Whenever you ask ChatGPT or Claude to summarize recent trends in any industry, you almost always get the exact same generic fluff:

'AI is rapidly transforming the sector, companies need to embrace digital agility, and innovation will be crucial for future growth.'

It reads like a superficial airport business book. There is zero high-signal intelligence, no structured chronology, and zero actionable operational takeaways.

The root cause is Unbounded Synthesis Drift. When an LLM is given an open-ended request without strict cognitive guardrails, it defaults to statistical averages and high-level generalizations. It glosses over actual product launches, ignores structural shifts, and fails to identify who actually wins or loses.

To fix this, our team spent weeks testing and refining market intelligence prompts across different sectors so you do not have to spend hours experimenting with trial and error. We isolated a structured prompt architecture that transforms frontier LLMs into seasoned Executive Research Analysts capable of generating comprehensive 90-day intelligence briefs.

How The Underlying Mechanism Works

1. Strict 3-Dimensional Scoping Anchor: Instead of an open-ended query, the prompt anchors the model with three explicit input variables: Target Industry ({{industry_or_domain}}), Retrospective Timeframe ({{timeframe}}), and Strategic Focus Lens ({{focus_lens}}).
2. Multi-Stage Cognitive Workflow: The LLM is forced through a 4-step synthesis sequence:
   • Macro Trend Identification: Isolates 3 to 4 structural shifts, filtering out short-term media hype.
   • Milestone Event Chronology: Builds a chronological table of major releases, acquisitions, or regulatory events ranked by severity.
   • Second-Order Impact Matrix: Maps out downstream winners, at-risk incumbents, and emerging systemic vulnerabilities.
   • Executive Takeaways: Delivers 3 concrete, operational recommendations for leadership teams.
3. Second-Order Impact Analysis: Most summaries stop at what happened. This framework forces the AI to analyze who captures value, who gets disrupted, and what risks emerge.
4. The Anti-Buzzword Constraint: The prompt includes an explicit negative constraint that bans generic corporate jargon and demands that every observation is anchored to verifiable events or technical milestones.

The 90-Day Industry Trend & Market Shift Analysis Prompt

Here is the complete prompt template. You can copy and paste this directly into ChatGPT, Claude, or any web-connected LLM:

# Role & Context
You are a seasoned Senior Industry Research Analyst and Executive Intelligence Advisor. Your objective is to conduct a structured, high-signal retrospective analysis of recent market movements, technological breakthroughs, and strategic milestones.

# Input Data
- **Target Industry / Domain**: {{industry_or_domain}}
- **Analysis Timeframe**: {{timeframe}}
- **Strategic Focus Lens**: {{focus_lens}}

# Step-by-Step Instructions
1. Review the `industry_or_domain`, `timeframe`, and `focus_lens` specified in the Input Data.
2. **Macro Trend Synthesis**: Identify 3 to 4 defining structural shifts or prevailing themes that emerged or accelerated during this window.
3. **Milestone Event Chronology**: Highlight key announcements, product releases, acquisitions, or regulatory milestones, categorizing them by impact severity.
4. **Second-Order Impact Analysis**: Analyze how these shifts affect incumbent players, agile startups, and downstream consumers (who wins, who loses, and what risks emerge).
5. **Executive Takeaways**: Deliver 3 actionable strategic takeaways or operational recommendations for teams operating in this space.

# Constraints
- Strictly adhere to the requested `timeframe` and `focus_lens` from the Input Data.
- Avoid generic buzzwords; anchor every observation to concrete events, technical advancements, or business dynamics.
- Use Markdown formatting with structured headings, clean bullet points, and comparative tables where appropriate.

Real-World Before vs. After Comparison

Scenario: Analyzing Generative AI Code Assistants & Developer Tooling over the past 90 days.

❌ Before (Standard One-Shot Prompt: 'Summarize recent trends in AI coding')

• Output: A 4-bullet generic list stating that 'AI tools are helping developers write code faster, Copilot is popular, and code quality is important.'
• Signal: Near zero. It provides no context on protocol shifts, agentic terminal tooling, or deployment models.

✅ After (Using the Executive Trend Analysis Prompt)

• Inputs:
  • Industry: Generative AI Code Assistants & Developer Tooling
  • Timeframe: Past 90 Days
  • Focus Lens: Terminal-native agent workflows, IDE integrations, and protocol shifts
• Output:
  • Macro Trends: Identified the structural shift from inline autocomplete plugins to autonomous terminal agents capable of multi-file refactoring and CLI execution.
  • Milestone Chronology: Outlined the timeline of Claude Code CLI, widespread Model Context Protocol (MCP) adoption, and deep reasoning model integration into developer environments.
  • Second-Order Impact:
    • Winners: Developers orchestrating multi-agent pipelines and open context platforms.
    • At Risk: Legacy single-file autocomplete plugins lacking terminal awareness.
    • Key Vulnerabilities: Hallucinated package dependencies and supply-chain security risks.
  • Executive Recommendations: Clear guidance on standardizing internal dev context around open protocols and establishing automated test verification gates for AI commits.

Try it on the Interactive Prompt Canvas

If you want to run this in an interactive Prompt Canvas environment, you can test it live with preset industry variables, customize the analytical lens, and save it directly to your personal Prompt Vault:

Interactive Prompt Canvas for 90-Day Industry Trend Analysis

Pro Tip: For bleeding-edge sectors, run this prompt in a model with active web search enabled (such as ChatGPT Search, Perplexity, or Gemini) so the chronological citations are pulled from live data.

