Interesting approach! I've thought about a similar method after reading about the PLATO platform.
When playing astro‑maze, the delay is noticeable, and in a 2D action game such delays are especially apparent. Games that don’t rely on tight real‑time input might perform better. (I'm connecting from Europe, though.)
If you add support for drawing from images (such as spritesheets or tilesheets) in the future, and the client stores those images and sounds locally, the entire screen could be drawn from these assets, so no pixel data would need to be transferred, only commands like "draw tile 56 at position (x, y)."
Thanks! But I get the impression that with Kokoro, a strong CPU still requires about two seconds to generate one sentence, which is too much of a delay for a TTS voice in an AAC app.
I'd rather accept a little compromise regarding the voice and intonation quality, as long as the TTS system doesn't frequently garble words. The AAC app is used on tablet PCs running from battery, so the lower the CPU usage and energy draw, the better.
I had already suspected that I hadn't found all the possibilities regarding Tortoise TTS, Coqui, Piper, etc. It is sometimes difficult to determine how good a TTS framework really is.
Do you possibly have links to the voices you found?
The sample sounds impressive, but based on their claim -- 'Streaming inference is faster than playback even on an A100 40GB for the 3 billion parameter model' -- I don't think this could run on a standard laptop.
Large text-to-speech and speech-to-text models have been greatly improving recently.
But I wish there were an offline, on-device, multilingual text-to-speech solution with good voices for a standard PC — one that doesn't require a GPU, tons of RAM, or max out the CPU.
In my research, I didn't find anything that fits the bill. People often mention Tortoise TTS, but I think it garbles words too often. The only plug-in solution for desktop apps I know of is the commercial and rather pricey Acapela SDK.
I hope someone can shrink those new neural network–based models to run efficiently on a typical computer. Ideally, it should run at under 50% CPU load on an average Windows laptop that’s several years old, and start speaking almost immediately (less than 400ms delay).
The same goes for speech-to-text. Whisper.cpp is fine, but last time I looked, it wasn't able to transcribe audio at real-time speed on a standard laptop.
I'd pay for something like this as long as it's less expensive than Acapela.
In my experience, this bug - lags and overheating when drawing with the Apple Pencil - exists since iPadOS 16. When searching for it on the web, I found lots of reports and no indication that it is solved, including by hardware replacements.
In any case, HN's guidelines ask to use the original title of an article, unless it is misleading or linkbait. I'd agree that Apple's software quality has been going down.
I've used Apple's Automator app to add a new custom Quick Action which does exactly this. After right-clicking a folder, the right-click menu shows my custom Quick Action to create an empty text file.
This requires about 5 to 10 minutes to set up. You'll find instructions for this on the web or via some LLM. I've looked right now for a suitable article, but the ones I've found are subtly different from my Quick Action. I've asked ChatGPT and its instructions seem to be correct.
In the example I mentioned, ChatGPT 4 did keep all essential statements of my texts when reproducing shorter versions of them. For example, it often wrote one high-level sentence which skillfully summarized a paragraph of the original text. As far as I understand, this is what the author meant by 'summarizing' vs. 'shortening (while missing essential statements)'.
I was impressed at those high-level summaries. If I had assigned this task to several humans, I'm not sure how many would have been able to achieve similar results.
Am I blind or is there no mention at all of the GPT model he used?
The author states his conclusions but doesn't give the reader the information required to examine the problem.
- Whether the article to be summarized fits into the tested GPT model's context size
- The prompt
- The number of attempts
- He doesn't always state which information in the summary, specifically, is missing or wrong
For example: "I first tried to let ChatGPT one of my key posts (...). ChatGPT made a total mess of it. What it said had little to do with the original post, and where it did, it said the opposite of what the post said." He doesn't say which statements of the original article were reproduced falsely by ChatGPT.
My experience is that ChatGPT 4 is good when summarizing articles, and extremely helpful when I need to shorten my own writing. Recently I had to write a grant application with a strict size limit of 10 pages, and ChatGPT 4 helped me a lot by skillfully condensing my chapters into shorter texts. The model's understanding of the (rather niche) topic was very good. I never fed it more than about two pages of text at once. It also adopted my style of writing to a sufficient degree. A hypothetical human who'd have to help on short notice probably would have needed a whole stressful day to do comparable work.
I prefer to work intensely and collaboratively in an office.
This is how I'd do it: Three in-office days, same weekdays for everyone (e.g. Monday to Wednesday). The choice to have a 5-day or 4-day week.
An energetic, quietly humming work atmosphere, with incidental information sharing and a spirit of collaboration, with colleagues present and nearby, sounds best for me personally. Among other advantages, the presence of coworkers helps me focus.
Different strokes for different folks. Obviously, the prerequisite is that it's a team of nice people you like being around.
Sure, people who prefer to work from home would leave that company. That doesn't mean that this company will lack talent. People who do want to work like that will join it.
We're currently building a new AAC device which allows users to write/speak quite a bit faster, and we're encountering exactly the hurdles you mention. Would you be willing to exchange some helpful pointers about how to bring an AAC device to market? Contact info in my profile.
Did your game require realistic physics collisions? If not, this might be unnecessary complexity. Almost no 2D shoot'em up game before 2000, and very few afterwards, go this route. Here's the common method to make a shmup with very simple rectangle comparisons:
But if your space debris objects are supposed to collide and agglomerate realistically, and if the player ships are supposed to have difficulty pushing a cluster of heavy objects out of the way, then using a physics library is sensible.
