Alex

It’s sports but it’s a microcosm of the bigger issue… Interesting to see all the Americans supporting the US presidential pressure on FIFA. Almost seems like they don’t understand what made this country great. Separation. Institutions. I long suspected it was never a superiority among the people or culture. Just a good start and good equilibrium, flukishly persistent, ready to be lost with the right motives or persona. I hope it is regained.

"risk of catching a deadly pathogen climbs exponentially with the length of the cannibalism chain, a risk even cooking can't eliminate, especially for prions" sciencex.com/news/2026-07-c…

We're excited to announce that Resolution has a $160M grant from Coefficient Giving: $108M unconditional, with a further $52M conditional on hiring and compute needs. We'll use it to grow teams across our research portfolio and invest heavily in research automation. 🧵

Никогда не надоест выискивать заново эту новость из 2018 года. Норвежцы сами предлагают Холанда в ЦСКА, просят всего 300 тысяч евро... В комментариях,кстати, есть чувак,который пишет "Зачем он? У нас есть Жиронкин". Сейчас Виталию Жиронкину 26.Он вроде бы в костромском Спартаке

@MauricioAChavez @ashleevance That's not true. The US is ranked 17th among the most expensive teams. Mexico is ranked 37th. transfermarkt.com/vereins-statis…

@ashleevance Off the top of my head, id venture to guess they have as many "top flight" players as the USA does, with a similar amounts of homegrown domestic league players.

Honest question from soccer novice. Why doesn't Mexico have top flight players when it's such a large, soccer crazed country?

@ashleevance This is a question that has long intrigued me: why does tiny Uruguay produce far more top-tier footballers than Mexico? Why is there such a huge gap in quality between Scotland and England? No one really knows the answer.

@VeselayaSobaka @Rational_Answer Прочитай всю статью Скотта просто.

@Rational_Answer Это что, оценка адекватности предсказаний моделей на примере одного прогноза, событие из которого ещё не произошло, а адекватность предсказания определяется тем, насколько оценка модели верна по мнению автора текста?

==Предсказательный гадалкослоп: прогнозируем Путина!== Скотт Александр написал интересный пост о том, что AI-суперпредсказатели (современные LLM со специальной обвязкой, заточенной под эту задачу) дескать уже сейчас соревнуются в корректной оценке вероятностей разных событий плюс-минус на равных с топовыми человечьими суперфоркастерами. Он там приводит в пример FutureSearch – один из таких специализированных ИИ-инструментов, который находится в более-менее открытом доступе. Естественно, я не мог упустить возможность потестировать его! Смотрите, я на той неделе писал о возможности сделать на Полимаркете ставку на то, что Путин останется у власти как минимум до конца текущего года, по «цене» 86% (что эквивалентно «доходности к погашению» в размере 37,5% годовых в долларах). Сейчас уже вероятность поднялась до 89% (прибыль 3,5% за неделю, noice!), но всё равно остается на глаз довольно низкой. Так вот, я попросил этот самый FutureSearch оценить вероятность ухода Путина с поста президента до конца года по любым причинам – результат можно наблюдать на приложенном скриншоте. Три «нейроисследователя» прошерстили 146 источников и пришли к выводу, что вероятность составляет около 5%: 1,5% по базовым медицинским причинам и 3,5% на прочие силовые методы. С первым компонентом всё понятно, а вот как конкретно эти ребята вышли на 3,5% – я, к сожалению, так и не понял. Но концептуально это не сильно далеко от моей собственной «прикидочной» оценки. Для сравнения, что ответили другие современные модели (без какой-либо специальной обвязки, просто ответы на тот же самый вопрос): 🐌 Claude Fable 5 – самая мощная на текущий момент базовая модель (на этой неделе платные юзеры Клода как раз еще имеют к ней доступ без доплаты): 3–5%, короче около 4%. По сути, почти тот же результат, что у FutureSearch выше. 🐌 Claude Opus 4.8: 4–8% – тут уже вероятность заметно выше. Вся вариация здесь и ниже идет в основном за счет того, как разные модели оценивают вероятность переворота в том или ином виде. 🐌 ChatGPT 5.5: 9% – этот сразу первым делом полез на Polymarket проверять вероятности и, видимо, заякорился на них. Написал, что «текущие 11% на Полимаркете могут быть завышены действиями не самых умных китов, но…» 🐌 Gemini 3.1 Pro: этот, по сути, вообще отказался оценивать вероятность самостоятельно. Сразу написал «бро, там на Полимаркете дают около 12% – я хз, чем тут тебе еще помочь можно». 🐌 Grok Expert: 11–13%. Этот тоже, кажется, на Полимаркет заякорился. Вывод: кажется, проверять вероятности на Fable / FutureSearch выглядит не самой глупой идеей.

