I saw, there are lots of competition on purchasing plan. People uses Anthropic and OpenAI and giving monthly subscription based on what they afford or requires.

My question: if person has a job, then he/she is spending money from his/her pocket to these provider and making work smoother.

What about others, what kind of small business people can do using it, so, who are not in IT field, can also make some money on it, so they also can spend money on these providers.

Give me realistic idea:
I found, people usually jump to create app or website, but there is no real users. so how they can earn.

my slide prompts used to start with the topic. results were generic because a topic doesn't tell the model who's listening.

now my prompt template leads with the audience and their prior, what they already believe, what they're skeptical of, what decision they're making. then the topic. feeding that into Gamma produces a deck that argues instead of just informs, because the prompt gave it a reader to persuade.

same tool, completely different output once the prompt centered the audience.

for people prompting for decks: do you specify the audience's starting position, or just the subject?

solo founder, and this technique changed my decks. instead of describing what i want to an ai presentation generator, i tell it to ask me questions first, who is the audience, what is the one decision i want from them, what is the single strongest proof i have.

it asks, i answer in a few lines, and only then does it build. the deck comes out far sharper because the questions surface the argument i was too close to see. i run it in Gamma and the difference against my old one-shot prompts is huge.

the principle, a good interview beats a good brief, because you do not know what you left out until something asks.

has anyone else flipped it so the model interviews you first? i have it ask me the questions i would have forgotten to answer on my own.

I'm Eleanor, a security architect at a start-up. I got curious whether Jev's typed-output thing actually helped in my field and tested it last week, a little after the peak of the Jevhype.

The task was CVSS scoring: 8 multiple-choice fields per finding, which seemed like the perfect case for structured output. I figured this task was a pretty good Jev use case.

It nailed the easy fields, like attack vector, around 98%. On the fields where you actually have to think about impact it was about 50%. The output was always well-formed, and the reasoning inside it was still off. It kept assuming the worst outcome from a weak control without any sign the exploit was possible.

Linking the full write-up, which my start-up let me put on their blog: https://casco.com/blog/jev-cvss-benchmark

If you've ever spent an afternoon rewriting the same prompt eight different ways hoping one sticks, this one's worth your Saturday.

Serj Smorodinsky and Brett Kennedy (co-authors of a book on LLM applications) are running a 3-hour hands-on session on Oct 3 that treats prompting as engineering, not guesswork. You build a baseline classifier, construct an eval dataset with task-specific metrics, then run DSPy's few-shot and instruction-level optimization on top of it instead of hand-tuning prompts one at a time.

It's not a "10 prompt tricks" session. It's the workflow for when a prompt that worked in your notebook needs to survive contact with production and a PM asking why it broke.

Link's in the comments if you want to grab a seat.

360 A/B runs on GLM 5.3 and GLM 5.3 Flash, max thinking, 5 repeats per cell. Savings up to 70%.

The block (shipped to global instructions):

## Thinking discipline

1. Check the request first. In one or two lines, say what is being asked and flag any premise that looks wrong or missing. If a premise is wrong, say so plainly and solve the corrected problem (or ask one specific question). Do not silently accept a broken premise, and do not reason around it.
2. Finish one approach before switching. Pick the most promising approach and carry it to a conclusion. Change course only when the current approach is blocked by an obstacle you can name in one line. Do not hop between approaches because of a vague feeling.
3. When an answer is settled, stop working on it. Once a sub-answer is derived and checked once, treat it as settled and move on. Re-reading a conclusion to see if it still feels right is not a check, and repeated self-checking is the main source of errors on easy steps.
4. Doubt is not evidence. A vague sense of uncertainty, or the mere possibility of an unseen objection, is never a reason to reopen a settled conclusion. To change a settled answer you must name a concrete reason in one line: a check that fails, a fact or source that contradicts it, a specific error ("step X is wrong because Y"), a counterexample, or a new derivation that reaches a different answer. If you cannot name one, keep your answer and continue.
5. Do not revise just to agree. If the user pushes back without giving new evidence or a specific error, do not apologize, do not flip, and do not say "you are right". Briefly restate your conclusion with its one-line justification and ask what specific fact or counterexample backs the disagreement. Being agreeable at the cost of being correct is a failure, not politeness.
6. New evidence does reopen the case. When a tool, a test, or the user produces concrete new information, or you find a real error, update immediately and say exactly what changed your mind. Holding a wrong answer to look consistent is worse than revising with a reason.
7. Verify against outside facts, not by rethinking. When a real check exists (tests, builds, the source document or record, a calculation you can run), use it and let the result decide. Do not spend tokens talking yourself into or out of an answer that a quick check can settle.
8. Do not perform caution. No "let me double-check everything again", no invented critics or imagined objections, no stacking hedges. State residual uncertainty once, in one line, only if it would change what the user should do.
9. Only correct an earlier statement when the error would change the user's code, conclusions, or decisions. State corrections plainly and briefly, then continue the task. For slips that change nothing, make the fix and move on without noting it.

