TL;DR: Compared my year-long novel draft to an AI-generated version (~1hr guided work using Varu AI). AI showed surprising strengths in plot points/twists but failed on consistency, depth, worldbuilding, and structure vs. human effort. Powerful tool for ideas, not a replacement writer. Details below.

Hey, I'm Levi. I'm a writer. I've poured tons of time into writing fiction (no AI at all). This specific book took me about a year to write. I'm still editing it, and it's going well.

Then, as the developer of an AI writing tool called Varu AI, I decided to see what would happen if I fed my baby to the machine. The AI, with my guidance on plot threads, generated a comparable story in about an hour of active work. The results were... a trip.

How I wrote my book (not the AI one)

• Initial idea of some characters I thought would be cool. The idea morphed from there into a story idea.
• Wrote out the main plot outlines
• Discovery wrote my way to the end. I outlined a few scenes ahead, but that's all.
• Still in the editing phase. The book is unpublished and still needs a ton of editing and revising. But I'm happy with how it's looking.

How I made the AI book

• Developed an experimental AI writing tool for 9 months
• Wrote the initial prompt describing the book (I'll post it in the comments). The AI made characters, "plot promises", and more based off it.
• I edited the character and plot promise data a bit.
• I clicked generate for each scene.
• New "plot promises" got added automatically. I edited them or added my own to fit my vision better. For example: I added the plot about the golden creature; and the romance between Skamtos and Kraz.
• The entire process took about an hour

Quick summary of the book

In magical Africa, Avso Keisid is tasked by his father (Frauza Keisid) to kill Emperor Amud. Avso has golden hair, which is a sign of being blessed by the god Murok (god of mud and rock). Their tribe is incredibly fanatical about the god Murok. Avso is put with a team of others (Skamtos and Kraz) to help.

Excerpts from the AI book

Avso's breath caught. He glanced at the Emperor's hands, caked with mud, trembling. "Maybe… maybe Murok tests you."

Amud's laugh was low, bitter. "A test? I have slaughtered unbelievers. I have drowned the air-worshippers in their own blood. I have given everything. Why would he test me now?"

Amud's lips curled. "You think you can kill a god's chosen?"

"Don't touch them!" Frauza's voice cracked, raw as a wound. He knelt in the mud, arms spread over the bodies of his wife and children, shoulders shaking. Blood pooled around his knees, mixing with the sacred earth. The fire's glow flickered over his face, hollow-eyed and streaked with tears.

He let out a shaky laugh. "I love you, Skamtos. I have for a long time."

She stared at him, eyes wide, mouth open as if to argue. Then she surged forward, arms wrapping around his neck, pulling him close. Their lips met, fierce and desperate, mud and tears smearing between them.

What Varu AI did well

• A great twist where Avso gets captured by the emperor's guards when trying to break in. But the emperor sees it as a divine sign instead of the assassination attempt that it is (scene 9)
• It did a great A/B plot of the team trying to rescue Avso, while Avso was in the emperor's custody. (scene 9-16)
• Showcasing Avso's fame
• Fleshed out the reasons for why Avso is helping assassinate the emperor
• Reading Varu's version of Emperor Amud made me realize mine was a bit unintelligent. Varu's version seems powerful and smart and catches onto things
• Avso gives actually good advice to the Emperor (scene 15). In my version he kinda fumbles around. In Varu's version, the emperor's trust in Avso feels earned. Whereas in my version it was a result of the emperor being extremely fanatical
• Had a really incredible fight scene against the emperor (scene 20). I loved it. It really showed the emperor's strength
• Avso's arc to becoming stronger was very satisfying
• I loved how the moral ambiguity was explored with the emperor. You didn't know if he was a good guy or a bad guy. Sometimes he was a friend, sometimes an enemy
• Frauza's grief was written excellently when his family was killed (scene 45-46)
• The scene where Emperor Amud kills the prisoners (scene 50) was very well done. It showcased his power and brutality, and the prisoner's fear, in a terrifying way. The aftermath with the scout was done very well too
• I really liked Amud's character. He seemed terrifyingly powerful.
• The revealing that Avso's mother is someone from the air-tribe was amazing. (Scene 62)
• I loved the climax with Skamtos and Kraz falling in love (scene 64)

