John Ma 💹 🧲

Design AI/Infra product @Meta prev @MeetApollo @scale_AI and @seiNetwork

[Codez @0xCodez]

https://t.co/eJB3TgyJwV

[Nate Herk @nateherk]

https://t.co/om55Y7mRCL

[Graeme @gkisokay]

https://t.co/ukIIw3wMjq

[Matt Van Horn @mvanhorn]

https://t.co/DM0CAuyprS

[Addy Osmani @addyosmani]

https://t.co/hIe0UX7z6T

[GREG ISENBERG @gregisenberg]

What does it actually mean to be AI native?

There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass):

1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents.

2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough.

3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong.

4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol.

5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter.

6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close.

7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction.

8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds.

9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system.

Full episode with @TheoTabah from @meetLCA on @startupideaspod. This is the stuff we normally keep internal but all the sauce is yours.

@TheoTabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI.

You are in for a treat

Become AI native in under 60 minutes

https://t.co/EzreBHFyIJ

Watch

[Kyle Jeong @kylejeong]

https://t.co/OTdN8Viv6g

[Codez @0xCodez]

Claude Code creator:

"I don't prompt Claude anymore. I write loops - and the loops do the work. My job is to write loops."

in 30 minutes Boris reveals his actual daily Claude Code setup.

Claude Code + loops + dynamic workflow

Worth more than a $500 vibe-coding course

[Exa Developers @ExaDevelopers]

Getting laid off sucks, finding your next job doesn't have to!

Claude (with Exa connector) can search the web for openings, research companies, and help you find jobs!

Try it now, link below 👇

[Elvis @elvissun]

https://t.co/DotZ3V6XhJ

[GREG ISENBERG @gregisenberg]

EVERYTHING YOU NEED TO KNOW ABOUT CHATGPT'S "LOVABLE KILLER" CODEX SITES (in 25 mins):

TLDR; the coolest part is that apps you build can update themselves autonomously

1. Codex Sites is not Replit or Lovable or Bolt. Those are great for one-prompting a full app. Codex Sites is for building apps that the agent keeps improving without you touching them.

2. Your personal website can update its own stats. Your internal dashboard can refresh its own data. Your product can add features while you sleep. The app is alive.

3. Start by invoking at-sites. Use realistic sample data. Always say "save for review, do not deploy." This unlocks building a real product, not a homepage.

4. Add persistent storage so the app remembers everything between visits. Without this it resets every time. Ask Codex to show you the data model before it builds.

5. Create safe actions. These are the specific things the agent is allowed to do to your app: add data, update cards, move things, score things. You define the boundaries. The agent operates within them.

6. Build skills so any future Codex chat knows how to interact with your app. The skill is basically a manual for the agent. Without it, every new chat starts from zero.

7. Save gate like a video game. Codex doesn't auto-save. Create checkpoints before you deploy so you can roll back if something breaks.

8. Close the autonomous loop. This is the magic. Once memory, safe actions, and skills are set up, the agent can update your app from any chat, any context, without you switching tabs.

9. Use the plugins most people are sleeping on. Figma, Canva, HeyGen for avatar videos, Game Studio for interactive experiences, FAL for image generation, Hugging Face for open source models. Worth adding a few.

10. The big picture: we went from building apps to raising apps. You set up the structure, the guardrails, and the skills. The agent does the rest. That's autonomous product building and it's here right now.

Tbh, Codex sites isn't perfect. Still a lot to be desired like domains, db, authentication etc.

But it's a glimpse into this idea that apps can be updated/improved upon automonously.

And Codex Sites is REALLY good if you live in Codex everyday. Which more and more of are.

And that's really cool. Will be interesting to see how Lovable, Bolt, Replit etc react to this.

full tutorial on @startupideaspod where you get your pods

https://t.co/vKRQL70Nds

watch

share with a friend

i'm rooting for you

What do you think of Codex and Codex sites?

[Alex Lieberman @businessbarista]

I built a content machine.

It turned me into a one-person media company, has driven tens of millions in pipeline for @tenex_labs, and is allergic to AI-slop.

It has also turned all of my employees into content creators.

I may opensource the whole thing, but for now, I'm going to share how I built it & how it works.

Feel free to copy & paste the steps to Claude/Codex if you want to build your own content machine.

Step 1: Map out the process

In order to make any of your work AI-native, you need to understand the way in which it's been done historically. This is why business context & domain expertise REALLY matters, even in a post-AI world.

Content has been my bread & butter for the last decade, so I started by pulling out an 8.5x11 sheet of printer paper and drawing the traditional process.

1) Look for inspiration
2) Pick a 10x content idea
3) Research the idea
4) Brain dump all of my thoughts about the idea
5) Decide the post format I want to create
6) Create a draft of the post
7) Edit the post
8) Create derivative versions of the post
9) Go live
10) Track performance

Step 2: Where am I needed vs. not needed?

I am needed for the first & final mile:

First mile: picking the idea/direction & providing all of the necessary context
Final mile: going through the final draft with a fine tooth comb & giving final sign-off.

AI can handle the rest:

Looking for inspiration, researching the idea, pulling my thoughts out, writing the post, doing a first edit, creating derivative content, and tracking performance.

Step 3: Build the Content Machine

The machine is one pipeline, run end-to-end or step-by-step. It is a directory of skills that mimic the steps in the content process that I've delegated.

