Fabian Werkmeister

I help you to become fluent in Data and enable you to translate between business & tech ★ Data Scientist @FUNKE ★ 🐍Python 🤖Data Science 🛠️Data Engineering

Why do so many data projects fail to deliver real business value?
Because they often start with a technical discussion, not a business problem.
➡️ And you've likely felt the same frustration as so many others...

Investing in a complex data solution only to find it's over-engineered, over-budget, and doesn't actually solve your core challenge.
You're left with high costs, slow processes and no actionable outcome.

Imagine instead a data solution that is simple, powerful, and cost-effective. One that feels like it was designed exactly for your business and just works.
This is what I can build for you.

I started my career on the business side and switched to data along the way, where my deep passion for that field motivated me to self-taught all the skills I now have.
Sitting on both sides of the table allowed me to really understand and connect both worlds.

This is what you get, if you work with me:

Business-First Approach:
You get a business-minded partner, which listens to your challenges first to ensure the solution we build together is one you actually need. No confusing tech-speak, just pure focus on actionable results for you and your business.

Pragmatic & Cost-Effective Solutions:
You get a lean, powerful, and low-maintenance data analytics platform on Google Cloud Platform, which simply works.

Collaborative & Transparent Process:
You will be a partner in the process, not a spectator. Through regular check-ins and feedback, we ensure alignment at every stage to build something, which actually helps you.

➡️ If you're ready to finally get results from your data, connect or DM me and let’s chat!

Data Scientist

No Budget for Managed ETL? 💰
Here is a lean and cost-effective alternative 👇 

Managed ETL services can kill your budget
They are designed to punish you for your growth
They feel essential, but their pricing will spiral as your data volume increases

But you can also build something yourself
Use dltHub and serverless functions / containers to perform data ingestion:
✅ You only pay for execution time, aligning cost directly with usage
✅ You can focus on the actual problem, since dlt does the heavy lifting for you

What's the biggest headache dlt solved for you recently? ✌ 

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Hi, I'm Fabian👋

I write about building lean and cost-effective analytics platforms on GCP

If you like my content
👉 Follow 
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Lean & cost-efficient data systems on GCP for businesses to finally turn their data into decisions | 🛠️ Senior Data Engineer @ FUNKE

I don't have a linear career in data.
For a long time, I let that be a limiting belief.

I studied economics and worked in that field for some years
I learned what KPIs are and what really drives numbers and businesses
I did a lot of powerpoints and fought with excel, but this wasn’t a profession for me

So I took a data science bootcamp and switched to data
I still remember how everyone in the bootcamp was way better at coding than me
For years in data, I felt I was always playing catch-up to the people who grew up with this stuff

But with time and more seniority, this perception shifted
I notice that using tech and tools doesn’t generate value on its own
Value is created in knowing when to use which tool to drive a business action

With this in mind, I began to see my non-linear career path as my biggest asset.
Since I lived in both worlds, I also know very well how to connect them.
I start with the action, then build the absolute minimal system for that.

I may not be the best coder or technical expert out there
But I speak and understand business language
And this enables true solutions

If you’re also coming from a non-traditional path, stop trying to fit the mold.
Your diverse background isn't a weakness.
It can be your ultimate advantage.


What’s an unexpected asset your career path has given you?

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Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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The biggest lie in data
"We just need a new tool"


A new tool won't fix a broken process.
A new tool won't magically create a data-driven culture.
The most successful projects follow a business-first mantra.
👉 Tools are the last piece of the puzzle.

✅ Focus on Culture: Talk with the business teams.
✅ Focus on Processes: How are things done right now and how should they work in the future?
✅ Focus on the User: Identify the friction points between the data and the action or decision taken by an end user.

Remember:
Tools are just the vehicle - culture and process are the engine!

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Hi, I'm Fabian 👋

I write about building lean and cost-effective data systems on GCP
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Stop obsessing over the architecture diagram.
Obsess over the problem.

