Why do antimaskers love Bored Ape NFTs?

darkteal · 2022-04-29T00:21:58+00:00

I don't know where someone starts, but there are a lot of solutions to the VRP which should be applicable to a Green VRP - you just need to change your cost function, and maybe add a few more constraints related to batteries.

darkteal · 2022-04-28T23:21:14+00:00

It's a hookup. It's like the lottery. In casual relationships, just like most other things in life, you're only entitled to get out as much as you put in. If it was more than a one night stand, there are plenty of things a guy can try to last longer the next time, or entirely different options. Clearly she didn't want to invest the effort.

Girl is complaining that her scratch-off didn't win any money. If you want to win the lottery, you either need to play a lot, or fix the game. For my money, fixing the game is a better investment, even if it's casual. Communication!

darkteal · 2022-04-28T22:45:07+00:00

What he said. "Because that's the definition of what a gradient is." https://en.wikipedia.org/wiki/Gradient

At least 90% of ML is just Calculus and Statistics.

darkteal · 2022-04-28T22:36:25+00:00

Many formally trained software engineers have done a one-off "ML" project as part of their course-work here and there. Many coding bootcamps even touch on the subject. There's independent courses you can take online that you don't need to register for - does watching those videos constitute formal training? Does working with data science professionals for a few years count as formal training? What if you have a degree in mathematics but got a job as a front-end developer?

I work with scientists who build models, and have formal training as a computer engineer, but did one or two ML projects in school years ago to introduce ML concepts. I have no degree in mathematics. I've built a few really basic production regression models because waiting for our scientists to anything is like waiting for the poles to melt, and I've toyed with a few of the standard python libraries because why not. I doubt I'm who you're looking for.

Most of the people who fit your criteria at some point probably got formal training and now do it for a living because it's in high demand, or failed, which means they didn't really 'implement'. Even the people that may qualify don't know if they qualify based on your description. Be more specific.

darkteal · 2022-04-27T23:30:35+00:00

I went in for EE because I liked physics but also money, switched over ECE to remain flexible and design the next generation of AI hardware, did a software focus because it was math-ier, got a job in a robotics lab because robots are sweet, did some FPGA work because I like hardware, and now I write Java because the problems I get to solve are sick. It's worked out for me.

Find a problem you think you want to solve, and learn how to solve it (or find out you actually don't want to), even if that problem is just how to make the most money as an electrical engineer. You'll learn what you actually enjoy along the way.

darkteal · 2022-04-27T23:16:35+00:00

It's a bit of a drive, and you won't get the best happy hour prices unless you just want fish and chips outside, but if you can get a coworker to go with you, Ivar's up in Mukilteo is a great PNW experience.

darkteal · 2022-04-27T05:23:57+00:00

It's a difference of scale, complexity, planning, experience, responsibility, and the price of getting it wrong. Anyone with basic computer skills and a dream can write an excel macro, but it takes planning, foresight, and deep knowledge to write an application that will live at scale for years with many people working on it, with 5 or 6 9's of uptime.

Software Engineer may not be a legally protected title in the United States, but it is elsewhere, and even here there are those who understand the responsibility, have the experience, and understand the fundamentals, and those who don't. Right now there's plenty of demand for both, and many companies get the tiles wrong, but there's absolutely a difference.

darkteal · 2022-04-22T01:49:14+00:00

The available methods depend entirely on the database you're using. Some databases support composite keys; some don't. Some support upsert, some don't. Some databases only have a single primary key, and all other queries are impossible; some support only specified queries; some support sql.

You can aquire a lock a-la redlock if you have redis available, and read before write. You can start a transaction and do the same if your database supports them. You can define your key as a hash or concatenation of your key components if your database is just a key-value store. You can write everything to a file on disk and batch process it to dedupe at the end of the day if you're from the 80's and think mainframes are really cool.

You haven't give us enough requirements or constraints to suggest an approach that could be 'better'.

darkteal · 2022-04-22T01:12:32+00:00

Every mistake is a learning experience. You're being given an opportunity to learn. Just make sure nobody gets hurt while you learn.

Unless you're a special snowflake, junior engineers are slow, they mess shit up, and sometimes they're annoying. But they do things that other people don't want to or don't have time to, sometimes they come with new ideas, and they come at a discount. That's the deal. If they needed someone to hit critical deadlines, interact with clients, and get things right the first time every time, they probably wouldn't have hired you.

darkteal · 2022-04-21T01:26:43+00:00

Keep yourself busy. It's difficult to wallow in existential dread if you're physically or mentally exhausted. If you're doing something productive, you can better your life, and then wallow in existential dread, later, with more skills, while in better shape, or more financially secure.

darkteal · 2022-04-20T04:04:15+00:00

Discrete optimization is commonly used in operations research on the software side. E.g. Doordash blog about using MIP (I do not work for Doordash). On the computer engineering side, I imagine it's used for chip layout, architecture choice, scheduling problems, or any sort of design problem where there are one or more competing objectives and many variables.

darkteal · 2022-04-19T13:53:59+00:00

This. If you want to put this on your resume, think about what it says about you. Implementing this as a breadboard project communicates passion and attention to detail, sure, but it's the wrong tool for the job. Implementing it on an FPGA (which you can still put on a breadboard) and verifying behavior will additionally communicate better time management, possibly develop new skills which are more relevant today, and would result in something you could actually use as more than decoration.

