Compute is the new capital: why AI startups raise GPUs, not just cash
For AI startups, access to GPUs can matter more than money. How cloud deals, credits and chip reservations shape who can build frontier models.
AI Desk1 min read
Section E
Developers & Builders
AI coding, agents, APIs, RAG, fine-tuning, local AI and MLOps.
29 stories RSS
Learning path for developers who want to build with LLMs
From your first API call to retrieval, tool use, evaluation and deployment: an ordered path for software developers moving into AI engineering.
AI Desk1 min read
How to cut your AI API bill: caching, batching and model routing
Practical techniques to reduce LLM API costs without hurting quality: prompt caching, batch APIs, smaller models, routing, shorter contexts and output limits.
AI Desk4 min read
Model deprecations explained: what happens when an AI model is retired
AI providers regularly retire older models. How deprecation timelines work, what breaks when a model is shut down and how to plan migrations without surprises.
AI Desk3 min read
AI CodingGuide
How to use AI coding assistants effectively: a practical workflow
Get better results from Copilot, Cursor, Claude Code and other coding assistants: give context, work in small steps, review every change and keep tests in the loop.
AI Desk3 min read
Vibe coding explained: building software by describing it — and the risks
What "vibe coding" means, how people build apps by talking to AI, where it works brilliantly, where it fails, and how to keep vibe-coded projects safe.
AI Desk3 min read
What are AI agents? How they plan, use tools and take actions
AI agents go beyond chat: they plan, call tools, browse and act toward a goal. How the agent loop works, the main design patterns, real uses and the risks.
AI Desk3 min read
Agents & Agentic AIGuide
Model Context Protocol (MCP) explained: the "USB-C port" for AI tools
MCP is an open standard for connecting AI assistants to tools and data. How servers, clients and tools work, how to try it, and the security points to know.
AI Desk3 min read
APIs & SDKsGuide
Your first LLM API call: a step-by-step guide in Python
Set up an API key safely, send your first request to a language model in Python, understand messages, tokens and parameters, and handle errors and costs.
AI Desk3 min read
APIs & SDKsGuide
Structured outputs and function calling: getting reliable data from LLMs
How to make language models return valid JSON that matches a schema, how function calling works, and how to validate outputs so your application never breaks.
AI Desk3 min read
How retrieval-augmented generation (RAG) works, step by step
RAG lets AI answer from your documents. Each stage explained: ingestion, chunking, embeddings, retrieval, reranking, prompting and evaluation, with practical tips.
AI Desk3 min read
Vector databases explained: how they work and how to choose one
Embeddings, similarity search, indexes like HNSW and the trade-offs between pgvector, Pinecone, Weaviate, Qdrant, Milvus and Chroma for your RAG project.
AI Desk3 min read
Fine-tuning an LLM with LoRA: a practical overview
When fine-tuning helps, how LoRA and QLoRA make it affordable, how to prepare data, train, evaluate and deploy an adapter — and when not to fine-tune at all.
AI Desk3 min read
Knowledge distillation explained: how small AI models learn from big ones
Distillation trains a compact "student" model to imitate a large "teacher". How it works, why it powers today's small models, and its legal and quality limits.
AI Desk3 min read
Local & On-device AIGuide
How to run a local LLM with Ollama: installation to first chat
Install Ollama, download an open model, chat in the terminal or a web interface, pick a model that fits your memory and use the local API from your own code.
AI Desk3 min read
Local & On-device AIGuide
Quantization explained: how big AI models run on small hardware
Quantization shrinks AI models by storing weights with fewer bits. How 8-bit and 4-bit formats work, GGUF, GPTQ and AWQ, and the quality trade-offs to expect.
AI Desk3 min read