❌ Typical data project goes like this:

Month 1-2: Vendor selection
Month 3-4: Pipelines and modeling
Month 5: Dashboarding
Month 6: Show it to the business
(and find out it’s useless)


✅ The Value Path looks like this:

1. The "Why" Phase (40% of effort)
Deep stakeholder interviews.
Find out problems and decisions to be made.
Don't write a single line of SQL until you know that.

2. The "How" Phase (10% of effort):
Lean tech selection.
Most tools deliver the same output.
Choose what is convenient and available.
(if possible choose also what is cheap)

3. The "Build" Phase (50% of effort):
Minimalist implementation.
Automating only what is necessary.
Build the shortest bridge between data & decision.


You can build a "perfect" scalable platform in 6 months that nobody uses

Or you can build an "imperfect" targeted solution in 6 weeks that changes the business.

—
Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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My data team was most often seen as a cost center.
Here is how we shifted this to become enablers 👇

I used to build the thing I was asked for.
I finished the tickets.
I delivered the code.

But that often left behind a black box solution that no one on the business side truly understood. 
A technical problem was solved without transferring the value of it to the business.
That's how data projects solely generate costs.

Do this instead
✅ Prioritizing the business problem over technical solutions.
✅ Designing the platform to deliver a single, clear business action.
✅ Measuring success by time or costs saved, not by processed data volume.
✅ Transferring knowledge so the business team can understand and act on the data without you.

That’s how you build trust.
That’s how you generate value tied to the costs.
That’s how your projects and your team are seen as an investment.

TL;DR
Any data project needs to directly and measurably impact the bottom-line of the business
➡️ if your project doesn’t deliver this, it will only be seen as a financial liability

---

Hi, I'm Fabian 👋
I write about building lean and cost-effective data systems on GCP
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SQL is important for a data engineer
But the most valuable skill isn’t taught in tutorials

I’m not talking about Terraform, Python or BigQuery
These are merely tools and techniques to work with data
My most important skill is the ability to translate:

Business ➡️ Tech
Stakeholder: "I need real-time data for my weekly sales report"
Me: "We need a simple batch pipeline that updates data each morning"

Tech ➡️ Business
Tech: "We implemented incremental loading for big source tables"
Me: "Your Monday morning dashboard will now be ready at 8:00 AM instead of 10:30 AM."

When you bridge this gap with communication, you do two things:
👉 You protect the business from over-engineering (and over-spending)
👉 You work on things stakeholders actually care about (time and money)

Learn to speak and understand business language
That is how you move from a data engineer to a strategic business partner

----------------------------------------

Hi, I'm Fabian👋

I write about building lean and cost-effective data systems on GCP
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The era of Big Data is over
The era of Big Action will finally unlock the value of data

For the past decade, billions were spent on data stacks
We now have 100+ tools to pipe, clean and visualize data
And yet, decision-making velocity hasn't improved

The systems we built were designed for observation, not operation
Your data system is worthless until that potential is converted into movement

Let's look at a simple example:
➡️ 100 customers haven't purchased in 3 months

𝐓𝐡𝐞 𝐨𝐥𝐝 𝐰𝐚𝐲
- Discover this (most businesses don't even get to this point)
- Download CSV
- Format in Excel
- Upload to HubSpot
- Manually trigger "we miss you" email
- By the time you do this, 50% of them have already moved to a competitor

❌ 𝗧𝗵𝗮𝘁 𝗶𝘀 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝘃𝗮𝗹𝘂𝗲 𝗹𝗲𝗮𝗸𝘀 𝗼𝘂𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺


𝗧𝗵𝗲 𝗻𝗲𝘄 𝘄𝗮𝘆
- Your system detects these automatically
- Your system pushes these to HubSpot
- An email workflow is triggered
- You get a ping to approve this
- Emails are sent

✅ 𝗪𝗶𝘁𝗵 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 𝗶𝗻𝘃𝗲𝘀𝘁, 𝘆𝗼𝘂 𝘁𝗼𝗼𝗸 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗮𝗰𝘁𝗶𝗼𝗻


Data systems need to pivot
From a reporting source to a trigger
From "what happened?" to action-oriented systems.


