42 related articles

Superlog Responder is a free open-source AI bug-fixing agent that integrates with Sentry and Datadog alerts to automatically perform root cause analysis and generate mergeable PRs.

Deep analysis of six core AI model issues: open-source vs closed-source models, inference throughput vs accuracy tradeoffs, benchmark gaming, distillation vs RL, reward hacking defenses, and dynamic quantization technology.

Learn how to parallelize Cursor browser Workers from serial to parallel execution using distributed Worker pools, proxy pools, token bucket algorithms, and exponential backoff to compress 2000-3000 page scraping tasks from hours to 15-20 minutes.

A developer proposes a Flex API-based slow mode for Codex, trading speed for nearly double the usage quota. We analyze the product logic, technical feasibility, and business challenges.

Deep dive into how graph engineering uses state machines and directed graphs to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.

Explore how graph engineering uses state machines and directed graph structures to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.

Openbase is a voice-driven AI coding agent management tool that lets developers dispatch tasks, steer agents, and approve code changes via phone. Deep analysis of its cross-platform sync and voice interaction advantages and limitations.

After heavy use of AI coding tools like Cursor and Claude, an indie developer discovers his debugging and code comprehension skills are eroding. Exploring the skill atrophy risks behind AI-boosted productivity.

Cursor Pro users hitting persistent errors? This deep dive covers the top causes — rate limits, proxy issues, account status — plus a 6-step troubleshooting guide.

Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.

Vibe Coding lets non-developers build real business software with AI. Explore Claude Code, Lovable, Replit, and Cursor — and learn how to build AI Agent workflows for your business.

The full GPT-5.6 model lineup is live. How can domestic developers access it at low cost via API relay stations? This article explains the principles, registration, token setup, client integration, and key risks.

An in-depth look at the Skills paradigm in AI programming: through intent routing and script encapsulation, let AI agents auto-manage multi-channel LLM APIs on a One API gateway for one-click distribution, health checks, and auto-degradation.

A senior developer's 24-hour deep test of Grok 4.5: a 1.5T-param MoE model at $2/M input tokens, with coding benchmarks rivaling GPT-5.5. Real performance, token efficiency, and limits explained.

FDE (Forward Deployed Engineer) is the hottest emerging role in the AI deployment wave, combining a technical CTO, full-stack AI engineer, and business consultant. Learn the two FDE tracks, core skills, and how to transition into one.

A deep dive into Harness Engineering — the third phase of AI coding. Based on research across 2,853 GitHub repos, explore agents.md, Skills, MCP, and eight configuration mechanisms to control your AI coding assistant.

Pure frontend roles are shrinking; AI Agent development is the high-salary divide. This guide breaks down the full skill tree for frontend engineers pivoting to AI: TypeScript, frameworks, AI productivity, and Agent core concepts (MCP, Tool Calling, Skill).

DeepSeek R1 lacks Function Calling and JSON Output by default. Qwen3's programmable thinking modes make it the top open-source agent choice. Key LLM selection pitfalls and MCP protocol updates.
Building a Coding Agent with LLM: A De…
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.

Developers spend 60%+ of their time understanding legacy code. Agentic Coding uses AI Agents to autonomously explore codebases, map hidden dependencies, and generate tests—turning legacy modernization from risky archaeology into a controlled engineering process.