Machine Learning

SerpApi's Markdown output reduces search result tokens from 24,723 to 6,435—a 74% cut that lowers costs and frees up context window space for AI agents.

JONI positions itself as an orchestration and execution layer above foundation models. Here's how its architecture and routing work in practice.

Five leading agentic coding CLIs compared across autonomy, tooling, permissions, cost, and real-world engineering workflows.

ArrowJS positions itself as the first UI framework built for AI coding agents. Here's what that means for data scientists building AI-powered apps.

NVIDIA NeMo Switchyard is an open-source routing layer that directs LLM requests to the right model based on complexity, saving cost and latency.

Spec-driven development with Claude Code fails when criteria have no failing state. Here's how to write specs that prevent agents from self-certifying completio

PagedAttention and RadixAttention solve the two core KV cache problems in LLM serving: memory fragmentation and redundant prefix computation.

A blind experiment comparing RAG and long-context retrieval on 12 questions across 32 articles, measuring cost, latency, and answer quality.

Three practical strategies for managing small LLM context windows, with Python examples covering sliding windows and token budgeting with RAG.

Learn how to fetch, clean, and convert webpages to Markdown, then use an LLM to return focused answers from page content.

Semi-supervised learning combines labeled and unlabeled data to train classifiers. Learn the core assumptions and algorithm types that make it work.

Harness, loop, and graph engineering solve distinct problems in agent design. Mixing them up leads to costly mistakes in production systems.