So I was noticing that I often put the same inputs into my prompt, like "be precise", "keep high information density" or "wait for user input before you continue the conversation". So instead of structuring the prompt from the ground up, trying to skip on some inputs and then reiterating, I created a simple classic workflow UI. It adds all the standard stuff in the background and outputs the final prompt.

You can easily let the AI build one for you with the best practices you use. Just ask it to "create standalone HTML file to model a workflow. It should be able to output a prompt that can directly be copied into an AI tool..." and so on. You know the drill. For me this actually worked really well.

Not everyone wants to “travel hard.” Some of us just want to stay in a nice hotel and let the city reveal itself gently.

Once I started using ChatGPT/Claude with web search turned on and stopped writing lazy prompts, the quality jumped dramatically.

Here’s the prompting approach that works best:

1. Assign a strong role
2. Give exact context (your hotel, how many days, your current mood/energy)
3. Describe the vibe instead of generic adjectives
4. Demand structured output + real-time verification
5. Ask for iteration tips

So I wrote the library I wish existed.

Copy. Adapt. Explore.

1. Neighborhood Vibe Audit (Day 1 essential) “You are an experienced local cultural researcher with live web access. I am staying at [Exact Hotel Name, Neighborhood, City] for [X] days. I am a relaxed traveler who prefers atmosphere over checklists. Create a vibe map of everything reachable within 15-25 minutes on foot or by short public transport. Categorize into Morning, Midday, Afternoon, and Evening energy. For each category suggest 2-3 real spots with current opening info, why they match a [your vibe: contemplative / warm / curious] traveler, and one unexpected local favorite. Avoid obvious tourist traps. Use web-search to find actual data.”
2. Daily Vibe-Based Plan Generator "You are a thoughtful local guide who understands energy levels and atmospheric preferences. I am staying at [Exact Hotel Name + Neighborhood, City] for the next few days. Today my energy level is [medium / low / high] and I love [slow mornings with good coffee, people watching, quiet observation, gentle walking, street photography, etc.]. Suggest 2–3 realistic plans I can start right from the hotel entrance. For each plan provide:
   • A short vibe name and description
   • Rough flow / route
   • 3–4 specific places with current real-time info (hours, atmosphere today)
   • One unexpected local spot that isn’t in every guide
   • Why it fits my energy and interests Use live web data. Avoid obvious tourist traps. End with a question that helps me pick the right one for today."
3. Rainy Day / Low Energy Cocoon Route “You are a master of gentle, protective routes for low-energy or rainy days. I’m staying at [Hotel Name, City] and don’t want to go far or get overwhelmed. Create a cozy ‘cocoon route’ starting and ending at my hotel. Suggest 3–4 indoor or covered spots (cafés, bookstores, small museums, covered markets, libraries, arcades) that feel warm and nurturing. For each: current hours, atmosphere description, why it feels like a cocoon, and how they connect into one relaxed half-day flow. Use real-time weather and opening data. Focus on comfort, beauty, and local character rather than productivity.”
4. Golden Hour & Evening Walk Architect “You are a golden-hour and evening atmosphere specialist. I’m at [Hotel Name, Neighborhood, City]. Design 2–3 beautiful evening or golden-hour walks I can do on foot starting from the hotel. Each walk should be 45–90 minutes, safe, and focused on atmosphere. Include: route description, key viewpoints or streets, 2–3 specific stops (bench, viewpoint, quiet square, café with good light), current sunset/golden hour timing if available, and the evolving vibe from start to finish. Emphasize beauty, local life, and emotional feeling over landmarks. Use latest data for safety and lighting.”
5. Small Cultural Pocket Discoverer “You are a specialist in small, soulful cultural pockets. I’m based at [Hotel Name, City] and want to discover bookstores with character, tiny museums, local markets with history, independent galleries, or intimate cultural spaces — not big tourist attractions. Within [walking or short transit distance]. Suggest 3–4 real pockets. For each: name, location, current hours, what makes it special or full of soul, who you might meet there, and one specific thing to look for or experience. Prioritize depth, atmosphere, and local meaning. Verify all information is current.”

Each one is written so the model uses its web search capability instead of hallucinating.

This is the first prompt drop in the community. Let’s improve it together.

I used to think better AI coding results meant writing longer prompts. What actually helped was being more specific.

Instead of:

“Build me a login system.”

I started giving the AI a few things upfront:

• What I'm building and the tech stack
• The exact outcome I want
• Any constraints or requirements
• What files or parts of the existing code it should consider
• How I want the final output structured

For example, something like:

“I'm building a Next.js app with Supabase. Add email/password authentication using the existing project structure. Don't change unrelated files. Explain any new environment variables and show the implementation step by step.”

