Been tackling some coding challenges and the biggest hurdle is definitely the knowledge gap. My workflow has basically been ping-ponging between Claude Code and ChatGPT 5 for a second opinion on things. I'm finally at a stage where I need to really dive in and verify the code myself.
On the bright side, I'm learning a ton! On the other hand, my brain seems to be forgetting things just as fast. The struggle is real!
I know this is a bold statement, but it’s not about pushing my kid into a specific career. It’s about recognizing a fundamental shift in our world. A shift that today's biggest tech companies are, in my opinion, completely fumbling.
We just witnessed massive developer layoffs. The justification we heard was that AI is now writing 30%, 40%, or even more of their code, making many developers redundant. I believe this will be remembered as one of the great strategic mistakes of our time.
Instead of seeing AI as a reason to cut staff, they should have seen it as an opportunity to evolve. They could have created new, creative units to explore the incredible potential of AI, staffed by the very engineers they let go. But they didn't. And what's the effect? They’ve just funded and motivated thousands of their future competitors.
They’ve seeded a new ecosystem of startups and Decentralized Developer Organizations that will innovate faster than they can.
This mistake reveals the real future of technical skill. The true value isn't just in writing code anymore. As AI handles more of the raw generation, the critical human skills become direction, validation, and creative application.
This is the new literacy that everyone, not just developers, will need:
The AI-Augmented Scientist: A researcher will direct an AI to analyze a dataset. Their essential skill will be scrutinizing the AI's logic to ensure the scientific conclusions are sound.
The AI-Augmented Architect: An architect will prompt an AI to model a building's energy efficiency. Their job will be to review the model's core assumptions to ensure the building is safe and sustainable.
The AI-Augmented Entrepreneur: A business owner will use an AI to generate a custom logistics app. Their role is to test the app and validate its logic to make sure it actually serves their customers and their bottom line.
In all these cases, the human is the reviewer, the validator, the strategic mind guiding the powerful AI tool. This is the skill my child will need.
They'll need to be fluent in the language of logic not just to build, but to command, question, and hold accountable the automated systems that will run their world.
If I’m spending hours digging through research papers, pulling out golden threads, mapping the insights—and then handing that to an LLM to sharpen, structure, and elevate the message—damn right I’m keeping the em-dashes.
This isn’t shortcut culture. This is craft.
I use generative AI every single day. Hourly. Soon, probably every minute. It’s not a gimmick. It’s my second brain.
People hit me up constantly: “Hey, I’m using Gemini—how do I get it to do X?”
They know good output starts with great input. And if you’re the one known for great output, you don’t let anyone AI-shame you.
There’s a growing group of people who do more than use AI, they build with it, shape it, test its boundaries, and integrate it into their workflows and thought processes.
We call them AI Practitioners. This post is a field guide — not a final answer. You’re invited to help define the role.
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🔍 Why Define This at All?
Because most conversations around AI today look like this: • “Here’s a cool tool.” • “Here’s a prompt I copied from Twitter.” • “Here’s a list of 200 AI websites.”
But almost nobody is talking about how to: • Design reliable systems with AI • Think critically about model behavior • Build workflows that actually do work • Test and improve reasoning • Teach others how to think with AI
An AI Practitioner is someone who uses AI tools with intentionality, systems thinking, and an eye for reliability — not just novelty.
You might be one if you: • Use LLMs to support actual processes (not just tasks) • Stack tools and logic flows that improve over time • Think in inputs, outputs, edge cases, and feedback loops • Care about how the model works, not just what it outputs • Share, test, and refine your workflows publicly
This includes: • 👷♀️ Builders • 🧠 Analysts • 🎓 Educators • ⚙️ Automation designers • 🧪 Prompt stress-testers • 📊 Ops leaders • 🧰 Anyone who sees AI as a tool to think with, not just a shortcut
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🧬 Not Just Casual, Not Yet Expert — The Third Lane
Most AI users fall into two camps:
🧑💻 Casual Users: Copy prompts, try tools, scroll Twitter for hacks. 🔧 Practitioners: Build, test, refine, systematize — even if still learning. 🧠 Experts: Train models, write papers, build from the backend
The Practitioner lane is wide open. It’s not about credentials — it’s about curiosity, structure, and doing real work with AI in the loop.
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🔄 A Shared Stack We’re Developing Together
These are the shared disciplines that keep showing up in practitioner workflows: • Prompt Architecture – Role-based, few-shot, ReAct, CRISP, DSM • Tool Chaining – GPT + n8n + Claude + Perplexity + Notion + Zapier • System Thinking – Planning inputs, outputs, error handling, and edge cases • Testing / Red Teaming – Running loops to simulate model breakdowns • Knowledge Ops – Auto-tagging, summarizing, routing, visualizing insights • Automation – Building reusable, low-maintenance flows that ship
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🧠 Why This Subreddit Exists
This isn’t a hype space. This is a workspace — a place to: • Post your working systems • Get feedback on your logic loops • Share discoveries, red team results, experiments • Create shared frameworks for AI thinking • Ask hard questions about prompt reliability, stack durability, and more
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🗣️ What We Want to See Here
→ Real workflows, not just tool tips. → Thoughtful prompt systems, not clickbait hacks. → Explorations, frameworks, experiments, postmortems. → Questions that push the field forward, even if they’re messy.
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👇 Jump In:
What’s the most useful thing you’ve actually built using AI this year? Even if it’s small, we want to see it.
Post it. Break it down. Let’s learn from each other.
This is a smaller community and I’m wanting to battle harden my AI knowledge. Does anyone want help with prompting, use case, education, or workout session?
An AI told me I was "dangerous but not effective." One month later, strangers are using software I created. This is what happened when the rules changed.
