I mean, I guess that’s the goal? I wasn’t getting much blue raz, just artificial lemonade. Wasn’t expecting the no-fizz, but I guess I didn’t mind it.

Celsius overall is definitely not my favorite brand, but I had limited selection at the hotel I’m staying in.

I just went on a rampage downloading shit again and again and again, trying to make a game that looks good because everything before it produced dogwater, but nothing is helping. Mind you, I'm vibe-coding all of this because I'm bored, but I see so many other people getting decent results, and I don't know what I'm doing wrong. I'm running Fable 5 too. As far as random connectors/skills/MCPs/plugins, I'm using:

• Godot 4.7
• GDScript
• Blender 5.1
• Python (bpy)
• glTF/GLB
• WebAssembly (Web export)
• Vercel
• Vercel CLI (via npx)
• ElevenLabs
• FastNoiseLite / NoiseTexture2D (Godot procedural textures)
• AudioStreamGenerator (Godot procedural audio)
• Jolt Physics (Godot physics engine)
• GL Compatibility renderer

What else do I need to be doing?

fyi I'm talking about physical game design & mechanics, not story building. My point is that it all looks like complete dogshit

Hey everyone,

I found a Molekule Air Pro left on the curb for free. It looks brand new, but when I plug it in, it immediately shows the screen in ⁠the first picture:

“Purifier error 001

Contact Molekule customer support”

Since I found it on the street, I probably can't contact support (and definitely can’t use a warranty).

Does anyone know how to fix this specific error code?

Hey everyone,

I found a Molekule Air Pro left on the curb for free. It looks brand new, but when I plug it in, it immediately shows the screen in ⁠the first picture:

“Purifier error 001

Contact Molekule customer support”

Since I found it on the street, I probably can't contact support (and definitely can’t use a warranty).

Does anyone know how to fix this specific error code?

Hi everyone,

Asking on behalf of a transfer student friend of mine. We heard a theory that an empty Final Exam Information section means there will be no in-person final, or like, it will be a paper instead of a final exam.

Is this accurate?

Also, bonus question: Are seminar courses "harder" than regular lecture courses? I know it's smaller class sizes and stuff, but I'm not entirely certain what that entails in terms of actual course difficulty and rigor.

Thanks in advance!

From "There's Something About Miriam" (2004) starring Miriam Rivera.

Participants settled for an undisclosed amount before the show aired.

In 2019 Rivera was found dead by apparent suicide. Rivera's spouse still believes it was murder

https://people.com/tv/miriam-rivera-the-first-openly-trans-reality-star-dead-at-38/

So focused on the strawberry one yesterday that I didn’t realize they have the OG!

i dont know which one I like more 🤔

What do y’all think of it?

’Twas pretty good

Hi, I'm a bit new to this subreddit, but I'm just curious why Gamabunta gets brought up so much in this server?

We could be discussing if Goku solos the verse and then someone will be like "well not if Naruto summons Gamabunta" and at this point i feel like a small, lonely child watching all of his friends laugh at an inside joke that came from a sleepover I missed

I watched Naruto when I was young enough that I didn't care about power scaling, I just liked watching superpowers and colors flying all over the screen and things blowing up

is gamabunta really that goated? could he really just sit on Isshiki's face to kill him?

tl;dr - they're too different in their function to compare 1:1, so it's better to use them both for different purposes

This is a follow-up post on the post I made about the unslop-text skill I built using the data from this post. One of the biggest questions I received in the comments was how unslop-text compares to the "humanizer" skill. So, rather than trying to sum it up in a few words, I figured I would just explain it in a separate post. What follows will be a written comparison of how both skills work. The body of the post will primarily focus on a detailed comparison of the two skills.

Yes, I'm going to use headers and bolded bullet points (it's easier to read).

No, I did not write this post with AI, nor did I "humanize" or "unslop" it using either of the skills.

