Claudio Lener

Father, Husband, Product Manager, Psychonaut.

[Claudio Lener @Claudiolener]

You'd hope AI consumer costs will go down but the savings will just go to the companies that are now heavily subsidized by investors' $$$

[Vaibhav Sisinty @VaibhavSisinty]

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

[signüll @signulll]

intelligence is the ability to add order to complexity.

[Alexander Whedon @alex_whedon]

Introducing SubQ - a major breakthrough in LLM intelligence.

It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA),

And the first frontier model with a 12 million token context window which is:

- 52x faster than FlashAttention at 1MM tokens
- Less than 5% the cost of Opus

Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention).

Only a small fraction actually matter.

@subquadratic finds and focuses only on the ones that do.

That's nearly 1,000x less compute and a new way for LLMs to scale.

[Kevin Patrick Mahaffey @dropalltables]

More people would be pro datacenter if every one had a beautiful open-to-the-public heated pool.

[BuBBliK @k1rallik]

🚨do you understand what just happened to mathematics..

A 23 year old with ZERO math degree opened ChatGPT on a Monday afternoon out of boredom.

80 minutes later - a 60-year-old unsolved problem was dead.

The problem? World's top mathematicians had tried for decades. Failed..
The tool? A $20/month subscription..
The effort? One single prompt..

And here's the wild part - the AI used a method everyone already knew existed. Nobody just thought to apply it HERE.

Terence Tao (literally the greatest living mathematician) called it "a meaningful contribution that goes well beyond solving this one problem"

We are not ready for what's coming next..

[signüll @signulll]

the craziest part now is that the modern computer probably has to be entirely reinvented, from scratch. pretty much like how jobs & co brought apple ii to market.

like not improved. not given a chatbot sidebar or something but really from the ground up like the iphone redefined what it meant to be a pocket computer.

the current paradigm for computers was built around a human staring at a screen, moving a cursor, opening apps, managing windows, naming files, remembering where things live, & manually translating intent into interface actions.

that made sense when the human was the runtime. but in an ai native world, it starts to look kinda ridiculous.

you can see this ridiculousness when you use computer use agents… they are useful sure, but they’re also obviously transitional. they’re teaching ai to operate machines designed for humans, which is clever, but also kind of absurd. it’s like making a robot hand so it can use a doorknob instead of asking why the door needs a knob at all. yes i know humans also need to use a door knob, but maybe in the future humans don’t need to use a computer, or at least what we think of a computer today at all.

this all leads to some interesting questions:

- what is a file when the system understands context?
- what is an app when intent can route itself?
- what is a desktop when work can be decomposed, executed, monitored, & summarized by agents?
- what is a browser when the agent can retrieve, compare, transact, & remember?
- what is an operating system when the primary user is no longer just a person, but a person plus a swarm of delegated intelligences? or no person at all.

the old computer assumed navigation.
the new computer has to assume a new kind of intention. the old computer organized information. the new computer has to try to organize agency.

we’re still in the hacky middle stage at the moment with sidebars, copilots, agents clicking through legacy ui, & automation layers sitting on top of 40 year old metaphors.

the new computer is likely one where memory, context, identity, permissions, tools, agents, & interfaces are native primitives. this means desktop, mobile, browser, apps, files, folders deserves another first principles look.

[ひえたコッペパン🥖 @coolpan967]

リンゴって、型に入れて育てると形まで変わるらしい。
気づいた人、天才すぎる。

[Claudio Lener @Claudiolener]

Aesthetically beautiful

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Zain Shah @zan2434]

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.

@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

[Claudio Lener @Claudiolener]

There's going to be little to stop research labs with frontier models to create their own native products that can immediately eat up entire industries.
Companies that rely on models from research labs as their main value proposition have little moat left. It's getting wild.

[Claude @claudeai]

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.

Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day.

[Harley Finkelstein @harleyf]

If bigger government meant better results, Canada would be crushing it.

We’re not.

So what are we actually optimizing for?

[Lucy Hargreaves @lucyhargreaves4]

Build Canada is a 6 person org. we don't have a government relations team, so i built one

it gives us real-time policy intel, generates daily briefs, tracks media + sentiment trends, and deep dives on bills, lobbying, gov spending etc

full demo below. would love your thoughts

[Lou @louszbd]

we open-sourced glm-5.1
agents could do about 20 steps by the end of last year. glm-5.1 can do 1,700 rn. autonomous work time may be the most important curve after scaling laws. glm-5.1 will be the first point on that curve that the open-source community can verify with their own hands.

hope y'all like it^^

[Aakash Gupta @aakashgupta]

There's a physicist at Stanford named Safi Bahcall who modeled this exact principle and the math is wild.

