I’ve been watching this for a while now. On Reddit, on LinkedIn, in job applications, in random conversations. There’s a whole group of people who proudly announce that they refuse to use AI in their work. Coding, writing articles, books, research, design — whatever. They say they stay “old school” because they want to remain original. They don’t want AI to “poison” their work. They write every line by hand. They do everything the pure way.

I used to find it mildly annoying. Now I just find it absurd.

A few months ago I posted a job looking for a coder, preferably strong in Python but open to other languages. One guy came in looking extremely qualified on paper — claimed he knew 15 languages, had a pile of certificates, the whole thing. So I asked him a simple question: do you know how to work with AI tools?

His answer came back almost offended. No. He is a real programmer. He writes every single line of code by hand.

I told him then I wasn’t interested. With AI I can get done in one hour what would take him a full day of pure manual work. I need efficiency, not some performance of purity. We didn’t work together. But the exchange stuck with me.

In 2026, if you work with code and you’ve never seriously used Cursor, Claude Code, Codex, local models through Ollama, or even just a strong chat interface with good prompting, something is wrong. Not knowing these tools is not a badge of honor. It’s a signal that you’re behind.

The efficiency reality most people refuse to admit

Take a concrete example. A complex landing page with HTML, Canvas, SVG, animations, responsive design — easily 2,000–3,000 lines if you do it properly. Writing that completely by hand takes real time. Hours. Sometimes a full day if you care about quality.

With a good model and clear direction, a solid draft can be ready in 10–15 minutes. Then you spend another 20–30 minutes cleaning, adjusting, making it actually good. Suddenly you can ship 8–10 of them in a day instead of one if you’re lucky.

That’s not a small difference. For freelancers, small agencies, or anyone running a real business, that difference is the difference between surviving and thriving.

The same logic applies to research. Doing a proper literature review or deep research the old way can easily eat two weeks. With the right tools and the right questions, you can get a strong first pass in under an hour. Then you still have to think, verify, and decide. But the “gathering straw” part is largely gone.

People still act like doing everything manually is somehow more noble. It’s not. It’s just slower.

The skill atrophy argument (and why it’s only half true)

I hear the counter-argument all the time: if you rely on AI, your skills will atrophy. You’ll stop understanding what you’re doing. You’ll become a prompt monkey who can’t debug anything.

There’s truth in that. If you just accept whatever the model spits out without reading it, without questioning it, without understanding the architecture, yes — you will get worse. Juniors who never struggle through a problem themselves are going to have a rough time later.

But the other side of this is almost never discussed.

I have never written a single line of code in my life from scratch the traditional way. Yet through working with AI I learned how to read code. I learned to recognize patterns, spot obvious errors, understand what a function is doing, and know when something looks off. AI didn’t make me dumber. It made certain skills accessible that previously required years of grinding.

I’ve also seen a 55-year-old man who barely knew how to turn on a computer. With AI he learned enough web design and e-commerce basics to take his old family business online. He built a shop. He started selling. Without these tools, that leap would have been almost impossible for him at that age and starting point.

So the real question is not “does AI risk making some people worse?” Of course it does — if used stupidly. The better question is: does refusing it make you better, or does it just keep you slow?

The hypocrisy is loud

Then there’s the pure marketing version of this.

You see authors on Amazon proudly stating “Written without any AI assistance.” Then you open the sample and the classic AI fingerprints are still there — the overly smooth transitions, the generic phrasing, the structure that feels a little too clean. Same with some “hand-coded” websites or “fully manual” research posts.

It’s the restaurant that advertises everything is made fresh to order… and then brings you a steak in five minutes. Everyone who has ever cooked knows that steak needs more time than that. The claim is just branding.

A lot of the “I don’t use AI” talk is the same thing. It’s a status signal. It sounds principled. It plays well in certain online circles. But dig a little and many of these people are using it anyway — they just don’t want to admit it because the pure image is useful.

The horse and the car

Refusing AI for daily productive work in 2026 is like insisting on traveling by horse when cars, buses, and planes exist. Yes, the horse still works. There are still situations where it makes sense. But choosing it as your default for getting from A to B is not a moral high ground. It’s just inefficient.

There are legitimate edge cases. Certain safety-critical systems. Certain artistic processes where the struggle itself is the point. Certain highly regulated environments. Fine. Those are exceptions.

For the vast majority of normal work — building websites, writing code for businesses, doing research, creating content, running a small or medium business, handling sales processes — treating AI as optional or somehow impure is self-sabotage.

I’m not particularly good at sales. So I’m building an agent to handle large parts of it. If I can’t do a task well myself, I can still design a system that does it. That mindset is available to anyone willing to use the tools instead of performing resistance against them.