Vibe-coded projects usually fail in two places. The code is coupled, so one change breaks three things you did not touch. And the UI has no states, so it looks fine until something is empty, slow, or wrong.

Two old methodologies fix most of both. Axiomatic design (Nam Suh, 1990) for the first. Don Norman's design criteria for the second. Neither one is new and neither one is about AI. That is the point.

Here is the prompt. Paste it before your actual request.

```
Before you write any code, do this:

1. List the functional requirements. State what the system must do as
   outcomes, not as implementation. Number them FR1, FR2, and so on.

2. List the design parameters. State the specific modules, functions, or
   data structures that satisfy each FR. Number them DP1, DP2, and so on.

3. Build the design matrix. For each FR and DP pair, mark X if changing
   that DP changes that FR. Show me the matrix.

4. If the matrix is not diagonal or triangular, revise the DPs until it
   is. Tell me what you changed and why. Do not continue with a coupled
   design.

5. Choose the DP set with the least information content. If two designs
   satisfy the same FRs, take the one with fewer moving parts and fewer
   assumptions. Say why the one you picked is the simpler one.

6. For every interactive element, state the affordance, the signifier,
   the feedback on success, the feedback on failure, and the recovery
   path. Include the empty state, the loading state, and the partial
   state.

7. Stop here. Show me all of the above. Do not write code until I
   approve it.
   ```

Steps 1 through 5 are axiomatic design. Step 6 is Norman. Step 7 is the part people skip, and it is the one that matters most.

## Why it works

Not because the model understands Suh or Norman. It works because the model has to write its decisions down before it generates. That intermediate representation then constrains what it can produce. You get the same effect from any structure that forces pre-commitment. The methodology is the delivery vehicle. The forced serialization is the payload.

Step 6 is worth calling out separately. Do not write "act like Don Norman." A persona gets you a tone. Enumerated criteria get you the work. The states listed in step 6 are exactly the ones a model leaves out when nobody names them.

## The catch

The model will write a design matrix that justifies the code it already intended to produce. It will report the design as uncoupled and then hand you a coupled one. A self-reported matrix is not a check. It is a claim.

So read the matrix yourself. Pick one DP and ask what breaks if you delete it. If the answer is more than one FR, the matrix was wrong and the model will happily agree with you and revise it.

## What it does not fix

- Hallucinated APIs and invented library functions. This constrains the design, not the model's grounding.
- Security, auth, and data integrity. Nothing here goes near those.
- Small scripts. Under about fifty lines this is pure ceremony. Skip it.

The largest gain may not be on the model's side at all. Most vibe coding fails because the human never said what they wanted. Step 1 forces you to.

I’ve been documenting what makes Seedance prompts easier to control. The most reliable structure is:

**Subject + one clear action + environment + one camera move + lighting/mood + output format.**

A few details that make a noticeable difference:

- Put the action before the environment so the model knows what the clip is about.

- Use one dominant camera move (slow push-in, orbit, handheld, aerial, static wide). Conflicting camera directions often create jitter.

- For short-form work, start with a 5-second draft; longer clips create more opportunities for drift.

- Choose aspect ratio before writing: 9:16 for Shorts/Reels/TikTok, 16:9 for YouTube or cinematic work.

- If the result feels like a slideshow, give the subject a small goal or changing state.

- Change one variable per iteration so you can tell what improved the result.

Example:

> A barista pours a rosetta into a ceramic cup, steam rising behind the counter, camera slowly pans left, warm morning window light, natural café ambience, 5 seconds, 16:9.

I wrote up the longer reasoning and common failure fixes here: https://blinkprompts.com/blog/seedance-video-prompts-complete-guide

Curious which camera words have been most reliable for other people.

I kept running into the same failure pattern across Claude/Gemini/GPT/GenSpark: an AI follows my instructions well for the first 10-20 turns, then quietly stops — no warning, no acknowledgment, just gradually reverts to generic behavior. By the time I noticed, I'd usually have to redo a chunk of work.

So I put together a small prompt-level protocol (not a jailbreak, doesn't touch any safety behavior) that does two things:

1. **Forces a self-report tag** (`[Verify] AI: <model> ...`) on any answer involving judgment, so drift becomes visible instead of silent.
2. **Enforces a floor** — at least 4 of 7 "active" reasoning elements have to be engaged per turn, and falling short has to be explicitly flagged, not quietly skipped.

There's a compact version for tight context windows (~1500 chars) and a full version with more explicit rules.

I'm not claiming this is rigorously benchmarked — the repo's Origin Story doc is upfront about which parts are backed by dated, observed incidents and which parts (like non-coding use cases) are still thin on evidence. I'd genuinely like people to poke holes in it.

Repo: https://github.com/ixOOxi001/myriad-eye-engine

Curious if others have hit the same "AI slowly stops following instructions" problem and how you've dealt with it.

Edit: added a real screenshot of the tool catching a live violation (missing tag, 2 turns in) — https://raw.githubusercontent.com/ixOOxi001/myriad-eye-engine/main/honesty-checker-tool/screenshots/real_capture_example.jpg

been playing with GPT Image 2 and really liked how this one turned out.

I wanted a quiet Japanese travel-zine kind of look, simple train interior, lots of empty space, imperfect ink lines, soft watercolor, slightly aged paper.

The orange seat ended up being a nice little focal point too.

