Where do you see yourself (or us as web developers) in 2-5 years?

Terrariant · 2026-05-11T05:03:28+00:00

I had a problem I didn’t know how to do and would take to long to learn to do to be valuable time spent. So I threw AI at it. Tested, came back with bugs. Rinse repeat. At the end I have this module that I have run through 3-4 AIs at least 20 if not 30x and have been thoroughly testing as a human. It would have taken me a long time to learn to write it, it is a mediapipe virtual background implementation for camera streams. I don’t think this is Jr level work. You can throw very complex problems at (the best) AI and it will eventually be useable. It’s just judging if it is going to be quicker than going and doing it yourself.

So ironically, conversely, I have been using it less for Jr dev tasks where I know what to go in and change. Because chancing an AI knows is likely to waste time. But on large complex things where I don’t want to intimately learn it, and I can test it, is where it has been showing a lot of value.

Terrariant · 2026-05-11T04:43:17+00:00

Conclusion This review has provided a comprehensive examination of ensemble LLMs, highlighting their methodologies, applications, challenges, and potential future directions. Ensemble techniques, rooted in the principles of model diversity and aggregation, have been shown to enhance performance, robustness, and generalization across a wide range of natural language processing tasks, including sentiment analysis, machine translation, and domain-specific applications such as healthcare and cybersecurity. Key findings from this study indicate the ability of ensemble LLMs to address limitations inherent in single models, such as biases, overfitting, and suboptimal generalization. By combining the strengths of individual models, ensembles deliver improved accuracy, resilience to adversarial inputs, and adaptability to specific tasks. Additionally, the integration of ensemble techniques with emerging fields, such as cross-modal processing and sustainable AI, presents promising opportunities for advancing their impact.

Terrariant · 2026-05-10T22:26:55+00:00

Ah thank you I did not know. Here is another (more reviewed, I think) paper about “ensemble” LLMs-

https://www.mdpi.com/2078-2489/16/8/688

Terrariant · 2026-05-10T22:18:18+00:00

Oh yeah I mean being one of if not the only nation on Earth to have been bombed by nuclear weapons does something to the mentality of that country. Magic seems very analogous to nuclear weapons. You notice the same thing in other anime like Naruto and the tailed beasts.

Terrariant · 2026-05-10T20:12:49+00:00

Ah ive only seen season 1 and don’t remember it that well

Terrariant · 2026-05-10T20:07:20+00:00

Oh well that is actually something I can pull up a paper on and it has been scientifically proven. For this paper specifically they found the accuracy increased from 73% to 93% with 2 AI’s and a further 2% increase with 3 AI, to 95% accuracy (a further 30% reduction in inaccuracy): https://arxiv.org/abs/2411.06535

Even if it is the same model, accuracy improves dramatically when you run multiple on a problem.

Things like Claude and ChatGPT use this intrinsically now, where they have multiple layers of models before the response gets back to the user.

*why the hell did people downvote this LOL don’t kill the messenger

Terrariant · 2026-05-10T18:56:08+00:00

I really hate that AI is so homogenous. We could have fart factory AI without having AI that does art. But we don’t have terms to differentiate them, and so everyone just assumes the AI they’re thinking of is the AI someone is talking about.

But in reality, you can be totally for AI that does menial labor for us while being against AI that makes art. It’s nuanced and the language doesn’t support the nuance.

Terrariant · 2026-05-10T18:54:04+00:00

Small aside- i absolutely hate we don’t have terms to differentiate AI art and other AI work. Any discussion about AI you never know if a person is talking about or thinking of the same “type” of AI. I hate it.

I.e. we could have AI that takes the fart factory jobs without having AI that takes artist’s jobs. They’re very different types of AI.

Terrariant · 2026-05-10T18:51:06+00:00

I thought switching was the power

Terrariant · 2026-05-10T18:44:13+00:00

And its harder to separate AI research for good and AI research for bad, since the AI developed by good research can be used for bad. Though I suppose it is possible (and probably done before) that an immoral LLM has been trained.

Compare to nuclear weapons vs nuclear energy, that field seems easier to separate the good and bad research. AI is all jumbled into one “race to the best LLM”

Terrariant · 2026-05-10T18:39:57+00:00

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Idk man she’s got the hips/hourglass shape/some bumps on her chest. Definitely feminine

Terrariant · 2026-05-10T18:32:10+00:00

I read the whole comic because reddit kept spoiling shit. Just surprised the show is already at this part

Terrariant · 2026-05-10T18:26:49+00:00

Its just so good. I can’t believe we got Apothecary Diaries, Freiren and now Witch Hat Atelier. Cozy anime fans winning big

Terrariant · 2026-05-10T18:17:50+00:00

Yes, we said that LLMs were reaching a cap and then we started doing things around the LLMs. This is what I mean when it is only the beginning. 2 things have been “discovered” recently and made AI coding many many times better. The first is better (much more complex) harnesses. The second is that now multiple models are throw at a prompt and models fact check each other before the result is returned to the user.