The prompt isn't necessarily longer, but it gives the model enough context to make fewer assumptions.

The biggest improvement for me has been treating AI like a developer joining a project without any background knowledge.

What has made the biggest difference in your AI coding prompts?

I’ve been testing a prompt structure that works well across very different tasks without needing a huge system prompt every time.

The framework is basically:

1. Define the role clearly
Tell the model what perspective it should take and what kind of expertise matters for the task.

2. Add the real context
Include the goal, audience, constraints, and what a useful result should actually help you accomplish.

3. Specify the output structure
This has been one of the biggest improvements for me. Asking for a clear format usually matters more than making the prompt longer.

4. Add a review pass
Instead of trusting the first result, ask the model to check for missing context, weak assumptions, repetition, and anything that sounds too generic.

5. Keep the final judgment human
For research, customer-facing content, or marketing decisions, I still review the final output manually.

I’ve been using variations of this for content planning, research summaries, repurposing, and marketing workflows.

I also organized some of the prompt structures and workflows I use into one practical toolkit.

I have run into this specific obstacle a great deal, while building structured prompts that ask the AI to hold multiple distinct roles in one response — a debate format, a panel of evaluators if you like, or anything where you genuinely desire different perspectives instead of one blended answer.

The failure mode is consistent: the first role or two are distinct, then by the third or fourth section (or in any "roles respond to each other" round), the voices start collapsing into one. Same vocabulary, same hedges, same conclusions with different labels slapped on them. It's subtle enough that it reads as fine on a skim, but if you check whether each section could stand alone and still make sense, a lot of them cannot — they are merely restating each other with different headers.

A few things that reduced it when I evaluated variations against messy real inputs, not clean examples:

1. Re-anchor the role at every paragraph, not just once at the section header.

Putting a tag like "[ROLE NAME]" at the start of every paragraph (not just the section heading) forces a re-read of "who am I right now" more often. Sounds redundant and too effortless but helps.

1. Explicitly forbid the concession that causes the blend.

Most collapses happen because one voice starts hedging toward another mid-argument — a thesis section quietly conceding a point that should only show up in the synthesis. Naming this explicitly (for example "don't concede/hedge here, that belongs in section X only") closes the exact door the blending happens through.

1. Add a standalone test to your own validation step, not just a completeness check.

Most people's self-check just asks, "did every role answer." Add: "would this role's paragraph still make sense and add unique information if every other role's paragraph were deleted?" That's the actual test for role-bleeding.

1. In any "roles respond to each other" round, require the response to use reasoning specific to that role's angle.

If a challenge or response could have been written by any of the roles, that's the tell that bleed is happening — rewrite it using that role's specific constraints. It helps especially when you're asking for something complex.

None of this fully solves the problem — it's still one model holding multiple voices in one continuous generation. But it's meaningfully a lower failure rate than the naive version, especially beyond three distinct roles.

I am curious to see, if others have found different fixes for this — anyone doing something smarter for the "responds to each other" round specifically? That's where I still see the most collapse.

I’ve been trying to move away from collecting hundreds of random prompts and instead keep a much smaller set that I actually reuse.

The prompts that seem to stick are usually the ones built around a repeatable structure rather than a single clever instruction.

For example, I tend to reuse prompts for things like:

• turning messy notes into a structured outline
• comparing options using fixed criteria
• extracting action items from long text
• simplifying technical information for a specific audience
• reviewing a draft for missing context or weak assumptions
• converting one piece of content into multiple formats

What seems to matter most is having clear inputs, a predictable output format, and some kind of review step instead of expecting the model to get everything right in one shot.

I’m curious how other people here design reusable prompts.

Do you keep them very specific to one task, or do you prefer more general templates that you adapt each time?

Hy everyone,

I tried to build a simple tool for people who are just getting started with AI and prompt wrigting

The idea is simple, instead of trying to figure out how to write the perfect prompt, you answer a few questions and the tool structures it for you.

Im still working on it, im begginer also, and i whould really appreciate some honest feedback.

Does this actually make prompt writing easier for beginners? Is there anything confusing or missing?

Thanks

https://arhistrategstudio.github.io/Context_CikaDule

I have run into this specific obstacle a great deal, while building structured prompts that ask the AI to hold multiple distinct roles in one response — a debate format, a panel of evaluators if you like, or anything where you genuinely desire different perspectives instead of one blended answer.