I'm going to try to list the most related similarities/differences between the two skills in the same sequential order so it's easiest to follow (i.e., bullet point 1 for "humanizer" will correspond to the same category in bullet point 1 of "unslop-text," and vice versa).

unslop-text:

1. Unslop-text gets all of its data from the research done in this post. In short, ~90,000 posts across >40-some subreddits were scanned for what people perceive as the most blatant AI giveaways/tells.
2. Unslop-text uses a scanner that has severity levels, JSON, CI exit code, and a "density score."
3. Unslop-text flags the issues and makes the fixes for you once you've set the style, but it won't choose the style or write the piece from scratch, and, like humanizer, it bans em dashes from the final version (sorry em-dashes ☹️)
4. Unslop-text also has voice calibration (pins a register and speaker, and can match your own writing sample) + it treats the over-corrected "trying-not-to-sound-like-AI" voice as its own tell.
5. Unslop-text ranks tells by how often readers cite them (per the data)
6. Unslop-text only catches surface tics, but structural tells like sentence rhythm and sycophancy still require a human to read it aloud.

humanizer

1. Humanizer gets all its data from Wikipedia's "Signs of AI writing" guide.
2. Humanizer is a prompt-only skill (i.e., no code)
3. Humanizer edits drafts by checking them against 33 specific patterns (see repo for reference). It flags remaining AI-like text and rewrites it while also completely banning em dashes from the final version.
4. Humanizer includes voice calibration, a "personality/soul" step, and version 2.8.0 system stability optimized for Claude Code and OpenCode.
5. Humanizer presents patterns as a numbered catalog and is not ranked by frequency or impact.
6. Humanizer fixes both surface and structural patterns in a single rewrite instead of relying on human input. This isn't necessarily "better," as it can still end up being very wrong, but it is "easier"

========================================

It's worth noting that this post was initially intended to provide a documented photographic comparison of each skill's output, but I realized that these skills are too different to charitably pit them against each other with a one-off side-by-side "unslop this text: _____" prompt.

The humanizer is built specifically to rewrite text and project a voice, while unslop-text acts strictly as a guardrail and scanner that refuses to impose an artificial style. Furthermore, neither tool can convincingly replicate human prose, as an LLM cannot entirely strip away its own underlying structural cadence. Because a machine-generated register persists regardless of surface-level fixes, judging which output sounds more human is an impossible metric that depends entirely on the quality of the initial input text.

Due to these blatant differences, I would posit that both skills should just be used for separate purposes rather than picking one over the other. The humanizer should be used as a quick rewriter for a one-shot cleanup into a default voice before you do a final review yourself. Unslop-text should be used as a structural auditor and CI-gate to scan for surface tells or to protect a voice you establish yourself. It is VERY UNLIKELY that it will give you finished prose that you are happy with in one shot. Both skills do reliably strip away surface-level AI markers, but neither can eliminate the underlying AI cadence, meaning the final step for both requires a human to read the text aloud.

========================================

While I am the creator of unslop-text, this post is not intended to bash or discredit the humanizer skill. Everything comes down to preference, and ultimately, your AI output will only be as good as what you put into it.

This is a follow-up for a skill I made based on the breakdown I posted of ~90,000 Reddit posts on what people actually flag as AI-written text. People asked for a tool they could use, and I had built it into a Claude skill, so this post is dedicated to that.

Unslop-text is built strictly on that data. The ranking is based on volume, where em dashes are at the top because they were the most cited tell in the corpus, well ahead of any specific buzzword. This ensures that the target is placed on the giveaways that trigger people most often.

The scanner is a plain Python script that catches surface stuff like em dashes, "as an AI language model," diction memes, and formatting tics. It runs in CI and gives you a slop score per file. But most of the strongest tells in the data are structural, like uniform sentence rhythm or a paragraph that sounds fluent and says nothing. No regex is going to catch those. So the skill flags them for a "read-aloud pass" so you can verify it yourself.

