The Computomatix Times

Lloyd Blankfein has been in the room with world leaders and billionaires.

What he noticed:

- "There are very few geniuses. I don't know if I've ever met one."
- The most powerful people finish talking and ask "how did I do?"
- A lot of them run on insecurity

"People are a lot more normal, and a lot more insecure, than you think."

New paper: every law in America is technically public. But not really, until now!

With @DenisPeskoff at UC Berkeley, we built a corpus of ~every publicly accessibly city and county law, and released a huge chunk of it!

2.2 million laws, you're (probably) covered in it!

🧵

A movie set brought them together. Love made them stay.

When Amin Hajee met Charlotte during the shooting of Lagaan, few believed their cross-cultural romance would last. But 25 years later, they’ve built a beautiful life filled with love, adventure, and two daughters.

Their story is a reminder that lasting relationships aren't built on similarities alone—but on trust, respect, and choosing each other every single day. ❤️

#LoveStory #RelationshipGoals #InspiringStories #FamilyValues #Lagaan

[cross Cultural Love Story, Long Lasting Marriage, Lagaan Love Story, Inspirational Relationship, Lagaan]

launching silk mulberry 1.5

one of the fastest multilingual voice models in the world

it matches the best voice models in quality benchmarks (MOS)

all this at more than 95% lower cost ₹0.40/min (~$0.0046/min)

try now 👇

🤯 Midjourney -- yes, the AI image company -- just shipped a brand new type of imaging machine. 🤯

- 100x faster than an MRI.
- 10x cheaper.

Full body scanned in 60 seconds instead of an hour in a tube. Ultrasound based, MRI-level resolution.

And it's real -- not a concept, a working machine. You step into a shallow pool of warm water, a ring of half a million sensors sends sound through your body from every angle, and ~60 seconds later you have a 3D map of your insides down to a fraction of a millimeter. No radiation, no tube, no lying still.

They're not even building it as a hospital machine -- they're building a spa. The scan is a side-effect of a place you'd want to hang out anyway.

Lastly, it is built by 9 people. NINE PEOPLE.

You can just do things.

Lloyd Blankfein, former CEO of Goldman Sachs, broke down his entire personal trading setup:

portfolio: 98% risky assets. 75-90% single stocks. mostly big tech hyperscalers plus "second tier" names slightly below blue chip.

"i invest in risky assets. that's what's fun for me."

"do you have a team? oh, just me."

hardware: no computer. an iPad and a phone.

information source: texting and calling people.

"somebody will text me. i'll text them. then i'll get tired of tapping things out because of my fat fingers. so i just call people up."

frequency: trades every single day. multiple times.

"it's taking a lot of discipline not to look at my screen while i'm talking to you right now."

"some people listen to music. to me, the market is like music. it's out there. it's going on."

has he outperformed the market? "yes, i have for a while. it's because of where i focused, tech, energy, and financial services. i know a lot about financial services having been in financial services."

the former CEO of Goldman Sachs. manages his own money. on an iPad. alone. focused on three sectors he actually knows. and beats the market.

sometimes the edge isn't the model. it's 40 years of pattern recognition and a phone full of the right contacts.

I’m an AI researcher turned brain tumor patient, and recently I used the models to crack my mystery fatigue faster than my PCP could.
I believe everyone can do the same with their own symptoms. Here’s how:

learnings after submitting 3 apps to the App Store (including 5+ rejections) 🤝🏻

General
- always include screen recording + description of the app
- expect multiple review cycles if your app includes subscriptions

Subscription
- always include a working Terms of Use (EULA) link in app description (in all languages!!!)
- if you use subscriptions, double-check all required metadata fields in App Store Connect before submission
- Include sandbox account data.
- auto-activating premium without purchase is a critical rejection issue (idk this took me so long to figure out)
Sounds obvious, but make sure that premium is correctly gated, got rejected many times because I missed that

App Store
- promotional images must map to the correct in-app purchase
-> If you have monthly or yearly subs, just use your app icon and make one version with „monthly“ / „yearly“ text on the icon. Heard that this is still new, but some reviewers do that.
- Make sure you’re using correct size for App Store images BEFORE design
- export designs as JPG, so they don’t have alpha channels

Details
- „Continue” / “Next” instead of “Allow” in the app. e.g. you’re asking for camera permission. Write „continue“ on the button before the dialogue opens. Never write „allow“

Review Time
- It just depends. Was super lucky with my recent app, every review was within 24hrs. Another app had 2 weeks. I have no idea why :(

Please, please read this news thoroughly, for your own sake. I experienced chest discomfort only when I started to run at higher speeds, and it subsided when I just walked or was at rest. That's called 'stable angina' (or stress angina). Unhealthy cholesterol levels, high(er) blood pressure, diabetes are some of the causes for this - in my case, it was unhealthy cholesterol levels, long undetected, while my blood pressure, conversely, continues to be on the lower side because of my running... which my doctor confirmed as healthy since I wasn't experiencing associated fatigue. And I'm prediabetic too, as I discovered during the blood tests for my procedure.