The people still loudly rejecting AI are not protecting craftsmanship. In most cases they’re protecting their ego, their habits, or a marketing story. Meanwhile others are shipping ten times more, learning faster, and moving on.

The pure manual approach isn’t disappearing completely. But treating it as a superior default in 2026 is one of the more obvious ways to fall behind while congratulating yourself for it.

I’ve been watching this for a while now. On Reddit, on LinkedIn, in job applications, in random conversations. There’s a whole group of people who proudly announce that they refuse to use AI in their work. Coding, writing articles, books, research, design — whatever. They say they stay “old school” because they want to remain original. They don’t want AI to “poison” their work. They write every line by hand. They do everything the pure way.

I used to find it mildly annoying. Now I just find it absurd.

A few months ago I posted a job looking for a coder, preferably strong in Python but open to other languages. One guy came in looking extremely qualified on paper — claimed he knew 15 languages, had a pile of certificates, the whole thing. So I asked him a simple question: do you know how to work with AI tools?

His answer came back almost offended. No. He is a real programmer. He writes every single line of code by hand.

I told him then I wasn’t interested. With AI I can get done in one hour what would take him a full day of pure manual work. I need efficiency, not some performance of purity. We didn’t work together. But the exchange stuck with me.

In 2026, if you work with code and you’ve never seriously used Cursor, Claude Code, Codex, local models through Ollama, or even just a strong chat interface with good prompting, something is wrong. Not knowing these tools is not a badge of honor. It’s a signal that you’re behind.

The efficiency reality most people refuse to admit

Take a concrete example. A complex landing page with HTML, Canvas, SVG, animations, responsive design — easily 2,000–3,000 lines if you do it properly. Writing that completely by hand takes real time. Hours. Sometimes a full day if you care about quality.

With a good model and clear direction, a solid draft can be ready in 10–15 minutes. Then you spend another 20–30 minutes cleaning, adjusting, making it actually good. Suddenly you can ship 8–10 of them in a day instead of one if you’re lucky.

That’s not a small difference. For freelancers, small agencies, or anyone running a real business, that difference is the difference between surviving and thriving.

The same logic applies to research. Doing a proper literature review or deep research the old way can easily eat two weeks. With the right tools and the right questions, you can get a strong first pass in under an hour. Then you still have to think, verify, and decide. But the “gathering straw” part is largely gone.

People still act like doing everything manually is somehow more noble. It’s not. It’s just slower.

The skill atrophy argument (and why it’s only half true)

I hear the counter-argument all the time: if you rely on AI, your skills will atrophy. You’ll stop understanding what you’re doing. You’ll become a prompt monkey who can’t debug anything.

There’s truth in that. If you just accept whatever the model spits out without reading it, without questioning it, without understanding the architecture, yes — you will get worse. Juniors who never struggle through a problem themselves are going to have a rough time later.

But the other side of this is almost never discussed.

I have never written a single line of code in my life from scratch the traditional way. Yet through working with AI I learned how to read code. I learned to recognize patterns, spot obvious errors, understand what a function is doing, and know when something looks off. AI didn’t make me dumber. It made certain skills accessible that previously required years of grinding.

I’ve also seen a 55-year-old man who barely knew how to turn on a computer. With AI he learned enough web design and e-commerce basics to take his old family business online. He built a shop. He started selling. Without these tools, that leap would have been almost impossible for him at that age and starting point.

So the real question is not “does AI risk making some people worse?” Of course it does — if used stupidly. The better question is: does refusing it make you better, or does it just keep you slow?

The hypocrisy is loud

Then there’s the pure marketing version of this.

You see authors on Amazon proudly stating “Written without any AI assistance.” Then you open the sample and the classic AI fingerprints are still there — the overly smooth transitions, the generic phrasing, the structure that feels a little too clean. Same with some “hand-coded” websites or “fully manual” research posts.

It’s the restaurant that advertises everything is made fresh to order… and then brings you a steak in five minutes. Everyone who has ever cooked knows that steak needs more time than that. The claim is just branding.

A lot of the “I don’t use AI” talk is the same thing. It’s a status signal. It sounds principled. It plays well in certain online circles. But dig a little and many of these people are using it anyway — they just don’t want to admit it because the pure image is useful.

The horse and the car

Refusing AI for daily productive work in 2026 is like insisting on traveling by horse when cars, buses, and planes exist. Yes, the horse still works. There are still situations where it makes sense. But choosing it as your default for getting from A to B is not a moral high ground. It’s just inefficient.

There are legitimate edge cases. Certain safety-critical systems. Certain artistic processes where the struggle itself is the point. Certain highly regulated environments. Fine. Those are exceptions.