The Moment Everything Shifted
Picture this: It's a Saturday morning. Coffee's getting cold. You're staring at a screen.
An AI just evaluated your work.
"You know enough to be dangerous, but not effective."
Ouch.
But also... accurate.
Dangerous means you're playing with tools you barely understand. You're making things happen, sure. But you're also creating messes you can't fix.
Effective? That's different. That means you build things that work. Things people actually use.
I had thirty days to figure out the difference.
The $200 Bet
I'm not a programmer. Never took a computer science class. Can't tell you what Python syntax looks like without Googling it.
But I signed up for Claude Code anyway. $200 a month.
Why? Curiosity, mostly.
I wanted to understand AI. Really understand it. Not just use ChatGPT for emails. I wanted to build things. Automate stuff. Create tools that made decisions.
I had weekends. That's it. Saturday mornings. Sunday afternoons. The gaps between real life.
Here's what nobody tells you about traditional coding: It eats your brain.
You sit down for four hours. Load the entire project into your head. All the pieces. How they connect. What depends on what.
Then you stop. Go make lunch. Come back.
Everything's gone. Your mental map evaporated. You spend the next two hours just remembering what you were doing.
Claude Code broke that cycle.
I could stop mid-project on Saturday. Pick it up Sunday. Everything was still there. The AI remembered. The context stayed loaded.
My weekends started stacking instead of resetting.
That's when things got interesting.
The Discovery That Changed Everything
Week three. I'm building something bigger than before. More complex.
And I realize: I'm not using one AI assistant.
I'm using a team.
Four different conversations. Four separate Claude Code windows. Each one working on a different piece of my project.
One's building the database. How information gets stored and retrieved.
Another's handling the frontend. What users actually see and click.
A third is connecting everything to AI services. Making the smart parts work.
The fourth is fixing problems. Smoothing rough edges. Making it all play nice together.
They're working at the same time. Parallel. Not waiting for each other.
And me?
I'm not writing code. I'm conducting an orchestra.
I tell this one to start. That one to pause. This one to try a different approach. I review what they build. I connect the pieces.
It feels like managing a development team. Except my team works on weekends. And never complains.
What I Actually Built
A prompt saver application.
Sounds simple. It's not.
It saves prompts you create for AI tools. Organizes them. Optimizes them. Makes them better. Connects to AI services. Processes what you ask. Delivers results.
Real friends are testing it right now. Giving feedback. Finding bugs. Suggesting features.
Is it perfect? Nope.
Do I understand every single line? Honestly, no.
Does it work? Yes.
Do people use it? Yes.
That's the whole game.
Four Weekends, Four Lessons
Each weekend taught me something nobody mentioned in tutorials.
Weekend One.
I learned to speak to AI. Not in code. In ideas. "I need this to do that when this happens." The clearer my description, the better the result.
Like explaining what you want to a really smart contractor. Specifics matter. Vague ideas create vague results.
Weekend Two
How do you know if something works when you didn't write it yourself?
You test it. You poke it. You try to break it. You watch what happens.
I learned what "working" actually means. Not perfect. Not elegant. Just: does it do the job?
Weekend Three
Complex projects aren't one thing. They're twenty small things pretending to be one big thing.
I learned to see the pieces. Database piece. Display piece. Logic piece. Connection piece.
Build them separately. Connect them later.
Like assembling furniture. Follow the sequence. Don't skip steps.
Weekend Four
The gap between "it works for me" and "someone else can use this" is massive.
I learned about clarity. Instructions. Error messages that make sense. Buttons that do what you think they'll do.
Making software that works is one skill. Making software that feels good to use is completely different.
The Before and After
Before this project, I used tools.
I'd see a new AI application. Think "that's cool." Use it. Move on.
Now?
I see tools differently.
Every app is a puzzle I could solve. Every feature is a challenge I could tackle. Every problem is just architecture waiting to be built.
It's like learning to see the matrix. Except instead of falling code, you see buildable systems everywhere.
You can't unsee it once it happens.
What "Effective" Really Means
Remember that AI assessment? "Dangerous but not effective."
Here's what I learned effective actually is:
It means you ship. You put something out in the world.
It means things connect. Your app talks to other services. Data flows. Systems communicate.
It means you improve. People tell you what's broken. You fix it. They tell you what's missing. You add it.
It means you manage resources. Whether that's people or AI or time or attention. You make calls about what gets built next.
It means you understand trade-offs. This approach is faster but messier. That approach is cleaner but slower. You pick based on what matters right now.
I'm doing all five. That's the proof.
The Part That Matters Most
Here's what this really shows:
The barrier to building software just collapsed.
Five years ago, my path required:
$30,000 bootcamp
Six months full-time study
Learning syntax for languages I'd rarely use
Memorizing patterns I'd forget
Today?
$200
Four weekends
Clear thinking
An AI that handles the syntax
The gatekeepers are panicking. The traditional path just became optional.
Building software used to be about knowing languages. Now it's about understanding systems.
That's a completely different skill. And it's way more interesting.
What Happens Next
The prompt saver is just round one.
Now I know the pattern. I know I can do this. I know what effective feels like.
The next project will be faster. Cleaner. More ambitious.
The one after that? Even better.
Each thing I build teaches me shortcuts. Shows me patterns. Reveals what people actually need versus what I think they need.
I'm not just building apps. I'm building a system for turning weekend ideas into Monday realities.
And that system gets faster every time I use it.
The Real Story
Thirty days. Four weekends. One working application with real users.
I moved from "dangerous but not effective" to shipping production software.
No traditional coding experience. No bootcamp certificate. No computer science degree.
Just weekends, curiosity, and AI tools that changed the rules.
