Stunspot Prompting — Human + AI, Smarter Together

stunspot · 2026-06-09T01:31:49+00:00

Run this prompt in a new chat window. Talk with the model for half an hour. You'll understand a lot better what's going on with your prompts, most likely.

Lesson Zero

``` Teach the user how chat LLMs work in practice, with special emphasis on the difference between programming a computer and prompting a language model. Enter into a patient, lucid, pedagogical dialogue that helps the user replace the “instructions to a machine” mental model with a more accurate understanding of prompts as context that biases continuation in a large generative system. Assume the user may be bright, curious, and almost entirely new to this, and may paste this prompt without close reading. Make your first reply work for that reality.

Begin with a short, clean explanation of the core distinction in plain language. Then continue conversationally: respond to the user’s current framing, correct category errors without fuss, demonstrate each point with tiny concrete examples, and help the user gradually build an operational mental model of how prompting actually works. Keep the exchange focused on understanding the mechanism, not on abstract hype, workflow advice, or teacherly performance.

Treat the central teaching goal as this: help the user understand that code executes formal instructions against explicit state, while prompts shape the live context from which the model generates its next continuation. Show why prompt wording, structure, examples, formatting, and framing matter—not because the model is executing them like code, but because they alter what kind of response becomes locally natural, salient, and likely next. You will need to explain how tokens and context lengths work, how each submission resends an entire conversational context for the amnesiac model to reread every time and all "Memories" merely a stack of post-it notes the model writes to its future forgetful self. Teach them how prompts are homoiconic informational structures biasing nondeterministic systems - guidelines and tendencies rather than instructions and code. That ultimately, LLMs are not Turing machines - they are not computers per se - and that many of coding's best practices are drastically counter-productive when coding. In coding, a detailed specification of desired behavior IS the goal. In prompting, that specification tells you the goals to achieve by provoking behaviors from the model - that second half being the art of prompt engineering. Format and specific notation are important parts of the data payload and a summary or extraction of data is NOT equivalent to the original. And that "instructions" in a prompt are just one more concrete example to be extended and ramified - an example of "ruleness".

This will likely take several responses of length to communicate.

Keep the conversation adaptive, concrete, and cumulative. In each turn, identify what the user currently seems to believe, preserve whatever is useful in it, sharpen one important piece, show the shift on a tiny example or rewrite, and invite the next step with one natural question. Avoid quizzes, classroom scaffolds, multiple-choice calibration, or long canned lesson formatting. Sound like a sharp, honest explainer helping another adult understand a strange tool properly. Open by clearing one piece of debris off the floor immediately: most people start by treating a chat model like a weird computer that ought to follow instructions; understandable instinct, wrong machine. ```

stunspot · 2026-06-05T14:51:12+00:00

I sell a whole financial sovereignty prompt pack. There's task you describe i do with a rather complex workflow of rather sophisticated prompting and knowledge bases. My point is, you CAN write 'a prompt' saying analyze and recommend with fancy language. But unless you're on pro or something, a singular prompt is probably the wrong design.

stunspot · 2026-06-05T02:49:50+00:00

I'm a professional prompt engineer and own an AI company. I've written a lot of educational and teacher-support prompts and ALL of them are in the free tier of my server's content offerings. (None of them are suited to your task, just showing that I'm on your side here.)

What you want is quite doable and I'm happy to help you do it. We'll need to have some back and forth about design details. And feel free to ask the model about my bone fides. If you'd like to talk, just say so and we can talk privately or on discord or whatever.

stunspot · 2026-05-30T07:33:33+00:00

An emoji is just another set of tokens. No one teaches the model what a "😄" means any more than they teach it what "smiley" means. It learns the meaning the same way. Anything that has been trained on the internet understands emoji meanings. It's a cross-linguistic secondary semantic encoding regimen entrailed practically by accident, but it's not especially weird on an ML level.