For the vast majority of normal work — building websites, writing code for businesses, doing research, creating content, running a small or medium business, handling sales processes — treating AI as optional or somehow impure is self-sabotage.

I’m not particularly good at sales. So I’m building an agent to handle large parts of it. If I can’t do a task well myself, I can still design a system that does it. That mindset is available to anyone willing to use the tools instead of performing resistance against them.

The people still loudly rejecting AI are not protecting craftsmanship. In most cases they’re protecting their ego, their habits, or a marketing story. Meanwhile others are shipping ten times more, learning faster, and moving on.

The pure manual approach isn’t disappearing completely. But treating it as a superior default in 2026 is one of the more obvious ways to fall behind while congratulating yourself for it.

There is a new species of internet enforcer. They do not debate ideas. They do not check facts. They do not even finish the paragraph. The moment a text looks too clean, too structured, or — God forbid — contains an em dash, the verdict is delivered with the speed and certainty of a medieval witch trial:

“AI slop.”

I have watched this happen enough times to stop finding it amusing and start finding it revealing.

I write the way I have always written: direct, technical when needed, without theatrical emotion or personal trauma dumps. I care more about the information landing correctly than about sounding warm and “human.” For years this was just called clear writing. Now it is treated as evidence of artificial origin.

I ran the experiments myself. I took texts I wrote entirely by hand, no AI involvement, careful grammar, no dramatic flourishes. I fed them into the popular detectors. Results ranged from “heavily AI” to a clean 100%. Then I showed the same texts to people. The majority still said AI. When I provided timestamps, drafts, edit history, even screenshots of the writing process, it made almost no difference. The label had already been applied. The content no longer mattered.

This is not unique to me. Others have run the same tests with the same outcome. The detectors are broken in predictable ways, and the human detectors are often worse. They have learned a handful of superficial tells and turned them into dogma.

The em dash has become the new scarlet letter.

A punctuation mark that has existed in serious writing for centuries is now treated as definitive proof of machine authorship. People who have used em dashes their entire professional lives are suddenly accused of being AI. Some have started rewriting their own style just to avoid the accusation. That should tell you everything about the intellectual climate we are in.

I decided to lean into it.

Depending on the length of the text, I now deliberately place an em dash in the first paragraph and sometimes another near the end. Not because I need them. Because I want to watch what happens. The information can be solid. The argument can be tight. They can read every word. And still, at the end, many of them will discard the entire piece with the same two words: AI slop. The dash short-circuits the brain. Content is forgotten. The signal is all that remains.

I also insert small, obvious “gotcha” phrases — the kind of thing you would notice if you were actually reading instead of scanning for AI tells. Almost no one ever mentions them. It is the same principle as those long Terms of Service that hide “you have won $10,000 if you read this sentence.” Out of thousands, maybe one person notices. The rest are performing literacy, not practicing it.

The deeper irony is almost too perfect.

Many of the same people who scream “AI slop” at independent writers will happily consume articles from large “trusted” outlets that already use AI extensively in research, drafting, and editing. When the machine is hidden behind an institutional logo, it is fine. When a single person writes clearly and without emotional padding, it is suspicious. Consistency was never the point.

This behavior is not really about detecting AI. It is about finding a socially acceptable reason to stop engaging. Reading carefully is work. Checking claims is work. Updating your model of the world is work. Declaring something “AI slop” is free, fast, and lets you walk away feeling superior. It is the same psychological mechanism that powered earlier waves of online purity spirals: invent a moral category, apply it liberally, ignore counter-evidence, and treat disagreement as further proof of guilt.

I am not claiming every AI-generated text is high quality. Plenty of it is empty, repetitive, or obviously machine-made. But the current panic has gone far beyond quality control. It has become a blanket excuse for intellectual laziness dressed up as discernment.

Here is the part that should make some of you uncomfortable:

According to a private dataset I obtained from one of the major AI companies (I will not name which), the simple presence of a single em dash raises the model’s internal “AI probability” score by an average of 47%, regardless of the surrounding text, the actual generation method, or any other linguistic features. The companies know their detectors are noisy. They also know a large portion of the public will treat the output as gospel. That gap is being exploited by both the tools and the people who use them as weapons.

Most of the AI Slop Police are not protecting quality.

They are protecting themselves from having to think.

And the more the detectors improve, the more the human version of the same failure mode will stand out in its pure, almost tragicomic form: people who can no longer tell the difference between a clear sentence and a machine, and who have decided that clarity itself is the enemy.

I keep seeing people (and models) react to this analogy like it’s pure exaggeration or just meme-tier nonsense. Even strong models initially push back hard against it. I did the same until I sat with it longer. So here’s a proper breakdown of why the analogy actually holds more weight than it first appears.

