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You don’t need a husband or a wife. You need just 1 great prompt. Here’s how to use @perplexity_ai or @grok to replace your spouse forever! Less bickering and more happiness!

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 stars

Introducing the Content Machine! This was the first time I walked through the mechanics of our anti-slop content system & how we drive 10,000,000+ impressions at @tenex_labs with a marketing team of...2. Thanks to @clairevo for having me on her show to share. Full-writeup & interview below... What is the Content Machine? A directory of daisy-chained skills that turn what you already say into publish-ready content. This is the key way to avoid turning into a slop cannon. It mines the places you already talk (Slack, Notion, Gmail, Linear, GitHub) plus what the internet is saying, finds the ideas worth writing, interviews you to extract the story, drafts it, and edits it to a 9/10 bar before you post. What are the principles of this system? 1) It is not a "write me a post" prompt. The core belief baked into it: the raw material must come from you. 2) The machine never invents your voice and never fabricates your insight. It does the research, the structure, and the editing, so your time goes only to the part only you can do. It focuses the human on the first & final mile of the content process. How is it structured? A two-layer split between the process & the person - The process layer is generic and shared: the pipeline, the content-type specs, the copywriting references, the onboarding flow. That is what lives in git and what gets shipped to other people. - The personal layer is yours and never leaves your machine: content-machine.config.md, creators// (profile, style guide, lessons), projects/, published/, oracle-reports/. All gitignored, and the desktop build script refuses to package any of it. What is the 10-step pipeline? 1) Creator Select. Multi-creator by design. It figures out who this run is for and loads their profile, style guide, and content lessons. Everything downstream is scoped to that person. - Onboarding (first run only). Scaffolds the workspace, auto-creates your Notion Vault, connects your sources, and builds your voice guide one of three ways: import a guide you already have, feed it writing samples, or sit for a short voice interview. 2) The Oracle. Two idea engines running in parallel: - Oracle scans what you wrote in the last 7 days across Slack, Notion, Gmail, Linear, and Git, hunting for "spikes," moments where you naturally said something worth expanding. - The Internet Reader scans what the world is saying, pulled only from the source list in your profile (handles, labs, outlets, keyword watchlist), plus a social sweep across Reddit, X, YouTube, Hacker News, and more (thanks /last30days & @mvanhorn). - Every idea is scored 0 to 10 (POV strength 25%, story potential 25%, emotional intensity 20%, lesson/framework 20%, depth 10%). Everything qualifying gets written to The Vault, a Notion database that is the durable idea bank. 2.5) Research. Before you get interviewed, a research agent builds a sourced brief: - key facts with links - current developments - what has already been said in-market - contrarian angles - open questions only you can answer. 3) Interview Panel. Six interviewer personas (Ferriss, Rogan, Larry King, Stern, Barbaro, Barbara Walters) ask you one question at a time, each chasing a different dimension: tactics, story, core truth, the hidden thing, clarity, emotional depth. It pushes back on vague answers and will not advance until it has 2 to 3 specific stories with real details. 4) Production. Your interview becomes a raw markdown file: stories, core insights, quotable moments, the emotional anchor, surprising reveals, the "so what." Your exact words are preserved. This file is the source of truth for everything that follows. 5) Refinement. Now it drafts, and only now. It must read your style guide, your content lessons, and the spec for the chosen format (LinkedIn post, X thread, long post, playbook, podcast promo, reaction post, article, and so on). 6) Writer's Council. Six reviewers score the draft: Morgan Housel (will this matter in 10 years), Tim Urban (is it confusing), Shaan Puri (would I stop scrolling, plus three alternate hooks), Greg Isenberg (what can someone steal), David Perell (is it personal, observational, playful), and a Slop Detector hunting AI tells. Each gives what's working, what needs work, a fix, and a score. 7) Revision Loop. Under 9/10 goes back around. The smart part: fixes get sorted into editorial (the machine rewrites it itself) and information gaps (only you have the answer), and information gaps route back to the Interview Panel with targeted questions rather than letting the machine make something up. Max 3 editorial cycles. At 9/10 the piece becomes the anchor. 8) Repurposing Engine. One anchor fans out into 10+ derivatives: X article, LinkedIn article, short X posts, short LinkedIn posts, a playbook if there is a framework in it. Each one is written native to its platform with a fresh hook, not cross-posted, and each runs the full council and revision loop to 9/10 on its own. This is the multiplier. 9) Distribution (optional, off by default). UTM tagging, a scheduled publishing queue, CRM capture of every touchpoint, attribution reporting back to pipeline, and marking the Vault row as Published. 10) The Learning Loop, always running. After you approve a piece it diffs your first draft against the final, extracts the pattern, and asks you to confirm it. Confirmed lessons go into content-lessons.md and override the style guide. Once a lesson proves out across a few projects it graduates into the style guide itself. Your first drafts get better over time instead of you re-explaining preferences. P.S. i'm thinking about opensourcing this. should i do it?

