DIY Build a workbench – Renovate

Looking for book recommendations

I am looking for some book recommendations. Currently, I am enrolled in a coding bootcamp (Thinkful's Engineering Immersion). I am looking for some books that would be crucial in my studies to become a software engineer. I was wondering if I could get some recommendations for what books you thought were the most beneficial in your studies?
EDIT: Also, here is a list of books that I am currently reading that I think are very important..
  1. Data Structures & Other Objects Using C++ by Michael Main and Walter Savitch This was a book that was the assigned reading for a CS class I took at UCSB that I am now re-reading.
  2. Programming Interview Exposed - Secrets to Landing Your Next Job by John Mongan and Noah Suojanen Suggestion from a talk I saw on YouTube
  3. Cracking the Coding Interview - 189 Programming Questions & Solutions by Gayle Laakmann McDowell
  4. Bitcoin and Cryptocurrency Technologies by Arvind Narayanan ... This one seemed interesting to me, so it's on my list to-read.
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PGA: No Frills DFS Data - Honda Classic Recap & Discussion of Golf Metrics
So, this slate was fantastic.

I had a player pool of 22 guys and only 3 missed the cut with another as an MDF. While I only had 1 guy in the top 5 this time, it was one of my most exposed players in Lucas Glover. I had 3 more at T9 so 4 of the top 11 guys and a bunch more T20 or better. I didn't have any lineups packed with the top 5 so didn't have any huge individual scores but when most lineups went 6/6 or 5/6 with a bunch of T20 or better players, it's always going to be a very good week despite not hitting yahtzee.

Again, to recap, here was my player pool in order of exposure.

T30 Justin Thomas
T4 Lucas Glover
MDF Graeme McDowell
T9 Sergio Garcia
T59 Zach Johnson
T36 Daniel Berger
T16 Michael Thompson
T59 Vaughn Taylor
T36 Gary Woodland
T51 Russell Knox
CUT Adam Scott
T20 Chesson Hadley
CUT Luke List
T16 Billy Horschel
T20 Brian Stuard
T36 Byeong Hun An
CUT Cameron Smith
T36 J.T. Poston
T9 Jason Kokrak
T9 Jim Furyk
T20 Matt Wallace
T20 Talor Gooch

My model once again pushed Furyk (it tends to really like him, Chez Reavie and Phil Michelson) but this time it wasn't overboard about it. At the end I didn't use him in any of the purely model driven lines but ended up trusting the model when I created the "homer line" where I choose 1-2 guys I really want added in and exclude a few I'm already heavy on so I could jam in Adam Scott again and the lineup said fill it out with Furyk. Was pleasantly surprised with a T9 from the guy and it will give me a little bit more faith when the model recommends him.

Now back to Adam Scott, this is why I limit my ability to directly construct a lineup to only 1 dart. The only things in Scott's favor were course history, tout coverage and Vegas odds. Everything else said he's a fine golfer but way too overpriced and since my model works rather holistically, all those things were already accounted for so I already had a smittering of him out there. Yet I bought into the narrative and jammed him in there. I don't regret the decision, I'd do it again. But this is exactly why I build a model, because if I built my 10x gpp lineups by hand, I'd likely have gone with him in a lot more lineups because his narrative was very compelling. The other guys to miss the cut in Smith and List, well, I stand by those choices as well. Half the field needs to be cut, so even if everyone golfed the game of their lives you'd still get half the field get cut despite hitting peak form. Kind of like if everyone went to an Ivy League then we'd have Yale PhDs flipping burgers kind of scenario. In short, don't worry about it. Even the best golfers will miss the cut.

You may also recall the model was suggesting Ortiz and Blayne and I vetoed them because I didn't feel the data was reliable. They both missed the cut. I would have been about 1/3 exposed to each had I not manually sifted through and error checked my lineups, something I sometimes don't get a chance to do because I didn't start running the model until near lock. It would have been disastrous had I not seen those unfamiliar names and decided to take a closer look.

