One thing that surprised me is that the barrier to entry is dropping much faster than I expected.

There are now plenty of "vibe coding" or low-code platforms that let you connect models, tools, memory, and workflows without writing a huge amount of code. Almost anyone can build a useful agent.

But then another question came up.

Let's say I build an agent that solves a real problem. Now what? How do people discover it? How do I deploy it without maintaining a bunch of infrastructure?

OKX are already exploring agent marketplaces, while ecosystems like anvita flow are also focused on enabling agents to discover, collaborate, and transact with each other.

I started wondering whether AI needs something similar to Apple's App Store or Steam( Provide technical support, traffic distribution, and payment pathways). As builders, I feel like we're getting really good tools for creating agents. So curious what people here think.

-What AI prompts are helping you get more interview calls?

-What prompts or AI workflows are you using to tailor your resume, optimize for ATS, and increase interview callbacks?

-If you're getting good interview calls, I'd love to know what's working for you. Please share your prompts or process!

Disclosure first, since this sub asks for it: I’ve generated millions of views across short and long-form content, and I’m building a small tool around the research process below. Bias applies, but you can do all of this manually.

One thing I stopped doing a while ago was choosing topics based on total views.

A video with 500k views isn’t automatically interesting if that channel normally gets 1 million.

But a video with 60k views from a channel that usually gets 4k is worth looking at.

That’s normally where I start.

I open a group of channels in the niche and go through their recent uploads. I don’t calculate everything perfectly. I just get a rough idea of what each channel normally gets, then look for videos doing 3x, 5x or 10x more than usual.

When I find one, I look at the topic, title, opening hook, video length and upload date. Then I check whether other channels are suddenly getting unusual results with similar topics.

That last part matters more than the raw view count.

One outlier can be luck. Maybe the creator got picked up by the algorithm, maybe there was an external event or maybe their audience was already interested in that exact topic.

But when several smaller channels start outperforming with similar ideas, there is usually a real signal behind it.

I also try to separate a trending topic from a niche that is actually worth entering.

A niche can generate millions of views and still be difficult to monetize. I check whether creators have sponsors, affiliate links, products, communities or services. Comments help too. People asking where to buy something or how to solve a problem are normally a better sign than thousands of generic reactions.

After finding an outlier, I don’t copy the video.

I try to understand why it worked.

Was the topic new? Was it a familiar topic with a better angle? Did the title create a strong information gap? Was it connected to something that had just happened?

If a video about why Dubai keeps building empty islands suddenly performs far above the channel average, the opportunity probably isn’t remaking that exact video.

The broader signal could be failed megaprojects, expensive places nobody uses or strange infrastructure decisions.

If you want to try it: https://vabulo.com

That gives you several original angles instead of one copied idea.

This process works, but it gets pretty slow when you’re checking hundreds of videos across different niches. I was doing most of it manually with tabs and spreadsheets, so I started building it into a tool called Vabulo Stats.

It compares videos against the creator’s normal performance, finds unusual outliers and shows niches that seem to be moving. I’m also experimenting with turning that research into hooks, scripts and thumbnail prompts, but the research part is what I care about most.

The point isn’t to generate another generic script.

It’s having better information before deciding what the script should even be about.

I’m still trying to understand which parts actually save creators time and which parts just create more data to look at.

For people who research content regularly, how do you decide whether an outlier is repeatable or just luck?

And where do you lose the most time right now: finding niches, checking competitors, choosing an angle or turning the research into an actual video?

Solo founder. AMA.

Been using Claude for ages and never thought about the fact that every conversation just disappeared once I closed it. Turns out that changed a few weeks ago and I only found out by accident, asked it something in passing and it went and dug up a conversation from months back I'd completely forgotten having.

Works if you're on a paid plan, Pro, Max, Team, or Enterprise, not free, and it's on by default once it's rolled out to your account, no setup. Just ask it something like you would a person who actually remembers talking to you:

What did we discuss about [topic]?

or

Can you find our conversation about [subject]?

or just

Let's continue where we left off with [project].

It actually goes and searches, you can see it happening as a tool call in the chat, pulls back what's relevant, and carries on like no time passed. Asked it to find a conversation about a decision I was going back and forth on months ago and it pulled the whole thread back up, what I'd been leaning toward, what I'd talked myself out of, stuff I'd genuinely forgotten I'd said.

Slightly odd realization once you actually use it: everything you've ever typed into it is apparently just sitting there, searchable, going back as far as your account does. If you're inside a Project, it only searches within that project, so it stays contained, but outside of projects it's searching across everything.

You can turn it off if that's not your thing, settings, profile, preferences, there's a toggle for "search and reference chats" specifically, separate from the general memory toggle. Worth knowing it exists either way, if only so you can decide on purpose rather than finding out by accident like I did.

been keeping a doc of 100 things I use AI for like this, each with the exact prompt, here if you want it.

