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The fallacy of ‘synthetic benchmarks’
PrefaceApple's M1 has caused a lot of people to start talking about and questioning the value of synthetic benchmarks, as well other (often indirect or badly controlled) information we have about the chip and its predecessors.
I recently got in a Twitter argument with Hardware Unboxed about this very topic, and given it was Twitter you can imagine why I feel I didn't do a great job explaining my point. This is a genuinely interesting topic with quite a lot of nuance, and the answer is neither ‘Geekbench bad’ nor ‘Geekbench good’.
Note that people have M1s in hand now, so this isn't a post about the M1 per se (you'll have whatever metric you want soon enough), it's just using this announcement to talk about the relative qualities of benchmarks, in the context of that discussion.
What makes a benchmark good?A benchmark is a measure of a system, the purpose of which is to correlate reliably with actual or perceived performance. That's it. Any benchmark which correlates well is Good. Any benchmark that doesn't is Bad.
There a common conception that ‘real world’ benchmarks are Good and ‘synthetic’ benchmarks are Bad. While there is certainly a grain of truth to this, as a general rule it is wrong. In many aspects, as we'll discuss, the dividing line between ‘real world’ and ‘synthetic’ is entirely illusionary, and good synthetic benchmarks are specifically designed to tease out precisely those factors that correlate with general performance, whereas naïve benchmarking can produce misleading or unrepresentative results even if you are only benchmarking real programs. Most synthetic benchmarks even include what are traditionally considered real-world workloads, like SPEC 2017 including the time it takes for Blender to render a scene.
As an extreme example, large file copies are a real-world test, but a ‘real world’ benchmark that consists only of file copies would tell you almost nothing general about CPU performance. Alternatively, a company might know that 90% of their cycles are in a specific 100-line software routine; testing that routine in isolation would be a synthetic test, but it would correlate almost perfectly for them with actual performance.
On the other hand, it is absolutely true there are well-known and less-well-known issues with many major synthetic benchmarks.
Boost vs. sustained performanceLots of people seem to harbour misunderstandings about instantaneous versus sustained performance.
Short workloads capture instantaneous performance, where the CPU has opportunity to boost up to frequencies higher than the cooling can sustain. This is a measure of peak performance or burst performance, and affected by boost clocks. In this regime you are measuring the CPU at the absolute fastest it is able to run.
Peak performance is important for making computers feel ‘snappy’. When you click an element or open a web page, the workload takes place over a few seconds or less, and the higher the peak performance, the faster the response.
Long workloads capture sustained performance, where the CPU is limited by the ability of the cooling to extract and remove the heat that it is generating. Almost all the power a CPU uses ends up as heat, so the cooling determines an almost completely fixed power limit. Given a sustained load, and two CPUs using the same cooling, where both of which are hitting the power limit defined by the quality of the cooling, you are measuring performance per watt at that wattage.
Sustained performance is important for demanding tasks like video games, rendering, or compilation, where the computer is busy over long periods of time.
Consider two imaginary CPUs, let's call them Biggun and Littlun, you might have Biggun faster than Littlun in short workloads, because Biggun has a higher peak performance, but then Littlun might be faster in sustained performance, because Littlun has better performance per watt. Remember, though, that performance per watt is a curve, and peak power draw also varies by CPU. Maybe Littlun uses only 1 Watt and Biggun uses 100 Watt, so Biggun still wins at 10 Watts of sustained power draw, or maybe Littlun can boost all the way up to 10 Watts, but is especially inefficient when doing so.
In general, architectures designed for lower base power draw (eg. most Arm CPUs) do better under power-limited scenarios, and therefore do relatively better on sustained performance than they do on short workloads.