My cash games went exceedingly well as I chose one of my lineups that did fairly well to use in cash. I cashed in every 50/50 and double up (sometimes outright winning them) and won all but 2 of my h2hs. There's a good story here about why, despite that I play most of my volume in cash, that I go with only 1 lineup. There's one specific player I've been matching up with quite a bit. It started out in lower stakes and I believe he's now tilted and trying to recover because he keeps upping the stakes but I keep taking em. This past slate he posted a $100 h2h and I took it. He then matched up with me in another one for $5. He decided to go with 2 lineups, one of them performed pretty poorly, another would have done very well in a GPP. Given how pleased I am writing about this, I bet you can imagine which one of those I lost and which I won. This is why I just create one cash lineup and stick with it because I've been on his side of things in the past. If he wins both then it wouldn't matter, if he loses both then it wouldn't matter. If he loses the $5 wins the $100 it doesn't matter... but if he loses the $100 but wins the $5 then he goes on crazy monkey tilt.

It doesn't matter at all that mathematically speaking it doesn't make a difference (so long as both lineups had equal assumed expectations), emotions still run high in this and unless you're doing very high volume at leveled stakes (not 2 matchups of 20x difference in size) and not going to track the individual results but look at the big picture then it's fine. But nobody does this, we aren't androids, when you win you win, when you lose you lose. This is why although I put way more in cash than gpp and bad cash lineup can sink me, I'm still taking a binary approach with cash games. I'm not taking a 75% indifference with a 25% chance of losing my god damn mind because the h2h that mattered was the one that failed. Fail like a stoic with a single cash lineup that gives 100% indifference.

Now then, some people have been asking me to go into more detail about about the data that use to create the lineups. I'll just reiterate again that I'm never going to explain how the sausage is made. But I will be serving plenty of sausage and give you a general idea what animal it came from.

Today I'm going to talk about specifically how most of my research really demonstrates just how stupid most golf stats are. I really want to be 100% sure and am in the process of scraping an absurdly large database containing several decades. And since I'm doing this on my free time, it'll take some time before I parse and analyze everything. I don't want to make the very bold claims I already believe to true without further studying the matter and really ensuring my thoughts are real and it's not the product of bad calculations or insufficient sample size. But, what I've discovered thus far, is that all those stats are just window dressing. Saying someone led the field shots gained x is fundamentally no different than saying "they did well and had a good tournament." Things like shots gained track results not process. So it's much like tracking wins and rbis. Yes, the best hitters and the best pitchers in baseball often lead the league in those metrics, but we all know why they aren't good predictive tools.

For example, when my beloved Red Sox signed Dante Bichette in 2001, there was all this talk about him having led the major leagues in RBIs the past few seasons. He just had his epic year, two years ago driving in 133 runs and the year before got 90. While he was aging and slowing down, I distinctly remember a lot confusion over why we signed this elite hitter but then used him in a platoon. I'd be at Fenway and as the Red Sox lost, people would openly question the wisdom of having one of the best hitters in the game ride it out on the bench. This was 2 years before Moneyball was published and while front offices knew the reality of the situation (third team in 2 years and out of the league after that season), the average hard core Red Sox fan would just scratch their head wondering why we didn't give Dante a little more of a chance to show he still had it.

I feel this is the situation today with golf and golf statistics while what we have today is an improvement of the past - we take it for granted that it comes with the same authority as so wOBA or usage. We know that the winners won, but we don't know much else and shots gained is basically more or less a fancy way to say someone did a better job. If someone gets a birdie on a par 4, their SG will improve by about... drumroll please... 1. So you could just simply compare scores - IE look at end of tournament standings. Yes, there is definitely some nuance and they do factor in the relative difficulty of that specific par 4 and if I didn't feel like there was some actionable data out there I wouldn't bother with any of this. But I believe that way too much weight is put into this, whether I'm right or wrong, I will follow up on this in much more detail once it's no longer a hunch but rather indisputable. The reason why gathering this data is difficult is that it's restricted - which itself should be a bit of a red flag.

I'll also be reading "Every Shot Counts" soon, which is a book written by the creator of the Shots Gained metric. I really don't want to make any further and sweeping judgements until I read the author's long and detailed explanation of the metric.

But really, we can all see the smoking gun for ourselves to see that the process by which they used to record shots gained is kept a secret and they don't disclose the data. Even prior to them ghosting us, access to the statistics themselves was restricted - you need to apply for access. The twitter account still exists and it's like everyone vanished into thin air, the last tweet was well over a year ago and simply a picture of a golf course as if nothing was about the change.