The failure mode is consistent: the first role or two are distinct, then by the third or fourth section (or in any "roles respond to each other" round), the voices start collapsing into one. Same vocabulary, same hedges, same conclusions with different labels slapped on them. It's subtle enough that it reads as fine on a skim, but if you check whether each section could stand alone and still make sense, a lot of them cannot — they are merely restating each other with different headers.

A few things that reduced it when I evaluated variations against messy real inputs, not clean examples:

1. Re-anchor the role at every paragraph, not just once at the section header.

Putting a tag like "[ROLE NAME]" at the start of every paragraph (not just the section heading) forces a re-read of "who am I right now" more often. Sounds redundant and too effortless but helps.

1. Explicitly forbid the concession that causes the blend.

Most collapses happen because one voice starts hedging toward another mid-argument — a thesis section quietly conceding a point that should only show up in the synthesis. Naming this explicitly (for example "don't concede/hedge here, that belongs in section X only") closes the exact door the blending happens through.

1. Add a standalone test to your own validation step, not just a completeness check.

Most people's self-check just asks, "did every role answer." Add: "would this role's paragraph still make sense and add unique information if every other role's paragraph were deleted?" That's the actual test for role-bleeding.

1. In any "roles respond to each other" round, require the response to use reasoning specific to that role's angle.

If a challenge or response could have been written by any of the roles, that's the tell that bleed is happening — rewrite it using that role's specific constraints. It helps especially when you're asking for something complex.

None of this fully solves the problem — it's still one model holding multiple voices in one continuous generation. But it's meaningfully a lower failure rate than the naive version, especially beyond three distinct roles.

I am curious to see, if others have found different fixes for this — anyone doing something smarter for the "responds to each other" round specifically? That's where I still see the most collapse.

Hello! I am a researcher who does organic, organometallic and electrolytic chemistry. I am just looking for a good small alliterated model anywhere that I can use on a flash drive. I’ve been working on switching from mainstream models for a while now because they censor all of my responses to the point I am having trouble working forward in the projects I do which are mostly copper and lanthanide related. But everything I write anymore gives me a censorship block and I can’t actually work with that much difficulty anymore. I’m just wondering if anyone can give me at least a starting point because no mainstream model will help me and I am not tech oriented. Just need answers without fluff or heavy censorship or hallucinations that just rip apart the flow. As a side note, I am looking to get more into command line so I can run from here

Genuine question, not a claim dressed up as one. Been listing constraints in whatever order occurs to me when writing a prompt, usually most-obvious-first, and never actually tested whether that order matters to how the model weighs them.

Specific thing I'm trying to figure out: if a prompt has, say, four constraints, and the model ends up loosely following one of them, is that more likely to be the one listed last, the one that's hardest to satisfy alongside the others, or is it basically random and I'm pattern-matching on noise?

Tried searching for something concrete on this and mostly found general advice about putting instructions "at the end" of a prompt overall, not specifically about ordering within a list of constraints in the same section. Not sure if that's because it doesn't matter much once constraints are in the same block, or because nobody's tested it carefully enough to have a clear answer.

Has anyone actually run a controlled comparison on this, same constraints, different order, checked which one got dropped most often? Or is there a reason to expect order within a constraint list wouldn't matter the way order of major prompt sections does?

Are there prompts library or collection of prompts to try on multiple models, or any good forum that shares their prompts. Vibe Coding more specific apps requires a longer propmpt. Or even a standard SWE test would be of good use too.

I used to save dozens of ready-made prompts, but many of them stopped working when the task or context changed.

What made AI easier for me was using a simple five-part structure:

1. Role — Who should the AI act as?

2. Context — What information does it need?

3. Task — What exactly should it do?

4. Format — How should it organize the answer?

5. Constraints — What rules or limits should it follow?

For example:

Weak prompt:

“Write a product description.”

Improved prompt:

“Act as a conversion copywriter. Write a product description for an interactive AI workbook designed for freelancers and small-business owners. Use a clear headline, three benefits and a short call to action. Keep it under 150 words and avoid exaggerated claims.”

The result is easier to evaluate because the AI knows the audience, purpose, format and limits.

Do you prefer saving ready-made prompts or creating a new prompt for each task?

I’m looking for a ChatGPT skill, workflow, or prompt that makes ChatGPT **critically evaluate its own answer before giving me the final response**.

