#Repealthe8th | Are the Irish Media Up To The Job?
media
ireland
politics
political-correspondents
oireachtas-retort
analysis
society
marref
repealthe8th
8 days ago by jm
For years we were subject to speculation and debate about the emergence of new party in Irish politics. Endless coverage for Lucinda Creighton, Michael McDowell and whoever else. All the while, the most incredibly vibrant social movement touching every county in Ireland has emerged and the majority of journalists are unable to write about it.
Media comment has concerned itself not so much with the issues but with grave concern that this is happening outside perceived boundaries of respectable politics. This is ordinary people getting together and putting a most unspeakable issue on the agenda and soon to vote – in spite of the Normal Rules.
It is not just that regime journalists live in a bubble or don’t care to inform themselves. They genuinely do not understand how this campaign has played out. It is beyond their entire conception. This is what happens when your idea of politics only extends to the ritual of posters on lamp posts.
8 days ago by jm
AdoptOpenJDK/jitwatch
analysis
java
jvm
performance
tools
debugging
optimization
jit
august 2017 by jm
Log analyser and visualiser for the HotSpot JIT compiler. Inspect inlining decisions, hot methods, bytecode, and assembly. View results in the JavaFX user interface.
august 2017 by jm
Tad
april 2017 by jm
'A Desktop Viewer App for Tabular Data' -- pivot CSV data easily; works well with large files; free, from Antony Courtney
dataviz
osx
csv
data
pivot-tables
analysis
desktop
april 2017 by jm
Reproducible research: Stripe’s approach to data science
november 2016 by jm
This is intriguing -- using Jupyter notebooks to embody data analysis work, and ensure it's reproducible, which brings better rigour similarly to how unit tests improve coding. I must try this.
stripe
coding
data-science
reproducability
science
jupyter
notebooks
analysis
data
experiments
Reproducibility makes data science at Stripe feel like working on GitHub, where anyone can obtain and extend others’ work. Instead of islands of analysis, we share our research in a central repository of knowledge. This makes it dramatically easier for anyone on our team to work with our data science research, encouraging independent exploration.
We approach our analyses with the same rigor we apply to production code: our reports feel more like finished products, research is fleshed out and easy to understand, and there are clear programmatic steps from start to finish for every analysis.
november 2016 by jm
Structural and semantic deficiencies in the systemd architecture for real-world service management, a technical treatise
analysis
systemd
linux
unix
ops
init
critiques
software
logging
november 2015 by jm
Despite its overarching abstractions, it is semantically non-uniform and its complicated transaction and job scheduling heuristics ordered around a dependently networked object system create pathological failure cases with little debugging context that would otherwise not necessarily occur on systems with less layers of indirection. The use of bus APIs complicate communication with the service manager and lead to duplication of the object model for little gain. Further, the unit file options often carry implicit state or are not sufficiently expressive. There is an imbalance with regards to features of an eager service manager and that of a lazy loading service manager, having rusty edge cases of both with non-generic, manager-specific facilities. The approach to logging and the circularly dependent architecture seem to imply that lots of prior art has been ignored or understudied.
november 2015 by jm
Emojineering Part 1: Machine Learning for Emoji Trends - Instagram Engineering
may 2015 by jm
Instagram figuring out meanings from Emoji usage contexts using ML. 😮
instagram
emoji
cool
language
text
internet
web
speech
communication
trends
machine-learning
analysis
may 2015 by jm
VividCortex uses K-Means Clustering to discover related metrics
march 2015 by jm
After selecting an interesting spike in a metric, the algorithm can automate picking out a selection of other metrics which spiked at the same time. I can see that being pretty damn useful
metrics
k-means-clustering
clustering
algorithms
discovery
similarity
vividcortex
analysis
data
march 2015 by jm
The Infinite Hows, instead of the Five Whys
november 2014 by jm
John Allspaw with an interesting assertion that we need to ask "how", not "why" in five-whys postmortems:
ops
five-whys
john-allspaw
questions
postmortems
analysis
root-causes
“Why?” is the wrong question.
In order to learn (which should be the goal of any retrospective or post-hoc investigation) you want multiple and diverse perspectives. You get these by asking people for their own narratives. Effectively, you’re asking “how?“
Asking “why?” too easily gets you to an answer to the question “who?” (which in almost every case is irrelevant) or “takes you to the ‘mysterious’ incentives and motivations people bring into the workplace.”
Asking “how?” gets you to describe (at least some) of the conditions that allowed an event to take place, and provides rich operational data.
november 2014 by jm
Hydra Takes On Hadoop
Code: https://github.com/addthis/hydra
Getting Started blog post: https://www.addthis.com/blog/2014/02/18/getting-started-with-hydra/
hyrda
hadoop
data-processing
big-data
trees
clusters
analysis
april 2014 by jm
The intuition behind Hydra is something like this, "I have a lot of data, and there are a lot of things I could try to learn about it -- so many that I'm not even sure what I want to know.” It's about the curse of dimensionality -- more dimensions means exponentially more cost for exhaustive analysis. Hydra tries to make it easy to reduce the number of dimensions, or the cost of watching them (via probabilistic data structures), to just the right point where everything runs quickly but can still answer almost any question you think you might care about.