The billionaire class’s baseless smear of all non-governmental charitable organizations as useless and rife with fraud is a smart move by people who would prefer not to pay more in taxes or be expected to give generously to charity.

Dear @FIFAcom The red card against @balogun was wrong, but so are the people who think your rules cannot be challenged. IF a lawyer were to appeal it on behalf of @USMNT, these are three routes available. Let me explain:

heaven on earth

Mathematical models show that for bamboo, mutants with longer cycles will tend to outreproduce ones with shorter cycles, until the whole forest has the longer cycle. So initially there was perhaps a mutant that flowered every 2 or 3 or 5 years. After a long cycle is established, the easiest way to get an even longer cycle is to double it. So 120 = 5 × 3 × 2 × 2 × 2.

We recently obtained the highest-resolution 3D images of the human brain ever taken from outside the skull. This is the first look. Introducing Aleph, a research lab building brain interfaces for the telepathic future. (1/n)

A new report argues that training AIs to be risk-averse – to treat resources as having diminishing marginal utility – could both preserve AIs’ usefulness (if they turn out aligned) and provide an extra line of defense (if they turn out misaligned). The authors sketch out some possible methods of training AIs to be risk-averse, and give reasons to be cautiously optimistic about these methods’ success. Read it here: forethought.org/research/risk-…

A new blog post from @Benthamsbulldog argues that eventually handing off high-stakes decisions about the future to philosophically competent, reflective AIs will result in much better outcomes than locking in current human values, retaining human control, or allowing whatever haphazard mix of human and AI power might otherwise emerge naturally. Read it here: newsletter.forethought.org/p/we-should-ha…

Me crucé con esto, Messi está casi 6 desviaciones estándar por encima de la media de delanteros de grandes ligas en cuanto a goles y asistencias en 90 minutos. Estadísticamente es prácticamente imposible que vivas para ver a alguien así

Stories have shapes: a comedy rises toward joy; a tragedy falls into loss. Inside an LLM, that’s visible more literally: as an LLM reads a story, its internal activations trace a wandering path that reflects the model’s sense of what kind of story it is reading. (1/5)

@salt___doll @paulg @infobeautiful @newmoney Should the expected increase in the value of assets be taken into account?

@paulg @infobeautiful @newmoney I still don't get how the borrowing on assets to avoid tax works. In ~10 years, you will pay more in interest than in tax if you sold. Sure, you continue clocking gains. Then it is not a tax saving strategy, but more levered stock exposure. What am I missing!?

How rich people avoid paying tax (Originally by Instgram user @newmoney.blog)

Most AI investing happens downstream of the frontier: a capability emerges, a category gets named, and capital rushes in. But by the time a category earns a clean box on a market map, the best builders have usually been living in the messy version for months. Agents. Reasoning. RL environments. World models. AI for Science. Recursive self-improvement. I call this frontier proximity: the ability to see what is becoming possible before it becomes consensus. My frontier proximity ladder: L0 Wrapper: uses today’s models. L1 Reactor: reacts fast to releases, but roadmap is downstream. L2 Anticipator: builds for where capabilities are going. L3 Native: depends on a non-obvious frontier bet. L4 Shaper: helps move the frontier itself. The point is not that every company needs to train models. Apps can have high frontier proximity if they understand what models will make possible next. Infra can have high frontier proximity if it knows what future agents, multimodal systems, robotics stacks, or scientific workflows will need. That is why we’re launching MoE Capital. MoE stands for Mixture of Experts. The idea is simple: build an AI fund around people closest to the frontier: frontier researchers, technical founders, AI-native builders, and seasoned operators. We don’t want to be another AI fund with a newsletter-level understanding of the frontier. We want to build the AI fund closest to the frontier. More in The Information: theinformation.com/newsletters/ai…

How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵

Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: radicalnumerics.ai/blog/radical-n… At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms. We founded Radical Numerics to turn the tide. And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins. The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life. Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now). 1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer). 2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated. We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up. Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives. Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap , and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs. Fortune article: fortune.com/2026/06/15/exc… Jobs: radicalnumerics.ai/join-us