Claude Code often hallucinates by inventing functions, imports, or test results, which wastes time; this article explains a four‑layer engineering setup—honesty rules, pre‑write verification, post‑write hooks, and a dedicated fact‑checking sub‑agent—to force the model to provide evidence before claiming code works.
Agnes AI is offering its text, image, and video model APIs for unlimited free use, prompting a shift in AI application development where high‑frequency, multi‑step workflows—such as agents, content editing, and short‑video generation—can be prototyped and iterated without the token‑cost barriers that previously limited small teams.
Jason Liu, the creator of the 13k‑star Instructor library and new Codex team member, reveals how to turn Codex into a persistent, autonomous agent using long‑living threads, Heartbeats + @computer, Goal mode, local memory storage, and validation loops for continuous work.
OpenAI identified API‑overhead as the new bottleneck after faster model inference and introduced a persistent WebSocket connection that caches conversation state, overlaps request phases, and preserves the original API shape, delivering up to a 40% end‑to‑end latency reduction and dramatically higher TPS.
Claude Managed Agents provides a fully managed cloud environment that handles agent loops, tool execution, context management, and session continuity, allowing developers to define an agent once and let Anthropic’s runtime take care of the rest, dramatically reducing infrastructure overhead.
The author reflects on moving from traditional backend work to AI development, describing how the shift brings longer hours but a more engaging, exploratory workload, a broader perspective, new documentation practices, and a changed hiring landscape.
This guide walks developers through the full lifecycle of OpenAI Codex—from understanding its role as a code‑agent, comparing it with Claude Code, and choosing the right usage mode, to installing the CLI, configuring MCP plugins, selecting models, and mastering the most common commands and shortcuts for an efficient AI‑augmented development workflow.
The March 30 AI daily digest curates recent open‑source voice input and TypeScript libraries, new development workflows, a 30B parameter model that runs on 24 GB GPUs, and NVIDIA's PivotRL research that reduces reinforcement‑learning rollouts while matching end‑to‑end performance, all with concrete benchmarks and links.
This article explains the common pitfalls of AI‑generated code, introduces the open‑source Superpowers framework that enforces a structured, test‑driven workflow, details its core skills and mandatory steps, shows cross‑platform installation for Claude Code, Codex and OpenCode, and offers practical tips for effective AI development.
This guide explains how to harness Cursor's AI agents for software development by covering agent harness components, planning modes, context management, rule and skill extensions, long‑running loops, image handling, common workflows like TDD and Git integration, parallel execution, cloud delegation, and debugging strategies.
Refly is an open‑source Agent Skills Builder that lets you create production‑grade AI workflows in minutes using natural language, offering versioned, reusable skills, runtime intervention, extensive tool integrations, and export options that outperform traditional automation platforms.
In this in‑depth interview, Peter Steinberger explains how AI agents let him submit and merge hundreds of commits daily, replace traditional code reviews with prompt‑driven requests, and redesign his development workflow around a closed‑loop validation system that reshapes modern software engineering.
This article provides a comprehensive technical overview of RAGFlow, an industrial‑grade Retrieval‑Augmented Generation platform, detailing its architecture, core components such as DeepDoc, intelligent chunking, embedding integration, multi‑stage retrieval, and agent workflow, while comparing it with traditional RAG shortcomings.
This tutorial walks through creating a full‑featured LLM Agent workflow using LangGraph, covering goal definition, task decomposition, execution nodes, state updates, re‑planning logic, and user feedback, while comparing ReAct and Reflexion approaches and providing complete Python code examples.
