Important unpublished Letter to the Editor of the American Israelite by my Mom "Cincinnati Jewish Community: Focus on Caring for People with Dementia" #CincinnatiJewishCommunity #Dementia #Alzheimers medium.com/@elzimmer18/cinci…

Last share of the day: I do have ~10k books & papers on my hard drive. Most are copyrighted, so I can't freely share them. However, I deployed a few agents to summarize them into useful .md files. These are useful for me, and are useful to put in my AI contexts. I am not entirely happy am still working on revising and improving them. They are grouped in the folders below. They are still < 700 items but will grow. I thought I'd make them available, at least for a while. Link to shared folder in reply. Enjoy.

Google just automated the PhD. They built an AI that reads a problem, forms hypotheses, runs experiments, writes the paper, AND simulates its own peer review. no human touches it. It’s called “ScientistTwo” It is a fully autonomous multi-agent framework that executes end-to-end machine learning research. Without a single human in the loop. You give it a fundamental challenge. That’s it. The AI independently navigates the literature. It establishes the baselines. It formulates novel hypotheses. Then it writes the code. It runs the experiments. It conducts its own ablation studies. It even argues with a simulated peer-review engine to refine its work before submitting. The results are staggering. Researchers tested ScientistTwo against the highest standards of human scientific achievement—papers accepted at top-tier conferences like NeurIPS and ICML. The AI improved human state-of-the-art results on 86 different tasks. It generated an average performance gain of 25.2% over the absolute best human models. It didn't hallucinate a single citation. It wrote fully executable, verified codebases. And it autonomously generated expert-level, publication-ready papers. We’ve spent the last two years using AI as a highly advanced intern to help us write code and summarize data. That era is over. ScientistTwo isn't an assistant. It is an autonomous scientific pioneer. It doesn't just summarize the frontier of human knowledge. It expands it. If an AI can autonomously hypothesize, test, and publish breakthroughs in machine learning... How long until it discovers something we don't even have the math to understand?

STOP WASTING HOURS TRYING TO FIGURE OUT WHAT TO LEARN IN AI. I put together one practical roadmap with videos, GitHub repos, guides, books, research papers, and courses. VIDEOS: 1. LLM Introduction — piped.video/watch?v=zjkBMFhN… 2. LLMs from Scratch — piped.video/watch?v=9vM4p9NN… 3. Agentic AI Overview (Stanford) — piped.video/watch?v=kJLiOGle… 4. Building & Evaluating Agents — piped.video/watch?v=d5EltXhb… 5. Building Effective Agents — piped.video/watch?v=D7_ipDqh… 6. Building Agents with MCP — piped.video/watch?v=kQmXtrmQ… 7. Building an Agent from Scratch — piped.video/watch?v=xzXdLRUy… 8. Philo Agents — piped.video/playlist?list=PL… GITHUB REPOS: 1. GenAI Agents — github.com/nirdiamant/GenAI_… 2. Microsoft AI Agents for Beginners — github.com/microsoft/ai-agen… 3. Prompt Engineering Guide — github.com/dair-ai/Prompt-En… 4. Hands-On Large Language Models — github.com/HandsOnLLM/Hands-… 5. GenAI Agents — github.com/NirDiamant/GenAI_… 6. Made with ML — github.com/GokuMohandas/Made… 7. Hands-On AI Engineering — github.com/Sumanth077/Hands-… 8. Awesome Generative AI Guide — github.com/aishwaryanr/aweso… 9. Designing Machine Learning Systems — github.com/chiphuyen/dmls-bo… 10. Machine Learning for Beginners — github.com/microsoft/ML-For-… 11. LLM Course — github.com/mlabonne/llm-cour… GUIDES: 1. Google's Agent Whitepaper — kaggle.com/whitepaper-agents 2. Google's Agent Companion — kaggle.com/whitepaper-agent-… 3. Building Effective Agents by Anthropic — anthropic.com/engineering/bu… 4. Claude Code Agentic Coding Practices — code.claude.com/docs/en/best… 5. OpenAI's Practical Guide to Building Agents — cdn.openai.com/business-guid… BOOKS: 1. Understanding Deep Learning — udlbook.github.io/udlbook/ 2. Building an LLM from Scratch — manning.com/books/build-a-la… 3. The LLM Engineering Handbook — oreilly.com/library/view/llm… 4. AI Agents: The Definitive Guide — oreilly.com/library/view/ai-… 5. Building Applications with AI Agents — oreilly.com/library/view/bui… 6. AI Agents with MCP — oreilly.com/library/view/ai-… 7. AI Engineering — oreilly.com/library/view/ai-… RESEARCH PAPERS: 1. ReAct — arxiv.org/abs/2210.03629 2. Generative Agents — arxiv.org/abs/2304.03442 3. Toolformer — proceedings.neurips.cc/paper… 4. Chain-of-Thought Prompting — arxiv.org/pdf/2201.11903 COURSES: 1. Hugging Face Agent Course — huggingface.co/learn/agents-… 2. MCP with Anthropic — deeplearning.ai/courses/mcp-… 3. Building Vector Databases with Pinecone — deeplearning.ai/courses/buil… 4. Vector Databases: Embeddings to Apps — deeplearning.ai/courses/vect… 5. Agent Memory — deeplearning.ai/courses/llms… No endless searching. No information overload. Just resources you can actually use. 🔖 Your future AI skill set will come from consistent building, experimenting, and learning. Repost so someone else can find this roadmap, and pls consider following @amisha_explains for more content around AI, Beauty, and businesses.