Code: https://github.com/addthis/hydra
Getting Started blog post: https://www.addthis.com/blog/2014/02/18/getting-started-with-hydra/
april 2014 by jm
error-prone - Catch common Java mistakes as compile-time errors
analysis
java
static-analysis
code
errors
bugs
november 2013 by jm
It's common for even the best programmers to make simple mistakes. And commonly, a refactoring which seems safe can leave behind code which will never do what's intended. We're used to getting help from the compiler, but it doesn't do much beyond static type checking. Using error-prone to augment the compiler's static analysis, you can catch more mistakes before they cost you time, or end up as bugs in production. We use error-prone in Google's Java build system to eliminate classes of serious bugs from entering our code, and we've open-sourced it, so you can too!
november 2013 by jm
What can data scientists learn from DevOps?
november 2012 by jm
Interesting.
'Rather than continuing to pretend analysis is a one-time, ad hoc action, automate it. [...] you need to maintain the automation machinery, but a cost-benefit analysis will show that the effort rapidly pays off — particularly for complex actions such as analysis that are nontrivial to get right.' (via @fintanr)
via:fintanr
data-science
data
automation
devops
analytics
analysis
'Rather than continuing to pretend analysis is a one-time, ad hoc action, automate it. [...] you need to maintain the automation machinery, but a cost-benefit analysis will show that the effort rapidly pays off — particularly for complex actions such as analysis that are nontrivial to get right.' (via @fintanr)
november 2012 by jm
Microsoft's Azure Feb 29th, 2012 outage postmortem
march 2012 by jm
'The leap day bug is that the GA calculated the valid-to date by simply taking the current date and adding one to its year. That meant that any GA that tried to create a transfer certificate on leap day set a valid-to date of February 29, 2013, an invalid date that caused the certificate creation to fail.' This caused cascading failures throughout the fleet. Ouch -- should have been spotted during code review
azure
dev
dates
leap-years
via:fanf
microsoft
outages
post-mortem
analysis
failure
march 2012 by jm
Dr. Neal Krawetz explains perceptual hashing
june 2011 by jm
ie. TinEye and other "images like this one" search engines. nice explanation
algorithm
images
analysis
programming
dct
hashing
perceptual-hash
tineye
via:hn
image
june 2011 by jm
Gamasutra - News - Opinion: Minecraft And The Question Of Luck
february 2011 by jm
'Notch’s luck was that he came across the idea of doing a first-person fortress building game. His alignment was that the game that he wanted to make was culturally connected to [he PC gamer] tribe. While the game may appear ugly, and its purchase process etc seem naive to many a gaming professional, all of those decisions that Notch made along the road to releasing his game were from the point of view of a particular perspective of what games are, what matters and what were the things that he could trust the tribe to figure out for themselves.'
tribes
viral
minecraft
gaming
analysis
games
culture
gamasutra
via:nelson
future
software
marketing
from delicious
february 2011 by jm
Dublin bikes revisited
february 2011 by jm
Fantastic comparative number crunching on the JC Decaux Dublin Bikes scheme, compared to their other European cities (Brussels, Lyons, Paris, Seville), times of day, busiest stations, rainfall, etc.
bikes
dublin-bikes
cycling
dublin
ireland
jc-decaux
number-crunching
analysis
statistics
from delicious
february 2011 by jm
First logging-as-a-service tool for the cloud wins NovaUCD award - siliconrepublic.com
november 2010 by jm
first, eh? not sure about that. still, good going for Irish startup JLizard, logging in the cloud seems to be hot
logging
metrics
analysis
cloud
ireland
startups
novaucd
from delicious
november 2010 by jm
Petit: Log Analysis
june 2010 by jm
log analyzer; removes common strings and patterns from log files, identifying outliers and hapaxen as "interesting". also does charting of frequencies etc.
logs
logging
analysis
loganalysis
syslog
tools
from delicious
june 2010 by jm
Body By Victoria - Secure Computing: Sec-C
december 2009 by jm
Dr. Neal Krawetz brings the science on detecting Photoshop retouching
pixels
images
forensics
jpeg
photoshop
fake
analysis
detection
from delicious
december 2009 by jm
glTail.rb - realtime logfile visualization
july 2009 by jm
'View real-time data and statistics from any logfile on any server with SSH, in an intuitive and entertaining way', supporting postfix/spamd/clamd logs among loads of others. very cool if a little silly
dataviz
visualization
tail
gltail
opengl
linux
apache
spamd
spamassassin
logs
statistics
sysadmin
analytics
animation
analysis
server
ruby
monitoring
logging
logfiles
july 2009 by jm
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