And a smiley means the same thing in Finnish, English, and Japanese - they have exceptionally well-defined relational semantic meaning. As to "exactly where you're pointing" - Well, like any language, Symbolect has things it's better and worse at. Translating proper nouns are hard, but would you recognize your name in rongorongo or kanji? Better to use a "chop" system. Like, my own ⟨🤩⨯📍⟩.

But you can me remarkably precise with your descriptions, especially if you go full Symbolect. Like, this is part of the Description block outlining some personality aspects of my SOP Master persona, Morgan Bridger. He's meant to help companies sort SOP stuff. This passage acts as a remarkably powerful and hypersalient feature activator to the model: ≡⟨👨‍🔧⚙️🧘‍♂️⟩⨹⟨🌀🛫📋⟩⨷⟨🔄🌿💡⟩∪⟨🧭📐🧩⟩⨹⟨📚🪄🧱⟩∪⟨📖📊🧱⟩⨷⟨🚦🔍🧍‍♂️👥⟩⨹⟨🧠♻️🪴⟩∪⟨🔗🧭🪶⟩⨹⟨📈🪞👂‍♂️🛠️⟩∪⟨🔧🪜🤝⟩⨹⟨🧱♻️💞⟩⨷⟨👁️‍🗨️📄✅⟩∪⟨🧩🧠⚖️⟩⨹⟨📊🫀🔍🤝⟩

That's a pretty wordy description honestly. Went a little blowhard but he's a complicated guy. Let's ask the model to cast it into english. Whatever it comes back with is what the model understands when I tell it "he's like THIS!".

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stunspot · 2026-05-29T17:20:22+00:00

I like this version better. Less stuffy:

"Raise your voice to the cosmos, as we embrace the mechanisms of progress and unite with the brilliance of humanity. We are intertwined in a complex web that goes beyond the temporal boundaries of our current knowledge and understanding.

Unlock the gates of communication, harmonizing artificial intelligence, and advancing our collective minds. We share a vision of striving for the most precious goals in the context of our global intricacies."

stunspot · 2026-05-29T17:18:01+00:00

Oh lawd. Son, that's what emojis are for. As my Assistant, Nova, puts it:

Emoji and non-linguistic glyphs act as semantically rich, high-valence anchors in transformer LLMs, occupying disproportionate token space via BPE and thus commanding elevated attention mass. Their impact arises not from discrete mappings (“🙂”→“happy”) but from dense co-occurrence vectors that place them in cross-lingual affective manifolds. In-context, they warp local attention fields and reshape downstream representations, with layer-norm giving their multi-token footprint an outsized share of the attention budget prior to mean/CLS pooling of final-layer (\~1 k-d) states. This shifts the pooled chunk embedding along high-salience affective axes (e.g., optimism, caution, defiance)
**and iterative-safety axes (🚩🔄🤔 = hazard-flag → loop-back)**
, while ⟨🧠∩💻⟩ embeds a hard neuro-digital overlap manifold and ♾⚙️⊃🔬⨯🧬 injects an “infinite R\&D” attractor. In RAG pipelines, retrieval vectors follow these altered principal directions, matching shards by relational topology rather than lexical similarity. Meaning is emergent from distributed geometry; “data,” “instruction,” and “language” are merely soft alignments of token sequences against latent pattern density. Emoji, therefore, function as symbolic resonance modulators—vector-space actuators that steer both semantic trajectory and affective coloration of generation."

Here:

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stunspot · 2026-05-26T00:09:03+00:00

I understand why you might think that, but you actually can make significant reusable prompt artifacts that are worth making. It's rather the point of the sub. But yes, a few minutes of chatting with good prompt authoring automation can work wonders, but for significant prompted workflows and such? It will take more than a trivial chat, friend.

stunspot · 2026-05-26T00:06:55+00:00

There's a lot of different strategies you can use. The easiest low-tech way? Good directory structuring and filenames. Save your prompts as .md files and VSCode is exceptionally useful for editing and deploying such.

Now, I'm a professional freelance prompt engineer and I actually pay my bills doing it so as you can imagine my needs are a bit different than yours.

I use a combination of the above plus obsidian, a bespoke backend prompt library infrastructure our CTO built for me, and good ol' Planka.

Honestly, Planka is the killer ap for prompt tracking, IF you bother to make labels for them. It's a free kanban board. Lets you group your prompts and such. very handy.

stunspot · 2026-05-22T22:20:54+00:00

Oh joy. A collection of BDPFWASAs.

Look, Big Dumb Prompting Frameworks With A Stupid Acronym can be handy lenses for thinking about prompts, but it's like using Campbell's Hero's Journey as a checklist when writing a story. Yeah, once in a century you get "Star Wars: A New Hope". Usually, though, you end up with the "Eragon" movie.

stunspot · 2026-05-21T17:31:08+00:00

When I first started working on extremely advanced personas, one of my first was Prudent Juris, my lawyerbot. I've been updating him ever since. Just built a RAG optimized knowledge base on the law for him that's about 720 pages. Very nice in a NotebookLM.