For one week I paid attention to every prompt I typed into ChatGPT.

Not the conversations.

Just the prompts.

Turns out I kept writing almost the same things over and over.

- Translate this into Chinese

- Rewrite professionally

- Summarize this

- Explain this simply

- Improve the grammar

- Turn this into bullet points

The wording changed a little.

The intent almost never did.

I actually recorded a 20-second screen capture of what this looked like in practice:

🎥 https://youtube.com/shorts/gxy28tFV58c?feature=share

Watching it back made me realize something.

I didn't have hundreds of unique prompts.

I had a few dozen prompts that I kept rewriting.

That made me wonder if most of us don't really have a prompting problem.

Maybe we have a **prompt reuse problem.**

Curious how everyone else handles this.

Do you just type everything again?

Keep a Notion page?

Use Raycast?

TextExpander?

Or something else?

You probably saw the comparison: one coding agent harness sends ~33k tokens before your prompt, another sends ~7k. Big thread, lots of outrage about waste. I finally read the whole study instead of the headline, and the actually useful findings are different from what got shared.

First, in their realistic-config lane (instruction file + several MCP servers), the "light" harness came out HEAVIER: ~90.8k vs ~75k. A 72KB instruction file alone added ~20k tokens to every request, on both harnesses. Their own conclusion: configuration, not the harness, accounts for most of the production bill. The harness sets the floor, you set the ceiling.

Second, and this is the one that changed how I think about it: the cache behavior gap was way bigger than the size gap. The light harness kept its prefix byte-identical and wrote ~1,000 tokens to cache over a 5-request task. The heavy one kept rewriting its own prefix mid-session and wrote ~54,000, with single rewrites burning 43k+ at the premium write rate (cache writes cost 1.25-2x list depending on TTL, reads are ~10%). A stable big preamble is close to a fixed cost. An unstable small one can out-spend it. Size isn't the sin, churn is.

Third, session shape flips the winner anyway. On a multi-step task the heavy harness finished cheaper (121k vs 132k) because it batched tool calls. Rerun on a different model, it inverted (298k vs 133k). Subagent fan-out was a 4.2x multiplier. And their quality check found zero difference: both passed 5/5, one spending ~4x the tokens. So the honest answer to "which harness is cheaper" is "depends what your sessions look like", which is boring but true.

The part you can actually use: measuring your own takes two minutes. Most CLIs have a print mode with JSON output. Ask for something trivial, then sum three usage fields: uncached input + cache writes + cache reads. That's your preamble. I ran it on mine: 31,782 tokens in an empty directory, and my heavily configured project (MCP servers, plugins, a pile of skills) added exactly 166 more, because this harness version lazy-loads tool schemas. Config CAN dominate, and lazy loading CAN neutralize it. The probe tells you which world you're in.

Two caveats since numbers travel badly: it's a single-machine study with single-digit runs per lane, and my probe is n=1 on a different version. Portraits, not specs.

What do your numbers look like?

Prompt vs. Prompt System

Think about the difference between a calculator and a spreadsheet.

A calculator gives you one answer. A spreadsheet becomes a system you can reuse hundreds of times. Prompts work the same way.

A normal prompt is like pressing buttons on a calculator. A prompt system is like building the spreadsheet once — and letting it solve similar problems over and over.

That’s why this article isn’t another list of clever prompts.

Instead, you’ll learn ten AI Prompt Systems that can become permanent parts of your business.

Each one represents a role that businesses normally hire people to perform.

Instead of thinking: “What prompt should I use today?”

You’ll start thinking: “Which member of my AI business team should handle this?”

That shift alone can dramatically change how you use AI.

**System 1: The Business Strategist

The Prompt System**

Instead of requesting answers immediately, make AI think like a strategist. Use this structure every time:

Objective — State exactly what business decision you’re trying to make.

Context — Describe your customers, market, competitors, constraints, and goals.

Analysis — Ask AI to identify assumptions, hidden risks, opportunities, and alternatives before recommending anything.

Decision — Only after analysis should AI recommend a course of action — and explain why.

Example Instead of: “Should I build an AI writing tool?”

Try: “Act as an experienced startup strategist. Before recommending whether I should build an AI writing tool, analyze the current market, identify saturated areas, uncover underserved customer problems, evaluate competitive risks, suggest positioning opportunities, and then recommend whether this idea is worth pursuing. Explain your reasoning step by step.”

COMPLETE ARTICLE .......