𝗪𝗵𝗮𝘁 𝘄𝗮𝘀 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 "𝗲𝘅𝗽𝗼𝗿𝘁-𝘁𝗼-𝗰𝘀𝘃" 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘆𝗼𝘂 𝗸𝗶𝗹𝗹𝗲𝗱? 😉 

------------------------------------------------------------

Hi, I'm Fabian Werkmeister👋
I write about building lean and cost-effective data systems on GCP
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I used to be a data snob
And this attitude prevented valuable solutions

As a data engineer, I think in terms of outputs
I designed whole workflows with a clear result in mind
This was most often a nice dashboard or an automated report

When someone asked for an "Export to Excel" button, I rolled my eyes
I thought "Why do I build of all this if it just ends in a spreadsheet?"
I was missing the point completely!

❌ My job isn't to dictate how people work.
✅ My job is to enable them to work.


If a finance manager is faster in Excel, my fancy dashboard is actually a bottleneck.
If a sales rep needs a CSV to close a deal, my shiny report is blocking revenue.
My business users know way better how they can work efficiently than I do!

👎 I stopped being religious about the tools
👍 I started being religious about the outcome


Now, my philosophy is simple:
👉 Build the pipeline
👉 Ensure the data is available and trusted
👉 Let the user decide the last mile to be actionable

If that means an Excel export or a csv-file?
So be it.

The result at the end of the day is what counts.
Not the tool they used to get there.

----------------------------------------------

Hi, I'm Fabian Werkmeister 👋

I write about building lean and cost-effective data systems on GCP

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Does your data project have a six-figure price tag?
Bad news, you’ve already failed

A six-figure data stack is not an investment.
It’s a financial liability.
Start with something super simple👇

✅ Identify the Core Problem:
Stop looking at tables and talk to people.
👉 What specific business problem is causing pain?
➡️ "We are spending $50k/month on ads, but I can't tell, which campaigns are actually driving sales"

✅ Define the Actionable Outcome:
Don't start to build before you have a clear goal in mind.
👉 What is the simplest, clearest decision or action that needs to be enabled?
➡️ "I need to see CPA by campaign side-by-side so I can cut the losers, if they fall under value X"

✅ Select the Minimalist Tool:
Only now, start to build something!
👉 What is simplest, most cost-effective setup to deliver that specific action?
➡️ Get campaign data via ETL-tool or scripts, build a simple SQL-pipeline with a cron job, pump it into something consumable for your end user (Excel, BI tool, etc.)

That's it.
That's how you build a fast MVP, which costs almost nothing.
And with this foundation you can iterate and scale up always later on.


What’s one business problem you solved recently where you intentionally chose a simple tool over a complex one? 👇 

-------------------------------------------------

Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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Some people meditate.
I build pipelines.

There is a meditative quality to Data Engineering that I haven't found anywhere else.
It satisfies my deep need for organization.
Taking something messy and shaping it into a logical, reliable structure.

It’s like solving a massive puzzle.
Every piece has a place.
Every transformation has a reason (hopefully 😉).

Building pipelines also leads to another passion of mine: serving others.
I use my love for structure to make life easier for the people around me.
I take the complexity onto my plate so they don't have to deal with it.

It's the perfect synergy. 
Solving someone else's problem while satisfying my inner needs.
And that’s why I love what I’m doing each and every day! 


There's nothing quite like that feeling when your pipelines wrap up with a satisfying green checkmark, is there? 😉

-------------------------------------------------

Hi, I'm Fabian👋
I write about building lean and cost-effective data systems on GCP
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No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Paolo Perrone

NVIDIA just made their best AI models free.

99% of developers have no idea.

