image
Everyone is excited about AI agents replacing junior engineers
But Nobody is talking about what happens 20 years later...
Today's org chart looks like this:
→ 1 Product Owner
→ 3 Senior Engineers
→ 4 AI Agents doing all the junior work
Clean. Efficient. No dead weight.
But here's what nobody is modeling 👇
Every senior in that picture was once a junior.
They got things wrong. Got corrected. Tried again.
They learned by doing the exact tasks we're now handing to agents.
Remove that stage, and you remove the entire growth path.
No junior → no mid-level in 5 years
No mid-level → no senior in 10
No senior → no one left to manage the agents in 20
The irony runs deeper than that
The AI agents in your 2026 org chart were built by engineers who had junior roles
The prompts guiding those agents were written by people who once wrote bad code
The systems they run were designed by humans who failed first, repeatedly
You can automate the task
You cannot automate the formation
So in 20 years, if we follow this model:
→ No new engineers entering the pipeline
→ AI agents that nobody truly understands anymore
→ And no one left who remembers how to build from scratch
We didn't build the future of tech.
We just delayed its collapse by one generation.
The real question isn't "how do we replace juniors with AI?"
It's "who trains the humans who will lead AI in 2045?"
Because that person?
Is currently a junior somewhere
Assuming we still let them exist...
📩 Join 30K builders and execs who never miss an AI agent update :
→ Weekly AI agent briefings
→ Free templates & exclusive giveaways
👉 https://lnkd.in/dvvUD3hA
image
Still confused about LLMs, RAG, AI Agents, and MCP?
Think of AI like a human body. 👇
1. LLM == Brain
It generates text by reasoning.
This is the core intelligence, nothing more.
2. RAG == Brain + Books
Take the brain, hand it a library.
Now it can pull from your docs and databases instead of guessing.
3. AI Agent == Brain + Hands
Give the brain the ability to act.
It uses memory and tools, not just knowledge.
4. MCP == Nervous System
This is what connects everything.
Without it, the brain, the books, and the hands can't talk to each other.
Most people learn these terms in isolation.
That's why they never stick.
Once you see them as one system, the whole stack makes sense.
→ LLM thinks
→ RAG remembers
→ AI Agent acts
→ MCP connects
Which piece is your team still missing?
📩 Want to go deeper? Join 30K builders and execs getting the AgentX Newsletter and get :
→ Weekly AI agent briefings
→ Free templates & exclusive giveaways
👉 https://lnkd.in/dvvUD3hA
image
Everyone in my feed reacts to AI news 24 hours after it matters.
I stopped doing that the day I followed and started checking Polymarket every morning.
One glance tells me:
→ Which AI company the market expects to win this month
→ Which launches insiders are confident about
→ Which "breakthroughs" nobody is willing to bet on
That last one is the real filter.
Hype that no one puts money behind is just hype.
And in tech, expectations move first, reality catches up later.
The people who see expectations shift early make better calls: on strategy, on positioning, on where to build.
You're already informed about AI.
This shows you what the informed money believes next.
👉 Follow Polymarket for daily AI, tech, and business signals.
image
🚨 A Netflix engineer just open-sourced the fix for the most expensive problem in AI: wasted tokens
It's called Headroom, and it might be the smartest fix to high token usage
Your agent reads a 10,000-token log file to find one error.
You paid for all 10,000 tokens.
The answer needed 1,200.
Headroom sits between your agent and the LLM and compresses everything before the model sees it.
JSON, code, logs, RAG chunks, each gets its own specialized compressor.
And it's reversible: the originals stay on your machine, so nothing is lost.
The results speak for themselves:
→ Up to 95% fewer tokens
→ Same accuracy on benchmarks
→ Zero changes to your code
Setup takes one minute:
1. pip install "headroom-ai[all]"
2. headroom wrap claude
Done.
Works with Claude Code, Cursor, Codex, and anything OpenAI-compatible.
Everything runs locally, fully open source.
The cheapest token is the one you never send.
🔗 Github repo: https://lnkd.in/e6tEeXcA
So, are you compressing your context, or just paying the bill?
📩 Join 30K builders and execs getting the AgentX Newsletter and get :
→ Weekly AI agent briefings
→ Free templates & exclusive giveaways
👉 https://lnkd.in/dvvUD3hA
image
Apple pays Google $1B. Google pays Apple $20B.
