Me in 2012 instead of learning: DSA System Design AI / ML Generative AI LLMs AI Agents RAG Vector Databases Embeddings Fine-tuning Prompt Engineering MCP AI Coding Agents Python Java C C++ Go Rust JavaScript TypeScript HTML CSS React Next.js Vue Angular Node.js Express.js NestJS Spring Boot Django FastAPI REST APIs GraphQL WebSockets SQL PostgreSQL MySQL MongoDB Redis Elasticsearch Kafka RabbitMQ Docker Kubernetes Terraform AWS Azure GCP Linux Git GitHub CI/CD DevOps Microservices Serverless Cloud Cybersecurity Data Engineering Data Science MLOps Data Analytics Blockchain Web3 Smart Contracts Solidity Edge Computing Distributed Systems Observability Prometheus Grafana OpenTelemetry Supabase Firebase Vercel Cloudflare PostgreSQL + pgvector LangChain LlamaIndex PyTorch TensorFlow Hugging Face Ollama OpenAI APIs Claude APIs Gemini APIs Model Context Protocol AI Infrastructure GPU Computing CUDA

More than 25 solar-powered smart nests have been installed in the Sun Island Scenic Area in Harbin, northeast China's Heilongjiang Province, providing wild mandarin ducks with safe and secluded spaces for nesting and hatching. So far this year, around 100 ducklings have been born in the scenic area.

the AI app tech stack is basically solved. nextjs, vercel, convex, clerk, stripe, tailwind. pick your models, ship. what's not solved is the layer underneath your agents. where does context live between sessions? how does cursor's memory stay separate from claude code's memory when they're both working the same repo? the stack gets you to demo day. scoped persistent memory is what gets you past it.

"Matryoshka Language Model Suites" Instead of training every model size separately, this paper nests 500M, 1.5B, and 3B models inside one architecture and trains them together. The smaller models are standalone checkpoints, get near-free distillation from the largest model, and share weights + KV cache for speculative decoding. And you still get the same performance, with 36% less training compute, and 14-26% faster speculative decoding. So a model family can become one jointly trained system instead of several independent models.

Robinhood plagiarism → the original on Solana → the fork on Robinhood Chain Carbon copy design, identical copy, exact same "borrow USD against tokenized stocks" mechanics. When a product is so good it gets stolen entirely -that’s a compliment. @NestUSD pioneered. @root_evm just pressed Ctrl+C + Ctrl+V. Just another day in crypto