Steve Zimmerman

[Steve Zimmerman @stevezim]

Important unpublished Letter to the Editor of the American Israelite by my Mom "Cincinnati Jewish Community: Focus on Caring for People with Dementia" #CincinnatiJewishCommunity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
https://t.co/lyhsQEHWWw

[Barack Obama @BarackObama]

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

[Tech with Mak @techNmak]

The full AI engineering curriculum is now free.

It's called AI Engineering from Scratch. 20 phases, 428 lessons, roughly 320 hours end to end. Free. MIT license. Runs on your own laptop.

The design principle that makes it different from everything else => every algorithm gets built from raw math before a single framework loads. Backprop by hand. Tokenizer by hand. Attention by hand. Agent loop by hand. Then you implement the same thing in PyTorch or sklearn. By the time the production library appears, you already know what it's doing underneath.

Every lesson ends with something you keep:
→ Prompt templates for any AI assistant
→ Skill files for Claude, Cursor, Codex, OpenClaw, Hermes 
→ Agent definitions you wrote the loop for yourself 
→ MCP servers built from scratch in Phase 13

428 lessons means 428 artifacts by the end. Tools you built and actually understand.

The full 20 phases:
→ Phase 0 - Setup & Tooling (12 lessons) 
→ Phase 1 - Math Foundations (22 lessons) 
→ Phase 2 - ML Fundamentals (18 lessons) 
→ Phase 3 - Deep Learning Core (13 lessons) 
→ Phase 4 - Computer Vision (28 lessons) 
→ Phase 5 - NLP (29 lessons) 
→ Phase 6 - Speech & Audio (17 lessons) 
→ Phase 7 - Transformers Deep Dive (14 lessons) 
→ Phase 8 - Generative AI (14 lessons) 
→ Phase 9 - Reinforcement Learning (12 lessons) 
→ Phase 10 - LLMs from Scratch (22 lessons) 
→ Phase 11 - LLM Engineering (15 lessons) 
→ Phase 12 - Multimodal AI (25 lessons) 
→ Phase 13 - Tools & Protocols (23 lessons) 
→ Phase 14 - Agent Engineering (42 lessons) 
→ Phase 15 - Autonomous Systems (22 lessons) 
→ Phase 16 - Multi-Agent & Swarms (25 lessons) 
→ Phase 17 - Infrastructure & Production (28 lessons) 
→ Phase 18 - Ethics, Safety & Alignment (30 lessons) 
→ Phase 19 - Capstone Projects (17 projects, 20-40 hours each)

Python, TypeScript, Rust, Julia throughout.

GitHub Repo: https://t.co/E2Rg09gnrR

[leopardracer @leopardracer]

Boris Cherny (creator of Claude Code at Anthropic) said he runs thousands of agents at once and hasn’t written a single line of code by hand in months

he talks to them, they build, test, and commit while he thinks about the next thing

one finance analyst did the same for investment research and now screens 17,000+ stocks and drafts equity reports while he drinks coffee

the only difference is one hour of setup

the setup guide is in the article below 👇

[Argona @Argona0x]

a 28-year-old in Berlin runs a 7-agent software factory off a remote server

she approves checkpoints from her phone at midnight; her workspace has no desk, no city, no fixed machine - any screen is just a terminal

she quoted $28,000 for a scope a local agency priced at $74,000 and told the client 'minimum 6 weeks.' the agents shipped a validated PR in 19 hours

the agency was still revising their proposal

i've been running a version of this for the past few months. the setup sounds absurd until the first time it works, and then you can't go back

the factory lives on a remote VPS - always on, eight tmux panes, already mid-session. ssh in from whatever screen is nearby: laptop at home, phone on a train, tablet at a café at 1am. the environment never moves. you're just a terminal window connecting to something that was already running

agencies price the way they do because their overhead is structural. a $74,000 quote on a 6-week scope is real math: account managers, a senior dev who gets rotated to a bigger client by week three, revision cycles that exist because context lives in fourteen slack threads instead of one file