This meme is funnier when you know what's behind it 👇
Apple pays $1B a year to run its new Siri on Gemini tech.
Google pays $20B+ a year to stay the default search engine on iPhone.
Rivals, paying each other billions.
And it's happening everywhere in AI:
→ Nvidia invests $100B in OpenAI, OpenAI buys Nvidia chips.
→ Big Tech is spending $750B on AI buildout this year alone.
→ Money keeps circling between the same few companies.
That's exactly why the bubble warnings are getting loud:
→ A leaked US Treasury report privately compares AI to the dotcom era.
→ The BIS warns the bubble could pop and hit the global economy.
But here's my take as someone building in AI every day:
The dotcom crash killed the hype companies.
It made the real builders more valuable than ever.
Same thing happens if this bubble pops.
Wrapper apps and prompt tricks disappear.
Agents with real ROI, RAG systems, and evaluation skills become the safest career bet in tech.
So enjoy the meme.
Then make sure your skills survive both endings of this story.
Bubble or genius: what does this handshake look like to you? 👇
image
Your AI agent doesn't have a quality problem
It has a shipping problem.
Before writing another line of code, answer 3 questions:
1. Who is the ONE person this is for?
- Not "founders". Not "marketers".
- A name. A face. Someone you can message today.
2. What is the ONE moment it must never fail?
- Every product has a single make-or-break interaction.
- For an invoice agent: the numbers are right.
- For a content agent: the draft sounds like the client.
- Everything outside that moment can be rough.
3. What can stay human?
The dirty secret of successful AI products:
- Half of them have a person quietly handling edge cases in v1.
- That's not cheating. That's learning what to automate next.
Answer these 3, and your build time drops from months to days.
Skip them, and you'll polish features nobody asked for.
The market doesn't reward the best-built AI product.
It rewards the one that showed up first and kept its word.
Which of the 3 questions is hardest for you? 👇
document
I gained 110,000 followers in 12 months with one embarrassingly simple format.
Not essays. I wrote 50+. Crickets.
Not videos. Hours of work for 12 likes.
Not carousels. Decent, but never explosive.
𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀.
Every time I posted one, it outperformed everything else 5 to 1.
The catch? I can't design. At all.
So here's my exact 2-minute workflow:
1️⃣ Open Gamma → "Create new AI"
2️⃣ Pick a visual style (I rotate between 3)
3️⃣ Paste your prompt + text
4️⃣ Post it
The 3 styles that consistently win:
→ Notebook sketch (my #1, feels human, not corporate)
→ Glass whiteboard
→ Classroom whiteboard
I put the exact copy-paste prompts for all 3 in the carousel below. Swipe → copy → post.
🔗 Try Gamma free: https://lnkd.in/eTSurfm5
P.S. If you've been posting for months with nothing to show for it, it's probably not your ideas. It's your format.
#GammaPartner
image
95% of AI agents can retrieve.
Almost none can investigate.
The difference is one architecture decision 👇
📌 A retrieval agent:
→ Takes your question
→ Runs one vector search
→ Summarizes whatever chunks come back
Fine for FAQs.
Useless when the answer spans people, events, and relationships across thousands of records.
📌 An investigative agent:
→ Decomposes the question into evidence-gathering tasks
→ Queries a Knowledge Graph multiple times
→ Cross-references findings like an analyst would
→ Remembers past investigations for follow-ups
The engine behind this is 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚: retrieval over connected knowledge instead of isolated chunks.
🗓 On July 31, I'm joining a 6-hour live bootcamp where we build exactly this
"An investigative AI agent, end to end"
Here's what we're building, step by step:
1️⃣ We connect an LLM agent to a real Knowledge Graph through a GraphRAG API, using nothing but natural language
2️⃣ Then we teach it to investigate: breaking complex questions into multi-step retrieval workflows
3️⃣ Next, we make it synthesize evidence from multiple graph queries into one sourced answer
4️⃣ Finally, we extend it with graph analytics, timeline analysis, and geospatial reasoning
No hours building graphs from scratch, no Cypher queries.
We work on a production-ready GraphRAG endpoint and focus purely on agent design.
By the end, I'll walk away with a working investigative agent!
Online, live, led by David Knickerbocker
🔗 Grab your spot and build with me: https://lnkd.in/eQ4ytgKv
🎁 Discount Code to whoever joins me : CHOROUK40
Will I see you there? 👇
