions.
This is not intended as a deep technical ...
#technology  #blockchain 
4 weeks ago by ekingery
Home - Hyperledger
Hyperledger is an open source collaborative effort created to advance cross-industry blockchain technologies. It is a global collaboration, hosted by The Linux Foundation, including leaders in finance, banking, IoT, supply chain, manufacturing and technology.

#technology  #blockchain 
4 weeks ago by ekingery
Architecture of the Hyperledger Blockchain Fabric
A blockchain is best understood in the model of state-machine replication [8], where a
service maintains some state and clients invoke operations that transform the state and generate outputs.
A blockchain emulates a “trusted” computing service through a distributed protocol, run by nodes connected
over the Internet. The service represents or creates an asset, in which all nodes have some stake.
The nodes share the common goal of running the service but do not necessarily trust each other for
mo...
#technology  #blockchain 
4 weeks ago by ekingery
Metabase
Metabase is the easy, open source way for everyone in your company to ask questions and learn from data.
#technology  #visualization  #analytics  #data.science 
5 weeks ago by ekingery
fastText
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.

#technology  #machine.learning  #public.good 
5 weeks ago by ekingery
JavaScript End to End Testing Framework | Cypress.io
Test your code, not your patience.
Cypress is the new standard in front-end testing that every developer and QA engineer needs.
#technology  #javascript  #testing  #quality 
6 weeks ago by ekingery
Readings in Database Systems, 5th Edition
Readings in Database Systems (commonly known as the "Red Book") has offered readers an opinionated take on both classic and cutting-edge research in the field of data management since 1988. Here, we present the Fifth Edition of the Red Book — the first in over ten years.
CHAPTERS
Preface [HTML] [PDF]
Background introduced by Michael Stonebraker [HTML] [PDF]
Traditional RDBMS Systems introduced by Michael Stonebraker [HTML] [PDF]
Techniques Everyone Should Know introduced by Peter Bai...
#technology  #data  #database 
6 weeks ago by ekingery
PostgreSQL Triggers and Stored Function Basics | Severalnines
In a previous article we discussed the PostgreSQL serial pseudo-type, which is useful for populating synthetic key values with incrementing integers. We saw that employing the serial data type keyword in a table data definition language (DDL) statement is implemented as an integer type column declaration that is populated, upon a database insert, with a default value derived from a simple function call. This automated behavior of invoking functional code as part of the integral response to data ...
#technology  #database  #postgresql 
6 weeks ago by ekingery
Psql Watch - Today I Learned
The Postgres REPL psql in includes a \watch command that repeatedly executes a command every n seconds. Here’s the official description:

\watch [ seconds ] Repeatedly execute the current query buffer (like \g) until interrupted or the query fails. Wait the specified number of seconds (default 2) between executions.

It executes the current query buffer, which you can print with \p:
#technology  #database  #postgresql 
7 weeks ago by ekingery
Calculating Summaries with Histogram Frequency Distributions · Advanced SQL · SILOTA
A histogram is a special type of column statistic that sorts values into buckets – as you might sort coins into buckets. Generating a histogram is a great way to understand the distribution of data. We'll look at multiple ways of generating histograms.
#technology  #sql  #statistics  #data.science  #postgresql 
11 weeks ago by ekingery
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora.
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of
discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each
item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in
turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of
text modeling, the topic probabilities provide an explicit representation of a document. We pre...
#technology  #machine.learning  #public.good 
12 weeks ago by ekingery
Rebel developers are trying to cure our smartphone addiction — with an app - The Washington Post
To understand why it’s so hard to pry yourself free from your phone, Facebook account and Twitter, you need to know about B.F. Skinner’s pigeons.

In the 1950s, Skinner began putting the birds in a box and training them to peck on a piece of plastic whenever they wanted food. Then the Harvard psychology researcher rigged the system so that not every peck would yield a tasty treat. It became random — a reward every three pecks, then five pecks, then two pecks.

The pigeons went crazy ...
#technology 
12 weeks ago by ekingery
A Gentle Introduction to Normality Tests in Python
An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods.

Parametric statistical methods assume that the data has a known and specific distribution, often a Gaussian distribution. If a data sample is not Gaussian, then the assumptions of parametric statistical tests are violated and nonparametric statistical methods must be used.