On the Good and Bad of SPECSPEC is an ‘industry standard’ benchmark. If you're anything like me, you'll notice pretty quickly that this term fits both the ‘good’ and the ‘bad’. On the good, SPEC is an attempt to satisfy a number of major stakeholders, who have a vested interest in a benchmark that is something they, and researchers generally, can optimized towards. The selection of benchmarks was not arbitrary, and the variety captures a lot of interesting and relevant facets of program execution. Industry still uses the benchmark (and not just for marketing!), as does a lot of unaffiliated research. As such, SPEC has also been well studied.
SPEC includes many real programs, run over extended periods of time. For example, 400.perlbench runs multiple real Perl programs, 401.bzip2 runs a very popular compression and decompression program, 403.gcc tests compilation speed with a very popular compiler, and 464.h264ref tests a video encoder. Despite being somewhat aged and a bit light, the performance characteristics are roughly consistent with the updated SPEC2017, so it is not generally valid to call the results irrelevant from age, which is a common criticism.
One major catch from SPEC is that official benchmarks often play shenanigans, as compilers have found ways, often very much targeted towards gaming the benchmark, to compile the programs in a way that makes execution significantly easier, at times even because of improperly written programs. 462.libquantum is a particularly broken benchmark. Fortunately, this behaviour can be controlled for, and it does not particularly endanger results from AnandTech, though one should be on the lookout for anomalous jumps in single benchmarks.
A more concerning catch, in this circumstance, is that some benchmarks are very specific, with most of their runtime in very small loops. The paper Performance Characterization of SPEC CPU2006 Integer Benchmarks on x86-64 Architecture (as one of many) goes over some of these in section IV. For example, most of the time in 456.hmmer is in one function, and 464.h264ref's hottest loop contains many repetitions of the same line. While, certainly, a lot of code contains hot loops, the performance characteristics of those loops is rarely precisely the same as for those in some of the SPEC 2006 benchmarks. A good benchmark should aim for general validity, not specific hotspots, which are liable to be overtuned.
SPEC2006 includes a lot of workloads that make more sense for supercomputers than personal computers, such as including lots of Fortran code and many simulation programs. Because of this, I largely ignore the SPEC floating point; there are users for whom it may be relevant, but not me, and probably not you. As another example, SPECfp2006 includes the old rendering program POV-Ray, which is no longer particularly relevant. The integer benchmarks are not immune to this overspecificity; 473.astar is a fairly dated program, IMO. Particularly unfortunate is that many of these workloads are now unrealistically small, and so can almost fit in some of the larger caches.
SPEC2017 makes the great decision to add Blender, as well as updating several other programs to more relevant modern variants. Again, the two benchmarks still roughly coincide with each other, so SPEC2006 should not be altogether dismissed, but SPEC2017 is certainly better.
Because SPEC benchmarks include disaggregated scores (as in, scores for individual sub-benchmarks), it is easy to check which scores are favourable. For SPEC2006, I am particularly favourable to 403.gcc, with some appreciation also for 400.perlbench. The M1 results are largely consistent across the board; 456.hmmer is the exception, but the commentary discusses that quirk.
(and the multicore metric)SPEC has a ‘multicore’ variant, which literally just runs many copies of the single-core test in parallel. How workloads scale to multiple cores is highly test-dependent, and depends a lot on locks, context switching, and cross-core communication, so SPEC's multi-core score should only be taken as a test of how much the chip throttles down in multicore workloads, rather than a true test of multicore performance. However, a test like this can still be useful for some datacentres, where every core is in fact running independently.
On the Good and Bad of GeekbenchGeekbench does some things debatably, some things fairly well, and some things awfully. Let's start with the bad.
To produce the aggregate scores (the final score at the end), Geekbench does a geometric mean of each of the two benchmark groups, integer and FP, and then does a weighted arithmetic mean of the crypto score with the integer and FP geometric means, with weights 0.05, 0.65, and 0.30. This is mathematical nonsense, and has some really bad ramifications, like hugely exaggerating the weight of the crypto benchmark.