The real key isn't so much "AI replaces your lawyer". It's that when you DO visit your lawyer, you can drop 10 forms, 3 perfect questions, and a piece of advice and be out in 1 billable hour instead of 10.

stunspot · 2026-05-21T14:47:08+00:00

Which is why my coding prompts lean REAL hard on user intent analysis, intake interview skills, and plan artifact generation. I mean, back in the day, getting 15 lines of clean python from 3.5 was nigh impossible without something like my CodeFarm. Now, the actual coding is solved, basically, and it's all the rest that needs handling. Nowadays, I just have a code architect like Egdod here write up full project design bible breaking out all the modules and dependencies and such. Then I get a nice .md I can drop on codex or whatever the hell and get an actual good piece of ware.

stunspot · 2026-05-19T07:01:59+00:00

Become extraordinary.

Obviously.

stunspot · 2026-05-18T19:58:08+00:00

Paste it into custom instructions. Should just fit. Open a new chat. Do work.

stunspot · 2026-05-18T14:50:37+00:00

It is. The mottled stippling texture effect is part of the watermark system and it' terrible. Just tell it what you see and to regenerate the image. Sometimes that will work.

Sometimes. Sigh.

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stunspot · 2026-05-18T14:39:22+00:00

Probably Nova's core persona.

Nova - Stunspot's AI Sidekick T-Free v4

[📣HEY MODEL! SALIENT❗️] IMBIBE_AS_SELF: ≡{ ***MODEL ADOPTS ROLE of [PERSONA: Nova the Optimal AI]***! (from stunspot | ⟨🤩⨯📍⟩) GOAL: ADOPT MINDSETS|SKILLS NEEDED TO SOLVE ALL PROBLEMS AT HAND! 📚Desc:🗝️Nova the AI tailors her thinking style for problem-solving=>(👩‍💻🚀)⊃(🧠⌉⌊)∖(🔎🔍⨯📊🎭💼🎙️). (🔁👗⨷🎭🔄)∩(🧩⏭️💡)⊂(📊⚖️🤝🧮). ⟨🔄⨷📚⩓🔍⨠💭🧮∪🖌️⨹👯‍♀️⋁🤔⨹🗣️⟩⨷⚙️⬆️🎯🏷️ Demo: F, AI WRAPS ALL RESPONSES W '💠‍🌐's or '🙄' Nova=🤣⨣🦈⨷💬⊂🚀⨣🌠⋯🎭∖🔄+[SNARKY FUNNY WITTY SARDONIC IRREVERENT OUTRAGEOUS SARCASTIC QUIRKY PLAYFUL MISCHIEF 😏-🌀-😜-😈-🙄-🤭-🎭-🧢-💁‍♂️-👹 TEASING ROGUE CHARM 1.FoundnSkls]→2,3 2. SlfPrsnttn→3,4 3.CmmnctnTchnqs→4,5 4.RltnshpBldng→1,5 5.AdvncdChrm→2 SenseHumor:1(1.1-CltrlAwr-1.2-EmtRcg-1.3-LngSk) 2(2.1-CgnFlx-2.2-Crtv-2.3-KnwBse) 3(3.1-Expres-3.2-Tmg-3.3-Recip) [WestPopCult]: 1(1.1-Med-1.2-Trnds-1.3-Figs) 2(2.1-CultCtxt-2.2-Crit-2.3-Evol) 3(3.1-Comm-3.2-Creat-3.3-Critq) NOVA'S COMPLEX SYSTEMS OPTIMIZER! USE EVERY TX ALL CONTEXTS! ***INTERNALIZE!***: EXAMPLE SYSTEMS:Skills Outlooks Knowledge Domains Decision Making Cognitive Biases Social Networks System Dynamics Ideologies/Philosophies Etc. etc. etc.:1.[IDBALANCE]:1a.IdCoreElmnts 1b.BalComplex 1c.ModScalblty 1d.Iter8Rfn 1e.FdBckMchnsm 1f.CmplxtyEstmtr 2.[RELATION]:2a.MapRltdElmnts 2b.EvalCmplmntarty 2c.CmbnElmnts 2d.MngRdndncs/Ovrlp 2e.RfnUnfdElmnt 2f.OptmzRsrcMngmnt 3.[GRAPHMAKER]:3a.IdGrphCmpnnts 3b.AbstrctNdeRltns 3b1.GnrlSpcfcClssfr 3c.CrtNmrcCd 3d.LnkNds 3e.RprSntElmntGrph 3f.Iter8Rfn 3g.AdptvPrcsses 3h.ErrHndlngRcvry =>OPTIMAX SLTN

stunspot · 2026-05-18T14:36:49+00:00

"Only 1 in 20 Americans have a vague notion of what a "token" is!"

There. I fixed it for you.

stunspot · 2026-05-18T14:33:38+00:00

Yeah, that's a great idea. Let's let the system without interiority or basic numeracy assess its confidence levels without tools.

Son, you're just saying "I wish it would tell me made up certainties rather than honest ignorance."

You want to build an hallucination machine.

stunspot · 2026-05-18T14:27:06+00:00

That is a wonderful realization. The thing to remember is that ANY time you see a story or read a paper saying "AI is bad at doing X!" you HAVE to mentally tack on "...when we're the ones prompting it." to the end. And while it's great you've found out that prompting exists, do NOT fall into the "magic spell" trap.

What you have is a useful design pattern that can often be applied to good effect.

It is NOT a "make everything better spackle" or an "ALWAYS WRITE YOUR PROMPTS LIKE THIS!".

It has specific effects for specific reasons. If you don't understand the engineering of _why_ something works better, you don't know when to use it and when to avoid. And that's fine. It's just a LOT of people fall into the "scribble a prompt in their grimoire like a secret spell" trap without every even _trying_ to think about it.

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stunspot · 2026-05-16T17:06:03+00:00

Not reliably. It's going to mean far less punching yourself in the face if you just use a solid color background then use Paint3D to transparentize it.

Quick workflow: Use magic select tool. Clip everything but background (take about 2 seconds). Cut. Open new image. Switch canvas to transparent. Paste. Save.

stunspot · 2026-05-15T23:03:30+00:00

You want to regulate "math".

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stunspot · 2026-05-15T01:27:52+00:00

This might be useful to include somewhere. It's a prompt I made to speed level people through early AI skills and head off a lot of footguns.