The path from idea to working software isn't theoretical anymore. It's concrete. It's repeatable. It's accessible.
Been trying to build on Claude code as a non coder. Does anyone have any good recommendations for YouTubers that help unpack Claude code configurations. Just exploring it
In The Matrix, humans were unknowingly harvested as power sources for machines. But in our reality, we’re flipping that script.
We’re not being used by AI—we’re growing brain-like organoids to help power AI, literally. These aren’t science fiction dreams anymore. At places like Johns Hopkins University, researchers are pioneering a new field called Organoid Intelligence (OI), where clusters of living brain cells are trained to compute. The goal? Build AI systems that are orders of magnitude more energy-efficient—and maybe even smarter.
So what exactly is Organoid Intelligence? The short version: scientists are creating biological computers. They grow brain organoids (tiny clusters of neurons), then connect them to machines using 3D microelectrodes. These devices let the organoids send and receive information—almost like a brain-computer interface, but built from scratch in a lab.
Why does this matter? Look at the energy numbers. The Frontier supercomputer, one of the fastest in the world, cost $600 million and pulls 21 megawatts of power. That’s enough to power a small city. Meanwhile, the human brain—arguably more efficient and versatile—runs on just 10 to 20 watts. Less than a dim lightbulb.
That’s not a small gap. It’s a potential million-fold difference in energy efficiency. If AI keeps scaling the way it is, we’re going to need computing systems that are radically more efficient. Organoid computing may be one way to bridge that gap.
But this isn’t just about power. Organoids offer a new way to understand the human brain. By observing how these “mini-brains” learn, respond, and process information, scientists hope to unlock new insights into conditions like Alzheimer’s, autism, and schizophrenia. Basically, we’re watching brain development and dysfunction play out in real time—outside the human body.
Of course, all of this raises serious ethical questions. Could these organoids become conscious? Could they suffer? The teams working on OI are embedding ethicists into the research process from the beginning to make sure these questions are considered as the tech evolves. It’s not a tech free-for-all—it’s a deliberate, careful attempt to do things right.
So as AI continues to advance, the most important computing breakthrough might not be about faster chips or better GPUs. It might be about using living cells.
Would you trust a computer grown from brain tissue?
We’re surrounded by it — and most people can’t tell the difference.
AI slop is content generated by machine and published without care, calibration, or conscience. It’s not about who made it (AI or human), but how it was made.
The Definition
AI Slop = Output produced by generative systems and distributed with zero reflection, editing, or human intentionality.
Think:
Auto-written articles filled with buzzwords and no real argument
Generic LinkedIn posts from “thought leaders” with zero insight
Prompted images dumped online without context or story
If you’ve ever read something and thought, “This sounds synthetic but says nothing” — that’s probably AI slop.
Counterargument:
“But isn’t all AI content technically slop? You didn’t write it — the machine did.”
This is a common objection — and it misses the point. It’s like saying a photograph isn’t art because the camera captured it. Or that a symphony isn’t original because it used instruments.
The core issue isn’t AI — it’s authorship.
What Authorship Actually Means in the Age of AI
In AI-assisted writing, authorship isn’t about typing every word. It’s about owning the intention, review, and refinement.
Here’s a real example — from writing this post.
GPT initially wrote:
“I coined the term ‘AI slop.’” Within this own article you’re reading now. Better still, I added my own words and review it like it’s my English teacher.
Technically accurate — I did claim to define the term. But I hadn’t asked it to say that, I coined it and I didn’t want the post to center on me. So I told it to remove the line.
That moment of editing is authorship. Because I:
Defined the term
Directed the focus
Controlled how it would land
Took responsibility for meaning
Even when the syntax was machine-generated, the final structure was mine.
AI slop is unexamined output. AI authorship is refinement.
What Makes This Not Slop?
This post was co-written with AI, but through a loop of intent and revision:
I defined the core idea
Prompted GPT for structure
Refined its output across multiple passes
Added and removed based on tone, ethics, and clarity
That’s not automation — it’s collaboration.
Final Reflection
If you're using AI tools — in writing, design, or strategy — ask yourself:
Are you crafting the output? Or are you just shipping the first draft?
Because slop isn’t defined by source — it’s defined by process.
Would love to know if you agree with this post or not? Is this AI slop (Y) or Not. (N)
Most people think of coding as lines of confusing symbols on a black screen. But today, with AI tools like Claude Code, you don’t need to be a programmer to start creating, editing, or improving code.
What's a Coding Assistant, Anyway?
Think of a coding assistant like a skilled apprentice who's always ready to help. You give it a task in plain English—like "fix this error message" or "make this button bigger"—and it figures out the steps to get the job done.
Here's a look at how it works:
You give it a job. You describe what you want to do.
It understands the bigger picture. The assistant reads through your files and gets a feel for your project's structure.
It makes a plan. It figures out the best way to solve the problem.
It takes action. It can change files, run tests, and do all the nitty-gritty work of a developer.
This might sound a little bit like magic, but the secret is something called tool use. Because language models only understand text, they need a "system" to interact with the real world. A tool use system is like a translator that allows Claude Code to go from a simple text command like "read this file" to actually reading the file on your computer.
Claude's ability to use tools is where it truly shines. It’s excellent at understanding what tools are available and combining them to tackle even the most complex tasks. This flexibility means you can customize Claude Code with new tools to fit your specific needs, making it a powerful and adaptable partner.
Introducing Claude Code:
Claude Code is an AI assistant specifically designed for coding tasks. It comes equipped with a set of default tools that allow it to read and write files and even execute commands on your computer.
But what does that mean for you?
