Tenet Healthcare

AI Product Director

Trilliant Health

Developed machine learning models to predict various data points in our claims and provider datasets. My role among them was:

• working with Health Plan Price Transparency (HPPT) data to develop a proprietary Rate and Quality Score for payers and providers to enable users to identify who the high-quality *and* affordable providers are
• using machine learning to predict providers' taxonomy classification to enhance our Provider Directory product using claims and NPPES features
• using claims data to infer patients' Primary Service Area (PSA) to enhance many of our products - like our Demand Forecast, Provider Directory, and Network Explorer reaching a ~47% increase in classification accuracy

Data Scientist IV

Unum

• Developed and implemented the company’s first ever AI Governance Framework, aligning AI with enterprise compliance goals and ensuring bias mitigation, model transparency, and reproducibility
• Led the zero-to-one vision, strategy, roadmap, and execution of NLP and AI projects, building scalable AI products across different functional domains while ensuring executive buy-in
• Consistently lead user interviews to discover common pains, needs, and friction points to better inform AI projects

Principal AI Product Manager

• Lead zero-to-one AI projects through Product Discovery, Delivery, and Iteration, while using best Build-Measure-Learn practices
• Orchestrated the cross-functional launch of a scalable NLP-as-a-Service platform (backed by LLMs and GenAI) to address document classification, semantic matching, entity extraction, and summarization leading to a 54% increase over baseline models, a 73% average time savings of common manual processes which drove an annual cost savings of $2.5M
• Championed quick experimentation and iteration on AI initiatives, leading to quicker development and release cycles
• Drove executive buy-in of AI products through heavily-researched operational usability, engineering feasibility, and business viability analyses

Senior Data Scientist / AI Product Manager

Nashville Software School

Data Science Instructor - Assistant (One Cohort)

The General®

•     Led end-to-end 0-1 product development and data science execution across multiple Claims-focused ML initiatives, managing roadmaps, feature definitions, and integration with core systems
•     Identified and prioritized high-impact ML use cases in collaboration with cross-functional stakeholders, translating business needs into scalable predictive models
•     Mentored and coached junior data scientists while also setting standards for model development, engineering best practices, and product delivery
•     Designed, developed, and deployed Silhouette, an internal ML infrastructure tool that automates cloud-based environment setup (AWS), version control (DVC/S3, Git/Bitbucket), and reproducible ML workflows (Pipenv, Cookiecutter)
•     Scaled internal tooling and drove cross-team adoption of Silhouette, reducing friction in deployment and improving reproducibility across ML projects

Lead Data Scientist

•     Shaped data science team vision, product strategy, and foundational infrastructure - establishing the team’s first tech stack, creating its first public-facing AI blog, and launching its first internship program
•     Led zero-to-one development of NLP product using claim notes (Word2Vec, LDA Topic Modeling, Concept Mining) to predict attorney involvement, delivering $2.3M in business savings
•     Built and trained a deep learning model (PyTorch) to classify vehicle damage from claim images, enhancing adjuster workflows
•     Used OCR to extract structured text from scanned claim documents to identify and surface specific types of claim denials
•     Developed a REST API web app to support labeling workflows, enabling scalable image annotation for model training
•     Owned product lifecycle and modeling for a total-loss vehicle prediction system, driving $2.5M in annual cost savings

Data Scientist II

• Perform various natural language processing tasks (R/tidytext, Python/gensim), including sentiment analysis, topic modeling, and Word2Vec/Doc2Vec
• Build a nation-wide price elasticity model using a Mixed Effects/Hierarchical Logistic Regression algorithm (with cross classified and nested random effects) to predict demand for a product at various pricing structures (R, Julia)
• Predict a claim's route by converting claim notes to Word2Vec word embeddings and feeding those word vectors into machine learning models for multi-class prediction (Python/Gensim/Scikit-learn)
• Build an interactive geo-spatial smoothing app (Python/Flask, PostGIS, Leaflet.js) for various key performance indicators using AWS S3 as the data store
• Collaboratively design and implement statistical techniques and machine learning models with team members and department heads
• Create pipeline to transfer data from SQL Server database on local server to PostgreSQL database on AWS EC2 instances
• Assist the data science team in transitioning from local servers to cloud computing on AWS
• Architect and implement RESTful APIs (Python/Flask, Celery, Redis, SQL) that expose various statistical models for consumption

