Ugaitz

In the end, everything is a gag. Do not take me too serious.

[Ugaitz @ugaitz]

@clairevo Is it better than Exa? How does it improve it?

[bbshare @shadouyoua]

硅谷PayPal帮主彼得·蒂尔认为，AI正终结过去200年，以数学能力为核心的精英体系。工程、量化等“数学型人才”的逻辑优势，正在被AI迅速替代，其职业护城河也正在消失。未来真正的竞争力，在于叙事、社交语境理解，以及把AI嵌入社会系统的语言型人才，这才是人类独特优势。

工业革命让肌肉贬值，AI革命正在让“纯计算的大脑”贬值。未来的胜出者，是那些能用语言驾驭算法、用叙事锚定价值的社会系统操盘手。

作为程序员： 别只埋头代码，而是要多练自然语言描述问题；学产品思维和用户研究，理解非技术需求；开发时优先考虑伦理、社会影响；选领域深耕；把代码变成“能讲故事”的系统。未来你的优势是“代码+叙事”。所以，年轻人别慌着放弃STEM，也别觉得学文科就躺赢了。真正该练的，是用AI思考更深的问、讲出更动人的故事、构建更复杂的社会系统。蒂尔指出了方向，但真正的游戏，才刚刚开始。

说白了，需要文理兼修，才能走得更远。

[Kevin Simback 🍷 @KSimback]

Be an AI lab with 5 people and $300k

>take a good base model like GLM 5.3 Flash
>focus on non-verifiable domains instead of math and coding
>don’t hire expensive experts and buy datasets for RL
>instead create the environments and build synthetic data
>do the RL on your synthetic data and aimed specifically at knowledge work
>publish a frontier class model at 60x lower cost

[GREG ISENBERG @gregisenberg]

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.

My observations:

1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.

2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.

3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.

4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.

5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.

6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.

They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.

7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.

8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.

Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.

9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.

10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!

11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.

12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.

13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.

14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.

15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.

16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.

17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?

18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.

19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.

20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.

21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.

Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.

Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.

Happy to be home and locked in.

Until next time!

[Andrew Curran @AndrewCurran_]

@bdambrosio @LinusEkenstam The cost is dropping faster than any transformative technology in history, 54 times faster than electricity, and it's been dropping at this same rate since GPT-3.

[GREG ISENBERG @gregisenberg]

https://t.co/ycmy4OWzKJ

[OpenAI @OpenAI]

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

[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.

[JUMPERZ @jumperz]

karpathy is showing one of the simplest AI architectures that actually works..

dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in.

the real insight is the loop...every query makes the wiki better. it compounds.. now thats a second brain building itself.

i think this is so good for agents if applied right

instead of pulling from shared memory every session, they build a living knowledge base that stays.

your coordinator is not just coordinating tasks anymore.. it is maintaining institutional knowledge so every execution adds something back to the base.

the bigger implication is crazy tho.

agents that own their own knowledge layer do not need infinite context windows, they need good file organisation and the ability to read their own indexes.

way cheaper, way more scalable, and way more inspectable than stuffing everything into one giant prompt.

[jack @jack]

https://t.co/jgZkBvYOPt

[JUMPERZ @jumperz]

Mark Cuban is calling AI the biggest job creation wave since the internet.. and when you look at the numbers it makes sense..

>33 million US businesses need AI integration and only 8.8% actually have it in production..

>the current market is $11B but the real opportunity is $165B to $495B... and all of that money is going to people who can set up AI systems for businesses, not people who can build AI from scratch

this is exactly why betting on agent orchestration makes sense

>most people hear "AI opportunity" and think they need to build the next chatgpt or learn to code when they don't..

>33 million businesses need someone to walk in, understand their workflow, and set up agents that actually do the work..not another SaaS subscription..

>68% of businesses say they use AI..

but dig into it and most of them are just prompting chatgpt for emails so the distance between

"i used a chatbot" and "AI runs my operations" is where the entire opportunity sits.. and no tool is closing that gap on its own, it needs people who understand both the business and the agentic tech...

ive also been saying this for long .. the people who know how to set up and run agent systems are about to be the most in demand people in tech, and most of them won't only be engineers..

