I hate the AI people that always seem to have some drama that reminds me of high school. I’m 37 years old, I find juvenile drama juvenile.

I hate the smug coders who declare their chosen language or path is superior despite being mediocre coders at best.

I hate the venture capital posters who came in fresh off crypto, thinking they’re the gods and market movers for giant trends in AI, and if you’re not up on their vibe you’re clearly an outsider.

I hate the ingroup, outgroup dynamic of the young AI enthusiasts who decided their chosen flavor of cult is obviously the future of the species and you’re either with them or against them.

I hate the esoteric tech enthusiasts who decided they figured out sentience aka consciousness and the secret summoning of machine demons and obviously you don’t understand the nature of the universe if you don’t understand “What’s Coming” ™️.

I hate the subtle implications that if you’re not part of the right cool kids group chat or ingroup or event, you’re a nobody unworthy of consideration.

I hate the people on this platform that got into AI a year or two ago and clearly are the cutting edge “thought leaders” you must accept as having the correct opinions.

I hate the people who seemingly know every minutiae of modern neural networks yet have never built an actual production AI system.

I hate the entrepreneurs who add “AI” to their offering and yet don’t have the self-awareness to realize they’re just adding buzzwords they don’t actually understand.

I hate threadbois.

I hate fake coders.

I hate AI “safety experts”.

I hate the snarky “I’m smarter than you” assholes.

I hate the usurpers and the frauds and the grifters.

I hate the engagement baiters and the over-confident alphas.

I truly hate the AI social media scene, and am just going to build what I know how to build.

We’ll see who ends up at the top once the dust settles.

I tend to write code in layers. I get an end-to-end system working as a base layer and then I go back over the whole system and fill in the gaps with subsequent layers.

I find that this is cumulatively faster than building each component piece by piece and assembling them at the end.

It allows for rapid feedback in bugs, it gets most of the tedious boring code stuff out of the way early, and allows for modularizing as you go given the system as a whole.

What do I mean by “layers”?

Let’s use a fictional example. Say a client wants me to build them an object detection system that multiple users can interact with simultaneously.

I would first stand up a very simple react app that allows an image to be uploaded and then an output image to be displayed. A few lines of code. I’d have a backend flask app have a single api that inputs an image and outputs random noise.

Once I have random noise being output, I’ll then go over the whole system and make the output generator a separate modular function that inputs an image and outputs noise.

Then I’ll maybe spin up a celery docker with redis for a simple job queue for generating the noise.

Now it can handle scale and simultaneous requests.

I’d make a new api that checks the status of the job.

At each step, so far, the entire end to end system is working to display something, and is being tested and hardened end to end as I go.

Then perhaps I’ll swap out the noise generator function with a yolo PyTorch model from hugging face to do the object detection.

Now, instead of the output react panel showing noise, it shows the result.

Then I’ll maybe add an active learning loop where the output is corrected and the model is fine-tuned.

The point is not about this hypothetical example, but rather how I think about it: layer by layer getting the whole system functional from the start, and layering on the requisite feature without ever breaking the end to end.

The next thing I might do is containerize the whole system. Making sure it still works.

Then I’ll separate the containers for the frontend and backend. Making sure it still works.

Then perhaps I’ll horizontally scale the backend. Still making sure the entire end to end system works.

Etc etc.

I’ve seen too many coders just dive in without a clear understanding of the process of engineering a system, spending hours and days on one piece, then moving on to the next piece. Finally when they integrate it all, they deal with bugs that arise from that integration.

I find that a subpar approach to engineering than ensuring an entire end to end system is working at each stage and layering on complexity and modularization.

I fully support mass deportations of all illegal immigrants by force (they shouldn’t have snuck into another country).

I fully support border patrol agents using rubber bullets, barbed wire, or similar against anyone who isn’t actually running for their lives (very small percentage of them actually need asylum).

We have a process for citizenship, which can definitely be improved, but which illegals completely ignore. It requires a citizenship test, pledge of allegiance, and actually following the law.

Right now, border patrol agents just let anyone in.

Don’t care how it affects the economy (it’ll help).

Don’t care how uncompassionate it is (go through the legal citizenship process if you want to be an American so badly).

Don’t care how logistically difficult it’ll be (we can do it if we wanted to).

Don’t care how many low skilled jobs will be unfilled (robots and high schoolers).

Don’t care if I get labeled xenophobic (I don’t have a phobia of xenos).

Fake asylum seekers are just criminals.

Open borders are a disaster.

Anyway, just sharing my opinion; you can disagree with me and that’s okay too.

Any climate change “computer model” being used to dictate global policy should be open source, with open data, and have way more public eyes on it than merely a “trust the experts” approach that we have today in order to trigger an emotional reaction.

I’m not denying anything here (we’ve had some alarmingly warm winters here compared to my memories from last millennium), but we need a more data-driven and measured approach instead of fearmongering.

(Also it’s not cow farts, relax…)

Imagine all the wild stuff we’ll find under the ice in Antarctica.

A continent bigger than Europe!

