Boosting impressions to maintain "passive income" is one possibility.
I’ve seen niche forums in the past insert affiliate links, so when somebody clicks on that HP graphing calculator they go straight to Amazon and the site operator gets a 2% commission. This was the approach of a website that plagiarized Obscure Sorrows and was discussed here on HN (https://news.ycombinator.com/item?id=48611823).
And then there’s tinfoil hat territory: massive influence operations that span multiple forums and Wikipedia and random blogs to support specific points of view about scientific claims, preferred technology approaches tied to commercial platforms, and even historical claims such as who or what developed a scientific concept.
For anyone unfamiliar with Anthem (including me, before I read the second Onion article) and curious about what happened, Wikipedia covers some of the executive decisions and leadership changes that hurt the effort:
Iirc it was a stupid C-suite decision to use Frostbyte across EA studios. It never made much technical sense and DICE lacked the bandwidth and capital to support so many teams and usecases.
Yes after the success of frostbite around Bad Company 2 and Battlefield 3 or so, EA decided it should be used in everything. Not unlike the mistake Capcom is making with REngine right now, if the performance woes of Monster Hunter Wilds and Dragons Dogma 2 are anything to go by.
Pretty sure there was even a Need for Speed game on Frostbite which was... huh.
I worked on three of them! While there are some serious issues with Frostbite and everyone loves blaming it for everything, its role in sabotaging huge projects is definitely overblown.
There are some issues with workflow and it is true that the UI definitely was, as many at EA said, "by programmers for programmers". But it's a very capable engine in the right hands.
That said, I'd pick Unreal any day for a AAA project and Unity or Unreal for smaller projects.
cool, i think Frostbite is really pretty honestly. performs well in BF but ive not played other games that use it knowingly.
Why would you pick unreal? It seems a lot of games made in it suffer performance issues. In my mind its a really unwieldy engine. (ofc, because its so feature packed, and perhaps because i am no professional engine dev :-))
For a professional project with at least 30+ developers the complexity of using Unreal is necessary and on par with any competent engine. In fact, I feel it is often much easier to do things well in Unreal.
I like Unreal because it's really quite intuitive and ergonomic for larger productions, fairly well documented, and has some of the coolest tech in the industry. Their rendering pipelines are beautiful and a joy to work with and I could rant all day about my love for Nanite and Lumen. It is light years ahead of Unity and much easier to understand and modify than Frostbite.
I will say that Unity has better support out of the box for custom shaders and compute than Unreal (and Frostbite).
Now, you mentioned performance. Funny thing: performance has been my primary professional concern for the last six years of my career. Unreal itself performs very well if you use it correctly and has really good documentation for best practices as well as amazing tools for measuring performance and indicating what is wrong. The tools in Frostbite were very lackluster. Unity has some fairly good stuff. Unreal feels so far ahead.
I believe the reason there are more visible Unreal games with performance issues is because there are a lot more noteworthy ambitious productions in Unreal. That means games which push what they can deliver, and often don't quite get there. And for smaller productions it's likely just a matter of less experienced teams not following the best practices or cranking settings up to 11 without proper performance budgeting.
It’s just a rerun. Bioware had an entire prototype of a game called “Agent”, a james bond style bioware RPG built on EA technology from sports games … until it became clear the technology to render madden NfL lacked something like “non flat surfaces” you’d need to build a world :/
The core issue is wanting to jump on the Fortnite hype train. Every exec wanted their own money printing machine. It's not unique to this studio: abandoning the core IP to pursue an Overwatch clone.
>Internally, the project was codenamed "Dylan" by BioWare, in reference to Bob Dylan in hopes that it would be a name that would be spoken about years later.
I can't speak to Ruby or other programming languages, but as a creator and occasional hacker with a media background, I have found AI to be incredibly liberating.
Now I can build things that would never have been possible without huge investments of time and/or money. The latest project: a hybrid human/AI tech newsroom that leverages LLM-driven advances in translation (https://asia-tech-news.ghost.io).
For my personal passions, LLMs have completely opened up areas that were extremely painful to deal with 5 or 10 years ago. Take genealogy: I used to manually transcribe old letters, and documents in foreign languages (often archaic terms) were impossible for me to work with without seeking out experts. The other night I had an AI go through a book of 200-year-old religious records and pick out references to potential ancestors. A few weeks earlier I worked on an old diary transcription (https://youtu.be/P6r1I-4T9is?si=qhaf3Wd5zZNjieEw) which I later shared with relatives to proof and enjoy. Such tasks are no longer a chore. It's fun again.
The closest comparison I can make was stumbling upon an old HTML tutorial on an MIT website 30+ years ago. All of a sudden, as a writer and creator, I was no longer tied to my employer's business model to get my words and images out to the world.