LAUNCHED: Microsoft has released MAI-Cyber-1-Flash, its first cybersecurity model. MAI scores 96% on the CyberGym vulnerability benchmark inside the company's MDASH agent harness, 12 points above Anthropic's Mythos at half the cost. Microsoft AI CEO Mustafa Suleyman: "Token cost is now the real constraint for defenders".

@Mechramc @karpathy @rasbt Been following you for quite some time. Great work bro! Congratulations 🥳 🙌 Does the book come in paperback or hard copy?

At 38, I had 17 years of program management under my belt. PgMP, PMP, enterprise SaaS. Zero machine learning experience. At that time, as everyone does, I started doing some Coursera courses but everything was vague. There were two things that helped - @karpathy and @rasbt. Once the concepts were broken down - I could build, break, and eventually come up with 100s of ideas that failed. I decided to pivot into AI research. Not a side hobby. A real pivot. I spend to this day at least a few hours every week reading latest papers and trying to break the implementation of the paper. Even now, I am participating in the ICLR huggingface paper reproduction challenge. Two years later at 40: 6 papers on arXiv, an NVIDIA Inception membership, founded Murai Labs, and an open source Tamil language model that is coming out soon. The book grew directly out of what I needed to build TamilLM from scratch. If it helped me learn how to ship a real model with no ML background, it can help you too. It is such a beautiful coincidence that TamilLM finished pretraining and Under The Hood launched on Amazon on the same day - it was not planned. (Kadavul irukaan Kumaru moment) 100+ copies sold on Leanpub. #1 bestseller there. Now available worldwide on Amazon. 8 free chapters at murailabs.com/under-the-hood (Chapters 4, 5, 8, 13, 18, 23, 27, 32). India Kindle: amazon.in/dp/B0H9FYVK3W US Kindle: amazon.com/dp/B0H9FYVK3W US Paperback: amazon.com/dp/B0H9FWPCDV Leanpub: leanpub.com/under-the-hood I started late. But not done yet - I am glad I started. 🙏

TamilLM has finished pre-training. 🎉 30 billion tokens. 57,221 steps. 1.5B parameters. ~48 hours of H100 time across two clusters (one of which our own safety guard assassinated 90 seconds after the first finish — the resumed re-run reproduced the lost model to four decimal places). Final report card, measured across ten registers of Tamil: 📜 Classical Sangam: 6.08 → 3.50 🕉️ Bhakti devotional: 6.66 → 3.67 📖 Literary prose: 4.69 → 2.87 🗞️ Modern formal: 4.52 → 2.69 Overall held-out loss: 4.61 → 2.84 The hard old registers — the reason this model exists — improved the most, all the way to the final step. Every checkpoint integrity-verified. Three copies on three storage domains before the GPUs go quiet. ~$2.2K all-in, failures and forensics included. It already completes the first Thirukkural from three words. Next: post-training, so it doesn't just continue Tamil — it converses in it. வளர்க தமிழ்🌱 P.S. Now looking for name suggestions for the model - as TamilLM is already taken on HF. Drop a comment with a model name - and the name with the most likes wins! 😃

Been saying I was building something cool, and it’s finally here. I built it for my dadi. She doesn't use multiple apps; she just uses WhatsApp. So I built an AI assistant that lives right there. She sends a voice note in Hindi, and her groceries arrive. It never buys anything on its own. It reads the order back and waits for her confirmation. Cash on delivery only. Voice by @ElevenLabs , orders via @Swiggy 's MCP tools, running on WhatsApp via @Meta . Agent is Nous Hermes 4 @NousResearch . Watch it in action below 👇. I’m writing a full technical deep-dive article on this architecture and will be dropping it soon.