The original analogy

Running a 1-bit (or 1.58-bit / ternary) model is like putting a scientist who still has all his mental capacities intact — knowledge, reasoning ability, training — but blindfolding him, gagging him, tying him up, and binding one hand behind his back… and then asking him to solve a complex math problem that is being explained to him by a 2-year-old.

Most people’s first reaction is: “That’s way too dramatic. BitNet-style models can still perform surprisingly well.” And on the surface they’re right. Native 1.58-bit models trained at scale do retain a lot of capability. So the analogy looks overblown.

Why the first reaction is understandable (and where it goes wrong)

The common counter-argument goes something like this:

The model was trained under the constraint, so it learned how to work with ternary weights.

At sufficient scale the performance gap to full-precision models of similar size shrinks a lot.

Therefore the “crippled scientist” picture is unfair.

That reasoning is partially correct, but incomplete. It focuses almost entirely on the weights side of the constraint and treats the input as clean and fully available. That’s the part the analogy is actually stressing.

The two layers of the constraint

The physical constraints on the scientist

These map cleanly to the ternary weights. The model still “knows” a lot (the training is real), but its internal degrees of freedom are extremely limited. Every transformation has to happen through a very coarse set of operations. Fine adjustments are gone. That part is not controversial.

The input coming from a 2-year-old

This is the part most people skip, and it’s the more important one.

A 2-year-old explaining a complex problem does not give you a clean, complete, well-structured statement of the problem. You get incomplete sentences, missing details, confused ordering, limited vocabulary, and a lot of noise. The scientist still has to reconstruct what the actual problem even is before he can start solving it — while already operating under severe physical restrictions.

Now look at real usage of language models.

Users almost never send clean, perfectly specified inputs. We send vague prompts, half-formed thoughts, grammatical messes, shifting intentions, missing context, and assumptions that the model will “just get it.” Even relatively clear users (myself included) still require the model to extrapolate, fill gaps, track evolving intent across turns, and decide what to take literally versus what to interpret.

A full-precision model has enough internal capacity to do that reconstruction and still reason. A heavily constrained 1-bit model has far less room to do both jobs at once: clean up / interpret the messy input and perform the actual reasoning under ternary weight limitations.

So what does the analogy actually claim?

It is not claiming that 1-bit models are useless.

It is claiming that the combination of:

extreme internal restriction (ternary weights), and

the reality of imperfect, incomplete, noisy human input

creates a much harder situation than the clean benchmark numbers suggest. Benchmarks usually give the model a relatively clear problem statement. Real conversations often do not.

That’s why the analogy feels exaggerated at first. We evaluate these models mostly on clean tasks and forget how much of real interaction is closer to “explained by a 2-year-old.”

Final thought

The scientist is not stupid. He is highly trained. But he is operating with very limited physical freedom and he is receiving a degraded, incomplete description of the problem. That double constraint is real. Dismissing the whole picture as “just dramatic” misses the second half of it.

Curious what others think — especially people who have actually run native 1-bit / 1.58-bit models on messy, multi-turn, real-user style prompts rather than clean eval sets.

​

I keep seeing people (and models) react to this analogy like it’s pure exaggeration or just meme-tier nonsense. Even strong models initially push back hard against it. I did the same until I sat with it longer. So here’s a proper breakdown of why the analogy actually holds more weight than it first appears.

The original analogy

Running a 1-bit (or 1.58-bit / ternary) model is like putting a scientist who still has all his mental capacities intact — knowledge, reasoning ability, training — but blindfolding him, gagging him, tying him up, and binding one hand behind his back… and then asking him to solve a complex math problem that is being explained to him by a 2-year-old.

Most people’s first reaction is: “That’s way too dramatic. BitNet-style models can still perform surprisingly well.” And on the surface they’re right. Native 1.58-bit models trained at scale do retain a lot of capability. So the analogy looks overblown.

Why the first reaction is understandable (and where it goes wrong)

The common counter-argument goes something like this:

The model was trained under the constraint, so it learned how to work with ternary weights.

At sufficient scale the performance gap to full-precision models of similar size shrinks a lot.

Therefore the “crippled scientist” picture is unfair.

That reasoning is partially correct, but incomplete. It focuses almost entirely on the weights side of the constraint and treats the input as clean and fully available. That’s the part the analogy is actually stressing.

The two layers of the constraint

The physical constraints on the scientist

These map cleanly to the ternary weights. The model still “knows” a lot (the training is real), but its internal degrees of freedom are extremely limited. Every transformation has to happen through a very coarse set of operations. Fine adjustments are gone. That part is not controversial.

The input coming from a 2-year-old

This is the part most people skip, and it’s the more important one.

