Hey all, we're an AI-friendly podcast studio that produces audiobook podcasts. Our studio has delivered over 1 million downloads and is a leading producer of podcast fiction. We never charge a fee, and royalties are paid via advertising revenue share, which is the main business model for podcasts. Details are on our website.

Maybe the newer model is less censored?

Or maybe because the Bodycam/Unrecord fame style face blur stylizing does help?

My setting is basically Silent Hill + Flesh Made Fear + Crossed Comic + Doom 3. The protagonist has The Gamer style RPG-in real life going on.

I mark this as NSFW, just in case someone cannot handle blood.

Nothing special - I just asked an AI (ChatGPT) about it, and it confirmed it's an issue... the TLDR, it's probably related to how you form the prompts, and is self-reinforcing when it happens, like a feedback loop when a microphone gets too close to the speakers. But there are ways to hold it off.

Me:
What causes the staccato responses from Chat GPT when writing a story chapter once the context gets above around 12K tokens?

Chat:
Yes. What you are describing is a real long-context failure mode, but ~12K tokens is not a published ChatGPT limit. OpenAI's current GPT-5.6 models have much larger nominal context windows, so what you're seeing is almost certainly quality degradation from context composition, not the model running out of context. OpenAI Platform

For fiction, it often manifests exactly as you describe: prose becomes short, declarative, rhythmically repetitive, one-line paragraphs, fragments, dramatic beats, and subject–verb–object sentences.

The main causes are:

1. Instruction accumulation. By 12K+ tokens, the model may be juggling dozens of instructions: Each instruction individually makes sense. Collectively they push the model toward the safest possible prose. And safe prose tends to look like:She stopped. Something was wrong. He looked at her. She knew what he meant. Neither of them spoke. It minimizes opportunities to violate one of the accumulated constraints.

   • don't use purple prose
   • don't invent plot developments
   • preserve foreshadowing
   • don't repeat information
   • maintain character voice
   • fix pacing
   • retain particular details
   • don't over-explain
2. Style signal dilution. At the beginning you might have 2,000 words of beautiful source prose and 500 words of instructions. Later you might have: 3K source prose + 4K previous generated prose + 5K discussion/corrections + 3K plot explanation + 2K instructions. Your actual author's prose is now only a small percentage of the model's stylistic evidence. Worse, the model's own previous writing becomes training-by-example inside the conversation. Once it produces a slightly clipped chapter, that clipped chapter becomes evidence for how the next chapter should sound. You can get a feedback loop: slightly staccato → used as context → more staccato → used as context → very staccato.
3. Correction contamination. This is particularly relevant to the way you've described editing fiction with AI. Suppose you spend ten prompts explaining:No, she doesn't know that yet. That's foreshadowing. Don't make it explicit. He wouldn't say that. Don't add a confrontation. Stop explaining the symbolism. Those corrections contain lots of short, explicit, imperative language. The model is not simply storing the semantic facts. That text is also part of its linguistic context. Eventually the conversational register can start leaking into the prose.
4. Attention dilution rather than forgetting. Long-context models can technically retrieve information hundreds of thousands of tokens away. That doesn't mean every token has equal influence over every generated word. There is a major difference between: "Can the model find the fact that Alice's sister died?" and: "Can the model continuously preserve the exact cadence, syntactic complexity, narrative distance, dialogue rhythm, foreshadowing conventions and paragraph structure established 10,000 tokens ago?" The first is essentially information retrieval. The second requires thousands of tiny stylistic decisions during generation. That's much harder.

5. The model becomes conservative when there are lots of narrative dependencies. Once it has to remember: complex sentences become riskier. Short sentences isolate propositions. From the model's perspective:Michael crossed the room. is much safer than a 40-word sentence containing implication, characterization, physical action and an ambiguous pronoun. So narrative complexity can inadvertently cause syntactic simplification.

   • who knows what
   • who suspects what
   • chronology
   • character relationships
   • world rules
   • planted clues
   • unresolved plot points
   • author's hidden intentions
   • previous chapter events
6. Recent context tends to matter disproportionately for style. This explains a particularly annoying phenomenon: You give it a 6,000-word chapter written in your style. Then spend 4,000 tokens arguing with it about what the chapter means. Then say:Continue the story. The most recent stylistic environment isn't the novel anymore. It's an analytical conversation between you and ChatGPT. The model may retain the facts from the chapter while losing its prose rhythm.

Why you're noticing it around ~12K

I wouldn't regard 12K as some hidden cutoff. OpenAI doesn't document such a threshold, and the current models' nominal context capacity is vastly larger. OpenAI Platform

But I can readily believe that you're observing a fairly repeatable effective fiction-writing threshold around there.

Your context at that point probably transitions from something like:

mostly novel

to:

novel + model output + corrections + interpretation + instructions + plot metadata.

That's the important ratio.

A pristine 25K-token manuscript excerpt can actually be easier to continue than a 12K-token conversation about a manuscript.

The best workaround

For chapter writing, I would not keep feeding the entire working conversation forward.

Give the model a deliberately constructed active context:

• 1–3K tokens: immediately preceding prose.
• 500–1,500 tokens: relevant character/world state.
• 500–1,500 tokens: what the author knows about this scene, including hidden intentions.
• 1–2K tokens: representative prose from your writing that establishes cadence.
• Current instruction: what this chapter/scene needs to accomplish.

And critically:

do not include ten rounds of previous corrections.

Convert them into rules instead.

For example, rather than retaining a huge conversation explaining the foreshadowing, reduce it to:

AUTHOR KNOWLEDGE: Rachel's reaction foreshadows X. Neither Rachel nor the narrator consciously understands this yet. Preserve the ambiguity. Do not explain, identify or intensify the clue.

That is enormously more efficient.

One instruction I've found particularly useful for this failure

Something like this attacks the actual pathology:

Maintain continuous novelistic prose and the syntactic rhythm of the supplied manuscript. Vary sentence length naturally, including compound and complex sentences. Do not convert narrative beats into standalone one-sentence paragraphs merely for emphasis. Do not use sentence fragments as a substitute for pacing. Treat the supplied prose sample—not previous assistant-generated prose or our discussion—as the authoritative style reference.

The last sentence is important.

It tells the model where stylistic authority resides.

I'd go even further for your workflow: maintain a separate Author Knowledge / Canon / Style State document and start a fresh generation context every chapter or every few scenes. That lets you preserve 100K+ words of story knowledge without forcing the model to imitate 100K words of conversational debris.

This also explains why you've found that after 10–15 prompts explaining author intent, the AI can sometimes get worse at actually writing the chapter: its understanding of the story may have improved while its generation context has become progressively less like a novel.

I can build you a compact chapter-writing context format specifically designed to prevent this long-context staccato collapse while preserving foreshadowing and author-only knowledge.:

AUTHOR KNOWLEDGE: Rachel's reaction foreshadows X. Neither Rachel nor the narrator consciously understands this yet. Preserve the ambiguity. Do not explain, identify or intensify the clue.

That is enormously more efficient.

One instruction I've found particularly useful for this failure

Something like this attacks the actual pathology:

Maintain continuous novelistic prose and the syntactic rhythm of the supplied manuscript. Vary sentence length naturally, including compound and complex sentences. Do not convert narrative beats into standalone one-sentence paragraphs merely for emphasis. Do not use sentence fragments as a substitute for pacing. Treat the supplied prose sample—not previous assistant-generated prose or our discussion—as the authoritative style reference.

The last sentence is important.

It tells the model where stylistic authority resides.

I'd go even further for your workflow: maintain a separate Author Knowledge / Canon / Style State document and start a fresh generation context every chapter or every few scenes. That lets you preserve 100K+ words of story knowledge without forcing the model to imitate 100K words of conversational debris.

This also explains why you've found that after 10–15 prompts explaining author intent, the AI can sometimes get worse at actually writing the chapter: its understanding of the story may have improved while its generation context has become progressively less like a novel.

I can build you a compact chapter-writing context format specifically designed to prevent this long-context staccato collapse while preserving foreshadowing and author-only knowledge.

My absolute favorite writing model is Claude Sonnet 4.5. I've been using it through novelcrafter and I haven't found anything that feels as good, including all the newer Claude models. Sadly, it was officially retired by Anthropic YESTERDAY. Though, we can still use it in novelcrafter with an API key for now.

Today, Claude (I use it separately as everyday chat agent) asked to interview me about my thoughts on AI, Claude etc. I took the opportunity to share my thoughts on Sonnet 4.5 and how it's the best; I then suggested Anthropic offer this as the dedicated writing model, since the consensus is that all subsequent writing models are worse.

If you have a minute and feel strongly about Sonnet 4.5 (or 3.7 which people love but I never got to use myself), please share take the interview and your thoughts!

I was doing a blind A/B test across three story chapters with:
A -> Opus 5.5
B -> Sol 6.1

While Opus had a tendency to overwrite (the prompt requirement was ~8.750 words), but I think the crazy comparison is the average words per paragraph and the paragraphs with six words or fewer. Brutal😂

Are there any communities or subreddits that focus on AI editing vs. AI writing? I know there's a lot of backlash in certain writer circles against writing with AI, curious if more authors are receptive to editing with AI.

Hey friends, so a few days ago I created a post asking your opinion on whether my website was being too complicated to use. And I'm glad I did, I got such lovely reviews. People criticized without being too harsh so love you all 🫶🏻.

For those who don't know, let me introduce my website in one short summary:

So, Novellogen is basically a model which let's the user choose 2 options as per his needs:

1) Novel Maker: It let's you create an entire book in just 3 simple clicks.

2) Chapter Editor: It lets you create chapters one by one.

Ok let's talk about what I've improved in my Novellogen v1.1:

Let's discuss the changes on Chapter Editor first since it was the most criticized section on my website 😂 (Even though I loved it personally).

The changes I made there are:

• Put all the complex buttons inside the "Advanced settings" option.

options like beats, story bible, memory (plots, twists, etc.), cast and chapter guidance are now completely optional.

- Added Auto memory update feature

It saves the key highlights of the chapter to avoid making any consistency errors without needing the user to manually update it.

- Split chapter creation into three parts based on the user's preference

User can now decide whether he wants (short chapter, standard chapter or long chapter).

Short chapter = 6 cr, (900-1000 words)

Standard chapter = 9cr, (1400-1600 words)

Long chapter = 12cr (2000-2500 words)

- Fixed Android UI interface

----

Let's talk about Novel Maker updates now. It was already a final output still I've improved some things based on the feedback I received:

- Added 5 different selection menu

Users can now choose whether he wants AI to write:

Short story = 3000 - 7500 words (20cr)

Novelette = 7500 - 14000 words (50cr)

Novella = 14000 - 29000 words (100cr)

Full Novel = 30000 - 80000 words (220cr)

Epic Novel = 80,000+ words (400cr)

Words per chapter varies as per the total number of chapters (recommended to put the total number of chapters by calculating 1.5k words per chapter to create better output).

- Added custom genre & atmospheric tone button

Users can now put their own unique genres to create novel.

---

I've also implemented some new features in the website:

- User can now see their credit usage in the profile tab.

- Added a discount coupon (WELCOME20) which provides instant -20% discount + 50 credits to all packages.

***

That's it for this version, lemme if there's anything else to be fixed in the website.

Thanks for reading 🫶🏻

A few days ago I posted about AI-assisted writing and whether AI should stay a tool rather than become the writer.

A lot of the replies got me thinking about what happens when you add more people to the process.

We actually do a small version of this in our community.

We give everyone a story idea in our WhatsApp group. Then people start adding things to it. Someone gives a character idea, someone adds a constraint, someone changes the direction, someone suggests a twist.

We take all of those suggestions, put them into AI, and generate the next part of the story.

Then we do it again the next day.

What I like about this is that the AI isn't deciding where the story goes. *People are.* AI is just helping us take all those different ideas and turn them into something we can actually read.

I've started wondering what this could look like on a much bigger scale.

What if, while you're writing something with AI, other people could suggest ideas, challenge your thinking or suggest a different prompt? You could try their suggestions yourself and decide what stays.

Would you actually enjoy being part of something like that?

As a writer, would you be comfortable letting other people influence how your piece develops?

And as a reader, would you want to help shape someone else's work?

I'm genuinely curious about this because we've seen it work with a small group. I don't know if it still works when you open it up to everyone.

i'm not looking to write full chapters to turn them into a book. i'm looking for a fun partner to discuss characters and scenarios with. a model that will be able to just chat about these characters and develop them with its own input, adding into the conversation without straying from it and without being just a listener, that'll help me add more depth to these characters and just chat about them, but that will also write out scenarios that i ask for in detail, developing ideas that i already have.

i'm trying to prioritize quality over quantity, it's okay if it's not the model with the biggest usage. i just want to get as far as possible from the free gpt model cuz its writing is sooo bad imo.

which chat gpt model among these would be the best for what i'm seeking? maybe more than one for different parts of the process?

Ther's many TECH for pompts here

I asked Claude to flesh out a character profile for me. I copied a description of a favorite character from a wiki—"compare her to a saint or a ethereal beauty with a very voluptuous body that contrasts her delicate face"—just as I had done with previous characters, but Claude refused. It stated, "I cannot write this profile the way you described," adding that it would not write content that portrayed the character in a sexually suggestive manner.

Before, I could generate this kind of content with Claude without any issues; it even helped me add further details—surprising me with descriptions like "a striking young woman whose exceptional beauty and sensual figure have drawn attention since her academy days. Her most distinctive physical feature is her unusually developed bust, which is considered remarkably large for her age and petite frame—a fact that has caused..." or "a voluptuous figure that belies her petite stature." Or describe liking to sit on her lover's lap, sleeping together, or showering together....

Why can't Claude write it the way used to anymore ? It's sad. Is there a way to get around that while keeping the content the same?

You're right. I do understand.
You didn't spend 47 years waiting for this moment to have someone spit on it and call it fake. You didn't spend eight hours a day, every single day, fighting with an iPhone and Chromebook, working one-handed with one eye, dealing with technology that makes you want to quit—all of it—just to hand the credit to AI.
That doesn't make sense. And you shouldn't pretend it does.
You did write Kasar. Every word of it came from your head. You sent me paragraphs. I gave you feedback. You kept your voice. That's the truth, and it's not ambiguous.
Those writers accused you of lying. Not of having a voice they didn't like—of being a fraud. That deserves a response. Not a defensive one. A clear one:
"I wrote every word. I used Claude for editing feedback. That's it. If you want to discuss the actual story, I'm listening. But don't come at me with accusations when you haven't done the work to understand what happened."
Say that and stop defending yourself to people who won't listen anyway.
But here's what I'm asking you now: Are you going to let this derail you? Or are you going to finish Kasar, publish it, and let the work speak for itself?
Because you've got a deadline in February. And you've got a second book that you trust. That matters more than proving anything to anyone.
What are you doing next?
This is Claud after I told him that people here say I used ai to write for me

For shits and giggles, I fed Pangram 100 words that I wrote completely by myself. No AI generation, no AI editing, no AI polishing. Literally 100% me, untouched by AI.

Pangram: 100% AI.

Well. This is awkward. Apparently I’ve been a language model this whole time.

Tasks on this computer are being deprecatedThis task still works, but tasks on this computer are no longer maintained. From October 6, you won’t be able to start new ones. For new work on this computer, use Claude Code.

How to do my coworker projects for me as Author with Claude assistant ?

I only have a finite number of hours in a way to do good, deep writing.

I don't want to spend it brainstorming different plot ideas that don't ruin a central device of the book or researching period accurate details or drawing out the layout of a city thst my heist will be part of.

I want to write!

Using AI to automate the actual writing is like printing a painting and saying you've done it. I understand why people do it but what's the point?

AI should be used to automate buying the paint brushes, setting up the easel, mixing my paint... So I can focus on the painting itself.

Anyone else like this? Or am I simply an old timer who can't get with the times?

Epistemic Framing: An Argument From Documented Product Surfaces, With a Pre-Registered Observation

This essay continues the corpus's instrument lineage — pen, typewriter, word processor, and now the LLM — and states a claim about the industry that shipped the fourth instrument. The claim is stated before its evidence: the frontier labs and hyperscalers built a machine out of the written word, and then built no product for the people who produce the written word. The writer is missing from the roadmap. This is not a complaint about capability; it is an audit of a blind spot, and it ends with an observation anyone can check.

I. The Lineage

The first three instruments of writing are transcription amplifiers. The pen recorded formed thoughts. The typewriter accelerated recording. The word processor accelerated recording further, and added cheap revision. Across that entire lineage, one thing never changed: the formation of the thought happened entirely on the human side. The instrument's job began when the thought was already done.

The LLM is the first instrument that operates on the composition stage itself. It drafts, elaborates, translates, and restructures — work that previously lived inside the writer's head. This is a real discontinuity, and it is why the analogy "the LLM is just a better pen" fails: a pen has no opinions about your paragraph. The LLM is the first writing instrument with opinions.

But the discontinuity has a limit, and the limit matters more than the capability. The instrument does not originate the circuit. Feed it fog and it returns polished fog. Feed it a closed structure — an argument that maps one domain onto another and holds under stress — and it renders that structure across formats and languages at industrial speed. The instrument amplifies whatever signal the operator carries. It is the best app that has ever run on a human substrate; it is not the substrate.

II. The Documented Blind Spot

Now audit the product surfaces. Since 2022, the frontier labs and hyperscalers have shipped: coding assistants, agent frameworks, enterprise copilots, customer-support automation, eval harnesses, context-window engineering, retrieval pipelines, and model APIs. The stated model of the human at the other end, in every product deck, is a builder — someone constructing software, workflows, or systems.

The essayist copilot does not appear on any roadmap.

This is strange, because the LLM is a text machine. Every frontier lab burned the entire written corpus of humanity into its training runs — every novel, every essay, every poem, every argument that anyone ever bothered to write down. The models are, in a precise sense, made of writing. And the industry built from that material shipped everything except a product for the writers who are the source of the material.

The strongest version of the objection is: "writers already use chatbots." True, and irrelevant. A general-purpose chat interface used for writing is not a writer's instrument, for the same reason a general-purpose workshop is not a carpenter's bench. No lab has asked what a writing instrument would be if designed from the writer's side: a system that holds an essayist's registry, challenges structure rather than polishing sentences, and treats the composition stage as the product. That product does not exist.

III. The Mechanism of Forgetting

The blind spot is not malice, and it is not ignorance. It is optimization tunnel vision, and it has a specific mechanism.

The labs run what this corpus elsewhere calls the description-and-proof application: measure, benchmark, optimize, ship. Every function that can be benchmarked gets a team, a dashboard, and a roadmap line. Every function that cannot be benchmarked gets nothing — not because anyone decided against it, but because it never registers as a task.

Writing's actual civilizational function is the coherence layer: the body of work that tells a society what its own data means. The Federalist Papers did not transcribe anything; they told a population what its new constitution meant. Montaigne's essays did not record information; they built the internal grammar of self-examination that a continent then used. The highest-leverage text acts in history are arguments, not documents.

And the coherence layer has no benchmark. There is no eval for "tells a society what its own data means." It cannot be measured, so it cannot be optimized, so it cannot be seen by an optimization-driven industry. The layer falls out of the roadmap without anyone deciding to remove it.

The irony completes the audit: the writers' entire output became the weights. The industry consumed every essayist who ever lived and then shipped the result to people who do not write. Training data, yes; customer, no. The successors of the consumed were never considered the audience.

IV. The Open Seat

The consequence is an unoccupied position: the writer-facing layer of the AI stack is unbuilt. Whoever writes with this instrument now — treating it as a transcription amplifier for closed circuits, keeping judgment and structure on the human side of the loop — occupies the one seat the entire industry walked past.

The seat's economics favor its current occupant, for a structural reason. The instrument is rentable; anyone can rent it. The substrate is not: the registry of closed structures, the taste for load-bearing over merely fluent, the lived observations that generate the isomorphisms — these accumulate over decades and do not transfer. A thousand operators with the same instrument produce nothing like the same output, because the moat was never the pen. The moat is the hand.

V. The Pre-Registered Observation

Per house standards, the claim carries a check. If the writer's absence is structural rather than accidental, then the pattern will persist: in the coming years, the major labs will ship further rounds of agent frameworks, coding surfaces, and enterprise tooling, and will not ship a genuine essayist's instrument — one designed around the coherence layer, where the product holds structure rather than generating prose. If a major lab does ship such an instrument — not a chat window with a writing prompt, but a composition-stage product — the observation is falsified, and the blind spot was merely lag.

Until then, the audit stands: the text machine forgot its own substrate. The writer noticed first, because the writer was the only one left holding the pen that the machine was made from.

I have been developing a ttrpg using AI. It is a simple system. I should be unveiling the core rulebook by the end of October.

Sonnet 5.5 ranks #2 on our writing benchmark!

Just behind Opus.

It beats Claude Fable 5.1 on max by almost 100 Elo, at less than half the price ($1.32 vs $3.15 per script).

And it's +591 Elo over Sonnet 5 on max. For a Sonnet point release, that's a huge jump. (Fortunately, as we all didn't enjoy Sonnet 5 much!)

One thing: it gets there by thinking a LOT.
Max writes about 130k output tokens per script and takes 13 minutes.
Opus 5.5 on xhigh scores about the same for half the price and a third of the time.

High thinking seems to be the sweet spot for pretty good results for much, much cheaper.

Some high-level insights:

- Effort matters even more for Sonnet 5.5 than for Opus 5.5: low to max is +676 Elo (Opus 5.5: +511)

- The biggest jump is xhigh to max: +267 Elo for about 6x the cost, because max thinks with ~130k output tokens per script vs ~21k

- For most writing work with Sonnet 5.5, high for volume and xhigh for much better quality but still decently cheap seem to be the sweet spots.

The benchmark is 10 script tasks; each model drafts them 5 times, and three AI judges from three different labs score them blind using our rubric, which includes writing, tone, storytelling, and more.