50 sitios web que parecen 'ilegales' pero son perfectamente legales 1. cobalt.tools — Descarga cualquier video de redes sociales 2. photopea.com — Photoshop gratis 3. temp-mail.org — Correo electrónico temporal con un clic 4. tinywow.com — Más de 100 herramientas gratuitas en un solo sitio 5. archive.org — Accede a cualquier página web antigua 6. libgen.li — Millones de libros de texto gratuitos 7. sci-hub.al — Artículos de investigación gratuitos 8. alternativeto.net — Encuentra alternativas gratuitas a aplicaciones 9. justwatch.com — Localiza dónde transmitir cualquier contenido 10. gutenberg.org — 70.000 libros clásicos gratuitos 11. pdfdrive.com — Descargas gratuitas de PDF 12. openculture.com — Cursos gratuitos de universidades de élite 13. wolframalpha.com — Resuelve cualquier problema matemático al instante 14. remove.bg — Elimina el fondo con un clic 15. cleanup.pictures — Borra objetos de fotos 16. unscreen.com — Elimina el fondo de videos gratis 17. squoosh.app — Comprime cualquier imagen gratis 18. excalidraw.com — Gráficos dibujados a mano gratis 19. carbon.now.sh — Convierte código en obras de arte 20. ray.so — Capturas de pantalla de código impresionantes 21. flightradar24.com — Rastrea cualquier vuelo en tiempo real 22. camelcamelcamel.com — Rastrea el historial de precios de Amazon 23. haveibeenpwned.com — Verifica si has sido hackeado 24. virustotal.com — Escanea malware en cualquier archivo 25. privnote.com — Envía mensajes autodestructivos 26. file.io — Comparte archivos que se eliminan automáticamente 27. archive.ph — Archiva cualquier página web para siempre 28. accountkiller.com — Elimina tu presencia de cualquier sitio web 29. radio.garden — Escucha cualquier emisora de radio del mundo 30. tunefind.com — Identifica las canciones de cualquier programa 31. musicforprogramming.net — Música para concentrarte 32. mynoise.net — Paisajes sonoros personalizados para enfocarte 33. annasarchive.org — Busca cada libro que se haya escrito 34. elicit.com — Asistente de IA para artículos de investigación 35. consensus.app — Busca consensos científicos 36. connectedpapers.com — Mapea investigaciones de forma visual 37. semanticscholar.org — Búsqueda académica gratuita 38. scispace.com — Comprende cualquier artículo de investigación 39. summarize.tech — Resume cualquier video de YouTube 40. phind.com — Búsqueda de IA para desarrolladores 41. regex101.com — Prueba cualquier expresión regular al instante 42. codebeautify.org — Formatea cualquier código de forma clara 43. explainshell.com — Entiende comandos de terminal 44. tldraw.com — Pizarra infinita en el navegador 45. downdetector.com — Verifica si un sitio web está caído 46. tineye.com — Búsqueda inversa de imágenes 47. fast.com — Prueba tu velocidad de internet 48. smallpdf.com — Edita PDF gratis 49. ilovepdf.com — Combina y divide PDF 50. 10minutemail.com — Correo temporal en segundos Todo legal. Todo gratis. Guárdalo antes de que se te olvide.🔖

We can finally say AI isn't killing jobs. A new paper from me, @tryramp, and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI's impact on jobs. Firms that adopt AI heavily grow headcount 10% over two years following adoption. Low adopters see no statistically significant change.

Baidu just open-sourced an OCR model that reads entire 40-page documents in one shot. It's called Unlimited-OCR. 3 billion parameters but only 500 million active during inference. Runs 100% locally on your machine. Why this matters: traditional OCR tools chop documents page by page. Tables that span two pages break. Reading order gets lost. Cross-page context disappears. Unlimited-OCR processes the whole document at once. 32K context window. Text, formulas, tables, reading order all preserved across pages. Output comes out as clean structured Markdown. → 93% accuracy on the standard benchmark. +6 points over the baseline. → Error rate stays below 0.11 even past 40 pages. → Multilingual out of the box. → 2.12 million downloads on Hugging Face last month. 14,600 GitHub stars. For context: Amazon Textract, Google Cloud Vision, and Azure Document Intelligence all charge per page. This runs locally for free.

Someone turned Claude into an entire company. 42 skills, organised like a real org chart (links below): Here is every department, and where to get each one. Developers Superpowers → github.com/obra/superpowe… Context7 → github.com/upstash/contex… Skill Creator → github.com/anthropics/ski… MCP Builder → github.com/anthropics/ski… Webapp Testing → github.com/anthropics/ski… Claude-Mem → github.com/thedotmack/cla… Designers UI UX Pro Max → github.com/nextlevelbuild… Taste → github.com/Leonxlnx/taste… Frontend Design → github.com/Leonxlnx/taste… Transitions → github.com/Jakubantalik/t… Web Artifacts → github.com/anthropics/ski… Brand Guidelines → github.com/anthropics/ski… Marketing 45 skills to run your marketing, from copywriting to SEO to lead magnets. Access them all here → github.com/coreyhaines31/… Social Media 17 skills to run your social media, from post writing to Reels to thumbnails. Access them all here → github.com/charlie947/soc… Finance 8 skills to run your finances, from statements to reconciliation to audits. Access them all here → claude.com/plugins/finance Small Business 31 skills to run your small business, from cash flow to payroll to invoicing. Access them all here → claude.com/plugins/small-… Legal 9 skills to handle your legal work, from contract review to NDAs to compliance. Access them all here → claude.com/plugins/legal Every skill on the chart is real and installable from the links above. Same departments. Same output. No payroll. Bookmark this.