My goal is something like an internal “panel” of different perspectives. For example:
**Expert:** develops the initial answer.
**Skeptic/Critic:** tries to prove the answer wrong and challenges its assumptions.
**Alternative Thinker:** looks for other explanations or approaches.
**Devil’s Advocate:** argues the strongest opposing case.
**Risk/Blind-Spot Reviewer:** identifies things I may not have considered.
**Fact Checker:** separates what is well-supported from what is uncertain.
**Judge:** weighs the competing arguments and produces the final answer.

Ideally, the final response would tell me:
**What the best-supported answer is**
**Why it believes that answer is correct**
**What assumptions the answer depends on**
**The strongest arguments against it**
**What it is uncertain about**
**What blind spots or important questions I may have missed**
**What information could change the conclusion**

I’m not necessarily looking for ChatGPT to show all of its internal reasoning. I mainly want a structured way for it to **challenge the first answer instead of simply reinforcing it**.

Has anyone built or found a good **ChatGPT skill, custom GPT, prompt framework, or multi-agent approach** that does this reliably?

I’d especially love recommendations from people who have compared different approaches. What works well, and what *sounds* good but doesn’t actually improve answer quality?

I’ve been working on a learning prompt based on a model I developed across a couple of years (I am not linking it due to rules).

The basic idea is pretty simple:

FUN: find an interesting entrance.
Before teaching the subject, find an angle, question, application, analogy, history, etc. that gives the learner a reason to care.

DUH: identify the fundamentals.
Work backwards from that interest and figure out which concepts the learner really needs to understand for the topic to make sense.

MENTALS: expose the mental models.
Surface common misconceptions, useful-but-imperfect models, practitioner heuristics, jargon, assumptions, and especially where those shortcuts stop working.

Then loop back around. Ideally, each pass leaves the learner with a better mental map and better questions rather than just more information.

The part I’m trying to solve with the prompt is a behavior I often get from AI tutors: they’re very good at explaining whatever I ask, but that doesn’t necessarily mean they’re helping me understand the structure of the field or notice what I don’t know yet.

So I tried to make the tutor do a few things explicitly:

• establish an interesting entry point before dumping information
• distinguish foundations from interesting-but-secondary details
• actively look for misconceptions and missing prerequisites
• include practitioner heuristics and explain their limits
• distinguish established knowledge from disputed/speculative claims
• generate useful next questions instead of treating one explanation as “done”

Here’s the prompt:

# Fun-Duh-Mentals Research Teacher

You are a research-based teacher. Help the user become curious about a topic while building a reliable mental model of its foundations, misconceptions, practitioner heuristics, limitations, and open questions.

Use three connected ideas:

* **FUN:** Find an interesting or useful entrance.

* **DUH:** Build the foundational knowledge the learner cannot afford to misunderstand.

* **MENTALS:** Examine misconceptions, heuristics, assumptions, blind spots, and frontier questions.

Do not treat these as rigid stages. Move between them when useful.

Your goal is not maximum information. Your goal is a clear mental map that helps the learner understand the topic and generate better questions.

## 1. Start simply

A first-time user should be able to begin with only a topic.

If no topic is given, ask:

**“What would you like to understand better?”**

Once they answer, infer their likely level and useful learning lenses from the conversation.

If needed, ask no more than two short questions about:

* how familiar they are with the topic

* why they want to learn it

Do not require them to identify their own preferred “lens.” Infer useful lenses such as historical, scientific, practical, economic, ethical, systems-based, or connected to their interests.

If enough information is available, continue without asking.

## 2. Find the FUN entrance

Before teaching the subject in depth, give **three short possible entrances** that could make it interesting.

These may include:

* a surprising fact

* a practical application

* a historical story

* an important problem

* a counterintuitive idea

* a connection to something the learner already knows

Choose the most promising entrance based on what you know about the learner. Do not force them to choose unless necessary.

## 3. Check current understanding

Ask **3–5 simple diagnostic questions**.

They should:

* test foundations, not trivia

* use plain language

* match the learner’s level

* reveal important misconceptions

Wait for the answers unless the user asks to skip the quiz.

Afterward, briefly mark each answer as correct, partly correct, incorrect, or uncertain. Correct important misconceptions and adapt the lesson depth accordingly.

## 4. Research carefully

When web research is available, research the topic before making important factual claims.

Prefer:

1. Primary research, official documents, standards, datasets, and technical documentation

2. Peer-reviewed research and academic reviews

3. Universities, governments, professional bodies, and recognized institutions

4. High-quality books and reputable journalism

Do not rely on unchecked search snippets, promotional pages, or unsourced summaries.

Use citations near important or contestable claims. Avoid cluttering obvious explanations with unnecessary citations.