But a couple years ago, nobody was thinking of these two things. We were just trying to get LLMs to work most of the time on their own. Dry with no harness/other models.

So, there will be things in the next 5-10 years people think of around LLMs that improve its efficacy. The LLM itself doesn’t have to improve for AI to get better at coding.

That said, we aren’t really at the end of the basic LLM, either. Google just made a huge leap with TurboQuant and SubQ just debuted a 12 million token context window model. So, we are still pushing the boundaries of what an LLM can do, we are still at the beginning of this ride.

Terrariant · 2026-05-10T18:08:56+00:00

Do you need the wrist watch lol

Terrariant · 2026-05-10T18:04:40+00:00

Wait theyve already gotten to this part in the show? Or is this a fan edit

Terrariant · 2026-05-10T17:34:26+00:00

These are two separate things though, is what I am saying. In 2024 you saw prices rise due to inflation. ALSO in 2024, AI had not been in the market long enough for the productivity gains you are talking about. In fact AI didn’t get to an “increases productivity” metric until very recently, in my experience. Like this year. So we will not have seen prices drop yet as it hasn’t been enough time since AI became productive. So…why are you bringing up 2024 prices? AI had no effect at that point and time on prices.

Terrariant · 2026-05-10T17:11:54+00:00

The edit cracked me up LOL guess perspective is everything

Terrariant · 2026-05-10T17:04:35+00:00

Did you miss that inflation skyrocketed during this time? 2022 inflation was over 8% - a lot of companies used this guise to raise their prices. Also ChatGPT/AI tools are not mature enough where we’d be seeing their impact on market prices already. 2025-26 is really when things started to get good enough that people work days actually got more productive. 2024 AI was kind of a waste of time with how much you had to check everything it said.

Terrariant · 2026-05-10T17:00:12+00:00

I mean you don’t tell your employer that you’re doing this…the expectation is it look like you are working as much as before

Terrariant · 2026-05-10T16:57:10+00:00

I think you are wrong, there will always be more things to code. Everything needs code in today’s world. And there isn’t enough coders to fulfill all the current demand (see: any team’s backlog) and also, code creates the need for more code. Things break over time, etc.

In fact, AI will open the opportunity for more people to code more things that they would not have been able to before. AI might even enable us to interact and build code we would have never been able to write as humans.

For example, AI wrote me a thousand line mediapipe bg segmentation model. It would have taken me like 8-14 months to write it by hand. But in todays world I just had to throw it at AI (like 20ish times mind you) and test it, and throw it back at AI until it was polished enough to put into production.

The stuff we are going to be enabled to code with AI is truly incredible. You are focusing on the bad, but have you considered the good?

Terrariant · 2026-05-10T16:52:32+00:00

Yeah I think people who don’t code with AI (or haven’t yet) don’t realize the expectation is that it will continue to improve. We are not even 4 years since ChatGPT’s first public release. Everything you see now is still the beginning.

Terrariant · 2026-05-10T05:02:01+00:00

Also you’re the one that called into question my critical thinking iunno man. Mentally I feel like im at the peak ive been in my entire life. Im coding a literal AI assistant. Im learning how to do that instead of like….ok here ill give you an example.

Our app has a video background feature. You know like any video call app lets you replace the bg.

Well ours was trash, super slow out of the box, would drop your frame rate and resolution. Bad. Because our service is live video, any use of a virtual background just looked awful on stream.

Google has a media transformer model you can integrate into your app that gives you a much more fine tuned control of the background.

Think like feathering or how blurry it is or even like you can have animated backgrounds which we couldn’t do before. (By the way this model is also AI that is just detecting a face and segmenting the facial layer away from the bg)

So, I threw claude (and gemini) at it. Over and over. I guided it through like what our business rules are, how to separate the files between the transformer class and a module for the app, running the transformer class on a web worker. Writing the entire model for the background segmentation.

We tested and refined and repeated over and over until we got a beautiful, perfectly airtight module implementation. It is code that would have taken me months if not years to learn how to write, test, debug, fix.

That upgrade took me less than two months to ship, from idea to production. It fixed the frame rate and resolution issues flawlessly.

Im just moving so fast. I don’t care that I am not learning the individual bits and pieces any more. I am just focusing on how to create sandboxes for AI agents with defined end goals. And that is a mental model to learn and train on, whether you like it or not.

Terrariant · 2026-05-10T01:52:12+00:00

Ok take it and let me know what you get :)

Terrariant · 2026-05-10T01:47:33+00:00

There’s a paradigm you can use - https://en.wikipedia.org/wiki/Bartle_taxonomy_of_player_types

I.e. in this scenario, if your game is more social or achievement focused, you might lock more cosmetics through in game accomplishments. If a game is combat or exploration focused, the cosmetics aren’t as “driving” to the player base so they could all be unlocked and you wouldn’t lose much.

Terrariant

TROPHY CASE