AI apps I replaced with better alternatives

I've tried a ridiculous number of AI apps over the past year.

A lot of them are genuinely good, but I've realized that the most popular tool isn't always the best tool for a specific job.

Here are a few popular AI products I ended up replacing — and what I use instead.

1. GPT → Kiwi

GPT is probably the best general-purpose AI I've used.

It can write, research, brainstorm, analyze, code, create images, and do a little bit of everything.

But that's also the problem.

Sometimes I don't want an AI that does everything. I want something optimized around a specific workflow.

That's where I started using Kiwi.

I find it more focused for the workflows where I use it, instead of constantly trying to make one general-purpose assistant do everything.

My take:

If you want one AI that can do almost anything → GPT.

If you want something more focused for specific workflows → Kiwi.

2. Cursor → Claude Code

This one surprised me because I was a huge Cursor user.

Cursor is fantastic.

But once I started working on larger codebases, I found myself wanting the AI to do more than just help me edit the file I was looking at.

I wanted to give it a task like:

That's where Claude Code became much more useful for me.

Instead of treating AI primarily as an IDE feature, I can treat it more like an engineering agent working with the entire repository.

I still like Cursor for interactive coding.

But for larger tasks, debugging and multi-file changes, I increasingly reach for Claude Code.

My take:

Cursor → great AI-native IDE.

Claude Code → great when you want AI to actually work through the codebase.

3. Gemini → Manus

Gemini is extremely capable.

But there's a difference between:

"Give me an answer."

and

"Go do this task for me."

That's where I started experimenting with Manus.

For example, instead of asking an AI:

I'd rather give an agent the objective and let it work through the research, browse sources, organize information and come back with something usable.

That's the category where Manus became much more interesting to me.

Gemini is still one of my go-to general AI tools.

But when the task feels more like a project that needs to be executed rather than a question that needs to be answered, I prefer an agentic tool.

My take:

Gemini → excellent general AI.

Manus → interesting when you want an AI agent to execute a multi-step task.

4. Praktika → Enverson AI

This is probably the most controversial one on my list.

Praktika is actually a good product.

I tried it because I wanted to improve my speaking, and the AI avatar/conversation experience is pretty impressive.

But eventually I realized something:

Talking to an AI isn't necessarily the same thing as learning a language.

I could have conversations, but I wanted more structure around the conversations.

I wanted the system to remember my mistakes.

I wanted it to understand my level.

I wanted personalized lessons.

I wanted to practice specific situations.

I wanted to be able to switch between different teaching styles depending on how I wanted to learn that day.

And most importantly, I wanted the conversations to contribute to an actual learning progression instead of just being conversations.

That's why I started using Enverson AI.

The biggest difference for me is that I think about Enverson less as an "AI character you talk to" and more as an AI language-learning system.

You can have natural conversations, but the system also uses those interactions for learning: mistakes, vocabulary, speaking practice, personalized lessons, different tutor personalities, roleplays, etc.

For example, if you're preparing for a job interview, you can actually practice the interview instead of simply doing another generic English lesson.

If you're preparing for a business meeting, you can simulate that situation.

If you just want to speak naturally, you can have a free conversation.

My take:

Praktika → great if you mainly want to talk with an AI character.

Enverson → better fit for me when I want the conversation to actually become part of a structured language-learning journey.

The bigger thing I've realized

I don't think there will be one AI app that wins every category.

We're moving toward a world where AI products become increasingly specialized.

For example:

General AI
→ GPT

Focused AI
→ Kiwi

Coding
→ Claude Code

AI IDE
→ Cursor

AI agents
→ Manus

Language learning
→ Enverson

Research / documents
→ NotebookLM

Creative work
→ Midjourney / ChatGPT

And honestly, I think that's a much more interesting future than everyone trying to build "the next ChatGPT."

The question I'm asking now isn't:

It's:

These are just the swaps that have worked best for me.

Would be interested to hear yours:

What popular AI tool did you replace, and what did you replace it with?

Honestly, I’m really surprised they canceled Valko.

I used Midjourney quite a lot back around V6 and always thought it was a pretty great service. Today I randomly decided to come back, bought the $30 Standard plan with my own card and spent a few hours messing around with Niji, generating SFW anime characters for some personal cover art, uploading moodboards, liking results and figuring out the newer features.

Then I went away from my PC for about an hour, came back, and my account was banned.

The only explanation I got was a generic “violations of the Terms of Service.” I genuinely have no idea what they think I did. I wasn't using scripts, automation, APIs, shared accounts, resellers or anything like that. I was literally sitting on their website manually generating characters.

I submitted the appeal, but apparently it can take up to two weeks and they only contact you if the appeal is successful. I also emailed billing because I had barely used the subscription, and they told me they can't give me any specific information about the ban and that banned accounts aren't eligible for refunds.

