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StackOverfllow -> Copy Code -> Doesn't work -> Study its working -> Read related info on other websites -> Code starts to make sense -> Do the required changes in code as per the understanding -> Got it working.
I only learn if the initial 2 steps fail. Else I skip the latter. -
Client: I can't login with my lastpass
Me: Oh, why not, how are you trying?
Client: So, I've entered my lastpass password into my bank account, and it says 'wrong login credentials'
Me: °-°17 -
Dogecoin hit USD $0.40 recently, which means it's time for the Crypto Rant.
TL;DR: Dogecoin is shit and is logically guaranteed to eventually fall unless it is fundamentally changed.
===========================
If you know how Crypto works under the hood, you can skip to the next section. If you don't, here's the general xyz-coin formula:
Money is sent via transactions, which are validated by *anybody*.
Since transactions are validated by anybody, the system needs to make sure you're not fucking it up on purpose.
The current idea (that most coins use today) is called proof-of-work. In short, you're given an extremely difficult task, and the general idea is you wouldn't be willing to do that work if you were just going to fuck up the system.
For validating these transactions, you are rewarded twofold:
1) You are given a fixed-size prize of the currency from the system itself. This is how new currency is introduced, or "minted" if you prefer.
2) You are given variable-size and user-determined prize called "transaction fees", but it could be more accurately called a "bribe" since it's sole purpose is to entice miners to add YOUR transaction to their block.
This system of validation and reward is called mining.
===========================
This smaller section compares the design o f BTC to Dogecoin - which will lead to my final argument
In BTC, the time between blocks (chunks of data which record transactions and are added to the chain, hence blockchain) is ten minutes. Every ten minutes, BTC transactions are validated and new Bitcoins are born.
In Dogecoin, the time between blocks is only one minute. In Theory, this means that mining Dogecoin is about ten times easier, because the system expects you to be able to solve the proof of work in an average of one minute.
The huge difference between BTC and Doge is the block reward (Fixed amount; new coins minted). The block reward for BTC is somewhat complicated compared to Doge: It started as 50 BTC per block and every 4 years it is halved ("the great halving"). Right now it's 6.25 BTC per block. Soon, the block reward will be almost nothing until BTC hits it's max of 21 million bitcoins "minted".
Dogecoin reward is 10,000 coins per block. And it will be that way for the end of time - no maximum, no great halving. And remember, for every 1 BTC block mined, 10 Doge blocks are mined.
===========================
Bitcoin and Dogecoin are now the two most popular coins in pop culture. What makes me angry is the widespread misunderstanding of the differences between the two. It is likely that most investors buy Dogecoin thinking they're getting in "early" because it's so cheap. They think it's cheap because it isn't as popular as Bitcoin yet. They're wrong. It's cheap because of what's outlined in section two of this rant.
Dogecoin is actually not very far off Bitcoin. Do the math: there's a bit over 100 billion Dogecoin in circulation (130b). There's about 20 million BTC. Calculate their total CURRENT values:
130b * $0.40 = 52b
20m * $60k = 1.2t
...and Doge is rising much, much faster than BTC because of the aforementioned lack of understanding.
The most common thing I hear about Doge is that "nobody expects it to reach Bitcoin levels" (referring to being worth 60k a fucking coin). They don't realize that if Doge gets to be worth just $10 a coin, it will not just reach Bitcoin levels but overtake Bitcoin in value ($1.3T).
===========================
It's worth highlighting that Dogecoin is literally designed to fail. Since it lacks a cap on new coins being introduced, it's just simple math that no matter how much Doge rises, it will eventually be worthless. And it won't take centuries, remember that 100k new Doge are mined EVERY TEN MINUTES. 1,440 minutes in a day * 10K per minute is 14.4 million new coins per day. That's damn near every Bitcoin to ever exist mined every day in Dogecoin
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I'm gonna fail my now-online uni course. I'm not understanding jackshit.
Fuck this covid bullshit.
Thank you for listening.16 -
I’m so fucking sick and tired of !devs telling me how simple a feature should be to implement.
Like motherfucker the most complicated thing you’ve ever done with a computer is attempt (and fail) at working with tables in Microsoft Word and you’re trying to tell me how long a new feature/K8s architecture/noSQL aggregation should take to implement?
A monitor cable wiggling loose paralyses you for hours but I’m supposed to bow down to your understanding of what is causing a bug?6 -
*Have idea.
*Start thinking about it.
*Start seriously thinking about it.