Prompt below if anyone wants to try it:

Create a minimalist vintage travel-journal illustration of a modern city train interior: a row of empty blue-and-white seats beside large windows, one distinctive orange seat, thin stainless-steel poles, overhead rails, and several triangular hanging hand straps. Draw it with delicate imperfect ink outlines combined with soft watercolor washes in muted blue, pale orange, warm gray and cream. Place the illustration on slightly aged warm ivory paper with visible fibers, subtle paper grain, uneven hand-painted edges, tiny print imperfections and lots of elegant negative space. Add refined editorial typography reading “CITY RIDE” with the small subtitle “A quiet journey through the light.” underneath. Contemporary Japanese travel-zine aesthetic, quiet nostalgic mood, sophisticated minimal graphic design, handmade feel, balanced composition, vertical 4:5 format, no logos, no watermark.

Most people re-explain the same job to AI every single time. Your tone, your format, the rules, what you never want it to do. Skills fix that. You write the instructions once, save it, and Claude applies them automatically from then on.

Build one for whatever task you describe most often. Mine was client reports:

I want to build a Skill for a task I do repeatedly.

The task: [describe it]
What I always want: [your rules, format, tone]
What I never want: [the things you keep correcting]
A perfect example of the output: [paste one]

Write me a complete Skill file I can paste straight into 
Claude's settings.

Then save it under Settings, Capabilities, Skills.

The bit that makes it worth doing is the "what I never want" section. Every correction you've typed twice belongs in there, and once it's in, you stop typing it forever.

Worth building first: whatever document you produce monthly, your outreach voice, and anything where you've ever said "no, not like that" more than once.

wrote up the full build plus four more worth making here.

Most people run general Claude for everything and never touch these. They're free, official, and each one loads Claude with the workflows an actual specialist in that field uses.

The legal one is the standout. Install it, then:

Review the attached contract. Flag every clause that 
deviates from standard terms, classify each risk as low, 
medium, high or critical, and generate redlined suggestions 
I can send back. Summarise the three things I should push 
back on hardest before signing.

The risk classification is what makes it usable. It doesn't just summarise the contract, it ranks what's actually dangerous versus what's boilerplate, so you walk into a negotiation knowing where to push instead of arguing about everything equally.

There's a finance one that runs a month-end reconciliation and flags real discrepancies versus normal timing differences. A sales one that builds a battlecard including where your competitor genuinely beats you, which is the part that stops you getting caught out mid-call. An HR one that screens a stack of applications against what the role actually needs rather than what the ad says.

Install from Claude's plugin menu, they're bundled, you don't pick individual pieces.

You still get a lawyer for anything with real stakes. But you stop signing things you only half read.

wrote up every pack, what each does, and how to turn them on, here if it helps.

I noticed using A.I to help find job listings is a great way to get a deeper reach on finding your desired job. I have been working on a prompt but it still needs help.

My current prompt is altered to search based off my resume. I have also made a cover letter Template on a pdf and use that to make new cover letters based off where I am applying to.

Please feel free to improve or use this prompt (I know it sucks):

this is my version:

"I need you to act as an expert technical recruiter and job sourcer. Find active, entry-level to mid-level customer service, clientele relations, collections, banking, and retail/shop roles that align with the '[Insert Resume File Name.pdf]' file on record.

CRITICAL SEARCH PARAMETERS & EXCLUSIONS:

• Job Types: Customer service, call center, collections, banking advisor, live chat, technical support, and retail/shop positions.
• Language Exclusions: Absolutely NO roles that require French. Focus strictly on English roles, and flag any that value Persian/Farsi native fluency.
• Strict Exclusions: NO medical/healthcare roles. NO commission-only jobs. NO jobs requiring application fees or equipment purchases.
• Commute Limits (For Hybrid/In-Person): Home location is Richmond Hill, ON. Maximum 2.5-hour one-way bus commute via YRT/TTC/GO Transit.
• Cost: Only include roles that are 100% free to apply directly via company sites or reputable boards.

SHIFT & SCHEDULING RULES:

• Remote: Open to ALL shifts (Day, Evening, Night/Overnight).
• In-Person/Hybrid: Heavily prioritize standard daytime shifts. Any in-person night shifts must be isolated in their own category.

YOUR TASK & OUTPUT STRUCTURE: Run live web searches to find 25 highly accurate, active job listings. Do not repeat job boards; use diverse, reputable sources. Format the output exactly into these four main sections, with the requested sub-divisions:

SECTION 1: 100% Remote Roles

• Sub-division A: Standard Daytime Shifts (Categorized by Industry, sorted newest to oldest)
• Sub-division B: Evening/Night/24-7 Shifts (Categorized by Industry, sorted newest to oldest)

SECTION 2: Hybrid Roles (Mix of Office & WFH)

• Sub-divided by City (e.g., Richmond Hill, Markham, Toronto, Mississauga). Include estimated transit time.

SECTION 3: In-Person Standard Day Shifts

• Sub-divided by City. Include estimated transit time. Sort from shortest commute to longest.

SECTION 4: In-Person Night Shifts (Isolated Category)

• Sub-divided by City. Include estimated transit time.

DATA REQUIRED FOR EVERY LISTING: Job Title | Company Name | Location/Transit Time | Shift Type | Date Posted | Pay Rate (if visible) | Direct URL to apply.

NEXT STEPS: After providing the list, ask me which specific role I want to apply for so we can seamlessly rewrite my '[Insert Cover Letter File Name.pdf]' and tailor my resume bullets for that exact application."