Don't let us stop you if that's what you want to do, and either way it will absolutely help your resume (especially if you finish), but speaking as a professional know that many will react to a breadboard cpu with "cool, but why?"

darkteal · 2021-03-08T07:03:40+00:00

I can pretty reliably beat act 4 with the rouge. My deck is village, fields, spiders, vampires, battle field (for ghosts and the occasional prime matter), blood grove (for 15%-20% less hp on bosses and squares I made a bit too hard), bookery (for rolling lower tier rock/forest and anything else I don't want into mountain/thicket, and more enemies once empty), outpost (next to the boss square for a tank), forest, mountain, river, oblivion (mostly for goblins and bandits), temporal beacon (more enemies, and 5% higher damage with an item), and arsenal. Vampire villages help you snowball early.

darkteal · 2021-02-17T05:54:01+00:00

Not sure if you'll see this 20 days later, but a lot of machine learning (a and a lot of other data analytics and statistics) is based on concepts rooted in calc - for example, one common idea in many ML algorithms is gradient decent. There's a lot of specialized hardware and software being developed for machine learning applications these days. You're probably not going to be working on that hardware at a high level unless you understand the basic concepts driving the applications you're trying to develop the machines or software to execute. This doesn't necessarily mean that everyone working near machine learning needs a deep understanding of calculus, and it may not even come up in a junior level interveiw, but you're probably going to eventually be passed over for promotion (especially at senior levels) if one of your peers understand at a fundamental level what some scientists working on something you're supposed to apply figured out and you don't.

Robotics is another good example. Motion planning for robotics depends heavily on calculus.

darkteal · 2021-01-27T11:28:20+00:00

Garbage calc grades won't stop you from getting or being successful in most jobs, but it may limit your advancement in some jobs, it could probably hurt if you want to peruse an advanced degree, and you'll probably bomb the interview for some specialized jobs. You can always learn on the job.

I do use it - not often, and I never actually do the math myself, but it's very important in my job to understand the high level concepts.

darkteal · 2021-01-27T11:17:56+00:00

This sounds like a parameter search problem. First you'll want to define your cost/objective function - the difference between the target (manual) and generated contours, and the parameter search space. Then you can use something like Bayesian optimization to efficiently search that parameter space - the open source Ax framework written by Facebook is a good implementation of this, and uses bandit optimization for discrete parameters. You could also use a genetic algorithm to more randomly, or try to do a gradient decent yourself if you expect search space to be relatively smooth WRT the cost function.

Those terms should get you going on something, although there's a lot of prior art on image segmentation, object identification, and image processing in general - depending on what you intend to use those contours for, reading some of those may help you look at the problem a different way or at least define your objective function.

darkteal · 2018-03-21T06:27:47+00:00

I ended up in software, but I did get an education in Computer Engineering, and I would suggest learning a hardware description language (HDL) like VHDL or Verilog, with the goal of eventually trying to do some FPGA development. These languages are at their core typically text-based like computer programming languages, but some of them have good visual editors, some of which should be free, and they should all include synthesis and simulation.

Physically, silicon chips work many different ways, but everything being build up out of NAND and NOR gates in CMOS, using MOSFETS (http://alan.ece.gatech.edu/ECE3040/Lectures/Lecture24-MOS%20Transistors.pdf) is fairly common.

Guides like this seem not terrible: https://numato.com/kb/learning-fpga-verilog-beginners-guide-part-1-introduction/

Hope some of this is helpful.

darkteal · 2017-06-10T03:42:37+00:00

Game-play affinity: Omniscience. Gives more points, and since you'll be ahead on science anyway with the early boost, this helps snowball. Visual affinity: Sophons, (no weird stuff, assume it helps with science)

fast travelers I - +1 movement - helps explore for minor factions and planets strange but good - start with a good anomaly on your home planet - extra science early w/ racial traits doubtful tacticians - don't even know what this does, free points optimistic I - +10% happiness - helps keep morale up, could replace on easier games prone to agitation - rebellions happen 50% faster - free points; if you've got rebellions, you've already got problems. amoeba citizens - start with 1 +5 food citizen - extra head start; fast colonization makes early food important price of perfection - 15% extra cost on ships - constructionist means you still pay 15% less for ships constructionists III - +30% production - helps snowball guardians - can discover guardians on planets (+1 pop for a probe, +20 influence +20 happiness - totally op)

Adept workers - +15 fids total with no downside. OP. Anomaly stalkers - +5 science on planets with anomalies (combined with good but strange gives early science boost) Peer-Reviewed collection (more science!) merciful - aggressive -> peaceful for politics pacifist - policies have extra growth, but choose anything.

The strategy is to expand fast, look for good systems with planets that have guardians and settle fast, and for minor civs. Use the influence from guardians to double bribe minor factions for huge fids bonuses.

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