Trevor D. McKee PhD 🇿🇦🇺🇸🇨🇦😷

Thanksgiving presents us with time to truly appreciate the good things we have in our lives - family, friends, and vocations- passion that inspires us to make the world a better place. In these turbulent times, its helpful to reflect and appreciate those good things. Best wishes

It’s Sup Fig S7. Jfc.

we will soon have solo founder billionaires this workflow will make you an engineering machine banger article by @elvissun

WE ARE LIVE! I REPEAT WE ARE LIVE! 🚨 If you like visual novels, casual gaming, and just good ol' personality quizzes with a twist, you're gonna LOVE this. Give it a try, let me know your results below, and share with a friend if you had fun!

@thequeenofmm Hi there 👋

@srishticodes Hey there! Nice to e-meet you, Im in Toronto working on AI for biomedical imaging

Twitter is cool. But it’s 100x better when you connect with people who code If you’re into tech, AI, DSA, Web development, Web3 or programming, say hi!

@michelleefang Building solutions for biomedical image analysis, Claude code is heavily involved, I would love a 6 mo sub if you are still handing out!

if you're vibe coding or building over the holidays, i want to gift one of you a 6 month subscription of claude pro to support <3 just drop a comment below. merry christmas!

@JA_Olaoye Hi there, data scientist here and on the road to AI founder (though still learning). Love to connect, and nice to emeet you 👋

Dear builders, I want to connect with: Data Specialist Data Scientist Data Engineer AI Founders AI Engineers AI Automation Specialists AI Web Developers AI Content Creators Full Stack Developers Product designers Web 3 enthusiasts Everyday Engineers UI/UX say hi and let’s build smarter together.

This GitHub repo isn’t a tutorial dump. It contains 28 production-ready AI projects you can actually use. Here’s what you’ll find inside: Machine Learning Projects → Airbnb price prediction → Flight fare calculator → Student performance tracker AI for Healthcare → Chest disease detection → Heart disease prediction → Diabetes risk analyzer Generative AI Applications → Live Gemini chatbot → Working medical assistant → Document analysis tool Computer Vision Projects → Hand tracking system → Medicine recognition app → OpenCV implementations Data Analysis Dashboards → E-commerce insights → Restaurant analytics → Cricket performance tracker And 10 advanced projects coming soon: → Deepfake detection → Brain tumor classification → Driver drowsiness alert system This isn’t just code files. These are end-to-end, working applications. Explore the repo here: lnkd.in/gNkDJTMD Save it for later. Repost ♻️ if you’re building with AI. Check my profile for more AI resources 👋

@ishaansehgal Hi @ishaansehgal Im building bioinformatics solutions for researchers in academia and pharma, and slowly learning how to accelerate what we are doing with AI assistance, looking to ge into the agentic space to supercharge what we can do. Would love to connect, here or on Linkedin

Dear builders, I want to connect with: AI founders Coding hackers YC alums Tech disruptors Product makers Voice interface designers Mobile devs Everyday engineers If that's you, say hi. Let's build smarter together.

@Kh5065 Congrats, and hi

When I started with 00 followers. Now I’m at 40k If you’re building too, reply "hi", and let’s connect 🔒📈🔥

@alexwtlf Advice for using twitter/x? Curate the algorithm- I used to follow exclusively scientists or clinicians in my area of interest - the algo is probably going to ask you to follow edgy political content slash musk tweets - just dont engage, or follow on separate account. Also, hi!

I’ve never used this information dump. But it feels like the time has come. Let’s see what it’s actually capable of. Any advice?

Claude Code (w/ Opus 4.5) is the most powerful agent in the world. And @AnthropicAI just released Skills. In this video: (1) What is a coding agent? (2) Using Claude Code as a general agent (3) Setting up Claude Skills in Claude Code (4) Building a skill that can post on x (5) How to learn more TIMESTAMPS 00:00 Intro 01:13 What is a coding agent 02:48 Using an Agent on our Computer 05:14 Claude Code as a general agent 07:32 using Claude Code for a General Task 10:12 Ok Let's Create Skills now that we understand agents 13:54 Creating Twitter Post Skill with Annotations 19:55 Having Claude Skill to POST on x 24:55 Conclusion

You don’t get what you deserve, you get what you negotiate.

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by @HaiqianYang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

OpenAI and Anthropic engineers don't prompt like everyone else. I've been reverse-engineering their techniques for 2.5 years across all AI models. Here are 5 prompting methods that get you AI engineer-level results:

This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly: Can LLMs actually discover science, or are they just good at talking about it? The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder: Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists? Here’s what the authors did differently 👇 • They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision • Tasks span biology, chemistry, and physics, not toy puzzles • Models must work with incomplete data, noisy results, and false leads • Success is measured by scientific progress, not fluency or confidence What they found is sobering. LLMs are decent at suggesting hypotheses, but brittle at everything that follows. ✓ They overfit to surface patterns ✓ They struggle to abandon bad hypotheses even when evidence contradicts them ✓ They confuse correlation for causation ✓ They hallucinate explanations when experiments fail ✓ They optimize for plausibility, not truth Most striking result: `High benchmark scores do not correlate with scientific discovery ability.` Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories. Why this matters: Real science is not one-shot reasoning. It’s feedback, failure, revision, and restraint. LLMs today: • Talk like scientists • Write like scientists • But don’t think like scientists yet The paper’s core takeaway: Scientific intelligence is not language intelligence. It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.” Until models can reliably do that, claims about “AI scientists” are mostly premature. This paper doesn’t hype AI. It defines the gap we still need to close. And that’s exactly why it’s important.

CellSAM uses an object detector, CellFinder, to detect cells and prompt the Segment Anything Model to generate segmentations with human-level performance across a range of bioimage data. nature.com/articles/s4159…

Empowering biomedical discovery with AI agents: Cell cell.com/cell/fulltext/…

Love this application for synthetic biology! Congrats @hhlee and @cultivariumFRX !

Stop wasting hours trying to learn AI. 📘📚 I have already done it for you. With one list. Zero confusion. And no fluff 📹 Videos: 1. LLM Introduction: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide - Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents - Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP - Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️ &follow for more stuff on building AI Agents.