Also, the PGA still insists "All strokes gained statistics are calculated using ShotLink, the PGA TOUR's real-time scoring system powered by CDW. But since it's so secretive, we really don't know much about it. I'm not talking conspiracies or anything, they could have a very good data collection system that's phenomenal, but the very notion that the PGA doesn't even bother telling anyone how the data is collected and yet nobody is asking any questions should tell you this isn't exactly the most objective market.

So basically, I'm very confused by Shots Gained as a metric, can find very little information on it and what I can find is out of date and contradictory and seems to imply it's more or less no different than a nuanced version of looking at the final standings. I want to say it's bullshit, but I'm just reserving final judgement and simply labeling as sketchy for now.

So then we should look at results yeah? Yes, but this is largely what pricing is based upon, so not much of an edge there. So shall we look at ranking? Yes, let's take a look at OWGR.

When I first started with golf, I knew nothing and had nothing to base anything on other than seeing their pricing and recent point accumulations. Since Tiger Woods wasn't playing in that event, it was all entirely new names, just names I'd hear in passing while switching off ESPN as they were starting their golf coverage. So naturally, when I saw each golfer had a world ranking, I viewed that as a cheat sheet. From the very beginning, one of the formulas I've used to develop lineups was as simple as putting together the golfers within budget that collectively had the lowest aggregate world ranking number. Why am I suddenly speaking in such specifics you ask? Because it's a horrible DFS metric and nobody else is doing it (I track gpps lineups to see what others are doing, there are a few of these more simple formulas that pop up periodically, this is not one of them) so it's not exactly as if disclosing this information will make my opponents that much stronger.

My OWGR lineup has in fact been the single worst performing in cash and the 2nd to worst performing in gpps of the dozens of lineup models I have. Thankfully, I don't play it because it's so bad but I keep tracking it and recording how it would have performed just for fun these days. The only lineup that performed worse than the OWGR lineup in GPPS, well that one heavily factors in OWGR as well :). OWGR is just a terrible, terrible metric for DFS. Yes, it will give you the cream of the crop like the Dustin Johnsons, but you can never afford a lineup of Dustin Johnsons, you'll have to start digging deeper and pulling up min priced guys like Satoshi Kodaira - mr bitcoin himself. Someone who if you've been reading my stuff, is the entire reason I stopped playing any lineup that had OWGR as a primary indicator.

Now Satoshi, despite being a pretty horrible DFS play most of the time, is a great example of everything wrong with OWGR. His Fedex Cup rank is currently 160 and has never been better than 93, but his world rank is perplexingly 59. In 2018, he played 18 tournaments and finished under par only twice. He missed more cuts than he made as well. I could be mistaken, but it seems that he got into some majors via a sponsor in 2017 and 2018 and managed to do alright in them. He also ended up winning one of the tournaments he played in last year.

When researching OWGR to figure out how it came about and how it is calculated, I learned a lot. Basically, it's nothing more than party planning. A golf course in Scotland wanted to figure out whom to invite to compete in their tournament and invented the system. It weighs the strength of the field very heavily in rewarding points- and the strength of the field is - yup - you guessed it - determined by people already ranked by the system. So if Dustin Johnson cloned himself and kept playing tournaments exclusive to him and his equally ranked clones, they'd forever hold onto the top rankings. If OWGR was an excel sheet, the creator would get an error popup upon loading it up each day due to circular references. So, Satoshi I'm sure is a great golfer, anyone there should be, but his ranking is very artificially skewered up because he managed to make the cut and finish around 50th in some really packed majors that had a lot of heavy hitters. In fact, the ranking system is so completely absurd, that any millionaire can get themselves world ranked pretty easily. They just need to do something like sponsor a Pro-Am at some odd but counted tour like the Alps Tour and then invite the guys ranked 1st, 2nd and 3rd to compete and filling out the rest of the field with toddlers and yourself. You would be assured a 4th place finish. Yet you didn't beat any of the top 3 golfers in the world. You just beat 100 toddlers. Yet you still get the high ranking because they get 45, 37 and 32 respective points for strength of field, which is greater than if you had a tournament of the golfers ranked 93rd through 200 playing. Finishing 4th behind the only 3 adults and beating 100 toddlers has the same impact as finishing 4th in a field of 107 of the greatest golfers in the world.