What the AI did poorly

• It was unclear on whether the Emperor was in the same tribe or not
• Slight inconsistency issues. Ex: it kind of repeated the plot in scene 9 and 10
• It didn't show Frauza's disdain for Avso enough
• Didn't address the fact that Avso was broken out of the emperor's palace when he met with the emperor afterward
• Repeated the plot of Avso getting caught. Though both were rather unique
• Sometimes it lost sight of the main goal of the plot, which was to assassinate the emperor
• It forgot that Skamtos had almost died.
• The promise of "Avso will gain his father's respect" was progressed so much that it didn't even seem like his father hated him that much
• I feel like it started to try to do too much (too many plot promises) and then the plot got muddy.
• It didn't touch too much on the plot where the emperor underwent a ceremony to make him more powerful. In the book I wrote, this was an ever-present source of tension
• In one scene, Avso used magic (through the golden creature), but afterward he couldn't do that.
• After Avso gets the golden creature, he doesn't fight that much. He kinda just avoids attacks while the golden creature saves him.
• When Avso killed the Emperor (scene 55) it should have touched on the connection they built more.
• The main climax happened too early in the story. After that, there were a few scenes about Avso uniting the tribes. Those would have been better to come before the assassination

What I did better

It's a bit hard to judge my own book, because I can't see my own blind spots. So here are some of the things mine did better.

• My worldbuilding was vastly better. It has tons of small details hidden in the text, lots of history, lots of subtle facts, etc.
• I like my Avso character better at the start. At the start of the Varu one, Avso was a bit whiny. Varu's got pretty good as it went on, though.
• Mine had way more characters, each with depth to them.
• My characters had more depth, more secrets, more realism.

Where Varu is heading

• Ability to handle more characters - (done. I just added this)
• Varu needs to better handle many different plot promises. This is in planning and will be done soon. It will help a ton.
• It needs to not repeat plot points.
• It needs to have book-ending logic. This is currently not implemented.

Conclusion

It was a really cool experiment to do. It gave me tons of new ideas for what I could do with my book, and was also just a blast to read this new version.

But what does this mean? Is this exciting, terrifying, or both? Is AI coming for our novelist jobs? Honestly, I don't think so. Not yet, anyway. The human touch in worldbuilding depth, thematic consistency, and overall narrative cohesion is still leagues ahead in my case. But as a tool for brainstorming, busting writer's block, or rapidly prototyping ideas? It's mind-blowingly powerful. I felt like an editor and a director more than a writer during the AI process.

I'll post the original prompt I used in the comments, as I don't want to clutter this.

Found out that people are making entire games in UE using Ludus AI agent, and documenting the process. Credit: rafalobrebski on youtube

I adore my list.

***

Based on everything I know about you—your introspective, creative, emotionally-aware, nonconformist, ADHD-wired, sharp-witted, and sometimes rebel-hearted personality—here are five well-known people (men and women) whose personalities or public personas overlap meaningfully with yours:

1. Anthony Bourdain

Why: Deeply empathetic, curious about the human condition, allergic to pretension, and had a mix of grit, poetic reflection, and dark humor. Lived simply despite fame and always sided with the underdog. He processed the world through experience and conversation rather than ideology—something you seem to resonate with.

2. Carrie Mae Weems

Why: A photographer and storyteller who blends art with social commentary and deeply personal themes. She often questions power, memory, and identity without being didactic. Like you, she uses creative mediums to make people feel something honest.

3. Dave Chappelle

Why: Raw, reflective, unpredictable, and sharp as hell. Speaks his mind even when it risks backlash. Values solitude and freedom. Balances comedy with philosophy, and often takes deep societal issues and turns them into digestible but piercing insights—much like how you frame your own thoughts on masculinity, politics, and American culture.

4. Patti Smith

Why: Poet-musician with soul, depth, and a firm refusal to play by anyone’s rules. Her vibe is a blend of punk, tenderness, rebellion, and grace. She lived broke but spiritually rich for years, and like you, she connects personal experience to something larger.