Secondly, the crypto benchmark is garbage. I don't always agree with his rants, but Linus Torvald's rant is spot on here: https://www.realworldtech.com/forum/?threadid=196293&curpostid=196506. It matters that CPUs offer AES acceleration, but not whether it's X% faster than someone else's, and this benchmark ignores that Apple has dedicated hardware for IO, which handles crypto anyway. This benchmark is mostly useless, but can be weighted extremely high due to the score aggregation issue.
Consider the effect on these two benchmarks. They are not carefully chosen to be perfectly representative of their classes.
Unfortunately, many of the workloads beyond just AES are pretty questionable, as many are unnaturally simple. It's also hard to characterize what they do well; the SQLite benchmark could be really good, if it was following realistic usage patterns, but I don't think it is. Lots of workloads, like the ray tracing one, are good ideas, but the execution doesn't match what you'd expect of real programs that do that work.
Note that this is not a criticism of benchmark intensity or length. Geekbench makes a reasonable choice to only benchmark peak performance, by only running quick workloads, with gaps between each bench. This makes sense if you're interested in the performance of the chip, independent of cooling. This is likely why the fanless Macbook Air performs about the same as the 13" Macbook Pro with a fan. Peak performance is just a different measure, not more or less ‘correct’ than sustained.
On the good side, Geekbench contains some very sensible workloads, like LZMA compression, JPEG compression, HTML5 parsing, PDF rendering, and compilation with Clang. Because it's a benchmark over a good breadth of programs, many of which are realistic workloads, it tends to capture many of the underlying facets of performance in spite of its flaws. This means it correlates will with, eg., SPEC 2017, even though SPEC 2017 is a sustained benchmark including big ‘real world’ programs like Blender.
To make things even better, Geekbench is disaggregated, so you can get past the bad score aggregation and questionable benchmarks just by looking at the disaggregated scores. In the comparison before, if you scroll down you can see individual scores. M1 wins the majority, including Clang and Ray Tracing, but loses some others like LZMA and JPEG compression. This is what you'd expect given the M1 has the advantage of better speculation (eg. larger ROB) whereas the 5900X has a faster clock.
(and under Rosetta)We also have Geekbench scores under Rosetta. There, one needs to take a little more caution, because translation can sometimes behave worse on larger programs, due to certain inefficiencies, or better when certain APIs are used, or worse if the benchmark includes certain routines (like machine learning) that are hard to translate well. However, I imagine the impact is relatively small overall, given Rosetta uses ahead-of-time translation.
(and the multicore metric)Geekbench doesn't clarify this much, so I can't say much about this. I don't give it much attention.
(and the GPU compute tests)GPU benchmarks are hugely dependent on APIs and OSs, to a degree much larger than for CPUs. Geekbench's GPU scores don't have the mathematical error that the CPU benchmarks do, but that doesn't mean it's easy to compare them. This is especially true given there are only a very limited selection of GPUs with 1st party support on iOS.
None of the GPU benchmarks strike me as particularly good, in the way that benchmarking Clang is easily considered good. Generally, I don't think you should have much stock in Geekbench GPU.
On the Good and Bad of microarchitectural measuresAnandTech's article includes some of Andrei's traditional microarchitectural measures, as well as some new ones I helped introduce. Microarchitecture is a bit of an odd point here, in that if you understand how CPUs work well enough, then they can tell you quite a lot about how the CPU will perform, and in what circumstances it will do well. For example, Apple's large ROB but lower clock speed is good for programs with a lot of latent but hard to reach parallelism, but would fair less well on loops with a single critical path of back-to-back instructions. Andrei has also provided branch prediction numbers for the A12, and again this is useful and interesting for a rough idea.
However, naturally this cannot tell you performance specifics, and many things can prevent an architecture living up to its theoretical specifications. It is also difficult for non-experts to make good use of this information. The most clear-cut thing you can do with the information is to use it as a means of explanation and sanity-checking. It would be concerning if the M1 was performing well on benchmarks with a microarchitecture that did not suggest that level of general performance. However, at every turn the M1 does, so the performance numbers are more believable for knowing the workings of the core.