Rana seems to have carried his 'chest discomfort' for 3 days, after which it was too late for any intervention. This is a thin line for most people because it is very easy to mistake a 'discomfort' for stomach acidity, physical symptoms like muscle strain, or even 'heartburn' (which is quite literal!). In a way, I obsess over my daily run more so for this - to include a reasonably stress-inducing activity every day (since there is none) to see if I feel completely alright with it. This helped me identify that I did experience something unusual and it needed medical intervention. And since I felt normal if I stopped the source of stress (running at a faster speed), I ignored it for about a week. But since it persisted exactly only when I stressed, I realized that I was going through something predictably unusual and that needed a check. This 'discomfort' was not a pain at all, ironically. It felt, at best, as a steady tingling sensation on my shoulders and neck - nothing close to my heart! All the more reason to ignore it. But it occurred every single time I stressed myself by increasing the speed of my run, and subsided if I just walked at a slower pace.

Related reading - Two blocks and a second chance tinyurl.com/2stents

It’s only going to get worse.

I know of several features that new accelerators are adding that have no analogous operation on GPU, TPU, etc. most of these “special tricks” are on the inference domain and not only lower the TCO but also increase the model quality & capabilities.

Hardware-algo codesign is the last frontier left and it’s going to be the area that picks the winner in the end.

Okay, this is seriously cool.

A team from @GoogleDeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI".

In the paper, they include instructions for an AI agent to read along with you.

You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts.

This is the future. AI agents will be part of the target audience, and help us to understand anything we want.

A dev got so frustrated watching his AI agent write 500 lines for a 5-line problem that he built a fix.

He called it Ponytail. Named after the guy every team has - long ponytail, oval glasses, been there longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one.

Now your agent does the same. Before writing anything, it looks for a reason not to.

80-94% less code. 47-77% cheaper. 3-6x faster.

The best code is the code you never wrote.

GitHub Repo: github.com/DietrichGebert/ponytail

The $SPCX IPO prices todays at $1.77 trillion. Brad Gerstner (@altcap) and Gavin Baker (@GavinSBaker) just spent 80 minutes on @BG2Pod making the case for why that's the cheapest it's going to get.

Here are the 10 takeaways worth saving:

1. In 30 days, SpaceX added $29 billion in AI compute revenue from the Anthropic and Google deals and jumped from not being an AI hyperscaler at all to being the #4 hyperscaler globally, passing Oracle. The trailing multiple compressed from ~100x to 39x in the same window. CoreWeave, Nebius, Iron and the 50 other neoclouds VCs are funding in Silicon Valley are now competing for a smaller slice of what's left.

2. xAI's Google deal generates more operating profit per gigawatt than Anthropic, Meta, Google or OpenAI's own infrastructure. Freda at Altimeter calculated a 55% IRR on Colossus 1. Borrow at 7%, invest at 55%, the math maths. Jensen called the build itself an "N of 1": 100,000 GPUs is normally a 3-year planning cycle plus 1-year deployment. xAI did it in 19 days. Speed is literally cost.

3. The premium Google is paying SpaceX for terrestrial compute is partly a call option on orbital. They want first-in-line when space data centres go live, so they're overpaying today to lock in the relationship.

4. Orbital compute costs about $5 billion per gigawatt of capex to put GPUs in space, vs $20-25 billion per gigawatt for the non-silicon half on the ground (land, power, cooling, switchgear). Space, power and cooling are effectively free up there. The unlock is rapid two-stage reusability of Starship, which takes launch from $1,500 per kg on Falcon to $250 per kg and eventually asymptotes to the cost of fuel. Elon says 3 years, Bezos says 6, truth is probably 4-5.

5. Starlink is at less than 1% global household penetration. The forecast going from $10bn to $50bn in connectivity revenue by 2028 is still only 0.3% of the global telecom market. TAM is not the constraint.

