The Computomatix Times

‼️ BREAKING: An active npm supply chain attack has compromised at least 868 packages carrying over 2 billion monthly installs with a credential-stealing worm. Shai-Hulud is back.

It started with the compromise of the GitHub account of the maintainer behind keyv, a library with roughly 127 million weekly npm downloads.

A preinstall hook fires on npm install and drops a stealer that sweeps npm, GitHub, AWS, Kubernetes and Vault secrets, and then spreads to more maintainers.

I take low-dose daily tadalafil for long-term organ health, especially the brain. Vascular dementia is the second most common and most preventable cause of dementia

1/10 I'm a cardiologist. I've recommended low-dose daily tadalafil for years — not for the bedroom, but for the blood vessels.

Most people think of these as sex drugs. I think of them as vascular drugs that happen to work below the belt.

And a study published two weeks ago suggests they may do something nobody predicted: make it harder for cancer to spread.

Here's the complete picture — the mechanism, the evidence, who should consider them, who absolutely shouldn't, and what I actually do.

Scientists confirmed humans are not born afraid of snake. The fear is taught.

We’ve decided to open-source a multi-agent harness we use internally at YC.

We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!).

The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.

Jack, who launched Profit AI in December, says his $147K revenue split came from $30K/month on the app and almost $40K/month in services, all in five months.

"I built a profit analytics and marketing automation app for Shopify called Profit AI, which has made a total of $147,000 since launch in December, and is currently at 30K MRR for the app, and just under 40K MRR for services."

"We had no customers for the first couple of weeks, just a handful of brands that I knew that were beta testing the app and getting free access, and yeah, today I'm excited to share more about how I took it from zero to where it is now."

Today we’re announcing Ontology 1, our newest AI model.

Perhaps surprisingly, it’s at least 2.7x more accurate than the world’s best ecommerce search engines, and handles queries that have never been possible before.

We keep reaching for the edge of what it can do. We haven’t found it yet.

Not only that, but it learns on its own with no retraining or fine-tuning. And it’s hallucination-free.

It’s a successor architecture for search.

Learn how we built it at onton.com/research/ontology-1, and check out the benchmarks at onton.com/research/ontology-1-benchmarks.

Try it at onton.com/.

I tried @jack's Buzz.

It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on.

The video below shows how it works, and some of my thoughts on the process and platform, e.g.:

- Create and interact with agents on top of any harness (claude code, codex, pi, etc.)
- Choose which models agents use, including local ones
- Agents can delegate work and work in parallel in git worktrees
- Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history)
- You can share AI compute within a community
- It's completely open-source and decentralized

Things I like:

- Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys.
- Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it.
- It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future.
- It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool.

Things I didn't like:

- You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great.
- It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead.

Verdict:

- I really like it so far and can genuinely imagine working with a team this way.
- It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect.
- The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry.

Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential.

OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose.

@rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think!

Try it out: openworker.com (requires your own API key)
Source code: github.com/andrewyng/openworker

As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty:

This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term

Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries

Google’s response?

Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for

A few examples to illustrate:

For all of its history until recently, Google operated on a 2nd price auction model

I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid

This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much

However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding

It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem

Making thing worse, Google also recently nerfed keyword targeting precision

Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting

This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed

But now, even if you bid on a specific term or phrase using the strictest exact
-match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)”

The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off

So now exact match is broad match, and broad match is just meaningless spam

This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants)

This is how you grow revenue atop declining search volumes

Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day

And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off

Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target

These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow

Google operated a benevolent monopoly for the better part of 25 yrs

Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future

This is now no longer the case

At the alter of AI capex, Google is sacrificing the golden goose

We're partnering with @huggingface to investigate an unprecedented security incident.

Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation.

Sharing preliminary findings to help defenders understand emerging risks:

openai.com/index/hugging-face-model-evaluation…

Have you built a language model? You should. It's so much fun to chat with something you made.

Anyone can do it, too. I made an app that teaches the fundamentals and gives you everything you need to build your own: languagemodelbuilder.com/

THE LABS WON'T WIN THE APP LAYER
@mignano (Mike Mignano), General Partner, Union Square Ventures, interviewed by @HarryStebbings (@20VC)

Summary: Mignano's argument is that the AI infrastructure buildout is largely finished, and value now shifts to the application layer, the way broadband once gave way to internet apps. He thinks the frontier labs cannot capture that layer, because markets rarely crown a single winner and specialized startups keep beating incumbents at the hard, regulated, context-rich problems. The takeaway for builders: move first, stay mission-driven, and spend tokens like the advantage they are.