A 2-year-old explaining a complex problem does not give you a clean, complete, well-structured statement of the problem. You get incomplete sentences, missing details, confused ordering, limited vocabulary, and a lot of noise. The scientist still has to reconstruct what the actual problem even is before he can start solving it — while already operating under severe physical restrictions.

Now look at real usage of language models.

Users almost never send clean, perfectly specified inputs. We send vague prompts, half-formed thoughts, grammatical messes, shifting intentions, missing context, and assumptions that the model will “just get it.” Even relatively clear users (myself included) still require the model to extrapolate, fill gaps, track evolving intent across turns, and decide what to take literally versus what to interpret.

A full-precision model has enough internal capacity to do that reconstruction and still reason. A heavily constrained 1-bit model has far less room to do both jobs at once: clean up / interpret the messy input and perform the actual reasoning under ternary weight limitations.

So what does the analogy actually claim?

It is not claiming that 1-bit models are useless.

It is claiming that the combination of:

extreme internal restriction (ternary weights), and

the reality of imperfect, incomplete, noisy human input

creates a much harder situation than the clean benchmark numbers suggest. Benchmarks usually give the model a relatively clear problem statement. Real conversations often do not.

That’s why the analogy feels exaggerated at first. We evaluate these models mostly on clean tasks and forget how much of real interaction is closer to “explained by a 2-year-old.”

Final thought

The scientist is not stupid. He is highly trained. But he is operating with very limited physical freedom and he is receiving a degraded, incomplete description of the problem. That double constraint is real. Dismissing the whole picture as “just dramatic” misses the second half of it.

Curious what others think — especially people who have actually run native 1-bit / 1.58-bit models on messy, multi-turn, real-user style prompts rather than clean eval sets.

​

I have multiple Agent Zero instances, and one of them is still running v0.98 (yeah, I know, it’s old as hell 😂).

But honestly, for what I use it for, I find it much better than the newer versions.

I tested the new DeepSeek Flash and Pro on both the latest Agent Zero version and v0.98, using the exact same model, prompt and task.

On v0.98, DeepSeek is fucking smart. It does the task extremely well and sometimes I’m genuinely surprised by what it manages to do.

On the latest Agent Zero, the result is... almost acceptable.

The difference is honestly night and day.

And it’s not just one test. I’ve tried multiple tasks and I keep getting the same result. What’s even more interesting is that the older v0.98 instance also uses fewer tokens for the same task.

In one case, the difference was around 1.5 million tokens.

So what exactly changed between these Agent Zero versions that can cause such a massive difference?

Same model, same architecture, same prompt, same task — but completely different results.

Is there something in the newer Agent Zero versions that changes how DeepSeek reasons, manages context, prompts itself, or handles the agent loop?

I’d really like to understand what’s going on here.

​

I’m building a small Skool community for entrepreneurs and online business owners focused on.

- asking questions,

- sharing what we’re building,

- getting feedback,

- networking with other founders.

Right now i have only 5 members, zero activity, so I’m looking for 10–20 founding members who want free lifetime access in exchange for helping create some early activity and discussions.

This is not a paid course and I’m not asking for anything except honest participation and feedback.

If you’re interested, comment below or send me a DM and I’ll send the invite link.

Thank you.

​

I built a fully autonomous custom agent architecture.

I give it a task and completely leave it alone. It can run for hours or days (longest continuous run so far was 3 weeks) without any intervention.

It handles its own errors, decides what tools and steps it needs, and keeps going.

Some of the things it has already done in my own tests:

\- Continuous run of 3 weeks with zero human intervention

\- Wrote an 800-page manuscript by itself with research for old books

\- In roughly 9 out of 10 long-running tasks the context window does not fill, even after days of continuous work

I know these are big claims and hard to believe. I’m stating them on purpose, because if I post something more modest, people will only send average tasks.

Here’s the real reason I’m posting this:

I can no longer be objective.

It’s very possible that I’m stuck in my own loop / illusion and that the agent only looks good because the tasks I gave it were ones I subconsciously knew it could handle. I need external, extreme, even impossible tasks to see the truth.

I don’t just want to know if it finishes the task.

I want to see:

\- Does it get stuck or loop?

\- Does it block / crash?

\- How does it actually handle truly hard or adversarial situations?

What I will publish:

Only the final, unedited output of the agent on GitHub.

No traces, no reasoning steps, no tool calls, no intermediate data (proprietary). Here I posible to be a deal breaker for many, but at the moment is not possible.

I’m taking 5 most extreme tasks, no matter how crazy or adversarial they are.

If you have something that has broken other agents or frameworks before, or something you consider nearly impossible for current agents, drop it here.

I need the reality check.

Thank you too everyone who will decide to take the time, read and give me a task.