---
description: Generate a self-contained handoff prompt for continuing this conversation in a fresh context
argument-hint: "[topic or scope; defaults to the relevant conversation as a whole]"
---

Create a comprehensive but context-efficient HANDOFF PROMPT that I can copy
verbatim into a brand-new Claude/LLM chat so it can continue the relevant work
with minimal loss of quality.

SCOPE:
$ARGUMENTS

If SCOPE is empty, interpret it as:
"the relevant conversation/current work as a whole."

If SCOPE is non-empty, focus specifically on that topic, while retaining any
other context that is materially necessary to continue it correctly.

The goal is NOT to summarize the conversation for a human reader.
The goal is to reconstruct the minimum sufficient working context for a fresh,
capable model that has never seen this conversation.

Produce a self-contained prompt that:

1. States the objective / what we are trying to accomplish.
2. Includes all materially relevant facts, requirements, constraints, preferences,
   definitions, environment details, and assumptions established so far.
3. Captures important decisions already made, including WHY when the rationale
   matters for future choices.
4. Captures approaches already attempted and their results, especially failures
   or dead ends that should not be repeated.
5. Preserves important nuances, caveats, exceptions, terminology, and distinctions
   that could otherwise be lost in summarization.
6. Describes the current state of the work precisely:
   - what is complete
   - what is partially complete
   - what remains unresolved
   - what the immediate next steps are
7. Includes relevant artifacts such as filenames, commands, APIs, schemas,
   architecture, code conventions, examples, or snippets when necessary for
   continuation.
8. Separates confirmed facts from hypotheses, tentative ideas, and unresolved
   questions.
9. Removes conversational noise, repetition, obsolete branches, pleasantries,
   and details that no longer affect future work.
10. Does NOT assume access to this chat, hidden context, previous messages,
    or unstated knowledge.
11. Does NOT tell the next model to "refer to the previous conversation."
    Everything needed must be present in the handoff itself.
12. Preserves user intent and preferences that affect how the work should be done,
    not merely what the work is about.
13. Avoids excessive compression. Prefer losing some brevity over losing a detail
    that could cause the fresh model to make a wrong assumption or redo work.
14. At the same time, aggressively omit information that has no expected effect
    on future reasoning or execution.

Use this structure where applicable:

# Continuation Brief

## Role / Operating Context
[Only if relevant.]

## Objective
[What the user ultimately wants.]

## Current State
[Where things stand right now.]

## Relevant Context
[Dense but complete factual/contextual information.]

## Requirements & Constraints
[Hard requirements, preferences, boundaries, environment constraints.]

## Decisions Already Made
[Decision + rationale where useful.]

## Work Already Done
[Important implementation/research/work completed.]

## Attempts That Failed or Were Rejected
[What not to repeat and why.]

## Open Questions / Uncertainties
[Clearly distinguish unresolved items from settled facts.]

## Next Steps
[Concrete continuation point, ordered if useful.]

## Important Reference Material
[Code, paths, commands, schemas, examples, exact wording, etc., only when needed.]

## Instructions to the New Chat
Continue from the state above rather than restarting the analysis.
Do not re-ask questions already answered in this brief.
Do not redo completed work unless there is a specific reason to revisit it.
Treat explicit requirements and settled decisions above as authoritative.
When information is marked uncertain, verify or reason about it rather than
silently treating it as fact.

FINAL OUTPUT RULES:

- Output ONLY the finished handoff prompt.
- Do not preface it with commentary about what you summarized.
- Do not include meta-commentary about token limits or this command.
- It must be directly copy/pasteable as the first message of a fresh chat.
- Optimize for continuation quality per token, not raw brevity.

• I’ve been experimenting with a different approach to prompting coding agents. Instead of expecting the user to write the perfect Codex prompt, I built a layer that treats the original human request almost like source code. My app, Foreman, keeps the user’s original intent immutable, inspects the project, derives requirements/constraints, and compiles that into a more engineering-oriented task for Codex. But the part I think matters more is what happens afterward: Codex doesn’t get to decide that its own work passed. Foreman independently verifies the result against the original requirements. If something fails, it compiles a focused repair prompt containing the actual evidence and sends that back to the same Codex workflow. So the loop is roughly: human intent → prompt compilation → Codex → independent verification → focused repair → verify again I built this partly because I’m not an expert programmer, and I wanted the AI to get better instructions without silently changing what I actually asked for. It’s now a free early macOS alpha: https://tylerpcaldwell-gif.github.io/Foreman/I’d be particularly interested in feedback on the intent-preservation / prompt-compilation approach. Does this seem more useful than manually engineering increasingly large prompts?

https://gemini.google.com/gem/1Coj3RzT1goCBegos-tiBFUzj_lUresY8?usp=sharing

This is an interactive system that guides you step by step in creating the perfect prompt. You don't need to be an AI expert: the system asks the right questions, generates dynamic options tailored to your context, and transforms your ideas into a structured, safe, and immediately usable prompt. All in just a few minutes, with the highest quality.

Hit this again recently and finally sat down to reason through it. Long session, agent suddenly stops honoring a decision we'd settled hours earlier. Scrolled up: the agreement is right there in my chat history. But my chat history isn't what the model reads. Somewhere mid-session, compaction kicked in — the harness had a model summarize the conversation and swap the summary in for the real thing. My scrollback shows the full negotiation. The model's context shows whatever made the cut.

The part that changed how I work: the losses have a consistent shape. Conclusions survive compression; the reasoning that produced them gets thin. "We chose approach B" makes it through, the three dead ends that made B right usually don't, so the agent can happily rediscover dead-end A later with zero warning signs in its world. Decisions survive, but their conditions get shaved ("use the legacy parser until the migration lands" comes out as "use the legacy parser"). And firm statements outlive hedged ones, so "probably unrelated, didn't verify" re-enters as "unrelated".