the factory collapses all of that into a CLAUDE.md

→ a 100-line markdown file at the repo root loads the entire project into every new agent session - stack, architectural rules, banned patterns - so no session starts blind and no context drifts between runs
→ agent one is read-only: maps the existing codebase, documents patterns, flags risks before any agent touches the code
→ agent two writes the user story and acceptance criteria, locking the exact definition of done before engineering starts
→ agent three produces the technical brief: data model changes, API shapes, a precise list of every file that will move - this locks before any builder runs
→ backend and frontend build in parallel but in isolation, each scoped to its own directories, so they can't reach across and corrupt each other's work
→ agent six writes acceptance tests against the original user story criteria before the implementation is considered complete
→ agent seven runs a final read-only audit: missing auth, tenant isolation gaps, any deviation from the brief gets flagged back into the loop before the pr is cut
→ three checkpoints pause the entire chain for human approval - story, spec, pre-merge - each one a 30-second phone tap when the upstream work was done right

the 19 hours is the output. what compressed was everything underneath: the pm relaying a question to an engineer who responds two days later, the architectural mistake that only surfaces after code is written, the context drift between sessions because the memory layer is a human brain instead of a file that loads before anything runs

the loop closes itself. validator flags a gap, builder fixes it, verifier confirms, pr is clean by morning

the agency sent their revised proposal at 9am. the pr had been merged for 14 hours

she approved the final checkpoint at midnight, 30 seconds on her phone. the agents were already done

the desk, the office, the fixed machine - she left them out

[GREG ISENBERG @gregisenberg]

I just got back from SF and I FEEL INSPIRED.

I spent 5 days with frontier AI model teams, AI startup founders, and 3 billionaires.

My takeaways:

1. I had lunch with 3 billionaires. All of them are buying SaaS companies and rebuilding them agent-first. They were deeply inspired by Bending Spoons and Ryan Cohen's eBay deal. Buy the company, cut the headcount, rebuild the tech, add agents, add features, make more valuable experience, raise prices.

2. The frontier model companies are hungry for usage data from the field. They can see API calls and token counts. They can't see the actual workflows. If you're deep in a niche using these models in ways the model companies haven't seen, that understanding is incredibly valuable. Usage intelligence is the new alpha.

3. Consumer AI is massively underbuilt. Every billboard in SF is either B2B inference infrastructure or vertical agent companies. The entire city is optimized for enterprise. Meanwhile you have companies like Cal AI doing $50M ARR in 18 months as a consumer app. I met with a cool few teams doing consumer AI (@paulscherer / @ekuyda)

4. MCP came up in literally every conversation. The companies exposing their product as MCP endpoints are getting pulled into deals they never pitched for. The ones that aren't are becoming invisible to agents. This is the new SEO. If agents can't find you, you don't exist. Building products for agents is the new zeitgeist in general.

5. Not uncommon for hot seed rounds to be $25-50 million valuations. I saw a Series A at $450 million

6. If I had a dollar every time someone mentioned "forward-deployed engineer" this trip I could have funded a seed round. It's the hottest role in SF right now. The person who sits between the agent and the customer, making sure everything actually works.

7. The mood around open source shifted. A year ago it felt like open source was chasing the frontier models. Now founders are telling me Gemma and DeepSeek are good enough for 80% of what they need at a fraction of the cost. The "which model do you use" conversation is being replaced by "which model for which task." Model loyalty kinda feels dead.

8. Voice agents came up more than I expected. Multiple founders told me voice is the interface for the next billion users. The billion people who will never type a prompt will absolutely talk to one.

9. The Obsidian community in SF is weirdly intense. Multiple founders showed me their vaults unprompted. Like showing someone your home gym. It's a flex now. The quality of your knowledge base (second brain?) is becoming a status symbol among builders.

