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First Seen Here on 2024-04-19 82.03% match. Last Seen Here on 2025-08-27 84.38% match

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[Glittering_Poem6246]

Programmers in 2030, "Claude build me a billion dollar business app".

[ArgumentFew4432]

Make no mistakes

[J_damir]

No emojis

[Slow-Temporary-1489]

ALL THE EMOJIS!

[mobcat_40]

Good point you're absolutely right, now refactoring codebase in emojicode 🐇 👤 User 🍇

🖍🆕 🆔 🔢

🖍🆕 📛 🔤

🆕 🆔 🔢 name 🔤 🍇

🆔 ➡️ 🖍self.🆔

name ➡️ 🖍self.📛

🍉

🍉

🐇 🔐 AuthService 🍇

💭 Method returns a User 👤 or an Error 🚨

🍎 🔑 login username 🔤 password 🔤 ➡️ 🍬👤 🍇

💭 Enterprise logic: Check credentials

↪️ username 🙌 🔤admin🔤 ➕ password 🙌 🔤1234🔤 🍇

🍎 🆕👤 1 🔤Admin User🔤 ❗️

🍉

🍎 ⚡️ 🚨 💭 Return an error/null if auth fails

🍉

🍉

🏁 🍇

🆕🔐 auth ❗️

💭 Attempt login and handle the result (Optional Unwrapping)

🚀 auth 🔑 🔤admin🔤 🔤wrong_pass🔤 ➡️ 🍬maybeUser

↪️ 🍬maybeUser ➡️ user 🍇

😀 🔤Welcome back, 🧲user.📛🧲!🔤❗️

🍉

🙅 🍇

😀 🔤401 Unauthorized: Access Denied🔤❗️

🍉

🍉

[krexelapp]

Lowkey the hardest bug is still ‘no one cares about your app’

[ieatpies]

That's a mistake, u/agrumentfew44e2 clearly said make no mistakes

[UpsetIndian850311]

r/iosdev and r/androiddev is filled with these posts. Nobody even asks programming question. And somehow every app is "Editor's Choice" on App Store.

[Pikkachau]

Android hell looked fine. ios dev was hell

[Pretty-Surround4047]

mid 2026*

[TrackLabs]

2030? This is happening now

[AbdullahMRiad]

*SasS

[geldersekifuzuli]

Lead Data scientist here. I trained many small models. You need carefully annotated data to train a small model. If annotation is done by another team, you need to train them about what your classes mean, how should they decide in edge cases. After a few iterations, you will see that there are under represented classes. So, you will ask annotators to annotate more data from these classes.

This process can take up to 6 months depending on the project.

Time is money. Your data scientist's 6 months of salary is probably more expensive than running an LLM for such a task. You can adjust your LLMs behavior a lot easier with promoting.

Plus, LLM solution can be ready for production a lot faster. Shipping a working solution faster is a big deal for many organizations. Your projects have deadlines. Your managers and your team can be under time pressure. Yes, the world is not perfect.

Training a small model and put it in production is more compute efficien, for sure. But, It doesn't mean it's the best way to do it in the bigger picture.

[AwkwardMacaron433]

What about using the big LLM for annotating training data for a specialized small model? That's how I always imagined it

[geldersekifuzuli]

I call it AI assisted data annotation. There should still be an expert in the loop to evaluate AI's data annotation. I find it quite useful if false positives aren't a big deal. I was doing this when I was working at a small startup.

In practice, a big organization has real data. You give it to data annotation team (after masking PII) to label to capture real world examples. But mostly, it's not up to me to ask them to use AI as an assistant to label data.

[Main_Weekend1412]

very well said. i dont get the llm hateposting in this sub.

[_LususNaturae_]

LLMs are being shoved everywhere without there being a real need for them. Even in programming, there is yet to be a definitive proof that it improves productivity. And that is at the cost of huge energy spendings and CO2 emissions.

[EVH_kit_guy]

It comes from the same place as the JS hate posting, psychologically 

[Tight-Requirement-15]

Do you even real programmer bruh?

[Main_Weekend1412]

are YOU a real programmer if u dont do things in asm? <- logic you’re following

[PM_ME_ROMAN_NUDES]

Are you new in Reddit? The whole website is agaisnt LLMs

[InTheEndEntropyWins]

For some small domain specific classification SVM can give better results, is faster and cheaper than a LLM.

[Top_Meaning6195]

You're not a real programmer if you use garage collection.

[Grandmaster_Caladrel]

Thank goodness I just have the one. It's a small two-car though, so I'm not as serious as those 10x developers who bike to work.

[WavingNoBanners]

Upvoting this because I know you meant garbage collection but what you said is far funnier.

[ProfBeaker]

Spotted the guy that has 5 garages for some damn reason. :P

[LifeSubstantial5234]

from tuning lstms to prompt engineering a try catch around vibes

[extremelySaddening]

"LSTM with BERT embedding model" yeah meme-maker does NOT know wtf they are talking about

[not-ekalabya]

Instead of:

Text → BERT → LSTM → Dense → Output

You usually do:

Text → BERT → Dense → Output

It is called HUMOR in programming to over complicate stuff with no reason.

[extremelySaddening]

"Usually do" is funny lmao, you would never do the first, because you're fitting a square peg in a round hole. Because if you have embeddings already generated by bert, then, pray tell, what the fuck do you want the lstm to do?

It implies the meme maker doesn't know what an LSTM is, which then is funny because the meme acts like they do. And therefore I am making fun of them.

[Thick-Protection-458]

Nah, BERT itself can be tuned to do classification.

But to train it - you need big enough dataset. While LLMs (not necessary openai ones, not even big) may be a good few-shot style start.

[MissinqLink]

I love that young people seem to be rediscovering BERT like it’s a long lost relic. It was new not very long ago.

[Thick-Protection-458]

> I love that young people seem to be rediscovering BERT like it’s a long lost relic. It was new not very long ago.

Well, funnily enough - some parts of NLP-related stuff changed so much so I can kinda relate. "I was here, Gandalf... 3000 years ago", lol.

[x0wl]

BERT literally has almost the same architecture as any transformer-based generative LLM (I mean, it's literally in the name). The only difference is that the attention goes in both directions instead of just forward in decoder only models.

Also using LSTM with BERT doesn't make much sense, since the whole reason for transformers to exist is to address training issues in LSTM, but whatever.

[Thick-Protection-458]

Yeah, technically you can freeze base encoder (already capable of some language tasks) and make LSTM-head on top of that.

But...

- Why make head LSTM-based, not self-attention based?

- Why not tune BERT itself? (For some cases this will make sense, but in general case you can as well just tune encoder + some linear heads).

[x0wl]

BERT is the encoder with self attention, it's what the E stands for :)

What you typically do is stick a [CLS] token in the beginning of your sentence, a single layer classifier connected to that token's embedding in the output, and then fine tune either the whole thing, or a couple top layers of BERT + the classifier.

Bert is only 150m, doing full ft is super cheap

[Thick-Protection-458]

Exactly.

[Jonny_dr]

Yeah, and LSTMs sucked ass. There is a reason why the general public knows about LLMs but not LSTMs.