*Push yourself to do it.
*Have a mini unconscious panic attack that tells you you're gonna fail.
*Do something else completely unrelated to what you wanted to do.
*Feel good about the meaningless shit you just achieved.
*Recognize you're procrastinating.
*Try to stop.
*You can't cause you're stupid and lack the control and discipline to trick your own mind.
*Watch video about how to overcome procrastination.
*Get a more firmer understanding of it.
*Realize you're a helpless piece of shit.
*Cry till dead inside.
*Resume daily mindless shit.1 -
# Retrospective as Backend engineer
Once upon a time, I was rejected by a startup who tries to snag me from another company that I was working with.
They are looking for Senior / Supervisor level backend engineer and my profile looks like a fit for them.
So they contacted me, arranged a technical test, system design test, and interview with their lead backend engineer who also happens to be co-founder of the startup.
## The Interview
As usual, they asked me what are my contribution to previous workplace.
I answered them with achievements that I think are the best for each company that I worked with, and how to technologically achieve them.
One of it includes designing and implementing a `CQRS+ES` system in the backend.
With complete capability of what I `brag` as `Time Machine` through replaying event.
## The Rejection
And of course I was rejected by the startup, maybe specifically by the co-founder. As I asked around on the reason of rejection from an insider.
They insisted I am a guy who overengineer thing that are not needed, by doing `CQRS+ES`, and only suitable for RND, non-production stuffs.
Nobody needs that kind of `Time Machine`.
## Ironically
After switching jobs (to another company), becoming fullstack developer, learning about react and redux.
I can reflect back on this past experience and say this:
The same company that says `CQRS+ES` is an over engineering, also uses `React+Redux`.
Never did they realize the concept behind `React+Redux` is very similar to `CQRS+ES`.
- Separation of concern
- CQRS: `Command` is separated from `Query`
- Redux: Side effect / `Action` in `Thunk` separated from the presentation
- Managing State of Application
- ES: Through sequence of `Event` produced by `Command`
- Redux: Through action data produced / dispatched by `Action`
- Replayability
- ES: Through replaying `Event` into the `Applier`
- Redux: Through replay `Action` which trigger dispatch to `Reducer`
---
The same company that says `CQRS` is an over engineering also uses `ElasticSearch+MySQL`.
Never did they realize they are separating `WRITE` database into `MySQL` as their `Single Source Of Truth`, and `READ` database into `ElasticSearch` is also inline with `CQRS` principle.
## Value as Backend Engineer
It's a sad days as Backend Engineer these days. At least in the country I live in.
Seems like being a backend engineer is often under-appreciated.
Company (or people) seems to think of backend engineer is the guy who ONLY makes `CRUD` API endpoint to database.
- I've heard from Fullstack engineer who comes from React background complains about Backend engineers have it easy by only doing CRUD without having to worry about application.
- The same guy fails when given task in Backend to make a simple round-robin ticketing system.
- I've seen company who only hires Fullstack engineer with strong Frontend experience, fails to have basic understanding of how SQL Transaction and Connection Pool works.
- I've seen company Fullstack engineer relies on ORM to do super complex query instead of writing proper SQL, and prefer to translate SQL into ORM query language.
- I've seen company Fullstack engineer with strong React background brags about Uncle Bob clean code but fail to know on how to do basic dependency injection.
- I've heard company who made webapp criticize my way of handling `session` through http secure cookie. Saying it's a bad practice and better to use local storage. Despite my argument of `secure` in the cookie and ability to control cookie via backend.18 -
Boss at meeting to us all: "All our data is going to be moved to this fantastic system x has made called Oracle. I don't think they created oracle."3
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It works. My code fucking works. It shouldn't. I got tons of errors, I changed to some obscure shit, expecting it to fail, but it fucking works. I should be happy, but I'm mad for not understanding how.3
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JavaScript is new the PHP.
Reading a stackoverflow question about async functions and await-usage, and lot of well-intended answers show no understanding of the concept. Some fail to understand that the scope is about promises. They don't know that, yes, *every* async function *always* returns a Promise and that within an async function it's synctically the same to do either `return x` or `Promise.resolve(x)` or `return new Promise((resolve) => {resolve(x)}), and they fail to realize what await does or doesn't do and are oblivious about how awaiting at certain stages can have huge performance impact when compared to either Promise.all or Bluebird.map.