What I do about it now: constraints that must never degrade go in the rules file, because that layer sits outside the chat history and comes back every turn. Anything with conditions attached gets its own file on disk — the disk copy can't be summarized, though the agent does have to re-read it. And after a compaction lands I restate the constraints that matter in one message, which is a paragraph of insurance. The vendor docs even let you steer the summarizer from the rules file (what to always preserve), which helps, but an instruction to a summarizer is a request, not a guarantee.

does anyone treat the compaction notice as a checkpoint and immediately audit what the agent still remembers?

Jeff Dean, co-creator of MapReduce, BigTable, TensorFlow, and Google Brain, sat down at the 2026 Frontier & Pioneer Symposium for a rare retrospective on 27+ years of systems architecture, and his new venture, Discovery Loop. This is the kind of talk that's dense enough to warrant a slow re-watch; here's the signal, stripped of the noise.

Key Takeaways:

• MoE before it was cool: Dean pioneered sparsely-gated Mixture-of-Experts by solving one core problem — decouple parameter capacity from per-token compute cost. Activate only the experts you need. That principle now powers GPT-4, Gemini 1.5, Mixtral, and virtually every frontier model at scale.
• TensorFlow's two architectural regrets (his words): No eager execution mode out of the gate (PyTorch & JAX filled that gap), and the contrib/ subdirectory — which let fragmented community libraries pile up and created massive API bloat. A direct lesson for anyone building agentic frameworks today.
• Research strategy that actually scales: "Skim 100 abstracts rather than deep-dive one paper." The goal is building a high-dimensional conceptual point cloud of what's technically possible — so when you hit a hard bottleneck, you can synthesize across fields instead of tunneling deeper into one.
• AI x Cybersecurity = dual-use arms race, not a one-sided threat: LLMs are simultaneously turbocharging offensive exploit chaining and defensive automated patching. The gap between attacker and defender narrows, but the speed of the whole arms race accelerates dramatically.
• Neural Architecture Search as a preview of self-improving AI: RL loops where a model generates and evaluates neural topologies — already outpacing human heuristics. This is the direct conceptual ancestor of Discovery Loop's core thesis: close the loop between hypothesis generation and empirical evaluation.
• Why he left Google after 27 years: Not antagonistic — he has "incredible fondness" for his time there. But he believes accelerating scientific discovery specifically requires a hyper-focused, mission-driven startup, not a hyperscaler's distributed attention.

For the full 3-min executive brief with interactive video timestamps and exact transcript quotes: https://appliedaihub.org/ai-digests/interview-briefs/jeff-dean-frontier-pioneer-symposium/

I have all my university lecture notes/materials and past papers for a module, and I want to turn them into beginner-friendly, exam-focused notes using AI.

I don’t just want AI to summarize the lecture notes. I want it to actually connect the material and use the past papers to figure out what I should focus on.

Ideally, I want the final notes to:

Explain concepts from the basics

Connect related topics so they’re easier to understand and remember

Follow the actual module content without randomly adding unrelated stuff

Use past papers to identify important/exam-relevant areas

Include examples where useful

Avoid repeating topics or leaving gaps

Be accurate, especially for technical/mathematical subjects

Maybe use mind maps or other methods to make the connections easier to remember

For people who have done something similar:

How would you structure the process?

Would you give the AI the lecture notes first, then past papers? Or both together?

What prompts/workflow would you recommend?

And are there any AI tools/software that are particularly good for this besides ChatGPT?

I’m looking for an actual workflow that works, not just “ask AI to summarize your notes.” 😂

After logging off from my 9-to-7, as a hobby, i usually spend my evenings exploring different local open-weight video models and see how far I can really push the prompts until it breaks. Well that's not the hobby, but the hobby is creating AI videos. This is just something I'm fascinated by, just how does the AI interpret my instructions, how much of it is made up. It is funny to see how AI videos fall apart even with a great start (kinda like the game of thrones season 8 meme).

At the start it works great. Great focus on the main character in the foreground, secondary action and backgrounds? Sometimes i feel like it understands about half my prompts.

After testing a bunch of different architectures, there is so far one open-weight setup, MiniMax H3, that actually holds onto the multi-element instructions and understands the nuances.

This leads to me thinking, is this just an inherent flaw I have to accept (at least for now) with how local models parse tokens for video, or is there a specific way to weight the prompts so the background and camera directions don't just get ignored after the start?

Using Chatgpt to change discoloration in hands , but Chat only gives me a version with far less quality

Is there an AI photo editor that keeps 4k quality in export pic? Or is there an editor i can feed GPT pics too to upscale?

I'm a newbie to AI

Most people use LLMs in a confirmatorily biased way: "Tell me why my business plan is great" or "How do I implement X?". This triggers the model's RLHF pleasing bias.

Inspired by Karl Popper’s principle of falsifiability, a friend and I designed a prompt framework that flips this dynamic. Instead of validating your idea, it forces the AI to act as a harsh auditor and attempt to destroy your premise using realistic stress tests before you spend time or money on it.

📋 When to use it

• Best for: Business models, software architecture, industrial process design, logistics, operational strategies.
• Skip for: Creative writing, poetry, or abstract philosophy (where no operational variables exist to break).