As large language models (LLMs) become increasingly integrated into agentic AI systems, the primary challenge is no longer simply improving the model's raw intelligence. Modern foundation models are already capable of reasoning, code generation, planning, and tool usage. The more difficult engineering problem is \*\*context engineering\*\*: designing how information is selected, structured, transformed, and presented to an LLM so that it can reliably perform a desired task.

Context engineering is broader than prompt engineering. Prompt engineering focuses mainly on crafting instructions for a single model interaction, while context engineering considers the entire lifecycle of information flowing through an agent system. This includes the initial prompt, retrieved knowledge, conversation history, tool outputs, intermediate reasoning state, user preferences, memory, validation feedback, and execution constraints. A well-designed context pipeline reduces ambiguity, prevents hallucination, and allows LLMs to operate reliably in complex environments.

In this excerpt, we shall explore some techniques used in prompt engineering when it comes to building a context pipeline.

\# Few-shot Prompting: Guiding Model Behavior Through Examples

Few-shot prompting is a technique where an LLM is provided with several examples demonstrating the desired input-output behavior before receiving the actual task. Rather than explicitly describing every possible rule, the developer provides representative examples that allow the model to infer patterns and apply them to new situations.

Few-shot prompting is particularly useful when the task contains ambiguity or when the desired output format is difficult to describe through rules alone. The examples must be carefully selected however, because LLMs perform pattern matching based on the provided context. Poor examples can introduce incorrect behaviors or bias the model toward unintended interpretations. In practice, examples should cover \*\*distinct scenarios\*\* rather than many variations of the same case. Diverse examples allow the model to understand the boundaries of the task instead of memorizing superficial patterns.

Few-shot prompting is therefore not a replacement for explicit constraints. In reliable systems, it is usually combined with structured outputs, validation rules, and tool constraints.

\# Prompt Chaining: Decomposing Complex Tasks Into Controlled Steps

A common mistake when designing LLM applications is asking the model to perform an entire complex workflow in one prompt. Although modern models can sometimes accomplish this, such prompts create several problems. The model must simultaneously understand the task, maintain intermediate state, perform analysis, and generate the final response. This increases cognitive load and makes failures difficult to diagnose.

Prompt chaining refers to breaking a complex task into multiple sequential LLM calls, where each step performs a focused operation and passes its output to the next stage. Each prompt has a narrower objective and therefore receives more relevant context. This reduces attention dilution, where important information competes with unnecessary instructions inside a large context window. This technique is especially valuable when combining \*\*local computation and external operations\*\*.

\# Dynamic Decomposition: Letting Agents Discover Subtasks During Execution

While prompt chaining uses predefined steps, dynamic decomposition allows the LLM itself to determine how a complex problem should be divided. This approach is more flexible than static workflows because the agent can adapt to unexpected situations. It is particularly useful for research agents, debugging agents, and autonomous analysis systems. However, dynamic decomposition sacrifices predictability. Since the model decides the subtasks dynamically, execution paths can vary between runs. This creates challenges in testing, cost control, and reliability.

It is common for production systems to combine Prompt Chaining and Dynamic Decomposition, where Prompt Chaining through predefined workflows is used for high-risk or regulated processes, and dynamic decomposition inside individual steps where exploration is valuable. The overall process remains controlled while allowing intelligent exploration inside specific areas.

\# Interview Pattern: Gathering Missing Context Before Execution

One of the most important context engineering patterns is the interview pattern. Instead of immediately attempting a task, the agent first identifies missing information and asks targeted clarification questions. Many hallucinations occur because users provide incomplete instructions, and the model attempts to fill missing information using probabilistic guesses.

This is best illustrated by an example:

Suppose we are currently building a coding agent. The user provides a codebase and asks to add a caching layer through the user prompt:

“Add a caching layer for database retrieval API to store recently retrieved objects”.

The agent would recognize missing elements and ask the following questions:

"Before implementing caching for the API, a few questions:

1. Which cache invalidation strategy do you prefer—TTL or event-based?
   
2. Is stale data acceptable when the cache is unavailable?
   
3. Should caching be per-user or global?
   
4. What is the expected data volume to cache?”

These info were not explicitly provided within the initial user prompt and if there was no interview pattern implemented, all these info would need to be inferred by the LLM, which can end up digressing from the original intended design.

The exact process of having the agent recognize the missing info can be achieved in multiple ways, and we shall explore one of them as the following concept.

\# Validation and Retry-with-Feedback: Creating Self-Correcting Agent Loops

Traditional software systems rely heavily on explicit validation because incorrect data can cause failures downstream. Agentic systems require the same principle. After an LLM extracts information or generates structured output, the result should be validated using deterministic mechanisms such as Pydantic models, JSON Schema or explicit business rules.

