This article explains how JD's team built an attribution‑analysis Agent that maps analysts' investigative steps into a plan‑and‑action loop, uses parallel search with pruning, script constraints, and dynamic structured memory to make data‑driven root‑cause analysis faster, more reliable, and interactive.
This article explains why LLMs are stateless, distinguishes short‑term from long‑term memory needs for agents, critiques common memory solutions, and then details Claude Code’s two‑layer architecture—static CLAUDE.md with six hierarchical files and a dynamic auto‑memory system that uses structured markdown, a lightweight selector model, and aging warnings—to provide a practical, source‑level blueprint for building robust agent memory.
Clawdbot (now Moltbot) upgrades its memory from a static file‑based store to a three‑layer dynamic knowledge graph that automatically extracts facts, aggregates weekly summaries, and preserves history, enabling the AI to stay current with a user’s life without manual edits.
This article explains the fundamentals of malloc and free, covering their signatures, how they interact with the brk/sbrk system calls, the structure of memory control blocks, multiple allocation strategies such as explicit free lists, segregated lists, buddy systems and tcmalloc, along with code examples and their trade‑offs.
This article explains the purpose, prototype, and usage of C's malloc function, delves into its underlying implementation using free‑list mechanisms, virtual‑memory translation, system calls like brk and mmap, and compares it with related functions such as calloc, realloc and new, while providing practical code examples and best‑practice guidelines.
This guide explains how to allocate, initialize, resize, and release heap memory in C using malloc, calloc, realloc, free, and memset, with detailed code examples and tips for avoiding memory leaks.
This guide explains typical C memory‑allocation errors, practical ways to avoid them, and introduces a lightweight logging system that automatically records allocations and frees to help developers detect memory leaks during debugging.
This article reviews Alibaba DAMO’s Conversational AI team research on low‑resource few‑shot learning, introducing induction and dynamic‑memory networks, detailing their architecture, experimental setup on ARSC, ODIC and miniRCV1 datasets, and demonstrating state‑of‑the‑art performance improvements.
