Tagged articles

knowledge flywheel

4 articles · Page 1 of 1

Oct 10, 2026 · R&D Management

Zero-Based R&D Rebuild: Tencent's AI Pipeline Delivers 68% of Requirements End-to-End

Tencent's OK platform rebuilt its R&D process from scratch using zero-based thinking, enabling AI to deliver 68% of 254 requirements end-to-end with 51% completed within two hours, replacing traditional handoffs with a nine-stage pipeline, dual quality gates, surgical coding discipline, and a knowledge flywheel that shifts human roles to high-level judgment.

AI-assisted developmentR&D process redesignTencent OK platform

0 likes · 9 min read

Zero-Based R&D Rebuild: Tencent's AI Pipeline Delivers 68% of Requirements End-to-End

Tencent Technical Engineering

Oct 9, 2026 · R&D Management

Beyond AI Coding: End-to-End AI Automation Reshapes R&D Pipelines

The article details OK Platform's AI-driven end-to-end R&D pipeline that automates nine stages from requirement to deployment, achieving 68% auto-completion and 51% within 2 hours, using zero-based thinking to eliminate human bottlenecks, with structured specs, quality gates, knowledge flywheel, and human-as-decision-maker model.

AI-driven R&DOK PlatformOrganizational Efficiency

0 likes · 48 min read

Beyond AI Coding: End-to-End AI Automation Reshapes R&D Pipelines

DataFunTalk

Mar 24, 2026 · Artificial Intelligence

Memory‑Based Self‑Evolution: Redefining LLM Agents Beyond Parameter Updates

This article examines the limitations of traditional supervised fine‑tuning and reinforcement learning for LLM agents, introduces a memory‑based self‑evolution paradigm with technologies such as Dynamic Cheatsheet, ReasoningBank, ACE and MemGen, and shows how building an experience bank can turn static models into continuously learning agents, especially in the insurance sector.

LLMMemory SystemsSelf-evolution

0 likes · 13 min read

Memory‑Based Self‑Evolution: Redefining LLM Agents Beyond Parameter Updates

DataFunTalk

Feb 14, 2026 · Artificial Intelligence

Memory‑Based Self‑Evolution: Enabling AI Agents to Learn Like Humans

This article explores a new agent‑optimization paradigm—Memory‑Based Self‑Evolution—detailing how dynamic memory systems such as Dynamic Cheatsheet, ReasoningBank, ACE, and MemGen transform LLM agents from static, parameter‑only models into continuously learning entities that can adapt to real‑world data, with a focus on insurance industry applications.

Agent MemoryLLMSelf-evolution

0 likes · 13 min read

Memory‑Based Self‑Evolution: Enabling AI Agents to Learn Like Humans