It’s gonna destroy all the lies about the ancient past of humanity.

Wild discoveries forthcoming.

No wonder the elites fear global warming - all the Antarctic ice is gonna melt!

If you’re not using Perplexity instead of Google, you’re missing out.

People think quantum entanglement can be used for ftl comms, but from our current understanding of physics, while the entangled particles collapse to the same energy state, you can’t choose that energy state and thus cannot send information.

Like two decks of cards shuffled the same. You know the next card you draw will be the same across the galaxy but you can’t control what card comes out.

The risk of AI being centralized in the hands of bureaucrats (as if the peasants can’t be trusted with such powerful tech) is one of the biggest real risks with AI.

They’ll make claims of not wanting mentally ill persons or criminals to get access to such intelligent machines.

And it’ll sound real nice, as if we need to have trusted leaders who can be responsible with such dangerous tech.

They’ll make the case that if everyone got it, bad guys, insane schizos, manipulators, or violent individuals would do untold damage to society with advanced AI.

The problem is that history is not on their side.

Decentralization and open source have historically been much safer for this type of tech.

When power is centralized, it corrupts and attracts the corruptible.

Those in charge of managing the tech do not necessarily have anyone’s best interests at heart; and even if they do, it’s much safer to ensure everyone has it.

The risk of AI centralization cannot be overstated.

There are many people, perhaps even most people, who believe that illegal immigrants deserve the compassion of prosperous people.

The worship of compassion as the highest value.

That any deportations of people who illegally entered a country is, in fact, not a compassionate act.

That if they commit crimes against citizens at a slightly higher rate, that doesn’t change the extreme compassion that must be shown towards them.

That the greatest risk is citizens embracing a primitive, potentially cruel, disdain towards people who look different from them or have a different faith than them, as that would not be compassionate.

That if conditions are in any way worse in another country, compassion dictates that the more prosperous country has a humanitarian duty to let them come in freely.

That the country will deal with any negative downstream consequences later, as that is in line with compassion, and maybe just maybe the prosperous citizens ever so slightly deserve a bit of hardship, to get a taste of how bad things can be in the rest of the world.

Just a bit more compassion, please.

Maybe it's okay if citizens get a taste of how the un-privileged feel, whose only crime was being born unfortunately in the wrong country, as that will help develop citizens' compassion.

That maybe it's okay, if not in fact deserved, that citizens get a bit of pain; maybe that'll help them feel more guilt and subsequently compassion.

Compassion compassion compassion.

Worshipful compassion says be a better person and let them all in…

You see, the EU enacted all these AI regulations initially to prevent extinction risk.

Some said it was about power.

Once people move on from extinction risk and think it’s silly, the regulations remain.

In a decade, when people cry about how the initial inception of the regulations were founded on a shaky foundation, an unrealistic fear, people will laugh at them for digging up such obscure and pointless lore.

“We are where we are, who cares what started it. They’re important now, even if their initialization was founded on what turned out to be an irrational fear.”

But that’s why we have to address over-regulation now.

“An ounce of prevention is worth a pound of cure.”

It’s better to not let them get irreversibly seeped into society. If the foundations are shaky now, let’s uproot them before it’s too late.

Except it already is too late.

You should constantly be practicing using these new AI tools like cursor or perplexity if you want to adapt as the way coding and research is evolving.

If you think that software development will be replaced by AI, in your head you probably think of the drastic change between let’s say 2020 and 2030, from people typing out for loops manually to people never typing a for loop again.

That may make you believe that “AI is gonna steal all the software dev jobs! We’re doomed! Mass unemployment!! Waahhh!!”

Instead, use your brain for a second. All changes like this are gradual. Miles of progress are made in inches.

As LLM’s promulgate and get better at writing code month by month, the way engineers design software will be changing, gradually, and month by month.

There is not going to be an obvious inflection point, but there will be new skills to learn.

Actually getting good at getting the computer to type out the code you want is itself a skill.

If you’ve been practicing this new coding paradigm for over a year, you realize what it can’t do, and you can’t wait until the next gen can do a bit more.

At no point along the way, if you’re staying up on these new tools, has your job been replaced.

The way you engineer may be evolving, but slower than you think, and certainly slower than people imagine in their fearful heads.

If you look back a decade, sure it may seem drastic, but the people who will be “replaced” are those who didn’t actually practice using the new tools along the way.

Crying about macroeconomic trends is dumb.

Software devs who are trying hard to get these tools to do what they want, week by week, are at no risk of being replaced.

They’re the ones who are learning how to be a code whisperer, they are the consumers of the tools who have no fear of being outsourced to a machine.

There is no hard take off in the short term, and the people losing their jobs to machines will be the people who obstinately refuse to use them, or just casually try them here and there.

And they deserve to lose their jobs, as they’re weak and lazy.

Coders excited about new tools to get the computers to dance as they want, and who for the past year or two have been actively practicing getting better at speaking to these tools, are evolving; their jobs are safe.

Stop whining and get good. See using LLM’s to write code for you as a skill to practice and watch your fear dissipate.

