There are 3 things to learn at any given time:
1. That which never changes i.e. humans, yourself and others
2. What you need to know to succeed right now i.e. deeper in your current tools and systems, or those you'll need to use next month
3. Whatever intrigues you. Maybe this is what you're asking: what's new to be intrigued by?
To the latter, I would say to start learning machine learning if you haven't already.
> I very frequently get the question: "What's going to change in the next 10 years?" And that is a very interesting question; it's a very common one. I almost never get the question: "What's not going to change in the next 10 years?" And I submit to you that that second question is actually the more important of the two -- because you can build a business strategy around the things that are stable in time. ... [I]n our retail business, we know that customers want low prices, and I know that's going to be true 10 years from now. They want fast delivery; they want vast selection.
> It's impossible to imagine a future 10 years from now where a customer comes up and says, "Jeff, I love Amazon; I just wish the prices were a little higher." "I love Amazon; I just wish you'd deliver a little more slowly." Impossible.
https://www.inc.com/jeff-haden/20-years-ago-jeff-bezos-said-...
When I write a near-future work of fiction, one set, say, a decade hence, there used to be a recipe that worked eerily well. Simply put, 90% of the next decade's stuff is already here today. Buildings are designed to last many years. Automobiles have a design life of about a decade, so half the cars on the road will probably still be around in 2027. People ... there will be new faces, aged ten and under, and some older people will have died, but most adults will still be around, albeit older and grayer. This is the 90% of the near future that's already here.
After the already-here 90%, another 9% of the future a decade hence used to be easily predictable. You look at trends dictated by physical limits, such as Moore's Law, and you look at Intel's road map, and you use a bit of creative extrapolation, and you won't go too far wrong. If I predict that in 2027 LTE cellular phones will be everywhere, 5G will be available for high bandwidth applications, and fallback to satellite data service will be available at a price, you won't laugh at me. It's not like I'm predicting that airliners will fly slower and Nazis will take over the United States, is it?
And therein lies the problem: it's the 1% of unknown unknowns that throws off all calculations. As it happens, airliners today are slower than they were in the 1970s, and don't get me started about Nazis. Nobody in 2007 was expecting a Nazi revival in 2017, right? (Only this time round Germans get to be the good guys.)
<http://www.antipope.org/charlie/blog-static/2018/01/dude-you...>
Multiple HN discussions: <https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...>
I've been porting Stable Diffusion (which isn't a small model) over to Elixir and as part of doing that have been starting/stopping my jarvislabs machine when I start/stop building. I've been spending about $1/day without trying to be efficient.
Also, fast.ai is a great resource for learning ML, I highly recommend it.
For specific programming languages, I think Elixir is a great investment.
Edit: Some systems reading if anyone is curious:
* Thinking in Systems by Donella H. Meadows
* Designing Freedom by Stafford Beer. I also recommend anything by Stafford Beer.
* Anything by Christopher Alexander
* Systems Thinking For Social Change: A Practical Guide to Solving Complex Problems, Avoiding Unintended Consequences, and Achieving Lasting Results: A new to me book that I haven't read but looks promising.
* Elixir sits on top of the BEAM (the Erlang VM), which has several decades of being battle tested.
* Elixir is at its core a functional language, with immutable only (not just by default) data, and comes with built-in processes and OTP, a library that provides ready-made abstractions on top of processes. It's very, very good at concurrency.
* Elixir has Mix, Hex, and ExUnit, which provide great tooling and package management.
* Elixir's ecosystem is rather vibrant and active. Phoenix, LiveView, Livebook, Nx, Axon, and more. Elixir generally takes the approach of building things on top of Elixir from scratch, which frees it from having to deal with impedeance mismatches. See Livebook (the Elixir notebook solution) and Nx/Axon, and all the other machine learning stuff going on in Elixir right now.
I highly recommend the presentation The Soul of Erlang And Elixir by Sasa Juric.
https://www.youtube.com/watch?v=JvBT4XBdoUE
It really gets at the core of what makes Elixir and Erlang special, but I'd say Elixir has a lot more quality of life improvements over Erlang.
Edit: I think I watched both the Angular course (after having used Angular for a while) and the React with TypeScript course (at the time I transitioned to React).
And with Udemy for the last few years, one can always just wait a few days and get the course at 90% off.
What a gigantic waste of time and effort that field has been. I still have yet to see anything useful come out of it. NFTs always are pointed to as the "useful" thing, but tbh, if I'm buying art, I'd much rather have some well regarded painters work hung in my living room.
Not some monkey as my twitter avatar. Shrug.
It just feels kind of scammy... but I largely feel that way about the stock market as well.
Take for instance identity management - hospital ticketing system, where the patient's records are linked directly and inextricably to the tickets, etc. You can request an appointment using a smart contract, and everything else will happen magically.
Maybe someone worked on such systems, but they didn't get any airtime. I think eventually it will happen.
- Brushless motor control: has now reached cheap commodity status and would be good to learn more about. In the past I was always stuck between DC toy motors and full 6 wire AC motor control.
- Vision system on a small micro with edge-AI model for deer detection. After decades of classic machine vision I think it's time to overcome my neural net reservations and plunge in with one of those microcontrollers that can run pre-trained models efficiently
- Battery charging dock that robot drives to autonomously: learning goal is Oregon weather capable contacts or even wireless charging
My instinct would be to do all this in C++ with some Python as high-level glue but maybe time to learn some Rust? Not sure yet.
