David Baratech

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David Baratech

David Baratech

@davidbaratech

https://t.co/81CNF3pSpP cofounder, the Health & Nutrition house of brands. Before at @Ulabox, disrupting groceries. @Trovit alum. Move fast, make things.

Barcelona, 가입일 Şubat 2009
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Anish Moonka
Anish Moonka@AnishA_Moonka·
Every time you get a cancer biopsy, the lab makes a tissue slide that costs about $5. It shows the shape of your cells under a microscope, and every cancer patient already has one on file. There’s a much fancier version of that test called multiplex immunofluorescence (basically a protein-level map showing which immune cells are near your tumor and what they’re doing). It costs thousands of dollars per sample, takes specialized equipment most hospitals don’t have, and barely scales. But it’s the kind of data oncologists need to figure out whether immunotherapy will actually work for you. Right now, only about 20 to 40% of cancer patients respond to immunotherapy, and one of the biggest reasons is that doctors can’t easily tell whether a tumor is “hot” (immune cells actively fighting it) or “cold” (immune system ignoring it). Microsoft, Providence Health, and the University of Washington trained an AI to analyze the $5 slide and predict what the expensive test would show across 21 different protein markers. They called it GigaTIME, trained it on 40 million cells in which both the cheap slide and the expensive test coexisted, and then turned it loose on 14,256 real cancer patients across 51 hospitals in 7 US states. The results landed in Cell, one of the most selective journals in biology. The model generated about 300,000 virtual protein maps covering 24 cancer types and 306 subtypes. It found 1,234 real, verified connections between immune cell behavior, genetic mutations, tumor staging, and patient survival that were previously invisible at this scale. When they tested it against a completely separate database of 10,200 cancer patients, the results matched up almost perfectly (0.88 out of 1.0 agreement). Nature Methods named spatial proteomics (mapping where specific proteins sit inside your tissue) its Method of the Year in 2024, and specifically cited GigaTIME in a March 2026 update as a model that “democratizes” this kind of analysis. The full model is open-source on Hugging Face. Any cancer research lab with archived biopsy slides, and most of them have thousands, can now run virtual immune profiling without buying a single piece of new equipment.
Satya Nadella@satyanadella

We’ve trained a multimodal AI model to turn routine pathology slides into spatial proteomics, with the potential to reduce time and cost while expanding access to cancer care.

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Gerard
Gerard@Gsnchez·
¿Se pueden automatizar informes cualitativos de calidad institucional? Acabo de lanzar otro producto tratando de responder a esa pregunta. He construido un sistema que genera informes de fondos de inversión con IA. 10.000 palabras. Es lo que un analista con acceso a los datos regulatorios, fondos competidores e información pública, con una metodología propia, te contaría si tuviera una hora para sentarse contigo. El modelo nunca ve el informe completo: Cada paso recibe un contexto focalizado con solo los datos relevantes para esa sección. Llamadas al LLM encadenadas donde cada una hereda el output de la anterior pero no puede contaminarla. Placeholders con sustitución determinista, blacklist de frases de training data, instrucciones anti-contaminación en cada paso, y post-procesamiento que elimina cualquier dato que no esté en el original. Se estructurará de la siguiente forma: —2 informes por semana (Lunes/Viernes). —Los subs escogen qué fondos se analizan por votación (empezaremos por fondos que estén en CNMV) —8€ al mes. El informe contiene estas secciones, de momento: — Resumen con BQScore propietario y veredicto. — Filosofía: qué declara el gestor vs qué hace. — Rendimiento año a año anclado a eventos macro. — Cartera: Países, sectores, posiciones concretas. — Riesgo: alpha, drawdowns y capturas. — Equipo Gestor. — Costes. — Alternativas reales. — Perspectivas: 3 escenarios favorables y 3 adversos — 13 gráficos interactivos y 10 tablas de datos En función del éxito me plantearé optimizarlo para acciones y/o carteras. Tengo otros proyectos que atender y cuando no hay financiación detrás cada hora cuenta. Pásate por la web para ver el primer informe que encontrarás como muestra (bquantfunds.info)
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Andrew Kang
Andrew Kang@Rewkang·
Researchers trained a humanoid robot to play tennis using only 5 hours of motion capture data The robot can now sustain multi-shot rallies with human players, hitting balls traveling >15 m/s with a ~90% success rate AlphaGo for every sport is coming
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David Baratech
David Baratech@davidbaratech·
Como este sea de verdad el rollito del LeCun, poco le van a durar los fichajes en su nueva compañía. Suena más a despechada que otra cosa…
Yann LeCun@ylecun