Take woodworking. For some people, the act and meditative work of building it piece by piece is the part that they strive for. For others, it’s the planning phase and the “how will all these pieces go together” phase. For even others, it’s the staining phase. Some people like every part, others like just one part and work through all that extra just to get the chance to do that next part. For some, while they may not enjoy the preface to the work they want, they appreciate it because they feel that blitzing past it cheapens the stage they love.
Everyone is different and AI is hitting everyone just a little bit differently, and that’s just in areas that directly impact their specific craft.
> Everyone is different and AI is hitting everyone just a little bit differently, and that’s just in areas that directly impact their specific craft.
That's a great point. I don't have experience with woodworking, but when I see a piece of good antique furniture with precise dove-tail joints that someone made hundreds of years ago, that brings me joy.
As a writer, and as a reader, I really value human-crafted insights, like many of the comments I see every day on HN. As many people have stated elsewhere, writing forces you to organize your thoughts.
But I also recognize that there is a place for machine-generated content, whether it's the weather report on my phone or the factory-produced chair I'm sitting on now.
I don't think the story of artificial intelligence is "humans will stop creating." AIs are powerful tools, but they shouldn't be regarded as mutually exclusive with people who like to create things. Some will create in old ways. Some will create in new ways. Others will move onto new ways of creating.
That factory produced chair is way more likely to end up as landfill than a hand-made one. The AI-generated slopware generates the landfill (ie pollution and the jobkilling) before it's delivered to you.
You can completely clone board games and steal from their original makers OR you can make something new that didn’t exist before because there’s tools to facilitate your work that you didn’t have until now
Transcription is one legitimate use for LLMs that archivists are excited about. It's a case of a tedious task that frees them up to do the more interesting parts of the jobs, and the product is something that can be easily compared to the original and verified. This is very different from something like writing prose or code.
It definitely varies person-by-person. People who enjoyed l33tcoding, the act of programming, and the building process feel one way vs. the people who are in it for the end product, or enjoy the design process feel another. In a way, it's almost like those who enjoy the journey vs. the result (although the design process is also a journey).
Personally I enjoyed the act of programming/building. I'm struggling to put this into words, but software engineers have often been interested in solving technical problems, rather than business problems. At least I've felt this personally. I'm working on shifting that enjoyment toward the requirements gathering/design process. At the end of the day, it's still problem solving (which is the root of what I enjoy). But now it's more focused on solving business problems vs. technical problems. Which at the end of the day, is what is valuable to the business.
From the business perspective, this is great, because the end goal of software for the business is "business value". But it is a tough shift for some people to make (me included).
Edited to add:
Writing code vs. designing software are both creative. Writing code has a much more concrete/objective feedback loop though.
I also particularly hate the feeling of AI being shoved down everyone's throat.
And I don't enjoy digging through the mess that AI creates, whenever I have a particular bug that it can't quite get right.
I note the Toronto announcement cites job creation in the very first sentence. I live near another snowy city where Waymo has been teased, but has yet to have any significant presence:
I get the concern over tokenmaxxing and dead-end projects, but they are going to lose talented people over such petty limits, and hold back the work they do for clients.
I question whether the numbers they are looking at capture everything that's going on.
$100/mo on Claude Max for me (probably headed to $200/mo by years end). The time saved on tedium, the analytic insights I can generate for clients, and the progress I can make on R&D is difficult to assess and measure within my small business P&L.
Perhaps the average employee at this organization is ill-equipped to make good use of AI. Efficiency and effectiveness might be a function of a good engineer wielding it, while a bad engineer might burn through tokens just to keep up.
Or maybe that's just me in the bargaining stage of my career collapsing.
This is not a "might". That's literally how I've seen it way too many times now.
The same employees that are already not the greatest, that take the longest, that get stuck in rabbit holes the most, that have to be corrected in code review the most (because they took a fundamentally wrong turn/decision and just ran with that) are the ones spending the most tokens to do more of exactly that and "faster".
They still take the longest because they do something with AI, badly, and then try to fix it, with AI, for more and more tokens. They get stuck in rabbit holes because AI takes them there just like they went there before by themselves but now it just also costs tokens on top. Code review usually means you catch a wrong turn within like the second sentence of their three page AI generated PR description that invalidates a huge 1500 line PR entirely. That thing cost lots of tokens and now needs to be redone for eveb more tokens.
The multitrack reel-to-reel tapes and two-track masters are likely sitting in boxes in someone's basement or storage unit, along with scores or hundreds of other recordings from the same studio (or record label, if they took possession).
Music studios are (or were) terrible businesses. Typically they were started by a certain type of musician who liked the process and maybe invested in the gear to record his or her own music, and then decided to turn it into a business. They required constant, expensive technology upgrades. They also require a steady stream of clients which is so hard to maintain as music evolves and new up-and-comers choose or are steered toward other studios.