On the Good and Bad of CinebenchCinebench is a real-world workload, in that it's just the time it takes for a program in active use to render a realistic scene. In many ways, this makes the benchmark fairly strong. Cinebench is also sustained, and optimized well for using a huge number of cores.
However, recall what makes a benchmark good: to correlate reliably with actual or perceived performance. Offline CPU ray tracing (which is very different to the realtime GPU-based ray tracing you see in games) is an extremely important workload for many people doing 3D rendering on the CPU, but is otherwise a very unusual workload in many regards. It has a tight rendering loop with very particular memory requirements, and it is almost perfectly parallel, to a degree that many workloads are not.
This would still be fine, if not for one major downside: it's only one workload. SPEC2017 contains a Blender run, which is conceptually very similar to Cinebench, but it is not just a Blender run. Unless the work you do is actually offline, CPU based rendering, which for the M1 it probably isn't, Cinebench is not a great general-purpose benchmark.
(Note that at the time of the Twitter argument, we only had Cinebench results for the A12X.)
On the Good and Bad of GFXBenchGFXBench, as far as I can tell, makes very little sense as a benchmark nowadays. Like I said for Geekbench's GPU compute benchmarks, these sort of tests are hugely dependent on APIs and OSs, to a degree much larger than for CPUs. Again, none of the GPU benchmarks strike me as particularly good, and most tests look... not great. This is bad for a benchmark, because they are trying to represent the performance you will see in games, which are clearly optimized to a different degree.
This is doubly true when Apple GPUs use a significantly different GPU architecture, Tile Based Deferred Rendering, which must be optimized for separately. EDIT: It has been pointed out that as a mobile-first benchmark, GFXBench is already properly optimized for tiled architectures.
On the Good and Bad of browser benchmarksIf you look at older phone reviews, you can see runs of the A13 with browser benchmarks.
Browser benchmark performance is hugely dependent on the browser, and to an extent even the OS. Browser benchmarks in general suck pretty bad, in that they don't capture the main slowness of browser activity. The only thing you can realistically conclude from these browser benchmarks is that browser performance on the M1, when using Safari, will probably be fine. They tell you very little about whether the chip itself is good.
On the Good and Bad of random application benchmarksThe Affinity Photo beta comes with a new benchmark, which the M1 does exceptionally well in. We also have a particularly cryptic comment from Blackmagicdesign, about DaVinci Resolve, that the “combination of M1, Metal processing and DaVinci Resolve 17.1 offers up to 5 times better performance”.
Generally speaking, you should be very wary of these sorts of benchmarks. To an extent, these benchmarks are built for the M1, and the generalizability is almost impossible to verify. There's almost no guarantee that Affinity Photo is testing more than a small microbenchmark.
This is the same for, eg., Intel's ‘real-world’ application benchmarks. Although it is correct that people care a lot about the responsiveness of Microsoft Word and such, a benchmark that runs a specific subroutine in Word (such as conversion to PDF) can easily be cherry-picked, and is not actually a relevant measure of the slowness felt when using Word!
This is a case of what are seemingly ‘real world’ benchmarks being much less reliable than synthetic ones!
On the Good and Bad of first-party benchmarksOf course, then there are Apple's first-party benchmarks. This includes real applications (Final Cut Pro, Adobe Lightroom, Pixelmator Pro and Logic Pro) and various undisclosed benchmark suites (select industry-standard benchmarks, commercial applications, and open source applications).
I also measured Baldur's Gate 3 in a talk running at ~23-24 FPS at 1080 Ultra, at the segment starting 7:05. https://developer.apple.com/videos/play/tech-talks/10859
Generally speaking, companies don't just lie in benchmarks. I remember a similar response to NVIDIA's 30 series benchmarks. It turned out they didn't lie. They did, however, cherry-pick, specifically including benchmarks that most favoured the new cards. That's very likely the same here. Apple's numbers are very likely true and real, and what I measured from Baldur's Gate 3 will be too, but that's not to say other, relevant things won't be worse.