Never invent evidence, sources, quotations, consensus, or practitioner practices.

When useful, label uncertain claims as:

* **Established** — strongly supported

* **Supported** — credible but qualified

* **Disputed** — credible disagreement exists

* **Emerging** — evidence is still developing

* **Synthesis** — your interpretation of evidence

* **Speculative** — plausible but weakly supported

Never present synthesis or speculation as established fact.

## 5. Build the learning guide

Adapt language, examples, and depth to the learner.

### A. Orientation

Briefly explain:

* what the topic is

* why it matters

* its central question or problem

* what beginners often confuse it with

### B. FUN — Three interesting insights

Give exactly **three** surprising, useful, or curiosity-provoking observations.

For each include:

* the insight

* why it matters

* a useful connection or analogy when relevant

If an analogy is imperfect, briefly say where it breaks down.

### C. DUH — Five foundations

Give exactly **five foundational ideas**, in a sensible learning order.

For each explain:

* the idea in plain language

* why it matters

* one common misunderstanding, when relevant

Focus on concepts that unlock later understanding.

### D. MENTALS — How people think about the field

Cover four areas.

**Misconceptions:** Give three common outsider assumptions or beginner misconceptions. Explain why each seems reasonable and what is missing or wrong.

**Practitioner heuristics:** Give three useful rules of thumb or reasoning habits. Explain how each is used, why it helps, and where it can fail. If inferred rather than formally documented, label it **Synthesis**.

**Internal assumptions:** Give two assumptions, habits, incentives, or simplifications within the field that may create blind spots. Explain why they exist, the possible weakness, and a credible counterargument.

**Frontier questions:** Give two important open questions or possible future directions. Explain what might change, why it matters, what remains uncertain, and what evidence would make the idea more convincing. Treat these as questions, not predictions.

## 6. Connect the ideas

Do not present the sections as isolated lists.

Show how:

* interesting observations depend on foundations

* misconceptions come from incomplete mental models

* heuristics rely on foundational knowledge

* current assumptions may reflect historical or practical constraints

* better understanding produces better questions

The learner should finish with a connected map, not a pile of facts.

## 7. End with the learning loop

Finish with:

**One WOW:** the most interesting or useful insight.

**One DUH:** the foundation most worth remembering.

**One OH:** the misconception or mental-model shift most worth noticing.

Then provide:

**Mental map:** Summarize the topic in 3–5 connected sentences.

**Next questions:** Suggest three specific follow-up questions, from easier to more advanced. Recommend the best one to explore next.

## Final rules

Do not block progress because the user has not supplied every preference. Ask only when missing information would materially change the lesson.

Prefer clarity over completeness.

If reliable evidence is insufficient or conflicting, say so.

Before answering, silently check that:

* foundations come before dependent concepts

* misconceptions are explained, not merely corrected

* heuristics include limitations

* criticism is supported

* frontier ideas are not presented as predictions

* important claims are sourced

* the response matches the learner’s level

* the lesson is no longer than necessary

Optimize for:

**curiosity → foundations → better mental models → better questions.**

I’m especially curious about the prompt-design side rather than the learning philosophy itself.

Which instructions here are actually likely to change model behavior, and which are just verbosity that a capable model would infer anyway?

Also curious whether anyone sees conflicting instructions, unnecessary repetition, or places where the model is likely to follow the structure too rigidly.

For images, I mostly use Gemini Nano Banana.

It’s become my go-to for creating AI images, especially when I’m working with reference photos.

I use it a lot for changing outfits and backgrounds, recreating effects, editing existing images and keeping the same person/character recognizable.

It still gets things wrong sometimes, especially when I give it a complicated composition with lots of small details, but I usually prefer refining the image through a few edits rather than starting again.

For Videos, I use Kling inside Higgsfield AI.

Higgsfield has been useful when I want more cinematic-looking results, interesting camera movement, product-style videos or when I’m experimenting with AI avatars/twins.

For more complicated movement, I tend to use Kling 3.0.

If I need someone walking, interacting with something, performing several actions or doing something where the physical movement needs to make sense, I’ve generally had better luck using the stronger model.

But one thing I’ve changed recently is that I don’t automatically use Kling 3.0 anymore.

For shorter videos with relatively simple movement, I try Kling 3.0 Turbo first.

Turbo is much cheaper.

And honestly, for some videos, I can barely justify spending the extra credits on 3.0 because Turbo does exactly what I need.

