Lula Rocha 🙃IQ21

Designer #Bitcoin #Lightning⚡️ #Nostr #npub1cul8tkag4h8rqare6e2h2qv77klwfhyqgtwwahknx58l385ep8eqvd2r52 ⚡️

[Sparrow Wallet 🐦 @SparrowWallet]

Unless @Apple's decision to terminate @craigraw's Apple Developer account is reversed by June 30, all new installs of Sparrow will fail, and development on macOS will end. If you value Sparrow, a repost would help. @AppleSupport

[Lula Rocha 🙃IQ21 @lularocha]

@TheAhmadOsman Anyone who starts with “Drop everything” gets blocked

[Michael Saylor @saylor]

When I gave this speech in October 2022, Bitcoin traded near $20,000, Strategy held 130,000 BTC worth about $2.6 billion, and $MSTR was ~$24 split-adjusted. Weeks later, after Bitcoin fell below $16,000, our debt exceeded the combined value of our BTC and cash reserves by ~$300 million, and $MSTR fell into the $13 range by year-end.

We stayed focused, strengthened the company, and executed our strategy. Since then, Strategy has raised over $60 billion of additional capital and invested it in Bitcoin, adding more than 716,000 BTC. Today, our BTC and USD reserves exceed debt by ~$48 billion. Thank you to everyone who believed, endured, and took the long view.

[Lula Rocha 🙃IQ21 @lularocha]

@0xSero Whoowww large LLMs!

[Lula Rocha 🙃IQ21 @lularocha]

@DavidOndrej1 Keep working hard to avoid hard working

[Lula Rocha 🙃IQ21 @lularocha]

@MengTo Love it! May I ask what preview tool you’re using? Thanks

[Lula Rocha 🙃IQ21 @lularocha]

Learning to see.

[Ihtesham Ali @ihtesham2005]

Two math olympiad champions wrote a training manual in 1993 on two old Macintosh computers, and every American kid who has won a major math competition in the last decade learned to think from it.

Their names are Sandor Lehoczky and Richard Rusczyk. The book is called The Art of Problem Solving. Most people in math know it as AoPS.

Since 2015, every single member of the US International Math Olympiad team has been an AoPS student. Not most of them. Every one.

That statistic sounds impossible until you understand what the book actually does.

Lehoczky and Rusczyk were not professors. They were competitors. Lehoczky earned the sole perfect AIME score in 1990 and led the national first place team. Rusczyk was a USA Mathematical Olympiad winner and a perfect AIME scorer in 1989. They had both survived the same brutal selection process the book was designed to train students for.

And the first thing they decided was that almost every existing math textbook was teaching the wrong thing.

School math gives you formulas. You memorize them. You apply them. You pass the test. Then you sit down in front of a real competition problem and the formula does not apply, and you have nothing underneath it.

That is the gap. The gap is not knowledge. It is thinking.

The entire premise of AoPS is that problem-solving is a transferable skill, not a bag of memorized tricks. A student who genuinely understands why a technique works can adapt it, combine it with something else, and deploy it in a context they have never seen before. A student who only memorized the technique freezes the moment the problem looks different.

The book teaches the difference between a formula and a method.

A formula tells you what to compute. A method tells you how to see. The students who win olympiads are not the ones who know more formulas. They are the ones who have trained themselves to look at an unfamiliar problem and recognize its structure. To see that this problem is secretly asking the same question as a problem they solved three weeks ago, just dressed differently.

Rusczyk calls this "learning to read the problem." Not reading the words. Reading what the problem is actually asking underneath the words.

The second thing they built into the book is tolerance for being stuck.

Most students treat confusion as a signal to stop. The book treats confusion as the starting point. Every chapter pushes students past the point where the obvious approach runs out. That moment of running out is not failure. That is where the actual thinking begins.

Lehoczky once described it this way. If you can solve a problem quickly, you are not learning. You are performing. Learning only happens when you are past the edge of what you already know.

The book was written on old Macintosh computers in 1993. Rusczyk launched the AoPS website in 2003. Today the community has over one million users. Thousands of students enroll in AoPS online courses every year. Most winners of every major American math competition are AoPS alumni.

A platform built by two kids who were good at math competitions has become the infrastructure that produces the next generation of mathematicians, engineers, and scientists who are good at thinking.