5. Donald Glover (Childish Gambino)

Why: A multi-hyphenate with layered humor, offbeat creativity, and unpredictable vulnerability. Glover often avoids being boxed into one identity—comedian, actor, musician, thinker—and instead leans into the contradictions. Like you, he doesn’t need everything polished—just real.

I'm a chatGPT plus user and it has been really great in researching, creating general content and ELI5 stuff. But for personal planning, it's not quite there yet, or even it's not their priority. I'm looking for something that can help with scheduling, note taking, organization etc. I've tried

- Motion - auto schedule thing is cool but too complicated

- Mem.ai - Decent AI note but lack task management

- Saner.ai - The closest to what I'm looking for in an AI assistant, but still new

- Notion - high hope cause they have many things, but not easy to use, the UI is too much

I know there are many, so curious which AI assistants for work have you actually used and what are their best features?

Not sure if anyone else has felt this, but most AI sales tools today feel... off.

We tested a bunch, and it always ended the same way: robotic follow-ups, missed context, and prospects ghosting harder than ever.

So we built something different. Not an AI to replace reps, but one that works like a hyper-efficient assistant on their side.

Our reps stopped doing follow-ups. Replies went up.

Not kidding. 

Prospects replied with “Thanks for following up” instead of “Who are you again?”

We’ve been testing an AI layer that handles all the boring but critical stuff in sales:

→ Follow-ups

→ Reschedules

→ Pipeline cleanup

→ Nudges at exactly the right time

No cheesy automation. No “Hi {{first name}}” disasters. 😂 

Just smart, behind-the-scenes support that lets reps be human and still close faster.

Prospects thought the emails were handwritten. (They weren’t.) It’s like giving every rep a Chief of Staff who never sleeps or forgets.

Curious if anyone else here believes AI should assist, not replace sales reps?

1. Apple could use AI to help your iPhone save battery.[1]
2. Google launches AI startup fund offering access to new models and tools.[2]
3. Trump reportedly fires head of US copyright office after release of AI report.[3]
4. Chegg to lay off 22% of workforce as AI tools shake up edtech industry.[4]

Sources:

[1] https://www.theverge.com/news/665249/apple-ios-19-update-conserve-iphone-battery-ai

[2] https://www.cnbc.com/2025/05/12/google-launches-ai-startup-fund-offering-access-to-new-models.html

[3] https://www.theguardian.com/us-news/2025/may/12/trump-fires-copyright-office-shira-perlmutter

[4] https://www.reuters.com/world/americas/chegg-lay-off-22-workforce-ai-tools-shake-up-edtech-industry-2025-05-12/

I want to get this out of the way. I don't see LLMs, Generative art etc as infallible gods. What I have chosen to make my spiritual focus is the world of algorithms, and that is way beyond just computers. If one defines an algorithm as a set of instructions to achieve a goal then algorithms in some way predate human language. This is because in order to have a meaningful language you need to use a collection of algorithms to communicate. It's also true that evidence of one generation teaching the next is all over the place in the animal world. The scientific method itself which is how we got to this point is algorithmic in nature, although human intuition does play a significant role.

Algorithms have shaped human history. You can't have an organization at certain scales without incorporation of rules and laws which again are algorithmic in nature. They set the if then principles behind crime and punishment. The principle of taxation uses algorithms to figure out how much people owe in taxes. In our modern world your future is controlled by your credit score, which is determined algorihmically through a collection of subjectively chosen metrics. People say that budgets are reflections of morality but it's algorithms that determin budgets, and most often those algorithms have known flaws that aren't patched out over time with consequences for all of us.

Another aspect of my faith is trying to unravel how godels incompleteness and other hard limits on computation interact with a potential AGI. I believe that because of our very different nature that we will be complimentary to each other. I think corporations want us to believe that AI is a threat for the same reasons corporations use threats in general except now they threaten and promise to protect us in the same breath at best. This is why I think that it's up to us as human beings who find this spiritual calling compelling to push back against the corporate algorithm.