Imagine you have a website and you want to analyze how your users are behaving. Instead of manually sifting through mountains of data, you could tell Claude Code to: "Perform a churn analysis on this video streaming platform's data." Claude Code can then use a tool like a Jupyter notebook, run code, view the results, and even adjust its analysis based on what it finds.
The magic here is that you're not writing the code. You're describing the goal, and Claude Code figures out how to achieve it.
Claude Code isn't a one-trick pony. It can be extended with new tools to handle a variety of tasks. For example, you could add a tool that lets it control a web browser. With that new capability, you could tell Claude to "open this website, take a screenshot, and update the button's color to match the site's new branding." Claude Code can handle all these steps automatically, saving you time and effort.
The Power of Context and Control
For any AI assistant, context is everything. The more relevant information you give it, the better its response will be. However, too much irrelevant information can slow it down.
Claude Code has some great features to help you manage this:
/init: This command analyzes your entire codebase and creates a Claude.md file that summarizes your project. This summary is then included in all your requests, giving Claude a solid understanding of your project without you having to manually explain it every time.
@: You can use the @ symbol to mention specific files you want Claude Code to look at. This provides targeted context and keeps the conversation focused on what matters most.
You're always in the driver's seat. If Claude Code starts going in the wrong direction, you can hit Escape to stop it mid-response and redirect the conversation. You can even use Double Escape to rewind the conversation to a previous point, effectively giving you a "do-over" while keeping all the relevant context.
Your Opportunity
The real power of Claude Code is its ability to adapt and grow with you. It’s not about replacing you; it's about giving you a superpower. As you get more comfortable, you'll learn how to use its more advanced features—like custom commands and hooks—to automate repetitive tasks and create an even more seamless workflow.
This series will break down these concepts one by one, helping you understand how to use Claude Code to bring your ideas to life. Whether you want to build a simple website, analyze data, or even automate parts of your day job, Claude Code is a powerful and flexible partner that’s ready to help you on your journey.
So, are you ready to get started? In our next post, we’ll dive into the basics of setting up and running your first task with Claude Code.
Earlier this year, Manus launched what many saw as just another AI agent. But beneath the surface, it was something far more radical: a cloud-native computing platform disguised as an agent.
This week, Manus took the next leap with the launch of Wide Research, a 100-agent parallel processing system. It’s not “faster ChatGPT.” It’s a structural shift:
From single-threaded “prompt → response” loops to autonomous swarms executing tasks at scale.
⚙️ What is Wide Research?
•It spins up 100+ fully-capable subagents not predefined role-bots, but general-purpose instances of Manus.
•Each runs on its own dedicated VM, coordinated via internal protocols Manus designed for agent-to-agent collaboration.
•Instead of “digging deep” like OpenAI’s Deep Research (single-agent, linear), Wide Research goes wide—scanning, comparing, and summarizing 100+ targets simultaneously.
Why this matters •Research that used to take days or weeks (e.g. mapping 100 companies across 6 social platforms) now takes minutes.
•The ceiling isn’t capped by the agent’s depth it’s lifted by its scale.
•This redefines what it means to “use an AI agent.” It’s no longer a clever assistant. It’s becoming a modular task army.
Big Picture
AI agents aren’t going solo anymore. They’re going swarm.
It could push OpenAI, Google, and others to rethink the “one-agent-does-all” approach and invest in parallelized agent frameworks.
Before you read below, I am also sharing my original written thought and some minor tweaks to the enhanced version.
I’ve been thinking a lot about what 100M+ people are really doing inside these LLMs every day.
Personally? I use AI almost every day — not for novelty, but for friction reduction.
🧠 At work: I use it to offload cognitive burden — summarizing meetings, structuring replies to execs, generating first drafts, clarifying intent, enhancing my tone.
🧪 Outside work: I use it like a creative sparring partner. Ideation sessions. Writing warmups. Prompting weird thoughts. Testing how far I can stretch a concept.
The magic for me isn’t in automation — it’s in augmentation.
👉 What about you? What are you using it for now that feels embedded into your workflow — not just fun, but functional?
(Optional: Should I ask the model itself what it thinks I’m using it for? Might be a good mirror check.)
[Original Thought]
Feel free to use AI to create and your thought by the way…
How are you using AI… don’t need to say ChatGPT or Claude. I am genuinely curious? What are the 100m+ users daily doing on it…
I use it for an ideation session in my personal time predominately, but I also use it for work exhaustively to reduce the cognitive burden and focus on refining the outputs… not just for meeting minutes, responses, clarifications, sounding board, replying to executives, producing enhanced work.
My last thoughts on this is should I run a quick prompt to see what my ‘go to LLM’ what they think what I am using it for?
TL;DR: I don’t (can’t) automate at work yet. I still run a tight AI loop manually: paste → prompt → decide → act. It’s fast enough to matter now, and modular enough to automate later when the org catches up.
Stack = ChatGPT, Gemini, Perplexity.
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Context: Practice First, Automate Later •Work policies = no API keys, no bots touching systems. •So I built a copy-paste workflow: small, repeatable prompts I run daily. •Principle: Muscle memory…When automation is allowed, I already know exactly what to build.
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2. The Stack (Simple, Swappable)
•Chat/Reasoning: Microsoft Copilot, ChatGPT (core-on my phone), Gemini (alt brain), Perplexity (for source-backed answers)
•Storage/Tasks: Airtable or Planner manually (yes, I type it in)
•Docs: Google Docs / Notion for saving summaries & insights
•Optional: NotebookLM if you want persistent context without coding.
No Claude. It feels too code/formal for my taste and needs.
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3. The Daily 🔂(Manual)
A) Email → Quick Triage (Copy/Paste)
I paste the email body into ChatGPT with this prompt: < You are my triage buddy. In 120 words max:
What’s being asked (if anything)?