It is not a detector, and it has no "house style." It strips the tells and makes you commit to a voice (that way the onus is on you to come up with your desired style rather than having it inevitably default to the same AI-isms). It is the same data as the original post, just repurposed for something usable in your own work.

Let me know if you have any recommendations or questions!

Repo and scanner: https://github.com/JCarterJohnson/vibecoded-design-tells/tree/main/unslop-ai-text (under /unslop-ai-text)

The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds about the data, the most obvious tell is the overused em dash (of course). Right behind that are flaws that software cannot easily scan. AI writing has a flat, predictable sentence rhythm and a constant, unnatural positivity. The paragraphs look polished but say nothing. This makes AI detection incredibly difficult. The signs that human readers trust the most are unfortunately the exact ones that software cannot measure.

Methodology:

I pulled the Arctic Shift Reddit archive: 89,239 posts across 47 subreddits (r/ChatGPT, r/WritingWithAI, r/SaaS, r/aiwars, r/ClaudeAI, r/Professors, r/Teachers, and the rest), 2021 to 2026. After filtering to posts that are actually about spotting AI writing, 7,984 were on-topic, split across three lanes: AI tools, writing, and SaaS. Every figure below is a share of those on-topic posts, not a raw count, because the topic barely existed before 2023 (26 on-topic posts in 2021, 86 in 2022) and then exploded (587 in 2023, 3,174 in 2025), so raw counts mostly track the subreddits growing.

It is important to note that a keyword pass badly miscounts this topic, so I hand-audited a 600-post sample to record what people actually cite as a tell, versus what a pattern merely matches.

Why does all AI writing converge on the same voice? Every model is tuned for a safe and agreeable register that reads as "good writing" to a grader, so everyone's default lands in the same place. One commenter put the effect plainly: "ChatGPT has a very recognizable cadence. And as soon as you catch it, it is impossible to focus on what's being written, because it's not even someone's actual thoughts." (r/ChatGPT)

The tells, ranked by how often people actually cite them:

Rank	Tell	What people say
1	The em dash (cited in 7.1% of audited posts, the top tell by a wide margin).	"Em dashes have become the single most reliable tell of AI-generated text." (r/ChatGPT)
2	A flat, uniform sentence rhythm (cited 4.0%, and no scanner can see it).	"Every YouTube video script I watch has the same cadence, the same verbiage, the same fucking chatGPT slop." (r/ChatGPT)
3	The "not just X, it's Y" cadence (cited 2.8%, the top sentence-level tell).	People list it right next to the punctuation: "even beyond the obvious em dashes and 'not just x, it's y'." (r/ChatGPT)
4	The five-paragraph shape and the "in conclusion" wrap-up (cited 2.5%).	They "leave in those super obvious lines like 'In conclusion, this essay has discussed...'." (r/ChatGPT)
5	The diction memes: "delve," "leverage," "seamless," "tapestry" (cited 1.3% as a cluster).	A prompt people pass around to fix it: "no telltale signs like em dashes, overused words like 'seamless'." (r/ChatGPT)
6	Leftover assistant boilerplate, the "as an AI language model" line (cited 1.2%).	The other line people forget to delete: "As an AI developed by OpenAI...". (r/ChatGPT)
7	The hollow scene-setting opener (cited 0.7%, low but iconic).	A whole post written in the voice, quoted as the example: "I wanted to take a moment to delve into something that's been on my mind lately. In today's fast-paced digital landscape..." (r/ClaudeAI)

Two tells belong in the top five but are missing from that table on purpose, because no keyword can catch them and the audited readers named them anyway. Sycophancy (the "great question!" opener, the reflexive refusal to take a side) is cited about as often as the antithesis cadence. So is saying nothing at length (i.e., prose that is grammatical and confident but makes no actual claim). A pattern-matcher is blind to both of those things so I could not check for them when I scanned for data, but they are obviously very real.