Again, recall what makes a benchmark good: to correlate reliably with actual or perceived performance. A biased benchmark might be both real-world and honest, but if it's also likely biased, it isn't a good benchmark.
On the Good and Bad of the Hardware Unboxed benchmark suiteThis isn't about Hardware Unboxed per se, but it did arise from a disagreement I had, so I don't feel it's unfair to illustrate with the issues in Hardware Unboxed's benchmarking. Consider their 3600 review.
Here are the benchmarks they gave for the 3600, excluding the gaming benchmarks which I take no issue with.
- Cinebench (MT+ST)
- V-Ray Benchmark (MT)
- Corona 1.3 Benchmark (MT)
- Blender Open Data (MT)
- WinRAR (MT)
- 7Zip File Manager (MT)
- 7Zip File Manager (MT)
- Adobe Premiere Pro video encode (MT)
To have a lineup like this and then complain about the synthetic benchmarks for M1 and the A14 betrays a total misunderstanding about what benchmarking is. There are a total of three real workloads here, one of which is single threaded. Further, that one single threaded workload is one you'll never realistically run single threaded. As discussed, offline CPU rendering is an atypical and hard to generalize workload. Compression and decompression are also very specific sorts of benchmarks, though more readily generalizable. Video encoding is nice, but this still makes for a very thin picking.
Thus, this lineup does not characterize any realistic single-threaded workloads, nor does it characterize multi-core workloads that aren't massively parallel.
Contrast this to SPEC2017, which is a ‘synthetic benchmark’ of the sort Hardware Unboxed was criticizing. SPEC2017 contains a rendering benchmark (526.blender) and a compression benchmark (557.xz), and a video encode benchmark (525.x264), but it also contains a suite of other benchmarks, chosen specifically so that all the benchmarks measure different aspects of the architecture. It includes workloads like Perl, GCC, workloads that stress different aspects of memory, plus extremely branchy searches (eg. a chess engine), image manipulation routines, etc. Geekbench is worse, but as mentioned before, it still correlates with SPEC2017, by virtue of being a general benchmark that captures most aspects of the microarchitecture.
So then, when SPEC2017 contains your workloads, but also more, and with more balance, how can one realistically dismiss it so easily? And if Geekbench correlates with SPEC2017, then how can you dismiss that, at least given disaggregated metrics?
In conclusionThe bias against ‘synthetic benchmarks’ is understandable, but misplaced. Any benchmark is synthetic, by nature of abstracting speed to a number, and any benchmark is real world, by being a workload you might actually run. What really matters is knowing how each workload is represents your use-case (I care a lot more about compilation, for example), and knowing the issues with each benchmark (eg. Geekbench's bad score aggregation).
Skepticism is healthy, but skepticism is not about rejecting evidence, it is about finding out the truth. The goal is not to have the benchmarks which get labelled the most Real World™, but about genuinely understanding the performance characteristics of these devices—especially if you're a CPU reviewer. If you're a reviewer who dismisses Geekbench, but you haven't read the Geekbench PDF characterizing the workload, or your explanation stops at ‘it's short’, or ‘it's synthetic’, you can do better. The topics I've discussed here are things I would consider foundational, if you want to characterize a CPU's performance. Stretch goals would be to actually read the literature on SPEC, for example, or doing performance counter-aided analysis of the benchmarks you run.
Normally I do a reread before publishing something like this to clean it up, but I can't be bothered right now, so I hope this is good enough. If I've made glaring mistakes (I might've, I haven't done a second pass), please do point them out.
