Brian Lopes @statmobile

🚨 Anthropic just showed a 27-minute workshop on how to actually write prompts for Claude. Taught by the people who built it. Free. No registration. No paywall. I've seen $300 courses that don't cover what they teach in the first 8 minutes. Watch the session

leopardracer @leopardracer

3 days ago

This AI analyst does $60,000 a day of research for about $450 Kimi K3 + GPT-6 Astra, but the real trick is a funnel, not one giant model: signal → plain code → Kimi K3 → GPT-6 Astra → human → sourced report step 1 → plain code does the math first, zero LLM cost step 2

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INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 2 hour with this. Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and bookmark it now.

Stanford AI engineering course: "Anyone can build an AI agent in 60 minutes" Prompt → Agent → Loop → Decision layer Stanford just released a course on building AI agents from scratch 00:00 - Build your first AI agent 48:13 - Create agents without coding 54:33 - Ship one that runs while you sleep the hour gets you an agent that works. the layer above it is what keeps it cheap once it runs all day every fork inside that loop is still a full frontier call until you move it This free course is better than most paid AI agent courses Bookmark and watch it today Then read the article below

Hanako @hanakoxbt

a month ago

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Stanford AI engineering course: “Anyone can build an AI agent in 60 minutes The real skill is building the harness that makes it reliable” Prompt → Agent → Harness → Revenue Stanford just released a complete course on building AI agents from scratch: 00:00 – Build your first AI agent 48:17 – Create agents without coding 54:39 – Turn agents into a $100K+ business While you scroll, someone else is learning Anthropic’s $750,000 skill set This free course is better than most paid AI agent programs Bookmark it and watch it today Then read the article below to learn how to build the harness around your agents

Ichigo @iiiichigo_chan

4 weeks ago

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27 years ago tonight, Pedro Martínez pitched 2 innings in the 1999 MLB All-Star Game in Boston. Starting for the American League, he faced the required minimum six batters and struck out five of them. Joe Buck on FOX Sports call.

🐧 Linus Torvalds gets angry when people say 99% of our code is written by AI, he told the audience at the Open Source Summit.

“My thoughts on tacos is that most places you go the tacos suck ass” -@EBoKnowss

OMG!!! It’s actually on the @claudeai YouTube page!! Microsoft ❤️ Claude!! m.youtube.com/watch?v=TQd_YQ…

I genuinely thought no one was recording😂 Shocked to see this online, but here's the workshop from yesterday's Code with Claude event!

Movez @0xMovez

4 months ago

Microsoft Senior AI developer just showed how they build AI agents with Claude at Microsoft. 34-minutes. free. By Microsoft team Opus 4.7 + 1,400+ pre-built MCP tools plug Claude into agent → give it tools → ship to production worth more than any $500 vibe-coding course.

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Python: Introducing Profiling-Explorer adamj.eu/tech/2026/04/0…

TIL Google did a 5 day Ai agents course covering Day 1 - Introduction to Agents:. Day 2 - Agent Tools & Interoperability with Model Context Protocol (MCP): Day 3 - Context Engineering: Sessions & Memory Day 4 - Agent Quality: Learn to build Day 5 - Prototype to Production

Terence Tao put it plainly: there is no evidence that LLMs exhibit genuine creativity. Yes, they have solved some Erdős problems. But these are low-hanging fruit, questions that attracted little attention and that yield once the right existing techniques are applied. That is not creativity. That is search plus recombination. Yes, LLM outputs can look impressive. But look at who is impressed: typically non-experts. Experts know very well that LLM performance gets terrible when you approach the frontier of human knowledge. And this is not a temporary gap. It reflects a structural limitation. We do not fully understand human creativity. But we do know a key property: Conceptual leaps: the ability to generate new representations, not just recombine existing ones. LLMs do not do this. They interpolate in representation space. They operate within existing conceptual frameworks; they do not create new ones. This is why we haven’t “yet seen them take the next step”.

Introducing the Google Workspace CLI: github.com/googleworkspac… - built for humans and agents. Google Drive, Gmail, Calendar, and every Workspace API. 40+ agent skills included.

I've just released a new version of typeagent, a Python library I've been working on since mid last year --more and more using Claude-- that implements memory for agents. Not originally my idea, I mostly ported the TypeScript version by Steve Lucco and Umesh Madan. This release was improved a lot by Bernhard Merkle. To install, use "pip install typeagent". Changelog: github.com/microsoft/type…

I feel like back in the day you had thick pepperoni. Anyway, Tony's is the bomb! Bumped into you getting take out one time. Now you're even more big time Mr. Ice, sir.

elio imbornone @elio82

8 months ago

First thing I eat when I come back home!!

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You're awesome, I love how you burn the world. Was a fan before, but never realized your personality. Burn it! All of it.

Asante Samuel @pick_six22

8 months ago

We need some entertainment. Where is Mr. Jerry Jones we need him to turn up

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Closed AI models no longer have an edge. There’s a free/cheaper open-source alternative for every one of them now.

Microsoft killed the GPU mafia 🤯 They finally open-sourced their 1-bit LLM inference framework called bitnet.cpp. It lets you run 100B parameter models on your local CPU without GPUs. - 6.17x faster inference - 82.2% less energy on CPUs 100% Open Source.

The lifecycle of a pure math theorem: - 1997: my PhD advisor asks me to work on one of his conjectures - 2000: I solve the simplest case and dream of generalizing my approach - 2003: after years of struggle, I come to the conclusion that my approach *cannot* generalize - 2006: after reading a paper by Daan Krammer, I have a lighting bulb moment and realize that my approach works in full generality *up to equivalence of categories*... this enables me to solve my advisor's conjecture... I then use it as an ingredient in the proof of a much older and more famous conjecture (the "K(π,1) conjecture for finite complex reflection groups") - 2007: I submit my article for publication - 2009: referee #1 gives up - 2010: 2 more referees have now given up, complaining that the paper is too hard to read - 2012: referee #4 is finally able to produce a report, the revision work starts - 2014: the paper is accepted for publication - 2015: the paper is published - 2007-2025: because the older conjecture overshadows the lesser known conjecture by my advisor, and because my paper is too difficult, virtually no-one asks any question about the "lighting bulb" categorical idea at the core of the proof - Jan 22, 2026: I received an inbound email from a mathematician from another hemisphere, inquiring about the categorical aspects - Jan 26, 2026: I have my first ever videocall discussing the specifics of this core component of my proof

Umut Şimşekli is a prominent researcher at the intersection of probability theory, statistics, and modern machine learning, best known for revealing the role of heavy-tailed phenomena in learning algorithms. His work showed that the noise in stochastic gradient descent (SGD) is often not Gaussian, as traditionally assumed, but heavy-tailed and better modeled by Lévy processes. This insight fundamentally changed how researchers understand optimization, generalization, and robustness in deep learning. By connecting SGD to stochastic differential equations driven by stable laws, Şimşekli provided a probabilistic explanation for why deep networks can escape sharp minima and generalize well. He has also contributed to Bayesian nonparametrics, PAC-Bayesian theory, and information-theoretic learning principles. These ideas now influence optimization design, uncertainty quantification, and robustness analysis in deep learning. His work bridges classical probability with practical AI, showing how subtle statistical properties of noise shape the success of modern learning systems.