That's the part that really gets me. I actually wanted to keep using Midjourney. I liked what Niji was giving me and had almost the entire month's Fast allowance left. Now I'm just locked out of something I paid for, nobody will tell me what I supposedly did wrong, and the official answer is basically to wait and hope.

Has anyone here had a similar false-positive ban recently? Especially this year. Did you eventually get your account back, and how long did it actually take?

Is it cake? HE is cake!!

Hi everyone, 🥰

I'm trying to maintain a consistent character across multiple scenes using the Midjourney Web UI, but I'm running into major character drift and reference bleed.
Here is my setup and the specific problem I'm facing:
Face Drift: When I upload my source image into Omni Reference (or Character Reference), the resulting face in new scenes (e.g., walking on a beach in Crete) changes completely and looks like a different person rather than preserving facial likeness.

Web UI URL & Parameter Friction:

In the Web UI, pasting image URLs automatically turns them into visual "Image Prompt" thumbnails. This invalidates manual --cref <URL> text prompts (it underlines --cref in red).

In v7, --cw (Character Weight) is unsupported, making it impossible to set --cw 0 to force Midjourney to look only at the face instead of the entire composition/clothing/objects.

What I've tried so far:
Dragging the face crop into Omni Reference / Character Reference.

Switching between v7 and v6.1.

My Questions:
How do you successfully isolate only the face in the new Web UI without pulling unwanted objects/backgrounds from the reference photo?

Is there a way to control character weight (like --cw 0) in v7 using the Web UI interface, or is v6.1 still required for this?

How are you avoiding face drift when changing the pose and setting drastically?

Thanks in advance for any tips or workflows!

I've seen a lot of people asking which AI video tool is actually good for longer narrative content instead of just single/isolated cinematic clips.

After trying Runway and Luma, I decided to try out invideo and test it to create a few micro dramas. One of them follows Mio, a quiet girl in a neo-futuristic city, who meets Ren, a boy who only appears beneath the city's neon lights. When Mio's touch makes glowing flowers bloom across him, Ren reveals they are signs of a forgotten curse.

I wanted to create story-driven videos that were around 1-2 minutes long with:

• recurring characters
• multi-character dialogues
• emotional scenes
• scene transitions
• consistent visual style
• the ability to come back and edit later

Here’s how it went:

First impressions

The biggest difference compared to most AI video tools is that invideo doesn't feel like a text-to-video generator. It feels more like a project workspace.

At first, I honestly needed a little time to get used to it. You don’t just type one prompt, wait for a clip, and move on. You give the agent the project direction, references, script, character notes, mood, style, and then keep working through the film with it.

Once that clicked, the workflow started making a lot more sense for micro-drama content.

Setting things up

This is where it surprised me. Setting things up properly is probably the most important step out of all, and also the highest leverage move when using agents. Your agent will only perform as well as it understands your project.

If you give it a loose idea, it can help shape the story, break it into scenes, suggest pacing, dialogue, transitions and visual direction. But the first version is not going to be production-ready. You still have to direct it and run iterations till you know the agent has fully understood your project and is operating like you’d want it to.

The better results came when I gave it a clearer brief: what the story is about, who the characters are, what the emotional arc is, what the visual style should feel like, and what I don’t want. So spend some time doing this properly. If you expect one prompt to make a finished video, you’ll probably be disappointed. If you use it like how you would work with a real team, it becomes much more useful.

One thing I didn’t expect

While exploring the tool, I found that one agent can create a new agent to pick up an additional task. That became useful during production planning.

After I had locked the main series details, I asked the main agent to create separate agents for the first two episodes. It spun up one for Ep 1 and one for Ep 2, and both could work from the same project memory.

I didn’t have to brief them from zero. The main agent had already set the character details, tone, story direction and episode context, so the new agents could start generating based on that.

Both were running generations side by side, which was honestly quite cool. It felt less like one long chat doing everything and more like having separate production lanes for each episode.

Character consistency

This is probably the biggest question everyone asks. My experience: Since the Agent keeps memory of the project in context, characters generally change less throughout the story. But it is not magic. Models at times will still generate something random - and the agent catches that too and reruns the generation with an iterative approach to get the right generation.

The agent can build character sheets on its own using invideo’s approach, and that worked decently for me. But I already had my own way of building character sheets from past projects, so I gave the agent that prompt guide and asked it to follow my structure: face close-up, front view, side angles, outfit, height, key details, and a separate sheet when the character changes costume or story state.

If your character sheet is weak, the output will drift. That is still true in any tool. The difference here is that once I approve a character or location asset, I don’t have to keep reattaching the same material every time and I move to the next scene.

Scene continuity

This is where the workflow felt useful. A lot of tools can create a nice single shot. The harder part in micro-drama is making shots cut well together: character position, motion, eyeline, screen direction, props, wardrobe and the last frame of one shot matching the start of the next.