50 legendary websites that feel like the internet’s hidden toolbox 🧰 1. unpaywall.org — Free research papers 2. openlibrary.org — Borrow books online 3. doaj.org — Free academic journals 4. gutenberg.org — 70K+ free books 5. openstax.org — Free textbooks 6. openculture.com — Free online courses 7. wolframalpha.com — Solve complex problems 8. elicit.org — AI research assistant 9. consensus.app — Research-backed AI answers 10. connectedpapers.com — Find research connections 11. semanticscholar.org — Academic search engine 12. scispace.com — Understand research papers easily 13. photopea.com — Free Photoshop alternative 14. squoosh.app — Compress & optimize images 15. remove.bg — Remove image backgrounds 16. cleanup.pictures — Remove objects from photos 17. unscreen.com — Remove video backgrounds 18. carbon.now.sh — Beautiful code images 19. ray.so — Share elegant code screenshots 20. phind.com — AI search engine for developers 21. regex101.com — Test and debug regex 22. codebeautify.org — Format & clean code 23. jsonformatter.org — Format & validate JSON 24. explainshell.com — Understand terminal commands 25. shots.so — Stunning product mockups 26. mediamodifier.com — Powerful online mockup generator 27. alternativeto.net — Find alternatives to any app 28. haveibeenpwned.com — Check data breaches 29. virustotal.com — Scan files & URLs for malware 30. privnote.com — Self-destructing text notes 31. temp-mail.org — Disposable temporary email 32. 10minutemail.com — 10-minute temporary email 33. file.io — Temporary & secure file sharing 34. radio.garden — Explore global live radio 35. music-map.com — Discover similar music & artists 36. tunefind.com — Find songs from movies & shows 37. musicforprogramming.net — Focus music for coding 38. mynoise.net — Custom background sounds 39. coffitivity.com — Ambient café sounds for productivity 40. justwatch.com — Find where to stream movies 41. archive.org — The internet’s digital library 42. archive.ph — Create a personal copy of any webpage 43. similarsites.com — Find related websites 44. summarize.tech — AI YouTube video summaries 45. raindrop.io — Ultimate smart bookmark manager 46. downdetector.com — Check real-time service outages 47. tineye.com — Smart reverse image search 48. fast.com — Quick internet speed test 49. smallpdf.com — All-in-one PDF tools 50. ilovepdf.com — Merge, split & convert PDFs Save this. You’ll definitely need some of these later! 🔖 Follow @lihazadn_Ai for more useful websites, AI tools & tech resources.

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): deeplearning.ai/the-batch/is… ]

How to Become an AI Engineer in 2026–27: A Step-by-Step Roadmap by Aqsazafar aqsazafar81.medium.com/how-t…

I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.

If you're 17 (or any age) and you want to learn to build LLMs from scratch, read chapters 15-16 of Deep Learning with Python, available online here: deeplearningwithpython.io/ch… In particular, chapter 15 has one of the best explanations of WHY dot-product attention works that you'll find anywhere.

There are 2 career paths in AI right now: The API Caller: Knows how to build with LLMs. The Architect: Knows how LLM systems are built. If you want to move toward the second, Stanford has one of the best free LLM engineering playlists on YouTube: CS336: Language Modeling from Scratch. The 2026 course has 19 lectures covering almost the entire LLM stack - ➡️ Build the model: Tokenization, Transformers, architectures, MoE ➡️ Understand the hardware: FLOPs, memory, GPUs, TPUs ➡️ Make it fast: Triton, GPU kernels, parallelism, distributed training ➡️ Train it: Scaling laws, data collection, filtering, deduplication ➡️ Run it: Inference, evaluation ➡️ Post-train it: SFT, RLHF, RLVR Plus multimodality. And you don’t only watch lectures. > You implement the tokenizer, Transformer and optimizer. > You write FlashAttention2 in Triton. > You build memory-efficient distributed training. > You turn raw Common Crawl dumps into pretraining data. > You fit a scaling law. > You use SFT + reinforcement learning to train a language model for mathematical reasoning. Stanford says students write at least an order of magnitude more code than in most other AI classes. Stanford CS336. Spring 2026. 19 lectures. Free on YouTube. Choose your path. (Playlist in the comments.) ♻️ Repost to save someone $$$ and a lot of confusion.

AI Coding Tools: What Changed in the Last 6 Months by Paolo Perrone medium.com/data-science-coll…

Good move by @JensenHuang. The Nvidia letter is well written and worth reading. As we saw with the OpenAI-Hugging Face hack, we need open models and harnesses for defense. Lets stop believing the PR that closed models are safer. - that's just regulatory capture.

Very happy to support this on behalf of Google. We have long benefited from open source, are big contributors to open source and in fact have consistently made open weights models with Gemma available from @GoogleDeepMind @demishassabis . Onwards!

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. @rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: openworker.com (requires your own API key) Source code: github.com/andrewyng/openwor…

Our first model, Inkling. Trained from scratch, weights are open, fine-tunable on Tinker today.

Best YouTube Channels To Learn AI in 2026 (No BS). Save it. 1. Fundamentals – 3Blue1Brown 2. Deep Learning – Andrej Karpathy 3. AI Research – Yannic Kilcher 4. Practical AI – AssemblyAI 5. LLMs – AI Explained 6. ML Theory – StatQuest 7. Papers Simplified – Two Minute Papers 8. GenAI – Matthew Berman 9. AI Agents – Nicholas Renotte 10. Applied ML – Krish Naik 11. PyTorch – Aladdin Persson 12. Math for ML – Serrano Academy 13. Industry Insights – Lex Fridman 14. Real-world AI – DeepLearningAI

Congrats to Coach Brown, Finals MVP Jalen Brunson, OG, and the rest of these incredible NBA Champion @NYKnicks! What a run!