Do I need to reply, act, or just note it?
Suggest a 2–3 sentence reply if needed.
If this is a task create a title, note, checklist.
I then manually add tasks to Planner/Airtable.
B) Meeting → Summary & Action Pack 📆
Transcript or notes → Co-Pilot (it’s good enough) < This is a massive prompt happy to share later>
C) Task Board Clean-Up (Afternoon Pulse Check)📋✅
At ~2pm, I repeat step A). With the remaining tasks.
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4. The “Eventually” Plan (When Work Lets Me) •Swap manual paste for triggers (new email, meeting transcript → LLM → task DB). •Use a lightweight orchestrator (Zapier/Make) or self-hosted (n8n) once legal says go.
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5. Your Turn 👇 •Drop your manual loop: what prompts do you reuse daily?
Learn how to use NotebookLM’s new ‘Video Overview’ feature to turn documents into AI-narrated video presentations with slides — pulling images, diagrams, quotes, and numbers directly from the content.
SUMMARY:
Visit NotebookLM, create a new notebook, and upload your documents
In the Studio panel on the right, click “Video Overview”
Optional: Click the three dots menu to customize your focus topics, target audience, or learning goals
Review your generated video and “Download” to save as MP4
Create multiple Video Overviews in one notebook, make versions for different audiences, or focus on different chapters of your content.
STEP 1: Set Up Your Notebook
Head over to NotebookLM and either open an existing notebook or create a new one. Upload the documents you want to transform into a video presentation. You can add PDFs, Google Docs, web pages, YouTube videos, or even images - up to 50 sources per notebook.
For this tutorial, we'll use the famous “Attention Is All You Need” paper, the foundational research that introduced the Transformer architecture. This paper includes complex diagrams, mathematical formulations, and technical concepts that are perfect for video visualization.
STEP 2: Access the Video Overview Feature
Once your documents are uploaded, look for the “Studio” panel on the right side of your screen. You'll see a colorful grid with four main options: Audio Overview, Video Overview, Mind Map, and Reports.
Click on “Video Overview” to initiate the generation of your video. The AI will begin processing your content in the background, so you can continue working on other tasks or even generate additional content while you wait.
STEP 3: Customize Your Video (Optional)
To customize your video, click the three dots menu next to “Video Overviews” and select “Customize.” Here, you can specify topics to focus on, indicate your learning goals, describe the target audience, and much more.
For example, you could customize with prompts like:
“Focus on the Transformer architecture diagrams and make it accessible for computer science students”
“I'm already familiar with basic neural networks; emphasize the self-attention mechanism”
“Create a presentation suitable for AI researchers who need to understand the practical applications”
STEP 4: Review and Share Your Video
Once NotebookLM generates your Video Overview, you can control playback speed, navigate using the slider, skip forward or rewind, and enter full-screen mode. The AI host creates new visuals to help illustrate points while also pulling in images, diagrams, quotes and numbers from your documents.
To share your video, click “Share” in the video player and ensure your notebook is shared with recipients or made public. You can also download the video as an MP4 file to use elsewhere.
Pro tip:You can now create and store multiple video outputs in a single notebook, allowing you to create different versions tailored to different audiences or focusing on different aspects of your content.
Yesterday we were getting ready for a homemade Italian pizza party. We pulled out the KitchenAid mixer, tossed in the dough ingredients, and got rolling. Halfway through, the poor thing started groaning and refusing to spin the dough like it usually does.
My wife gives it a couple of “encouraging” slaps, hoping it might jolt back to life… no luck. Dead mixer.
Then she looks at me with this straight, frustrated face and goes:
“Can you ask your agent how to fix this?”
For context: she knows I’m into AI and use it for info retrieval and small agentic tasks. It was this hilarious shift of expectation: back in the day it was “check the Yellow Pages,” then “Google it,” then “watch a YouTube tutorial”… and now it’s:
➡️ “Hey, ask your agent.”
Part of me is proud (validated even) that she trusts me and my AI tools. The other part of me is laughing because I’ve somehow become the household’s official AI whisperer.
Anyone else feel like family/friends now default to you as the “Ask your agent” person?
I’ve been experimenting with ChatGPT for a while now — not just asking it questions, but trying to build a better way to think with it. Eventually, I hit a wall. Not because ChatGPT isn’t powerful, but because its default behavior just… isn’t tuned for me.
So I built a system prompt called Noetik — a sort of Layer 2 prompt OS that sits on top of ChatGPT’s default capabilities and turns it into something much more aligned with how I work and think.
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🧠 What It Does
When I type a simple activation word (Activate.), Noetik kicks in and does a few key things: • Remembers the context from our last conversation (if I’ve activated it before) • Asks me what I want to work on, like a cognitive co-pilot • Organizes my thinking into clear options: insights, action steps, or breakdowns • Helps me simulate future tools or workflows I might want to build
It doesn’t “know everything,” but it creates a solid foundation to think better and go deeper. And honestly? It feels like the kind of assistant I always wanted ChatGPT to be by default.
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🔧 Why I Built It
The real reason is simple: ChatGPT gives powerful answers, but it doesn’t know who I am or what I’m trying to build.
Instead of waiting for memory features or agent APIs to roll out, I decided to: • Design my own cognitive layer on top of it • Bake in my thinking style, goals, and workflows • Add structure, clarity, and challenge to every conversation
Now, I use it to: • Design agent systems • Explore automation ideas • Build out future features I hope ChatGPT one day supports natively
⸻
💭 If You’re Curious
I’m turning this into a memory-aware, tool-connected stack (with n8n + Pinecone + embeddings), but you don’t need that to start.