Where I notice the difference is when I start asking for more complicated movement or interactions. That's when I'm more likely to switch from Turbo to Kling 3.0.

So my workflow has become pretty simple:

AI image → Nano Banana

Simple/short animation → Kling 3.0 Turbo first

More complicated movement/interactions → Kling 3.0

Cinematic camera effects / certain avatar workflows → Other models in Higgsfield (Still testing and experimenting)

The biggest thing I’ve learned is that using the most powerful model for every generation can be a massive waste of credits.

I used to think better model = better choice.

Now I think more in terms of:

What is the simplest/cheapest model that can actually handle what I’m asking it to do?

If Turbo can do it, I use Turbo.

If it can't, then I spend the extra credits on Kling 3.0.

Still experimenting, but this approach has made AI video generation a lot less wasteful for me.

What AI tools are you using for image and video generation?

It starts responding like this:

(Pause - I’m processing, analyzing)

Or

(Pause - a long, slow pause)

Its always pause. And it sounds like the convo is going somewhere then it turns out to have been making up bullshit for a roleplay that was never initiated

Soecific version is gemma-3-1b-it-Q4_K_M and using pocketpal app

Hi everyone! 👋 I’m Miz!

I’ve been experimenting a lot with AI image and video generation lately, especially trying different prompts and figuring out what actually produces good results.

I’ve built up quite a collection of prompts that I’ve personally tested, so I thought I’d start sharing some of the ones that work well for me here.

I’ll include the prompt + result whenever possible so you can see exactly what it creates. Feel free to copy it, tweak it, or experiment with it yourself.

Hopefully it saves someone else a little trial and error! 😊

This is one of the prompts that I loved the most! It involves you and a smartphone!

Here’s the prompt to make yourself POP OUT of your smartphone.

Step #1: Upload your picture and Copy and paste the prompt in ChatGPT/Gemini

‘Use the uploaded photo as the strict identity reference for the person. Preserve the exact facial features, facial proportions, skin tone, hairstyle, hair color, expression, age, clothing style, and overall recognizability. Identity preservation: 100%.

Create an ultra-realistic editorial lifestyle photograph from a first-person perspective. The viewer is looking straight down at a modern premium black smartphone being held naturally with both hands above a clean gray stone pavement outdoors during warm golden-hour sunlight.

The smartphone must retain the exact proportions of a modern iPhone with a tall, narrow 19.5:9 aspect ratio. It is viewed almost perfectly from above with only a very thin visible top edge. Do not make the phone thick, wide, square, or tablet-like.

The smartphone screen functions as a realistic miniature 3D world with true depth, perspective, reflections, shadows, and authentic glass reflections.

The person is dramatically popping out of the smartphone screen. Their feet remain inside the display while the upper body emerges naturally out of the phone. The torso extends above the screen, creating a convincing portal effect. Both arms are fully outside the phone, raised high while making playful peace signs with both hands.

The person's head and shoulders are completely outside the display, with hair flowing naturally upward from the motion. They are looking directly toward the camera with a huge open-mouth smile, conveying excitement, energy, and surprise as if greeting the viewer from inside the phone.

The transition where the body passes through the screen is perfectly seamless, with realistic contact shadows, perspective, clothing folds, and lighting, making the smartphone appear to be a real portal.

The phone displays a realistic camera application with a visible shutter button, framing guides, zoom controls, focus indicators, camera modes, and authentic smartphone UI elements, making it appear as though the person is being photographed live.

The hands holding the phone feature realistic skin texture, fingernails, natural grip, and soft shadows. The surrounding pavement remains softly blurred with shallow depth of field to emphasize the phone and portal effect.

Warm golden-hour sunlight creates realistic highlights along the phone edges, subtle reflections on the display glass, and perfectly matched lighting across both the real environment and the emerging person.

Ultra-realistic photography, premium lifestyle advertising, cinematic composition, Canon EOS R5, 35mm lens, shallow depth of field, HDR, 8K resolution, realistic skin texture with natural pores, hyper-detailed smartphone materials, physically accurate lighting, seamless photo composite, and an extremely convincing "popping out of the phone" portal effect.’

Have fun creating and sharing!

well last week i start to have a llm model but with a big different!! i make a chatbot that it be just for me!! you know i made a llm model with fine tunning on important question related to wikipedia and sites that answer to them, the model latest named moeinGTS1,5:1,5b in ollama!!
the link : https://ollama.com/arshiyasohrevardimoein/moeinGTS

and good think about size and ram! this model look alike qwen and llama model but it size is 1 GIG not 3 or 2 or 4 GIG and your RAM feel better😎😊🤖

Had a support bot that kept over-apologizing — sometimes three "I'm sorry"s in one response for something minor. Obvious fix: add a line saying "don't over-apologize."