Finishing 4th and beating 100 toddlers will grant you the same amount of points as finishing 20th at a major. That's how utterly stupid this rating system is. Obviously I'm using some extreme edge cases, it's very likely they would see through that scheme and not count it, but you get the idea of how inconsistent the system is. If you simply altered the PGA tour to the top 3 golfers and then a bunch of amateurs, those amateurs would soon arbitrarily be some of the highest rated in the world themselves, thus feeding itself.

This is why I call my OWGR model Ouroboros

Dustin Johnson doesn't play defense. He isn't jumping out of the sand trap and blocking your approach shot. Him finishing in front of you has zero impact on how well you performed compared to him. Yet if you simply show up and play in enough events where he easily beats you, you'll end up with a solid world ranking. This is an absurd system. When I researched OWGR, I was simply shocked it was how some random guy created an invitation list for a tournament and because golf feels the need to be so full of tradition they just made that the official world rankings.

Don't get me wrong, the top OWGR guys are all very good DFS plays because they are winners. However, after a certain point you're not dealing with anything at all reliable. I'm not sure at which point it gets diluted, but after a certain point, that metric becomes just as unstable as Bitcoin. I find it very amusing that the indicator that showed me the flaws with OWGR after a certain stage is named Satoshi. I'm also fully aware of how difficult it is to quantify something so intangible as golf. However, there's no doubt in my mind that there must be a significantly better manner than what is currently used.

But, whether or not my hunch is right or wrong, we still have a system where the data is all secretly gathered and stored by the PGA. That's something everyone should be aware of as they set their lineups.

Good luck everyone. Will dive deeper into the shots gained after I get around to buying and reading the book and finally finish analyzing that data. I could very well come back here in two weeks apologizing for my ignorance that gave me the gall to question such genius. In the meantime, good luck grinding out there and I'll post again in a few days with my player pool for the next event.
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PGA: No Frills DFS Data - Honda Classic

Edit: Just went through some more auxillary research on some of the fresh names appearing in my player pool for the first time. Needless to say, I without question believe that these guys are showing up due to a lack of data rather than good data. This is not something I really thought about when building the model and this is the first situation in which it has arisen. Later on I'll remove them from the original player pool and rerun the models that chose them and go with the new variations. It probably won't change things significantly, just a lot less Roberto Castro and maybe some others :)