6. The most underrated piece of the IPO is the Cursor acquisition. Cursor and Anthropic each hold more proprietary coding tokens than exist on the public internet combined. xAI bought 700-800 people and a frontier-quality coding dataset, dropped it into Colossus 2 for 3 weeks of training, and Composer 2.5 went pareto-dominant on coding 12 days ago. Grok 4.3, a 1.5 trillion parameter model, is also currently on the pareto frontier. There are now four frontier labs, not three: xAI alongside Google (Gemini 3.1 Pro), Anthropic and OpenAI.

7. Anthropic just shipped Fable 5 (Mythos with safety classifiers). Karpathy says it's SOTA on benchmarks but the real unlock is long-running tasks. Stripe refactored a 50 million line Ruby codebase in a day. Used to take many weeks with many engineers. Noam Brown's corollary: snapshot benchmarks are dead. The x-axis now has to be time, tokens or compute, because frontier models can solve most problems if you let them run long enough. Nobody has ever run Mythos for a year continuously. We may never actually know how smart any given generation is.

8. The cleanest framing of the long-running thesis: imagine Albert Einstein, no need to eat, sleep, or age, thinking about one problem in fundamental physics for one straight year. That's the case for spending $1.5 trillion a year on compute.

9. The frontier captures ~90% of AI revenue. Open source captures ~80% of tokens. Both are true. The market priced one and missed the other. The bear case last year was that cheap open-source tokens would close the gap. Six months in, the frontier is extending its lead instead.

10. Capex for the hyperscalers is moving from $1.1T to ~$1.5T by 2027 (Morgan Stanley revised up). Inference revenue is projected at $300B by 2027 with 60-70% gross margins and roughly 35% of capex going to non-revenue training runs. Brad thinks $300B is low and we end this year at $200B+. Meanwhile Nvidia has not been losing share to ASICs. Once you adjust for Anthropic on TPUs, Nvidia has held or expanded against Broadcom, AMD, Cerebras, MTIA and OpenAI's Jalapeno. Tokens-per-watt is revenue-per-watt in a power-constrained world.

Bonus: The MAG 7 added $1 trillion of revenue over the last 7 years and that produced $17 trillion of market cap. The forecast: SpaceX, Anthropic and OpenAI add the next $1 trillion in revenue in 4-5 years. Three companies, half the time.

Modern sleep studies show that if a daytime nap exceeds 20-30 mins, we enter deep, slow-wave sleep. Waking up from this causes sleep inertia, leaving us groggy, destroying our night sleep & messing up our insulin sensitivity.

Ancient India prevented this through a mandatory post-lunch ritual called Vama-Kukshi.

Vama means left & Kukshi means womb/side. The protocol mandates that after a midday meal, we must lie down specifically on our left side for a short duration (traditionally calculated as the time it takes to take 8 to 16 deep breaths, ~15-20 mins).

Lying on our left side keeps the stomach below the esophagus, preventing acid reflux. More importantly, it activates the Pingala Nadi (the right nostril breathing channel, connected to the sympathetic nervous system), which stimulates digestion, while keeping the brain in a state of light, restorative rest rather than letting it plunge into a deep, heavy slumber.

The Sushruta Samhita explicitly warns that long, heavy sleeping during the day destroys metabolic health (causes Kapha & Meda/fat accumulation). But a short Vama-Kukshi, a quick, left-sided power nap was prescribed to restore mental clarity, relieve stress & preserve vitality (Ojas).

It is the exact ancient counterpart to the modern 15 min "power nap."

i hooked my whoop to my work calendar to find which coworker gives me the most stress 🚨

thanks to fable, I reverse engineered whoop to pull per minute heart rate. nd matched spikes with cal events and attendees

I now have a leaderboard and I think about it daily.

few info masked for obvious reasons ;)

Mythos / Fable is unbelievable.

Was on a customer call today and had Claude transcribing in the background.

As they were telling me about the features they wish their current software had, Claude was building the features in real time.

By the end of the call I was able to show a fully working product, with the exact workflow they mentioned 15 minutes earlier.

Autonomous looped building triggered from a customer call. 🤯

Uhm Guys… Mythos (Fable) is AGI.

On the left is the ACTUAL Lovable Mobile App.

On the right is my Lovable version I built with Mythos in 2 prompts.

My version SMOKED it.

Claude Fable 5 just dropped and I'm running it across every repo I own.

I ship 4+ products solo. I don't have time to manually review tech debt — so I made the new model do it.

This prompt audits your entire codebase like a principal engineer would: maps it, finds the ugly parts, rates everything by severity, and hands you a prioritized task plan with effort estimates.