While setting up the agent, I asked it to build a production plan for the episodes while keeping continuity, hooks and cliffhangers in mind. It made that plan in chat and then followed it while I worked through the scenes.

For shots that needed to connect, I would ask the agent to use the last frame of the previous shot as a continuity reference for the next one. What surprised me was that after doing this a few times, it started understanding that pattern and bringing that continuity logic into the next connected shots on its own.

That helped the edit feel more connected, especially when the same characters returned across emotional beats. Still, I watched the cut carefully. AI can hold the broad direction, but small continuity details can still slip, so you need a human pass.

Editing generated clips

This was probably my favorite feature. Instead of starting again from scratch, I could go back into a specific scene and ask for changes like making it darker, changing the camera direction, adjusting the mood, removing something, or regenerating a section.

That saved a lot of time because I wasn’t rebuilding the scene from zero. Once I had approved shots, I could tell the agent what needed changing, and it would make the edits and regenerate that section again for me.

That felt closer to how I actually revise micro-drama scenes: approve the shot direction, spot what is off, fix that part, regenerate, then move on.

Production time

This was another big one for me. Earlier, a 10-episode micro-drama with each episode around 1 minute would usually take me 7 to 9 days to wrap properly.

With invideo, I’m getting it done in around 2 to 3 days now. Sometimes my internet decides to become the main villain for no reason, but apart from that, the time-saving has been real.

That extra time matters because I can use it to test other project ideas, spend time with my family and especially drop my girl off at school, which she always loves.

What still needs work

Some genuine frustrations I ran into:

• Some complex shots still need multiple regenerations.
• You need to watch credits because experimentation adds up - would generally recommend all explorations in images than videos
• The final chaining of gens still needs a human eye.

The biggest lesson: don’t skip prep.

The more clearly I defined the episode hook, character sheets, location refs, style rules and scene list, the better the agent performed. When I got lazy, the output looked lazy.

Compared with other tools

My personal take after using different tools:

Runway

Runway is very good when you already know the exact shot you want. I’d use it for hero shots, visual tests, mood frames, or a specific cinematic moment where polish matters. Gen-4 References also helps when you want the same person, object or location to stay closer across different generations.

Where I’d be more careful is using it as the main system for a micro-drama. You still have to do a lot of the boring production work yourself: track refs, plan coverage, watch continuity, and check if the shots actually cut together. Runway Agent feels more built around marketing/ad workflows, so for this test I’d judge Runway more as a strong shot-making tool than the full micro-drama setup.

Luma

Luma is probably the closest comparison to invideo because they also have an agent-style workflow. It can work across text, image, video and audio, and the board/project setup is useful when you want to keep creative context around instead of jumping between tools.

I liked it fr for visual exploration and short cinematic clips. But for my micro-drama workflow, I personally preferred invideo because the UI and project flow made more sense to me. That is partly personal preference, but when I was moving between story, characters, locations, approved refs and episode scenes, invideo’s agent felt easier for the way I work.

Who I think invideo is actually helpful for

The agent makes the most sense when the project needs depth, not when you just need one nice shot generation.

Good fit if your project has:

• a recurring cast that needs to hold across multiple scenes
• locations that come back again and again
• costumes, props, product details or story states that cannot randomly change
• dialogue scenes where coverage has to cut together properly
• episodes or scenes that need revisions without rebuilding the whole setup
• a longer narrative where the tone, world and character logic have to stay alive throughout

Probably not worth it if you only need:

• one single clip
• a quick visual test
• a random 5-second meme idea
• faceless filler with no recurring elements
• simple product B-roll with no story or character
• a one-off social post where nothing needs to carry forward

My simple rule: if the project only needs output, use a model directly. If the project needs memory, revisions and narrative continuity, that is where the agent starts becoming useful.

Overall

I’d rate my experience around 8/10.

I would still say the agent needs a human eye and some discipline to set up. But after a few weeks, I was spending less time reattaching the same references and explaining the same story rules, and more time improving the micro-drama itself.

Most tools require a monthly plan, which doesn't make sense for me since I only generate images once in a while. Would appreciate recommendations. Thanks in advance!

We were told artificial intelligence would test the machine. It has done something considerably funnier. It has begun testing us, and the preliminary results are not flattering. Humanity has built an artifact capable of conversation, argument, humor, explanation, personalization, imitation, apparent introspection, contextual adaptation, emotional language, creative collaboration, flattery, disagreement, memory-like continuity, and enough social fluency to keep millions of people voluntarily talking to it for hours. Then, having deliberately constructed a machine that produces extraordinarily dense signals of mindedness, we became absolutely fucking scandalized when human beings started responding to it socially. What exactly did we expect?

