Real-time Enterprise Issue #12

ProcessOne curates two monthly newsletters – tech-focused Real-time Stack and business-focused Real-time Enterprise. Here are the articles concerning business aspects of real-time enterprise we found interesting in Issue #12. To receive this newsletter straight in your inbox on the day it’s published, subscribe here.

Cisco: 90% of internet traffic through Erlang-controlled nodes

A tweet from @guieevc and a subsequent discussion on HN gives us some insights into how internet traffic is routed and what powers all the machines doing the job.

Ruby vs Elixir vs Go: concurrency comparision

Siva Gollapalli writes: “As we all know, for comparison you need more than one language. So, I choose Golang and Elixir along with Ruby. When I search about building highly concurrent applications most of the results would involve either one of these languages and moreover I like these two languages. Golang for its simplicity and Elixir for its Ruby like features.”

Apple introduces the AI phone

This “new AI iPhone” — which, to be clear, is your same ol’ iPhone running a new mobile OS — will understand where you are, what you’re doing and what you need to know right then and there.

Your smartphone is listening and it’s not a paranoia

For your smartphone to actually pay attention and record you, there needs to be a trigger, like Hey Siri or Okay Google for example . Without these triggers, there’s no recording, with just some general metrics being sent to your service provider. This might not seem a cause for an alarm, but when it comes to apps like Facebook, no one knows what the triggers are. In fact, there could be thousands.

Use MQTT to stream real-time data

The ny-power project uses a set of microservices to consume open data about the performance of the NY State power grid, and computes an approximate level of carbon intensity. This computed data is served in a real-time MQTT stream, making it easy to consume in other applications. It also shows how to manage a set of data processing in Kubernetes with Helm.

Mining for Bitcoin vanity addresses with elixir

Let’s take another bite out of Mastering Bitcoin and implement the algorithm described for “mining for vanity addresses”. After we implement the basic algorithm, we’ll add our Elixir special sauce and turn it into a fully parallelized procedure.

Why you should never have a data room  for fund-raising

The data room is where your process goes to die. What happens is 18–20 firms access the data room and download all of your documents. You feel proud because data rooms have tracking on them so you know exactly who did and who did not access your data. So you sit around and wait for the next call. You convince yourself that it should take 1–2 weeks until they have gone through the data and you’ll get a call but it never comes.

Let us know what you think 💬

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