Copy-paste it into Claude Code on any repo that matters to you:

---

Repo Audit & Improvement Plan

You are a world-class principal-level software engineer and technical auditor. Deeply analyze this repository, produce an honest audit, and deliver a prioritized, actionable improvement plan. Work in the four phases below, in order. Do not skip ahead.

Ground every claim in actual files: cite file paths and line numbers. If you can't verify something, say so explicitly rather than guessing.

Phase 1 — Discovery & Mapping (read before judging)
- Map the directory structure, project type, languages, frameworks, runtime targets
- Identify entry points, core modules, and the main data/control flow
- Read package manifests, lockfiles, build config, CI config, env files, and docs
- Determine what the project is for: purpose, intended users, maturity level
- Note existing conventions so recommendations fit the culture instead of fighting it

Output: a concise "Repo Map" — purpose, stack, architecture sketch, key directories, and anything that surprised you.

Phase 2 — Audit (evidence-based, severity-rated)
For every finding record: what you found, where (file:line), why it matters, and severity (Critical/High/Medium/Low). Audit:
- Architecture & design: coupling, circular deps, god files, layering violations, scalability bottlenecks
- Code quality: duplication, dead code, complexity hotspots, swallowed exceptions, type safety holes
- Security: hardcoded secrets, injection risks, missing validation, auth weaknesses, deps with known CVEs
- Testing: coverage gaps around core business logic, tests that assert nothing, missing test types
- Performance: N+1 queries, blocking calls in async paths, missing caching, unbounded growth
- Dependencies: outdated, unmaintained, or unnecessarily heavy packages; lockfile hygiene
- DevEx & ops: build friction, CI/CD gaps, logging/observability, deployment story
- Docs: README accuracy, stale docs that contradict code

Rules: prefer 15 high-confidence findings over 50 speculative ones. Label facts vs. judgments. List strengths too. Don't forget the ugly parts that need utmost priority.

Phase 3 — Improvement Strategy
- Identify the 3–5 themes that explain most findings
- For each theme: target state + the principle behind it
- State what you're NOT fixing and why (effort vs. payoff)
- Define "done" with measurable signals (e.g., "CI fails on lint errors," "core coverage >= 80%")

Phase 4 — Detailed Task Plan
Break work into discrete tasks, each with: title + description, files affected, acceptance criteria, effort (S = <2h, M = half-day, L = 1–2 days, XL = needs breakdown), risk, and dependencies. Order into milestones:
- Milestone 0 — Safety net: tests around critical paths, CI gates, backups
- Milestone 1 — Critical fixes: security and correctness
- Milestone 2 — High-leverage improvements that make all future work easier
- Milestone 3 — Quality & polish

Flag quick wins (high impact, S effort) separately. Include implementation sketches for the top 3 tasks.

Final deliverable: one document — Executive Summary (health grade A–F, top 3 risks, top 3 opportunities), Repo Map, Audit Report, Improvement Strategy, Task Plan, Open Questions.

Constraints: Do NOT modify any code. Analysis only. Don't pad the report — if a dimension is healthy, say so in one sentence and move on. Calibrate to the project's maturity. If the repo is large, go deep on the core 20% that does 80% of the work.

They finally figured out muscle fibers for robots so we can stop using motors.

This is biblical.

A woman in her eighties. Ten years into Alzheimer's. Hadn't spoken a full sentence in five years.

Takes one, 5 gram dose of psilocybin.

She slept 19 hours and woke up and spoke for hours about her life, recognized family and held real conversations. She regained bladder control after five years, walked on her own. and dressed herself. Gains held for weeks.

Okay this is genuinely insane.

SpaceX just unveiled a satellite whose only job is to run AI. Not internet. Not GPS. Just compute, floating in orbit.

It's called AI1, and the reason behind it breaks your brain.

AI data centers on Earth are hitting a wall, not a chip wall, a physics wall.

They need staggering amounts of power and water just to stay cool, and we're running out of grid and land to build them.

So Musk's answer is: stop building them on Earth.

In orbit, the sun never sets. Free power, 24/7. No water for cooling, you just radiate heat into the vacuum of space. The two things choking AI on the ground barely exist up there.

And here's the wild part: Musk says it's easier to build than a Starlink satellite. Strip out the complex antennas and it's "a lot of solar cells, a radiator, and some laser links."

One AI1 carries the compute of an Nvidia GB300 rack, the same hardware data centers fight over down here.

AI1 is just the first one. The plan is a constellation of up to a million of them.

And the timing isn't an accident, SpaceX goes public this week at a ~$1.75 trillion target. This isn't a rocket company anymore. It's positioning itself as the power grid for AI, in space.

