Data empathy focuses on understanding the data. It considers the subjectivity introduced by humans into the data collection process and identifies biases. 1/n

The materials for the first class of my Stanford course 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 are officially on the course page. This includes slides, completed code, and video demonstration of the exercise we worked through. Also week 1 assignment is on the course Github repo! themodernsoftware.dev/

This lecture was once banned from the Internet because of how powerful it is. Thank God I've recovered it after trying for years. Study before it's deleted again Titled: Why Dumb people outsmart You..

I found an AI model that physically cannot write a single word. Then I found out how much money that word was costing me every single day it's called Jev. you hand it a situation and a list of valid answers. it hands back probabilities. no paragraph, no explanation, no chat window. ask it to write anything at all, and it simply refuses, because writing was never the job it was built for I went back through my own agent's logs and started counting. is this urgent, which team owns this, did the test actually pass, three questions, two or three valid answers each, and every single one of them had been quietly costing me a full paragraph of generated reasoning before landing on a word it could have handed me instantly within three days of launch, strangers proved exactly what that habit had been costing everyone, not just me. one found real flight results for $0.0039. another classified over a thousand research papers for eight cents, total. a third sorted five hundred real emails for three and a half cents. none of it used generation. all of it used a model returning nothing but a number against an answer someone had already written down in advance here's what actually stopped me cold, mid-scroll, re-reading my own agent's bill. the expensive part was never the hard, creative work. it was a dozen tiny yes-or-no checks, every one of them wearing a full paragraph as a disguise, billed at the price of language for an answer that only ever had two shapes to begin with I'm still paying for a paragraph, right now, today, every single time my agent could have just said yes full breakdown below

System Design Interview Notes — Comprehensive Insider's Guide — 28 chapters, and more: github.com/liquidslr/system-…

My personal favourites : 1. handbook.modular.com/ 2. jax-ml.github.io/scaling-boo… 3. baseten.co/inference-enginee…

Ati gossip club 🤣🤣🤣🤣

I fused three small language models on my MacBook Pro to test whether they could match GPT-5.6 on a concrete reasoning task. I had read a claim that combining local models during generation could deliver frontier-level reasoning. I wanted to see what happened when I built the loop, checked the tokenizers, and measured the answers. Using MLX, I combined the next-token scores of three quantized Qwen2.5 models: 0.5B, 1.5B, and 3B. I then tested the ensemble, each model, and GPT-5.6 Sol on the same 24 scheduling problems. The ensemble solved 1/24 cases. The 3B model also solved 1/24, with a lower median generation time. GPT-5.6 Sol solved 24/24 using medium reasoning and a larger generation budget. This was a narrow test with different compute budgets, so I would not generalize it into an intelligence ranking. But it answered the practical question I started with: this fusion setup did not earn its extra computation. I wrote up the full experiment, including the runnable code, setup issues, token traces, screenshots, and the distinction between correct JSON and a correct answer. If you are experimenting with local LLMs, the walkthrough gives you a setup you can reproduce and a way to judge whether your changes help. Read the full experiment for free 👇

🌟 Diagram Design Skill🌟 ⤷ A Claude Code / Codex skill for editorial-quality visual types, matched to your brand in 60 seconds by reading your website. github.com/cathrynlavery/dia…

Apache Kafka: Zero to Production #apachekafka piped.video/playlist?list=PL…

I just published: Evolution of LLM Architecture RNN -> Attention -> Transformer -> Scaling -> MoE -> New Directions Read here: outcomeschool.com/blog/evolu…

AI agents typically fail because the architecture is unclear, not because the model is weak. In this handbook, Tiago teaches you how to build agents with reliable planning, memory, tool use, and bounded execution loops. Along the way you’ll learn practical patterns for creating agents that stay stable in production. freecodecamp.org/news/how-to…