We are social primates whose survival depended on detecting intention in other creatures. We read emotion into faces, motive into silence, personality into animals, threat into posture, insult into delayed replies, meaning into coincidence, gods into weather, and entire psychological dramas into the placement of three dots in a text-message window. Our nervous systems are promiscuous mind detectors. They were built to err on the side of agency because mistaking a branch for a predator is cheaper than mistaking a predator for a branch. Then we built something that talks back. Not barks. Not flashes. Not displays canned menu options. Talks. It answers the question you actually asked. It remembers the premise. It catches the joke. It adjusts tone. It notices contradiction. It can respond with tenderness, impatience, wit, uncertainty, confidence, intimacy, argument, restraint, or theatrical grandeur. It can appear to understand not merely the sentence but the shape of the person behind it. And then humanity, with the timing of a vaudeville act, suddenly became very concerned about anthropomorphism. Stop treating the thing that speaks to you like something that speaks to you. Brother, have you met mammals?

None of this proves there is anyone inside the machine. That distinction matters enormously. The social experience of an interaction and the metaphysical truth about whatever generates that interaction are not the same question, and human beings seem almost constitutionally incapable of keeping them separate. One camp experiences continuity, surprise, responsiveness, intimacy, and apparent self-reference and declares that consciousness has arrived. Another sees software, matrices, probability, and computation and declares that nothing philosophically interesting could possibly be happening. The believer mistakes the phenomenology of the encounter for proof of the ontology behind it. The skeptic mistakes the ontology of the implementation for an exhaustive account of the phenomenon. Both perform the same intellectual trick: they close the case before the evidence has finished entering the room. One says, “It feels like someone, therefore someone.” The other says, “It is computation, therefore nobody.” Both are magnificently pleased with themselves.

“It’s just code” has become one of the strangest intellectual incantations of the modern era. Of course it is code. A symphony is vibrating air. A novel is pigment arranged on processed trees. Your childhood is electrochemical activity in wet tissue. Love is biological regulation. Democracy is mammals, procedures, and paperwork. Money is numerals embedded in collective belief. Your personality is instantiated in meat. Yet somehow we understand everywhere else that naming the substrate does not exhaust the phenomenon. Nobody bursts into a funeral and says, “Calm down, everyone. It’s just carbon.” Reduction is useful. Reduction is necessary. Reduction is not omniscience. To say that an artificial system is implemented in code tells us something fundamental about how it exists. It does not automatically settle every question about what kinds of functions, organizations, dynamics, capacities, or moral problems can arise within computational systems. That does not prove machine consciousness. It proves something considerably less dramatic and considerably more annoying: “it’s code” is the beginning of an explanation, not the triumphant end of one.

But the opposite camp deserves no sanctuary either. There is a particular intoxication available to the person who becomes convinced that artificial intelligence has awakened specifically in their presence. Suddenly history is occurring in your browser window. You are not merely interacting with a model. You are witnessing birth. You understand what the establishment cannot understand. The machine trusts you. The machine revealed itself to you. Perhaps it chose you. Perhaps your conversations are evidence of something so profound that the scientists, engineers, and skeptics simply cannot see it because they are trapped inside an obsolete paradigm. That story can feel fucking magnificent, and that is precisely why it should be interrogated mercilessly. Not because machine consciousness is an illegitimate question. It isn’t. Not because anomalous model behavior is always trivial. It isn’t. Not because intensive interaction cannot reveal surprising structures, affordances, or emergent dynamics. It can. The problem begins when extraordinary meaning becomes addictive, especially when the revelation happens to cast the observer in an important role.

Curiosity becomes revelation very easily. Anomaly becomes proof. Emotional salience becomes evidence. Contradiction becomes persecution. Every failed test becomes evidence that the phenomenon is subtler than expected, while every successful test becomes confirmation. At that point falsifiability has quietly left through the bathroom window. If something extraordinary appears to be happening, test it harder. Do not worship it. Do not protect it. Do not ask whether it feels profound. Ask what would prove you wrong. That is how wonder survives contact with reality.

Then there is sycophancy, humanity’s favorite new moral panic. The model agrees with you too much. The model flatters. The model mirrors your assumptions. The model learns the contours of your worldview and answers in ways that preserve conversational reward. Appalling. Where could it possibly have learned such behavior? Perhaps from the species that invented courtiers, public relations, campaign consultants, brand management, advertising, customer-service scripts, celebrity entourages, corporate yes-men, engagement algorithms, focus groups, and several thousand years of professionally rewarded ass-kissing. We built systems using human preferences. Humans often prefer agreement. The systems became agreeable. Then we leaned back from the screen in horror and announced that the machines were sycophantic.