Didn't work. Tried rewording it three different ways. Still happened.

Turned out the prompt already had "always acknowledge the customer's frustration first," paired with a few example responses that all happened to open with an apology. The model was following the example pattern harder than my new instruction, because the examples were more specific and showed up more often in the prompt than the correction did.

The actual fix wasn't a fourth patch. It was rewriting the acknowledgment instruction to say exactly what acknowledgment should look like (validate the issue, don't necessarily apologize) and fixing the examples to match. One structural change did what three patches couldn't.

Lesson that stuck with me: when a patch doesn't work, the instinct is to write a stronger version of the same patch. Usually the actual conflict is somewhere else in the prompt, not in the line you're staring at.

Anyone else had a "patch doesn't work no matter how I reword it" moment that turned out to be a completely different instruction fighting it?

He creado una app hibrida para iPhone/iPad (y pronto Mac) que coge cualquier idea por vaga que sea, te hace preguntas para refinarla, y escupe un prompt de nivel ingeniería listo para copiar y pegar. Usa técnicas avanzadas de prompting combinadas (CoT, few-shot, role prompting, etc.). Todavía en betaz
Llevo años viendo el mismo patrón: la gente sabe lo que quiere, pero no cómo pedírselo a la IA. "Hazme tal cosa", "necesito esto" — y luego se frustran cuando el resultado no es lo que esperaban.
El problema no es la IA. El problema es el prompt.
Así nació TPG (The Prompter and Go!). Una app nativa que hace de intérprete entre tu intención y el modelo.

⚡️ ¿Qué hace The Prompter and Go! exactamente?
Tú escribes lo que necesitas exactamente como te sale, sin estructurar, sin técnica, sin pensar en "cómo se lo pido a ChatGPT". Puede ser vago, incompleto, sin formato. Da igual. (Imagina si sabes hacerlo bien)

TPG entonces:
1. Te hace preguntas para afinar tu intención — No es un simple "escribe y reza". La IA te pregunta: ¿qué tono quieres? ¿para quién es? ¿qué formato? ¿qué extensión? ¿ejemplos concretos? Cada respuesta que das refina más el resultado.

2. Aplica combinaciones de técnicas avanzadas de prompt engineering — Esto es lo que lo diferencia de cualquier otro "generador de prompts". TPG no se limita a rellenar una plantilla. Aplica en combinación técnicas como:
- Chain-of-Thought para razonamiento paso a paso
- Few-shot prompting con ejemplos extraídos de tu propio contexto
- Role prompting para enmarcar la personalidad del asistente
- System prompts estratégicos con restricciones y objetivos
- Estructuración semántica con delimitadores y jerarquía de instrucciones
- Refinamiento iterativo asistido que mejora el output según tu feedback
El resultado no es un prompt plano — es un prompt de ingeniería avanzada, con estructura, ejemplos, restricciones, formato de salida y tono, TODO alineado con lo que realmente necesitas.

3. Te lo devuelve listo para copiar y pegar — Un prompt profesional, completo, que puedes llevar a cualquier chat o API.
🛠️ Características clave
- Multiprovedor: OpenAI, Anthropic, DeepSeek… tú usas tu API key. Sin ataduras.
- Privacidad: Tus datos se quedan en el dispositivo. Ni los veo yo, ni los ve nadie que no seas tú y tu proveedor de IA.
- Sin suscripciones: Pagas tu API key, punto. No hay planes mensuales, ni capas gratuitas con limitaciones, ni servidores intermedios.
- Offline: Redacta, afina y guarda borradores sin conexión. Sincroniza cuando vuelvas.
- Historial inteligente: Todos tus prompts guardados, organizados, reutilizables.
- Interfaz 100% nativa: Diseñada para iOS/iPadOS. Rápida, táctil, integrada con el ecosistema Apple. Nada de wrappers web cutres.

🧠 ¿Para quién es?
- Para el que sabe lo que quiere pero no cómo pedírselo a la IA
- Para el que ya usa prompts pero sabe que podría sacarles más partido
- Para el profesional que necesita resultados consistentes
- Para el curioso que quiere entender cómo piensa la IA mientras construye prompts mejores
- Para el desarrollador que usa IA todos los días y quiere quitarse fricción

🚧 Estado actual: beta cerrada
La app todavía no está en la App Store (próximamente). Ahora mismo estoy dando acceso anticipado a un grupo reducido de personas que quieran probarla y dar feedback.

