Ben created the Wardley Mapping Canvas as a way to break down the steps of mapping into bite-sized pieces. He published a guest post about the canvas for Miro (a real-time collaboration tool, formerly RealtimeBoard), and the canvas has since been added to the official template library for all users.Mapping Canvas — Hired Thought
total2Several people have asked why I make a big deal out of the Dhall configuration language being “total” (i.e. not Turing-complete) and this post will summarize the two main reasons:If Dhall is total, that implies that the language got several other things correct“Not Turing-complete” is a signaling mechanism that appeals to Dhall’s target audience“Because of the…
Ever since we started to use Kanban I have been thinking about how the ”perfect” kanban board would look like. I have searched for answers in the Kanban literature, but usually the boards shown there are simple examples to get you started, rather than ”evolved” or ”advanced” variants. Shortly after I had the idea with the priority pyramid I discussed it with some other persons in the agile community. One of them gave the advice, ”Why don’t you rotate the pyramid 90 degrees and connect it to a kanban board?”. This is what I came up with.
This is some sort of advanced or “ultimate” kanban board to aim for. At least for now, our kaizen efforts will for sure evolve it in the future 🙂 The picture holds quite a lot of information, let me walk you through the arrow from left to right. Let’s get started!
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Since 2009 there has been a “Cambrian Explosion” of NoSQL databases, but information on data modeling with these new data stores feels hard to come by.
My weapon of choice for over a year now has been ArangoDB. While ArangoDB is pretty conscientious about having good documentation, there has been something missing for me: criteria for making modeling decisions.
Like most (all?) graph databases, ArangoDB allows you to model your data with a property graph. The building blocks of a property graph are attributes, vertices and edges. What makes data modelling with ArangoDB (and any other graph database) difficult is deciding between them.
To start with we need a little terminology. Since a blog is a well known thing, we can use a post with some comments and some tags as our test data to illustrate the idea.
Sparse vs Compact
Modeling our blog post with as…
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Recently, I’ve been working on a project that gave me a chance to get back to JVM world and take a nice look into its current state. Having spend a lot more time in .NET world last decade, it was very refreshing to experience it again. Highlights for me are working with Groovy (very nice and easy, but powerful and fast) and vertx.io application platform.
So… I simply love and admire the design of vertx.io; although it provides powerful set of features, it uses asynchronous messaging and reactive programming principles, it makes most of required infrastructure glue code disappear, by providing right abstractions and lightweight APIs that never gets in your way.
Here is the more complete list of the features straight from the their website:
- Simple actor-like concurrency model. Vert.x allows you to write all your code as single threaded, freeing you from many of the pitfalls of multi-threaded programming. (No more
volatileor explicit locking).
- Vert.x takes advantage of the JVM and scales seamlessly over available cores without having to manually fork multiple servers and handle inter process communication between them.
- Vert.x provides real power and simplicity, without being simplistic. Configuration and boiler-plate is kept to a minimum.
- Vert.x includes a powerful module system and public module registry, so you can easily re-use and share Vert.x modules with others.
- Vert.x can be embedded in your existing Java applications.
VisualStudio 2015 and C# 6.0 are out available for preview!!
I decided that a good way to do that would be to try implement a couple of monads in C#. Monads offer a very nice way to separate core logic from the boilerplate code. They are used quite a lot in many programming languages, and in C# they are foundation for Linq. So, I was wondering how easy/hard it would be to implement them in pure C# (not with Linq) and would they end up being practical to use in a OOP language like C#?
First, a basic intuition about monads:
monad = data container + some computation + public interface (‘From’ and ‘Bind’)
- Data container – in C#, simply a class
- Computation – repetitive logic that can be extracted from the main/core operation
- ‘From’ – function that wraps a value inside the monad – in C#, constructor or static factory
- ‘Bind’ – function that applies main/core operation within the context of internal computation
Lets try to implement two simple monads: Maybe (Option) and Writer monad.
Maybe monad’s internal computation checks for ‘null’ values, making top level code path clear of null checks.
Writer monad’s internal computation allows to accumulate additional information as we progress through chain of operations, hiding away explicit logging statements from the top level code path.
and here is how it is used:
You can get full source code by going to this GitHub page. I may continue to play around with this, potentially add other monads and more complex examples of use…
This is a written (expanded) narrative of the content from a talk I first gave at PhillyETE on April 23rd, 2014. It mostly follows the flow of the presentation given then, but with a level of detail that I hope enhances clarity of the ideas therein. The talk’s original slides are available, though the key illustrations and bullet points contained therein are replicated (and somewhat enhanced) below. When audio/video of the talk is published, I will update this page to link to it. Discussion about this piece has taken place on Hacker News, reddit, and Lobsters.
I have two claims of which I would like to convince you today:
- The notion of the networked application API is an unsalvageable anachronism that fails to account for the necessary complexities of distributed systems.
- There exist a set of formalisms that do account for these complexities, but which are…
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Lots of programming problems can be modeled by pipe-lining data through series of sequential or parallel processing steps. This data flow allows us to separate computational tasks into meaningful modules and
get more focused code base that is easier to debug and reason about. For example, a pipeline with three steps that takes input ‘req’ and successfully process it, would look like this:
And if at any stage there is error during the processing, we would skip the rest and return error…
Now, there are many ways to implement error handling, but this can easily end up in a messy combination of conditional statements and/or throwing and catching exceptions to stop processing. This adds noise to the real code logic and makes composing tasks harder… I’ve recently started to use Either monad to help with this this problem and am very happy about the results:
Here, I’m using Either implementation from the nice functional library Falktale…
Till next time…