My co-founder @ArmanHezarkhani broke down Kimi K3 (new 2.8T parameter Chinese model) on @FoxBusiness today with @cvpane. We had our engineers testing it all day before he went on air. Here's the gist: 1) K3 is very good. As good, if not better, than many of the American frontier models. 2) The cost story is being misread. Everyone expects a Chinese model to be the cheap one. Per token, K3 is competitive. Per run (the actual job you outsource to the model), it's as expensive as the frontier models. But it's supposedly open source, so developers will attack the cost curve. Give it weeks, not quarters. 3) The playbook should look familiar. It's the same one China ran on solar panels & EVs: flood the market with cheap supply, wait for the addiction, then move the price. 4) The internet & the space race were funded by the US government, and innovators competed on top of the platform. AI got funded by private markets, and now the same companies that spent those trillions are getting hamstrung right as China gives its models away. 5) On guardrails: the bad guys will have completely unconstrained tools no matter what we do. Foreign adversaries, and bad actors here at home. If the good guys don't have equally powerful tools, only one side is armed. 6) Commoditized models are actually good news for the hyperscalers. All that open source intelligence has to run somewhere, and Google & AWS will get paid a lot of money to run it. 7) The labs saw this coming too. It's why Anthropic & OpenAI are racing up the application layer instead of just selling tokens.

We're introducing Claude for Teachers: free access to premium Claude capabilities for verified K-12 educators in the US, with a library of teaching skills and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states. claude.com/solutions/teac…

I taught JEE physics for years. That paper breaks strong kids in three hours. This exam is five hours of theory and five hours of lab work, and these five did close to perfect scores on it. Let me tell you what actually happened. The International Physics Olympiad is the world championship of school physics. It was the 56th edition. Held in Bucaramanga, Colombia, from July 5 to 12. 381 students. More than 85 countries. Every one of them the best physics student their country could find. India sent five kids. All five came back with gold. Their names are Kanishk Jain from Pune. Riddhesh Anant Bendale from Indore. Rishit Garg from Dwarka in Delhi. Shresth Suraiya from Mumbai. Svarit Joshi from Ahmedabad. We know a hundred cricketers by their nickname and not one of these boys. :) That clean sweep put India at joint World Number One. Tied with China, Russia, Kazakhstan, South Korea and Taiwan. Those are countries that pour serious money and national pride into science education. We are standing level with them. Now here is what the exam actually was. Two papers. Each five hours long. The theory paper had three problems. One on the thermodynamics of paramagnetic cooling. One on the photoionisation of ozone. One on the dynamics of electron positron pairs. The experimental paper was another five hours in a lab, working through heat transfer and thermodynamic processes in fluids. That means you get given equipment you have never seen, and you have to design your own experiment, take your own readings, handle the errors, and reach a real answer. Not multiple choice. No shortcuts. No pattern recognition. You either understand physics or you sit there for five hours. HBCSE says the Indian students were near perfect on theory and excellent on the practical too. Now, this was India's 27th appearance at the IPhO. Across all those years, about 44 percent of Indian students have won gold, 41 percent silver, 10 percent bronze. In the last ten years, every single Indian student has come home with a medal. 62 percent gold, 38 percent silver. Not one kid has gone and come back empty handed in a decade. Five golds in one year has happened only twice. This year, and in 2018. So who built this. The programme is run by HBCSE, the Homi Bhabha Centre for Science Education. It sits under TIFR, which sits under the Department of Atomic Energy. They run the whole funnel. A national exam, then a national olympiad, then a brutal selection and training camp, and out of everyone in the country, five kids get on a plane. The team was led by Professor Anwesh Mazumdar of HBCSE-TIFR and Dr Leena Joshi from St Xavier's College, Mumbai. The scientific observers were Professor Ananda Dasgupta from IISER Kolkata and Nisha Kelkar from Gogate-Joglekar College in Ratnagiri. Yes. Ratnagiri. A college in a small coastal town in Maharashtra. This is public education doing something the private coaching industry could never do on its own. The coaching industry is very good at one thing. Teaching you to solve a known problem fast. That is what JEE and NEET reward, and I say that with love because I was part of that world. But an olympiad paper does not have a known type. There is no shortcut chapter. There is no formula sheet that saves you. You have to sit with a problem you have never seen and think. That is a completely different muscle. And a government funded centre has been quietly building it in Indian teenagers for 27 years. So yes, be proud. Loudly. HBCSE also shared that around 64 percent of India's olympiad medallists go on to do a PhD. But only about 32 percent of medallists end up settling in India. I do not say that to spoil the moment. These kids owe the country nothing. They earned every option they have. But it should tell us something. We are excellent at finding this talent. We are excellent at training it. We are still not great at giving it somewhere worth staying. Congratulations Kanishk, Riddhesh, Rishit, Shresth and Svarit. This is one of the best things an Indian did this year and most of the country will never hear about it.