Thoughts on the Slate
This slate is pretty interesting. It’s a real mess after a few guys and everyone is priced up so you can’t even afford the guys you’d rather not play anyway. Furthermore, given how it’s one of the most difficult courses on the PGA tour, that adds a whole new layer to it all. In fact, it’s very likely that the cut line will be in the positive. People are talking about not making mistakes, but looking at the player pool and the unforgiving course, I challenge anyone to find a lineup that is “safe” in this one. In theory, my model (focuses upon picking winners instead of guys who may finish top 20 etc) should thrive here, going for the guys who can win it all and hoping whichever scabs formed around them happen to make the cut.
Things started with quite a hiccup though. The first lineup I spit out only used up 2/3 of available salary. This was a huge red flag so I had to dig deep into the code to figure out what went wrong. It’s not uncommon for my lineups to leave some money on the table, but what can I say, pricing is pretty efficient on an aggregate level. Regardless of what metrics you are chasing, chances are the guys who costs a little more as a group will performs a little better at it than the guys who cost a little less. I’ve found this to be true for every sport. While you’ll find the good values here and there, for the most part it’s a pretty efficient market. As this was a regular problem, I ended up developing an error checking system, so now each time I load up the model and import all the data, there theoretically should be a process which automatically runs and verifies everything is properly done. I’d been spending more time fixing errors than doing anything else, so if this works (doubtful) then I’ll have a lot more free time to analyze & write :)
The Player Pool
Here’s the raw player pool I had from my model overall (after I fixed the bug), as always, it’s in order of exposure.
I was very pleased to see that Justin Thomas was so loved by my model. As I mentioned last week, I wanted my higher exposure guys going forward to only be top 10 players in the slate. First week since wanting to go in that direction it occurred naturally.
What I didn’t like is that it chose a lot of people I’d simply never before heard of. I also got a bunch of people only once – that almost never happens. The single appearance wasn’t very compelling to me, it made it feel like they happened to fit that particular lineup and not really this slate in general. Furthermore, a bunch of the guys I looked up weren’t very encouraging. For example, I assumed that maybe Blayne Barber was a hot new phenom joining the tour as a rookie like Cameron Champ. Nope, he’s 29 years old and his Wikipedia page begins and ends with result from 2012. Yikes. I mean even Padraig Harrington won this one, but one thing I’ve found in my research is that while it pays to avoid the chalk sometimes, there’s no need to get that level of cute.
I also didn’t like the fairly high exposure I had to the guys beneath JT. Now, these are all fine golfers, and yes, I agree that they likely will outperform their peers at their price ranges. Yet, they are still priced like that for a reason and that reason is risk. Having gone two slate in a row 7 or 8 of the guys to finish top 10 but still failed because I went high exposure on one guy who bombed – I’m severely lowering the exposure threshold for those mid tier guys in the second round.
The Trim
If this is your first time reading, what I normally do is create way too many lineups with various metrics. Some being something very simple, others being very complex – probably too much for my own sake. Over time, I remove the losers and add replacements.
I first trimmed down the lineups that had heavy exposure to GloveJohnson/BergeHadley. After that I removed the lineups that produced the guys that were way too cute for my liking like Blayne Barber. That reduced down my list considerably and from there it was a pretty straightforward process of picking the 10 I liked the most.
Here is the curated player pool, again in order of exposure
These guys will only be playing in ghost lineups for tracking purposes going forward having not made the starting 10.
Was a bit upset that Barnrat didn’t make it as he’s an all or nothing kind of guy I wouldn’t mind having a small piece of in a tourney like this. Happy to be gone with Bitcoin Kodaira, he’s great evidence of the golf world ranking system being either highly subjective, meaningless or simply a silly metric. He’s always in a bargain bin and never performs despite hovering around top 50 world ranking. I’m always intrigued by that mystique but alas had some extra time today and added his name to Wikipedia after Blayne and was left even more bewildered by that ranking than ever. I believe I’ll be fading him until I know more about how these rankings are developed – or at least until his peripherals improve a bit.
Sad to see Grillo go but he’s not exactly the bargain he normally is and I’m not paying premium for bargain nostalgia. I can’t commit to Blayne, but, if you guys are looking for a very silly dart, my model really liked him and chose him for multiple independent variables. I can’t rule out though that it was caused by a lack of complete data combined with his price tag and the garbage heaps surrounding him.
I wanted more of Billy Horschel but never intervened, I also wanted more of Adam Scott, but only because he was barely represented and jammed him into one where he was the odd man out just barely. He was only guy to receive an artificial boost whereas everyone else got their exposure simply by surviving the purge.
Notable Fades
I find this to be both a joy and curse. I don’t see anything wrong with either of these two. It’s just that my model seemed to always prefer spending up a little more for Justin or dropping down a bit for someone like Berger or Woodland. One of those two could very easily win this thing. Yet, at the same time, I need to draw the line somewhere. I simply can’t roster everyone. And remember, even Padraig Harrington (a relation to the original poker nit Dan Harrington) won this one so you can’t quite get caught up in the “could win” narrative. Had my model selected either of them even once, I certainly would have kept them and maybe even given them the invisible hand to add to another lineup or so. I’m going to do some more research and really think about this, maybe diverge myself of some JT and pump these guys in, but as it stands now, that appears unlikely. If anyone has done any studies or anything on optimal exposure etc I’d love it if you could point me in the right direction.
I’m also fading Niemann, like Grillo, he’s an old nostalgic bargain bin performer that I used to love jamming in there. But the model says no and I will follow.
Here is the finalized player list after removing the bad data guys. As I predicted, it hasn’t changed much, but the end result is more of a even keel approach. Without the good min priced guys, the model shied away from JT a little bit and went more middle of the road.