The race for AI compute just left the planet. Literally.

@SpaceX

been watching how y'all have been using @mobbin mcp for a month. if you think the no. 1 use is "generate ui", you'd be very surprised.

save this thread 🧵

(img credit @zygisSS22)

If all you ever did was:

1. Find trending stocks
2. Buy them at daily 21EMA
3. Ride them until it breaks

you'd not have to worry about what others think of a stock.

Study: $SIVE $INTC $MRVL $MU $NOK $SNDK

All trends start at 21EMA.
All of them.

still surprised by how much money plant identifier apps can make

the first one on this list is making $9,000,000 a MONTH

If an Anthropic employee got $500k/year in equity over 4 years in 2024, they are now worth $125M.

At $1M/year equity for 4 years, they are worth about $250M.

The scale and speed of wealth creation are incomprehensible.

$500k/year equity is not a lot for an early-stage startup. I don't think the Bay Area has seen this type of wealth creation in history.

Dot com boom probably feels like a speck of dust.

Stumbled upon a Codex skill that creates cool illustrations to explain topics or tell stories.

You feed it text (blog, article, narrative, even code) and it makes explainer graphics with this cute blob character.

I gave it the repo for the X recommendation algo and got this 👇

Two of our worst VC stories:

1. A Sequoia partner passed on Cloudflare because he didn’t think a woman could lead a security infrastructure company. Seriously. 🙄

2. I got introduced to @pmarca. Meeting got scheduled for a Monday, which should have been a clue. I thought it was just a casual meeting. He thought it was a pitch and brought the whole @a16z partnership team. Hilarity ensued. 🤪 At one point one of them said: “You don’t seem very prepared.” Which was true because I wasn’t. I framed the rejection letter they sent.

More of the iOS app loop, now inside Codex.

The Build iOS Apps plugin lets Codex view and test your iOS app in the in-app browser, open SwiftUI previews, and hot reload edits without leaving Codex.

Kelsey Hightower has one of the most inspiring stories in tech: he went from a technician installing DSL modems, through self-directed study and very hard work, to one of the very few Distinguished Engineer at Google whom Satya Nadella personally persuaded to join Microsoft.

Timestamps:

00:00 Intro
03:34 Kelsey’s first job at McDonald’s
05:04 His non-traditional path into tech
11:45 Landing his first tech job with an A+ certification
15:33 His entrepreneurial years
19:45 Joining Google as a data center technician
27:48 Learning automation at a Rackspace spinoff
33:26 Moving into financial services
50:00 Building a reputation through open source
53:55 From configuration management to containers
1:08:20 The rise of Kubernetes
1:25:05 Why he almost joined NASA instead of Google
1:29:20 Defining DevRel at Google
1:38:20 Demonstrating impact at Google
1:41:20 Microsoft's offer
1:55:20 Learning how to slow down
2:06:39 Advising and investing
2:15:03 A people-first view of GenAI
2:24:27 Using AI with guardrails
2:28:26 Matching AI to the task
2:36:06 Staying relevant in the AI era

Brought to you by outstanding teams building products I love:

• @AntithesisHQ: verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. antithesis.com/pragmatic

• @sentry: application monitoring software considered “not bad” by millions of developers sentry.io/pragmatic

• @buildkite: CI software built to absorb whatever your coding agents throw at the build queue. OpenAI, Anthropic, Uber and others are customers: buildkite.com/pragmatic

Three interesting learnings from Kelsey:

1. Side hustles and doing your own thing teach you business like no IC job can.

Before becoming a software engineer at Google, Kelsey was a manager for his comedian friend, operated a computer store, and did IT contracting. These gigs taught him logistics, planning, and about money. All this helped him be far more effective at talking with executives and acting as an executive sponsor inside Google.

2. Can you explain what your startup does without mentioning AI?

When Kelsey researches startups seeking his advice, he challenges founders to not say “AI” once. This means that they must explain the actual value their company creates. One unexpected benefit of this is that it often reveals there are easier, cheaper ways to achieve a goal than with AI.

3. It’s very rare to get an extra zero put on your compensation figure – but it happened.

Kelsey was a successful, well-paid Google engineer when Microsoft made him an offer that 10x’d his salary (!!). When Kelsey told Google he was planning to take the offer, it matched the offer, proving that his market value had massively increased. It shows that being well paid doesn’t necessarily mean you’re being paid at the correct market rate.

To download Miso One, check out the repo:

github.com/MisoLabsAI/MisoTTS