"Lambda is a building block, not a tool
Lambda is not well documented
Lambda is terrible at error handling"
- Lambda is a building block, not a tool
- Lambda is not well documented
- Lambda is terrible at error handling
about half those steps (8-10, depending) are things you’ll have to repeat for every endpoint you create.
Does 8-10 manual configuration per endpoint, every time you roll out a new version, sound like fun?
If you want HTTPS (and you should, it being 2016 and all), you need to add that into the mix as well.
without a spec it’s a guess-and-test game
I often seemed to be missing output when I was experimenting, which complicated life.
there doesn’t seem to be a way to get Lambda in Python to return anything but HTTP 200
The combination means that the most effective tool here is to always return a dictionary with an element indicating whether the request succeeded or failed, since you don’t have HTTP status codes to work with.
wrapping your entire service in a big
try/catchblock seems critical to have any control at all.
"“The transition from an Internet of websites to an Internet of mobile apps and social platforms, and Facebook in particular, is no longer coming — it is here, declared the Times."
“The transition from an Internet of websites to an Internet of mobile apps and social platforms, and Facebook in particular, is no longer coming — it is here, declared the Times.
Facebook recently demonstrated chat bots that allow users to interact directly with media companies through its Messenger app. “Messenger is going to be the next big platform for sharing privately, and for helping you connect with services in all kinds of new ways,” said Zuckerberg.
"AngularJS Backend-less Development Using a $httpBackend Mock"
"Curry–Howard correspondence (also known as the Curry–Howard isomorphism or equivalence, or the proofs-as-programs and propositions- or formulae-as-types interpretation) is the direct relationship between computer programs and mathematical proofs"
"Neural networks are one approach to machine learning that attempts to deal with the problem of large data dimensionality. The neural network approach uses a fixed number of basis functions - in contrast to methods such as support vector machines that attempt to adapt the number of basis functions - that are themselves parameterized by the model parameters. This is a significant departure from linear regression and logistic regression methods where the models consisted of linear combinations of fixed basis functions, ϕ(x)ϕ(x), that dependend only on the input vector, xx. In neural networks, the basis functions can now depend on both the model parameters and the input vector and thus take the form ϕ(x|w)ϕ(x|w).
"The guide contains twenty-four design patterns that are useful in cloud-hosted applications. Each pattern is provided in a common format that describes the context and problem, the solution, issues and considerations for applying the pattern, and an example based on Microsoft Azure. Each pattern also includes links to other related patterns.
The design patterns are allocated to one or more of the following eight categories: availability, data management, design and implementation, messaging, management and monitoring, performance and scalibility, resiliency, and secuity."
"An open source and collaborative framework for extracting the data you need from websites.
In a fast, simple, yet extensible way."
"This is where OLAD, or One Line A Day, comes in. It started as a challenge(really more of a demand) to my team with a few simple guidelines:
In 15 minutes or less, come up with any product or idea that you want to develop using whatever technology you want.
Every day you must write a minimum of 1 line of compilable code(not necessarily runnable/testable).
Every Friday, everyone demos what they have done that week.
"Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. So let’s take a look at some simple techniques for peeking inside these networks."
The network typically consists of 10-30 stacked layers of artificial neurons
"A bare bones neural network implementation to describe the inner workings of backpropagation."
"In this article, I’m not going to wish for unicorns. But there are some low hanging fruit (as far as I naively can see), which could be introduced into the Java language without great risk. "
"The principles behind this in Computer Science is named after
Covariance - ? extends MyClass,
Contravariance - ? super MyClass and
Invariance/non-Variance - MyClass
The picture below should explain the concept."
"The Open Graph protocol enables any web page to become a rich object in a social graph. For instance, this is used on Facebook to allow any web page to have the same functionality as any other object on Facebook.
While many different technologies and schemas exist and could be combined together, there isn't a single technology which provides enough information to richly represent any web page within the social graph. The Open Graph protocol builds on these existing technologies and gives developers one thing to implement. Developer simplicity is a key goal of the Open Graph protocol which has informed many of the technical design decisions.
"The aim of dimple is to open up the power and flexibility of d3 to analysts. It aims to give a gentle learning curve and minimal code to achieve something productive. It also exposes the d3 objects so you can pick them up and run to create some really cool stuff."
"CPU and memory profiling has never been easier, and smarter at the same time. YourKit has developed a revolutionary approach to profiling applications at both development and production stages, bringing unparalleled benefits to professional Java developers. "
Good post on basic of thread pools but also the common scenario of submitting explosive tasks for execution.
"HELLO! Today we're going to talk about THREAD POOLS and PARALLELIZING COMPUTATION. I learned a couple of things about this over the last few days. This is mostly going to be about Java & the JVM. It turns out that there are lots of things to know about concurrency on the JVM, but luckily, lots of people know those things so you can learn them!
A thread pool lets you run computations in more than one thread at the same time. Let's say I have a Super Slow Function, and I want to run it on 10000 things, and I have 32 cores on my CPU. Then I can run my function 32 times faster! Here's what that looks like in Python."
"Share anything from the Web.
Watch movies, play games, or even karaoke. Together."