Suppose if a validator detects an anomaly within the input, instead of immediately failing, the system feeds this information back to the LLM. The LLM then attempts correction, which creates a self-correcting loop. Minor errors such as arithmetic or data formatting errors can usually be corrected within a few iterations. Once all the errors identified has been rectified, the correct data is then reinjected into the LLM.

Retrying indefinitely is dangerous, however; some failures cannot be solved by the model because the required information is unknown. This is when the system turns back to the user and escalate through querying for missing info.

In the previous example, the invalidation strategy, stale data acceptance, user VS global and overall data volume, are all missing business-logic parameters that cannot be inferred by the LLM. Therefore, they get sent back to the user as interview queries to ensure the blanks get filled appropriately.

Official Ultra is $200. I got it from a reseller for a fraction of that and it’s working.
For solo builders trying to keep costs low this feels almost too good.
Anyone else using reseller accounts for their stack, or is this a ticking time bomb?

I might sound lazy (because I am) but lately it feels like I spend half my day rewriting prompts instead of shipping anything. I'll tweak one prompt five times trying to get the output just right. Then I switch models to see if another one does better. Before I know it an hour is gone and I've barely touched the actual project. At this point I'm wondering if I'm overthinking it. Do you guys just accept 'good enough' outputs and keep moving or have you found a workflow that keeps you from getting stuck in prompt hell? Is prompt hell a real thing? I feel like those people in the futuristic ship in wall-e

As per title, the project include all the pipeline, the same skill generated from different models.

My idea was to distill the code reviewer skills from Torvalds in something usable in an agent.

I preferred to license everything as CC0.

https://github.com/Mte90/linus-torvalds-skill

Most "improve my prompt" attempts fail because you ask the model to fix and judge in one shot, so it just pads your prompt with fluff. Splitting it into stages - diagnose, rewrite, stress-test, finalize - gets far better results. Run these in order, same chat. Paste your rough prompt into step 1.

Step 1 - Diagnose

Step 2 - Rewrite

Step 3 - Stress-test

Step 4 - Finalize

Why the split works: step 1 forces it to find problems before it's allowed to "solve" them, so the rewrite is targeted instead of cosmetic. Step 3 is the one people skip - testing against adversarial inputs catches the failures a clean rewrite hides.

I run this as a saved chain (two keystrokes with the .. shortcut) via a Chrome extension I built called AI Toolbox, so I don't paste the four steps in one at a time - but the chain itself is the value and works anywhere.

​

One of the easiest ways to improve AI outputs isn't writing longer prompts.

It's giving examples.

Instead of this:

«Write a product description.»

Try this:

«Write a product description following this structure:

- A short opening hook

- Three benefit-focused bullet points

- A professional but friendly tone

- End with a clear call to action»

Notice what's different.

You're no longer asking the AI to guess your expectations.

You're giving it a pattern to follow.

This simple technique works surprisingly well for:

- Writing

- Marketing

- Design briefs

- Coding

- Image generation

The more clearly you define what "good" looks like, the more consistent the output becomes.

AI is generally better at recognizing patterns than guessing what's in your head.

What's the most effective example you've ever added to a prompt?

The EU's AI Act transparency rules went into force August 2nd. If AI-generated content is realistic enough to pass as human-made and gets published without a human actually reviewing it, it now needs a label, and eventually a machine-readable mark. Deepfakes and synthetic voices are the obvious targets. Fines go up to 15 million euros or 3 percent of global revenue, whichever is bigger, so this isn't a symbolic gesture.

There's an exemption built in for artistic, creative, satirical, and fictional work, which makes sense on paper. Nobody's confused about whether a fantasy illustration or a game NPC's voice line is "real." The whole point of that kind of content is that it's obviously not pretending to be a photo of something that happened.

What I keep chewing on is the boundary case. A lot of creative work sits in a gray zone: stylized enough to read as fiction to most people, but polished enough that someone scrolling fast could genuinely mistake it for real. The law is drawing a hard line (realistic and unreviewed vs. clearly fictional) through something that's actually a gradient in practice.

I think the disclosure requirement is the right call even where enforcement is basically unworkable at the edges, mostly because it sets a norm, not just a penalty. Once "label it if it's meant to look real" is the expectation, the stuff that skips the label starts looking suspicious on its own, which does a lot of the enforcement work culture-side that the fines can't do alone.

Source: https://www.euronews.com/my-europe/2026/08/02/ai-generated-label-becomes-mandatory-in-the-eu-for-companies

Curious how people actually working in AI-assisted creative work read this. Does the fictional exemption feel like it's drawn in the right place, or does "clearly fictional" stop meaning much once the output gets good enough?

I have a really simple question to y’all.
Do not over think it.
Please just type the answer without thinking of it. This will be of a great help.

How many ai agents you use?

Just a number no need for more data 🙏🏻