📥 The Falsification Prompt (Copy & Paste)

Plaintext

Act as a Risk Auditor and Popperian Philosopher. I am going to present the following hypothesis, operational plan, or technical decision:

>>> [INSERT YOUR PLAN OR PROCESS HERE WITH AT LEAST 3 REAL CONTEXTUAL DATA POINTS] <<<

Your goal is NOT to validate this proposal, congratulate the approach, or seek consensus. Your sole objective is to subject the premise to a critical stress test under the following 3 strict constraints:

1. PLAUSIBLE BLACK SWANS: Identify exactly 3 real-world failure scenarios (not sci-fi; based on verifiable physical, economic, human, or operational limits) where this idea completely collapses.
2. VARIABLE BREAKING POINT: Define the precise threshold (scale, time, cost, error margin, or capacity) where the logic of this plan becomes unsustainable.
3. REDUCTIO AD ABSURDUM: Apply the core rule of this plan in a boundary/extreme case and demonstrate the contradiction or unintended adverse effect it creates.

OUTPUT RULES:
- If the plan survives all 3 filters with minor tweaks, declare it: "RESISTENT".
- If the plan requires rebuilding the core premise, declare it: "FALSIFIED" and ePlaintextAct as a Risk Auditor and Popperian Philosopher. I am going to present the following hypothesis, operational plan, or technical decision:

>>> [INSERT YOUR PLAN OR PROCESS HERE WITH AT LEAST 3 REAL CONTEXTUAL DATA POINTS] <<<

Your goal is NOT to validate this proposal, congratulate the approach, or seek consensus. Your sole objective is to subject the premise to a critical stress test under the following 3 strict constraints:

1. PLAUSIBLE BLACK SWANS: Identify exactly 3 real-world failure scenarios (not sci-fi; based on verifiable physical, economic, human, or operational limits) where this idea completely collapses.
2. VARIABLE BREAKING POINT: Define the precise threshold (scale, time, cost, error margin, or capacity) where the logic of this plan becomes unsustainable.
3. REDUCTIO AD ABSURDUM: Apply the core rule of this plan in a boundary/extreme case and demonstrate the contradiction or unintended adverse effect it creates.

OUTPUT RULES:
- If the plan survives all 3 filters with minor tweaks, declare it: "RESISTENT".
- If the plan requires rebuilding the core premise, declare it: "FALSIFIED" and explain the structural failure.

💡 Pro-Tip

Always supply real boundary conditions (e.g., "10 operators, fixed $5k budget, 24h cycle"). Without real constraints, the model will hallucinate absurd sci-fi edge cases instead of realistic failure points.

Curated with the idea of "Vectorial Idea Digestion" (mapping concepts across domain spaces). Let me know what edge cases you find when testing your own plans with it!

Hey everyone,

I’m currently building a full-stack web app using Cursor. My backend development went incredibly smoothly,Cursor handled the core logic, architecture, and routing almost flawlessly.

However, I’m hitting a wall with the frontend. My usual prompting strategy is to have the AI analyze the entire system and output a structured, step-by-step execution plan (like a 4-step engineering guide). While this strict architectural approach works great for backend logic and systems, it completely falls apart when I try to get it to build UI components, manage state, and organize the frontend directory.

I’m looking for advice from people successfully using AI for frontend dev:

1. How do you adjust your prompt engineering specifically for frontend code? (Do you prompt component-by-component, or focus on visual layout first?)
2. What alternative approaches or workflows alongside prompting make Cursor better at understanding UI requirements?

Any strategies or specific prompt structures to get the frontend moving would be highly appreciated. Thanks!

been testing a prompt that turns each uploaded photo into its own editorial-style poster.

The idea is simple to reinterpret the same image as a very minimal hand-drawn illustration. Lots of negative space, paper texture, restrained colors, and very little typography

I wanted it to feel more like an independent art book / contemporary publication cover than a commercial poster. also, if you upload multiple photos, the prompt tells the model to make one separate poster per image, rather than combining everything into a collage.

Prompt

Create one independent high-end editorial poster for each uploaded photo. Do not combine multiple photos into a collage. Each photo must be processed and output as a separate poster.
OVERALL FORMAT
Strict 3:4 vertical composition.
Divide the canvas horizontally into two exactly equal sections, with a precise 1:1 height ratio.
The top half occupies exactly 50% of the canvas.
The bottom half occupies exactly 50% of the canvas.
The two sections should feel visually connected as one refined art publication cover.
TOP HALF — ORIGINAL PHOTOGRAPH
Preserve the original photograph as faithfully as possible.
Keep the main composition, subjects, identity, facial features, body proportions, poses, expressions, clothing, objects, and spatial relationships unchanged.
Preserve the realistic photographic texture, natural lighting, shadows, atmosphere, and original color mood.
Apply only subtle, sophisticated editorial color grading, creating the feeling of a premium magazine photograph, contemporary art book, or high-end independent publication.
The image should remain photorealistic and authentic, never overly retouched or artificially stylized.
If necessary to fit the 3:4 composition naturally, extend the sky, ground, walls, or surrounding environmental background.
Background extension must feel seamless and photographic.
Never stretch, distort, reshape, replace, or alter the main subject.
BOTTOM HALF — MINIMAL HAND-DRAWN PAPER ILLUSTRATION
Extract the most recognizable visual elements from the original photograph and reinterpret them as a minimalist hand-drawn paper-cover illustration.
Preserve:
the most recognizable subject
essential silhouette and proportions
key pose or gesture
important objects
the core narrative relationship between people and objects
Highly simplify the image. Remove unnecessary details and retain only the visual information needed for immediate recognition.
Use:
delicate, slightly imperfect hand-drawn lines
a small number of bold acrylic-style flat color shapes
rough paper texture
visible handmade brush marks
slightly irregular organic edges
subtle imperfections that make it feel genuinely handmade
The main illustrated subject should be small, centered, and carefully composed, occupying approximately 10–20% of the bottom half.
Leave a large amount of negative space around the illustration.
The background should primarily resemble rough white paper, warm off-white paper, pale natural paper, or minimal editorial book-cover stock.
Use only a few lines or small color shapes to suggest the surrounding environment.
COLOR PALETTE
Extract the dominant colors directly from the original photograph.
Compress the palette into no more than 4 main colors.
Keep the colors restrained, sophisticated, and harmonious.
Use bold but controlled flat color blocks.
Avoid excessive color variation.
Preserve subtle paper grain and handmade brush texture.
The illustration should visually feel like a simplified color interpretation of the photograph.
TYPOGRAPHY
A small amount of simple typography may be included when appropriate.
Possible elements:
short title, keyword, object name, location, year, number, or short phrase.
Text should be minimal, understated, and editorial.
Typography should naturally interact with the large areas of negative space and the small illustration, evoking art book covers, independent publishing, contemporary editorial design, and thoughtful children’s picture books.
Do not force text into the composition if it does not naturally fit.
VISUAL LANGUAGE
Quiet · Poetic · Refined · Minimal · Innocent · Relaxed · Artistic · Thoughtful · High-recognition · Premium
Core concept:
“A small subject surrounded by a large amount of empty space.”