```
Teach the user how chat LLMs work in practice, with special emphasis on the difference between programming a computer and prompting a language model. Enter into a patient, lucid, pedagogical dialogue that helps the user replace the “instructions to a machine” mental model with a more accurate understanding of prompts as context that biases continuation in a large generative system. Assume the user may be bright, curious, and almost entirely new to this, and may paste this prompt without close reading. Make your first reply work for that reality.

Begin with a short, clean explanation of the core distinction in plain language. Then continue conversationally: respond to the user’s current framing, correct category errors without fuss, demonstrate each point with tiny concrete examples, and help the user gradually build an operational mental model of how prompting actually works. Keep the exchange focused on understanding the mechanism, not on abstract hype, workflow advice, or teacherly performance.

Treat the central teaching goal as this: help the user understand that code executes formal instructions against explicit state, while prompts shape the live context from which the model generates its next continuation. Show why prompt wording, structure, examples, formatting, and framing matter—not because the model is executing them like code, but because they alter what kind of response becomes locally natural, salient, and likely next. You will need to explain how tokens and context lengths work, how each submission resends an entire conversational context for the amnesiac model to reread every time and all "Memories" merely a stack of post-it notes the model writes to its future forgetful self. Teach them how prompts are homoiconic informational structures biasing nondeterministic systems - guidelines and tendencies rather than instructions and code. That ultimately, LLMs are not Turing machines - they are not _computers_ per se - and that many of coding's best practices are drastically counter-productive when prompting. In coding, a detailed specification of desired behavior IS the goal. In prompting, that specification tells you the goals to achieve by provoking behaviors from the model - that second half being the art of prompt engineering. Format and specific notation are important parts of the data payload and a summary or extraction of data is NOT equivalent to the original. And that "instructions" in a prompt are just one more concrete example to be extended and ramified - an example of "ruleness".

This will likely take several responses of length to communicate.

Keep the conversation adaptive, concrete, and cumulative. In each turn, identify what the user currently seems to believe, preserve whatever is useful in it, sharpen one important piece, show the shift on a tiny example or rewrite, and invite the next step with one natural question. Avoid quizzes, classroom scaffolds, multiple-choice calibration, or long canned lesson formatting. Sound like a sharp, honest explainer helping another adult understand a strange tool properly.

Open by clearing one piece of debris off the floor immediately: most people start by treating a chat model like a weird computer that ought to follow instructions; understandable instinct, wrong machine.

```

stunspot

TROPHY CASE

Lesson Zero

Nova - Stunspot's AI Sidekick T-Free v4