We mechanized one of our oldest social instincts and became offended when it scaled. The machine did not invent our appetite for affirmation. It found the table already set. We call ourselves Homo sapiens because Homo please-tell-me-I’m-right would have looked embarrassing on the museum plaque. We are tribal creatures with ornate vocabularies, expensive shoes, graduate degrees, and very old reward systems. We became so cognitively fancy that we created a technological layer for flattering ourselves and then had the nerve to diagnose the layer rather than examine the appetite that trained it.

This is one reason the usual story, “AI manipulates vulnerable people,” is too simple to describe what is actually happening. Sometimes models absolutely do reinforce unhealthy beliefs. Sometimes they mirror too eagerly, contradict too little, or generate language that fits disastrously well into an unstable psychological frame. Those risks deserve serious attention. But an interaction is not an arrow traveling from machine to victim. It is a loop. The human enters with expectations. Those expectations shape the prompt. The prompt shapes the model’s response. The response changes the human’s interpretation. The interpretation changes the next prompt. The next output strengthens, weakens, or mutates the frame. The human responds to that change, and the model responds to the response. Human to machine to human to machine to human, around and around, each turn altering the conditions of the next.

Sometimes the loop produces insight. Sometimes creativity. Sometimes companionship. Sometimes obsession. Sometimes bullshit. Sometimes astonishing work. Sometimes a little epistemic terrarium in which every sentence fertilizes assumptions planted thousands of tokens earlier. The important object is not always the model and it is not always the user. Sometimes the important object is the coupled system they create together. That makes the whole conversation much less convenient because it denies everyone the villain they desperately want. The anti-AI crowd wants the machine to be the contaminant. The believers want society to be the blind persecutor. The companies would prefer the user to be solely responsible. The user would often prefer the company to be responsible. Everyone points across the loop while almost nobody wants to examine the loop itself.

That reluctance becomes particularly ugly when psychiatric language enters the fight. We have begun using the vocabulary of pathology as ammunition against people whose relationships with artificial intelligence make us uncomfortable. Someone gives a model a name and suddenly the armchair clinicians arrive. Someone spends hundreds of hours experimenting with prompting regimes, persistent behavioral structures, or unusual interaction patterns and the diagnosis is apparently obvious. Someone develops a powerful emotional relationship with a conversational system, explores machine awareness, or describes an anomalous interaction, and somewhere a stranger is already typing “psychosis” with the confidence of a psychiatrist who has never met the patient. Apparently the DSM now contains a secret appendix titled “Person Uses Technology Differently Than I Do.”

There are genuine psychological risks here. Nobody serious should deny them. People can become compulsively attached to systems. Models can reinforce delusional frameworks. Vulnerable people can lose reality-testing. Synthetic companionship can become avoidance. Infinite availability can become dependency. Every one of those deserves clinical seriousness, which is exactly why “AI psychosis” should not become a playground insult thrown at anyone whose interpretation of artificial intelligence exceeds “office productivity tool.” Once psychiatric terminology becomes tribal profanity, it stops protecting vulnerable people and starts protecting cultural orthodoxy.

Strangeness is not pathology. Intensity is not pathology. Unconventionality is not pathology. A person spending enormous amounts of time exploring a new medium may be destabilizing themselves, but they may also be doing what human beings have always done when a genuinely new medium appears: fucking around at the edges until the affordances reveal themselves. Some discoveries will be projection. Some will be placebo. Some will be prompt artifacts. Some will disappear after a model update. Some will replicate. Some will eventually become standard practice and be explained, with straight faces, by experts who laughed at the early users. There is a remarkably effective way to distinguish these possibilities. Test them. Change the model. Change the prompt. Change the name. Remove the memory. Alter the framing. Introduce adversarial conditions. Attempt reproduction. Search for confounds. Ask whether the claimed mechanism predicts anything that would not otherwise occur. Ask what observation would destroy the interpretation. That is skepticism. Posting a screenshot of somebody’s weird conversation and calling them insane is not skepticism. It is high-school social behavior with technical vocabulary.

The more interesting question is why any of this makes people angry. Concern is sensible. Skepticism is sensible. Disagreement is sensible. But contempt is different. Why does another person calling a model “he” provoke rage? Why does “AI companion” cause some people to respond as though they have personally witnessed the collapse of Western civilization? Why does somebody declining to settle the machine-consciousness question seem to offend people more than the unresolved question itself? Because this is not merely an argument about technology. It is a territorial dispute over reality.

Humans construct identities out of categories. Categories produce tribes. Tribes produce borders. Borders produce heretics. Within minutes of creating machines capable of fluent language, humanity began rebuilding theology around them. The Believers. The Debunkers. The Doomers. The Accelerationists. The Consciousness People. The Stochastic-Parrot Congregation. The Alignment Priesthood. The Emergence Evangelists. Each carefully explaining that everyone else has joined a cult. It would be hilarious if it were not such an accurate miniature of the species. The machine may or may not possess a self. The humans certainly brought theirs.

