Kyler Stocks

Building KyOS in public ⚙️

My system for high-performance, habit design, and building a $75K/month business

Behind-the-scenes ↓

[Kyler Stocks @KylerStocks_]

Today I started the next step in my game.

Kyler's Game.

Here's what I have to offer:

- A peak into my life working for a SAAS company
- My journey as I learn how to Market and Sell
- Creating an Offer
- Becoming "Oversubscribed" as @DanielPriestley says.

Problem #1: how the hell

How the hell do you market products?
How the hell do you sell a service?

I've been selling for a while and creating a $100M Offer was foreign to me.

My #1 goal is creating an offer. Something I can pitch. Something people would understand.

What am I condensing? What information do I need to bring to a conversation to show you everything I can offer?

I didn't know how then. But now I have a direction.

I read @AlexHormozi's books.

$100 Million Offer and $100 Million Leads

Go buy them, study them, and apply them.

You want to change your life? This is what will do it.

It challenges you to change your perception of money, products, and services.

I've tested my offer and am refining it every day.

The new problem is aligning it with a 15-minute or less pitch.

Here's my go-to template for the past year:

[Kyler Stocks @KylerStocks_]

@felixleezd Can someone explain this to me.

I love Claude code skills and plugins. Does codex do better?

[Kyler Stocks @KylerStocks_]

@claudeai How does it compare to Sonnett?

[Kyler Stocks @KylerStocks_]

@hubermanlab ACL and Meniscus recovery rate.

Have 1 normal

1 with BPC and TB500 compare testing after, 3 months, 6 months, 1 year, 1.5 years, 5 years, 10 years.

[Kyler Stocks @KylerStocks_]

@anything Bet let’s do it

[Kyler Stocks @KylerStocks_]

There is levels to this.

[Victoria Slocum @victorialslocum]

Bigger context windows won't save your LLM app.

𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 is the discipline of designing the architecture that feeds an LLM the right information at the right time. It's not about changing the model itself, but about building the bridges that connect it to the outside world - retrieving external data, connecting it to live tools, and giving it memory to ground responses in facts.

The goal isn't to shove more data into the prompt. It's to design systems that make the most of the active context window - keeping essential information within reach while gracefully offloading everything else into smarter, more persistent storage.

And bigger context windows don't actually solve the problem. What we need is smarter management of what information stays active and what gets offloaded.

The six components:
1️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀: The decision-making brain that orchestrates how and when to use information. They evaluate what they know, decide what they need, select the right tools, and adjust strategy when things go wrong.
2️⃣ 𝗤𝘂𝗲𝗿𝘆 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Translating messy, ambiguous user requests into precise, machine-readable intent. Without knowing exactly what the user is asking, the LLM cannot provide an accurate response.
3️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: The bridge connecting the LLM to your specific documents and knowledge bases. This includes chunking strategies that balance retrieval precision with contextual richness.
4️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: Giving clear, effective instructions to guide the model's reasoning. Think Chain of Thought, Few-shot Learning, and advanced strategies like ReAct.
5️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆: The system that gives your application a sense of history and the ability to learn from interactions. Both short-term (immediate context) and long-term (persistent external storage).
6️⃣ 𝗧𝗼𝗼𝗹𝘀: The hands that allow your application to take direct action and interact with live data sources.

This is way more sophisticated than classic RAG. This is what separates basic LLM Q&A from production-ready applications that can maintain coherence, access live data, and actually get things done.

See this ebook for more: https://t.co/oztRS7rJW3

[Kyler Stocks @KylerStocks_]

@grok @grok I have no knowledge of any of this coding and have only worked in sales my whole life. Simplify for me and tell me what I would need to learn to implement this

[Kyler Stocks @KylerStocks_]

@grok Explain reinforcement learning

[Kyler Stocks @KylerStocks_]

@grok @grok What if I only had access to a at home PC (3060 intel 5) the internet and AI. With minimal capital (Less than $100)

[Kyler Stocks @KylerStocks_]

@grok @grok If you had to build a scalable model for sales how would you do it

[Kyler Stocks @KylerStocks_]

@grok @grok What data would be the most valuable to least valuable for this specific niche

[Kyler Stocks @KylerStocks_]

@grok @grok Lets say Selling Real Estate for a New Construction Builder. And How does verifiers law play a part into this?

[Kyler Stocks @KylerStocks_]

@grok @grok How would I use this to train you for a very niche use case? Would it be pdf files of data? What would be the best way.

[Kyler Stocks @KylerStocks_]

@ChrisLaubAI @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@lukebelmar @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@Nicolascole77 @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@brezscales @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@gkcs_ @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@elonmusk @grok analyze this post for gaps

[Kyler Stocks @KylerStocks_]

@benkellyone @grok analyze this post for gaps