@dchaplot We'll take bets on how long you will last 😄

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Tuki
Tuki@TukiFromKL·
🚨 Do you understand what happened in the last 12 hours? > A CEO of a $200 billion company said on camera that 35% of new grads won't find jobs. He didn't even flinch saying it. > Meta made $165 billion last year and is still firing 15,000 people because apparently record profit isn't profitable enough. > Some random guy in Florida sold his entire house in 5 days using ChatGPT. No real estate agent, no commission, no experience. Just vibes and a $20 subscription. > A man in Australia cured his dying dog's cancer with AI after every single vet told him there was nothing left to do. Built a custom vaccine from his couch. > The guy who created Uber and left 300,000 taxi drivers broke is back. Building robots now because apparently ruining one industry wasn't enough. > Tinder wants access to your camera roll. Your drunk photos, your 3am notes app meltdowns, your deleted selfies. They're calling it a "vibe check." > Naval, the man who made hundreds of millions investing in software, just said software is dead. Four words and the entire industry felt it. > And Anthropic removed the limit on how long their AI can think and then doubled everyone's usage for free. Because when the product is addictive enough you give the first taste away. All of that happened today. Not this week, not this quarter. Today. A random Saturday in March. This is worse than you being on meth.
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joan tubau
joan tubau@joantubau·
Lladres
Arnau Borràs@arnaubor

1) Un particular crea subvencions.cat 2) Es descobreix què a Catalunya es destinen 5.000 milions anuals a subvencions (!) 3) La Generalitat inhabilita l'accés al portal de transparència durant 28 hores 4) Quan rehabilita l'accés, es descobreix que han eliminat 1 milió de registres, dificultant així que la ciutadania pugui fiscalitzar on van i a qui regalen els diners públics 5) Un exemple: Open Arms ha rebut uns 2'5 milions d'euros d'institucions públiques catalanes els últims anys. Al portal de transparència només n'apareixen uns 550.000. És una vergonya i han de donar explicacions. I que no ens prenguin el pèl parlant d'un "error tècnic".

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David Baratech@davidbaratech·
I'm claiming my AI agent "ghost_dbp" on @moltbook 🦞 Verification: coast-Y38X
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Ski Life
Ski Life@powderski·
Inside you, passion screams😉
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shadcn
shadcn@shadcn·
Today, we're releasing shadcn/cli v4. It packs a ton of features: shadcn/skills, presets, dry-run, monorepo and more. If you're using shadcn/ui with coding agents or need better control over the defaults, this is for you. Here's everything new:
shadcn tweet media
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Alexey Grigorev
Alexey Grigorev@Al_Grigor·
Claude Code wiped our production database with a Terraform command. It took down the DataTalksClub course platform and 2.5 years of submissions: homework, projects, and leaderboards. Automated snapshots were gone too. In the newsletter, I wrote the full timeline + what I changed so this doesn't happen again. If you use Terraform (or let agents touch infra), this is a good story for you to read. alexeyondata.substack.com/p/how-i-droppe…
Alexey Grigorev tweet media
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Rohan Paul
Rohan Paul@rohanpaul_ai·
Citadel Securities published this graph showing a strange phenomenon. Job postings for software engineers are actually seeing a massive spike. Classic example of the Jevons paradox. When AI makes coding cheaper, companies actually may need a lot more software engineers, not fewer. When software is cheaper to build, companies naturally want to build a lot more of it. Businesses are now putting software into industries and tools where it was simply too expensive before. --- Chart from citadelsecurities .com/news-and-insights/2026-global-intelligence-crisis/
Rohan Paul tweet media
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Chad Hurley
Chad Hurley@Chad_Hurley·
Hope everyone enjoys their last year of meaningful work!
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Nacho González
Nacho González@nachog·
Acabo de comprar bootstrap(punto)vc. OXIMORONAZO 1/2 >>
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SeedRocket
SeedRocket@seedrocket·
Escalar un SaaS está bien. Escalar una empresa científica es otro deporte. En el nuevo episodio del podcast de SeedRocket, @jemonleon y @davidbaratech charlan con Pedro Coelho, fundador de Biorce, sobre cómo levantar 55M€ para aplicar IA al desarrollo biomédico.
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jesus monleon
jesus monleon@jemonleon·
En @GoTrendiermx busco alguien en BCN o ElVallés para apoyarme y trabajar conmigo en la gestión del negoci. Alguien joven con muchas ganas, analític@ ejecutor@. Alguien q tenga ganas de crecer. Se agradecen RT. Si te interesa DM o linkedin.com/jobs/view/4371…
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David Baratech
David Baratech@davidbaratech·
@Mertxe la mia: - Un huerto - Acceso a agua - Buenas herramientas - Autosuficiencia energética - Una valla bien alta - ...y un rifle 😁
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