Théophile

Intrepid web & tech explorer. Ambitious engineer. Entrepreneur spirit.

[Millie Marconi @MillieMarconnni]

A British biologist looked at 200,000 years of human history and found that the entire reason humans broke out of poverty was not intelligence, not language, not even agriculture, but one mechanism so simple a 6-year-old could explain it.

His name is Matt Ridley.

He is a zoologist by training, an evolutionary biologist by career, and in 2010 he wrote a book called The Rational Optimist that quietly argued the most important fact about human progress had been hiding in plain sight for the entire history of economics.

Naval Ravikant has been telling people to read everything Ridley has ever written for the last 15 years. The reason is the argument inside this one book.

For 200,000 years, anatomically modern humans walked around with the same brain you have right now. Same skull size. Same neural architecture. Same raw capacity for language, planning, and abstract thought.

For roughly 190,000 of those years, almost nothing happened. Generation after generation lived and died inside the same Stone Age toolkit their great-great-grandparents had used. Then somewhere around 50,000 years ago, the line on the chart of human progress started to tick upward. Then it bent. Then it exploded.

The question Ridley spent years on was the only question that mattered. What changed.

It was not the brain. The brain had been the same for 190,000 years. It was not language, which had existed long before the takeoff. It was not even agriculture, which arrived only 10,000 years ago and was actually preceded by the upward bend, not the cause of it.

What changed was that humans started trading with strangers.

This sounds too small to be the answer. Ridley argues that it is the answer to almost everything. The moment one human exchanged a useful object with another human from a different group, something happened that no other species on earth had ever done.

Two ideas that had developed in isolation came into contact. The flint knapper learned what the spear maker had figured out. The fisherman from the coast learned what the hunter from the forest had figured out. The two pieces of knowledge fused into something neither side could have produced alone.

Ridley calls this ideas having sex. The phrase sounds frivolous and it is meant to. The point is that ideas, like genes, get better when they combine with other ideas from different lineages.

An idea sitting inside one head, no matter how brilliant the head, eventually hits a ceiling. The same idea exposed to ten thousand other ideas does something genes do under sexual reproduction. It mixes. It recombines. It produces offspring nobody planned.

The cleanest proof of this argument is the most uncomfortable case study in the book. Tasmania.

Around 10,000 years ago, rising sea levels cut Tasmania off from mainland Australia. A population of roughly 4,000 humans was now isolated on an island, with no possibility of contact with the rest of humanity. They had the same brains. The same language. The same starting toolkit as their cousins 150 kilometers north. The natural experiment was now running.

What happened next is something no economist or geneticist had ever predicted.

The mainland Australians kept inventing. Boomerangs. Spear-throwers. Fishing nets. Bone needles for sewing fitted clothes. Watercraft with paddles. Their technology compounded slowly across the centuries.

The Tasmanians went the other way. They did not just fail to invent the new tools their cousins were developing. They started losing the tools they already had. Fishing was abandoned within a few thousand years. Bone tools disappeared. Fitted clothing disappeared. They forgot how to make fire from scratch and started carrying lit firebrands from camp to camp instead, relighting their fires from a neighbor's whenever their own went out.

By the time European explorers arrived in the 17th century, the Tasmanians had the simplest toolkit of any human society ever recorded. Their material culture had gone backward for 8,000 years.

The archaeologist Rhys Jones called it a slow strangulation of the mind.

Joseph Henrich at Harvard later proved with formal mathematical models that there was nothing wrong with Tasmanian brains. There was something wrong with their network. A toolkit requires a critical mass of people exchanging skills to maintain itself.

The act of teaching a skill is imperfect. Every generation loses a small percentage of what the last generation knew. If your population is large enough and trading widely enough, those losses get caught and corrected by someone else who still remembers.

If your population shrinks below a certain threshold and stops mixing with outsiders, the small losses compound until entire technologies disappear.

This is the part that should haunt anyone reading this in 2026.

Intelligence is not a property of the individual brain. Intelligence is a property of the network the brain is connected to. A genius in isolation will produce less than a mediocre thinker inside a dense exchange of other mediocre thinkers.

The thing your ancestors needed in order to break out of 190,000 years of stagnation was not better brains. It was better connections between brains they already had.

The implication for any individual is direct and uncomfortable. If you are smart and isolated, you will be outproduced by people half as smart who are connected.

The most successful people in any field are almost never the smartest people in it. They are the ones positioned at the intersection of the most idea flows. They are reading more authors than their competitors. They are talking to more people from more disciplines. They are in the rooms where ideas from different lineages bump into each other.

Ridley ends the book on the line that sounds optimistic but is actually a warning its this "The future will be invented by people who connect ideas, not by people who guard them."

[Marko Slavnic @Markoslavnic]

The quality of animation you can create on your own is truly amazing. We really are just limited by our imaginations at this point. Go tell your story!

Made in @runwayml in a few hours and a handful of gens.

[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.

[Théophile @TheBuild3r]

@blinkdotnew Claw

[Blink.new @blinkdotnew]

Introducing Blink Claw - the first platform to hire unlimited AI employees that run your business 24/7.

180+ AI models included. Gmail, Slack, LinkedIn, HubSpot - one-click connect. No API keys, no $600 Mac Mini.

Reply "Claw" + RT. Your first agent is on us. ($50 - 200 creds)

[Clone @clonerobotics]

Building a fully actuated, human-level robotic hand by strength, speed, and range of motion is hard. Making it incredibly durable is even more challenging.

Clone has pioneered anthropomorphic hands with 27 degrees of freedom, human-level grip strength and speed, and the most durable artificial muscles in the world.

