briankelly

We really ought to come up with some new terms in this space... it is just confusing to mix "strong consistency" in with "eventual consistency" in any way.

I’m pretty sure the term came from Google (at least that is where I heard it first described) and just referred to a backend engineer with speciality in this area. Now usually these roles have “distributed systems” in the title, even if you aren’t really on the inside of the systems. That or “systems and infrastructure”, “data infrastructure”, or “AI/ML infrastructure” or sometimes “MLE” for those kinds of orgs. Or back to good ole “big data” now that it’s no longer tacked on everything.

Anyone investing is in a k hole

Wet bulb temp would be better to look at for that reason IMO.

I wonder if any Romans evidence had any evidence of population-level delirium from lead consumption...

Personally I think an updated camcorder would be incredible. CCD with global shutter and in a pocketable format with modern stabilization, a video analog to the rx100. Way too niche though.

I’ve seen people who seem to know what they’re talking about cast doubt that you could create your own probiotic yogurt in a typical kitchen since other strains are likely to dominate without a sterile environment and careful methods.

Location: CO, USA

Remote: Preferred

Willing to relocate: Yes

Technologies: distributed systems, high-scalability data-processing, Scala, Java, Python, Spark, Cassandra

Résumé/CV: https://drive.google.com/file/d/1URPefghzIgNSBR5eoq2AnyaDq9r...

Email: brianpatrickkelly (at) outlook (dot) com

LinkedIn: https://www.linkedin.com/in/brian-patrick-kelly/

Hello! My name is Brian and I have around 7 years of experience - the bulk of which I spent developing high-scalability storage software for HPC and enterprise. I focused on high-throughput data processing components that automate HSM and data lifecycle policies/operations primarily built with Scala, Spark, and Cassandra. Primarily passionate about systems-level software but open to a variety of roles. Thank you for reading.

In my opinion it will or already has redefined our conceptual models of intelligence - just like physical models of atoms or gravitational mechanics evolved and newer models replace the older. The older models aren't invalidated (all models are wrong, after all), but their limits are better understood.

People are waiting for this Prometheus-level moment with AI where it resembles us exactly but exceeds our capabilities, but I don't think that's necessary. It parallels humanity explaining Nature in our own image as God and claiming it was the other way around.

If jaw placement is an issue you can get corrective surgery without an apnea diagnosis (AFAIK). There’s a lot of info on Reddit, you can generally tell from your side profile or bite if it is a potential culprit.

It’s way more about control for the free software crowd. I do get it for tools of learning personally - managing your own knowledge base is important.

Never trust a wealthy guru.

Fair enough, but this did read that way to me:

> why I am very sad that we could not achieve reform without resorting to measures such as threatening the international student admission process

In what way does this or anything else Trump has done or indicated to do advance the state of education towards the goals of the reform you are talking about?

Well I don’t think it was kids still in school telling the truck drivers that so I’m not sure what karma has to do with it.

And what about the rest of human pyramid working under the director employed in these productions?

Nobody will watch anyone's "creations". The TV or whatever device watch on will observe what you and everyone else engage with and how you interact with it and create content for you on the fly, just like Instagram and TikTok's feeds do now.

AI creatives can enjoy the brief blip in time where they might get someone else to watch what they've created before their skills become obsolete in an exponentially faster rate just like everyone else's.

> Often times, "Stream vs. Batch" is discussed as if it’s one or the other, but to me this does not make that much sense really.

Just seems like a flawed premise to me since lambda architecture is the context in which streaming for data processing is frequently introduced. The batch vs stream discussion is more about the implementation side - tools or techniques best used for one aren’t best suited for the other since batch processing is usually optimized for throughput and streaming is usually optimized for latency. For example vectorization is useful for the former and code generation is useful for the latter.

The tooling is pretty easy to learn if scary looking at first and becoming a JVM expert, at least in the context of GC tuning, might take you a week or less. Some of the defaults are surprising though.

And I think there is some parallel with the kernel vs GC and mmap vs buffer pools - the GC simply has better context in the scope of the application. With other processes in the picture, though, yeah there is some provisioning complexity there.

Good to get your hands in the dirt as well.

My school started us off in microcontroller programming in C then Java for intro to data structures and OOP then back to C (and MIPS assembly) for systems/OS/concurrency. One thing I appreciate Java over Python for DS/Algo is the clear delineation between abstract data type and underlying data structure - IMO is easy to get a wrong mental model with Python. But teaching OS concepts in Java seems a little crazy to me.

Having a hard time finding it now, but someone put together a benchmark with two categories - naive and optimized - comparing implementations across languages with a workload vaguely resembling a real-world business application server with a mix of long and short lived objects. Java was at the top of the naive benchmark by a landslide and behind C and C++ (edit: and probably Rust) for the optimized ranking, but with a gap before the rest of the field.

With the JVM you basically outsource all the work you need to do in C/C++ to optimize memory management and a typical developer is going to have a hell of a time beating it for non-trivial, heterogenous workloads. The main disadvantage (at least as I understand) is the memory overhead that Java objects incur which prevent it from being fully optimized the way you can with C/C++.

The lore in the game Morrowind has a similar feel, though in a mythology-history instead of modern history sense, with a lot of Rashomon-style ambiguity.

Flink is designed around streaming first, while Spark is built around batch first and you're likely best off selecting accordingly. Though any streaming application likely needs batch processing to some degree. Latency vs throughput.

That would be because they aren’t meaningfully competing here. They both profit tremendously by the status quo and so do not step on each other’s toes.

My point was more about the two having separate use cases - but Kafka handles write throughput much easier compared to an RDBMS due to lack of transactions and not needing to manipulate the primary table data structures.

Yeah, it's Google and Facebook - not the ISP.

It's more of a commit log/write-ahead log/replication stream than a DBMS - consider that DBMSs typically include these in addition to their primary storage.

Very glad to see an active chapter near me - sounds awesome and I plan on checking it out.