[Garry Tan @garrytan]

Boil the Oceans

You know the phrase: “don’t boil the ocean.” Everyone’s said it in some overly ambitious meeting. It’s good advice in normal times. It keeps teams focused. It prevents scope creep. But we are no longer in normal times, and I think it’s time to retire saying it.

Artificial Superintelligence means it’s time to boil the ocean. We’ll start with a few lakes first.

I was recently with a university endowment’s head of private investing who told me their engineers were terrified for their jobs after seeing what Claude Code could do. And I get it — that’s the natural first reaction. But it’s the wrong one. It’s a zero-sum reaction to a positive-sum moment.

Instead of worrying about doing the same thing we’ve been doing for cheaper, why not focus on doing the thing we never even dreamed of doing? Why can’t that endowment achieve 50% net IRR instead of 10%? Why can’t a startup deliver a service that is 100x better than the incumbent? Why can’t we have fusion energy? Why can’t we talk to every single user and have a perfect understanding of every bug in our product?

These aren’t rhetorical questions anymore. They’re engineering problems with paths to solutions.

Here is what I think is actually going on with the fear: our fear of the future is directly proportional to how small our ambitions are. If your plan is to keep doing exactly what you’re doing, then yes, a machine that can do it faster and cheaper is terrifying. But if your plan is to do something dramatically bigger, then the machine is the best news you’ve ever gotten.

If you’re a worker — someone who trades labor for a living — this is the moment to become a builder. Start a business. And if you’re already management or capital, it’s time to go 10x more hardcore on what your aspirations could be. Not eking out 5% efficiency gains. Not increasing profit margins 2% by lowering cost and firing people. Those are the old games. The new question is: what would it look like to build a product or service so good that people would happily pay 10x what they pay now?

The net result of this is more jobs, not fewer. As Ryan Petersen likes to say, the human desire for more things is absolutely limitless. We can actually fulfill that desire now — if we have the agency to prompt it for ourselves.

Buckminster Fuller coined the term “ephemeralization” in 1938: doing more and more with less and less until eventually you can do everything with nothing. His entire vision of progress was about technology enabling radical expansion of human capability through dematerialization. He traced this from stone bridges to iron trusses to steel cables — each iteration stronger, longer, lighter, cheaper. He wasn’t describing job destruction. He was describing civilization getting better at being civilization.

This is Jevons Paradox for everything. When you make a resource dramatically more efficient, you don’t use less of it — you use vastly more. Steam engines didn’t reduce coal consumption. They made coal so useful that demand exploded. The same thing is about to happen with intelligence, with labor, with every service and product we can imagine.

But Jevons Paradox doesn’t activate on its own. It requires capital and management to actually raise their ambitions — to boil lakes and oceans instead of drowning them in committee

That’s what startups have always been good at: moving fast in the face of radical uncertainty, building for the 10x future while everyone else is optimizing for the 1.05x present.

Time to start.

[Ugaitz @ugaitz]

@hugomercierooo Twin

[Andrej Karpathy @karpathy]

Agency > Intelligence

I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency?

Grok explanation is ~close:

“Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path.

People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next.

It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”

[Ugaitz @ugaitz]

@IreneMontero @grok que opinas al respecto? Fundaméntalo con datos.

[Haider. @haider1]

Microsoft AI CEO, Mustafa Suleyman:

By the end of 2025, models will move from one-shot answers to continuous planning

With persistent memory and long-horizon planning, models will become deeply human-like

no new algorithm, mostly compute and scale

[Ugaitz @ugaitz]

Really makes you go 🤔
https://t.co/KWzUFKcLKd

[Ugaitz @ugaitz]

@_carminis Muy Camus.

[Ugaitz @ugaitz]

@invertiresvivir Opinión del IE0002639775?

[Ugaitz @ugaitz]

Worth the read. Dario Amodei — On DeepSeek and Export Controls https://t.co/K0Muqlk19N