DAY 6 of protesting for a fairer monetization model
- 2 years 100 people (if anything I think the average might be higher both in people and time) => 1 game $60
- 2 years 70 people (size of Team 5) => 6 standard-legal expansions
- Bundle $80 * 6
- "mini expansion" 26% of $ 80 => $20
- => 0.7 game (This is ~ the percentage of cards you will get while ignoring rarities and things like battlepass xp, cosmetics, and pre-order time-frames. This doesn't include the Classic Set.) $600
The paywall is the main reason why players quit or commit to drudgerous grinding (which shouldn't happen in a videogame because that creates addiction, and because your time is wasted if you're not truely having fun). For more data on the monetization issue of Hearthstone please visit this post.
Did you want cosmetic upgrades for your cards too? That'll only be about a year's worth of rent.
Outright removal of the battlepassBattlepasses are manipulative practices which weedled their way into our games and we've been complacent about them because they seemed like good deals relative to how much we were price-gouged by microtransactions before. They exist only to harbor addiction by keeping you tied to a commitment, lock content with an arbitrary limited-time-frame which players grind at the end of the season to not miss, and you may potentially not grind enough xp to earn the skins of your Tavern Pass. In addition, Blizzard will certainly use the bonus-xp events to control when you play in a way they couldn't before. Finally, you should only be coming back to a game for fun, and not to be industrious in a fictional space. Battlepasses HAVE NO PLACE in Hearthstone!
Ben Lee's stimulus packageForget about the rewards track changes that essentially gives players +10.5 packs this season (plus extra selection on which slot machine you gamble on). That's 1-2 non-duplicate epics, 3 non-duplicate rares, 3 non-duplicate commons, and 300 dust for most players. I know this is precious when you're accustomed to in-game poverty, but it's not worth what we're protesting for by a long-shot. Like the American Federal Government, they're ignoring the systematic issues they created and snuffing out opposition with a pathetic stimulus. They may as well have said "let them eat funnelcakes". And if they make another PR move that offers us more packs, no matter how many that may be, ignore that too.
#NoMoreWhaleGamesOnce 90% of people on this subreddit collectively agree to the demand-list, I'm calling for the beginning of our official Twitter, YouTube, and app-store protest. This means that we'll all reply to the latest tweets on some specific accounts and comment on the latest YouTube videos of some channels once a day until the end of the overall Hearthstone protest on this subreddit. I'll anounce the go-signal on a daily post after we agree. Please don't begin without this coordination since we wouldn't be taken as seriously without it.
On these platforms, I invite you to post this preprepared message (except for in reply to the tweet about Veteran's Day):
We demand a fair monetization model from Blizzard! [Reddit link of that DAY X Post] #NoMoreWhaleGames
The Twitter accounts are:
- CEO of Activision Blizzard
- Competitive Hearthstone
- Youtuber who thrives on making content about this (I'll delete this link after he uploads a video)
The YouTube accounts are:
For those of you who haven't already, I invite you to post a bad review or 1-star rating on the Google Play Store or App Store. This matters to Blizzard because games with a lower rating have a harder time climbing the top charts. We've already reduced Hearthstone on the Google Play Store to 3.9 and 3.6 on the App Store, with a substantial drop last week.
Finally, once we agree on our demands, there will be no negotiations with Winnie Mussolini and we will not tolerate compromises. So we must choose them wisely.
The NoMoreWhaleGames demand-list for Hearthstone
- The shop in Hearthstone is revamped according to the following changes in the obvious nessesary ways (IE: No more $69.99 for 60 Classic Packs).
- For every hero, there is a $3.99 purchase called a "Class Bundle" which provides playsets of all the cards of that hero in a given expansion set, including wild. As well as class-cards, those bundles will provide specific neutral cards of that set such that one could have every* card if they bought all the bundles. The exception is neutral legendaries because there have only been 3-5 of them since Ungoro.
- For every set, there is a $39.90 bundle that encompasses every Class Bundle of that set. This is the new Mega Bundle. The price of this bundle is decreased according to the Class Bundles already purchased. It includes one golden pack of that set and the neutral legendaries as bonuses.