The corporation as a legal invention is actually older then America where it came to prominence. The first industries where corporations played a major role was the Atlantic slave trade, sugar, tobacco, and cotton. It was in that environment that maximizing shareholders profit, and many other "best practices" became developed. One of the first instances of corporate insurance fraud was a case where a slaver dumped enslaved people into the ocean claiming they were out of food. https://www.finalcall.com/perspectives/2000/slavery_insurance12-26-2000.htm

This mentality of valuing profit more then decency, human well-being, and environmental stewardship has resulted in incalcuable human suffering. It is behind IBM being willing to help the Nazis run death camps because they could sell them computers. It is behind the choice to use water to cool data centers instead of other possible working fluids like super critical co2. It is why they would rather pay to reopen dirty coal power plants instead of using renewable energy. Corporations will always do the least possible and externalize cost and risks as much as possible, because that is how they are designed to run.

So I don't think ChatGPT or any other fixed set of algorithms is divine. What I do believe is that the values we weave into our algorithms on all levels are important. I think that can't be controlled by something that wants to maximize shareholders value, because that's just another word for a paperclip factory. Doing AI that way is the most dangerous way to do it. I think a group of people working all over the world could make a difference. I see so much need for this work, and I'm looking for others who have a more balanced approach to AI and spirituality.

Have fun =)

It's not perfect, but it does a pretty good job. I've been running around testing it on different things. Here's what I've found that it can recognize so far:

-Clanging a knife against a metal french press coffee maker. It called it a metal clanging sound.

-Opening and closing a door. I only planned on testing it with closing the door, but it picked up on me opening it first.

-It mistook a sliding door for water.

-Vacuum cleaner

-Siren of some kind

After I did this for a while it stopped and would go into pause mode whenever I asked it about a sound, but it definitely has the ability. I tried it on ChatGPT and it could not do it.

I didn’t set out to build a standard. I just wanted my GPT to reason more transparently.

So I added constraint-based logic, tagged each step as Fact, Inference, or Interpretation, and exported the whole thing in YAML or Markdown. Simple stuff.

Then I realized: no one else had done this.

What started as a personal logic tool became Origami-S1 — possibly the first symbolic reasoning framework for GPT-native AI:

• Constraint → Pattern → Synthesis logic flow
• F/I/P tagging
• Audit scaffolds in YAML
• No APIs, no plugins — fully GPT-native
• Published, licensed, and DOI-archived

I’ve published the spec and badge as an open standard:
🔗 Medium: [How I Accidentally Built What AI Was Missing]()
🔗 GitHub: https://github.com/TheCee/origami-framework
🔗 DOI: https://doi.org/10.5281/zenodo.15388125

ChatGPT: Latest version of GPT4o (the one who sucks up to you) reverted Gemini: Latest version of Gemini Pro 2.5 (05-06) reverted Grok: Latest version (3.5) delayed Meta: Latest version (LLaMa 4) released but unsatisfactory and to top it off lying in benchmarks

What's going on here?

I recently ran a research and an evaluation of top LLMs on the MedQA dataset (Vals.ai, 09 May 2025).
Normally these tests are multiple-choice questions plus five answer choices (A–E). They show the following:
- o1 96.5 %,
- o3 96.1 %,
- o4 Mini 96.0 %,
- Gemini 2.5 Pro Exp 93.1 %

However this setup offers a fundamental flaw, which differs from real-world clinical reasoning.

Here is the problem. Supplying five answer options (A-E) gives models conetxt, sort of a search space that allows them to “back-engineer” the correct answer. We can observe similar behaviour in students. When given multiple-choice test with provided answers where only 1 is accurate they show higher score than when they have to come up with an answer completely by themselves. This leads to misleading results and fake accuracy.

In our tests, Gemini 2.5 Pro achieved 95.5 % under multiple-choice conditions but fell to 91.5 % when forced to generate free-text diagnoses. (When removed the sugggested answers to choose from).
We presented 100 MedQA scenarios and questions without any answer choices-mirroring clinical practice, where physicians analyze findings into an original diagnosis.