10. Maybe it was just the people I met but the age of the founders is shifting. I met more founders over 40 this trip than any trip before and more founders under age 21 than ever before. Founders getting older and younger at the same time.

11. I spoke to a lot of fast-growing startups, VCs and frontier models who are hiring content creators right now.

12. The restaurant scene in SF is actually better than it's been in years. Founders are going out more. Alcohol is out, not surprisingly.

13. SF doesn't feel like the only place anymore. We all have access to the same frontier models. We all read the same X feed. A founder in NYC or Lagos is calling the same APIs as a founder in SoMa. So in the past it felt like SF was always lightyears ahead, doesn't feel that way anymore. It's okay not to live in SF and have BIG DREAMS.

14. The coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people. I had a few startup ideas here....

15. Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats, none of them use any AI at all.

16. I heard the phrase "agent debt" for the first time. Like technical debt but for agents. When you hack together an agent workflow fast and never clean it up, the system prompts conflict, the memory gets polluted, the tools overlap. 6 months later the agent is doing weird things and nobody knows why lol.

17. Met a few people who carry two phones now. One for personal. One that's basically an agent terminal running Telegram or iMessage connections to their agent fleet.

It's always amazing to get that dose of inspiration in SF. I FEEL INSPIRED.

But I'm so happy to be back home, locked in and building.

We're 12-18 months into a shift that will take 15 years to play out. The urgency in every conversation was real.

What an incredible time to be building.

[Avid @Av1dlive]

the anthropic claude for finance lecture is the best free hour in quant AI right now.

bookmark & watch today. It's the most valuable 1 hour in quant AI right now. Then read article below.

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/KiYB50RKqY

[CyrilXBT @cyrilXBT]

MARC ANDREESSEN JUST WENT ON ROGAN AND DROPPED THE MOST IMPORTANT AI ALPHA OF THE YEAR.

3 hours and 20 minutes of podcast.

Here are the 17 things worth your attention.

1. AGI is already here. Marc thinks the line was crossed 3 months ago with GPT-5.5, Claude 4.6, Gemini 3, and Grok 4.3. Nobody noticed because the field moves too fast for anyone to register the milestones anymore.

2. For almost any topic the top AI models now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone.

3. Every doctor is secretly using ChatGPT in the exam room. They turn around the second you stop talking and type your symptoms in. Some do it while you are still sitting there. His quote: "At that point you are asking what do I need you for."

4. When AI refuses to answer something he wants to know he tells it he is writing a novel. "Walk me through how the bad guy robs the bank." It explains almost anything if it thinks it is helping you write fiction.

5. When something is too complex he says "explain it like I am 10." Then "like I am 5." Then "like I am 2." He keeps going until it actually clicks.

6. When he wants to understand a tough topic he does not ask what the right answer is. He asks the AI to steelman one side then steelman the other. Then he decides for himself.

7. For big questions he tells the AI to pretend to be a panel of experts. "Be a doctor, a lawyer, a historian, a psychologist, and argue this out with each other." Then he reads the debate.

8. Pay attention to the exact moment you think "I do not know how to figure this out." Most people give up there. That is the moment you should open the AI.

9. The only real skill left in using AI is knowing what to ask. The models can do almost anything you can describe in plain English. The bottleneck lives in your own head.

10. You can send AI photos of almost anything medical now and get a real answer. Skin rashes. Blood test results. The new models read images not just text. A free 24/7 second opinion on anything.

11. The one type of therapy clinically proven to work is cognitive behavioral therapy. It is also something an AI can fully do on its own. Every person on earth is about to have access to a real therapist for free anytime they want.

12. AI is solving math problems open for 100 years that no human mathematician could crack. Same thing is starting in physics, chemistry, and biology. Expect cancer cures and weird new physics breakthroughs in the next few years.

13. The best AI coders in Silicon Valley now make $50 million a year. One person. That number tells you how big this thing actually is when you strip away all the doom takes.