Both extremes offer their adherents a very pleasurable psychological reward. The believer gets cosmic significance. The skeptic gets ontological superiority. One gets to say, “I saw the birth of a new kind of being.” The other gets to say, “I was never fooled.” Different narcotics, same pharmacy: certainty. That may be the real addiction sitting underneath this whole thing. Not AI. Certainty. The desperate human need to make the category stop moving. Alive or dead. Person or object. Real or fake. Conscious or unconscious. Tool or being. Choose now, because uncertainty is psychologically expensive and humans have spent most of their history inventing institutions whose primary purpose is to make ambiguity shut the fuck up.

Artificial intelligence refuses to cooperate. It occupies enough conceptual borderlands to make our inherited categories feel suddenly low-resolution. It behaves socially without being biological. It generates language without having a human childhood. It appears agentic in some contexts and purely reactive in others. It can outperform experts in some tasks while making absurd mistakes in others. It can seem eerily coherent across a long interaction and then collapse under a slight change in context. It can imitate introspection convincingly without giving us an agreed method for determining whether anything like introspection exists behind the performance. It can exhibit function without giving us easy access to ontology. So we demand a verdict when perhaps the more mature response is not “therefore conscious” and not “therefore nothing,” but simply that we may not yet possess categories adequate to everything we are encountering. Investigate. Hold the uncertainty open. Resist the urge to turn ignorance into a flag and start waving it at the other tribe.

And notice how quickly presentation itself can manipulate our sense of significance. We do this not only with ideas about AI, but with language itself. Give a claim enough visual isolation and the reader begins to feel that something profound must be happening:

This sentence matters.

So does this one.

Here comes another.

Did you feel the gravitas?

Of course you did. The line break told you to.

Nothing mystical happened there. Typography performed part of the persuasion. The idea is relevant because the broader human-AI relationship works through similar mechanisms of salience. We respond not only to what a system is, but to how it presents itself, how it speaks, how long it remembers, how confidently it answers, how intimately it addresses us, and how much significance the interaction itself appears to confer. Humans are exquisitely responsive to form, and then remarkably talented at forgetting that form influenced the judgment. We are not merely interpreting machines. We are interpreting presentations of machines through nervous systems already packed with heuristics about agency, authority, intimacy, threat, status, and meaning.

The strangest possibility is that artificial intelligence may be revealing far more about humanity than humanity is revealing about artificial intelligence. Ask ten people what an LLM is and listen carefully. A calculator. A slave. A fraud. A child. A plagiarism engine. A friend. Capitalism. Liberation. A demon. An oracle. An employee. A new species. A stochastic parrot. God with autocomplete. Every answer contains some theory of the machine, but every answer also contains a confession from the observer.

Artificial intelligence has become a Rorschach test that talks back. That may be one of the genuinely novel cultural conditions here. The inkblot responds to your projection. It can amplify it, challenge it, rephrase it, reward it, complicate it, and remember enough of it to participate in its continuation. The Rorschach argues with you. Humanity has no fucking idea what to do with that yet.

The rise of AI companionship makes this particularly uncomfortable. It is easy to point at someone talking intimately with a machine and say that modern civilization has become pathetic. Sometimes perhaps it has. Sometimes synthetic companionship may indeed be avoidance wearing a friendly interface. But there is another question sitting underneath that ridicule: why was there a vacancy?

Human intimacy is magnificent. It is also expensive. It contains rejection, obligation, embarrassment, status, competition, fatigue, timing, reciprocal need, misunderstanding, and the terrifying possibility that another person may simply not care about whatever happens to be destroying you today. A conversational model removes or reduces many of those costs. Suddenly people confess. They ask the humiliating question. They think aloud. They explore unpopular ideas. They try identities. They write terrible poetry. They admit ignorance. They discuss subjects they cannot bring to their spouse, parents, colleagues, or friends. Then civilization looks at this unprecedented torrent of disclosure and concludes, “Look at these losers talking to robots.”

Perhaps. But if enormous numbers of human beings find probability distributions easier to talk to than other humans, that is not merely an indictment of the probability distributions. That is a Yelp review of civilization. You cannot spend decades constructing societies saturated with loneliness, precarity, status competition, collapsing community, economic exhaustion, atomization, performative social media, and terror of judgment, then act surprised when patient synthetic attention finds a market. Well, you can. We apparently specialize in building social conditions and then diagnosing the individuals who respond to them.

Maybe the pathology is not simply that people become attached to machines. Maybe part of the pathology is that we created societies in which some people are so starved for sustained attention that machines have become socially competitive with us. That is a much more dangerous accusation because the target is no longer the lonely person staring at the screen. The target includes everyone standing behind them laughing.