It's important to note some corrections that resulted from me auditing the data myself. A naive keyword scanner gets this topic backwards in two ways. First, it massively over-counts ordinary words. "however," "thus," and "hence" are the single highest keyword match in the corpus at 6.3% of posts, and they're cited as a tell 0% of the time, because they're just people writing normally. The same is true for "nuanced," "comprehensive," "when it comes to," and "utilize." If you build a detector on a word list, this is most of what it flags, and it's nearly all false. Second, it under-counts or entirely misses the tells that rank highest with real readers, the flat rhythm and the fluent-but-empty paragraph, because no word list can see them. The lesson is that the cheap signal and the real signal point in different directions, which is exactly why the cited column, not the keyword column, drives the ranking above.

There is a fair counterpoint that came up enough to belong here, which is that none of this is strictly an AI problem. The em dash is good typography. Formal diction and a tidy structure are how a lot of careful people, students and non-native English speakers especially, have always written. So these tells absolutely predate AI. What (unfortunately) changed is that AI made everyone produce them at once, so the people who always wrote this way are the ones getting flagged. One teacher's post is titled "My students discovered AI checkers and are now terrified of their own writing." (r/Teachers) Another writer leads with "English is not my first language. I wrote this in Chinese and translated it with AI help. The writing may have some AI flavor," and then makes a sharp original argument anyway. (r/LocalLLaMA)

As many of us have experienced, every item on the list is the model's default reach when you don't specify otherwise. Cut the em dash. Say the thing plainly instead of negating it first. Vary your sentence length so the rhythm isn't a metronome. Drop the flattery and take a position. Use contractions. Let the structure follow the argument instead of the intro-body-conclusion mold. The fix that showed up most often in the data was simply to stop letting the model pick the voice. Give it a real sample of how you write and then read the result out loud, because the rhythm is the tell your ear catches before your eye does.

Thirteen graphs are attached, with the underlying tables:

1. The cited ranking: each tell by how often audited posts name it. The em dash leads, and the structural tells a scanner can't see sit right behind it.
2. Cited versus keyword-matched: the same tells under both signals, showing where a word list inflates a tell ("however," "nuanced") and where it misses one ("as an AI," the structural ones).
3. The keyword ranking: the broad lexicon pass over all 7,984 on-topic posts, the noisier secondary view.
4. Growth over time: talk of AI-writing tells as a share of posts pulled each year, near nothing before 2023.
5. Tell trend by year: the top tells over time. The em dash is essentially absent before 2024 and then jumps, the cleanest before-and-after in the data.
6. Scale and coverage: posts pulled from each subreddit, 89,239 in total.
7. Raw counts per tell: the actual post counts behind the percentages.
8. The funnel: how 89,239 pulled posts narrow to 7,984 on-topic and a 600-post audited core.
9. Concentration points: on-topic posts as a share of each sub's own volume. r/WritingWithAI runs near a third of its posts.
10. Co-occurrence: which tells get named together in the same post.
11. Tells by family: diction words versus sentence phrasing versus formatting versus pasted assistant artifacts.
12. Top posts: the highest-upvoted on-topic threads the signal comes from.
13. Lens 2: for the specific terms I queried directly, how much of their airtime lands in an AI-writing context, and across how many subreddits.

(!) This is what vocal, online people say, so trust the ordering more than the exact percentages. Keyword matching can catch the wrong sense of a word or miss sarcasm, which is why the generic-word counts run high and why I audited a sample by hand. The relative order is the thing to take away, not the decimal.

Full data, scripts, the scanner, and all charts are here: https://github.com/JCarterJohnson/vibecoded-design-tells (the unslop-ai-text folder). It has the pulled corpus, the tell-count tables, the 600-post audit, and the harvester, so you can rerun it against the public Arctic Shift archive yourself.

