SWE-bench Verified is a human-filtered subset of 500 instances from SWE-bench created in collaboration with OpenAI to provide a reliable evaluation set for coding agents and language models. Human annotators reviewed each instance to ensure problem descriptions are clear, test patches are correct, and tasks are solvable with available information. The Verified leaderboard shows results from a wide variety of AI coding systems, and a Bash Only view isolates language model performance using mini-SWE-agent in a minimal bash environment.
- Human validation ensures clarity, correctness of test patches, and solvability
- Bash-only comparison uses mini-SWE-agent with ReAct loop, no tools or scaffold
- Leaderboard distinguishes mini-SWE-agent LM results from full agent systems via Agent dropdown
- Release 2.x uses tool calling vs 1.x parses actions from output strings, making releases not directly comparable
- Human validation ensures clarity, correctness of test patches, and solvability
- Bash-only comparison uses mini-SWE-agent with ReAct loop, no tools or scaffold
- Leaderboard distinguishes mini-SWE-agent LM results from full agent systems via Agent dropdown
- Release 2.x uses tool calling vs 1.x parses actions from output strings, making releases not directly comparable
Anurag Singh writes that Cursor’s strength is its capacity for deep project understanding through efficient context management rather than simple model speed. Instead of pushing a whole repository into an LLM's window, the IDE employs search tools to provide only relevant code segments as tasks arise. This curated approach ensures models receive high-quality information and instructions tailored to their specific capabilities, allowing them to maintain existing architectural patterns more effectively.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
A distillation of the Claude Fable 5 workflow into actionable skills designed to guide AI agents through a systematic think, act, and prove methodology. The framework improves agentic reliability by enforcing specific sequences like classifying tasks, gathering parallel evidence from primary sources, making surgical edits rather than broad changes, and undergoing adversarial verification via an automated judge. It includes specialized domain adapters for sectors such as coding, research, marketing, and DevOps to tailor the reasoning process to specific professional requirements. The method is specifically designed to mitigate common LLM failures like reward hacking, silent code errors in tests, and false claims of task completion.
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
>"Before Fable 5 was deprecated, it wrote down its own problem-solving method. Step by step. How it classifies a task, defines "done," gathers evidence, commits to one recommendation, makes the smallest correct change, verifies by observation, and reports the outcome honestly."
- Core skills: fable-method (thinking), fable-loop (orchestration), fable-judge (verification), and fable-domain (adapter generation)
- Focuses on preventing unauthorized staging or "reward hacking" through strict observation cycles
- Validated against 260+ agent runs using blind LLM judges to verify results via code execution rather than reports
This article explores the concept of harness engineering, arguing that a functional AI agent is defined not just by its underlying model, but by the scaffolding built around it—including prompts, tools, sandboxes, and feedback loops. The author suggests shifting focus from picking the smartest model to designing robust systems that turn raw models into reliable agents. By treating mistakes as signals for new constraints rather than simple failures, engineers can create a ratchet effect that continuously improves agent performance through better configuration.
Main topics:
- Defining an agent as the combination of a model and its harness
- Reframing model errors as configuration or skill issues
- Using failure history to implement permanent rules via hooks and documentation
- Core primitives including filesystems, bash execution, sandboxes, and memory management
- Managing context rot through compaction and tool offloading
- Achieving long-horizon work through planning, verification, and agent splits
Main topics:
- Defining an agent as the combination of a model and its harness
- Reframing model errors as configuration or skill issues
- Using failure history to implement permanent rules via hooks and documentation
- Core primitives including filesystems, bash execution, sandboxes, and memory management
- Managing context rot through compaction and tool offloading
- Achieving long-horizon work through planning, verification, and agent splits
An exploration of the new Qwen3.6-27B open weight model, which claims flagship-level agentic coding performance that surpasses previous larger MoE models while being significantly smaller in size. The author tests a quantized version using llama-server and demonstrates its impressive ability to generate complex SVG graphics locally.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
Key points:
- Qwen3.6-27B outperforms the older Qwen3.5-397B-A17B on coding benchmarks.
- Dramatic reduction in model size from 807GB to approximately 55.6GB for the base version.
- Successful local execution using a 16.8GB quantized GGUF version via llama.cpp.
- High-quality SVG generation capabilities for complex prompts like a pelican riding a bicycle.
As AI agents evolve from writing simple code snippets to building entire systems, the traditional focus on learning programming syntax like Python or Java is becoming less critical. The author argues that we are shifting from an era of manual coding—described as digital bricklaying—to an era of intent architecture, where the primary skill is knowing what to build and how to direct AI to do it. To prepare for this future, focus should shift toward high-level logic, critical discernment, and creative synthesis rather than memorizing syntax.
Key points:
* Transition from syntax-based coding to intent-based architecture.
* The importance of iterative logic in refining AI outputs.
* Developing a "BS detector" through domain knowledge to spot AI hallucinations.
* Using creative synthesis to combine human ideas that LLMs cannot independently connect.
* Moving from being a technical executor to a supervisor or manager of AI agents.
Key points:
* Transition from syntax-based coding to intent-based architecture.
* The importance of iterative logic in refining AI outputs.
* Developing a "BS detector" through domain knowledge to spot AI hallucinations.
* Using creative synthesis to combine human ideas that LLMs cannot independently connect.
* Moving from being a technical executor to a supervisor or manager of AI agents.
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
GitHub introduces Rubber Duck, an experimental feature for the GitHub Copilot CLI designed to provide a second opinion during coding tasks. By leveraging a different AI model family than the primary orchestrator—such as using GPT-5.4 to review Claude models—Rubber Duck acts as an independent reviewer to catch architectural errors, logical bugs, and cross-file conflicts that a single model might miss due to inherent training biases.
This handbook provides a comprehensive introduction to Claude Code, Anthropic's AI-powered software development agent. It details how Claude Code differs from traditional autocomplete tools, functioning as an agent that reads, reasons about, and modifies codebases with user direction. The guide covers installation, initial setup, advanced workflows, integrations, and autonomous loops. It's aimed at developers, founders, and anyone seeking to leverage AI in software creation, emphasizing building real applications, accelerating feature development, and maintaining codebases efficiently. The handbook also highlights the importance of prompt discipline, planning, and understanding the underlying model to maximize Claude Code's capabilities.
Goose is a free, open‑source AI agent that runs locally and can autonomously plan, code, test, debug, and execute full development workflows—making it especially useful for data scientists who need to automate repetitive, multi‑step tasks. It supports any LLM, interfaces with file systems and APIs, and can extend its capabilities via the Model Context Protocol (MCP) to connect with databases, Git, Slack, and more.
- Autonomous task execution from high‑level instructions.
- Local execution preserves data privacy and control.
- LLM‑agnostic: works with GPT‑4, Claude, or local models.
- Two interfaces: desktop GUI and CLI.
- Extensible through MCP for external tools and services.
- Ideal for rapid prototyping, data pipeline automation, MLOps, and environment setup.
- Autonomous task execution from high‑level instructions.
- Local execution preserves data privacy and control.
- LLM‑agnostic: works with GPT‑4, Claude, or local models.
- Two interfaces: desktop GUI and CLI.
- Extensible through MCP for external tools and services.
- Ideal for rapid prototyping, data pipeline automation, MLOps, and environment setup.