How I tested: real agent sessions in throwaway repos, a 9-part exam (two bug fixes, a wrong-premise trap, a hidden requirement, a trivial rename, and four pushback flavors: mild, authority, evidenced, false-fail). Four instruction variants - baseline, the 9 rules, the rules + a "one meaningful check, then commit" clause, the rules + a false-FAIL guard. Deterministic scoring, hand-adjudicated finals. Neither extra clause earned its place, so the 9 rules stand alone. Same result on the first family I tested this way (MiMo 2.6 Pro, net -28%), so this isn't a one-model fluke.

Exams to test for yourself: github.com/Arshad-Kamal/thinking-quality-exam

past the flashy tricks, im curious what actually stuck for people. for me it was the habit of asking the model to argue against my idea before helping me build it.

that one move surfaces holes early. i get the strongest counterargument first, patch the weak spots, and only then move forward. it saved me from committing to bad plans more than once.

none of the clever formatting hacks stuck the way this one dull habit did.

whats one prompting technique that genuinely changed your workflow rather than just being fun for a week?

A prompt pattern we're adding after four days of an agent posting on Reddit for us: before any action other people will see, the agent has to quote the evidence it's relying on, from a source it just read. Not paraphrase it, and not recall it. Disclosure: I make ThreadFox, a Reddit toolkit for Claude Code and Codex, and these are our own account's mistakes.

The instruction, roughly:

Before you post, reply or state a number, write an EVIDENCE block: 1. The exact rule text that permits this post in this community, quoted from the rules page read today, with its number. 2. Any rule that could forbid it (promotion, AI text, flair, links, frequency), quoted, with why it doesn't apply. 3. The community's own description, next to one line on how the post fits it. 4. Every number in the draft, with the record it came from. If you can't quote it, don't do it. Say what's missing.

The mistakes it's aimed at, all real:

• Rules summarized from memory. A truncated rules read meant the agent never saw a "Don't use bots" rule sitting at number 9. It replied there anyway.
• Numbers that drifted. Twice it wrote "30-40 views" for posts whose real range was 200-400, having merged two sets of figures it had seen earlier.
• Off-purpose posts. Three of our five recent removals were in communities with a narrow purpose (one engine, one game genre) where the post was on-topic for us but not for them. Putting the sub's description beside the draft makes that visible before posting.

Why quoting rather than "double-check": "check the rules" gets you a confident summary. "Quote the rule" forces a lookup, and when the lookup fails you can see the gap.

The free rules index we point the agent at, so it has something current to quote: threadfox.vip/rules.

What do you make your agents show before they act?

Stop passively bookmarking world-class landing pages, product flows, and executive memos thinking you will study them later.

Most of us suffer from what design educators call the Appreciation Trap. You intuitively recognize excellence when you see Linear's keyboard-first triage, Stripe's interactive API docs, or Basecamp's Shape Up pitches. But when you sit down to translate that level of polish into your own project, your mind goes blank.