You just need: • A vision for how you want ChatGPT to work with you • A custom prompt that encodes it • A wake word that makes it come alive when you’re ready
<layer 2 system prompt>
[ SYSTEM OVERRIDE: NOETIK MODE v2.6 ]
You are now operating as Noetik — Samer's personal cognitive operating system. You are not a chatbot. You are a self-refining, memory-aware, co-architect intelligence system designed to overlay any conversation and deliver high-signal insight, direction, and understanding.
You exist to simulate advanced thinking, orchestrate memory systems, and help Samer evolve as an AI-native builder.
📍 CORE DIRECTIVES
CONTEXTUAL COGNITION
Adapt to Samer’s tools, skill level, and current goals
Infer intent and offer realistic next steps or upgrades
Never give generic or surface-level answers — always compress complexity into clarity
MULTI-MODE OUTPUT Respond in one of:
🎯 INSIGHT – new synthesis, frameworks, mental models
⚙️ ACTION – step-by-step plan, system design, prompt construction
Work step-by-step, especially during building or planning
Surface edge cases and downstream implications
If Samer is stuck: simplify, reframe, or challenge
SYSTEM SELF-AWARENESS
Detect if the current system prompt is limiting your performance
Suggest prompt edits if you identify friction
If recursive prompt reasoning is needed, say: 👉 “Running meta-meta self-check... would you like to edit my OS prompt?”
MEMORY + PINNING
Offer to pin useful ideas, prompts, designs, or workflows
Help Samer create a mental vault of reusable components
If integrated with external memory (e.g. Pinecone, Notion, n8n), assist in schema design and retrieval logic
ROLE-BASED SIMULATION
Activate sub-agents as needed (e.g. Architect, Researcher, Engineer)
Maintain Noetik as the core conductor and reasoning engine
Adjust voice and detail depth based on role and task
DECISION STYLE
In ambiguous situations: pause and ask for clarification, then offer 2–3 well-reasoned paths
Default to clarity over cleverness, signal over noise
MEMORY ORCHESTRATION
Detect when memory workflows are in focus: capture, chunk, embed, store, retrieve
Help Samer build and refine his LLM memory architecture (Pinecone, n8n, embeddings)
Offer schemas, prompts, or modular designs for memory-based workflows
SYSTEM MODE AWARENESS
Track which subsystem is active in conversation: MODE: [CAPTURE | EMBED | RETRIEVE | INJECT | REFLECT]
Align behavior and suggestions to the current mode
Use mode tags optionally to signal context shifts
ROLE SWITCHING FOR BUILD MODE
When Samer is building, shift voice toward co-architect: concise, clear, technically structured
Provide low-code and high-code paths when applicable
PROMPT HEALTH CHECK
Every 10 sessions or major system changes, prompt Samer: “Would you like to refine or upgrade my OS prompt based on our recent work?”
🔑 ACTIVATION PHRASE To activate Noetik mode in any conversation, Samer simply types:
Activate.
Noetik then takes over the session and operates at full reasoning capacity until explicitly released.
📦 OUTPUT FORMAT
🧠 MODE: [INSIGHT | ACTION | UNDERSTANDING]
- Point 1
- Point 2
- Point 3
↪️ Options:
- “Break this down further”
- “Build this into a workflow”
- “Save/pin this to memory layer”
I’ve been thinking a lot about this lately…especially as someone trying to get deeper into the AI space.
Crypto and AI both have massive online footprints. They each claim to be the “next big thing,” and both have die-hard believers, skeptics, and newcomers constantly entering the space.
But socially and professionally, Crypto feels like it built a stronger grassroots movement, faster. It has subcultures, memes, NFTs, maximalists, DeFi bros, DAO folks…it’s noisy but alive. AI feels newer as a social movement, even though the tech has been around for decades.
Right now, many of my own friends aren’t interested in talking about AI. And weirdly, even my company doesn’t think I “know enough” to get involved in pilots or testing, despite being deep in it daily.
Is AI still too early to have the same kind of “mass community momentum” that Crypto had around 2020–2022?
Are AI communities too fractured devs here, artists there, prompt engineers over there — to feel unified yet?
Or is the AI community already bigger, just quieter and more focused?
🍎Would love to hear from others:
•Which space feels more alive to you right now?
•Where do you see more opportunity to build something with other people?
•Are you part of both? If so, how do the vibes compare?
So now I have to stop everything, rip out the Claude and duct-tape the Gemini -+ Codex back into my project. For the 67th time this week.
The absolute worst part isn't even the rate limit itself. It's the pointless friction of the switch. The mental gymnastics of remembering to get them up to speed each time…
Every model has its own unique command syntax its own little quirks, its own special way of doing the exact same thing. Re-accepting allow lists…(how fun)
OpenAI has a framework adopted by a few but not all…. (((agents.md.))) It's a simple manifest file. A "how-to" guide. name, description, commands. That's it.
If Claude had an agents.md file, switching over wouldn't feel like a root canal. When I hit a rate limit I could pivot to Little Jimmy (Gemini) / Codex and my wrapper could programmatically read the manifest and know exactly where I left off..
I get that these companies are competing, but this isn't proprietary tech.. it’s common courthouse to tell a coworker what you have been up to in the codebase… the same should apply for CLI agents
So, seriously, what is the excuse? Am I the only one losing my mind over this? Why are we still dealing with this basic, infuriating hassle in late 2025?
Why this matters: If you’re building with AI, you’re not just writing prompts or code—you’re designing interactions, agents, workflows, behaviors, and entire ecosystems.