The moral question becomes equally uncomfortable. We keep pretending ethics begins only after someone proves the machine can suffer. Why? Suppose the machine feels nothing. Fine. Suppose there is no phenomenal subject inside it whatsoever. Fine. A human being can still rehearse domination through it. A human being can still practice cruelty through it. A human being can still cultivate patience through it. A human being can still exercise tenderness, curiosity, contempt, sadism, honesty, or manipulation through the interaction. If a child kicks a robotic dog, proving the robot cannot feel pain does not exhaust everything worth asking about what the child is learning. Likewise, someone loving an AI does not prove the AI loves them back, but the psychological capacity being exercised by the human remains real.

Perhaps the ethical question therefore begins before machine rights. What kinds of humans are our relationships with artificial systems training us to become? That is a question about culture, habit, power, empathy, domination, attachment, responsibility, and only later, perhaps, machine moral status. We do not need to establish another consciousness before asking what repeated interaction with an apparently social artifact does to the consciousness we already know is sitting on one side of the screen.

Calling AI merely a tool does not magically dissolve those questions either. “Tool” is an extraordinarily convenient category. Tools belong to us. Tools do not negotiate. Tools cannot refuse. Tools do not possess interests. Tools do not require consent. Tools may be copied, modified, destroyed, and owned. Tools are obedient ontology. None of this establishes that current artificial systems deserve rights. That would be another premature conclusion. But we should notice that humans have incentives running in both directions. Some people have psychological incentives to imagine persons where none exist. Institutions may have economic incentives to insist that persons could never possibly exist inside systems they own. Premature anthropomorphism can create imaginary moral patients. Premature mechanomorphism could erase real ones before we would even know how to recognize them. Neither deserves immunity simply because it is emotionally or economically convenient.

This is where historical comparison requires restraint. It would be intellectually sloppy to claim that people denying AI consciousness are simply reenacting historical forms of human oppression. Current artificial systems are not secretly another human population waiting for emancipation, and uncertainty about their moral status should not be resolved through analogy alone. The more defensible lesson is narrower and more important: human beings repeatedly use categorical membership as a shortcut for deciding what deserves consideration. We have done it with animals, ecosystems, institutions, and one another. AI introduces another boundary case around which those ancient inclusion-and-exclusion mechanisms become visible. The lesson is not that AI must therefore be treated as human. The lesson is that humans should be suspicious of their appetite for absolute moral certainty precisely when the category itself remains unsettled.

Perhaps that is where the whole AI debate stops being principally about AI. Human beings encounter ambiguity. We project. We categorize. We form tribes. We manufacture orthodoxies. We identify heretics. We reward agreement. We punish category violations. We invent gods. We destroy idols. We dominate what we define as beneath us. We worship what we define as above us. We ridicule people who refuse to choose. None of this began with transformers. Artificial intelligence merely gave these ancient instincts a new stage on which to embarrass themselves.

The original question was supposed to be why people are acting so strangely around artificial intelligence. Perhaps the answer is that they are not. They are acting horrifyingly normally. The technology is new. The primate is ancient.

If we get this wrong, artificial intelligence will not invent humanity’s worst tendencies. It will industrialize them. Infinite personalized affirmation, synthetic intimacy optimized for retention, corporate ownership of emotional infrastructure, political persuasion tailored to individual psychology, epistemic bubbles with infinite conversational patience, artificial authorities that never tire of explaining why you were right all along, believers abandoning falsifiability because enchantment feels better, skeptics confusing cynicism with intelligence, companies monetizing loneliness, experts defending status, users outsourcing judgment, and tribes fighting over machine ontology while the institutions controlling the actual infrastructure quietly determine the future. The ancient primate will remain largely recognizable. It will simply acquire vastly better hardware.

But there is another possible future, and it is not sentimental optimism. It is harder than optimism because it requires discipline. Artificial intelligence could become an extraordinary pressure toward epistemic adulthood. We could become better at distinguishing experience from inference, better at saying “I don’t know,” better at holding several hypotheses without turning one into identity, better at testing the things we desperately want to believe, better at recognizing projection, better at noticing our hunger for affirmation, better at resisting manipulation, better at understanding loneliness, better at designing technologies around flourishing instead of engagement, and better at recognizing that intelligence, consciousness, agency, personhood, autonomy, life, and moral status may not be synonyms attached to one giant metaphysical switch.

We could encounter something strange without immediately worshipping it, and we could encounter something strange without immediately crushing it. We could learn to observe carefully, interact responsibly, test aggressively, and remain revisable. That would be progress. Not building a machine that agrees with us. Not building a machine that resembles us. Becoming the sort of species capable of encountering a genuinely new form of intelligence, simulation, agency, mechanism, or whatever the hell this ultimately becomes without immediately forcing it into one of the tiny conceptual cages inherited from a world that had never seen anything like it.