Data Scientist I

Perception Health (Acquired by Huron)

•	Built machine learning models (Python) that used lab data to predict if a patient would be admitted or not and visualized results using an ROC curve and confusion matrix
•	Worked alongside Perception Health's Principal Data Scientist to analyze physicians’ referral patterns and consult hospitals on how to best reduce out-of-network referrals
•	Used GIS (PostGIS, Leaflet.js) techniques to visualize healthcare trends geographically
•	Lead production on Perception Health's flagship product since March 2015 to the present
•	Built customizable, interactive dashboards (Leaflet.js, D3.js, Crossfilter.js) into the primary platform
•	Optimized SQL queries and data preparation for faster graph rendering and page-load times
•	Utilized Agile methodologies (JIRA) to organize releases while ramping up features
•	Worked with the lead architect to optimize SQL scripts across the primary product's platform
•	Converted the platform to a RESTful API for more efficiency and to decouple the server-side code from the client side templates
•	Designed and developed an internal SQL application (Python, Flask) for non-programmers to easily upload data into a database, making repetitive communication with the database faster and more memory efficient

Software Engineer Project Lead

Perception Health

• Built machine learning models (Python, scikit-learn) that used lab data to predict if a patient would be admitted or not and visualized results using an ROC curve and confusion matrix
• Worked alongside Perception Health's Principal Data Scientist to analyze physicians’ referral patterns and consult hospitals on how to best reduce out-of-network referrals

Data Scientist / Software Engineer

Relode, LLC

• Designed and wrote programs that efficiently scraped useful data from various websites using Python and created web applications to be used internally by Relode sales representatives to access this data.
• Uploaded and maintained captured data in a MySQL database.
• Wrote Python scripts that reformated and correctly structured raw, unstructured data.

Software Developer (Contractor)

Digital Reasoning

• Parsed corrupted XML files in Python using ElementTree and Regular Expressions in order to pin point the corruption
• Designed and wrote programs that efficiently cleaned and structured the corrupt data in order to increase the accuracy rate of the firm’s predictive models

Checkd.in

• Employed web-parsing techniques to convert unstructured, raw data into a workable format to perform statistical analyses
• Utilized Facebook's Social Graph API to scrape useful data
• Designed and wrote programs that parsed and statistically analyzed Facebook data using Python

Software Developer (Intern)

Building data products @ Gridsearch | Principal DS @ Trilliant Health

I partner with businesses to bring data and AI-centric products and features to market. 

I also build machine-learning apps with and for my sons. Currently, I’m building a Natural Language Understanding app I call LADNER (Locating Assignments and Dates using Named Entity Recognition) that locates homework assignments and due dates on his teachers’ websites and emails, and then automatically adds them to my Trello calendar daily so we don’t miss homework assignments or quizzes. 

Before that, I built a machine-learning app called Achoo that predicted my son's inhaler usage using air quality, weather, and allergen data.

In my career and hobbies, I build machine learning and data-centric software that makes life easier.

💡Timothy Dobbins💡

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

The best AI engineers master PyTorch. The rest just import it.

Here's how to join the first group:

1️⃣ Master the Foundation
🔸 NumPy arrays → PyTorch tensors: https://lnkd.in/grmapDsK
🔸 Tensor operations that matter: https://lnkd.in/gjk3ErXu

2️⃣ Learn PyTorch Basics (Skip the Fluff)
🔸 60-minute blitz (actually 60 mins): https://lnkd.in/gU86ZGqt
🔸 What torch.nn really does: https://lnkd.in/gEmCGg6R
🔸 Daniel Bourke's 1-day crash course: https://lnkd.in/gCdBG_jc

3️⃣ Understand Autograd (The Magic Behind It All)
🔸 Karpathy's micrograd repo: https://lnkd.in/g_hTUmji
🔸 Karpathy's micrograd video: https://lnkd.in/gxM7h9E9
🔸 See gradients flow in real-time: https://lnkd.in/gKdwgAzz

4️⃣ Build Real Networks
🔸 Aladdin's playlist (underrated gem): https://lnkd.in/gP3k_rNx
🔸 NLP from scratch (no shortcuts): https://lnkd.in/g77inpPa
🔸 Adversarial examples (break things): https://lnkd.in/gFDzkep8

5️⃣ Production PyTorch (Where 90% Fail)
🔸 Distributed training without tears: https://lnkd.in/gdPZDnxB
🔸 Profile before you scale: https://lnkd.in/gfs7JQ6R
🔸 Mixed precision (2x speedup): https://lnkd.in/gikyETYK
🔸 Performance tuning secrets: https://lnkd.in/g8BE7J28

The uncomfortable truth:

Most "PyTorch experts" can't debug a CUDA out-of-memory error.
They copy tutorials. They call high-level APIs.

But when production breaks? Crickets.

Start with micrograd. Build intuition.
Then scale up. One tensor at a time.

What PyTorch concept finally clicked for you?

♻️ Repost to help someone skip the 3am debugging sessions

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.

Your AI agent has dementia. LangChain just dropped the cure: 

DeepAgents CLI with actual persistent memory.

This project solves the most infuriating problem in AI development, agents that forget everything the moment you close your terminal. 

It gives your AI assistant an actual memory that persists across sessions. 

Key Features:

✅ True persistent memory
Your agent writes markdown notes to ~/.deepagents/memories/ and reads them before every response. Like a human taking notes.

✅ File operations that work: 
Read, write, and edit files in your project with diff previews and human approval. No more "I can't access your filesystem."

✅ Shell command execution
Run tests, install packages, deploy code - all with your permission. Because we're not insane.

✅ Multi-agent personalities
Create specialized agents (backend-dev, frontend-wizard, devops-guru) each with their own memory and expertise.

Getting started is stupidly simple. 
Just install and run: pip install deepagents-cli && deepagents

Harrison said they built this because copy-pasting context 47 times a day is insanity (we agree).

Check out the code, see the full toolset, and contribute on GitHub!

♻️ Repost if you're done being your AI's memory bank

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

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

Plexe is a Python library that turns "I need to predict customer churn" into a working ML model.

No feature engineering. No pipeline code. Just natural language intent and your data.

It integrates with OpenAI, Anthropic, and Ray, making it easier to build sentiment analysis, fraud detection, and forecasting models in minutes instead of weeks.

Plexe doesn't just build the model - it generates a thorough evaluation report showing exactly where and why it performs well (or doesn't).

A powerful tool for anyone working at the intersection of product development and machine learning.

♻️ Repost if you've ever deployed a model and just hoped it worked.

🎁 Launch special: Get $20 free credits → https://console.plexe.ai
Code: LAUNCHDAY20

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

I just watched 4 AI agents compete on the same coding task.

Then an AI judge picked the winner.

The best code became the pull request.

This is how coding agents should work.

BLACKBOX.AI AI just shipped Multi-Agent Execution.

Here's what happens:

You give one prompt: "Implement OAuth authentication"

4 agents run in parallel: 
→ Claude Code 
→ Blackbox CLI 
→ Gemini
→ Codex 

Each builds their own solution.
You watch all 4 logs side by side in real-time.

Then an AI judge evaluates: 
✅ Code quality 
✅ Error-proneness 
✅ Efficiency 
✅ Edge case 

The best one wins. That code becomes your PR.

Why this matters:
Every AI coding agent has blind spots.

🔹 Gemini: Ships fast. Weak debugger
🔹 Codex: Blazing speed. Loses context
🔹 Claude: Nails architecture. Struggles with debugging.

Running one agent = hoping you picked the right one.
Running all of them = letting the best approach win.

The insight:
We've been using AI agents like single players.

The future is ensemble.
Run multiple approaches. Compare. Select the best.

Still guessing which AI to use?
♻️ Repost to save someone from picking the wrong agent again.

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The Battle

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