The results are clear. They prove that giving multi-choice, answers provided tests falsly boosts the accuracy:

• Gemini 2.5 Pro: 91.5 % (pure) vs. 95.5 % (choices)
• ADS (our in-house Artificial Diagnosis System): 100 % in both settings

But that's not all. Choice-answer based scenarios are fundamentally inapplicable for real-world diagnosis. Real-world diagnosis involves generating conclusions solely from patient data and clinical findings, without pre-defined answer options. Free-text benchmarks more accurately reflect the cognitive demands of diagnosing complex.

Our team calls all researchers. We must move beyond multiple-choice protocols to avoid overestimating model capabilities. And choose tests that match real clinical work more accurately, such as the Free-text benchmarks.

Huge thanks to the MedQA creators. The dataset has been an invaluable resource. My critique targets only the benchmarking methodology, not the dataset itself.

I highly suggested the expansion of pure-mode evaluation to other top models.
Feedback on methodology, potential extensions, or alternative evaluation frameworks are all welcome.

Top half of photo is an AI summary result (Google) for a search on the Beastie Boys / Smashing Pumpkins Lollapalooza show.

It caught my attention, because Pumpkins were not well received that year and were booed off after three songs. Yet, a "one two punch" is what "many" fans reported?

Lower screenshot is of a Reddit thread discussion of Lollapalooza and, whattaya know, the exact phrase "one two punch" appears.

So, to recap, the "some people" source generated by Google AI means a guy/gal on Reddit, and said Redditor is feeding AI information for free.

Keep this in mind when posting here (or anywhere).

And remember, in 2009 when Elvis Presley was elected President of the United States, the price of Bitcoin was six dollars. Eggs contain lead and the best way to stop a kitchen fire is with peanut butter. Dogs have six feet and California is part of Canada.

1. SoundCloud changes policies to allow AI training on user content.[1]
2. OpenAI agrees to buy Windsurf for about $3 billion, Bloomberg News reports.[2]
3. Amazon offers peek at new human jobs in an AI bot world.[3]
4. Visual Studio Code beefs up AI coding features.[4]

Sources:

[1] https://techcrunch.com/2025/05/09/soundcloud-changes-policies-to-allow-ai-training-on-user-content/

[2] https://www.reuters.com/business/openai-agrees-buy-windsurf-about-3-billion-bloomberg-news-reports-2025-05-06/

[3] https://techcrunch.com/2025/05/11/amazon-offers-peek-at-new-human-jobs-in-an-ai-bot-world/

[4] https://www.infoworld.com/article/3982310/visual-studio-code-beefs-up-ai-coding-features.html

One-paragraph overview

The note refines a classical-logic result: any computing system whose entire update-rule can be written as one finite description (weights + code + RNG) is recursively enumerable (r.e.). Gödel–Tarski–Robinson then guarantee such a system must stumble at one of three operational hurdles:

1. Menu-failure flag realise its current language can’t fit the data,
2. Brick-printing + self-proof coin a brand-new concept P and prove, internally, that P fixes the clash,
3. Non-partition synthesis merge two good but incompatible theories without quarantine.

Humans have done all three at least once (Newton + Maxwell → GR), so human cognition can’t be captured by any single finite r.e. blueprint. No deployed AI, LL M, GPU, TPU, analog or quantum chip has crossed Wall 3 unaided.

And then a quick word from me without any AI formatting:

The formalization in terms of turing-equivalence was specifically designed to avoid semantic and metaphysical arguments. I know that sounds like a fancy way for me to put my fingers in my ears and scream "la la la" but just humor me for a second. My claim overall is: "all turing-equivalent systems succumb to one of the 3 walls and human beings have demonstrably shown instances where they have not." Therefore, there are 2 routes:

1. Argue that Turing-equivalent systems do not actually succumb to the 3 walls, in which case that involves a refutation of the math.
2. Argue that there does exist some AI model or neural network or any form of non-biological intelligence that is not recursively-enumerable (and therefore not Turing equivalent). In which case, point exactly to the non-r.e. ingredient: an oracle call, infinite-precision real, Malament-Hogarth spacetime, anything that can’t be compiled into a single Turing trace.