The formulas you memorized in school will eventually be obsolete.

The thinking you trained will not.

What is one problem in your life right now that you have been avoiding because you do not yet know the right formula to solve it?

[Lula Rocha 🙃IQ21 @lularocha]

@ODELLXYZ @bjackson2718 https://t.co/tllnDTbTgw (@nvk make sense?)

[Lula Rocha 🙃IQ21 @lularocha]

@nvk BTC enters Black Friday Mode = work hard, stack harder.

[Lula Rocha 🙃IQ21 @lularocha]

It’s official: #BTCBlackFriday

[Lula Rocha 🙃IQ21 @lularocha]

@0xSero I’m kind of new to all of this, but from what I’ve seen so far you’re damn good in what you do and very generous, so that’s all very well deserved. 🤝

[Daniel Batten @DSBatten]

At the end of 2023 I met the person who ran GreenpeaceUSA's campaign against Bitcoin. I expected it to be tense.

But it wasn't.

He told me he'd read almost all of my responses to the posts they'd made on Twitter.

"I wish we'd engaged with you and people like you from the outset," he said.

Two months later he left Greenpeace.

A few months after that they ended the campaign entirely.

It was the most well-funded, yet the worst result in their history. I keep coming back to that phrase.

"People like you." Not me, but a whole group: Troy Cross, Margot Paez, Elliot David, Susie Violet Ward and many others.

We were working independently, with no budget and no coordination.

We just shared data and conviction. It is a great story of a decentralized response beating a multi-million dollar centralized campaign, by a combination of having the truth behind us, and expressing that truth in such a way that reasonable people could see.

That recipe has two parts, and it's like giving a glass of water to someone dying of thirst.

The first part is truth, there must be water in the glass.

The second part is the container, how you hold and deliver that truth matters just as much.

You can have perfect data and still lose people if the glass doesn't reach them, or if the energy behind it makes them flinch instead of drink.

We won because the data was right AND because enough people delivered it with the kind of energy that made opponents think rather than react.

And this recipe, we can use again and again throughout Bitcoin's adoption journey, and each time a Bitcoiner builds a new Bitcoin project that involves outreach to non-Bitcoiners.

[Lula Rocha 🙃IQ21 @lularocha]

I always had a feeling there was something wrong with Figma…

[Nav Toor @heynavtoor]

Adobe tried to buy Figma for $20 billion in 2022.

The deal collapsed. So Figma went public on the NYSE in July 2025 instead. Ticker FIG. Public company. Quarterly earnings. Wall Street pressure.

You know what happens to design tools after they IPO.

In March 2025, Figma raised the Professional Full seat 33%. From $15 to $20 a month. Organization seats jumped to $55. Enterprise to $90.

Then they took Dev Mode, which was free during beta, and locked it behind a paid seat. Your developers now pay extra to inspect the designs your designers already paid to create.

In March 2026, Figma started charging for AI credits on top.

If Figma raises prices again, you pay.
If Figma gets acquired, you pray.
If Figma shuts down, your files die with it.

Your design system. On their servers. In a proprietary format only their app can read. To draw rectangles on a screen.

There is an open source design platform that runs on your hardware. Stores your files in plain SVG. Costs $0 forever for unlimited users.

It is called Penpot. 45,700+ stars on GitHub.

A full Figma-grade design platform built on open web standards. Vector editing. Components. Design tokens to W3C spec. Flex and Grid layouts. Real-time multiplayer. Interactive prototyping.

Here's what it does:

→ Real-time collaboration. Live cursors. Comments in line.
→ Components, variants, shared libraries.
→ Auto layout, Flex, CSS Grid. The tool outputs production CSS, not lookalike CSS.
→ Interactive prototypes with overlays, animations, and flows.
→ Inspect tab. Free. Built in. Every developer grabs production CSS, SVG, HTML without a separate seat.
→ Plugin ecosystem. Figma import to migrate your files.
→ Self-host on Docker in one command. Your designs never leave your network.

Here's the wildest part:

Figma stores your designs in a proprietary format only Figma can read.

Penpot files are SVG. The same format your browser has rendered for 25 years. Open them in any editor. Open them in 20 years. Nobody can lock you out.

