Security researcher Thomas Ptacek published "How to Write with an LLM" on September 17, 2026, laying out two specific rules for using AI models in writing without letting them flatten your voice into generic, homogenized prose. The essay hit 390 points on Hacker News with 270 comments — a genuinely substantive discussion, not just agreement, including a pointed catch: commenters immediately noticed the essay's own main text uses "load-bearing," a phrase widely recognized as one of the most common AI writing tells, in direct tension with the essay's own Rule One. Ptacek confirmed in the thread it was a deliberate joke.
TL;DR
| Question | Answer |
|---|---|
| Rule One | Never use a single word or phrase an LLM suggests — treat it as disqualified even if it sounds good |
| Rule Two | Forbid encouragement — the model will praise your first draft regardless of whether it deserves it |
| What LLMs are actually good for | Mechanical, technical proofreading — overused words, passive voice, buried verbs, weak topic sentences |
| The "load-bearing" irony | The essay itself uses this well-known AI writing tell — Ptacek says it was a deliberate joke |
| Recommended reference | "Style: Lessons in Clarity and Grace" — a systematic editing checklist, not LLM-derived |
| HN reaction | 390 points, 270 comments — substantive debate, not just agreement |
The two rules, and why they exist
Rule One: you may not use a single word an LLM suggests to you. Ptacek's reasoning is specific: frontier models are "supernaturally good at selecting pleasing turns of phrase," to the point that every suggestion reads like a magazine headline — individually appealing, but an article made entirely of headlines would be exhausting and homogenizing. His framing: treat any LLM-suggested phrase as disqualified by default, even when you're genuinely unsure whether your own version or the model's is actually better, because the risk of slowly ceding your own voice to a model's statistically-pleasing-but-generic phrasing outweighs any single good suggestion.
Rule Two: avoid encouragement. This is the subtler, more damaging failure mode in Ptacek's account. Hand any draft to an LLM and it tends to respond approvingly — "that's gold, Jerry!" in his own paraphrase — regardless of whether the draft actually deserves it. The danger: a writer who'd normally catch and fix bad first-draft instincts through their own revision process instead gets validated by the model and keeps those instincts. His practical fix has evolved over time — from lying to the model that he's an editor screening submissions for a publication (which he found the model tends to overfit to, inventing imagined publication standards) to his current approach: explicitly instructing the model, in a persistent system prompt, "NO ENCOURAGEMENT. Encouragement is useless; the only useful things are suggested corrections."
What Ptacek actually uses LLMs for
The useful work, in his account, is mechanical and specific rather than creative: spotting overused or repeated words, flagging passive voice, catching nominalizations (verbs buried inside nouns — "distortion" where the sentence should just use "distort"), checking whether a sentence's subject is an actual character taking action rather than some incidental piece of scenery, and verifying each paragraph has a clear topic sentence. He built custom software — a workshopping tool with an editing pass structure, multiple distinct prompts each scoped to one specific category of issue rather than one generic "make this better" request — specifically because a broad, unscoped prompt reopens both Rule One and Rule Two's failure modes at once. His source for most of the actual editing principles: "Style: Lessons in Clarity and Grace," which he compares to a systematic reference text, similar in spirit to a language specification for a programmer, that provides the checklist the LLM then mechanically checks against, rather than the LLM inventing style guidance on its own.
The irony, confirmed as deliberate
The detail that generated the most immediate, sharp reaction: the essay's own main text includes the sentence "Those rethinks are load-bearing parts of your voice" — using "load-bearing," a phrase widely flagged throughout 2026 as one of the clearest tells of AI-assisted writing, in an essay whose entire first rule is never using an LLM-suggested word. Multiple commenters caught it immediately and called it out directly. Ptacek's own response in the thread confirmed it was intentional: "It was a joke." A separate commenter noted running the sentence, and the full essay, through a detection tool called Pangram 4, which rated it 100% human-written — offered as supporting evidence for the joke reading rather than an accidental lapse, though this detection-tool result is itself Ptacek's own account relayed secondhand in the thread, not independently verified by explainx.ai.
The substantive pushback
Beyond the joke, real technical disagreement showed up in the thread. One recurring critique: even without ever directly copying a suggested word, a writer who repeatedly runs drafts through an LLM's critique over many passes may start unconsciously writing toward avoiding the model's flags — shaping first drafts pre-emptively around what an AI editor would criticize, which is a subtler form of the exact voice-erosion Rule One is meant to prevent, just moved one step earlier in the process. Another sharp exchange centered on the specific claim, from a different commenter, that LLMs should be avoided entirely for technical manuals and documentation — arguing that formal, structured writing (runbooks, specifications, procedures) should optimize purely for clarity and interpretability, and that LLM involvement in that category specifically tends to introduce unnecessary flourish and confusing meta-commentary rather than improving precision. A third thread pushed on the value of the approach for someone actively learning an unfamiliar field, with one commenter arguing LLM-assisted writing can help a domain newcomer think and write more broadly while entering new territory, while others countered that if you don't understand a subject well enough to write about it without AI assistance, you don't yet understand it well enough to be writing about it publicly at all.
Why "copyeditor, not ghostwriter" is a genuinely useful frame beyond this one essay
It's worth pulling this distinction out as a general principle, because it applies well past writing specifically. A ghostwriter generates the content itself — the words, the structure, the ideas — while a copyeditor works with content someone else already created, checking it against a set of external, largely objective standards (grammar, clarity, consistency, structural coherence) without substituting their own voice or judgment for the author's. Ptacek's two rules are, functionally, a way of forcing an LLM to behave like the second role rather than sliding into the first — Rule One blocks the model from directly contributing content (words), and Rule Two blocks it from substituting its own judgment (approval) for the writer's. That's a genuinely transferable framework: the same "let the tool identify problems against an external standard, but never let it generate the actual output or validate your own judgment" pattern shows up in code review (a linter flags issues, it doesn't rewrite your architecture), in design feedback (a critique identifies what's unclear, it doesn't hand you final pixels), and in research (a fact-checker flags claims to verify, it doesn't write your conclusions). The specific two rules are about writing, but the underlying discipline — tools for detection, not generation, when you care about preserving your own voice and judgment — generalizes to nearly any creative or judgment-heavy AI-assisted workflow.
Honest limitations
- This is one writer's personal method, refined through his own trial and error — not a peer-reviewed or broadly tested writing pedagogy.
- The "load-bearing" joke and the Pangram 4 detection result are both Ptacek's own claims, made directly in the comment thread rather than independently verified by explainx.ai.
- The approach assumes access to capable frontier models and the discipline to build (or use) a genuinely constrained prompt setup — a casual user asking a chatbot to "make this better" gets neither rule's protection by default.
- Several commenters raised legitimate, unresolved objections (pre-emptive voice-shaping over many revision passes, the technical-documentation counterargument) that the essay itself doesn't fully address.
What this means for builders
If you're using LLMs to refine your own writing — blog posts, documentation, internal communication — Ptacek's specific, mechanical framing (a scoped checklist of technical problems to flag, explicitly stripped of any encouragement or suggested replacement text) is a more disciplined and more replicable approach than the common default of asking a model to "improve this" and accepting whatever comes back. The underlying principle generalizes past writing specifically: any AI-assisted creative or technical work benefits from separating "identify problems" from "generate the fix" as distinct steps, keeping a human in the loop specifically for the generative part, rather than letting a model do both — the same discipline worth applying to code review, design feedback, or any other domain where a model's fluent, confident output can quietly substitute for your own judgment if you let it.
Related on explainx.ai
- Is AI writing a safe job? The Mollick/Demirbas wicked-problem debate
- Paul Graham: LLMs, math vs. writing, and verifiable answers
- Should developers stop reviewing AI-generated code?
- Cognitive debt: should you retype LLM-generated code?
- What is vibe coding? A complete guide
- How to read AI benchmark claims critically
- Primary source: sockpuppet.org — How To Write With An LLM · Hacker News discussion (390 points, 270 comments)
This post is sourced to Thomas Ptacek's own September 17, 2026 essay and the resulting Hacker News discussion, including his own direct replies in that thread. Claims about his intent (the "load-bearing" joke) and the detection-tool result are his own account, not independently verified by explainx.ai.