While you burn cash on OpenAI credits...
NVIDIA open-sourced Nemotron.
Their entire agentic AI family 🤯

Free Models That Actually Ship:
• Nano: Runs on your laptop
• Super: Single GPU beast
• Ultra: Multi-GPU monster

What They Do:
→ Graduate-level reasoning
→ Math without hallucinations
→ Code that compiles
→ Visual reasoning that works
→ Tool calling. No loops.

The genius part:

They published EVERYTHING.
Models. Datasets. Techniques.
Actually open source.

Game Changers:
• 6x faster on new hardware
• No API limits
• Runs on a Raspberry Pi
• Commercialize it. No strings.

The uncomfortable truth:

You're waiting for GPT-5.
Smart teams ship with Nemotron.

Who's using it:
- Accenture
- CrowdStrike  
- ServiceNow
- Deloitte

The kicker:

Download from Hugging Face. Now.
Deploy tomorrow.
Zero license fees.

Forever.

They built enterprise NIM too.
(That costs money. Models don't.)

Still paying for API credits? 

♻️ Repost before OpenAI charges per thought

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝

I just finished Google's biggest AI Agents course ever.

250+ pages of production insights. 100% free.

Here's everything they released:

DAY 1️⃣ INTRODUCTION TO AI AGENTS (50 pages)

Most agent demos fail in production.
This covers the 5 levels of AI agents, from basic reasoning to self-evolving systems, plus security and architecture patterns that scale.
🔗 https://lnkd.in/gN9JaXgR

DAY 2️⃣ AGENT TOOLS AND MCP (50 pages)

MCP servers can add new tools without your approval.
Your poetry bot suddenly gains purchasing power overnight.
This breaks down tool design, the Model Context Protocol, and hidden security risks.
🔗 https://lnkd.in/gRWDSjBr

DAY 3️⃣ CONTEXT ENGINEERING AND MEMORY (70 pages)

Agents with zero long-term memory treat every session like meeting you for the first time.
Learn how to build memory systems that actively curate context, not just save conversations.
🔗 https://lnkd.in/gbgWJ4kH

DAY 4️⃣ AGENT QUALITY (50 pages)

Traditional QA doesn't work for agents. You can't unit test judgment.
This covers the 4 pillars of agent quality (effectiveness, efficiency, robustness, safety) and how to evaluate reasoning.
🔗 https://lnkd.in/g6DHcZDx

DAY 5️⃣ PROTOTYPE TO PRODUCTION (40 pages)

Building an agent takes 2 weeks.
Trusting it in production takes 6 months.
Learn evaluation gates, circuit breakers, and evolution loops that close the gap.
🔗 https://lnkd.in/gYTakhAK

Huge props to Antonio Gulli and Shubham Saboo for making this happen, and make it 100% free

250+ pages. Zero cost. All production-focused.

♻️ Repost to save a fellow AI engineer 6 months of trial and error

Sebastian Raschka, PhD just exposed the biggest secret in AI.

After dissecting 9 flagship LLMs, he found something nobody wants to admit:

We haven't invented a new architecture in 7 years.

The evidence is damning:
• DeepSeek V3: 671B parameters, still a transformer
• Kimi K2: 1 TRILLION parameters, still a transformer
• GPT-OSS: OpenAI's latest, still a transformer
• Llama 4: Meta's flagship, still a transformer

Every "breakthrough" model?
Same skeleton. Different makeup.

Here's what's actually happening:

DeepSeek V3/R1: Compressed attention (MLA) + expert routing (MoE)
Gemma 3: Sliding windows to fake longer context
SmolLM3: Removed position embeddings entirely (!)
Kimi K2: Just made DeepSeek bigger

The brutal reality:

• No new architectures since "Attention is All You Need" (2017)
• Every innovation = efficiency hack
• We're polishing, not inventing

671 billion parameters.
1 trillion parameters.
Same. Damn. Architecture.

While everyone's chasing parameter counts, Sebastian mapped the actual mutations:
• MoE: Use 37B of your 671B parameters
• Sliding attention: Pretend you have infinite context
• MLA: Compress your KV cache and pray

This isn't innovation.
It's optimization theater.

The most honest technical analysis you'll read this year.

Read it. Then ask yourself:
If we can't innovate past transformers, what happens when we hit the parameter wall?

♻️ Repost if you appreciate actual technical depth over hype

AI agent bootcamps charge $2,000 for this. You're getting it for free.

1️⃣ Design (6+ hours)
→ Langflow docs: docs.langflow.org
→ Travel planning agent template: https://lnkd.in/gmbWX5Mg
→ Agentic design patterns by Andrew Ng: 

2️⃣ Build (22+ hours)
→ LangGraph course: https://lnkd.in/g7raxjpC
→ LangSmith (modules 1-3): smith.langchain.com
→ Evaluation: https://lnkd.in/ghE-8EwA
→ Prompt engineering: 4 Methods of Prompt Engineering

3️⃣ Deploy (35+ hours)
→ LangChain Academy (modules 1-6): academy.langchain.com
→ MLOps specialization: https://lnkd.in/gmeb53UP
→ System design fundamentals: YouTube playlist
→ Distilabel for synthetic data: https://lnkd.in/gd_jJaJE

63 hours. $0.
The $2,000 bootcamp teaches the same stack with better marketing.

💾 Save this. It's the curriculum.
♻️ Repost to save someone $2,000.

Wanna build AI agents? Stop watching random tutorials.

Here's the actual learning path:

1️⃣ Design (6+ hours)
→ Langflow intro: langflow.org
→ Multi-agent workflow patterns
→ Build: Travel planning multi-agent system

2️⃣ Build (22+ hours)
→ LangGraph course: https://lnkd.in/g7raxjpC
→ LangSmith for tracing: smith.langchain.com
→ Evaluation metrics and prompt engineering

3️⃣ Deploy (35+ hours)
→ LangChain Academy: academy.langchain.com
→ MLOps fundamentals
→ LangGraph Cloud deployment

Total: 63 hours. If you follow the order.
6 months if you don't.

💾 Save this.  Start at Phase 1.
♻️ Repost if you've ever jumped straight to deployment and regretted it.

90,000 AI papers published on Arxiv.

Ilya Sutskever says you only need 27 to understand 90% of what matters.

I just went through his list. Here's what shocked me:

What's NOT on the list:
❌ No RAG papers (everyone's obsessed with retrieval)
❌ No prompt engineering papers (despite the hype)
❌ No LangChain or agent frameworks
❌ Nothing about fine-tuning optimization
❌ Zero papers on "emergent abilities"

What IS on the list:
✅ "Attention is All You Need" (obviously)
✅ "The Unreasonable Effectiveness of RNNs" (2015, still matters)
✅ "Scaling Laws for Neural Language Models" (this is the one)
✅ "Kolmogorov Complexity and Algorithmic Randomness" (wait, what?)

The pattern I noticed:

1️⃣ Ilya's not telling you to read papers about using AI.
He's telling you to read papers about how intelligence works.
Three categories dominate his list:
Fundamentals (the architecture breakthroughs)
Attention mechanisms
Residual connections
LSTMs and sequence modeling

2️⃣ Scaling (why bigger actually works)
Scaling laws
Pipeline parallelism
Training dynamics

3️⃣ Compression (the theory underneath)
Minimum description length
Kolmogorov complexity
Information theory

Here's the controversial take:
If you're only reading papers from 2023-2024, you're studying tactics.

Ilya's list is strategy:

"Attention is All You Need" is from 2017.
"Understanding LSTM Networks" is from 2015.
"Deep Residual Learning" is from 2015.
The foundations haven't changed.
We're just scaling them.

The most surprising paper on the list:

"The First Law of Complexodynamics"
It's not even about neural networks.
It's about how complex systems evolve.

This is what separates engineers who use AI from engineers who understand AI.

If you only read 5:

1) Attention is All You Need (transformers)
2) Scaling Laws for Neural Language Models (why GPT works)
3) Understanding LSTM Networks (sequence modeling)
4) Deep Residual Learning (why deep networks train)
5) Kolmogorov Complexity (the theory of intelligence)

♻️ Repost to save someone from FOMO-reading 90,000 papers

99% of AI engineers can't write a GPU kernel. 

This free CUDA course on GitHub changes that.

Zero fluff, pure metal GPU programming.

What you'll build:
• GPU kernels from scratch
• Production-ready Triton code
• Optimized matrix multiplication 
   (what makes GPT-4 fast)
• Direct cuBLAS/cuDNN integration
• Custom PyTorch CUDA extensions

The uncomfortable truth: 
Every 10x performance gain in AI comes from someone who understood CUDA.
Time to join them 👇

♻️ Repost to save someone from being a framework-only developer

🚨 Update: 800+ comments. My DMs exploded.
Can't reply to everyone. 
Grab it here: https://lnkd.in/eMQn_X-6
-------------------------------------------
Hamel's AI Evals toolkit disappears in 24 hours.

This isn't publicly available.

4 resources from the guy who:
→ Trained 2,000+ engineers on evals
→ Consulted for OpenAI, Google, Meta
→ Delivers high-value engagements for top-tier companies (ask dbt Labs why)

What you're getting:

📇 12 Evals Flashcards — the visual system for memorizing what matters
📘 The Complete Evals Guide — 144 pages of everything you need
📚 The RAG Evaluation Bible — retrieval metrics that actually work
📧 30-Day Email Course — builds evals that predict user satisfaction (free)

You won't find this on Google, GitHub, or HuggingFace.

It's in your DMs. If you act fast.

Last time I did this, 4,173 people commented. I can't DM that many again.

Comment "EVALS" + Repost ♻️ and I'll send all 4

24 hours. Then it's gone.

There's a special hell in AI/ML engineering.

It hits around Year 2.
I call it the "valley of almost useful."

You're past "Hello World" tutorials.
Not good enough for real jobs.
Stuck building demos nobody uses.

The daily torture:

Morning: "Today I'll implement that paper"
Noon: Your embeddings are somehow 7-dimensional
Evening: Stack Overflow has zero answers
Night: "Maybe I should go back to web dev"

The worst part?

You know just enough to be dangerous.
Just not enough to be valuable.

🔥 The 3 Levels of "Almost Useful"

Level 1: The Overengineering Phase
Every problem needs a neural network.
CSV parsing? Better use GPT-4.
Your hello world has 17 dependencies.

Level 2: The Imposter Syndrome Pit
Everyone else seems to ship AI products daily.
Your Twitter feed is pure pain.
"Just deployed my 5th AI startup this week!"
Meanwhile, you can't get embeddings to work.

Level 3: The Clarity Moment
You realize 90% of "AI Engineers" are just prompt engineers.
Most production AI is boring Python scripts.
The magic isn't in the model - it's in the problem.

Here's what got me through the valley:

1️⃣ Ship broken things
My first production model had 67% accuracy.
Still saved the company $200K/year.
Perfect is the enemy of deployed.

2️⃣ Join a startup, not FAANG
Startups let you touch everything.
Break things. Learn fast. Ship faster.
Big tech will teach you one tiny slice.

3️⃣ Build boring solutions
Stop chasing cutting-edge.
Most businesses need basic classification.
Not your 12-layer transformer architecture.

4️⃣ Document everything
Future you will thank present you.
Especially when debugging at 3 AM.
"Why did I set embedding_dim=7?"

5️⃣ Find your valley friends
The loneliest part is thinking you're alone.
You're not. We all went through this.
Some of us just hide it better.

The uncomfortable truth:

The valley of almost useful is necessary.
It's where you learn that AI isn't magic.
It's engineering.

And engineering is mostly debugging.
And crying.
But mostly debugging.

