Daniel Barrios Mogollón

#100Devs Committed to become a Full Stack Web Dev!

[Wes Bos @wesbos]

the four horsemen of the apocalypse

[aditya @adxtyahq]

for anyone asking where to learn this stuff:

• RAG → https://t.co/4bzbUIwV5g
• Agentic RAG → https://t.co/IotOiGmV1Y
• AI Agents → https://t.co/nEeMnVJQbk
• Multi-Agent Systems → https://t.co/pavDPVJEFj
• LangGraph → https://t.co/3miEqqFzF0
• LangGraph (code) → https://t.co/v7kxHZXqba
• MCP → https://t.co/lKawRb4etX
• Memory Systems → https://t.co/LSaT2UaPAS
• Evals → https://t.co/vxChxa1kqQ
• Context Engineering → search "Context Engineering Survey" on arXiv

and please skip the "build an ai agent in 10 minutes" videos
build something, watch it fail, then figure out why.

[Anish Moonka @anishmoonka]

Toru Miyazaki gave 11 cats with advanced kidney disease an experimental injection. 15 others didn’t get it. A year later, 9 of the 11 treated cats were alive. Only 3 of the 15 untreated cats survived. He just filed for approval, and the drug fixes a defect only cats have.

Most cats die from one thing: their kidneys fail. By age 10, 4 in 10 cats already have chronic kidney disease, and by age 15, the rate doubles to 8 in 10. Once diagnosed, a cat has about 2 years left.

The reason kidney disease hits cats so hard is a broken protein in their blood. All mammals carry a protein that helps the kidneys clean out waste. In humans and dogs, the protein floats freely and goes to work when the kidneys are in trouble. In cats, it stays stuck to another protein and can’t get loose. So the waste piles up, and the kidneys eventually give out.

Miyazaki originally found the protein in 1999, back when he was at the University of Tokyo. He figured out the cat-specific glitch in 2015. The paper he published in the Veterinary Journal in February laid out the trial. The injection is a working version of the missing protein. His company, the Institute for AIM Medicine, filed the approval paperwork with Japan’s Ministry of Agriculture on April 24, 2026. If the review clears, the drug goes on sale in spring 2027.

The 30-year lifespan figure in the tweet is Miyazaki’s own projection of what cats could reach without kidney disease. The trial only ran a year, and the average cat today lives 15. Most die from the same disease this injection treats.

The research almost died in 2020. After running out of funding during COVID, Miyazaki went public. Cat owners across Japan responded by sending in 300 million yen, around 2 million dollars total. He resigned from the University of Tokyo and worked on the drug full time. The treatment in front of regulators today exists because cat lovers refused to let the research die.

[Miguel Ángel Durán @midudev]

Este repositorio es una joya. Te da todos los pasos e instrucciones para proteger y asegurar tu servidor Linux.

Perfecto por si tienes un servidor propio o VPS:
https://t.co/yJD2GnXFPj

[Neo Kim @systemdesignone]

6 algorithms that'll help you load balance traffic:

1 Round robin
→ Load balancer forwards the request to the servers in a sequential order

2 Weighted round robin
→ Load balancer forwards the request based on server capacity

3 Least connections
→ Load balancer forwards the request to the server with the fewest connections

4 IP hash
→ Load balancer forwards the request based on client's IP address

5 Least response time
→ Load balancer forwards the request to the fastest responding server

6 Adaptive
→ Load balancer forwards the request based on server health

What else should make this list?

[Miguel Ángel Durán @midudev]

¡Vaya tesoro! Colección de APIs gratuitas de modelos de Inteligencia Artificial. Sin pagos y con límites claros.

✓ +20 modelos disponibles
✓ ChatGPT, DeepSeek, Gemini, Qwen y más
✓ Con requests/minuto y tokens por día

→ https://t.co/6j9yC6kwab

[Guillermo Casaus @_guillecasaus]

Existe un emulador de Android que se ejecuta dentro de Docker.

Te permite lanzar un dispositivo Android completo en tu ordenador con un solo comando.

Incluye aceleración por GPU, soporte KVM y ejecución headless.

Es gratis y 100% open-source 👇

[Brad Smith @BradSmi]

Today we’re introducing a new Legal Agent in @Microsoft Word, built to support the precision and rigor legal work demands. Every clause matters. Every redline tells a story. That’s why this agent was built to follow the structured workflows lawyers use while keeping them fully in control.

Early in my career, I asked for a computer on my desk because I believed technology could change how lawyers work. It did. Today, I believe this next generation of tools will do the same, grounded in trust and responsible use.

[Marko Denic @denicmarko]

I found this curated list of free courses with certifications.