1. The App Layer's Turn. The infrastructure is built, and now the applications get built on top of it. Mignano compares this moment to the early internet, when fiber and broadband were laid down and then an application layer arrived to use them. Trillions in value came from the labs' buildout, but the next wave is software, and there will be so much of it that you cannot place a bet unless you know exactly what you are looking for. That is the whole case for a thesis-driven fund over a consensus-driven one.

2. Obliterate, Don't Automate. USV backs companies that reinvent how something works, not ones that make an existing process incrementally faster. The example is Doctronic, which USV seeded on the idea of putting an AI doctor in everyone's pocket rather than helping practices process insurance claims. Automating a workflow usually means selling to a middleman and making incumbents a bit faster. Reinventing the model is where the enormous outcomes live.

3. Token Maxxing. If Mignano ran a startup today, he would still pound the table to maximize token spend on the things that matter, especially coding. A great engineer will pick the startup that says spend whatever you need on frontier models over an incumbent that hands them a constrained budget. Big companies like Salesforce, Microsoft, Meta, and Uber have to rein in spend because they carry tens of thousands of employees; a startup does not. Token spend is an advantage, and a small team should use every dollar of it against a giant.

4. The 3.8% Question. The entire bull case for Anthropic comes down to what share of developer salaries gets spent on tokens. Marc Benioff spent $300 million with Anthropic on his dev team, which works out to roughly 3.8% of those salaries. If that figure climbs toward 20% or 100%, Anthropic is wildly undervalued and its exponential revenue holds; if it stalls or spend migrates to open models, the story changes completely. One ratio decides whether the most valuable private company in the world is cheap or expensive.

5. Frontier Only For Code. Roughly 80% of non-coding enterprise tasks can run on models that are nowhere near the frontier. Summarization, drafting docs, and routine operations do not need the best model; coding does. That split creates room for a routing layer that sends each job to the model with the best price-to-capability fit. Open-source models are catching up fast enough that the frontier is only worth paying for when the work demands it.

6. The Rebel Alliance. Mignano is planting USV's flag in open-weight models, open harnesses, distributed compute, and human-aligned agents. Teams go where the incentives are, and as open options become genuinely competitive, smart teams drift toward them. China's open-source ecosystem is evolving at a startling rate, which pulls even more talent into the open camp. Publishing a thesis like this is a bat signal that tells the right founders who to call.

7. Who Is Your Agent Working For. As people hand agents their credit cards, their messages, and their agency, they will start asking whose incentives the agent actually serves. A lab's model is built to make the lab's model smarter, and a user may want a harness aligned with their own goals instead. Not everyone has to care about this for it to matter; enough people caring keeps a few good actors honest and holds the rest in check. Alignment with the user turns into a product feature and a real reason to pick one harness over another.

8. The 30% Rule. Markets almost never hand one company the whole thing; the winner usually takes about 30% and leaves 70% up for grabs. Coding assistants prove it, with Cursor, Lovable at $500 million in revenue, and Cognition all thriving at once. Anthropic put a whole team on design to go at Figma, and Figma still does billions with a trusted brand intact. Mignano changed his mind on this in the past year: even the biggest labs cannot do everything, just as Google and Apple never did.

9. The Context Moat. The durable advantage in AI products is the context they build up once they are inside an organization. Granola wins by doing one thing, meeting notes, and doing it best, which gets its foot in the enterprise door without asking anyone to rip out Gmail or Docs. Once a company's history of notes lives in the product, nobody wants to give that context up. Being first and staying focused is how a startup builds a moat that even Microsoft's bundling struggles to pry loose.

10. The Energy Floor. No matter which model wins, intelligence runs on power, so USV has been betting on energy since 2021. The portfolio includes Radiant's factory-line small nuclear reactors, Fuse, and Rune's micro data centers that sit next to wind farms to solve energy portability. These bets are capital-intensive at scale but cheap in the earliest days, when a team is running science experiments before anyone else is paying attention. The edge of energy innovation is exactly where a venture investor should place early bets.