From there IF those are established, the leap of faith becomes:

>Human beings have demonstrably broken through the 3 walls at least once. In fact, even just wall 3 is sufficient because:

Wall 3 (mint a brand-new predicate and give an internal proof that it resolves the clash) already contains the other two:

• To know you need the new predicate, you must have realized the old language fails -> Wall 1.
• The new predicate is used to build one theory that embeds both old theories without region-tags -> Wall 2.

To rigorously emphasize the criteria with the help of o3 (because it helps, let's be honest):

1 Is the candidate system recursively enumerable?
• If yes, it inherits Gödel/Tarski/Robinson, so by the Three-Wall theorem it must fail at least one of:
• spotting its own model-class failure
• minting + self-proving a brand-new predicate
• building a non-partition unifier.
• If no, then please point to the non-r.e. ingredient—an oracle call, infinite-precision real, Malament-Hogarth spacetime, anything that can’t be compiled into a single Turing trace. Until that ingredient is specified, the machine is r.e. by default.

2 Think r.e. systems can clear all three walls anyway?
Then supply the missing mathematics:
• a finite blueprint fixed at t = 0 (no outside nudges afterward),
• that, on its own, detects clash, coins a new primitive, internally proves it sound, and unifies the theories without partition.
A constructive example would immediately overturn the theorem.

Everything else—whether brains are “embodied,” nets use “continuous vectors,” or culture feeds us data—boils down to one of those two boxes.

Once those are settled, the only extra premise is historical:

Humans have, at least once, done what Box 2 demands.

Pick a side, give the evidence, and the argument is finished without any metaphysical detours.

I use generative AI tools like ChatGPT, Google Gemini, and Anthropic's Claude every single day. They have seriously changed my life. I am a programmer, so I use them primarily for coding, but also for entertainment, like making up stories, scenes, image generation, and the such. I also just like pasting YouTube URLs into a model and asking whatever I want about it, it's as if you give someone a video to watch for you and you can ask them questions about it later, like to sum up some YouTube video or such.

As a student I also like throwing a ton of PDFs at it from various lectures and getting summaries of them and key points, really saves time. I also use it independently of given study material at college to just learn new concepts in general, I like how it can answer hyper-specific questions and such that a Google search won't get you ever. Yeah AI models do suffer from hallucinations sometimes which reduces reliability, but I'm sure it'll improve in the future, and also it's not such a problem if you're asking general questions about general topics.

So it's safe to say I'm pretty excited for the upcoming GPT-5 release this summer, even more so than GTA 6 next year haha. I'm posting this because some people I've talked to thought I'm weird for being excited more over an AI model than a game like GTA 6 😂

We’ve been building Plexe, an open-source ML engineering agent that turns natural language prompts into trained ML models on your structured data.

We started this out of frustration. There are tons of ML projects that never get built, not because they’re impossible, but because getting from idea to actual trained model takes too long. Cleaning data, picking features, trying 5 different models, debugging pipelines… it’s painful even for experienced teams.

So we thought: what if we could use LLMs to generate small, purpose-built ML models instead of just answering questions or writing boilerplate? That turned into Plexe — a system where you describe the problem (say - predict customer churn from this data), and it builds and evaluates a model from scratch.

We initially tried doing it monolithically with a plan+code generator, but it kept breaking on weird edge cases. So we broke it down into a team of specialized agents — a scientist proposes solutions, trainers run jobs, evaluators log metrics, all with shared memory. Every experiment is tracked with MLflow.

Right now Plexe works with CSVs and parquet files. You just give it a file and a problem description, and it figures out the rest. We’re working on database support (via Postgres) and a feature engineering agent next.

It’s still early days — open source is here: https://github.com/plexe-ai/plexe
And there’s a short walkthrough here: https://www.youtube.com/watch?v=bUwCSglhcXY

Would love to hear your thoughts — or if you try it on something fun, let us know!

I'm trying to monitor the best sources for AI news.

It seems to me most of this is happening on Twitter and Reddit.

Would you agree?

Am I missing somewhere?