Blackfynn is hiring. We power neuroscience by linking all kinds of data like EEG, fMRI, and pathology data, and organizing that data so that investigators can concentrate on doing science instead of tedious data management. We use Python & Scala, graph databases and Postgres. We work out of our office in Philadelphia. email me with any questions: jim@blackfynn.com

I built a company on a .net stack a long time ago; it was fine. The biggest problem is SQL Server used to cost a lot of money and was kind of a waste in the face of postgres. As far as the programming ecosystem; C# as a language is great, but so much of the standard library and 3rd party libraries are old and crusty. Some were legitimately great though, like Dapper for talking to the DB, and ASP.net MVC was a decent web framework.

i use swagger with akka-http using annotations to automatically generate the swagger doc from the scala source code. mostly works pretty well, but occasionally the scala data structures gets mangled in translation, which is pretty annoying. but then i recall what i did before, which was manually documenting stuff or worse, nothing at all.

I like how they completely ignore Tinkerpop/Gremlin.

“Please stop doing X” posts are a dime a dozen and rarely offer actionable advice more prescriptive than the simple “stop doing this thing.”

Let’s get more posts that say, “Before doing creating something new, here’s how to evaluate what exists for your use case.”

I mean, if you’re in Python, yeah. If you’re using a functional language this is seldom the case.

“‘Oh just thread your application.’ Anyone that says that is basically an idiot, not appreciating the problems.”

always hard. somewhat easier with immutable data structures

Happy Birthday Lobsters! I’m grateful for the decent and civil space where people can share interesting stuff.

Whenever I read about people’s first hand encounters with Steve Jobs, I’m always impressed by what a jerk he was.

I’m working on a video course (for Pluralsight) about dealing with temporal data in PostgreSQL. It’s hard going but these things always are, and the topic is fascinating to me. There’s a lot of research and I’m learning interesting things about Postgres in the process.

I’ve been working on a streaming time series service. The client requests a time span, a list of channels, and some resampling parameters, and my service returns resampled waveform data for each channel sent as a stream over a websocket.

This week I’m incorporating neural spike events, and working to get streaming input working from a neurostimulation / sensor device.

For fun, this weekend I sat down with my daughter and made some silly pictures with the haskell Diagrams library, which I have to say, is the most elegant, well thought out API I’ve ever seen. We made a big smiley face.

So I taught students Python, Java, HTML, CSS, and JavaScript — real programming languages that you type, not silly blocks

I have a hard time taking an article seriously when the author describes HTML (on its own) as a programming language. It’s a markup language. It’s not Turing complete unless you manage to hack it with CSS.

Why are the slot machines purely relying on pseudorandomness? There seem to be some sources of “true” randomness that they can use to seed the PRG without requiring additional hardware, e.g. the trailing digits of exact timing of various events (so, for instance, pressing a button or inserting a dollar bill exactly on the hour and 0.001 seconds after the hour lead to completely different results - I highly doubt any human, even an app-assisted one, is capable of being precise enough on timing to exploit that).

I’m building a graph store backed by redis and fronted by a rest api. Its going to hold some medical/neuroscience data, so the security requirements are a bit special. eventually we’ll be running some machine learning algos over it, to build “annotating” edges, so this is all foundational from the fun stuff that comes next.

For my “old job” I’m doing medical insurance claims ETL. To get ready for my new job, I’m learning about streaming epilepsy detection for EEG data following the results here: http://ieeexplore.ieee.org/xpl/login.jsp?reload=true&tp=&arnumber=1020545&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1020545

It’ll probably just be a toy (or maybe just a proof of concept) at first since I don’t know much about EEGs (yet!)

No mainstream language today allows you to write a library to extend its type system

hmmmm that’s the most interesting sentence in the article. There’s alot of ways to extend type systems, so I’m not sure what the author has in mind exactly. I seem to remember somebody adding some flavor of dependent types into Haskell, which would certainly qualify.

I’m building a healthcare analytics system aggregating disease/diagnosis, prescriptions/procedures, and patient demographics over a large population of (anonymized) health insurance claims. Its not going to un-fuck the american healthcare system, but hey - more data shouldn’t hurt either.

I hadn’t actually thought of this as on-topic here, until I realized we have the cogsci tag… :)

I have a longstanding disagreement with this position, just to admit my bias up front. And I’m aware that the below views are controversial. I don’t intend to get into any fights about them, but I want to offer them.

Also, I’ve met people who find the brain-as-computer metaphor to be deeply upsetting, emotionally. If that’s you, fair warning that I go into some detail below, and I do advise skipping the rest of this or at least making sure you feel prepared.

Forgive me for this introduction to computing, but I need to be clear: computers really do operate on symbolic representations of the world. They really store and retrieve. They really process. They really have physical memories. They really are guided in everything they do, without exception, by algorithms.

Humans, on the other hand, do not – never did, never will. Given this reality, why do so many scientists talk about our mental life as if we were computers?

But then we keep seeing studies like the one described in this SciAm article, beginning to investigate exactly that symbolic representation. I can’t find them right now, but I’ve seen similar news items go by with regard to visual and spatial memory.

Certainly I’ll readily agree that “algorithm” suggests a system that works more often than it fails, and that’s not how I’d describe the brain. I’ll give this author that part of the discussion.

The brain is architecturally very different from a silicon computer. It has far more in common with a distributed system than with a single CPU core; all sorts of neurological phenomena come down to things happening out of sync with each other, and I’m not sure any have ever come down to an invalid pointer. (Glossolalia is interesting to think about in that context, but there’s no reason to suspect that there’s any analogue of pointers or addresses. But surely I don’t need to explain how suggestive the nature of neurons is as far as the possible mechanisms for structured information.)

Humanity understands the brain even less than it understands the security implications of Intel’s management engine. That present lack of understanding makes it easy to get away with claims that it’s fundamentally incomprehensible, that it does do certain things that it manifestly doesn’t, and that it doesn’t do things that it manifestly does.

Surely, processing information is the one thing we can be certain the brain actually does!

“the lyf so short, the craft so long to lerne.”

building monster ETL pipeline with Spark and Scala. I just learned last night how to use implicits to add custom methods on DataFrames, and it really cleaned up my code. Spark is definitely more expressive and more fun to write than hadoop jobs, but it also seems less stable. (random OOM problems if you’re not super careful)