The Hand in this demo is missing middle flexor muscles, but here’s an update on the neural joint controller.

[Théophile @TheBuild3r]

@felixprehn AI infrastructure is the new moat !

[Reads with Ravi @readswithravi]

The most powerful passage from Zen and The Art of Motorcycle Maintenance by Robert M. Pirsig:

[Steph Smith @stephsmithio]

this lesson just keeps on lesson-ing

[Joe Barrow @barrowjoseph]

Convert any PDF into a fillable form with 🪄AI magic. I built a free tool that: (a) takes seconds, (b) outperforms all the commercial solutions, and (c) is open source!

[Aadit Sheth @aaditsh]

BREAKING: OpenAI launches Agent Builder, the most anticipated product of the year.

This release will completely redefine how you work.

Here are 50 powerful use cases to try now:

1. Automate email sorting and intelligent auto-responses.
2. Personal calendar + life concierge for scheduling, travel, and errands.
3. Continuous note summarizer for meetings, research, and calls.
4. Social-media agent that writes, posts, replies, and tracks engagement.
5. AI financial tracker that reconciles accounts and predicts expenses.
6. End-to-end recruiting pipeline agent (JD → screening → scheduling).
7. Sales enablement agent handling lead scoring, outreach, and follow-ups.
8. Finance-close agent that reconciles ledgers and reports nightly.
9. Legal agent drafting, redlining, and reviewing contracts.
10. Procurement agent comparing suppliers and negotiating deals.
11. Market-research agent analyzing live trends and insights.
12. Competitive-intelligence agent monitoring rivals and filings.
13. Investor-research agent scanning SEC data and market sentiment.
14. Academic & patent-exploration agent for R&D teams.
15. Policy and compliance monitoring agent for enterprises.
16. Unified customer-support agent (email, chat, social).
17. Content-marketing agent generating blogs, visuals, and SEO drafts.
18. Ad-optimization agent managing multi-channel performance.
19. Personalized e-commerce shopping assistant.
20. Real-time product-feedback and sentiment-analysis agent.
21. DevOps deployment agent for automated code shipping.
22. Code-review and testing agent ensuring clean commits.
23. API-builder agent that auto-connects systems (no-code integrations).
24. Data-pipeline maintenance and ETL monitoring agent.
25. Security audit agent scanning infrastructure and codebases.
26. Personalized tutoring agent for adaptive learning.
27. Corporate L&D (learning & development) agent designing training paths.
28. Research summarizer agent for analysts and professors.
29. Language-learning conversational agent.
30. Skill-certification and grading agent.
31. Agent store for publishing and monetizing reusable agents.
32. Data-licensing agents that monetize proprietary datasets.
33. Agent-ops management agent for debugging and evaluation.
34. Virtual “employee” agents performing recurring company roles.
35. Agent-driven marketplaces where agents negotiate logistics or pricing.
36. Agent analytics and observability dashboards.
37. Policy & safety governance agents (AI compliance layer).
38. Agent-to-agent protocol builders for negotiation and permissions.
39. Legal & liability-tracking agents for digital labor.
40. Agent-driven advertising and bidding systems.
41. “Agent universities” teaching orchestration and logic design.
42. Reputation-score agents rating trustworthiness of other agents.
43. Agent-as-a-service marketplaces (like Shopify themes for intelligence).
44. Embedded agents in consumer devices and appliances.
45. Vertical domain-specific agents (healthcare, finance, logistics, media).
46. Cross-platform workflow orchestration agent connecting multiple ecosystems.
47. AI project-manager agent coordinating multi-team execution.
48. Knowledge-management agent indexing internal docs and chats.
49. Event-planning and coordination agent.
50. Personal research and idea-generation companion.

[Deep Psychology @DeepPsycho_HQ]

This is GOLD!!!

[Brendan Jowett @jowettbrendan]

Claude 4 Sonnet is terrifyingly good.

But most people are using it like a basic chatbot.

I've used it to generate content, automate deep research, build apps, and more.

Here are 10 real ways to unlock its power:

[Loic @LoicReco]

Anthropic released their complete prompt engineering guide.

99% won't read it. I spent 3 days testing every single technique.

While you're saying "please" and "thank you" to AI, smart operators are using advanced techniques to 10x their output.

Here's how you can do the same:

[Ruben Hassid @rubenhassid]

BREAKING: Stanford just surveyed 1,500 workers and AI experts about which jobs AI will actually replace and automate.

Turns out, we've been building AI for all the WRONG jobs.

Here's what they discovered:

(hint: the "AI takeover" is happening backwards)

[Julien | Tech & Invests @JulienTechInvst]

Et si Broadcom était en position délicate ?

À la suite des résultats d’Nvidia un point m’a fait tiquer: Meta et Google sont désormais clients de Spectrum-X, concurrente de TomaHawk 5. Du coup, une question se pose: est-ce que Broadcom est en danger ?

Thread 🧵

[jpa @josephpalbanese]

new project: cleo (kindle + llm) 📚

cleo is an ios app that pairs whatever book you're reading on kindle with an llm (o3) — ask questions, get recaps/summaries, or listen/discuss (think audiobooks but interactive). the llm has context on exactly where you are + the book contents.

best part? no complex setup needed. just link your kindle account and that's it. it just works.

reply / rt for testflight invite 👇

[Alex Vacca @itsalexvacca]

Anthropic's CEO claims AI hallucinates less than humans.

Bold statement.

So I decided to test it by feeding the same FAKE theories to ChatGPT, Claude, and Gemini to see which one calls me out first.

The results shocked me 🧵