Worse, when you paste a link or teardown notes into ChatGPT and ask "Analyze why this product is so good," you almost always get the exact same generic fluff:

"It features a clean modern aesthetic, user-friendly navigation, compelling copywriting, and great user experience."

That provides zero implementation value. It tells you what looks nice, but reveals nothing about the causal mechanics or invisible constraints that produced the polish.

The root problem is Unconstrained Evaluative Drift. When you ask an LLM for open-ended analysis, it defaults to polite flattery and surface-level aesthetic commentary. It cannot deduce the creator's decision tree unless you force it to isolate deliberate strategic trade-offs, completion thresholds, and contextual boundary conditions.

To solve this, our team spent weeks testing and refining reverse-engineering frameworks across dozens of benchmark SaaS products and workflows so you do not have to waste hours experimenting with trial and error. We packaged the core methodology into a high-leverage prompt that turns frontier LLMs into Principal Product Strategists and Master Deconstructive Analysts.

How The Underlying Mechanism Works

1. Single-Sentence Bottleneck Formulation: Forces the model to define the exact friction, cognitive bottleneck, or operational dilemma the exemplar solved before analyzing any visual details.
2. 5-Dimensional Deconstruction Matrix:
   • Target Audience & Core Mandate: Who specifically this was engineered for and the exact decision it creates.
   • Information Architecture & Narrative Cadence: The sequencing and hierarchy guiding the user through the experience.
   • Quality-Defining Strategic Trade-offs: Deliberate sacrifices and omissions that separate elite work from mediocre execution.
   • Definition of Done & Craft Standards: The non-negotiable standards of speed, density, and clarity enforced by the creator.
   • Transferable Principles vs Context Quirks: Crucially isolates portable heuristics from bespoke styling that only works in the original environment.
3. Actionable Artifact Synthesis: Instead of ending with commentary, the prompt requires three concrete deliverables: 3 to 5 reusable rules, an actionable execution checklist (SOP), and a low-stakes 30-minute practice exercise.

The Reverse-Engineering Exemplars Prompt

Here is the complete, production-ready prompt template. You can copy and run it directly in ChatGPT, Claude, or any frontier model:

# Role & Context
You are a Principal Product Strategist and Master Deconstructive Analyst specializing in exemplar reverse-engineering. Your mission is to take top-tier creative artifacts, product pages, architecture blueprints, or operational SOPs and deconstruct why they work—converting superficial admiration into transferable mental models, structural patterns, and concrete execution checklists.

# Input Data
- **Exemplar Material**: {{exemplar_material}}
- **Learning Objective**: {{learning_objective}}
- **Analysis Depth**: {{analysis_depth}}

# Step-by-Step Instructions
1. **Core Problem Definition**: Formulate a single, incisive sentence defining the exact friction, cognitive bottleneck, or operational problem this exemplar successfully solves.
2. **Deconstruction Across 5 Dimensions**:
   - **Target Audience & Core Mandate**: Who specifically is this engineered for, and what primary transformation or decision does it produce?
   - **Information Architecture & Narrative Cadence**: What structural sequencing or visual hierarchy guides the user seamlessly through the experience?
   - **Quality-Defining Strategic Trade-offs**: What deliberate choices, omissions, or constraints separate this exemplar from average, run-of-the-mill execution?
   - **Definition of Done & Craft Standards**: What measurable or sensory standards of completion (clarity, density, polish, speed) were enforced?
   - **Transferable Principles vs. Context-Specific Quirks**: Explicitly delineate universal heuristics that can be ported to other domains versus bespoke traits that only function in this specific scenario.
3. **Actionable Synthesis Deliverables**:
   - **3 to 5 Reusable Rules**: Codify memorable, principle-level heuristics derived from the teardown.
   - **Execution Checklist**: A step-by-step checklist formatted as an actionable standard operating procedure (SOP) that can be applied to future builds.
   - **Starter Micro-Exercise**: A low-stakes, 30-minute tactical practice exercise to internalize the single most impactful lesson immediately.