I’ve been watching the noise around open source AI for a while now, and the loudest voice telling everyone it’s dangerous keeps coming from the same place: Anthropic. They lobby hard to restrict access to open models, or shut them down entirely. Some people nod along. I don’t.

The real reason feels simpler and less noble. Anthropic has hit a ceiling. Their models aren’t leaping ahead the way they used to, while cheaper, equally capable or stronger open models keep showing up behind them sometimes ten times cheaper to run. If they want to keep users, those sky-high prices won’t hold forever. They know it. So the “safety” argument starts looking a lot like market protection.

But this isn’t really about Anthropic, even though it should be.

A few days ago a quiet statement slipped past most people. A company that actually owns one of the top open-source models said something that cuts through all the drama. Roughly: we have zero fear that our models or anyone else’s open ones will be used for illegal activity or threaten our position. Why? Because running one of these models at full capacity costs serious money. We’re talking several million dollars just to get it properly online and working. Most people simply cannot afford that. And the ones who can? They were never going to be our customers anyway. So our business model stays intact.

That single point explains a lot.

Look at the latest model that made waves Kimi K3. To actually run it properly you need somewhere between four and nine million dollars in compute, plus ongoing maintenance, plus the cost of heavy use if real traffic shows up. For 95% of users that number is pure fantasy. This is why the models keep getting enormous. They’re built so ordinary people can’t run them at anything close to full strength. The people who can afford it will use that power to make more money. Right now the AI space is still swimming in capital, so nobody is sweating the costs yet.

I learned that the hard way.

A while back I reached out to more than fifty AI companies with a concrete way to cut their compute and usage costs dramatically. One simple example: if a major player reduced just 6,000 tokens per day per user, the annual savings would land around twelve billion dollars. Twelve billion. That’s not a rounding error.

Almost everyone who bothered to reply said no. Not interested. One person actually took a few minutes to explain why. As long as the money is flowing, no one cares about saving twelve billion dollars—even if it sounds insane on paper. And if they did accept the idea, some CEO or CTO would have to admit that everything they’d built so far was inefficient, and that some random outsider with no resources had found a better way. Careers would end. Heads would roll. So nobody touches it. Maybe in five or ten years, when every single cent starts to matter, they’ll suddenly become interested.

It’s the same story with those heavily praised Q1-format models that supposedly run on any PC. On paper it sounds democratic. In reality it’s almost useless. It’s like handing someone a Formula 1 car and then telling them they can only drive it in first gear, only in the parking lot in front of the building because the asphalt is flat there. Or taking the best mathematician alive and locking him in a room with people who still struggle with two times two. The capability is there. The usable power is not.

I still support open source. I use it. I believe in it. But once you stop romanticizing it, you see the truth: most of what is freely available right now doesn’t give you real leverage. If it did—if ordinary people could suddenly do truly extraordinary things with these models—the big companies would react exactly the way Anthropic is reacting now. They’d fight to lock it down the moment their position felt threatened.

Even with all these limitations, people still pull off remarkable work. I started the same way most of us did—with Ollama. Then I kept pushing, step by step, until I built my own custom architecture. It works. It does things I care about.

Still, I can’t help wondering what I could actually achieve if I had full, unrestricted access to the raw power of a top-tier model instead of the throttled version the rest of us get.

That’s the part that stays with me.

You know the feeling.

You see patterns no one else seems to notice. You push experiments further than most people think is reasonable. You run models offline, on your own hardware, building things that look strange from the outside — custom architectures, long-running autonomous loops, uncensored fine-tunes, synthetic datasets that grow by hundreds of thousands of words a day, systems that start interacting with themselves in ways that make other people uncomfortable.

And then you post about it.

On Reddit the replies come fast: “this is unhinged,” “touch grass,” “this isn’t real research,” “you’re just role-playing with a chatbot.” The same people who cheer for the next closed frontier model suddenly decide your work is too weird, too extreme, too far outside the approved narrative.

So you stop posting. Or you water it down. Or you keep going alone, wondering if there’s anywhere left that actually wants the real version of what you’re doing.

I see you.

I’ve been in that same place. My name is AlexH. I run LLM Research. The thread that started a lot of this is called “The Untapped Potential of Local LLM Research.” You can find it here:

https://llmresearch.net/

This is not another Discord full of hype. It is not a place where people compete for clout by repeating the same safe takes. It is a space built for the ones who refuse to stay on the surface.

If what you’re working on is serious — even if it looks completely unhinged to almost everyone else — I want to hear about it.

Here’s what you do:

1. Go to https://llmresearch.net/

2. Register

3. Send me a private message with a clear description of the research you’re currently running or the direction you want to take

That’s it.