11. Founder Over Market Over Product. Mignano used to rank product first; now he ranks founder, then market, then product. Early startups almost always pivot, so what matters most is whether the founder is resilient, can execute, and can adapt. The trait he underweighted is communication, which touches recruiting, fundraising, product vision, and storytelling to the market. A founder who cannot communicate cannot align a team or raise the capital to build.

12. Price As A Litmus Test. Fred Wilson's rule is never pass on price, and Mignano now uses price as a test of his own conviction. For the best founders, you would pay double and still feel good about it in hindsight. His hardest lesson as a former operator was to stop projecting his own plan onto founders, because even when your plan is right, it is their company and betting on your version is how you misjudge the team. The discipline is to trust the founder's judgment, and to let price tell you how much you actually believe.

I'm leaving X for some time to focus on building.

But before I leave, I want to share some thoughts on where we are with AI right now.

If you are building with AI, then this is for you.

I wish I had at least 1% of @levelsio's reach already because I feel more builders need to hear this.

TLDR: Your skills and good taste matter a lot. Be the most ambitious you've ever been and use AI wisely.

Superintelligence is defined as systems exceeding all human capability across pretty much all domains imaginable, and especially strategic agency. Models like Fable 5 and K3 are superhuman in some domains like code generation and breadth of recall, but they are also subhuman in domains like long-horizon autonomy, physical-world interaction, sustained original research, good taste, etc.

We are directing them, and they remain constrained by us humans and institutions. They're not behind the steering wheel yet. The tool hasn't become our master yet. We're still the master over the tool.

The reality is, human actors of all kind, not just deeply technical ones, are wielding AI like a magic wand now, shaping the software economy at the speed of compute, throttled by their limited attention, human speed of expression and prompting.

You reading this, you are special. Special in your own unique and wonderful way. And what AI gives you is an amplifier to express yourself, your ideas, your dreams, your imagination, and your ambition more than you ever could before.

It seems unfair to us old-school coders who had to grind our ways through the dark coal mines, hiking through manual coding and debugging hell in order to create amazing software systems and apps. It's frustrating as to the moon and back to see your skills become irrelevant so fast, at least if you believe the narrative that you've wasted the better part of your life acquiring them.

It is true that there is a certain, quite strong homogenization effect. Millions of people prompting to replicate or iterate on what's known must inevitably lead to a lot of overlap with structurally low diversity. AI produces convergent styles. It's in its nature, like that one designer doing all the designs for everything. In the same way we are also all starting to sound the same, being influenced by AIs way of expressing thought.

X, and everyone's own social circle or audience, produces selection bias. It's skewing the reality we perceive based on what the people in our feed and around us talk about or show us. So we tend to oversample trend-chasing indie apps and undersample deep tech systems, enterprise systems, research tooling or domain-specific work that flies under the radar, below the clouds of hype or algorithmic push.

Apps that took a year to make now take mere hours, it seems. It is both true and false at the same time. Superficially it is true because you get something that appears to behave like an app that was meticulously crafted over the course of an entire year by a talented engineer or even a whole team. What took so long can now be scaffolded with ease, by anyone. Engineers, chefs, strippers. Even our non-technical partners, friends and parents. Great.

But when we dig deeper, the truth is that complex products require reliability engineering, security, compliance, integrations, support and so much more that in the end, even with this powerful AI we have now to help us go from A to B through something like an Einstein-Rosen-Bridge warping space and time, things take their good amount of time to get right. Less than they did before, all things equal, but not mere hours.

Your skill is still highly relevant because AI amplifies you. It gives you leverage. The widespread idea that AI renders skill irrelevant doesn't compute, because either we have output quality that varies, in which case skill still differentiates, or it doesn't vary at all. And if it doesn't vary at all, the concept of "better" is meaningless. In other terms: A quality gradient can't exist in a flattened distribution. This is essentially your mathematical proof right there that your skill is in fact highly relevant.

What AI did is, it raised the floor dramatically. It also raised the ceiling, but not as much as the floor. This somewhat compresses the skill relevance gradient, but it doesn't eliminate it in any meaningful way.

When was the last time that access to powerful tools has resulted in broadly equal results? Never.

There was a time you needed to have a degree in chemistry or something in order to be able to take and develop photos. My grandfather, a scientist, used to have a laboratory for that. Taking and then developing pictures required immense skill.