Grasping promises is hard in the beginning, I get that. But please don't share your lack of understanding as fact. -
Our original backend codebase used so-called "clean architecture" and really put the Java back in JavaScript (actually TypeScript, but hopefully you get my meaning).
For every endpoint I want to create, our design needs at least two classes that are basically callable singletons. If my understanding doesn't fail me, it could have been simple old functions... I think?10 -
Why don't most products follow a minimalist approach right till the end? Most start ups start like that. But when things begin to fall apart or become better they tend to deviate. While the earlier reason is understandable (because no one likes to fail so they'll do anything to not fail), the second reason seems to me more of an organisational creation than what the users want.
From my understanding as the product becomes popular positions (managerial or product) created need to justify their presence. What do they do? So the breath of fresh air brings in a lot of garbage that may not be required and would be in deviance from the main product idea.
It is debatable that audiences would not accept such ideas that are being brought in, because hey audiences are smart. And they are. But the organisation in order to justify the original wrong decision tends to push their new features (through offers or marketing campaigns). This makes the organisation invested into a wrong direction and security of jobs of the new managers/product people. Win win situation, but lose lose for the organisation and the original product. -
analogy for overfitting :
cramming a math problem by heart even the digits of any problem for exam.
now if the exact same problem comes to exam i pass with full marks else if just the digits are changed however the concept is same and simce i mugged up it all rather than understanding it i fail. -
The hype of Artificial Intelligence and Neutral Net gets me sick by the day.
We all know that the potential power of AI’s give stock prices a bump and bolster investor confidence. But too many companies are reluctant to address its very real limits. It has evidently become a taboo to discuss AI’s shortcomings and the limitations of machine learning, neural nets, and deep learning. However, if we want to strategically deploy these technologies in enterprises, we really need to talk about its weaknesses.
AI lacks common sense. AI may be able to recognize that within a photo, there’s a man on a horse. But it probably won’t appreciate that the figures are actually a bronze sculpture of a man on a horse, not an actual man on an actual horse.
Let's consider the lesson offered by Margaret Mitchell, a research scientist at Google. Mitchell helps develop computers that can communicate about what they see and understand. As she feeds images and data to AIs, she asks them questions about what they “see.” In one case, Mitchell fed an AI lots of input about fun things and activities. When Mitchell showed the AI an image of a koala bear, it said, “Cute creature!” But when she showed the AI a picture of a house violently burning down, the AI exclaimed, “That’s awesome!”
The AI selected this response due to the orange and red colors it scanned in the photo; these fiery tones were frequently associated with positive responses in the AI’s input data set. It’s stories like these that demonstrate AI’s inevitable gaps, blind spots, and complete lack of common sense.
AI is data-hungry and brittle. Neural nets require far too much data to match human intellects. In most cases, they require thousands or millions of examples to learn from. Worse still, each time you need to recognize a new type of item, you have to start from scratch.
Algorithmic problem-solving is also severely hampered by the quality of data it’s fed. If an AI hasn’t been explicitly told how to answer a question, it can’t reason it out. It cannot respond to an unexpected change if it hasn’t been programmed to anticipate it.
Today’s business world is filled with disruptions and events—from physical to economic to political—and these disruptions require interpretation and flexibility. Algorithms alone cannot handle that.
"AI lacks intuition". Humans use intuition to navigate the physical world. When you pivot and swing to hit a tennis ball or step off a sidewalk to cross the street, you do so without a thought—things that would require a robot so much processing power that it’s almost inconceivable that we would engineer them.
Algorithms get trapped in local optima. When assigned a task, a computer program may find solutions that are close by in the search process—known as the local optimum—but fail to find the best of all possible solutions. Finding the best global solution would require understanding context and changing context, or thinking creatively about the problem and potential solutions. Humans can do that. They can connect seemingly disparate concepts and come up with out-of-the-box thinking that solves problems in novel ways. AI cannot.
"AI can’t explain itself". AI may come up with the right answers, but even researchers who train AI systems often do not understand how an algorithm reached a specific conclusion. This is very problematic when AI is used in the context of medical diagnoses, for example, or in any environment where decisions have non-trivial consequences. What the algorithm has “learned” remains a mystery to everyone. Even if the AI is right, people will not trust its analytical output.
Artificial Intelligence offers tremendous opportunities and capabilities but it can’t see the world as we humans do. All we need do is work on its weaknesses and have them sorted out rather than have it overly hyped with make-believes and ignore its limitations in plain sight.
Ref: https://thriveglobal.com/stories/...6
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