He calls it "phase transitions in human networks." When you're stationary, your probability of a lucky event is limited to your existing surface area: the people you already know, the places you already go, the ideas you've already been exposed to. Your opportunity window is fixed.

When you move, your collision rate with new nodes in a network increases nonlinearly. Double your movement (new conversations, new cities, new projects) and your probability of a serendipitous encounter doesn't double. It roughly quadruples. Because each new node connects you to their entire network, not just to them.

Richard Wiseman ran a 10-year study at the University of Hertfordshire tracking self-described "lucky" and "unlucky" people. The single biggest differentiator wasn't IQ, education, or family money. Lucky people scored significantly higher on one trait: openness to experience. They talked to strangers more, varied their routines more, and said yes to invitations at nearly twice the rate.

The "unlucky" group followed the same routes, ate at the same restaurants, and talked to the same 5 people. Their networks were closed loops. No new inputs, no new collisions.

Luck isn't random. Luck is surface area. And surface area is a function of movement.

The lobster emoji is doing more work than most people realize. Lobsters grow by shedding their shell when it gets too tight. The growth requires a period of total vulnerability. No protection, no armor, soft body exposed to the ocean.

That's the cost of movement nobody posts about. You have to be uncomfortable first. The new shell only hardens after you've already moved.

[Claudio Lener @Claudiolener]

This is not the way we should do this. People are important - especially those with valuable knowledge. We should empower them further with AI tools instead of simply getting rid of them

[Milk Road AI @MilkRoadAI]

This is WILD.

A secret workplace war just broke out in China and it has gone fully viral on GitHub.

Companies started ordering their workers to document all their knowledge as AI "skill files."

Why? to replace those same workers with AI but workers figured out the plan fast so they fired back.

Someone built a tool called colleague.skill, software that scrapes a coworker's chat logs, emails, and work docs from Chinese platforms like Feishu and DingTalk, then clones them into an AI agent.

The idea was savage, digitize your colleague before they digitize you, hand the AI clone to the company, and watch your coworker get laid off while you survive.

A real GitHub project that exploded in popularity in days but then someone else entered the chat and changed everything.

A developer released anti-distill.skill, a tool that takes the skill file your company forces you to write, then strips out every piece of real knowledge before you hand it in.

The output looks perfectly professional, totally complete, impressively detailed but every critical insight has been secretly removed.

Your company gets a hollow shell while you keep the real knowledge locked away in a private backup.

The tool even has three intensity levels, light, medium, and heavy depending on how closely your bosses are watching.

Companies across China have been building AI digital twins of departed employees, feeding their old chat histories and documents into large models to produce clones that keep working after the humans are gone.

One verified case is that an employee left, and their replacement was literally an AI trained on every message they ever sent.

The anti-distill tool went viral on GitHub within hours of being posted, racking up stars faster than almost anything trending that week.

The implications reach far beyond China's borders.

Every knowledge worker on earth now faces a version of this question, when your company asks you to document your process, they may be building the tools to replace you.

[Claudio Lener @Claudiolener]

Obsidian for the win

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Claudio Lener @Claudiolener]

@krispuckett Maybe it wasn't mentioned, but I don't think it necessarily excludes it. With people focused at the "edge", the human touch can still exist, and I agree that is exactly what needs to be preserved in this new direction.

[Claudio Lener @Claudiolener]

@kalturnbull @jack This is a really interesting concept! It makes me think of a control plane for teams. I'd love to see a demo of it on the homepage without making me do extra clicks and try a demo.

[Claudio Lener @Claudiolener]

Block is going full-in on their re-org to align with new AI capabilities leveraging world models as systems that capture data and share intelligence, with human power at the edge to interact with reality in a way that AI cannot.

[jack @jack]

https://t.co/jgZkBvYOPt

[Claudio Lener @Claudiolener]

It was only a matter of time after OpenAI engulfed OpenClaw that we'd see Anthropic push features to match the autonomous agent paradigm.

It still impresses me how quickly Anthropic is able to deliver (seemingly) high quality features in their growing AI portfolio

[JUMPERZ @jumperz]

dug through the leaked claude code source.

44 hidden feature flags. 20+ unshipped features...

background agents, multi-agent orchestration, cron scheduling, voice mode, browser control.

it's all built.. no wonder they releasing a new feature every 2 days.. everything already done..