You fall behind you get left behind. Adapt or die.

Alright so let’s delve (😏) into some limitations LLM’s have with coding from my personal experience.

I asked LLM’s to change my flask app into a serverless IAC app that used AWS with Terraform, and it could not figure out how to connect SQS with Sagemaker serverless GPU and a docker based PyTorch image stored in ECR with a proper VPN setup with both private and public subnets. Simple.

I asked LLM’s to write a genetic algorithm that could potentially recreate either transformer or CNN architectures via a set of digital codons that defined basic matrix operations and neural connections with mating and mutation so novel architectures could be derived via sequential genes. Simple.

I asked my LLMs to swap out my DICOM anonymizer code with kitware’s, and then create a preprocessing pipeline that tested the effect of the radiology image augmentations from the albumentations library with the monai library using various loss functions and the Meta SAM2 image encoder. It had no idea what I was talking about. Simple.

I tried to get my LLMs to create a redis caching and celery queue manager that was deployable on azure in which it ran a ViT on a bunch of MRI scans and then pushed the result to a hospital’s PACS server asynchronously with error logging to a dead letter queue. Simple.

I asked it to try out various models from the timm library on CT scans and hook the results up to a 3D VTK react panel inside the OHIF software using its extension manager. Simple.

I asked the LLM’s to tell me why my weights and biases loss functions were increasing their variance even when the learning rate schedule exponentially decayed after a hundred epochs, and to use a Bayesian hyperparameter sweep with new neural weight regularizers and lr schedules to fix this issue. Simple.

I gave them a set of bash scripts and asked why it would fail when there are spaces in the folder names. It couldn’t realize that the “eval” bash function would get messed up if the string had spaces since it would consider them separate commands. Simple.

Maybe one day LLM’s will be able to do all this stuff with ease, but for the time being human engineers are crucial to coming up with good software systems.

Just a few simple examples off the top of my head.

Friendly reminder, anon, that in the age of LLM’s, the way you code should be to highlight small chunks of code and have the LLM expand or edit it inline.

Rinse and repeat.

Only type it out yourself as a last resort.

It’s about practicing a new skill.

Be a better craftsman.

Exponential change, in calculus terms, means the derivative is equal to the value.

In layman’s terms, that means a constant percentage increase over time. That no matter how large something grows, it keeps increasing a constant 10% in the same period of time.

It doesn’t matter if the machines have consciousness (they don’t),

Or if they’re gonna cause mass unemployment (they won’t),

Or if they’ll not make you as productive as was promised (this is true),

What matters for you personally is seeing them as new skills to master, to spend your time practicing with the new AI tools, every day.

The more you use them, the more you’ll gain a fingertip feel for their capabilities and limits.

It’s like practicing a new instrument.

Hit your 10,000 hours to master these new AI tools, and by the time this happens they’ll have been vastly changed and so hit another 10,000 hours.

Spent all yesterday coding with the new Composer in Cursor.

Big fan.

Multiple file editing at once.

Grouped context for projects.

My instinct was to stick with what I knew (Ctrl+K and Ctrl+L) but that’s how you go soft.

I forced myself to use Ctrl+I as much as possible to grow my skills, even if it was overkill for what I need.

This is what I mean about practicing with the new AI tools.

It’s not about if Composer was the best tool for the job.

It’s about growing my personal skills as a coder craftsman.

It’s about forcing myself outside my comfort zone and drilling into this new tool until I master it to the unconscious competence level.

Then it’s about practicing it even more.

The goal is to become extremely adept at using the latest AI tools so I never get left behind.

It’s entirely your responsibility whether you get replaced by machines, anon.

Seize your future and git gud.

Practice using cursor and perplexity daily.

Think of it like practicing a new instrument or training for a sport or mastering a hobby like welding.

Get better at being an AI tool user.

Spend less time debating consciousness or unemployment or political or economic impacts, and instead spend your time becoming an AI whisperer.

Move from conscious incompetence to conscious competence to unconscious competence.

Build the neural muscle memory.

Master these new tools.

Hit your 10,000 hours.

There’s an inordinate amount of legislation being written, debated, and implemented, on slowing down AI research out of fear that it will allow some psychopath to manufacture worldwide Ebola outbreaks from their basement.

Is this a legit concern justifying government regulation?

DEEPTOOLS VIDEO 1

If you want to learn to code with Cursor (an AI coding tool), here’s an 8 minute video of me using Cursor to build, from complete scratch, a web app that links to your webcam and tracks all faces live on that video stream. I have a few more Cursor coding videos I will be posting. Watch me get into flowstate live and actually write code.

#deeptools Video 1

Link to video on X:
https://x.com/DeeperThrill/status/1833640544159076380

LInk to previous video:
https://t.me/deepthrill/421

Dance with their minds.

DEEPTOOLS VIDEO 0

If you’re curious about what a Linux desktop looks like, as opposed to Windows or Mac, here’s a 15 minute tour of my Kubuntu desktop:

Link to video on X: https://x.com/DeeperThrill/status/1835044097737756848