- Goal: increase the amount of natural light in my apartment during the day
- Mount a flat mirror on a motorized sun-tracking attachment on south-facing balcony railing
- Set up geometry, software, and motor to rotate the mirror so that it always reflects a beam of sunlight into the apartment, trained on a target area between the wall and ceiling, above eye level of occupants
1. What (recently or distantly) acquired skill(s) have proven most useful to you?
2. What skills don't you have but you regret or year for most?
I like @bckr's guidance (<https://news.ycombinator.com/item?id=34055079>), and suggest that there are abilities with greater persistence which are often underappreciated.
Systems Thinking. It helps you understand how components interact to form a system, and how to change it. Books:
- The Goal: https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0...
- Thinking in Systems: https://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp...
Pertinent for 2023, learn about costs. If you're an engineer, understand how much the services you're responsible for are costing. How can you reduce that cost? Can you optimize costs enough to save your monthly salary?
Learning how to learn new stuff
I feel like a low-code platform that can be stood up on infrastructure (cloud or otherwise) owned by a company could provide a lot of value. Many companies and especially local governments have unique infrastructure requirements that make using a random website for a business function a non-starter. If they could bring a properly supported low-code platform to their infrastructure i could see that being super productive for a lot of the simple use cases they encounter.
Stand out to your employers, acquire knowledge and skills that makes you truly special. Not the fastest programmer in the hottest language.
E.g. Be able to use chatGPT to help you code, but not if you're maybe a junior and can't discern good code from crap. It should be basically a subordinate who you do code reviews with, not the other way around, though when it's on it's game if you don't understand a concept it can explain it pretty damn good. Again that's assuming it isn't making things up.
It would be nice if there were a toggle, or slider for: truthfulness, and reliability. Where basically it has little creativity to 'create' things that don't exist, unless reliability is set to 'creative' or 'low'. If I'm writing a fiction novel, that's what I want. If I'm coding it isn't.
I think AI consulting and workflow management will be big in the coming years. It's obvious so many things that we can do with this tech to us, but to many people they just don't 'get' it, and there's money in showing them.
* GPU programming (GPUs have consistently kept up with Moore-like laws)
* FPGA/ASIC design (hard but price for all of these is dropping rapidly, so becoming more accessible)
* Bitcoin/cryptocurrency related tech, including standing up your own miner, full node, or understanding how to build applications on top of it (web3/etc.) (despite the hate, cryptocurrencies are still around and thriving)
* Solar and battery related tech (solar prices continue to drop, as does battery technology. Consumers ROI on solar installations are approaching 2-5 years instead of 10+).
Understanding "fundamentals", either in terms of computer science education or mathematics, I think is also critical but I don't really know what fundamental math should be focused on, in the short term. It's easy to say "neural networks" but proficiency in that area is mostly about learning frameworks (as a snapshot of right now) and little to do with some underlying theoretical understanding.
In terms of specific languages or frameworks, just a word of warning. What language/frameworks that were popular 10 years ago are still relevant today? Many people gain utility both from using and from being paid to manage frameworks (and to a certain extent languages) but they tend to be ephemeral.
One piece of advice that I think was pretty good was to avoid the "stampeding hoards". One can "win" at the game of being the best at what's fashionable now but the greater utility is in understanding more fundamental skills with the added benefit of, should a skill become fashionable later, being well versed in it when it does.
Kubernetes is still incredibly relevant but growth is slowing down (mostly because it ate the world already).
WASM and eBPF are hot new technologies but still niche.
CDK landed last year and will probably become more and more relevant for new projects vs vanilla Terraform.
When used for yourself, it's a tool for thinking, organizing information, and understand your inner workings.
When interacting with others, it can be persuasive but kind, eye opening but focused, or walk on any fine line you can imagine. You can teach, educate, warn, debate,... with the tone you like. It's a skill that enables both strategy and empathy.
Cloud technologies: AWS/kubernetes/docker
Languages: English/any other native lang of country that you are trying to settle in
Low level? It's about as far from "low level" as you can get. I would barely call it a programming language.
That being said, I do think it is a worthwhile skill to learn.
Also, I think a certain level of cloud agnosticism is possible. A lot of things like App Engine-esque services and FaaS can be boiled down to a core subset that can be agnostic. Once you get into managed queues and such, then you start losing some agnosticism for sure.
Highlights are hard to name because different people like different features!
The packaging/dependency situation is pretty much solved now. Cabal v2 has been a godsend. That used to be the big issue. Stack is still around and works fine. ghcup makes installing everything easy. And Nix support is excellent and offers diversity of choice between nixpkgs and haskell.nix (which opens your way to proper x-compilation!)
I don't use it, but the Haskell Language Server is also vastly improved.
The library ecosystem is also the best it's ever been. Again, depends on what you wanna do. But there are more people and companies out there contributing to Haskell OSS than ever.
The compiler and RTS also keep solving problems and pushing the bleeding edge. Compact regions, a new latency-optimized GC, and the eventlog have greatly improved the RTS. And linear types landed recently - still nascent but still a big deal. And dependent types are definitely on their way.
Related: the ability to radically change one’s mind on things you believe strongly.
* be empathic
* be humble
None of them are new but still trendy
I'm not a fan of any of those :D
I feel I don't see jobs in those languages as frequently as I do in other more modern languages, but I at least really enjoy the feeling of knowing a tiny bit about what goes on behind the scenes.