[Quincy Larson @ossia]

https://t.co/nuIS7xJw5C

[Priyanka Vergadia @pvergadia]

99% of AI Engineer interviews are these 7 concepts repeated:

→ Prompt Engineering: control the model without touching weights
→ RAG: inject fresh knowledge, kill hallucinations
→ Fine-Tuning: own the model's behavior end-to-end
→ Vector Embeddings: semantic search at machine speed
→ Chain-of-Thought: force step-by-step reasoning, unlock hard problems
→ Eval Metrics: BLEU, ROUGE, LLM-as-judge. Measure or you're guessing.
→ MLOps: CI/CD, drift detection, production pipelines. Ship it or it doesn't exist.

Master these 7. Pass 90% of AI interviews.

[Andrew Ng @AndrewYNg]

New course: Spec-Driven Development with Coding Agents, built in partnership with @jetbrains, and taught by @paulweveritt.

Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way.

A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity.

Skills you'll gain:
- Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start
- Plan, implement, and validate features in iterative loops using a spec as your agent's guide
- Apply the same repeatable workflow to both new and legacy codebases
- Package your workflow into a portable agent skill that works across agents and IDEs

Join and write specs that keep your coding agent on track!
https://t.co/hI4GwuvhtN

[Miguel Ángel Durán @midudev]

La verdad es que lo siento por la frase pero sigo pensando que destruye el rendimiento de la página sin necesidad alguna y que lo mejor es que la gente despierte sobre eso. 😂

Pero estoy de acuerdo que todo son trade-offs. Sólo que ahora mismo los de Google Fonts son demasiados.

Eso sí, también se pueden bajar las fuentes de Google Fonts y hospedarlas y ya está, sólo que Font Source es mucho más cómodo para hacerlo.

Aquí tienes una fuente con todas las explicaciones que necesitas más detalladas:
https://t.co/iSiCrIYkBm

Por otro lado, creo que es importante hablar de esto, ya que mucha gente tiene la falsa creencia que la MEJOR manera de usar fuentes en tu web es Google Fonts.

Todavía hay gente que cree que se cachean entre usuarios y es algo que dejo de hacerse por seguridad desde hace tiempo (https://t.co/Z6Hntaew2G)

Sin contar a nivel europeo los problemas de GDPR siendo consideradas ilegales si no se pedía permiso del usuario: https://t.co/pW5t1g0zw8

Y es que no me da un tuit para hablar de eso. Creo que es fácil entablar una conversación, preguntar y aportar las ideas en el mismo hilo. Creo que este ha sido una oportunidad perdida para eso.

Ah, y Google Fonts sigue siendo útil. Me parece un catálogo genial para encontrar fuentes y para prototipar rápido. :)

[Daniel Barrios Mogollón @dbmcoder]

@neondatabase @traversymedia Opencode with VSCode or Antigravity.

[Dhanian 🗯️ @e_opore]

If I had to start with full stack development in this AI era, I'd learn these concepts:

1. HTML5

2. CSS3

3. JavaScript (ES2026+)

4. TypeScript

5. Responsive Design

6. Accessibility (a11y)

7. DOM Manipulation

8. Browser APIs

9. Fetch API

10. Async/Await

11. Git & GitHub

12. Package Managers (npm, pnpm, yarn)

13. Build Tools (Vite, Webpack)

14. React

15. Next.js

16. Server Components

17. Client vs Server Rendering

18. State Management

19. Context API

20. Redux / Zustand

21. Tailwind CSS

22. UI Libraries

23. Design Systems

24. REST APIs

25. GraphQL

26. API Integration

27. Authentication (JWT, OAuth)

28. Authorization

29. WebSockets

30. Forms & Validation

31. Error Handling

32. Testing (Jest, Playwright)

33. Performance Optimization

34. Code Splitting

35. Lazy Loading

36. SEO Optimization

37. Backend Basics

38. Node.js

39. Express.js

40. API Design

41. Microservices

42. Serverless Functions

43. Databases (SQL)

44. Databases (NoSQL)

45. ORMs (Prisma, Drizzle)

46. Caching (Redis)

47. File Uploads

48. Cloud Storage

49. Docker

50. CI/CD

51. Deployment (Vercel, AWS)

52. Environment Variables

53. Security Best Practices

54. HTTPS / TLS

55. Rate Limiting

56. Logging & Monitoring

57. Observability

58. Scalability

59. Load Balancing

60. CDN

61. Edge Functions

62. Progressive Web Apps (PWA)

63. Mobile Responsiveness

64. Cross-Browser Compatibility

65. Web Performance Metrics

66. Lighthouse Optimization

67. AI APIs (OpenAI, etc.)

68. Prompt Engineering

69. AI SDK Integration

70. RAG (Retrieval-Augmented Generation)

71. Vector Databases

72. Embeddings

73. AI Chatbots

74. AI Agents

75. Automation Workflows

76. Background Jobs

77. Event-Driven Architecture

78. Webhooks

79. Streaming Responses

80. Real-Time Apps

81. Feature Flags

82. A/B Testing

83. Analytics Integration

84. User Tracking

85. Personalization

86. Payments (Stripe)

87. Subscription Systems

88. SaaS Architecture

89. Multi-Tenancy

90. Role-Based Access Control (RBAC)

91. API Gateways

92. Service Mesh

93. Monorepos

94. Turborepo / Nx

95. Code Quality (ESLint, Prettier)

96. Documentation

97. Technical Writing

98. System Design Basics

99. Debugging Skills

100. Product Thinking

(...and more concepts)

===

👋 PS - Want a detailed breakdown of each concept?

Read right now:

→ Grab the Modern Full Stack Latest Edition Handbook:
https://t.co/bY2fvlAWxa

===

💾 Save this for later & RT to help others learn full stack development.

👤 Follow @e_opore + turn on notifications.

[Daniel Barrios Mogollón @dbmcoder]

@diegomarino @thenetninjauk Acabo de recordar que siempre vuelvo acá para la config inicial. @TheOdinProject

https://t.co/YZr9oZEPpF

[Daniel Barrios Mogollón @dbmcoder]

@diegomarino Conciso, directo y completo. @thenetninjauk

https://t.co/ucszZA1AXv

[Planet @planet]

Today marks a historic return to the moon for @NASA! 🚀 We captured this view of NASA’s Space Launch System rocket and Orion spacecraft on the launch pad earlier today. Here we go!

(1 & 2) SkySat • Kennedy Space Center, Florida, United States • April 1, 2026

[Mabel Hernández @MabelHdezM]

En la era de la #InteligenciaArtificial🤖: “¿El PDF está quedando obsoleto?”

Hace unos días me encontré con una publicación donde se cuestionaba si el PDF📄estaba en camino hacia el desuso frente a CRD (Compute-Ready Document).

🔸Entre otros aspectos, la publicación se enfocaba en dos puntos:

1⃣. Cuando un modelo procesa un PDF mediante sistemas de recuperación (RAG), el documento se fragmenta sin criterio semántico. Provocando:

☑️Métodos se separan de resultados.
☑️Gráficas de sus leyendas.
☑️Conclusiones sin contexto.

Este problema no es solo técnico. Es un tema de #ArquitecturaDeLaInformación y también de integridad científica, ya que, si la IA no distingue estructura ni procedencia, ¿cómo diferencia investigaciones científicas de contenido fraudulento?

2⃣. El Compute-Ready Document (CRD) como reemplazo del PDF. Al ser documentos diseñados, desde su origen, para ser comprendidos por IA, con datos estructurados, metadatos y trazabilidad (DOI, ORCID, revisión por pares).

Pero aquí abro el debate👇🏽:

🔹I. Suponiendo que el PDF está rumbo a la obsolescencia, ¿quién debe definir el nuevo formato del conocimiento computable? ¿Las universidades, organismos, editoriales… o las grandes empresas tecnológicas?

🔹II. Cuando una IA genera una respuesta basada en cientos de artículos académicos, ¿cómo se distribuye la autoría, el crédito y la responsabilidad jurídica? ¿Estamos frente a un vacío del #DerechoTIC o ante la necesidad de nuevos estándares de trazabilidad?

🔹III. Si el valor ya no está en “leer el paper” sino en “usar la respuesta generada por IA”,
¿quién captura ese valor? ¿La comunidad científica o las plataformas que intermedian el conocimiento?

🔹IV. En caso de que la IA se convierta en la principal intermediaria del conocimiento científico, ¿quién audita sus sesgos? ¿Quién garantiza que no reordene prioridades científicas según criterios opacos?

☝️Cierro con una pregunta final: ¿Estamos rediseñando la arquitectura del conocimiento pensando en el interés público o en la eficiencia del mercado tecnológico?

[Shu @shuding]

In case you didn’t know, React didn’t invent RSC, SSR, or Hydration, conceptually:

SSR is Express:

app.get('/', (req, res) => {
res.render('index', { name: 'John' })
})

RSC is Pug:

if name == "Bob"
button(class="btn") Hello Bob
else
button(class="btn") My name is #{name}

Hydration is jQuery:

$('.btn').click(...)

That’s why there’s no window in SSR, and you can’t set onClick in RSC, and you have hydration errors in jQuery too (such as a typo of .btn) but with a less fancy name.