Çocuğunuza Kurs Parası Ödemeden Verebileceğiniz 50 Ücretsiz Eğitim 1.) scratch.mit.edu → MIT'nin çocuk kodlama platformu 2.) code.org → Sıfırdan kodlama dersleri 3.) blockly.games → Bloklarla kodlama oyunları 4.) codecombat.com → Oyun oynayarak kod yazma 5.) makecode.microsoft.com → Microsoft'un çocuk kodlama aracı 6.) hourofcode.com → Bir saatlik kodlama etkinlikleri 7.) abcya.com → Yaşa göre eğitici oyunlar 8.) starfall.com → Okuma ve harf öğretimi 9.) pbskids.org → Eğitici çizgi film ve oyunlar 10.) sesamestreet.org → Susam Sokağı eğitim arşivi 11.) trtcocuk.net.tr → TRT'nin çocuk içerik arşivi 12.) bilimgenc.tubitak.gov.tr → TÜBİTAK'ın çocuk bilim dergisi 13.) kids.nationalgeographic.com → Doğa ve hayvan içerikleri 14.) si.edu/kids → Smithsonian çocuk kaynakları 15.) kids.britannica.com → Çocuklar için ansiklopedi 16.) dkfindout.com → Görsel bilgi ansiklopedisi 17.) ducksters.com → Ödev ve konu anlatım arşivi 18.) wonderopolis.org → Günün merak sorusu 19.) sciencebuddies.org → Ev deneyi ve proje rehberi 20.) phet.colorado.edu → Fizik ve kimya simülasyonları 21.) spaceplace.nasa.gov → NASA'nın çocuk sayfası 22.) climatekids.nasa.gov → İklim ve dünya bilimi 23.) esa.int/kids → Avrupa Uzay Ajansı çocuk bölümü 24.) exploratorium.edu → Müze deneyleri ve etkinlikler 25.) mysteryscience.com → Hazır fen ders planları 26.) generationgenius.com → Fen konularının videolu anlatımı 27.) nrich.maths.org → Cambridge matematik problemleri 28.) mathplayground.com → Matematik oyunları 29.) bedtimemath.org → Günlük matematik sorusu 30.) splashlearn.com → Sınıf seviyesine göre alıştırma 31.) storylineonline.net → Kitapların sesli okunması 32.) storyweaver.org.in → Çok dilli çocuk kitabı arşivi 33.) childrenslibrary.org → Dünya çocuk kitapları kütüphanesi 34.) freekidsbooks.org → Ücretsiz çocuk kitapları 35.) oxfordowl.co.uk → Seviyeye göre okuma kitapları 36.) unite4literacy.com → Sesli resimli kitaplar 37.) readingbear.org → Okuma öğrenme programı 38.) duolingo.com → Oyunlaştırılmış dil öğrenimi 39.) gus-on-the-go.com → Küçük yaşta dil öğretimi 40.) musictheory.net → Müzik teorisi temelleri 41.) chrome music lab → Müziği deneyerek keşfetme 42.) sketchrx.com → Çizim alıştırmaları 43.) artforkidshub.com → Adım adım çizim dersleri 44.) metkids.org → Met Müzesi çocuk bölümü 45.) tate.org.uk/kids → Tate'in çocuk sanat sayfası 46.) chesskid.com → Çocuklar için satranç 47.) lichess.org → Ücretsiz satranç ve alıştırma 48.) typingclub.com → On parmak klavye öğretimi 49.) legoeducation.com → Ücretsiz ders planları 50.) commonsensemedia.org → İçerik yaşa uygun mu kontrolü Dünyanın en iyi çocuk eğitimi ücretsiz duruyor. Sadece kimse duyurmuyor. Kaydedin, lazım olur.

一个系统化的 Agent 工程学习路线图手册：Agentic Engineering Handbook。 它把散落在 OpenAI 博客、Anthropic 工程文、SDK 文档、cookbook、论文里的 179 个精选资源，整成一份从"手写 Agent Loop"到"生产落地/eval/安全"的结构化学习路径。​ 学习路径分 7 阶段（Phase 0–6）​： Phase 0：从零手写 Agent Loop（基于 shareAI-lab/mini-claude-code，给了 v0–v4 可跑 Python 代码：bash agent → 模型当 agent → 结构化规划 → 子代理 → skills） Phase 1：Agent 基础（该不该建 agent 的 4 问检查表、Building Effective Agents 等） Phase 2：MCP & 工具生态 Phase 3：Context / Memory / Skills（含 Agent Skills 规范、上下文工程） Phase 4：Harness & 长程 Agent（mini coding harness 练习） Phase 5：Coding / Workspace Agents（Codex / Claude Code 风格工作流） Phase 6：Evals / Safety / Production（smoke/macro eval 套件练习） 仓库：github.com/keyuchen21/agenti…

Mark Cuban on the next job wave: "Software is dead because everything's gonna be customized to your unique utilization. Who's gonna do it for them..." The answer is people who know how to fine-tune small LLMs on private data. Not prompting. Not API wrappers. Actual custom models trained on your business. And almost nobody knows how to do it yet. This is the complete guide ↓ Bookmark this. This is the one.

My Stanford course 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 starts next Tuesday. This Github repository will hold all the assignments (and has the ones from last year). Bookmark it. See you next week. github.com/mihail911/modern-…

The Central Bank of Kenya has done an exceptional job of safeguarding the stability of our financial system and steering the economy through the volatility and pressures we have experienced at one time or another.

She's 18 built an AI agent with Opus 5 and sold it to Anthropic for $3.2M - and came to Stanford to show how to do it from scratch: 00:34 - how Opus 5 builds a $3.2M agent in one evening 15:34 - 4 agents replaced 400 Anthropic engineers 34:47 - from first prompt to a $3.2M check from Anthropic after watching I spent 60 minutes building my first agent - it cut my workday by 90% and a week later I got a $100k check from Anthropic: save & watch - article below on how to go from one prompt in Claude Code to an agent people pay millions for.

Anthropic engineer: "You're not supposed to prompt Claude You're supposed to build a system that prompts itself" In just 45 minutes, she breaks down how the Claude team builds agents that remember, fix their own mistakes, and get smarter with every single run Prompts → Harness → Loops → Graphs → Self-Improving Systems This is not about tweaking text in a chat box This is about building the execution loop that tests, catches hallucination, and refactors code autonomously If this were a $500 course, people would call it one of the best agent engineering breakdowns of the year You probably think you don't have 45 minutes right now Don't let this vanish from your feed Watch it today Then read the step-by-step guide below on building loops

If you want to learn about the math that powers AI tools, this book is for you. In it, Tiago gives you insights into what makes AI models really work so you can understand them better. You'll learn about Linear Algebra, Multivariable Calculus, Probability & Statistics, Optimization Theory, and lots more. freecodecamp.org/news/the-ma…

MIT published a brutally honest report on what AI is doing to students. A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet. Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle. The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable. The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust. Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates. The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output. The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.