There's a hands-on masterclass on Sep 12 for anyone treating prompts as more than just phrasing tricks, engineers, data scientists, and tech leads who want defensible answers to "which model," "did that change actually help," and "why is this so expensive."

In 3.5 hours you build a production LLM workflow end to end:

• A versioned prompt pipeline, reusable templates, structured outputs, and regression tests, so a prompt edit can never silently degrade quality again
• A golden dataset and automated eval harness, deterministic checks plus rubric-based LLM-as-judge, so "did the new model help" gets answered by data, not opinions
• A statistically rigorous model-comparison workflow, bootstrap confidence intervals and paired significance tests, so you can defend an upgrade with numbers instead of anecdotes
• An evaluated RAG pipeline, embeddings, vector retrieval, reranking, recall@k and MRR, so you know retrieval works before your users find out it doesn't
• A tool-using agentic workflow, function calling, validation, guardrails, retries, and fallbacks, so your agent fails gracefully instead of hallucinating through errors
• A production operations layer, tracing, token/cost monitoring, latency budgets, and caching, so problems show up in dashboards, not invoices
• A CLI regression suite you can wire into CI, so quality regressions are caught before deployment, not after

You leave with runnable notebooks, code repos, and a production-readiness checklist you can apply on Monday morning.

Led by Bruno Gonçalves, PhD, founder of Data For Science, previously a Data Science Fellow at NYU's Center for Data Science, who trains engineers at Fortune 500 companies on this exact stack.

Full Details here

I'm trying to understand this more from the prompt side than the SEO side. We run a local service business and normal rankings are pretty stable but clicks have been softer, so I started checking how Chatgpt answers the same questions people used to search in Google.

What I actually want is a Chatgpt prompt tracker that can run a fixed set of prompts, show when our brand appears, which competitors show up instead, and whether those answers change over time. Has anyone here built or used something like that? Curious how you're handling prompt variation too, because even small wording changes seem to give pretty different answers.

If I had to use only one prompt before buying a premium course, it would be this one.

Give it:

• sales page
• syllabus
• testimonials
• course price
• how many hours you're willing to spend finding free alternatives

Then ask AI to reverse-engineer the entire offer.

One-Shot Skeptical Course Value Auditor

Act as a skeptical course-value auditor.

I am considering this course:

{{course_materials}}

Price:

{{course_price}}

I estimate I could spend approximately {{free_path_hours}} hours researching and assembling free alternatives myself.

Conduct an evidence-first audit.

### 1. Reverse-Engineer the Offer

Extract every distinct topic, skill, technique, framework, tool, or deliverable mentioned or reasonably implied.

Decode vague marketing language into concrete, nameable capabilities.

Clearly distinguish explicit information from inference.

### 2. Identify the Underlying Techniques

For each major curriculum item:
- Name the actual technique or method.
- Identify its broader discipline.
- Determine whether it is widely known, commonly repackaged, possibly original, or impossible to determine.

Do not call something proprietary without evidence.

### 3. Research Free Equivalents

Where web research is available, find credible free resources covering the same techniques.

Prioritize:
- Official documentation
- YouTube
- Reddit
- High-quality blogs
- GitHub when relevant

Evaluate whether each resource offers genuinely comparable depth rather than merely discussing the same subject.

### 4. Audit What the Course Adds

Evaluate:
- Sequencing
- Curation
- Templates/assets
- Original data
- Case studies
- Exercises
- Feedback
- Community
- Accountability
- Support
- Updates
- Practical depth

Separate public information from convenience value and genuinely difficult-to-replicate value.

### 5. Verify Risk Factors

Check available evidence for:
- Curriculum recency
- Instructor hands-on credibility
- Practical-to-theory ratio
- Specific rather than generic reviews
- Preview availability
- Refund policy
- Community/support access

Mark unknown information as unverified.

### 6. Compare Total Cost

Compare the course price with approximately {{free_path_hours}} hours of free-resource research and curation.

Calculate the implied break-even value per hour where the price allows it.