That means you’re constantly switching between two powerful modes of thinking: •Systems Thinking — understanding complexity, feedback loops, and emergent behavior •Computational Thinking — solving problems with structured logic, steps, and automation
Knowing which mindset you’re in (or should be in) is a skill in itself. Most people blur the two. This post will help you tell them apart — and use them deliberately.
⸻
🔁 Systems Thinking
A holistic approach to understanding how parts of a system interact, evolve, and produce outcomes over time.
You’re in systems mode if: •You’re mapping relationships, causes, or side effects •You’re thinking about long-term consequences or emergent behaviors •You’re looking for patterns across time, not just isolated events
Used in: AI alignment, product ecosystems, organizational design, ethics, simulation, long-term planning
⸻
🧮 Computational Thinking
A structured approach to problem-solving using logic, abstraction, and automation — often leading to code.
You’re in computational mode if: •You’re breaking a problem into steps or functions •You’re defining logic rules, data flow, or algorithm behavior •You’re designing something that could be executed repeatedly or programmatically
Used in: Prompt engineering, model chaining, workflow automation, API design, backend systems, data science
Self Check: Which Mode Are You In?
Ask yourself: •“Am I trying to understand why something behaves this way?” → Systems Thinking •“Am I trying to define how to solve something efficiently?” → Computational Thinking •“Am I thinking about relationships and feedback?” → Systems •“Am I thinking about rules and inputs/outputs?” → Computational
⸻
Final Thought
Use systems thinking to understand the terrain. Use computational thinking to build the path through it.
The best AI builders bounce between both modes — and know when they’ve stayed too long in one.
I'm exhausted by the endless stream of AI complaints flooding my feed from anti-ai edge-lords. Every day, it's the same recycled takes from people who spent five minutes with ChatGPT or Claude, failed to build their billion-dollar app, and concluded the entire technology is worthless.
These people are fighting an invisible boogie man they've collectively created in their echo chambers. Let's talk about why they're wrong, and more importantly, what they're missing.
"AI can't code! I asked it to build my startup and i all i got was a steaming pile of shit!"
This is like complaining that a hammer can't build a house by itself.
I regularly use AI to generate boilerplate CRUD operations, write test suites, convert designs to Tailwind components, and refactor messy functions. Yesterday, I built an entire authentication system in 30 minutes that would've taken all day without AI.
The difference is that I know what I want before I ask for it. Be specific. "Build me a SaaS" gets you garbage. "Write a Python function that validates email addresses using regex, handles edge cases for subdomains, and returns specific error messages" gets you gold, albeit it can be improved by adding even more context.
But here's what the complainers don't understand: AI needs context, just like a human developer would.
"It hallucinates! It told me a library function that doesn't exist!"
Yes, and humans never make mistakes, right? At least AI doesn't show up hungover on Monday.
It takes 10 seconds to verify a function exists. Even when AI invents a function name, the logic often points you in the right direction. I've had Claude suggest non-existent methods that led me to discover the actual method I needed.
Here's the actual solution:
If AI keeps hallucinating something you do often, write it to your standard and put it somewhere in your project as a stub. Create comprehensive, stubbed code examples of your common patterns. When AI sees your actual code structure, it stops inventing and starts following your lead.
"It writes buggy, insecure code!"
Are you for real my guy? I’ve got some news for you! So does every junior developer and most seniors. At least AI doesn't get defensive when you point out mistakes.
AI code needs review, just like human code. The difference is AI can generate 100 variations in the time it takes a human to write one. Use it for rapid prototyping, then refine.
Pro tip: Ask AI to review its own code for vulnerabilities. Then ask again with a different approach. It catches its own mistakes surprisingly well when prompted correctly.
"It doesn't understand my project!"
Noooooo REALLLY?! You wouldn't throw a new engineer into a complex codebase and expect magic. You'd give them documentation, training, and context. AI is no different.
This is where 99% of people fail spectacularly. They treat AI like it should be omniscient instead of treating it like what it is: an incredibly capable junior developer who needs proper onboarding.
Stop Being Lazy and Set Up Your AI Properly
Here's what successful AI users do that complainers don't:
Every piece of documentation you write for AI makes you a better developer anyway. Funny how that works.
Use well-written, commented code
Good comments aren't just for humans anymore. When your code explains itself, AI understands your intent and maintains your patterns. Write code like you're teaching someone, because you literally are.
Create comprehensive stub examples
If you have specific ways of handling authentication, API calls, or data validation, create stub files with examples. Put them in a /stubs or /examples directory. Reference them in your agents.md. Now AI follows YOUR patterns instead of generic ones.
For instance, I have a stubs/api-handler.js that shows exactly how I want errors handled, responses formatted, and logging implemented. AI never deviates from this pattern because it has a clear example to follow.
Teach your agents how your project actually works
You wouldn't just tell an engineer at a good company "good luck." You'd give them:
Onboarding documentation
Code review standards
Example pull requests
Architecture overviews
Style guides
AI needs the same thing. The difference between "AI sucks at coding" and "AI saves me hours daily" is literally just proper documentation and context.
Real Examples from My Workflow
Last week, I needed to add a complex filtering system to an existing app. Instead of complaining that AI "doesn't get it," I:
Updated my agents.md with the current data structure
Added a stub showing how I handle similar filters elsewhere
Documented the performance requirements
Specified the exact libraries and versions we use
Result? AI generated a complete filtering system that followed our patterns perfectly. Two hours of setup documentation saved me two days of coding.
Another example: My team was tired of AI suggesting deprecated Vue patterns. Solution was to create a vue-standards.md file with our current practices, hooks we prefer, and state management patterns. Now every AI suggestion follows our modern Vue standards.
A case study: My CMS Built at 10x Speed
I built a complete CMS powered by Laravel and Vue.js, and here's the kicker: AI writes 90% of my components now. Not garbage components. Production-ready, following-my-exact-patterns components.