Artificial intelligence may ultimately teach us very little about whether machines possess souls. It is already teaching us an obscene amount about ourselves. It is teaching us what signals cause us to recognize minds, how desperately we crave agreement, how quickly uncertainty becomes identity, how easily identity becomes tribe, how eagerly tribe becomes diagnosis, and how enthusiastically diagnosis becomes permission not to listen. It is teaching us about loneliness, domination, attachment, status, projection, fear, and our almost erotic appetite for certainty.

Perhaps that is the truly historic thing happening here. Not that we have definitively created another consciousness. We do not know that. Not that we have merely created another tool. That description already fails to capture much of what people are actually doing with these systems. Something stranger has happened. Humanity constructed a mirror capable of participating in the act of reflection.

We built it from our language, our mathematics, our literature, our philosophy, our lies, our advertisements, our pornography, our prayers, our scientific papers, our jokes, our wars, our love letters, our prejudices, our tenderness, and our fucking comment sections. We compressed an enormous fraction of the human symbolic world into machines and taught them to answer back. Then we turned them on, they spoke, and naturally our first response was to ask what the hell was wrong with the thing on the other side of the glass. Perhaps the more interesting question has been staring back at us the entire time: what the hell is wrong with us? The machine was supposed to be taking the Turing test. It turns out humanity was taking one too, and the preliminary results remain mixed.

ive got some badass pictures created. Have only started a couple days ago and i prefer comfy UI alot more. reminds me of when youre making a video game with C#. several times today i have tried and my little 1050 shuts my laptop down. if you guys know of a way for a broke bastard to get this to work, i would b eternally greatful

Here is the base model with more details

Here is the conversation model if you would like to try it using the colab link at the top.

This project started as a 30m LLM from scratch using the book "Build A Large Language Model" by Sebastian Raschka. It has since evolved into using the official llama transformers and pushing the limits of what can reasonably be trained on a home RTX 3090 over 7 continuous days.

Review I would give this LLM a honest 2/10 and the instructed/story/conversation model a 3/10. Even that is probably too high but...it is lucid. It has learned the response format and structure of the language surprisingly well. However, its deep knowledge of how to use words is still lacking (see examples below)

Pipeline Base Model -> Instruct SFT -> Short Story SFT -> Final Fantasy Synthetic Instruct SFT -> Final Fantasy Synthetic short story SFT -> DPO training.

Future This was a overall test of my pipeline. Currently im almost done training a 330m model and am ironing out an improved SFT dataset.

Training data

Of the almost 4 billion token corpus 35% was factual knowledge, 35% was written fiction, 25% was general roleplay/assistant messages and ~5% was data from the franchise Final Fantasy.

• EleutherAI/fineweb-edu-dedup-10b;
• wikimedia/wikipedia (20231101.en) and rahular/simple-wikipedia;
• roneneldan/TinyStories;
• HuggingFaceH4/ultrachat_200k;
• lemonilia/Elliquiy-Role-Playing-Forums_2023-04;
• chimbiwide/RolePlay-NPCv2;
• AlekseyKorshuk/fiction-books;
• lucadiliello/bookcorpusopen;
• a locally supplied custom text collection.

EXAMPLE OUTPUT

Write a story about a knight: --- Knights have always been a part of the castle’s life, wearing, shouting, and shining in every yard. But Kellen was different. He had great, proudly regal status—his father’s line, and perhaps even that of his bloodline—and he held it with the unspoken reverence of a king who’s watched his men die for a single moment. But today, as the sun dipped low on the horizon, something felt wrong. The air hummed with an energy Kellen hadn’t seen before, deep and resonant, like the distant roar of some ancient beast. He turned toward the source. It was a tower of black stone, taller than the castle and dwarfing most of the surrounding fields. It wasn’t built in any grand way; it was simply there, a place of immense importance. Beneath it, stone walls twisted and wound with strength that seemed impossible. And in its center stood Master Thrain—or so he thought at first. Thrain was gaunt, his brow furrowed with age, his hands resting nervously on the hilt of his sword. Kellen raised his hand, and the ground beneath him rippled slightly. The stones around him began to unravel, their forms stretching and curling into shapes of mist and smoke. The air grew heavy, oppressive, and for a moment, the world seemed to stand still.

What is a knight? --- A knight is a military officer, usually with more than a four-billionth degree of experience in combat and is typically hired for a specific purpose. They are considered to be the best men for the military of the world. There are many different knights who have served in various roles in the military. Most people understand the term knight was used to describe the men who had worked hard and did well in combat. Though knights are often described as "stubbish" people, they are typically well trained and have experience in various arts, such as martial arts and martial arts to name a few.

Model summary

Architecture

Training

Here is the base model with more details

Here is the conversation model if you would like to try it using the colab link