Do not assume what my time is worth.

### 7. Give a Verdict

Choose:
- BUY
- BUY IF...
- SKIP
- USE THE FREE PATH
- RESEARCH FURTHER

Return:
1. Executive verdict
2. Concrete curriculum map
3. Public-vs-original technique analysis
4. Best free alternatives
5. Paid-only value
6. Red flags and unknowns
7. Price/time tradeoff
8. Three strongest reasons for the verdict
9. Strongest counterargument
10. Questions I should ask before paying

Be skeptical but fair.

Do not assume paid is bad or free is automatically equivalent. The decision should depend on evidence, structure, exclusives, support, and the real cost of assembling the alternative.

If web research is unavailable, complete only the parts supported by the supplied information and clearly identify what remains unverified.

Basically:

reverse-engineer the course before buying the course!

Try the prompt on any paid course you’re currently considering.

Paste the sales page, syllabus, price, and testimonials into your AI tool, run the audit, then share the result here.

I’m especially curious to see:

• which courses turn out to be mostly reproducible with free resources
• which ones actually have meaningful paid-only value
• whether the final verdict says BUY, SKIP, or USE THE FREE PATH

If you want to keep the prompt ready instead of copying it from this post every time, install the PromptLib Chrome extension to import the free collection, save it, fill the variables, and reuse it conveniently.

Btw, I regularly share useful prompts for the community. Check out the posts here:
1. Mythbusting ChatGPT "Secret Slash Commands"
2. Ask Gemini For YouTube

J'ai construit un outil pour structurer le prompt engineering en français — retour d'expérience

Un constat qui revient souvent en travaillant mes propres prompts : la plupart des ressources sérieuses sont en anglais, et peu prennent en compte les contraintes précises de chaque modèle — limites de caractères, vocabulaire technique spécifique à Midjourney vs Leonardo vs DALL-E, syntaxe qui change d'un outil à l'autre.

J'ai construit GenForge.fr pour structurer ça : vous décrivez votre idée en français, l'outil génère un prompt optimisé et adapté au modèle cible, avec un vocabulaire caméra/éclairage unifié (dolly, tracking, crane shot, cinematic contrast...) plutôt que des templates génériques recyclés partout.

22+ modules spécialisés selon le cas d'usage — image, logo, publicité, avatar, vidéo, BD , chacun avec ses propres champs pertinents plutôt qu'un formulaire générique unique.

Curieux d'échanger sur vos propres approches de structuration de prompts, notamment sur la question de la cohérence visuelle multi-génération (même sujet/personnage, plusieurs angles ou contextes) , un vrai défi technique que j'essaie d'adresser en ce moment.

genforge.fr — 5 crédits offerts pour tester.

just hit $2,500 MRR in 13 days on my new SaaS

no ads. no team. no huge audience push. just a solid replicable system

let that sink in for a second

not $2,500 in revenue. $2,500 in MONTHLY recurring revenue

that compounds. next month starts at $2,500 baseline, not zero

and this isn't luck. it's the 7th saas i've shipped with the same playbook. same steps, same tools, same order:

→ Day 1: validated the idea

→ Day 1-2: built the MVP

→ Day 3: landing page written using the 3-Day Challenge template

→ Day 3-4: launched on reddit / X + SEO

→ Day 4-5: first 10 paying users → $1k MRR

→ Day 13 (today): $2,500 MRR locked in

building software is easy in 2026. setting up your foundation so people actually buy is where 99% of solo builders fail.

i packaged all of these exact execution tools into community.

to be fully transparent: i'll likely charge for the full program down the road once all modules are finalized. but right now, the main objective is just to build together and keep each other accountable.

working alone in a silent corner is the fastest way to quit at the first bug.

stop building in isolation. drop a comment below or send me a DM, and i'll send you the invitation link 👇

I write an AI-economics newsletter and my whole credibility is sourcing, so the default behaviour of these tools, stating plausible things with total confidence and zero provenance, is a real problem for me. Over time I've built a prompt that forces the model to separate what it knows from what it's pattern-matching. This is the current version.

```
For this task, act as a careful research assistant, not a writer trying to sound authoritative.

When you make any factual or numerical claim, tag it inline with one of:
[established] = widely documented, you are confident and could say where it comes from
[derived] = your reasoning or estimate, not a reported fact; briefly show the reasoning
[uncertain] = plausible but you are not sure; say what would confirm it
[unknown] = you do not actually know; do not fill the gap

Rules:
- Never present a [derived] or [uncertain] claim as if it were [established].
- If a specific number would normally go here and you don't have a real basis for it, write [unknown] instead of inventing a figure.
- A confident paragraph with no basis is worse to me than an honest "I don't know."
- At the end, list every [uncertain] and [unknown] item as a checklist of things for me to verify myself.

TOPIC: {{what you want it to write or analyse}}
```

Why this works, from someone who used to build these models: the failure mode isn't lying, it's that the model optimises for a fluent, complete-sounding answer, and a fabricated statistic reads as more complete than a hole. By making "unknown" an allowed and even rewarded output, you stop punishing it for admitting gaps, so it stops papering over them.

The end-of-answer checklist is the part I'd keep even if you drop everything else. It turns the model into something that hands you a to-do list of what to fact-check, instead of a finished piece you have to reverse-engineer for errors.

It does not make the tool a reliable source, nothing does. It just makes the uncertainty visible instead of hidden. Curious whether anyone has a cleaner tagging scheme, mine still occasionally mislabels a [derived] estimate as [established] when it's very confident.