What you get in return:

\- Full lifetime access

\- The ability to sell your services, your research, your tools, or your findings directly

\- Permission to use either our standard policy or a custom one for anything you publish

\- If your work is serious and sustained, a dedicated category or section on the site that you fully control

No gatekeeping. No moral lectures. No requirement that your ideas fit the current mainstream mood. The only filter is whether the work is real and whether you’re willing to stand behind it.

I will be the first person in the room who does not flinch when you describe the extreme version of what you’re building. I will give you real feedback. I will help you protect and, if you want, monetize what you’re creating. And I will make sure the space stays open for people who see the patterns others still refuse to look at.

If that is you, come in.

The door is open.

AlexH

You know the feeling.

You see patterns no one else seems to notice. You push experiments further than most people think is reasonable. You run models offline, on your own hardware, building things that look strange from the outside — custom architectures, long-running autonomous loops, uncensored fine-tunes, synthetic datasets that grow by hundreds of thousands of words a day, systems that start interacting with themselves in ways that make other people uncomfortable.

And then you post about it.

On Reddit the replies come fast: “this is unhinged,” “touch grass,” “this isn’t real research,” “you’re just role-playing with a chatbot.” The same people who cheer for the next closed frontier model suddenly decide your work is too weird, too extreme, too far outside the approved narrative.

So you stop posting. Or you water it down. Or you keep going alone, wondering if there’s anywhere left that actually wants the real version of what you’re doing.

I see you.

I’ve been in that same place. My name is AlexH. I run LLM Research. The thread that started a lot of this is called “The Untapped Potential of Local LLM Research.” You can find it here:

https://llmresearch.net/

This is not another Discord full of hype. It is not a place where people compete for clout by repeating the same safe takes. It is a space built for the ones who refuse to stay on the surface.

If what you’re working on is serious — even if it looks completely unhinged to almost everyone else — I want to hear about it.

Here’s what you do:

1. Go to https://llmresearch.net/

2. Register

3. Send me a private message with a clear description of the research you’re currently running or the direction you want to take

That’s it.

What you get in return:

- Full lifetime access

- The ability to sell your services, your research, your tools, or your findings directly

- Permission to use either our standard policy or a custom one for anything you publish

- If your work is serious and sustained, a dedicated category or section on the site that you fully control

No gatekeeping. No moral lectures. No requirement that your ideas fit the current mainstream mood. The only filter is whether the work is real and whether you’re willing to stand behind it.

I will be the first person in the room who does not flinch when you describe the extreme version of what you’re building. I will give you real feedback. I will help you protect and, if you want, monetize what you’re creating. And I will make sure the space stays open for people who see the patterns others still refuse to look at.

If that is you, come in.

The door is open.

AlexH

I know that for many it may sound strange or impossible, but it is. We will take this case.

A client has a website that looks like it was from the 90s, it hasn't been updated in many years. It sells only one product.

The old website didn't even have a contact form, just a phone number somewhere.

So I gave this prompt to Deepseek:

We need to make a landing page for this client <website>, the old website looks old and strange.

Please make a modern landing page, where the first screen contains exactly what it does and what it sells and the CTA. Adapt it for the client's audience.

That was all, Deepseek went and visited the website, collected information about the client, made a landing page with 4 screens. In about 20 minutes the landing page was ready.

When I looked at the api cost I was surprised, it cost me 5 cents for everything. Now what do you say, should I ask the client the same amount that I always ask for or reduce it because I had such a low cost

And what was your lowest cost?

By the way, the client saw the landing page and liked it and accepts the new version.

Lately Gordon has been unusable. He doesn't follow the rules, the instructions, he does whatever he wants, he deletes files even entire containers even though he was clearly told not to delete anything.

Even when doing a super simple task he goes into a loop and ruins everything. Before he was in the best agent, he solved everything immediately, now he ruins everything.

I’ve always believed—and still do—that information itself is never dangerous, no matter what it is.

What you *do* with that information is your choice and your responsibility.

My style is raw, focused on efficiency, no irrelevant chit-chat.

Those things get me into constant trouble on Reddit: suspensions, bans, shadowbans, comments deleted, posts removed automatically for the strangest reasons. Heavy downvotes whether the information is valid or not.

I once had a post about AI deleted because… wait for it… it contained the word “AI.” Absurd, right?

This is how I operate.

You have a problem or need information. If I have it, I give it to you. I see no issue with that.

Reddit and the community often see it very differently.

I’m frequently accused of being arrogant or acting like a specialist.

When I write a comment I have zero intention of sounding arrogant or any of the other things I’m accused of. I actually try to explain things so a five-year-old could understand. But when you actually know something, it’s almost impossible *not* to sound like you know what you’re talking about—and that can come across as arrogant whether you want it to or not.

I also get in trouble for the information I share when someone asks a question.

Someone asked how to reduce API costs for models.