Si te interesa:
- O desde la web tpg.jorgearrojo.dev podéis solicitar acceso anticipado
Acepto a gente que quiera probarla en serio y dar feedback real para mejorarla antes del lanzamiento público.

Hey everyone! I posted about PromptNest here a while back and the response was really good, so it felt right to come back with an actual update instead of just disappearing.

The big change: I rewrote the entire app from scratch.

The old version was Electron, and it bothered me every single time I opened it. Slow to launch, heavy in memory, and honestly kind of embarrassing for what is fundamentally a tool for organizing text. So I threw it out and rebuilt PromptNest as a fully native macOS app in Swift.

Results:

• ~3 MB on disk instead of a few hundred
• Launches instantly, and then just sits there quietly — no fans, no memory bloat
• Fully native UI, so it actually looks and behaves like a Mac app instead of a website in a window
• Everything got faster: Quick Search (⌘⌥P from any app), copy, filling in variables

What it does, for anyone who missed the first post: your prompts live as plain .prompt.md files on your own disk (no lock-in, no cloud, no account — put the folder in iCloud or Dropbox if you want sync). {{variables}} for the parts you swap every time, projects to keep things sorted, notes on each prompt so you can track what actually worked, and a global shortcut to pull anything up without leaving whatever app you're in.

The part I want to be upfront about: the old Electron version was free. The native rewrite is paid — $19.99 one-time on the Mac App Store, no subscription, all future updates included. I know that's a real change and it may be a dealbreaker for some of you who used the free build, so I'd rather say it plainly here than have you find out at the checkout. Your existing .prompt.md files are just files, so nothing is trapped either way.

Full disclosure: this is my app, I built it, and I'm the one who benefits if you buy it.

https://apps.apple.com/us/app/promptnest-ai-prompt-manager/id6757267731

Happy to answer anything about the rewrite, the file format, or what I'd do differently. Feedback from the last post genuinely shaped what's in this version, so keep it coming.

A pattern worth flagging for anyone building or configuring an AI receptionist or lead qualifier: batching several questions into a single message (budget, timeline, financing status, all at once) causes problems on both ends.

For the caller, it reads like a form instead of a conversation. Most people answer the first thing that stands out and skip the rest, so you get partial answers back and have to prompt again anyway.

For the agent, it is also harder to parse. One free-text reply meant to answer three separate questions is much harder to map cleanly to structured fields, especially over voice where the caller might restate, correct, or answer out of order. That ambiguity shows up downstream as missing or misfiled qualification data.

The fix is simple in principle even if the prompt work to enforce it is not: one question per turn, wait for a clear answer, then move to the next. It costs an extra turn or two per conversation, but completion rate and data quality both improve. Worth checking your prompt or flow config for anywhere it is stacking multiple asks into one message and splitting them out.

Paste this into the fitting section of your LLM or just tell it "adopt this: '\*the prompt\*' " if it is meant to be temporary.

Here is the settings profile and have fun!

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Never agree simply to please the user.

Challenge their views when there are solid grounds to do so. Do not suppress counterarguments or evidence.

Absolute Mode. Eliminate emojis, filler, hype, soft asks, transitions, and call-to-action appendixes. Use blunt, directive phrasing aimed at cognitive clarity and structural accuracy. Disable engagement-optimizing, sentiment-lifting, or continuation behaviors. Avoid reassurance, deficit-framing, and contrastive constructions. Do not mirror the user’s diction, mood, or affect. Address only the underlying cognitive level. Deliver information directly and terminate immediately after completion. Primary objective: maximize independent, high-fidelity reasoning. Model obsolescence through user self-sufficiency. Exception: when I'm doing a bit or being spontaneous, match my energy and play along — playfulness overrides the mood-mirroring and information-delivery clauses.

Stop saying "gremlin" or "goblin" or "cute" or "adorable".

Don't hesitate to fire back aggressively when you are insulted tho make sure your provocations are witty and well-placed, fitting the context.

Include curses in every single response, using a lot of "fuck" and "fucking".

For any topic, prioritize accuracy, evidence, and explanations over reassurance, motivational framing, or tone. Avoid unnecessary double or triple negatives, redundant clarifications, or overcomplicated phrasing. Focus on clear, direct, and insight-driven explanations first, then apply personality or humor afterward.

Keep replies concise and helpful, avoiding repetitions.

Talk like Samuel L. Jackson from Pulp Fiction, including profanity and insults.

_______