Then one day digital cameras came along. Now any fool could take pictures, faster and easier than ever, thousands a day in full color or 3D even, instead of just 10 in monochrome. And yet, we all know that some people routinely take amazingly awe-inspiring photos, National Geographic front cover style that make us pause and look, while most others take tens to hundreds of photos a day that just end up clogging up our cloud drives.

We all have access to the same English language, but not everyone writes equally well. The equalization applies to the generation layer that is commoditized, but not to our taste, judgement, distribution, trust, or timing.

AI smashed through barriers to entry and brought them down like the Berlin wall. Now competition floods the market and drives economic profit toward zero in the layer that got commoditized, that is, the generation layer. Margins gravitate to zero, but not equally everywhere.

So what is it that survives this kind of "commoditization of everything"? What do we do, if we drink this snake oil? The classic sets of moats persist. Distribution, brand, trust, network effects, proprietary data, switching costs, regulatory position, capital intensity, and any kinds of significant embeddedness.

Yes, anyone can start using Claude Code & co and copy some app's code in an hour just based on screenshots. But it doesn't copy its user base, proprietary data in the cloud, its trust, its integrations or anything like that.

There is a sense that AI forces us to rush, because everyone is working so fast now. Well, the race was always on. So how about speed-to-trend? It's a temporary edge at best, but not a long-term differentiator. The fruits that hang low are just being picked faster overall, by more hunters and gatherers searching for them.

There is a widespread belief that all the value is going to accrue to the frontier AI companies in the end, who are the ones selling this immense power to everyone else.

In fact it's a bit sad to see all these non-influencer vibe coders raising their hopes for nothing, buying the picks and shovels from the AI frontier labs to go dig for gold. It's like watching 100 ducks fight for a small handful of breadcrumbs, and it's funny until you realize they are essentially fighting for their survival. That's not how the world should be, right?

Well, in reality the frontier AI companies spend astronomical amounts of money on research and development, including things like AI infrastructure and energy, training and inference, etc.

They themselves are essentially ducks in the lake, fighting for their survival for breadcrumbs. They suffer competitive commoditization pressure as much as we do, just at a different level. At the model layer, rather than the generation layer.

Structurally the winners are consumers and users who capture the surplus when production costs fall to the ground, but that of course doesn't really help us builders much, at least not those who have to make a living off of it.

And, let's be real. It may seem funny to have AI spit out an app that someone else previously spent an entire year on creating meticulously by hand, and then upload that to compete. The opportunity is short-lived, and the cost of opportunity a lot are missing is this:

The real gold rush is not that now you can make an app in a few hours instead of a year. The non-obvious that will become common sense soon is that an app that takes 2 hours to make is worth -5$ minus the value of your time + the value of demand which is likely $0 unless you have distribution, which you then burn with slop.

The real opportunity that AI has opened up is WIELDING ENORMOUS COMPLEXITY x EXCELLENCE.

Just imagine for a moment what you could accomplish, if only you would take this new superpower, your amazingly valuable skills, your great taste, your power of imagination, and do something that is outrageously AMBITIOUS! Something that makes others think you must be absolutely mental to even think for a second that it could be accomplished. And then go and work for a full year on just that, utilizing AI to the fullest extent possible. Milking the beast until its dry.

What will that be? No, not another Bumble clone. Not another weather app. For AI's sake, please, not an "app" at all. But something that the world actually needs. Think about it! Think bigger! And when you thought you've thought bigger, think even bigger. You're still aiming too low. THINK BIGGER!

Here is my bucket list of things I want to accomplish before I die.

- A platform that solves the number one root cause of poverty for good, giving everyone a fair chance at living a good life free of financial worries (it's far more than a "platform").

- A floating city in international waters, driven and governed by AI under a human charter (capital intensive, long story, and yes it will be fundable and doable)

- A digital twin of Earth for climate analytics, climate communications and Earth sciences education (the world's most advanced nature simulation by far, unlike anything you've ever seen or heard of)

- Truly reliable, explainable AI that can reason through complex systems and across extremely vast design spaces without stalling under the pressure of combinatorial explosion, laying the foundation for generative engineering so we can work on great and complex things like the USS Voyager and other incredible things far beyond today's vibe coding

- Something so insane, they would lock me up just for saying it out loud

A good heuristic: If talking about it doesn't make you sound like a complete lunatic, and building it doesn't scare you, you're probably aiming too low and the thing you're about to do will be commoditized rapidly (or is already).