Feedback: Your introduction "From Mario bouncing off a Goomba..." might be a bit misleading IMO because most games like the classic Super Mario titles on NES and SNES do not require and did not use most of these calculations.
Game development beginners often have the wrong impression that they need rigid body collision calculations or a 2D physics engine like Box2D to handle collisions. That's true if you want to make a game like Pool or something with collapsing stacks of crates like Angry Birds.
But for a 2D platformer you only need to detect collisions by comparing (axis-aligned) rectangles and to handle collisions by changing the moving character's X and Y coordinates (to undo an overlap) or setting the character's Y velocity (after using the jump button, or after landing on a Goomba's head).
This also makes it easier for the developer to finetune exactly how moving the character should feel like. (This includes inertia, but this inertia is usually not physically realistic.) Trying to use realistic physics as a gamedev beginner can easily lead to floaty and unsatisfying movement.
1. He isn't working on this exclusively. In the update notes, he mentions that he's making the game in parallel to a job. He recently finished his degree and sometimes takes time off for his next project. In 2021 he said, 'As I’ve mentioned before, game development is not a full time job for me, it’s just a hobby that consumes a lot of my nights and weekends.' Obviously this must be considered when evaluating the revenue.
2. After he finishes the game, it will still have a long tail of sales over the following years. He can develop another game, whose long tail will overlap with the first one, then with a third game, etc. But I don't know how to estimate these long tails after the first couple of years.
3. It seems to be his first game on Steam.
This is actually a very interesting case. I think he does most things right (though he doesn't seem to do enough marketing on Twitter). I assume that he'll arrive at a place where he has the option to go full-time sooner or later.
Surely you're not claiming that a game with fewer than 1000 reviews is "languishing"? Any indie game with 1000 reviews is very successful.
Picking your first example, Deadeye Deepfake Simulacrum has about 460 reviews, roughly 20,000 copies sold, an estimated revenue of about $120,000 while it's still in early access, and apparently a developer team size of 1. I'd guess that this game will net the developer several hundred thousand dollars over the next 10 years.
(Regarding multiplayer games: yes, these are a different case because they need to reach some critical mass to have a chance.)
I hope that this doesn't come across as moving the goalposts, but for me it's a given that popular Youtubers can multiply the success of a game, and that this depends to a large extent on luck. For me, the crux of the matter in discussions about game development profitability and survivorship bias is not how much luck influences the maximum possible success of a game.
For me the relevant question is: If you develop a carefully crafted, fun game based on a game concept you have reason to believe will have a decently sized target audience, will you make enough money for a living with some kind of predictability, or does even this depend on luck?
Now when looking at The Void Rains Upon Her Heart, I'd like to know the sales figures before the jump in popularity. You can filter Steam reviews to the period before the game's popularity surged in June 2023. At that time, there were 561 reviews (or 338 reviews for copies sold via Steam). Using the common sales estimation trick of multiplying the number of reviews with 50, we get approximately 28,000 (or 17,000) owners before June 2023. I think the price fluctuated between $9.99 and $6.99. Assuming that after Steam's 30% cut and taxes there are about $3 left on average for each sold copy, then 3 * 25,000 = $75,000. According to the game's credits on MobyGames it was made by a single person. Depending on how long he took, maybe it is reasonable to assume that the game was en route to profitability anyway?
I think that extreme outliers like Flappy Bird (which the developer developed over a couple of days and probably didn't expect any significant return) just muddle these discussions. They're irrelevant if you want to suss out what happens in the usual case.
Then it seems that "survivorship bias" is some kind of unfalsifiable and self-fulfilling belief.
Secondly, these sorts of discussions usually don't define any concrete amount of success that a game is supposed to achieve. What is "no traction" supposed to mean? Aegis Defenders has 1,656 reviews at the moment. There's the assumption floating about that you can roughly multiply this by 50 to get the number of owners, which would turn out to about 80,000. The price point is fluctuating between $19.99 and $4.99. Will it net the developer/publisher less money over its lifetime than its development cost?
In any case, I think that one of the biggest factors is not merely the game's quality, but whether there are a lot of players hungry for a game's specific concept and genre. Making an "excellent game" in an oversaturated genre, or in a genre where games require some network effect to take off (any multiplayer game), is much more risky. Don't just make something good; make something that a lot of people want even when the product is less than perfect.
EDIT: VG Insights estimates $770k gross revenue. That's just for the Steam version. The game was also published on PlayStation 4 and Switch. The developer team seems to have been small.
Based on your reply to the sibling comment, you're just pointing out the "contrast" to the success of Balatro? I honestly don't get the point. I don't think the particular amount of success that break-out hits achieve matters to the discussion. If Balatro had a million reviews, would you expect Aegis Defenders to also have a lot more reviews and sales? I think this isn't relevant to the question whether excellent games will succeed (for me this means: enable the developers to make a good living) with some predictability or whether it's due to luck, and whether there exist a lot of excellent but unknown games.
When playing astro‑maze, the delay is noticeable, and in a 2D action game such delays are especially apparent. Games that don’t rely on tight real‑time input might perform better. (I'm connecting from Europe, though.)
If you add support for drawing from images (such as spritesheets or tilesheets) in the future, and the client stores those images and sounds locally, the entire screen could be drawn from these assets, so no pixel data would need to be transferred, only commands like "draw tile 56 at position (x, y)."
(By the way, opening abstra.io in a German-language browser leads to https://www.abstra.io/deundefined which shows a 404 error.)