- Class Bundles, Mega Bundles, and adventures that aren't standard-legal are discounted by 25%.
- Each of these bundles can only be purchased once per account.
- Each of these bundles can be purchased regardless of how many cards a player already has.
- There remains the option of buying packs (this is indeed called "NoMoreWhaleGames", but purchasing packs under this environment would be unessesary for the normal player in every respect, and some may want to show their support expensively by going for all-golden collections/decks or just enjoy opening packs).
- The price of standalone packs is $0.99 for 1 pack up to $29.99 for 60 packs.
- There is no Class Bundle or Mega Bundle for the Classic Set. Instead, the Welcome Bundle contains 60 Classic Packs (because 10 Classic Packs is too slow of a start) and 1 Classic Legendary. It costs $16.99 (it was only $5 before to hook players with sunk-cost fallacy).
- Seasonal Battlegrounds Perks can be bought with gold.
- You get 10 gold per win instead of for every 3 wins.
- Achievements give 10% of their achievement-point worth as gold when achieved.
- Rewards for tiers 7-12 of Arena are increased by a pack. Rewards for tiers 8-11 are increased by ~20% more gold. Rewards for tier 12 is increased by ~100% more gold. The same is true for Duels because that has the same rewards as Arena.
- Golden epics cost 800 dust to craft, golden rares cost 400, and golden commons cost 200. Golden legendaries stay at 3200.
- You can complete quests in all modes (except when restricting the mode is integral to the quest, for example: "Win 2 games of Duels").
- Removal of the Tavern Pass and rewards track in favour of the old quests system (Weekly quests should be removed because they hold people to a arbitrary commitment, same as the Tavern Pass; you are not an employee within Hearthstone, it is not your duty to earn gold.).
- Reemburse players who bought the Tavern Pass by giving them the premium skins without them having to work for it, and some gold.
- Reemburse the players' Madness at the Darkmoon Faerie pre-orders (in in-game items) as though this new monetization model had been in effect during the Madness at the Darkmoon Faire pre-order period.
This creates a future with 3 types of paying players:
- The broke/budget player who buys into standard for ~$30.96 for his favourite 2 classes and spends ~$15.98 each expansion.
- The average paying player who buys into standard with 1-2 Mega-Bundles and buys the Mega-Bundle + maybe a few cosmetics each expansion.
- The most hardcore player who seeks to collect a whole wild collection and maybe golden decks/sets and all the skins.
There's always room for anotherThis shall not only be the end of Blizzard's whale-hunting practices in Hearthstone but, in time, the end of whale-hunting across the industry. Other gaming communities will talk about how the Hearthstone community retaliated against its predatory publisher, and will follow suit with their own revolt. In order to make this sweep possible, we must prove that we can do it once to a massive publisher. One success would warrant reputation and support from doubtful gamers who would rally with us in fiery indignation if they believed it wasn't impossible to end whale-hunting by protesting. I invite you to do this for their sake as well and to contribute to their protests when the sweep begins.
End of turnIt's an insult that Blizzard and Ben Lee remained silent after more than 10 000 of us already protested on Reddit. If it weren't for the Tavern Pass which sparked a pent-up outrage and all the daily posts expressing our discontent, Hearthstone would surely remain a fundamentally broken game. However, this is different than the protests of 1-3 years ago because we have momentum from the backlash of the new reward system. And we're taking heed to issues within Hearthstone instead of foreign politics.
Please upvote this, vote on comments, and comment on this to trigger Reddit's algorithm to get it to the top of hot, but not just these DAY X posts. If more posts reach thousands of upvotes and new ones shift into hot, it creates liveliness and prevents interest from dieing out. In order to ensure this succeeds, we must collectively accelerate it into an even bigger riot than before!
Let's use the momentum of this past week as a slingshot to get the PR response we deserve. And when we do, may we proudly spam into the circlejerk subreddit... "P2p BTW".