There are a range of techniques that you can use to check if your data sample devi...
#technology  #machine.learning  #statistics 
june 2018 by ekingery
Screenshots and screencasts
ubuntu screenshot instructions

Quickly take a screenshot of the desktop, a window, or an area at any time using these global keyboard shortcuts:

Prt Scrn to take a screenshot of the desktop.

Alt+Prt Scrn to take a screenshot of a window.

Shift+Prt Scrn to take a screenshot of an area you select.

When you use a keyboard shortcut, the image is automatically saved in your Pictures folder in your home folder with a file name that begins with Screenshot and includes the date and time it was taken.

If you do not have a Pictures folder, the images will be saved in your home folder instead.

You can also hold down Ctrl with any of the above shortcuts to copy the screenshot image to the clipboard instead of saving it.
#technology  #ubuntu 
june 2018 by ekingery
Meet the campaign connecting affluent techies with progressive candidates around the country - The Verge
Paul Spencer, a Congressional candidate in Little Rock, Arkansas, has never worked at a tech company. He doesn’t represent tech industry issues. He doesn’t even own a laptop or smartphone. He typically dictates the tweets on his campaign’s official Twitter account; occasionally he’ll type them out on a campaign staffer’s computer. Sometime last year, he was tagged in a tweet with someone going by the handle of @Pinboard, who was telling Spencer that he could raise money for him.
#technology  #politics 
june 2018 by ekingery
Machine Learning Crash Course  |  Google Developers
Machine Learning Crash Course
with TensorFlow APIs
Google's fast-paced, practical introduction to machine learning
#technology  #machine.learning  #tutorial 
june 2018 by ekingery
Prodigy - Radically efficient machine teaching
Radically efficient machine teaching.
An annotation tool powered
by active learning
#technology  #machine.learning 
june 2018 by ekingery
Killing the Coding Interview | Pete Holiday's Blog
Let me get straight to the point: you don’t have to see a person’s code to figure out whether they’re a good developer.

Over the past ten or so years, I’ve interviewed a lot of engineers. In that time, I’ve developed a set of techniques that allow me to quickly and accurately evaluate a developer without seeing their code.

I’m now convinced that it’s not only possible, but objectively better to do it that way.
#technology  #interviewing  #recruiting  #hiring 
may 2018 by ekingery
Keras Documentation
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
Supports both convolutional networks and recurrent networks, as well as combinations of the two.
Runs seamlessly on CPU and GPU.
#technology  #machine.learning 
may 2018 by ekingery
httpbin(1): HTTP Client Testing Service
Testing an HTTP Library can become difficult sometimes. RequestBin is fantastic for testing POST requests, but doesn't let you control the response. This exists to cover all kinds of HTTP scenarios. Additional endpoints are being considered.
#technology  #networking  #web.programming  #api 
april 2018 by ekingery
Syncthing
Syncthing replaces proprietary sync and cloud services with something open, trustworthy and decentralized. Your data is your data alone and you deserve to choose where it is stored, if it is shared with some third party and how it's transmitted over the Internet.
#technology  #backups 
april 2018 by ekingery
Standard Ebooks: Free and liberated ebooks, carefully produced for the true book lover.
Standard Ebooks is a volunteer driven, not-for-profit project that produces new editions of public domain ebooks that are lovingly formatted, open source, and free.