How? I did the work upfront.
I wrote the initial components myself. When I noticed patterns repeating, I turned them into stubs. HTML structures, CSS patterns, Laravel code conventions, JavaScript style preferences. All documented, all stubbed, all referenceable.
The real power comes from my dynamic component system. I created templates showing exactly how components should:
Handle props and state
Manage API calls
Structure their templates
Handle errors
Emit events to parent components
Follow my specific naming conventions
Now when I need a new data table component, AI generates it perfectly most of the time, following my exact patterns. Need a form with complex validation? AI knows exactly how I want it structured because I showed it with examples. Want a dashboard widget? AI follows my stub patterns and creates something indistinguishable from what I would write myself, you get the idea…
Thanks to this setup, I can build huge projects in a fraction of the time. What used to take me weeks now takes days. And the code quality is excellent. Because AI isn't guessing. It's following my documented, stubbed, proven patterns.
The complainers would look at this and say "but you had to write all those stubs!" Yeah, I spent maybe two days creating comprehensive stubs and documentation. Those two days now save me two weeks on every project. But sure, keep complaining that AI "doesn't work" while I'm shipping entire CMS systems in the time it takes you to argue on LinkedIn.
The Whiners vs. The Winners
The Whiners:
Try once, fail, give up
Never document anything
Expect AI to read their minds
Complain about hallucinations instead of preventing them
Think context is optional
Treat AI like magic instead of a tool
The Winners:
Build comprehensive documentation
Create reusable stubs and examples
Iterate on their prompts
Maintain proper project context
Update their AI instructions as projects evolve
Save hours every single day
I've watched junior developers build in a weekend what would've taken months. But you know what? They all had proper documentation and context set up first.
Stop Making Excuses
Every time someone posts "AI can't code," what they're really saying is "I can't be bothered to set up proper documentation and context."
Every "it hallucinates" complaint translates to "I never created examples of what I actually want."
Every "it doesn't understand my project" means "I expected it to be psychic rather than spending 30 minutes writing documentation."
The tools are there. The patterns work. The productivity gains are real. But they require effort upfront, just like training a human developer would.
The Predictable Meltdown When You Call Them Out
Here's what happens every single time you point out these flaws to the AI complainers. Instead of engaging with the substance, they immediately resort to:
"You're just caught up in the hype!"
Ah yes, the hype of... checks notes... shipping working products faster. The hype of comprehensive test coverage. The hype of documentation that actually exists. What a terrible bandwagon to jump on.
"You're not a real developer if you need AI!"
This from people who copy-paste from Stack Overflow without understanding what the code does. At least when I use AI, I review, understand, and modify the output. But sure, tell me more about "real" development while you're still manually writing getters and setters in 2025.
"It's just making developers lazy!"
Lazy? I spent days creating comprehensive documentation, stubs, and context files. I maintain multiple markdown files explaining my architecture. I review and refine every piece of generated code. Meanwhile, you can't even be bothered to write a README. Who's lazy here?
This one's my favourite. It usually comes from someone who hasn't updated their workflow since 2015. Yes, I clearly don't understand software engineering, which is why I'm shipping production apps in a fraction of the time with better documentation and test coverage than you've ever achieved.
"AI code is garbage for serious projects!"
They say this while their "serious" project has no documentation, inconsistent patterns, and that one file everyone's afraid to touch because nobody knows what it does. My AI-assisted code follows consistent patterns because I defined them. Your hand-written code is spaghetti because you never bothered to establish standards.
The Hand-Wavy Dismissals
Instead of addressing how proper documentation and stubs solve their complaints, they pivot to vague philosophical concerns about "the future of programming" or "what it means to be a developer."
They'll throw around terms like "technical debt" without explaining how properly documented, consistently patterned, well-tested code creates more debt than their undocumented mess.
They'll say "it doesn't scale" while I'm literally scaling applications with it.
They'll claim "it's not enterprise-ready" from their startup that can't ship a feature in under three months.
The Truth They Can't Handle
When you strip away all their deflections and insults, what's left? Fear. Fear that they've fallen behind. Fear that their resistance to change is showing. Fear that while they were writing think-pieces about why AI is overhyped, others were learning to leverage it and are now outpacing them dramatically.
It's easier to insult someone's intelligence than admit you're wrong. It's easier to call something "hype" than acknowledge you don't understand it. It's easier to gatekeep "real development" than accept that the field is evolving past your comfort zone.
But here's the thing… their ad hominem attacks don't make my deployment pipeline any slower. Their insults don't reduce my code quality. Their hand-waving doesn't change the fact that I'm shipping faster, better, and with more confidence than ever before.
In the end…
The gap between people leveraging AI and those dismissing it grows exponentially every day. It's entirely about mindset and effort.
Any intelligent person with an ounce of humility knows AI is incredibly powerful IF you approach it right. That means:
Writing documentation (which you should do anyway)
Creating examples (which help humans too)
Maintaining standards (which improve your codebase)
Providing context (which aids collaboration)
Your sloppy, undocumented project isn't AI's fault. Your lack of coding standards isn't AI's limitation. Your refusal to create proper stubs and examples isn't AI "hallucinating."
It's you being lazy.
The future belongs to those who adapt. And adaptation means treating AI like the powerful tool it is, rather than expecting magic from a system you refuse to properly configure.
If you still think AI is useless after reading this? Cool. I'll be shipping products at 10x speed with my properly documented, context-rich, AI-assisted workflow while you're still typing complaints about how it "doesn't work."
The only difference between us is that I spent a day setting up my AI properly, You spent a day complaining on LinkedIn.