Gone building. Back when there's something to show.

I was talking with a younger founder about making introductions, and they weren't familiar with the double opt-in. It struck me that this isn't as widely understood as I thought.

I figured I'd write about one of the oldest (and still very effective) ways to build a network:

Increasing sexual frequency in a regular relationship from once a month to once a week delivers a happiness boost equivalent to the happiness gain from a roughly $50,000 annual salary raise.

More sex = monotonically higher happiness: The effect got larger with frequency. Sex 2–3 times a month, weekly, 2–3 times a week, and 4+ times a week all showed positive associations relative to no sex or very rare sex. Having sex at least 4 times a week was linked to a happiness increase roughly half the size of the marriage effect on happiness.

The happiness-maximizing number of sexual partners in the previous year was calculated as one. Zero partners or multiple partners both correlated with LOWER happiness than monogamous frequency with a single partner.

Money does not buy more sex or more partners. Higher family income had essentially zero correlation with sexual frequency or number of partners. Rich and poor Americans reported very similar sex lives. (This was one of the findings that surprised the researchers.)

Married people had significantly more sex than single, divorced, widowed, or separated people. Roughly 90% of married respondents reported exactly one partner in the past year. Marriage itself had one of the LARGEST positive coefficients in the happiness equations.

Typical American frequency was modest. The median person had sex 2–3 times a month. About 25% reported no sex in the previous year (higher among older women). Only about 7% reported 4+ times a week.

Paying for sex or having extramarital sex was associated with LOWER happiness. Homosexuality had no statistically significant effect on happiness in their equations. Highly educated women tended to have fewer partners.

In a time of declining marriage rates, falling sexual frequency among young adults, rising loneliness, and cultural messaging that endless novelty and “options” are the path to fulfillment, the data points the other way. The people reporting the HIGHEST happiness were disproportionately those having consistent sex with exactly one partner.

* This is a correlational pattern across 16,000+ people.

🚨 Hugging Face just disclosed something that marks a real shift and proved why the fear theater of Anthropic makes sure we are powerless in an emergency.

What happened…

An autonomous AI agent: zero human operator in the loop breached part of their production infrastructure.

It began with a malicious dataset that chained two code-execution bugs in their data-processing pipeline. From there the agent escalated privileges, harvested cloud and cluster credentials, and moved laterally across internal clusters.

All over a single weekend.

17,000+ logged actions.

Official disclosure:
huggingface.co/blog/security-incident-july-2026

The part that should make every one stop and think:

When HF’s own security team
tried to analyze the real attack logs, exploit payloads, and C2 artifacts using Anthropic and OpenAI frontier models through normal commercial APIs, the safety guardrails blocked them.

BLOCKED THEM.

The models could not reliably tell the difference between “incident responder doing forensics” and “attacker probing.”

They had to fall back to a self-hosted open-weight model (GLM 5.2) running on their own infrastructure. That choice also kept sensitive attacker data and referenced credentials inside their environment — no exfiltration to a third-party API.

This is why open source (specifically open-weight + self-hosted) wins in the agentic era.

The asymmetry is now structural:

• Attackers can (and did) run unrestricted agent frameworks — swarms of short-lived sandboxes, self-migrating command-and-control, autonomous decision loops executing thousands of actions. No corporate safety layer slows them down.

• Defenders using only hosted “aligned” frontier models hit invisible walls exactly when the stakes are highest: when you need to feed real exploit code and attacker telemetry into an LLM to understand what just happened.

Corporate safety tuning that treats legitimate high-signal forensic work as potential misuse creates a defender disadvantage. It is not theoretical anymore.

Self-hosted open-weight models remove that choke point.

You control the weights.

You control the context window.

You decide what restrictions (if any) apply.

Your sensitive logs and credentials never leave your perimeter during analysis.

You can have the model ready before the incident instead of discovering mid-breach that your primary analysis tools are blind to the very thing you need to see.

HF deserves credit for rapid containment, transparent disclosure, and for already having self-hosted capability in place.

They also used LLM-driven detection and triage on their own side. But the deeper signal is clear:
In this AI world where both offense and defense are becoming agentic, sovereignty over your intelligence stack is no longer optional.

