¿Para qué pagar $19.99 al mes por jugar en la nube a juegos que ya compraste?
Y desde enero de 2026, GeForce NOW limita el uso a 100 horas mensuales.
Este proyecto de GitHub convierte tu PC, servidor o NAS en tu propio servidor de Steam para jugar remotamente.
Se llama Steam Headless y funciona dentro de un contenedor Docker.
Instalas Steam una vez y puedes acceder a tus juegos desde:
→ Navegador
→ Steam Link
→ Moonlight
→ Otro cliente de Steam
→ Teléfono, tablet o TV
Y lo mejor:
No necesitas pagar una suscripción mensual por el servicio.
El proyecto incluye:
→ Steam + Proton para juegos de Windows en Linux
→ Servidor compatible con Moonlight
→ Sunshine para streaming
→ Escritorio XFCE accesible mediante noVNC
→ Audio y vídeo desde el navegador
→ Soporte para mandos
→ NVIDIA, AMD e Intel
→ Heroic, Lutris y EmuDeck instalables fácilmente
Incluso puedes montar tu propia biblioteca de juegos en /mnt/games y mantenerla persistente.
Todo corre dentro de Docker, así que puedes desplegar tu propio servidor de gaming en segundos.
Compatible con:
→ Docker Compose
→ Unraid
→ Ubuntu Server
Y es open source bajo GPL-2.0.
El repositorio ya supera las 4.500 estrellas en GitHub
Si tienes un PC con buena GPU que está desaprovechado…
Esto básicamente lo convierte en tu propio GeForce NOW privado!
Github en primer comentario:
Instead of watching an hour of Netflix, watch this 2-hour Stanford lecture, which will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.
One classroom. One camera. Filmed from the back row.
He gave away a 50%-a-year method to 30 people.
Book Mark this vedio and watch when you are free!!
A hedge fund returned 50% a year for ten years straight. The man behind it once taught the entire method for free.
1) His name is Joel Greenblatt. He ran Gotham Capital from 1985 to 1994 — nearly 50% annual returns, ten years straight. Almost nobody sustains that for even one year.
2) In 1995, he returned all outside capital, kept managing his own money, and walked into a Columbia classroom.
3) He taught 30 students the entire method for free. No bank, no fund, no business school has ever promoted the recording.
4) His first lesson: corners of the market where the usual buyers are structurally forced to sell — regardless of price. Spinoffs, restructurings, index-exit dumps. He didn't teach a formula. He taught why these opportunities exist, and why they persist even after everyone knows about them.
5) The uncomfortable part — he held very few positions. Barely any diversification. Runs directly against what Columbia teaches two floors down, for $80K a year in tuition.
6) Every screener is free today. Every filing is searchable. The constraint was never information — it was knowing which information to ignore.
She built 100+ agents for Anthropic and made $1.3M - and in 60 minutes leaked everything she knows at Stanford:
02:07 - her first agent for Anthropic brought her $1.3M
08:34 - agents replace a team of 50 engineers worth $200k a month
19:47 - one agent did overnight what the company planned for 5 years
after watching I launched my first agent - $7k in the first week and zero employees needed.
Save & watch - the article below is step by step how to build your first agent like hers.
Anthropic just released a 2-hour course to getting a $500k AI engineering job.
and deleted it two days later.
I watched the recording last night. The course runs 2 hours. Took me 5. I kept pausing to try what they showed in Claude.
Posting recording below.
Watch it today, then read the step-by-step guide on building loops below.
THIS GUY MAKES COMPLEX AI AGENT CONCEPTS RIDICULOUSLY EASY TO UNDERSTAND.
No jargon wall. No assuming you already know what a reward model or an evaluation harness is.
Just a straight line from "I have no idea how agents actually work" to "oh, that's genuinely simple."
The best explainers do not simplify by cutting corners. They simplify by finding the one analogy that makes the whole thing click.
Ten million people have watched an MIT professor accidentally destroy the executive coaching industry.
He filmed the lecture once in January 2018 and died eighteen months later.
Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in one hour for free.
His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years.
Every January for four decades, he gave a lecture called "How to Speak."
His entire framework fits on a napkin.
Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you."
That last rule alone has probably cost the executive coaching industry a hundred million dollars.
"Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order."
That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk.
Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train.
The lecture is free on MIT OpenCourseWare. The textbook is free on his page.
Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin.
The napkin is free. The willingness to actually use it in your next meeting is the entire edge.
The founder of a Chinese AI company valued at over $20,000,000,000 has just given a 40-minute class on agent swarms
The clearest explanation I've seen of large-scale AI systems
Swap it for your 2 hours of Netflix tonight.
Google just dropped a free 2-hour course on complete agent engineering
How to turn one prompt into a system that keeps running while you sleep:
38:46 - Build your first AI agent
54:46 - Connect agents to MCP tools
1:12:43 - Run four different agent loops
1:20:57 - Turn those loops into graphs
2:22:31 - Build the complete autonomous system
Most people are still building one agent and stopping there
Google is already teaching the entire stack:
Agents → Tools → Loops → Graphs → Autonomous Systems
Single agents are the old workflow
Systems that keep running without you are the new one
This free course is worth more than most paid agent engineering bootcamps
Bookmark and watch it today
Then read the full graph engineering playbook below
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