The Struggle
Taking an early look at the player pool for this one, I couldn’t wait to see what my model delivered. Last week I discovered my model could produce barbells when it saw it fit and I was thinking this slate could produce plenty. I was quite surprised when it only produced a single barbell with 3 studs and 3 min priced guys. It did do some mini ones but only went extreme once. Still, that’s a pretty dramatic improvement from never to once and all on it’s own without any nudges.
While I haven’t yet modified lineup creation at it’s root, I did add in a few more different iterations that deal with less variance. I know I’m reacting the recent poor performance, so I’m not jamming those in yet, just studying the concept of it right now. I’ve also been thinking a bit about normalizing my lineups a bit. For example, lineup A and lineup B may be produced from different metrics. While those two lineups individually failed, had they been combined, it would have produced a winner. The idea would be to create all my varying models and their lineups use that not for those lineups but to populate a player pool. I would then take the players original exposure levels and reduce it logarithmically to give it a more even distribution. Then from there use a solver to produce my 10 lineups from the pool, with a goal of maximizing salary usage while still following the general exposure guidelines for each player. I have yet to actually research this enough to know yet if this is a stupid idea or not.
Although I work with a lot of people who could know the answer to this, I don’t want to be asking colleagues to help me out with my sports betting system :). This is especially true since I’m a consultant who parachutes in for a week or so then is never seen again so basically all interactions with people at work are first impressions. This is also why I prefer to post stuff here anonymously. I don’t want someone who is considering working with me to google my name and see that I enjoy sports gambling and used to be a professional poker player who now blogs about tranny alley in Macau :) But, getting back to my point, although I work heavily with data and code for a living as an independent product consultant, I do not code nor work with the raw data. I merely look at curated reports and work with the coders. So building this has been a great learning process, but nonetheless a major struggle. Furthermore, as someone who was a liberal arts major, I simply have no idea what most of those math symbols mean. Code is quite intuitive once you learn the non-intuitive parts, but learning mathematical concepts to use in expanding my models often leads to running into a brick wall. I can chug along just fine and then boom, out of nowhere there’s a whole bunch of squiggly lined gibberish on the screen that I can’t even begin to understand. The problem is I’m far too advanced to gain any help from a lay person but don’t understand a thing when talking to an actual expert – who only knows how to discuss it online as an expert using mathematical notation instead of words. The spectrum of online help with statistics seems to begin at sesame street and jump straight into Hieroglyphics. My point is that even though I’m literally paid to look at data and come up with action plans based largely upon it, even for me this is a constant struggle and I am learning a lot by doing this. Even if it never works, I’m sure somehow I’m adding skills that will come in handy outside of DFS.
Although building these models is a massive time suck, it’s pretty rewarding the first time you add something to it and it somehow works. Over the all star break, I finally adapted my hockey model (which I’d only used for cash) to also spit out stacked gpp lineups. So now it takes seconds to have all my gpp lineups at the ready. This is something I’m very proud about having accomplished. Just really upset that I didn’t set aside the time to do this earlier in the year. It really kicked my ass trying to figure out how to do it, spend many hours staring at the screen trying to figure out how to code it to build the stack using varying flexibility and contingencies I wanted and then it finally worked and I couldn’t believe it.
Good luck everyone, jam in Blayne if you have the testicular fortitude :)
As always, always appreciate feedback, even the negative stuff
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Sublicious Farms now accepts bitcoins! grow your own mushrooms at home with bitcoin.

Sublicious Farms now accepts bitcoins! grow your own mushrooms at home with bitcoin. submitted by subfarms to Bitcoin [link] [comments]

Circle CEO and Peter Thiel attending the Bilderberg Meetup

This is an automatic summary, original reduced by 61%.
Bilderberg, that infamously secretive annual meeting of some of the most influential people in global politics and commerce, gets underway today and there will be a trio of Irish businessmen behind the closed doors in Chantilly, Virginia.
While the discussions that go on at the gathering are never divulged, Bilderberg's official website helpfully lists each year's attendees, as well as the key topics which will be broached - top of the list being "The Trump Administration: A progress report".
Making his third Bilderberg appearance will be Mullingar's own Michael O'Leary.
In March, the Limerick-born, San Francisco-based siblings joined the Forbes Rich List elite for the first time.
The 26-year-old John was the youngest self-made billionaire on the list.
In previous years, Garret FitzGerald, Michael Noonan, Simon Coveney and Michael McDowell have all flown the Irish flag at the closed summit.
Summary Source | FAQ | Theory | Feedback | Top keywords: list#1 year#2 Bilderberg#3 Michael#4 John#5
Post found in /Bitcoin and /BitcoinAll.
NOTICE: This thread is for discussing the submission topic. Please do not discuss the concept of the autotldr bot here.
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