The feature Figma charges your developers extra for, Penpot gives away. Without asking permission.

Figma Professional: $20/month per seat. A 10-person team: $2,400/year.
Figma Organization: $55/month per Full seat. A 50-person org: $33,000/year.

Penpot: $0. Unlimited users. Unlimited files. Unlimited teams. Self-hosted. Free forever.

45,700+ stars. 2,700+ forks. 250+ contributors. MPL-2.0 license. Backed by a community that believes design tools should be free.

Your designs. Your files. Your standards.

100% Open Source.

(Link in the comments)

[Lula Rocha 🙃IQ21 @lularocha]

@mattpocockuk is the real deal.

[Matt Pocock @mattpocockuk]

Tons of folks are piling in here saying that AFK agents are a myth.

I have been using them to ship these GitHub repos:

mattpocock/evalite
mattpocock/sandcastle
mattpocock/software-factory (might be public by the time you see this)

Here are a few steps to making this work, and some reality checks.

Definitions

Let's split this into the day shift and the night shift. Day shift is planning/review/QA, night shift is AFK implementation.

Day Shift (part 1)

1. Use /grill-me to align with the AI
2. Use /to-prd and /to-issues to create a PRD (the destination) and implementation steps as separate tickets, which can be grabbed in parallel (the journey)
3. The PRD is a ticket, but it's not an actionable step. You just put the user stories there

This is pure requirements gathering shit, same as it ever was.

Night Shift

1. I run a planner agent which looks at all the tickets and sees what can be worked on now, and what's blocked
2. The planner agent then kicks off multiple agents (sandboxed using Sandcastle, my OSS tool) to implement the code
3. I then have an automated reviewer agent look at the commits produced - one agent per implementation. This checks alignment to the original PRD, as well as code quality
4. These commits end up on branches that get PR'd to main
5. The planner agent runs again until all work has been completed

The review is a crucial step - it's saved me MANY times. I am planning to massively increase the amount of review I do, hopefully with multiple agents.

But guess what - AFK agents sometimes produce bad code. This can happen because of:

a. The original plan was bad because the best solution was something different
b. The original plan was bad because it didn't take into account all the unknown unknowns, and the AI had to make some decisions during the coding session which were bad
c. The plan was good, but the AI just shat the bed (twice, once in the review stage, once during implementation)
d. Your codebase is bad and the feedback loops don't tell the agent if it did a good job or not

So... QA:

Day Shift (part 2)

1. QA all of the branches created
2. Create follow-up issues, potentially editing the original PRD to adjust the destination

This will usually take a long time, often as long as planning. But then you kick off the night shift again.

Once QA is all done, you review the important bits of code manually, usually in PR's. There isn't anything better than the PR UI right now, so that's what we're stuck with.

Wake-up Calls

1. If you let the AI run all night unbounded by planning, it's going to produce shit code
2. Mostly, my loops finish before I go to bed, it's just the night shift catching up to the day shift
3. The only reason I do AFK at all is because it allows me to automate review and totally not give a shit about latency
4. I always run night and day shift in parallel. I can't plan that far ahead (skill issue, probably). I need working code to base my plans from, so I'm aggressively QA-ing stuff that lands

[Lula Rocha 🙃IQ21 @lularocha]

@felixleezd @ADPList Design

[Lula Rocha 🙃IQ21 @lularocha]

@LynAldenContact Pareto

[Lula Rocha 🙃IQ21 @lularocha]

You in FULL CONTROL

[Andrej Karpathy @karpathy]

Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.

I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something:

1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable.
2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information.
3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy.
4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data.

So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :)

Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.

[Lula Rocha 🙃IQ21 @lularocha]

Understanding how much high quality Human input it takes to get high quality AI output. 🔥🔥🔥🔥

[Matt Pocock @mattpocockuk]

The skills:

https://t.co/h9JB7mvS77

[Lula Rocha 🙃IQ21 @lularocha]

@DSBatten Not sure if would be that easy to replace MC, maybe for payments but in the long run. I said "for payments" because payments is only part of what MC does ... Still thinking about this.

[Lula Rocha 🙃IQ21 @lularocha]

Then… Grok said … (this a test inspired by you know who recent post)

https://t.co/hVFYUDYRPL

[nic carter @nic_carter]

@elonmusk gork said: