Thoughtworks

Global Service Portfolio Lead for Data & AI Strategy services, including strategic enablement for Data & AI Governance, Data Modernization, AI readiness and other related client objectives.

Director, Data & AI Strategy

Leading our data strategy and data governance services, shaping the offering and approaches to create value-driven solutions for our clients in Europe

Principal Data & AI Strategist

DataQG

DataQG is the leading Data Enablement and Community Platform designed to help organizations upskill their workforce in data literacy.

Education Advisory Board Member

DAMA Germany

Lead marketing planning, execution and monitoring to grow and engage the community and to establish thought leadership and knowledge exchange in all things Data Management & Data Governance in Germany

Head of Marketing

Technics Publications

Author - "Humanizing AI Strategy - Leading AI with Sense and Soul"

Author - "Humanizing Data Strategy - Leading Data with the Head and the Heart"

Ambassador

adidas

Ensuring product data is consistent, accurate and available and making sure it’s used in a secure and meaningful way. We govern product data end to end, from data creation through data processing all the way to data usage and enrichment.

Senior Director, Product Data Governance

Lead in Social, PR and Voice of Consumer Analytics to integrate Consumer Insights from Social Media, PR, Search Behavior, Product Reviews and Experience NPS feedback into any decision-making in brand strategy, campaign planning and in-activation optimization.

Director, Digital Analytics - Social, PR & Voice of the Consumer

Lead in Voice of the Consumer Analytics with focus on integrated insights based on consumer feedback across digital channels (e.g. Digital Brand Perception on Social Media, Product Ratings / Reviews on Website, Search Behaviour on Google)

Director, Digital Analytics - Voice of the Consumer

Creation of Digital Marketing Performance KPI Framework to measure success within branding activities and consumer perception on strategic and operational level

Lead standardisation and alignment of eCom KPI definitions and reporting methodology across Digital Analytics, BI/Finance, Markets and Business Units

Governance of holistic eCom business performance KPI framework across digital and financial teams

Senior Manager, Digital Analytics

TD Reply

Project Manager of projects in Digital Strategy & Performance Optimization

adidas: On-site Project Manager and Product Owner of KPI Frameworks and Dashboard Visualizations at Digital Brand Commerce

Miele: Account Management of International Brand Marketing and coordination with over 10 markets

Support in Digital Performance Management projects of Coca Cola, Volkswagen and Döhler

Knowledge Management & Advisory between German and Chinese Office

Senior Consultant

Lead of projects in Digital Strategy & Market Research

Key account manager of VW brand marketing

Development of Social Media strategies

Setting up processes and guidelines for crisis management

Consultant

Junior Consultant / Master Thesis

Student Worker

Berlin Institute of Technology (Technische Universität Berlin)

Analysis of business models and market analysis of various industries

Research Assistant - Chair of Strategic Leadership & Global Management

I’m a Data & AI leader by day, a musician at night, and an optimist at heart. 

After 12+ years in data analytics, data governance, consumer insights, digital transformation and AI innovation, I grew fond of the human aspect - how to communicate, collaborate and get creative around data and AI.

My passion lies in making data and AI more approachable and fun, and I often use unconventional methods to do so - with music, humor or other creative outlets. 

Nice to meet you! 
Let’s have a (virtual) coffee?

Tiankai Feng

Data quality has always been a little bit of a catch-all for all kinds of data complaints. Whenever data looks a little different than expected, it immediately must be a data quality problem. 

The rapid increase of AI-driven use cases for data now puts even more attention to the concept of data quality, because AI use cases often require expanded historical data, higher granularity, higher speed and a different focus - like unstructured data for context. 

One thing has not changed: the only way to manage data quality in the right way is to move away from generic complaints about it to detailed conversations about what "high quality" data actually means, and what currently is missing to get there - to then improve data quality together towards a defined state. 

When in doubt, dad jokes can also help - to not only break the ice, but to also exercise authority by positioning yourself as someone's dad. 😁 

Let me know if you need any help.

#dataquality #datagovernance #tiankaistuff

Data & AI Strategy Director @ Thoughtworks | Author of “Humanizing AI Strategy” | TEDx Speaker | Data Musician

This is an appreciation post for everyone working in data governance.

While most data governance people I talk to are really driven and passionate about making data better for their organization, they are also the most modest and self critical - believing they always can do more things better. 

Facing resistance and misunderstandings around what data governance even is trying to do, being a great empath and communicator became a necessity - making them great collaborators and people to hang out with (if you give them a chance).

Since data governance conferences very quickly turn into group therapy, I know we are not getting the attention and acknowledgement we deserve - and although the hype around AI has finally put us in the spotlight, we are now spending even more time and efforts clarifying requirements and objectives, and setting the right expectations.

So, to everyone working in data governance: you are doing your best and it’s awesome - just a little longer and you can recharge during the break. There’s a lot of exciting work to do in 2026.

Data governance rules. 

#datagovernance #appreciation #tiankaistuff

Fun with data literacy in Kindergarten?! 

Yesterday I was invited to read “The Data Detective at the carnival” to the kids at my son’s Kindergarten group - a book that I translated for the German community and donated to the Kindergarten before. 

I was first a little worried that talking to kids between 4 and 6 years old about qualitative and quantitative data would be too difficult - oh how wrong I was! 

We had the most fun letting the kids count and describe things not only in the book but also in the room and in their lives - and everyone was excited to get a “data detective certificate” afterwards! 

But the most rewarding thing of all is how proud my son was that his dad came to Kindergarten to read a book to everyone - showing up as his cool dad is probably my favorite speaking engagement ever. 

Thanks to Chandra Donelson and Jordan Morrow for writing such a wonderful book! 

#dataliteracy #education #tiankaistuff

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In a desperate attempt to make data jobs cool for the next generation, Fortnite launches the new “data squad” skins! 

They even come with their own signature powers: 

👉 the data engineer can build pipelines to move faster but everytime a pipeline breaks the player is teleported next to the pipeline to fix it for an hour

👉 the data scientist can predict other players’ movements but needs to clean data in a spreadsheet for 30 minutes first 

👉 the data analyst puts random dashboards all across the map to confuse other players with KPIs and metrics 

👉 the data product owner can build powerful products but with every minute without a RoI the player loses power 

👉 the data governor can change the rules of the game entirely but other players only have to comply optionally 

Can’t wait for the AI roles skins to drop with equally as much realism! 

P.S. this is satire and not real. 

#data #humor #tiankaistuff

Receiving this kind of feedback after my talk and musical performance at the Forward Data Conference is truly so rewarding.

"People will forget what you said. People will forget what you did. But people will never forget how you made them feel”, the great Maya Angelou once said - that’s why sharing positivity and joy with others is for me equally as important as exchanging knowledge. 

After all, there are two things I love to spread: Happiness and germs. 

Just kidding, it’s Nutella. And germs. 😁

How we make people feel is our real legacy, y’all. Have a great weekend everyone!

#conference #mindset #tiankaistuff

Here are five reasons why you should buy my book "Humanizing Data Strategy":

👉 My mom calls it "better than expected"

👉 Critics call it "marxism for data" and "DEI Cr*p"

👉 It looks great on a book shelf

👉 That's why your kids will love to draw stuff onto the cover

👉 It's currently on Black Friday sales (but apparently only in Germany)

It's also about the human side of data and introduces the framework of the 5Cs (competence, collaboration, communication, creativity & conscience) to make data strategy truly human-centric, but those are just minor details. 😁 

Maybe those that read this book already can leave more reasons to buy it in the comments. Thanks in advance!

#datastrategy #author #tiankaistuff

“You’re too sensitive.” 

One of the worst things to say to someone who’s upset - and it never has made anyone less sensitive, ever. 

There’s always a reason why we are triggered by certain things based on our unique experiences, and this kind of statement shows ignorance to that. 

We can say many other things instead, like: 

“I’m sorry you’re upset - could you help me understand why this bothers you so much?”

“I think we can look at this from a different angle, what about…” 

“What can I do right now to make this situation better? I want to help make this right.”

It’s always easy to judge someone else, but trying to put yourself in someone else’s shoes is where real human connection starts. 

Be human, y’all! 

#empathy #collaboration #tiankaistuff

I’m really happy about anyone who voluntarily listens to my data music, but almost a thousand listeners from 51 countries is crazy! 

2025 was also very exciting because I’ve never performed music live so much as this year - I even got to play on a real concert piano thanks to the Inspari A/S data & AI day, and I got to write and perform a new original conference song for the Forward Data Conference! 

I know that making music about Data & AI is not everyone’s cup of tea - but as someone who’s passionate about the human side of data & AI, isn’t that subjectivity what truly shows our humanity? 

As long as my music brings a net positive to the data and AI community, I’ll keep on doing it - thank you all. 

P.S. I have some very cool new music coming before the year ends, stay tuned! 

#music #personalbranding #tiankaistuff

What do virtual meetings and Disney movies have in common? 

Not a lot, but I created this musical parody anyway. You’re welcome. 

And now everyone: 
🎶 can you see my screen right now? 🎶

YouTube link in comments. 

#meetings #humor #tiankaistuff

Forward Data Conference, here I come! 

This year’s final station of what for me felt like the “humanizing data & AI world tour” will be in Paris - and it will be a doozy! 

I’ll be giving a talk about the key success criteria of truly scaling AI, and I can’t wait to meet data friends like Joe Reis, Eevamaija Virtanen, Jessica Talisman, Andrea Gioia and Gaëlle SERET! 

The forward data conference was an excellent experience already last year, and from what I’ve seen so far Yoann Benoit, François Laurain and the team took it to really the next level this year.

Oh yeah, the scarf indoors is protecting my voice because there may or may not be some never before seen or heard music by yours truly tomorrow… 🎶

à demain!

#conference #data #tiankaistuff

Congratulations to Greg Freeman on publishing The Data & AI Literacy Bible - a book that’s timely, important and truly insightful! 

I had the honor to review and endorse this book, and what I really love about it is the focus on human confidence, growth and actions - aspects that are often forgotten in conceptual and abstract conversations around literacy. 

This having said, I’ve had the chance to meet various Data Literacy Academy folks and they’re all awesome - you can talk to them and find out yourself 🙂

The book is now out on amazon, link in the comments! 

#dataliteracy #book #tiankaistuff

There are many reasons why AI governance is important right now, but the biggest undeniable reason for me is that we need to admit that human-beings aren't perfect and make mistakes, and that we should do everything we can do prevent machines from making the same ones - faster and on bigger scale. 

I was asked to contribute to the "Document automation trends in 2026" report by Rossum and focused on the importance and challenges of AI governance, and there are many other interesting trends to read about that are not only specific to document automation but to AI use cases in general.

On a side note, I love how we are all trying our best to predict the next year, when AI specifically this year showed us that many things are truly unpredictable. I guess we all want some certainty for comfort, and I'm not blaming anyone.

Link to check out the full report in the comments!

Huge thanks to Julie BIASION ✨ for inviting me to contribute!  

#aigovernance #trends #tiankaistuff

🎶 EXCITING NEWS!🎶  

As a token of gratitude for all the opportunities and encouragement I got this year for my data & AI music, I want to give back with something very special: MY FIRST EVER (VIRTUAL) LIVE CONCERT!

Join me in an exclusive livestream where I'll perform my most iconic songs about, data & AI and so much more - and since it's live, of course I'll engage with all of your questions and comments. 

My only question for you is: Which of my songs do you want to see me perform live? The setlist is not completely done yet so I can still make changes 😎 

See you there!

#data #music #tiankaistuff Thoughtworks

TIANKAI LIVE IN CONCERT - a musical end of year celebration

How do I make my AI strategy human-centric, and why does it matter?! 

I’m so excited to return to DataQG to give a community webinar exactly on that topic tomorrow. 

Everyone can join, and there will be a live Q&A - so if you want to visit one of the best data governance communities and watch me improvise answers to your trick questions, join us! 

Thanks to my friend Philip Black for the invitation!

Link to sign up in the comments. 

#aistrategy #webinar #tiankaistuff

I often think about Formula 1 races as best practice environment where actual data-driven decisions are being made - a lot of them at an incredible speed! 

Being able to do that requires not only a well connected ecosystem of machines, tools, and systems - but also an incredibly well-oiled (get it?) team that understands how to work together in a high stakes environment. 

This is why I’m very excited about this upcoming Alteryx webinar with the Lisa Jarman, PhD from the McLaren Group Formula Team to exactly discuss how data-driven decisions are being made there, and I can’t wait to learn and get inspired!

See you at the webinar?

Link to register here: https://lnkd.in/dJi4dKKi

#webinar #dataanalytics #tiankaistuff

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

6 hours → 2 minutes.

One line of code.

I ran HDBSCAN on 2 million embeddings yesterday.

Snowflake quietly shipped NVIDIA's cuML and cuDF preinstalled.

No CUDA setup.

Just:

'''
import cudf.pandas
cudf.pandas.install()

import cuml
cuml.accel.install()
'''

Your entire pandas + scikit-learn pipeline → GPU. Instantly.

The benchmarks:

→ Random Forest: 5x faster
→ UMAP: 25x faster
→ TSNE: 72x faster
→ HDBSCAN: 200x faster

Same code. Same libraries. Just faster.

No rewriting. No CUDA boilerplate. No infrastructure work.

If you're running scikit-learn on Snowflake CPUs right now, you're burning money for no reason.

💾 Save this before you waste another weekend on clustering
♻️ Repost to save your team's weekends too

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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