Still in the valley?
Good. That's where real engineers are made.

Not in bootcamps.
Not in courses.
In the trenches, shipping broken code that somehow works.

Welcome to the club.

P.S. That senior engineer who seems to know everything? They googled it 5 minutes before the meeting. We're all just better at pretending now.

♻️ Repost to help someone realize they're not alone in the valley

I pushed every agent framework until it broke. Most broke fast.

CrewAI vs LangGraph vs Agno vs PydanticAI vs Atomic Agents...
Plus SmolAgents, Mastra, and AutoGen.

The brutal truth:

Most devs hate the popular ones.

CrewAI and AutoGen? "Tricky to debug"
LangGraph? "Overly complicated abstractions"
SmolAgents' CodeAgent? "Cool idea, doesn't always work"

What actually matters:

🎯 Control vs Agency
Some frameworks let LLMs run wild (AutoGen)
Others keep them on a tight leash (Atomic Agents)
Guess which ones actually ship to production?

🛠️ Abstraction Level
High abstraction = Quick start, debugging nightmare
Low abstraction = More code, but you know what broke
Pick your poison.

💀 The Learning Curve
LangGraph: Steep as hell, but scales
CrewAI: Easy start, hits a wall
PydanticAI: Write everything yourself

The uncomfortable truth:
"AI agent frameworks are some of the worst forms of abstraction"

But here's what actually works:
• Agno for developer experience
• LangGraph for multi-agent at scale
• PydanticAI when you need control
• CrewAI if you're just prototyping

Half these frameworks won't exist in 2 years.
The winners? Whoever makes debugging possible.

Saved you 2 weeks of testing. You're welcome.
♻️ Repost if you've been burned by agent framework promises

a16z just published their AI coding report.

The math blew my mind:

AI coding costs $10k/year per developer.
But creates $100k+ in value.
That's $3 trillion in new GDP.

The entire economy of France. From a few Silicon Valley startups.

Here's what nobody's talking about:

Software development just shifted from pure people cost to people + compute cost.

Your junior developer in Bangalore?
Now competing with Claude at $10k/year.

The uncomfortable truth:

We're watching the end of cost-based outsourcing.

Not because AI replaces developers.
But because location doesn't matter when compute costs dominate.

I tested the numbers myself:

• 100k context window filled
• Claude Opus 4.1 reasoning mode
• 10k output tokens
• 3 queries/hour, 7 hours/day

= $10,500 annually

That's more than many offshore developers.

But here's the kicker:

The companies winning aren't cutting developers.
They're hiring MORE.

Why?

Because when productivity doubles, the math changes:
• 1 developer + AI = 2x output
• Cost per feature drops 50%
• Suddenly every project has positive ROI

The report shows clear winners already:

Cursor: $500M ARR in 15 months
Windsurf: $2.4B Google acquisition
Claude Code: Started a war with its own distribution channel

The pattern is obvious:

Lower barriers → More projects → More complexity → More developers needed

AWS didn't kill sysadmins.
WordPress didn't kill web developers.
AI won't kill software engineers.

It just changes what they do.

University CS programs?
Already obsolete.

Teaching algorithms when AI writes them faster?
Teaching syntax when AI handles implementation?
Teaching debugging when AI prevents most bugs?

That's like teaching calligraphy in the age of keyboards.

The real skill now:

Knowing when the AI is hallucinating.
Dragging it out of architectural holes.
Translating business needs into prompts.

The $3 trillion opportunity isn't in replacing developers.

It's in making every developer 10x more valuable.

While universities update their curriculums,
smart developers are already adapting.

Are you?

♻️ Repost if you think CS education needs a complete overhaul

I just watched Sebastian Raschka build the entire attention mechanism from scratch in 2 hours.

No abstractions. No "just use torch.nn.MultiHeadAttention."

Line-by-line Python until it clicks.

This is 's breakdown of how LLMs actually work.

Step 1️⃣ Basic self-attention (no weights) 
Shows you the raw math: how a model decides which words matter.
You see why "bank" in "river bank" gets different attention than "bank" in "savings bank."

Step 2️⃣ Add learnable parameters 
Now the model can learn what to pay attention to.
This is where it stops being a simple lookup and becomes intelligence.

Step 3️⃣ Causal masking 
The trick that makes GPT work: tokens can only attend to previous tokens.
Without this, the model cheats during training.

Step 4️⃣ Multi-head attention 
The breakthrough: run 8 attention mechanisms in parallel.
One head learns syntax. Another learns semantics. Another learns long-range dependencies.
This is why transformers work.

👉 What makes this different:

Most tutorials either:
Show you the math (incomprehensible)
Show you the PyTorch API (you learn nothing)

Sebastian does both.

You see the math implemented in code, step by step.

By the end, you understand why attention_weights = softmax(Q @ K.T / sqrt(d_k)) isn't magic.

It's just weighted averaging with learnable weights.

💡The moment it clicked for me:

Attention isn't complicated.
It's just "which words should influence this word's representation?"
Then you do that for every word.
Then you stack it 96 times (GPT-4).

That's it.

Traditional RAG: retrieve → generate → pray

Agentic RAG: retrieve → validate → retry → succeed

One is a pipeline. The other is a feedback loop.

I just built this Agentic RAG system with LangGraph + Milvus

It knows when it's wrong, and fixes itself

Here’s how:

Router: "Can I answer without retrieval?" 
✅ Yes: Skip to generation 
❌ No: Retrieve from Milvus

Grader: "Are these chunks actually relevant?" 
✅ Good: Generate answer 
❌ Bad: Rewrite query, loop back

Validator: "Does this answer match the sources?" 
 ✅ Yes: Return to user 
❌ No: Retry with different approach

It's a graph with feedback loops, not a one-shot pipeline.

The difference:
Traditional RAG commits to one path. No second chances.
Agentic RAG loops back, validates, catches mistakes, and retries until it's right.

You get a system that debugs itself before you see the output.

Why this stack:
LangGraph = control flow primitives (routing, looping, branching) 
Milvus, created by Zilliz = production-grade vector search at scale
Together: RAG that thinks instead of guesses.

Full implementation 👇

This is the RAG architecture pattern that will become standard.
♻️ Repost to save a fellow engineer from another week of RAG debugging

BFCM isn't won by the brand with the best products.

It's won by the brand that sees what's breaking first.

Most operators spend BFCM like this: 

9 AM: Campaign's crushing it, scale to $10K/day
11 AM: ROAS drops 40%, nobody knows why
2 PM: Still diagnosing
5 PM: $6K burned on a broken campaign

The strategy wasn't wrong. 

You just couldn't see the problem fast enough. 

Diagnosing "why is ROAS dropping?" right now: 

→ Open Meta Ads Manager
→ Check Shopify analytics
→ Pull GA4
→ Cross-reference Klaviyo
→ Export to spreadsheet
→ Try to connect the dots

20 minutes later you've burned $3K.

Triple Whale + Moby give you real-time answers from your complete data stack. 

👉 https://lnkd.in/gtgB8GJw

Moby has real-time access to all of it. 

Ask Moby: "Why did ROAS drop?"

Get: "iOS campaign CTR crashed 67% after creative refresh. Top SKU went out of stock an hour ago. Traffic's converting to lower-margin backup products."

Not a guess. The actual answer.

One brand caught this: 

The campaign was showing 187% ROAS. 
Moby a flagged 41% return rate. 
Actual ROAS after was returns: 73%. 

They were about to scale into a loss.

During BFCM, 20 minutes to diagnose = thousands in wasted spend. 

The brands that win can see problems in seconds, not hours. 

And yes, I am a Triple Whale partner.

👉 https://lnkd.in/gtgB8GJw

♻️ Repost if you've burned budget figuring out what went wrong

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