giro x

[Yasmine Khosrowshahi @yasminekho]

Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...

And why AI hasn't actually learned to "see" the way we do.

It completely changes how you think about Human Intelligence v/s Artificial Intelligence:

1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.

2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.

3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.

4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.

5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.

6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.

7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.

8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.

9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.

10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.

11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.

12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.

13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.

14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.

15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.

16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.

Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[giro x @GermanOcaa3]

@elzorrotacneno Cómo vas a respetar a Israel pendejete

[giro x @GermanOcaa3]

@elijs_dev Excelente algoritmo

[giro x @GermanOcaa3]

@PM_Navegaciones @fisgonmonero Lo puede firmar toda europa y sus restos van a seguir aquí. Será castigo eterno jajaja

[Earth Hippy 🌎🕊️💚 @hippyygoat]

Incredible and brave Palestinian Journalist, Shaimaa Youssef.
📍 Hebron

259 PALESTINIAN JOURNALISTS HAVE BEEN KILLED BY ISRAEL SINCE OCT 2023

[giro x @GermanOcaa3]

Que se llenó el puente @MaraLezama
Y ya no están dejando subir a los 30 minutos de inicio. #PuenteNichupté

[giro x @GermanOcaa3]

Ahora que el lenguaje no es una barrera, quiero preguntarles cuántos partidos políticos tienen sus países? Aquí en México tenemos demasiados

[giro x @GermanOcaa3]

@TEQROO_Oficial que opina de que Gino Segura, ya este haciendo campañas publicitarias por todo Quintana Roo y aún no es periodo de campañas?

[giro x @GermanOcaa3]

@MaraLezama Entonces si no hay medicamentos, levantamos un reporte... Básicamente siguieron la misma analogía para las personas desaparecidas llamen y creen una carpeta de investigación. No te vamos a dar tu medicina inmediatamente...porque no hay pero ya tenemos tu reporte 👍

[giro x @GermanOcaa3]

@DotCSV Prefiero que se acaben mis token con anthropic que darle dinero a gpt para financiar la vigilancia automática masiva y drones asesinos para Israel

[giro x @GermanOcaa3]

@eltemagv Y tú qué mataste a tu hermano para vivir de sus empresas y dinero?

[La Derecha Diario México @DerechaDiarioMX]

🚨🇲🇽 | DENUNCIAR MATA: Anaid Belén Ramírez fue asesinada a golpes junto con sus perros, justo después de denunciar que delincuentes habían matado a una de sus mascotas en Tultitlán: Según reportes los delincuentes tienen familiares en el Ministerio Público que le avisaron a los homicidas de la denuncia.

[giro x @GermanOcaa3]

@soyrichlira @gonzalochale @freeswinger3 Que triste 😭, pero es cierto. Alguna posibilidad de hacer una meeting en 2 semanas?

[Loza @AngelilloJr]

@PRI_Nacional CÍNICOS HDSPM!!

[giro x @GermanOcaa3]

@soyrichlira @gonzalochale @freeswinger3 Si jalo, puede ser en algún lugar más céntrico? Aquí en Cancún hay una comunidad que se llama talent networks que organiza este tipo de eventos https://t.co/SjgUzZUa00

[giro x @GermanOcaa3]

@MrBeast PewDiePie

[Kartik @code_kartik]

Used just 3–4 prompts and somehow I’m already at 41% usage? That doesn’t add up.

Either the tracking is off, or the usage model isn’t as transparent as it should be.

Anyone else noticing this?

cc: @AnthropicAI

[Alex Volkov @ AI Engineer @altryne]

My feed is showing me a bunch of folks who tapped out their whole usage limits on Mon/Tue.

Is this your experience? Please comment, I want to understand how widespread this is