Ebook projects like Project Gutenberg transcribe ebooks and make them available for the widest number of reading devices. Standard Ebooks takes ebooks from sources like Project Gutenberg, formats and typesets them using a carefully designed and professional-grade style manual, lightly modernizes them, fully proofreads and corrects them, and then builds them to create a new edition that takes advantage of state-of-the-art ereader and browser technology.
#technology  #literature 
april 2018 by ekingery
Announcing Civiqs: the coolest thing I've helped build since Daily Kos
Behold, Civiqs—a division of Kos Media, separate from Daily Kos, with its own team but under the same corporate umbrella. And what does Civiqs offer? Exactly what we hoped for: the ability to scientifically track data EVERY SINGLE DAY on dozens of issues, candidates, and campaigns, so we can see how events affect public opinion in real time.
#politics  #technology 
march 2018 by ekingery
Arch Linux and the XPS 13 9360
This post is basically going to get you through an Arch Linux installation, with full disk encryption, hibernate and hybrid sleep support.
#technology  #tutorial  #linux 
march 2018 by ekingery
Email tracking, scheduling, templates, send later, and surveys for Gmail | Mixmax
Powerful analytics, automation, and enhancements for your outbound communications.
#technology  #automation  #email  #marketing  #sales  AList 
february 2018 by ekingery
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 
february 2018 by ekingery
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 
february 2018 by ekingery
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 
january 2018 by ekingery
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 
january 2018 by ekingery
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 
january 2018 by ekingery
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 
january 2018 by ekingery
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 
january 2018 by ekingery
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 
january 2018 by ekingery
httpbin(1): HTTP Client Testing Service
httpbin(1): HTTP Request & Response Service - troubleshoot rpc / webhooks, slack integrations, etc
#technology  #api  #programming 
january 2018 by ekingery
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 
december 2017 by ekingery
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 
december 2017 by ekingery
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 
december 2017 by ekingery
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 
december 2017 by ekingery
Import.io | Extract data from the web
Web scraping service, data extraction, etc.
#technology  #api 
december 2017 by ekingery
gopass - the team password manager
gopass is a simple but powerful password manager for your terminal.
#technology  #security  #golang 
november 2017 by ekingery
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 by ekingery
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 by ekingery
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 by ekingery
[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 by ekingery
Building High-level Features Using Large Scale Unsupervised Learning
We consider the problem of building highlevel,
class-specific feature detectors from
only unlabeled data. For example, is it possible
to learn a face detector using only unlabeled
images? To answer this, we train a 9-
layered locally connected sparse autoencoder
with pooling and local contrast normalization
on a large dataset of images (the model has
1 billion connections, the dataset has 10 million
200x200 pixel images downloaded from
the Internet). We train this network using
model parallelism and asynchronous SGD on
a cluster with 1,000 machines (16,000 cores)
for three days. Contrary to what appears to
be a widely-held intuition, our experimental
results reveal that it is possible to train a face
detector without having to label images as
containing a face or not. Control experiments
show that this feature detector is robust not
only to translation but also to scaling and
out-of-plane rotation. We also find that the
same network is sensitive to other high-level
concepts such as cat faces and human bodies.
Starting with these learned features, we
trained our network to obtain 15.8% accuracy
in recognizing 22,000 object categories
from ImageNet, a leap of 70% relative improvement
over the previous state-of-the-art.
#technology  #machine.learning 
october 2017 by ekingery
HTTP Status Codes
HTTP Status Codes
This page is created from HTTP status code information found at ietf.org and Wikipedia. Click on the category heading or the status code link to read more.
#technology  #http  #rest  #api 
september 2017 by ekingery
Documenting Hate News Index | ProPublica
This page lists media reports, collected by Google News, about hate crimes and bias incidents. The “keywords” column contains names and places found in the news reports in the “articles” column. The larger the word, the more prevalent it is in those stories. Select one of the words to see a list of news stories that contain it. Download the data.
#technology  #data  #analytics  #machine.learning  #public.good 
september 2017 by ekingery
Common Crawl
We build and maintain an open repository of web crawl data that can be accessed and analyzed by anyone.
#technology  #data  #data.science  #analytics 
september 2017 by ekingery
Gandalf Decision Engine · Apiary
Gandalf is a Open-Source Decision Engine for Big-Data.
#technology  #database  #public.good 
august 2017 by ekingery
Why Product Market Fit Isn't Enough — Brian Balfour's Coelevate
One of my main observations is that there are certain companies where growth seems to come easily, like guiding a boulder down hill. These companies grow despite having organizational chaos, not executing the “best” growth practices, and missing low hanging fruit. I refer to these companies as Smooth Sailers - a little effort for lots of speed.

In other companies, growth feels much harder. It feels like pushing a boulder up hill. Despite executing the best growth practices, picking the low hanging fruit, and having a great team, they struggle to grow. I refer to these companies as Tugboats - a lot of effort for little speed.

What is the difference between these two types of companies? This is a question I’ve pondered for a long time and have pieced together a framework to explain the difference. The framework has many implications for how you seek out growth and build a company. 
#technology  #entrepreneurship  #startup  #business 
august 2017 by ekingery
A Few Useful Things to Know About Machine Learning
Machine learning algorithms can figure out how to perform
important tasks by generalizing from examples. This is often
feasible and cost-effective where manual programming
is not. As more data becomes available, more ambitious
problems can be tackled. As a result, machine learning is
widely used in computer science and other fields. However,
developing successful machine learning applications requires
a substantial amount of “black art” that is hard to find in
textbooks. This article summarizes twelve key lessons that
machine learning researchers and practitioners have learned.
These include pitfalls to avoid, important issues to focus on,
and answers to common questions
#technology  #data.science  #machine.learning  #public.good 
july 2017 by ekingery
Deep Learning Project
An end to end implementation of a Machine Learning pipeline
#technology  #data.science  #machine.learning  #public.good 
july 2017 by ekingery
KPI dashboard software for businesses | Geckoboard
LIVE TV DASHBOARD SOFTWARE FOR BUSINESSES
Geckoboard helps everyone in your team focus on work that improves the metrics that matter to your business.
#technology  #metrics  #visualization  #management 
july 2017 by ekingery
Friends IRL
We're all experts at something.
CHICAGO WEBFRIENDS is a monthly gathering of designers, developers, and friends from all over the city of Chicago. Join us each month as a member of our group shares the insights, knowledge, and skills they're passionate about!
#technology  AList  #networking 
july 2017 by ekingery
Scale: API For Human Intelligence
API For Human Intelligence
Humans On-Demand. Get high quality results within minutes.
#technology  #data.science  #data  #machine.learning  #public.good 
june 2017 by ekingery
Tiny
Hi, we're tiny.
We start, buy, and invest in wonderful internet businesses.
COMPANIES WE OWN:

Dribbble
Show & tell for designers
MetaLab
Product design agency
Pixel Union
Ecommerce apps and themes
Designer News
News for designers
Flow
Beautiful project management
Crew
Creative freelancer network
We Work Remotely
Remote job board
WWR
COMPANIES WE'VE INVESTED IN:

Need/WantMediaCore (acquired)BufferEligibleMediMapMedeo (acquired)1QBitGiftbitTinyMob (acquired)CareGuidePeer (acquired)NotarizeCheckfrontHazelPulsraWuuThisOpenSpaceUpserveConversioSendWithUsThriveVessyl (acquired)SketchDeckXperiel
INTERESTED IN SELLING US A BUSINESS?

We aren't your typical buyer. We won't try to flip your business in 3-5 years. We won't mess with your team and culture. We won't lock you into golden handcuffs or push complex deal terms. We won't ruin your life with months of unnecessary due diligence. We won't renegotiate and grind you on terms. We'll make an offer within 7 days and close in 30. We keep things dead simple.

We started Tiny to create the buyer we wish we could have sold to. Here's a post we wrote about it.

The companies we buy usually fall into three buckets:
Bootstrapped

You've scaled your business with real customers and revenue. It's profitable ($500k+), but you're burnt out and don't know how to take it to the next level or want to do something new.

Venture That Can't 10x

You've raised some money and built a good business with serious revenue and maybe some profit, but you can't achieve venture scale. Your investors need a soft landing, and you want to sell to someone who appreciate your efforts.

Distressed

Ahhhh! Your hair is on fire and things are a bit of a mess, but you have serious revenue ($5MM or more) and think this thing could be profitable in the right hands. Help!

We typically look for businesses that have been in business for at least 3-5 years, have significant profits (minimum $500k, ideally $2MM+, as high as $20MM), and have a high quality team in place. We are open to owners sticking around, leaving cold turkey, or transitioning out over time. We'll work with you to transition.

We like simple internet businesses that have high margins, don't require tons of people or complex technology, and have a competitive advantage that protects them from competitors. For example: A dominant brand, a large and loyal userbase, a technology/IP advantage, etc.