I worked, briefly, in a London recording studio. It had a good run because its co-owner had a steady stream of Hollywood work and could afford the upgrades to the latest technology, which in turn attracted the latest pop artists. But even it struggled with the analog to digital conversion wave. A problem with the property that required extensive construction work derailed operations for months and it never recovered after that.
The arrival of PC recording software is often cited as the death knell for professional studios, but I think the model died decades earlier and was only sustained by constant supply of briefly successful and competent musician/producers who thought they could make a business out of their talents and gear.
If you're interested in this type of thing, check out "Sound City" (2013) which tracks the journey of a Neve console through LA recording history and eventually to Dave Grohl's private studio. https://www.youtube.com/watch?v=Jwskv2G0X8A
In the 80s one of my high school teachers said her husband was an engineer who worked on surveillance technology and described how windows could be used as recording devices. People's voices inside caused the window to have minute vibrations, which could be measured by a laser pointing at the window's exterior and reflected back and then turned into audio output. Essentially, turning a window into a microphone diaphragm. It blew my mind.
I just looked it up, the technique was apparently invented by a Soviet engineer 40 years prior - Leon Theremin, who is also known for the musical wand heard on Led Zeppelin II and various 1960s horror movie soundtracks.
Vaguely similar, since electrical wires emit EM fields that are disturbed by human bodies, you can use a special camera to see who's in a building based on those disturbances. I'm not sure how high-fidelity or easy to spoil it is, but this was real technology at least 15 years ago.
This is a feature in modern wifi routers and every single one is capable of detecting presence of multiple people within an apartment. With some training, movement can be distinguished from the data.
I saw a video at some point of someone building one of these, I'm not sure it would have worked terribly well for actual use in the outdoors but the sound quality was much better than I expected at close range. I wonder how you defend against this, short of having your important meetings in depressing windowless basements?
The amusing aspect is that this is so easy that multiple good ways someone could hobby program this long before AI was a thing. The sound of a bunch of keypresses may just blur together to our human ears, but if you try, you can tell that different keys make different noises, which is really the key insight. Between the different noises, the possible directionality you can get from a stereo (or more) pickup, and characteristic delay patterns during typing various bi-grams, there's a ton of information leakage in a recording of a keyboard.
Oh, I thought we were talking about being able to distinguish which key was pressed using wifi-radar, like one can do with deanonymizing someone via wifi gait analysis.
the agent never stops and says, “Wait, this is something another model could do cheaper and faster.” It just plows on with the slow, expensive model. Conversely, the agent never says, “This model is too dumb for this task. Let me tag in a smarter one.”
This is a pretty big failing, which is compounded by the fact that most humans don't know which model to pick, or make assumptions based on Anthropic's hierarchy or "effort" involved.
Like Fable: your toughest challenges. You mean, like Fields Medal toughest challenges? Or analyzing and updating three monster spreadsheet toughest challenges? Or writing a new novel in the style of William Gibson toughest challenges?
It's often nearly impossible to know in advance how hard the task will be, so the model has to guess, and will inevitably miss. Just because you know how hard the task is, doesn't mean it's obvious. Often it's only obvious to you, because you have additional context that models are missing.
A bad guess can lead to either context rot (if subagent wasn't used), or slower execution (if subagent was used but it turned out to be a bad idea). Both are bad UX.
Now, we could improve accuracy by having the harness do a research on each task before executing... which leads to even slower execution, also bad UX.
OTOH, would you trust the vendor to pick the best model for you? Would you trust them not to prioritize their own load-balancing concerns first?
The descriptions are near-useless and tend to flip around, as model families are not released in sync anymore, that's true, but fortunately, thanks in a big way to subscription pricing, the choice is simple: start with the best model on offer, and when you run out of quota, downgrade to the next best (or briefly switch providers).
Instead of trying to guess the difficulty level for each task, I think it’s better to define the model for each role in advance. It’s hard to know the difficulty level ahead of time, but we do know the roles in advance.
yeah totally agree that it does not happen on it own. what worked for me is making it explicit i.e when a task changes character i start a new session with fresh handoff (create your /handoff skill and have the original write it then paste it to the new one) surely this can be automated, im trying something with sessionStart hooks but its still at early stages. however i have found out that the briefing is what matters way more than which model reads it
I've been thinking an AI could be trained to bid on jobs. Probably too many real world problems to make it practical, but I can't get it out of my head.
I’ve seen niche forums in the past insert affiliate links, so when somebody clicks on that HP graphing calculator they go straight to Amazon and the site operator gets a 2% commission. This was the approach of a website that plagiarized Obscure Sorrows and was discussed here on HN (https://news.ycombinator.com/item?id=48611823).
And then there’s tinfoil hat territory: massive influence operations that span multiple forums and Wikipedia and random blogs to support specific points of view about scientific claims, preferred technology approaches tied to commercial platforms, and even historical claims such as who or what developed a scientific concept.
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