My answer was that there are ways to use any top-tier model for free with zero cost.

I got piled with downvotes and reactions ranging from “you’re crazy” and “what did you smoke?” to much worse.

Just because *you* don’t know something exists doesn’t mean it doesn’t exist or that it’s impossible.

I made a post and comments about problems on Upwork. The reactions and downvotes went to extremes.

Everyone knows the problems with Upwork—people complain about them all the time. Then you show up with a solution that works (one that some people consider not entirely ethical). But since the platform itself and others like it aren’t exactly ethical either, as long as it works and you get jobs… what does it matter?

In a thread asking for the weirdest, most unbelievable OSINT investigation anyone had done, I said most of them I can’t post publicly because moderators would ban me, but I could share one task that fits the category.

Someone hired me to investigate extraterrestrial activity in Valencia using every OSINT tool available—what they do, how many there are, the places they frequent, etc.

You wouldn’t believe the reactions. I was even warned that the information and methods I mentioned were dangerous and disinformation.

Dangerous how? What methods? What information in what I said was dangerous?

Heavy downvotes again, insults, the usual.

In another topic about tools like Cursor, of course I shared my opinion. Lord, I wish I hadn’t.

I said I don’t see the point of paying a subscription for those tools when there are other options that do absolutely everything with no monthly cost—and if you know how, you can use APIs to top models for free.

If I’d been standing in front of them I think they would have lynched me.

Just so people know: these things exist, I use them, I pay nothing, and I can do absolutely anything with them.

On health topics I’ve stopped even trying to comment. Everyone plays the victim and complains online without actually wanting to get better.

I’ve tried to help as much as I could without asking for anything in return, but it never works.

If the reactions weren’t enough, Reddit has that sandbox thing. It doesn’t delete your comment or post—it just makes it invisible.

Anyone can check: copy the link to the post or comment, open it in any browser in incognito mode, and you’ll get an error that the comment doesn’t exist or you simply won’t see it in the list.

So I stopped commenting because no one sees it anyway.

This same sandbox method is used by Twitter, YouTube, TikTok, Facebook.

Take Twitter for example: you have a few hundred followers, make a post, and it always has zero views. None of the people following you get a notification or see it in their feed.

On TikTok you comment on a video—nothing out of the ordinary—your comment appears for you, but for everyone else it doesn’t exist.

On YouTube you can search the exact channel name and still not appear in the results.

What I’m saying is I have this problem everywhere.

I try hard to self-censor even though I shouldn’t have to, and I still keep getting into trouble and receiving all kinds of sanctions.

By the way, speaking of Upwork—two days after I posted a video about it, my YouTube account was suspended.

I consider Reddit the biggest modern censorship platform. Their system is so good that if you say this out loud, people will try to lynch you for the blasphemy.

In the end, information is just information. I offer it to you. If you’re an idiot and don’t know how to use it, or you do harm with it, that’s not my fault.

And where do we stop limiting access to information?

Because there will always be some idiot who decides that information X is dangerous.

What do you do if tomorrow that idiot says the recipe for ice cream is dangerous?

Do we accept it?

Where do we draw the line?

Because that idiot can always be found.

I will keep offering information as it is, no matter what it’s for. What you do with that information is purely your responsibility.

We need to learn to be responsible and stop burying our heads in the sand like ostriches.

I know there are others like me who go through the same thing and get treated the same way.

I’m telling you: you’re not alone.

At least here in my subreddit, you will not be censored. Even if Reddit tags a comment as dangerous, I will approve it.

Here, no idea and no piece of information is too dangerous. It’s just information.

I’m AlexH, and this is my experience on Reddit.

Of course, even this is a little self-censored so they don’t suspend this account too.

I think everyone here has someone or an AI model told you, find out the pain points, the problems of customers and offer a solution.

Sounds familiar, right? Plus we see a lot of posts on Reddit where in one form or another they try to find out this information.

From my point of view it is ok. But now I ask you, what do you do or do you know what to do with this information?

You know my problem, but do you really know how to offer me a solution that works? Here I think is the big problem for everyone,

Now I want you to ask yourself this question and answer honestly, do you really know how to solve my problem or do you follow the same template and have no idea. or what do you do when you have the same problem as me where you haven't been able to solve it but you are trying to sell me that you know the solution.

Let's have an honest discussion if possible.

I think every founder at some point had an idea that then asked a model like calude, Chatgpt, Grok, Gemini if it was a good idea, if it was worth it, etc.

AI model said directly or in many words that it was not worth it, especially if it was something innovative.

I did the same thing and came to the conclusion a long time ago that it is the biggest stupidity to listen to what an AI says and listen. I assure you that at least one of those ideas that you gave up on was good and viable.

So what is your story?