If you're concerned your business doesn't fit the bill, feel free to send it to us anyway. We'll take a look and let you know within a couple days if it's a fit.
#technology  #entrepreneurship  #venture.capital  #financial 
june 2017 by ekingery
« earlier      
per page:    204080120160

related tags

#12:38  #agile  #AI  #algorithms  #analytics  #android  #angular  #apache  #api  #architecture  #archive  #art  #artificial.intelligence  #auth  #automation  #aws  #backbone  #backups  #bdd  #benchmarking  #bi  #bitcoin  #blockchain  #blog  #books  #browser  #build  #business  #business.intelligence  #caching  #calendar  #campfire  #career  #cartography  #cassandra  #CD  #chat  #chatops  #cheatsheets  #chicago  #child  #ci  #cli  #clojure  #cloud  #cms  #coding  #collaboration  #comedy  #communication  #community  #compensation  #computer.science  #conference  #consulting  #consumer  #containerization  #continous.delivery  #contracting  #coroutines  #countdown  #creativity  #credit  #crowdsourcing  #cryptography  #css  #cto  #culture  #currency  #data  #data.science  #database  #datacenter  #DDD  #deals  #debug  #deploy  #design  #design.for.hackers  #design.patterns  #desktop.support  #development  #devops  #diagram  #digital.art  #diversity  #diy  #dns  #documentation  #domain.driven.design  #economics  #edge  #editing  #editor  #education  #email  #embedded  #emoticons  #encoding  #encryption  #engineering  #entrepreneurship  #equity  #estimation  #etl  #event.driven  #exif  #export  #feedback  #fibers  #finance  #financial  #fitness  #fonts  #frameworks  #freelance  #functional.programming  #generators  #geochat  #geolocation  #git  #gitflow  #github  #go  #golang  #google  #google+  #government  #gpg  #graphdb  #graphics  #grunt  #hack  #hacking  #hadoop  #hardware  #haskell  #health  #heroku  #hiring  #hosting  #howto  #html  #html5  #htpc  #http  #human.resources  #humor  #I18N  #icons  #id3  #ide  #identity  #im  #incubator  #infosec  #infrastructure  #innovation  #interviewing  #investing  #io  #ios  #iphone  #irc  #java  #javascript  #jelly  #jenkins  #jobs  #joomla  #journalism  #jruby  #lambdadark  #languages  #last.fm  #lastfm  #latency  #law  #leadership  #league  #lean  #learning  #legal  #licensing  #linux  #literature  #mac  #machine.learning  #management  #markdown  #marketing  #math  #mathematica  #md5  #meetup  #merge  #messaging  #metaprogramming  #meteor  #metrics  #micro.services  #mobile  #monitoring  #mp3  #music  #mvc  #mysql  #networking  #newsgroup  #nlp  #node  #nosql  #notifications  #oauth  #opensource  #operations  #optimization  #organization  #payment  #payments  #performance  #philosophy  #phone  #photography  #php  #plex  #politics  #postgresql  #power  #presentation  #privacy  #process  #product  #productivity  #profiles  #profiling  #programming  #project.management  #proxy  #psychology  #public.good  #puppet  #python  #qa  #quality  #query.builder  #r  #racism  #rails  #real.estate  #real.time  #recruiting  #recycling  #refactoring  #regex  #registrar  #release  #reliability  #remarx  #remote.work  #resources  #rest  #reviews  #router  #rss  #ruby  #ruby.decorators  #SaaS  #salary  #sales  #saml  #scala  #scalability  #scrum  #search  #secure.delete  #security  #serialization  #serverless  #sharing  #shell  #shopping  #simhash  #slack  #SMS  #SOA  #social  #software  #software.architecture  #software.engineering  #software.process  #solr  #sql  #ssl  #sso  #startup  #stategy  #statistics  #stock  #storage  #strace  #surveillance  #survey  #sysadmin  #taxes  #tdd  #team.building  #technology  #telecommuting  #tent  #terminal  #testing  #time.management  #todo  #tool  #torrent  #traffic  #training  #tunnel  #tutorial  #tv  #twitter  #type  #ubuntu  #ui  #ux  #VC  #venture.capital  #vim  #viral  #virtualization  #visualization  #voip  #vuejs  #weather  #web  #web.programming  #web.serving  #wellspring  #wifi  #wiki  #windows  #wireframe  #xbmc  #xkcd  #xmarks  #zeromq  AList  clojure  DataScience  DDDesign  HTML5  infographic  linktuesday  PHP  prodmgmt  ux  vim 

Copy this bookmark:



description:


tags: