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

LangChain just open-sourced their answer to Claude Code.

9.9K GitHub stars. 5 hours old.

Deep Agents: the batteries-included agent harness. MIT licensed. FREE.

Here's why this is a big deal:

Claude Code is amazing but closed-source and locked to Anthropic.

Deep Agents gives you the same architecture. Any model. Any provider.

What's included out of the box:

1️⃣ Planning
write_todos for task breakdown and progress tracking. The agent plans before it codes.

2️⃣ Filesystem access
read_file, write_file, edit_file, ls, glob, grep. Full codebase navigation.

3️⃣ Shell execution
Run commands with sandboxing. Build, test, deploy from the agent.

4️⃣ Sub-agents
Spawn isolated workers with their own context windows. Parallel work without context pollution.

5️⃣ Context management
Auto-summarization when conversations get long. Large outputs saved to files. The problem Claude Code solved, now OPEN SOURCE.

Two lines to start:

pip install deepagents

agent = create_deep_agent()

Works with Claude, OpenAI, Google, or any LangChain-compatible model. Swap providers without rebuilding.

Built on LangGraph. Streaming, persistence, checkpointing: all production-ready.

The CLI adds web search, remote sandboxes, persistent memory, custom skills, and human-in-the-loop approval.

From their README: "This project was primarily inspired by Claude Code."

They're not hiding it. They're DEMOCRATIZING it.

Are you still locked into a single AI coding provider? 👇

♻️ Repost for someone who wants Claude Code capabilities without the lock-in

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝

My Meta Staff Engineer friends shared their Claude Code setup with me.

Changed how I build software FOREVER.

10 tips. Here's why they matter:

1️⃣ Always start in Plan Mode
Never execute first. Argue with Claude. Challenge its assumptions. Spend 80% of your time here. The code generation is the easy part.

2️⃣ Run /init on every project
Claude scans your codebase and creates a CLAUDE.md rules file. This controls everything. Skip this and Claude is guessing blind.

3️⃣ CLAUDE.md under 300 lines
Every line bloats your context window. More bloat = worse output. Only add what's unique to your project. Think lint rules, not docs.

4️⃣ Build a validation loop
Build, test, compile commands inside CLAUDE.md. Claude self-corrects when it can validate its own output. Without this, it hallucinates fixes.

5️⃣ Interrupt early, interrupt often
Claude going off track? Escape. Don't wait. Don't hope. Fresh context beats bloated context every single time.

6️⃣ /clear between features
Old context contaminates new tasks. One feature per session. Start clean or pay the token tax.

7️⃣ Multiple instances simultaneously
This is the REAL unlock. Multiple terminals. Multiple Claude sessions. One builds, one plans, one debugs. Jump between them like Starcraft.

8️⃣ Never manually edit rules
"Update the rules so we never do this again." One sentence. Claude maintains its own CLAUDE.md. Stop hand-editing config files.

9️⃣ Audit with /context regularly
MCPs destroy your token budget. /context shows the biggest offenders. Disable what you don't need. Most engineers never check this.

🔟 Sub-agents for atomic tasks ONLY
Don't create CEO agents and product agents. Keep shared context in ONE session. Sub-agents lose the reasoning chain. Bring work to the context, not context to the work.

The meta-lesson:

AI coding is 80% context engineering, 20% code generation.

Most engineers optimize the 20%.

These 10 tips optimize the 80%.

That's why they ship faster than entire teams.

Are you still treating Claude Code like a chatbot? 👇

♻️ Repost for someone using Claude Code who hasn't optimized their workflow

2 founders built Claude Code but for marketing, and it's already compressing 10-month execution cycles into 10 days

After multi-billion dollar exits to Salesforce and Oracle, Tom Chavez and Vivek Vaidya noticed something:

AI eliminated manual work for engineers, but marketers were still dealing with analysis and dashboards.

I was watching their conversation with Navin Chaddha from Mayfield and it struck me what has driven their success as serial entrepreneurs:

•⁠ ⁠“A bad decision made quickly is better than a good decision made slowly.”
•⁠ ⁠“Everything we used to do in 10 months, we now try to do in 10 days.”

Hear them break it all down:

You don't need 14 courses to build AI agents.

You need 6 weeks and this path:

Week 1️⃣: Understand the pattern
→ Anthropic's Building Effective Agents: https://lnkd.in/ew-rnBgD
→ Chip Huyen's agent explainer: https://lnkd.in/gKNqjeGZ
→ 3 hours total. No courses yet.

Week 2️⃣: Build something ugly
→ Langflow (no-code, 1 hour to ship): langflow.org
→ A research agent. A Slack bot. Anything.
→ Now you have context for everything else.

Week 3️⃣–4️⃣: Understand what you built
→ AI Agents in LangGraph: https://lnkd.in/g7raxjpC
→ Agent Memory: https://lnkd.in/ehtwbCVz
→ Rebuild your ugly thing. Properly.

Week 5️⃣–6️⃣: Deploy it
→ LangSmith for tracing: smith.langchain.com
→ Railway or Render for hosting
→ Now it's a portfolio piece.

The trap:
"I need to understand transformers first."
"I should take the Andrew Ng course first."
"I'll start when I have more time."

Build ugly. Learn why it's ugly. Fix it.

💾 Save for your next "where do I even start" spiral
♻️ Repost if you've bookmarked 47 courses and finished zero

Everyone's building AI agent demos.

Palantir shipped an end-to-end agentic architecture.

12 layers. The ENTIRE production stack. Here's what's inside:

1️⃣ Secure LLM integration
GPT, Gemini, Claude, Llama, Meta: commercial and open-source. Zero data retention by providers. Swap models without rebuilding.

2️⃣ Context Engineering
Real-time, pro-code and no-code. Contextual data, contextual logic, and systems of action feeding the Ontology.

3️⃣ The Ontology
Not a data lake. A digital twin with semantic, kinetic, and dynamic layers. Human+AI decision model built in. This is their moat.

4️⃣ Agent Lifecycle
Agent building, orchestration, and evaluation suites. All integrated. No stitching 5 tools together.

5️⃣ Security & Governance
Role-based, marking-based, purpose-based controls. System-wide branching. Approvals. Checkpoints. What regulated industries ACTUALLY need.

6️⃣ End-to-end Observability
Every tool call. Every data access. Every decision traced. Full model catalog.

Plus 6 more layers: tool services, multimodal compute, development environments, human+AI applications, package/release/deploy, and enterprise automation.

12 layers. One architecture. Production-ready.

The framework wars were the warmup.

The real game is agentic architecture.

Are you still debating agent frameworks? 👇

♻️ Repost for someone building agents who hasn't seen this architecture

Google just gave every AI coding assistant NATIVE knowledge of how to build agents.

15,600 GitHub stars. One command. FREE.

Your coding assistant doesn't know ADK. It hallucinates APIs and invents methods that don't exist.

Here's why this changes everything:

npx skills add google/adk-docs/skills -y

One command. Your coding agent now knows:

1️⃣ ADK Cheatsheet
Full Python API reference. Every method, every parameter. No more hallucinated imports.

2️⃣ ADK Dev Guide
Development lifecycle and coding guidelines. The patterns Google's own team uses.

3️⃣ ADK Deploy Guide
Agent Engine and Cloud Run deployment. Prototype to production in the same workflow.

4️⃣ ADK Eval Guide
Evaluation methodology and scoring. Test your agents before they hit production.

5️⃣ ADK Observability Guide
Tracing, logging, and integrations. See what your agents are ACTUALLY doing.

6️⃣ ADK Scaffold
New agent project in seconds. Right structure from the start.

Works with everything:

⚡ Claude Code: one command install
⚡ Cursor: MCP server config
⚡ Gemini CLI: extension install
⚡ Any MCP-compatible tool

Plus a docs MCP server that connects your editor directly to ADK documentation. Always up to date. Machine-readable.

What this replaces:

Hours of Googling API docs → $0
Debugging hallucinated methods → $0
Manually reading changelogs → $0

ADK has 15,600 stars, 2,800+ dependent projects, and now ships in Python, TypeScript, Go, and Java.

Google isn't just building an agent framework.

They're teaching YOUR coding assistant how to use it.

🔗 Install skills: npx skills add google/adk-docs/skills -y
🔗 Full docs: google.github.io/adk-docs

Are you still letting your coding assistant hallucinate ADK APIs? 👇

♻️ Repost for someone building with Google ADK who doesn't know about this

Google just dropped a FREE agent starter pack.

One command. 60 seconds. Full production stack.

3,100+ GitHub stars. Here's why:

5 agent templates, ready to deploy:

1️⃣ ReAct agent (ADK)
2️⃣ RAG agent: document retrieval + Q&A
3️⃣ Multi-agent with A2A Protocol
4️⃣ Live API: real-time multimodal
5️⃣ LangGraph base

That's the starting point. The infrastructure is what makes it DIFFERENT:

⚡ Cloud Run + Agent Engine deployment
⚡ Terraform configs included
⚡ CI/CD with Cloud Build + GitHub Actions
⚡ Vertex AI evaluation built in
⚡ Interactive playground for testing

One command:

uvx agent-starter-pack create my-awesome-agent

No boilerplate. No config hell. No "works on my machine."

Google gave away what most teams spend weeks building.

For $0.

Which template are you starting with? 👇

♻️ Repost for someone building agents from scratch

The complete AI Agents learning stack for 2026.

31 resources. Every single one free.

Most engineers bookmark 50 resources and finish ZERO.

This isn't a bookmark dump. It's a 5-phase path:

📹 UNDERSTAND

1️⃣ Intro to Large Language Models (Karpathy) → https://lnkd.in/egCPxzEx
2️⃣ Deep Dive into LLMs (Karpathy) → https://lnkd.in/euMCHnyv
3️⃣ Agentic AI Overview (Stanford) → https://lnkd.in/etjh6sYZ
4️⃣ Building and Evaluating Agents → https://lnkd.in/e45S6ZcZ
5️⃣ Building Effective Agents → https://lnkd.in/eKEaupVF
6️⃣ Building Agents with MCP → https://lnkd.in/eVhWN9kK
7️⃣ Building an Agent from Scratch → https://lnkd.in/eY3c5npC

📜 READ THE SCIENCE

1️⃣ ReAct → arxiv.org/abs/2210.03629
2️⃣ Chain-of-Thought Prompting → arxiv.org/abs/2201.11903
3️⃣ Tree of Thoughts → arxiv.org/abs/2305.10601
4️⃣ Reflexion → arxiv.org/abs/2303.11366
5️⃣ Toolformer → arxiv.org/abs/2302.04761
6️⃣ Generative Agents → arxiv.org/abs/2304.03442
7️⃣ RAG Survey → arxiv.org/abs/2312.10997

🗺️ STUDY THE ARCHITECTURES

1️⃣ Google's Agent Whitepaper → https://lnkd.in/ejiEnCdK
2️⃣ Building Effective Agents (Anthropic) → https://lnkd.in/ew-rnBgD
3️⃣ OpenAI's Practical Guide → https://lnkd.in/ef2vzH2C
4️⃣ Claude Code Best Practices → https://lnkd.in/eRBfw9k5
5️⃣ Google's Agent Companion → https://lnkd.in/eezmZrVs

🧑‍🏫 BUILD

1️⃣ HuggingFace Agent Course → https://lnkd.in/gxSFVqhY
2️⃣ MCP with Anthropic → https://lnkd.in/efqmGWaR
3️⃣ Agent Design Patterns → https://lnkd.in/gHhvHAKq
4️⃣ Multi-Agent Systems → https://lnkd.in/gkg3qzMG
5️⃣ Building Browser Agents → https://lnkd.in/etMGU_bi
6️⃣ Computer Use with Anthropic → https://lnkd.in/edR3Qg94
7️⃣ Evaluating AI Agents → https://lnkd.in/eFFiUue5
8️⃣ Building & Evaluating RAG Apps → https://lnkd.in/gWAbB363
9️⃣ Agent Memory → https://lnkd.in/eCf57tqh

🗂️ SHIP

1️⃣ GenAI Agents → https://lnkd.in/gykAzWMt
2️⃣ Microsoft AI Agents for Beginners → https://lnkd.in/efzMR5aU

5 phases. 31 resources. $0.

Bootcamps charge $5,000+ for LESS.

Which phase are you starting with? 👇

♻️ Repost for someone who wants to build agents but doesn't know where to start.

RAG, CoT, ReAct, RLHF, LoRA, MoE

What do these acronyms actually mean?

If you're breaking into AI engineering, these come up constantly. 

Here's the breakdown:

🔹 RAG (Retrieval Augmented Generation)
Instead of relying only on what the model "knows," you retrieve relevant documents and feed them into the prompt. Reduces hallucination. Powers most production AI apps.

🔹 CoT (Chain of Thought)
Prompting the model to "think step by step" before answering. Dramatically improves reasoning on complex tasks. Free performance boost.

🔹 ReAct (Reasoning + Acting)
Agent pattern where the model alternates between thinking and taking actions. Think → Act → Observe → Repeat. Foundation of most agent frameworks.

🔹 RLHF (Reinforcement Learning from Human Feedback)
How ChatGPT became ChatGPT. Train the model to prefer outputs humans rate highly. Why models are helpful instead of just predictive.

🔹 LoRA (Low-Rank Adaptation)
Fine-tune a model by training only a small number of new parameters. Makes fine-tuning accessible without massive GPU costs.

🔹 MoE (Mixture of Experts)
Model architecture where only a subset of parameters activate per token. How Mixtral and Nemotron run 30B+ params on consumer hardware.

Frameworks change every quarter.
These acronyms won't.

💾 Save for your next "wait, what does that stand for?" moment
♻️ Repost so they don't have to ask you.

taplio

The Battle

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Paolo Perrone

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The Battle

💡Timothy Dobbins💡

Paolo Perrone

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Likes

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