Daliana Liu

Daliana Liu Coaching

90% senior IC and leaders in tech got stuck in their careers because they lack two things: a) communication skills and b) understanding of their goals and strengths.

There are 3 ways I coach my clients:

1. Develop the communication skills to deliver high-impact projects and get promoted.
2. Build a trustworthy personal brand to attract high-quality opportunities or future customers.
3. Get clarity on your niche and design a career path that works best for you.

Want to reach your full potential without burning out?

I offer courses and 1-on-1 coaching: DM me on LinkedIn or email daliana@dalianaliu.com with your bio and the area you need coaching.

Join my newsletter with 20k readers: https://dalianaliu.kit.com/profile

Founder, Coach to Senior ICs, Tech Execs, Founders

The Data Scientist Show

• Built the podcast to the Top 100 US Tech Podcast.

• Built partnerships and advised GTM/content strategies for leading B2B Data & AI startups like Statsig (Acq. by OpenAI), Eppo (Acq. by Datadog), NVIDIA, Monte Carlo Data, Predibase (Acq. by Rubrik), SingleStore, Galileo AI, etc.

• Loved by the audience, highly rated on Apple and Spotify (4.8/5)

It's a deep dive into data scientists' day-to-day work, how they tackle technical problems, and their career journeys. This podcast helps you grow a successful career in data science. Listening to an episode is like having lunch with an experienced mentor. Guests are data science practitioners from various industries, AI researchers, economists, and CTOs of AI companies.

It has been re-branded to "Daliana's Game". 
Old episodes are still available on Apple, Spotify, YouTube, and all podcast platforms.

Founder, Host, Content Partnership Advisor

Predibase

• Developed technical content and led the online and in-person workshops to educate thousands of AI/ML practitioners on declarative ML and LLM finetuning.

• Advised the company and co-founders on data & AI content strategies and personal branding.

• Created a strategic partnership with Deeplearning.ai, which led to course collaborations with Andrew Ng's team and boosted the awareness of the product.

Senior Data Scientist/Developer Advocate

Amazon Web Services (AWS)

Machine Learning Solutions Lab: I work with AWS customers to translate the business problems to ML problems and build the models for them.
 
• Led the project with Sportradar to develop a Soccer Goal Predictor using action recognition models. The solution will help broadcasters with storytelling and fan engagement.
• Led the project with UIUC Football coaches to improve game preparation. Our ML model automated the process and reduced the workflow from hours to under 10 mins, and it provides more insights for the coaches.

Senior Data Scientist - Machine Learning

Data Scientist

Amazon

• A/B Testing (experiment design, causal inference, decision making, metrics deep dive)
Collecting data, generating features, and building machine learning models for prediction with customer behavioral data to shorten experimentation time.

• Designed success metrics for marketing and business for better and faster decision making
Created data visualization tools and analytics report 

• Gave company-wide training and workshops on A/B testing and data-driven decision-making

• Created a knowledge-sharing internal newsletter on "A/B testing learnings and best practices". Grew the list to 1,000+ subscribers.

Boingo Wireless

• Won 1st place in the Boingo Wireless Hackathon by creating a time series model using trigonometric functions to forecast daily ad capacity. 

• As the first and only data analyst, I led time series analysis and capacity forecasting projects and delivered business insights to marketing and sales executives.

Business Intelligence Data Analyst

SMIF UCI

• Student Managed Investment Fund (SMIF) Team

• Our team won 1st place in the CFA Orange County portfolio management competition in 2012.

• Conducted top-bottom research pertinent to the $33,000 fund’s investment objective, including economic research, industries research, and asset allocation.

Equity Research Analyst

I'm a career coach and entrepreneur. Previously, I was a Senior Data Scientist at Amazon with 7 years of experience in data science & ML. 

When I started my career, I felt lost. I struggled to meet expectations and feared I wasn't good enough. I had to figure out how to navigate my career and advocate for myself. Eventually, I got promoted to a senior data scientist at Amazon, leading high-impact projects and mentoring data scientists and MLEs. My career advice has been featured on Business Insider, Amazon Science Blog, and VentureBeat.

In my coaching program, I help data science/ML professionals advance their careers and future-proof themselves without the burnout. 

If you want to explore coaching, join my newsletter https://dalianaliu.kit.com/profile

Last night felt surreal. I went to The Monkey King (猴王悟空) opera with Chip Huyen and Khailee Ng — three friends from China, Vietnam, and Malaysia, all connected by the same childhood memory.

For anyone who didn't grow up with him: The Monkey King is a rebellious, brilliant, rule-breaking monkey who went on a journey to retrieve Buddhist scriptures. One of the most iconic characters in Chinese culture.

Seeing his story performed in English and Mandarin at the San Francisco Opera was magical.

Thank you to the NVIDIA team for the invitation — and especially to Jensen Huang and his family, who served as Honorary Chairs of this world premiere. They partnered with SF Opera to bring this iconic Asian story to a global stage.

We got there early to join a pre-show interview with Jensen, someone asked him “what do you like the most about the Monkey King?” He smiled and said: "The monkey is a little naughty."

That’s right. The Monkey King isn’t a traditional hero. He's fearless, resiliant, yet mischievous. He can fight anyone, outsmart demons, and endure the darkest challenges — he was buried under a mountain for 500 years.

Yet he emerges still playful, still pure at heart. 

He has seen the weight of the world, but somehow keeps his childlike innocence. I wonder if Jensen sees a bit of his own resilience and playful nature in Monkey King's story.

The show was a beautiful blend of Western Opera, Chinese Opera, acrobatics, and immersive light design. The New York Times called it "a jaw-dropping feat of music theater. Worth looking into if you're in the Bay Area!

I hope more tech leaders continue to support the arts in this city — bringing people together in real life.

Helping tech leaders and senior ICs gain visibility, build optionality, and achieve their versions of success | Ex-Amazon Sr. Data Scientist

Someone just asked me: "If layoffs can happen anytime, does getting a promotion still matter?"
It depends ⤵

If you're already at that level and learning to advocate for yourself, influence others, and navigate politics: yes. Go get what you deserve. Those skills transfer everywhere.

But if chasing the title is a distraction, if there's another team where you'd learn more even if it delays the promotion: skip it.

The real questions: 
1. Am I building skills that compound? 
2. How much value does this title actually add to my resume and personal brand?

Your technical skills might be obsolete in 3 years. Your ability to influence stakeholders, lead cross-functional teams, and advocate for your impact compounds forever.

Either way, stay engaged. Build the relationships. Learn from every project.

The goal isn't to collect titles. It's to build the skills & your personal brand to make you valuable in any market.

I believe 3 skills are essential to win in the next 5 years. I break them down in my "Contractor Economy Starter Kit" article. Get full access: https://lnkd.in/exDzawDb

Five things I'm grateful for in 2025:

1. My digital nomad journey in Asia & US

After living in 5 cities in Asia, I finally lived in NYC for 5 months as my last stop. I'm grateful for the new friends I made along the way. Except this time, I made friends differently. 

I used to go to a lot of meetups when visiting a new city, but this time, I only relied on friends' referrals. I asked them who they think I should meet. The connection is so much deeper when it's vetted by people you trust. Quality > Quantity.

2. My teachers outside of Tech 

I haven’t written a line of code in two years. Instead, I treated coaching like a second Master’s degree. I realized giving people a strategy isn’t enough. You have to work on their emotion. 

Because emotion is what actually drives decisions. To the executive coaches and healers who taught me how to help people overcome fear and self-doubt: Thank you. This work is more rewarding than any code I’ve ever shipped.

3. The Client Wins 

Nothing beats this. I watched clients transform: 
→ From terrified of presenting to winning executive buy-in. 
→ From stalled careers to securing Senior promotions. 
→ From "scared to post" to landing speaking invites at conferences. 
→ From undercharging to launching their dream products. 

I always believe in my frameworks, but seeing real results motivates me to share this with more people!

4. The Community - You

From the quiet lurkers to those who send heartfelt emails and LinkedIn comments — thank you. I read every response. Even if we haven't met, reading your stories feels like hearing from a friend. You fill my "jar of love" and keep me going. 🤎

5. Time with Family (The most important one) 

I got to see my grandpa before he passed away this year. That moment clarified everything. It reminded me exactly why I chose this path of freedom. I didn’t leave the corporate ladder just to travel. I left so I could be present for the moments that actually matter.

All of these things led me here. And I want to help you find that same freedom and fulfillment for 2026.

So, for Thanksgiving, I am doing an "All-In" special offer as a gift to you. I am unlocking ALL my course materials (20+ hours) + a 1-on-1 Strategy Call with me to map out your next career move.

Whether you want to climb the corporate ladder or build your optionality, this bundle covers the exact frameworks I used to get promoted in Big Tech and build a successful personal brand. They are from my $2,000+ courses and are now only $199 for Thanksgiving week.

Claim the offer: 
Register to see the details: https://lnkd.in/e7PmgfpW

Thank you for being part of this journey.

Daliana

How top 1% data scientists communicate with stakeholders - a checklist I wish I had 8 years ago (pay attention to the last one):

We are officially in the "wrapping up" season. How you communicate your wins now determines the projects (and promotion) you get in 2026. 

Here are 3 things I learned from the top 1% data scientists:

1. Put it in a "what if" scenario using business metrics 

❌ Don't say, "The model has 85% accuracy in predicting customer churn" 

✅ Say "If we implement this model, we could prevent losing ~850 out of every 1000 at-risk customers, saving approximately $1.2M in annual revenue based on our average customer lifetime value."

2. Use English terms instead of jargon 

❌ Don't say, "We used XGBoost with SHAP values for feature importance." 

✅ Say "We built a system that learns from past customer behavior, and it shows us the top 3 warning signs before a customer leaves - price sensitivity, declining usage, and support tickets."

3. Help your stakeholders explain it to their stakeholders 

❌ Don't assume your job is done when you get direct stakeholder buy-in. 

✅ Your mission: Make your stakeholders look like heroes to their bosses! Package your insights into ready-to-use slides with compelling narratives. Remember: Your work's success depends not just on your direct stakeholder's approval but on their ability to champion it up the chain.

If you want to drive impact and get promoted in 2026, you have to stop thinking like an "executor" and start thinking like a "partner."

You need to know: 
→ How to influence stakeholders without authority. 
→ How to present to Executives (so they actually listen). 
→ How to make your impact visible to the people who control your budget.

I have helped data scientists/MLEs get promoted and receive excellent performance reviews by teaching them the "Corporate Mastery" frameworks that nobody teaches in school.

For Thanksgiving, I am unlocking my entire playbook. I’ve put ALL my courses into one "All-In" Bundle. It includes:
1. Senior Promotion Strategies: How to get promoted without burning out.
2. Stakeholder Management: How to get buy-in for your ideas.
3. Executive Presentation: Turning "shy presenter" into "confident speaker."
4. DS/ML Project Strategies: How to identify and deliver high-impact projects
5. LinkedIn Personal Branding: Attract high-quality career opportunities 

Plus, you get a 45-minute 1-on-1 Strategy Call with me. We will sit down and map out exactly how you get to the next level in 2026.

Total Value: $2,000+, previously only available through group coaching.
Thanksgiving Offer: $199 for 6 weeks. You'll have plenty of time to binge-watch the 20 hours of on-demand courses during the holidays.

How to claim your offer:
Register here to view the details: https://lnkd.in/enGsKSwB

I am unlocking ALL my courses.

Whether you want to climb the corporate ladder or build a path outside of it, this "All Courses" bundle covers the exact frameworks I used to get promoted in Big Tech and build a successful personal brand that helps me grow my business.

What’s Inside:

Part 1: DS/ML Career Accelerator
↳Senior Promotion Strategies: How to get promoted without burning out.
↳Stakeholder Management & Influence: How to influence stakeholders and get buy-in 
↳Impact Creation: How to find high-impact projects and iterate for success

Part 2: Personal Branding & Freedom
↳LinkedIn Mastery: How to build a trustworthy brand and write viral posts.
↳Niche Finding: Future-proof your career by owning a specific problem.
↳The Creator Business Roadmap: How to package your knowledge and launch your side-hustle.

This isn't just theory. It's the exact playbook my clients used to: 
✅ Secure senior-level promotion.
✅ Turn a "shy" presentation style into C-Suite buy-in.
✅ Earn top-tier performance reviews at FAANG companies.
✅ Build personal brands that led to speaking invites.
✅ Created engaging LinkedIn posts that attract paying clients.
✅ Launch websites and products after years of procrastination.

For the next 48 hours, get full access for $199. (If you bought these separately, it would be $2,000+ ) You'll have 6 weeks to watch the content, which is plenty of time during the holidays.

🎁 THE BONUS: You also get a 45-minute 1-on-1 Strategy Call with me. We will sit down, review your situation, and map out your next career move.​

Claim this offer before it expires. 

Comment "Unlock" and I'll send you the details.

You should have an "Independent Contractor Mindset" even if you have a full-time job today.
Some people think that means 3 things: 

→ Do the bare minimum 
→ Stop caring about your coworkers
→ Ignore work relationships, just ship technical work

That's the OPPOSITE of what I mean.

The misconception is to think that Contractor Mindset means protecting yourself by disengaging.

Actually, the best contractors do exactly what the best employees do: 
→ They maintain deep relationships with clients 
→ They understand the client's broader business needs 
→ They become trusted advisors, not just executors 
→ They get bigger contracts, more equity, strategic influence

So, what's the difference?
Here's what the Contractor Mindset looks like in practice:

→ You're fully engaged in your current role AND you're keeping your LinkedIn updated and building your personal brand.
→ You're pursuing that promotion AND you're learning stakeholder management that works anywhere.
→ You're committed to your team's success AND you're protecting time for family, friends, and side projects.

The Contractor Mindset isn't about checking out. 

It's about staying engaged, gaining transferable skills, and building optionality outside your job.

------
I believe 3 skills are essential to win in the Contractor Economy. I break them down in my "Contractor Economy Starter Kit" article. Get full access by clicking the View My Newsletter button ☝🏼.

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.

The AI bubble is 75% of US GDP growth.

Read that again.

I spent the weekend analyzing Deutsche Bank's latest report.
What I found made me cancel my AI startup plans.

The dot-com bubble was 15.6% of GDP when it popped.
AI is already 5x bigger. And growing.

Here's the brutal reality nobody wants to admit:

1️⃣ Every AI Company Is Bleeding Cash
• OpenAI: Losing money on every $200/month subscription
• Break-even price: $2,000/month (10x current)
• 2026 projected loss: $14 billion
• 2029 projected loss: "Hundreds of billions"

2️⃣ AI Infrastructure Is a Money Pit
Data centers need $2 trillion annual revenue to break even.
Actual AI industry revenue: $390 billion.
That's a $1.6 trillion gap. Every. Single. Year.

3️⃣ AI Adoption Is Dead in the Water
• 95% of AI pilots fail (MIT study)
• AI coding tools make devs SLOWER (METR report)
• 77% of workers say AI increased their workload
• Adoption rates missed predictions 2 years running

The uncomfortable truth:

This isn't 1999. When the dot-com bubble popped, e-commerce was real.
It bounced back.

AI profitability is mathematically impossible.
The efficient compute frontier guarantees it.
Floridi's Conjecture proves agentic AI can't exist.

When this pops, there's no bouncing back.

What's keeping it alive? Your 401k.
70% of US venture capital is flowing into AI.
Half the S&P 500's growth is just tech companies building data centers.

Remove AI spending and US growth is already at zero.
We're IN the recession. AI is just hiding it.

♻️ Repost before your retirement fund discovers math

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

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

🚨 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.

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.

The Lovable for Data Analysis Just Dropped

Upload a file → Ask a question → Get your answer.

No Python, no SQL, no notebooks required.

Compare that to what you’re doing now: 

❎ Write SQL queries or Python scripts 
❎ Debug when they break
❎ Switch between notebooks, terminals, dashboards 
❎ Wait for a data scientist to help you

This agent actually understands your data:

✅ Sees your structure, knows how to query it 
✅ Maintains context across your workflow 
✅ Fixes its own errors
✅ Generates insights, not code you need to interpret

This is a big deal for anyone doing real data analysis work. 

You get the power of a data science team without needing to become one.

The best part?

I've got $15 in free credits for you to test it (no expiration date).

♻️ Repost + comment "GIVE ME MY WEEKENDS BACK 😭" and I'll send you the link

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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