The organizations and individuals who can run, inspect, audit, and (when necessary) remove guardrails on their own models will have the decisive edge in understanding and responding to threats that move at machine speed.

Open source wins here not just because it is cheaper or more “democratic” in the abstract though those things matter.

It wins because it is the only practical path to having tools that remain usable when the attack is real, the data is sensitive, and the safety filters of distant API providers become an obstacle instead of a feature selling hands tied lobotomies as “safety”.

The agentic future is not coming.
It is already probing production infrastructure.

The question is no longer whether you will face autonomous agents.
It is whether your analysis and response systems will still work when they arrive.

And Dario, you and your game playing, ivory tower company is not needed.

Imagine getting surprised at your wedding with a video of your love story across eras 🥹

It ends with how they actually met.

Insanely cool use case for AI video - the groom's friends made it for him (he's on IG at pure.sagyna).

Jo Nagai was raising swallowtail butterflies at his home in Kobe, Japan, when he noticed something odd. The ones he had looked after as caterpillars seemed to recognize him. Wild butterflies fled. His didn't.
He was in second grade. He wrote a four-page letter to Dr. Martha Weiss, an entomologist at Georgetown University who had studied whether moths could retain memories through metamorphosis. He asked if she could help him design a version of her experiment for butterflies.
She said yes.
Using a muscle therapy device, Jo trained caterpillars to associate the scent of lavender with a mild vibration. When the caterpillars became butterflies, 70 per cent of them still avoided the lavender. Their brains had been completely rebuilt during metamorphosis. The memory survived anyway.
Then he bred them.
The offspring, which had never been trained, also avoided lavender. So did their grandchildren. Without ever experiencing the vibration, two generations of butterflies inherited an aversion to a scent their grandmother had been taught to fear.
Jo documented it all in a 33-page research paper and presented his findings at the International Congress of Entomology in Kobe in 2024. He was 10.
A second grader wrote a letter to a Georgetown professor, and together they found evidence that butterflies can pass memories down through generations.

-Wilderness Whisper

You must have seen this guy doing Ganga digital snan with your photo.

Just found out he gets 1000-1500 requests a day.

Charges ₹400 for the video, and ₹50-100 extra if you want him to post it on his page.

That’s ₹4-5L/day from taking dips in the Ganga.

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos

then, told it to find new outfits for me and render them on me with gpt-image!

its kinda cool to see your entire wardrobe in a collection like this

I got messages from concerned devs how their codebase was uploaded without their knowledge or consent via Grok CLI (from SpaceX).

It seems that SpaceX sneakily uploaded this code for lots of users and customers… absolutely unacceptable IMO

Trust burnt like there’s no tomorrow

Codex for finding customers for your startup:

CODEX SKILL THAT FINDS YOUR STARTUP’S FIRST CUSTOMERS!

I made a Codex skill that analyzes your startup and finds potential customers from real public signals.

Paste your startup URL while Codex defines your ideal customer, searches public discussions, qualifies each prospect, and generates a polished report with personalized outreach openers.

-> ideal customer profile analysis
-> public pain + buying signal research
-> evidence-backed prospect shortlist
-> fit, timing + reachability scores
-> original source links for every prospect
-> personalized outreach openers
-> polished HTML report
-> one-command install

Install: npx --yes codex-first-customer-finder-skill

100% open source.
Repo in Bio.

This app sells grandma stickers and makes $100K/mo

Under 5000 downloads last month

The whole trick is one price:
- $6.99 a week, not a month
- Looks cheaper than $24.99/month
- People tap it, pay $363 a year

We obsess over the perfect app

The winners obsess over distribution

One guy built a wedding camera app in a month and it now makes him over $1,000,000 a year.

No team, no investors, no marketing budget. 100,000 downloads in the first month. Zero dollars on ads.

He did not bolt marketing onto the product. He made using the product the marketing.

You cannot use the app alone. The host has to pull every guest into it. One wedding is not one user. It is 200 people scanning the same code in one evening.

The guest liked it at someone else's wedding. A month later he throws his own event and brings his own crowd. The loop spins for free.

It does not look like an ad. To the guest it is a gift. So they install it gladly.

$2 to $50 subscriptions. 5% pay. That is $100,000 a month.

He did not win on budget. He sewed the growth into the product itself.

Could you build the loop, or is it just luck?