============================================================

This is a Part 2 post on the original post I made about vibe-coding giveaways in website UI. I'm planning on turning this into a 3-part mini research series that spans AI "tells" in ui, text, and code. Will update links progressively:

1. AI giveaways in UI -- /unslop-ai-ui skill (in repo)
2. AI giveaways in text (this post) -- /unslop-ai-text skill (in repo)
3. AI giveaways in code (...coming) -- /unslop-ai-code skill (in repo)

The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds about the data, the most obvious tell is the overused em dash (of course). Right behind that are flaws that software cannot easily scan. AI writing has a flat, predictable sentence rhythm and a constant, unnatural positivity. The paragraphs look polished but say nothing. This makes AI detection incredibly difficult. The signs that human readers trust the most are unfortunately the exact ones that software cannot measure.

Methodology:

I pulled the Arctic Shift Reddit archive: 89,239 posts across 47 subreddits (r/ChatGPT, r/WritingWithAI, r/SaaS, r/aiwars, r/ClaudeAI, r/Professors, r/Teachers, and the rest), 2021 to 2026. After filtering to posts that are actually about spotting AI writing, 7,984 were on-topic, split across three lanes: AI tools, writing, and SaaS. Every figure below is a share of those on-topic posts, not a raw count, because the topic barely existed before 2023 (26 on-topic posts in 2021, 86 in 2022) and then exploded (587 in 2023, 3,174 in 2025), so raw counts mostly track the subreddits growing.

It is important to note that a keyword pass badly miscounts this topic, so I hand-audited a 600-post sample to record what people actually cite as a tell, versus what a pattern merely matches.

Why does all AI writing converge on the same voice? Every model is tuned for a safe and agreeable register that reads as "good writing" to a grader, so everyone's default lands in the same place. One commenter put the effect plainly: "ChatGPT has a very recognizable cadence. And as soon as you catch it, it is impossible to focus on what's being written, because it's not even someone's actual thoughts." (r/ChatGPT)

The tells, ranked by how often people actually cite them:

Rank	Tell	What people say
1	The em dash (cited in 7.1% of audited posts, the top tell by a wide margin).	"Em dashes have become the single most reliable tell of AI-generated text." (r/ChatGPT)
2	A flat, uniform sentence rhythm (cited 4.0%, and no scanner can see it).	"Every YouTube video script I watch has the same cadence, the same verbiage, the same fucking chatGPT slop." (r/ChatGPT)
3	The "not just X, it's Y" cadence (cited 2.8%, the top sentence-level tell).	People list it right next to the punctuation: "even beyond the obvious em dashes and 'not just x, it's y'." (r/ChatGPT)
4	The five-paragraph shape and the "in conclusion" wrap-up (cited 2.5%).	They "leave in those super obvious lines like 'In conclusion, this essay has discussed...'." (r/ChatGPT)
5	The diction memes: "delve," "leverage," "seamless," "tapestry" (cited 1.3% as a cluster).	A prompt people pass around to fix it: "no telltale signs like em dashes, overused words like 'seamless'." (r/ChatGPT)
6	Leftover assistant boilerplate, the "as an AI language model" line (cited 1.2%).	The other line people forget to delete: "As an AI developed by OpenAI...". (r/ChatGPT)
7	The hollow scene-setting opener (cited 0.7%, low but iconic).	A whole post written in the voice, quoted as the example: "I wanted to take a moment to delve into something that's been on my mind lately. In today's fast-paced digital landscape..." (r/ClaudeAI)

Two tells belong in the top five but are missing from that table on purpose, because no keyword can catch them and the audited readers named them anyway. Sycophancy (the "great question!" opener, the reflexive refusal to take a side) is cited about as often as the antithesis cadence. So is saying nothing at length (i.e., prose that is grammatical and confident but makes no actual claim). A pattern-matcher is blind to both of those things so I could not check for them when I scanned for data, but they are obviously very real.

It's important to note some corrections that resulted from me auditing the data myself. A naive keyword scanner gets this topic backwards in two ways. First, it massively over-counts ordinary words. "however," "thus," and "hence" are the single highest keyword match in the corpus at 6.3% of posts, and they're cited as a tell 0% of the time, because they're just people writing normally. The same is true for "nuanced," "comprehensive," "when it comes to," and "utilize." If you build a detector on a word list, this is most of what it flags, and it's nearly all false. Second, it under-counts or entirely misses the tells that rank highest with real readers, the flat rhythm and the fluent-but-empty paragraph, because no word list can see them. The lesson is that the cheap signal and the real signal point in different directions, which is exactly why the cited column, not the keyword column, drives the ranking above.

There is a fair counterpoint that came up enough to belong here, which is that none of this is strictly an AI problem. The em dash is good typography. Formal diction and a tidy structure are how a lot of careful people, students and non-native English speakers especially, have always written. So these tells absolutely predate AI. What (unfortunately) changed is that AI made everyone produce them at once, so the people who always wrote this way are the ones getting flagged. One teacher's post is titled "My students discovered AI checkers and are now terrified of their own writing." (r/Teachers) Another writer leads with "English is not my first language. I wrote this in Chinese and translated it with AI help. The writing may have some AI flavor," and then makes a sharp original argument anyway. (r/LocalLLaMA)

As many of us have experienced, every item on the list is the model's default reach when you don't specify otherwise. Cut the em dash. Say the thing plainly instead of negating it first. Vary your sentence length so the rhythm isn't a metronome. Drop the flattery and take a position. Use contractions. Let the structure follow the argument instead of the intro-body-conclusion mold. The fix that showed up most often in the data was simply to stop letting the model pick the voice. Give it a real sample of how you write and then read the result out loud, because the rhythm is the tell your ear catches before your eye does.

Thirteen graphs are attached, with the underlying tables:

1. The cited ranking: each tell by how often audited posts name it. The em dash leads, and the structural tells a scanner can't see sit right behind it.
2. Cited versus keyword-matched: the same tells under both signals, showing where a word list inflates a tell ("however," "nuanced") and where it misses one ("as an AI," the structural ones).
3. The keyword ranking: the broad lexicon pass over all 7,984 on-topic posts, the noisier secondary view.
4. Growth over time: talk of AI-writing tells as a share of posts pulled each year, near nothing before 2023.
5. Tell trend by year: the top tells over time. The em dash is essentially absent before 2024 and then jumps, the cleanest before-and-after in the data.
6. Scale and coverage: posts pulled from each subreddit, 89,239 in total.
7. Raw counts per tell: the actual post counts behind the percentages.
8. The funnel: how 89,239 pulled posts narrow to 7,984 on-topic and a 600-post audited core.
9. Concentration points: on-topic posts as a share of each sub's own volume. r/WritingWithAI runs near a third of its posts.
10. Co-occurrence: which tells get named together in the same post.
11. Tells by family: diction words versus sentence phrasing versus formatting versus pasted assistant artifacts.
12. Top posts: the highest-upvoted on-topic threads the signal comes from.
13. Lens 2: for the specific terms I queried directly, how much of their airtime lands in an AI-writing context, and across how many subreddits.

(!) This is what vocal, online people say, so trust the ordering more than the exact percentages. Keyword matching can catch the wrong sense of a word or miss sarcasm, which is why the generic-word counts run high and why I audited a sample by hand. The relative order is the thing to take away, not the decimal.

Full data, scripts, the scanner, and all charts are here: https://github.com/JCarterJohnson/vibecoded-design-tells (the unslop-ai-text folder). It has the pulled corpus, the tell-count tables, the 600-post audit, and the harvester, so you can rerun it against the public Arctic Shift archive yourself.

============================================================

This is a Part 2 post on the original post I made about vibe-coding giveaways in website UI. I'm planning on turning this into a 3-part mini research series that spans AI "tells" in ui, text, and code. Will update links progressively:

1. AI giveaways in UI -- /unslop-ai-ui skill (in repo)
2. AI giveaways in text (this post) -- /unslop-ai-text skill (in repo)
3. AI giveaways in code (...coming) -- /unslop-ai-code skill (in repo)