14. One friend paid $200 to decode his entire DNA. Then gave the AI his DNA, blood test results, and Apple Watch data. The AI built him a full health dashboard and started telling him exactly what to fix.

15. Another friend put two cameras in his home jiu jitsu gym. AI watches him spar and gives him technique notes after every round. A world-class coach at every practice for free.

16. The best programmers in Silicon Valley now run 20 AI coding bots simultaneously. Each bot writes code while they review the others. They call themselves AI vampires because going to bed means 20 workers stop and you lose money every hour you sleep.

17. The obvious next step: the bots will run their own bots. One human running 20 bots each running 20 more. One person. One laptop. 1,000 AI workers. This is months away not years.

Bookmark this before you watch the full podcast.

Follow @cyrilXBT for every AI insight worth your attention the moment it surfaces.

[Steve Zimmerman @stevezim]

https://t.co/YaFzw3e1PG

[Nico @nicos_ai]

GITHUB ACABA DE LANZAR LA CERTIFICACIÓN OFICIAL DE UNO DE LOS ROLES TECH MÁS IMPORTANTES DE 2026

→ Agentic AI Developer (GH-600)

Y es la primera vez que trabajar con agentes de IA se convierte oficialmente en una disciplina reconocida de ingeniería.

Ya no hablamos de:
• prompt engineering
• vibe coding
• automatizaciones simples

Hablamos de un nuevo perfil técnico:

→ Agentic AI Developer

La persona que:
• coordina agentes de IA
• construye workflows autónomos
• integra agentes en entornos reales
• supervisa fallos en producción
• evita errores críticos en pipelines CI/CD
• sabe cuándo un agente no es fiable

Antes:
→ “Trabajo con agentes de IA” era difícil de validar.

Ahora:
→ GitHub certifica oficialmente ese skillset.

Y eso cambia el mercado.

Las empresas van a necesitar este perfil.
Pero todavía hay muy pocos developers especializados en ello.

Si ya trabajas con:
• Copilot
• Codex
• Claude Code
• workflows agentic
• automatizaciones con IA

Probablemente ya estés haciendo este trabajo.

GH-600 es la forma de demostrarlo.

Guárdate esto 🔖

[Shruti Codes @Shruti_0810]

I canceled Spotify.
I canceled Disney+.
I canceled Apple TV+.

No more monthly payments.

Claude turned my laptop into a free entertainment hub that’s better than all of them *combined*.

Here are 9 prompts that rebuild the whole system for free (Save this).

[Stephanie Zhan @stephzhan]

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer.

The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling.

We talk about what it means to build seriously in the agent era. Not just moving faster. Building new things, with new tools, while preserving the parts that still require human taste, judgment, and understanding.

[Steve Zimmerman @stevezim]

%AndrejKaparthy

[Andrej Karpathy @karpathy]

This is the the quote I've been citing a lot recently.

[Richard Seroter @rseroter]

0-to-50 in record time.

We've now got dozens of fully-managed remote MCP servers that let your agents easily interact with your favorite @googlecloud services.

Infra, AI, databases, ops, security, docs, Workspace, you name it.
https://t.co/u7LZhEnT1B

[NeilXbt @neil_xbt]

ANDREJ KARPATHY COULD HAVE CHARGED $500 FOR THIS WALKTHROUGH.

He put it on YouTube.

Every way he personally uses LLMs in his own life. Thinking models. Deep research. File uploads. Python interpreter. Claude Artifacts.

Not theory. Not benchmarks.

The actual daily workflow of the person who built Tesla Autopilot and co-founded OpenAI.

2 hours walking through his personal LLM workflow.

The gap between people who watch this week and those who save it for later is not 2 hours.

It is everything those 2 hours quietly change about how you work for the rest of your career.

[Steve Zimmerman @stevezim]

'Michael' The Movie - Why Don't People Know About It? https://t.co/4nGvVKXNR0 via @YouTube