1) The Oracle [AI]
Mines my Slack, Notion, call transcripts and Gmail for spikes, moments I naturally said something worth expanding, while the Internet Reader curates an external feed of X accounts & websites I've selected.

Qualifying ideas (≥6/10) are written to The Vault (a notion database of content ideas).

2) Select the idea from The Vault [Human]

3) The Researcher [AI]
Before any interview, build a sourced research-report.md: TL;DR, key facts with links, current developments, what's already been said, contrarian angles, and open questions for the interview. Claims are adversarially checked; fact is separated from opinion.

4) Interview Panel [AI + Human]
Six world-class interviewers (Joe Rogan, Howard Stern, Michael Barbaro, etc) ask 12–15 questions, one at a time, each pushing a different dimension...and never satisfied with vague answers. Won't advance without 2–3 specific stories, real numbers, and emotional specificity.

5) Production [AI]
The interview becomes a raw .md file: transcript, key stories, core insights, quotable moments, emotional anchor, surprising reveals, and the "so what." This raw file is sacred: my exact words, never paraphrased away.

6) Refinement [AI + Human]
I tell the machine what content type I want to create. It reads my custom style guide + past feedback lessons + content-type spec, then drafts in my voice...pulling real stories and quotes from the raw file. The #1 rule: write like you're texting a friend. Supports long posts, LinkedIn, X threads, and more.

7) Writer's Council [AI]
Six expert reviewers (Shaan Puri, Morgan Housel, David Perell, etc) score the draft through their own lens, splitting fixes into editorial (the machine can rewrite) and information gaps (only the creator can answer...these route back to the interview panel).

8) Revision Loop [AI]
Iterate until council scores 9/10.

9) Repurposing Engine [AI]
One anchor → 10+ natively-formatted derivatives, each re-hooked for its platform and each held to the same full Council → revision bar of 9/10. This is how two people produce like a hundred.

10) Final revision [Human]

11) Learning Loop [AI]
After approval, the machine compares first draft vs. final, extracts confirmed lessons, and saves them to that creator's content-lessons.md. Every future first draft starts smarter. Lessons override the style guide when they conflict.

Feel free to steal the machine & ask me any questions about how it works!

[Nous Research @NousResearch]

The next evolution of Hermes Agent is here!

Introducing Hermes Desktop: everything you love about Hermes, now native on your machine.

First demoed in Jensen's GTC keynote, it's now in public preview.

[Jonata Santos @_jonatasantos]

how to build anything rn:

- get a hetzner, do, or hostinger vps
- host hermes on it
- add gbrain or implement your own memory vault using qmd + sql
- set up hermes with codex auth -> gpt-5.5 / no reasoning / fast mode
- install orca on your macbook and phone with tailscale to have a nice ide to work on both
- before starting any work, ask hermes to conduct deep research on the subject and save it to gbrain as source material for the project
- use the `/grill-me` skill or a similar prompt to uncover as many unknowns as possible. save results to memory too
- define/write clear evals for every project to determine whether a run was successful
- have hermes iterate over the project until all evals pass, saving all learnings to the vault along the way
- whenever it gets stuck, use memory + a new research or `/grill-me` session to unblock it

rinse and repeat until the work is done. pay attention to the process. develop a feeling for how long tasks should take and do not be afraid to stop a model mid session to ask for status and why it's taking so long.

[Alex Finn @AlexFinn]

Hermes Agent is the most important tool you can set up right now

It's automated my workflows and multiplied my revenue in just months

You NEED to be taking advantage of it

Here is step by step how to set it up and get the absolute most out of it:

[Starter Story @starter_story]

she built a $60K/month SaaS in 2 months using just LinkedIn posts

no ads. no VC. no fancy growth hacks.

just one platform that everyone overlooks.

here's how Lara did it:

1. she started on LinkedIn by accident - just trying to get a job. instead, she built a following, scaled a personal branding agency to 6 figures, then hit 7 figures through education. all from LinkedIn content.

2. she and her cofounder Jake had a problem: finding and storing high-performing content was eating their entire day. so Jake built a Chrome extension to manage their swipe files. they called it Kleo.

3. they shared it with a few close friends. then posted about it 2-3 times on LinkedIn. that was literally the entire launch strategy.

4. 80,000+ users downloaded it in months. zero paid ads. pure organic distribution built on trust.

5. when they decided to monetize - they already had the demand. a waitlist of 18,000 people. ONE post alone drove 1,000+ waitlist signups.

6. they launched in beta. hit $30K in 4 days. $60K MRR by month two. 918 paying subscribers and still growing.

the secret they cracked?

LinkedIn has 1 billion users. only 1% post. only 0.1% post every week.
while everyone chases virality on X or TikTok - LinkedIn is sitting wide open. and 1,000 likes there is worth 10x more anywhere else, because every decision-maker is on that platform.

the biggest takeaway here:

the tool you build for yourself, to solve YOUR daily problem, might be the one that builds your business.

Lara didn't set out to build a SaaS. she set out to post better content.

the market just told her what she was good at - and allocated her a business.

[Nate Herk @nateherk]

https://t.co/eKfr3LGRpS

[Machina @EXM7777]

https://t.co/ip0CFHxG7R

[Shubham Saboo @Saboo_Shubham_]

https://t.co/ttTq8aAn1j

[GREG ISENBERG @gregisenberg]

How to use Obsidian with Claude in 61 seconds