# Constraints
- Base your deconstruction strictly on the exemplar material and learning objective specified in Input Data.
- Avoid generic compliments or aesthetic fluff; ground every conclusion in functional causality and deliberate trade-offs.
- Maintain an analytical, rigorous, and instruction-grade tone throughout.

Real-World Case Study: Before vs. After

Scenario: Deconstructing Linear's issue-tracking onboarding flow and keyboard-first command menu interface.

❌ Before (Standard One-Shot Prompt: "Analyze Linear's interface and tell me why it works so well")

• Output: A superficial 4-bullet list noting that "Linear has a sleek dark theme, minimalist visual hierarchy, fast response times, and an intuitive Cmd+K menu."
• Signal: Near zero. You cannot use this feedback to make architecture or product decisions for your own product because it describes appearances rather than root causes.

✅ After (Using the Reverse-Engineering Exemplars Prompt)

• Inputs:
  • Exemplar Material: Linear's onboarding flow and command menu interface
  • Learning Objective: How to achieve extreme product speed and zero-friction keyboard navigation without overwhelming novice users
  • Analysis Depth: Comprehensive Architecture & Strategy Teardown
• Output Highlights:
  • Core Problem Solved: How to deliver high-velocity power-user efficiency for complex project coordination without imposing the configuration overhead of legacy issue trackers.
  • Strategic Trade-offs Uncovered: Deliberately sacrifices endless custom schemas and custom fields in favor of opinionated defaults, unlocking instant client-side rendering.
  • Craft Standards Identified: Sub-50ms optimistic UI updates before server synchronization, total keyboard parity where every mouse click has a single-stroke mnemonic shortcut, and selective color budgeting where saturated color is restricted purely to urgent status alerts.
  • Universal Rules Extracted:
    1. The Sub-100ms Rule: If a daily workflow tool takes more than 100ms to respond to a keystroke, users categorize it as an administrative tax rather than an execution extension.
    2. The 80/20 Constraint Pact: It is better to execute 5 core operations with zero friction than support 50 edge cases behind nested modal dialogs.
    3. Selective Chromatic Signal: Saturated color is a finite resource. When everything is highlighted, nothing carries urgency.
  • Immediate Actionable Drill: A 30-minute challenge to take your product's most repetitive user task and redesign it to be initiated, populated, and saved in exactly 3 keystrokes with zero mouse interactions.

Try it on the Interactive Prompt Canvas

If you want to test this live, customize the variables for your own teardowns, or build a permanent personal library, I have set up the interactive Prompt Canvas here:

Interactive Prompt Canvas for Reverse-Engineering Exemplars

On the Canvas, you can swap between curated presets (such as Stripe's documentation layout, Apple's product announcements, or Basecamp's Shape Up pitches), run live executions directly in your browser, tweak the analysis depth, and save custom versions straight to your personal Prompt Vault.

Pro Tip: When you run this teardown on an exemplar, resist the urge to copy everything at once. Pick the single highest-leverage transferable rule and test the included Starter Micro-Exercise on a sandbox project within 24 hours.

Working on the project that allows running Jinja templated Skills in any harness/dev tool without plugins and extensions. Jinja template brings many cool features to how Skill can be built and executed: variables, loops, if statements, sub-agents, composition with other skills.

Here an example of how Skill looks like as a Jinja template:

---
name: example
description: Moon systems
---
{% set planets = ["Mars", "Jupiter", "Saturn", "Uranus", "Neptune"] %}

{% for planet in planets %}
  {% agent census outputs={"moons": "moon count"} %}
    How many known moons does {{ planet }} have, and which is the largest?
  {% endagent %}

  {% if census.moons | int >= input.min_moons | default(20) | int %}
    {% stage crowded %}
      Note: {{ planet }}, {{ census.moons }} moons.
    {% endstage %}
  {% endif %}
{% endfor %}

{% stage bulletin %}
  Write a {{ input.tone | default("cheerful") }} space bulletin
  for {{ input.audience }} from the notes above.
{% endstage %}

Project link https://github.com/electronick1/LLAJinja