OpenProse is a declarative language for standing model work in which you describe desired world states in Markdown contracts and a reconciler handles execution. Rather than scripting sequential agent steps that drift over time, you declare what must stay true and the system determines how much work is needed to keep reality matching that declaration. It ships as a skill for coding agents like Claude Code or Codex CLI with no separate binary or server to run.
- The core unit is a "Responsibility" whose Maintains section defines material fields and a content-hash fingerprint to avoid redundant re-runs
- The dependency graph self-wires: a node's Requires section subscribes to upstream Maintains facets, so structure emerges from the contracts rather than being explicitly drawn
- Five kinds exist: responsibility, function, gateway, pattern, and test
- Continuity (when a node wakes) is a first-class contract section, not an afterthought
- Optional imperative ProseScript plans are available for cases requiring exact choreography
- The core unit is a "Responsibility" whose Maintains section defines material fields and a content-hash fingerprint to avoid redundant re-runs
- The dependency graph self-wires: a node's Requires section subscribes to upstream Maintains facets, so structure emerges from the contracts rather than being explicitly drawn
- Five kinds exist: responsibility, function, gateway, pattern, and test
- Continuity (when a node wakes) is a first-class contract section, not an afterthought
- Optional imperative ProseScript plans are available for cases requiring exact choreography
Beau Carnes writes about a new hands-on beginner's course on the freeCodeCamp.org YouTube channel designed to help developers master OpenAI Codex. The tutorial covers essential topics including installation, pricing tiers, and interface navigation, while also exploring advanced workflows like Plan Mode and Go Mode for autonomous software development.
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
Anirudh Ramanathan writes that while Anthropic suggests code is no longer the primary bottleneck in development, organizations cannot adopt a single, rigid software development life cycle (SDLC) for all changes. Instead, effective management requires a variety of processes tailored to the risk and complexity of each change—ranging from simple documentation fixes to high-stakes schema migrations—utilizing state machines that react to external evidence rather than fixed workflows.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
Frederic Lardinois writes that Harness field CTO Martin Reynolds is addressing the surge in pull requests caused by coding agents, which can increase new code volume from 1.5x to as much as 50x. To manage this "review bottleneck," Harness has launched a rebuilt Code Repository and an AI Code Review product designed specifically for high-frequency agent traffic rather than just human teams. The company's approach focuses on using a software delivery knowledge graph to provide reviewers with context quickly, helping them distinguish critical code changes from routine dependency updates.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
- Coding agents can increase the volume of pull requests by 10x to 50x compared to traditional developer workflows.
- Harness rebuilt its repository service as an "AI-first" platform that is Kubernetes-based and runs across multiple clouds.
- The new AI Code Review tool integrates with existing GitHub repositories, allowing teams to use it without migrating their entire codebase.
Pushpak Chhajed writes about the evolution of project rule systems for AI coding agents, explaining why Laravel Boost moved away from complex semantic search layers in favor of a simple markdown-based approach. To prevent instruction files like `CLAUDE.md` from becoming bloated and consuming excessive context, the team implemented a system using `.ai/rules` containing specific Markdown files linked by a generated two-column index. This "progressive disclosure" method allows agents to efficiently locate relevant project conventions without overwhelming their prompt window or requiring complex vector databases for small rule sets.
- The system uses an automatically updated `index.md` file to help agents map current file paths to specific rule files.
- Agents are encouraged to use a combination of index matching and `grep -rin` to find rules that span multiple directories.
- This approach aligns with advice from the Anthropic Claude Code team regarding progressive disclosure in agentic workflows.
- The solution avoids "staleness" risks associated with maintaining separate vector embeddings for small collections of files.
- The system uses an automatically updated `index.md` file to help agents map current file paths to specific rule files.
- Agents are encouraged to use a combination of index matching and `grep -rin` to find rules that span multiple directories.
- This approach aligns with advice from the Anthropic Claude Code team regarding progressive disclosure in agentic workflows.
- The solution avoids "staleness" risks associated with maintaining separate vector embeddings for small collections of files.
Leela Kumili writes about DoorDash's Flux platform, which moved LLM-based coding agent workloads from developer laptops to cloud infrastructure, automating 130,000 engineering tasks in a single month and supporting over 25,000 automated code reviews per week. The platform was built after local execution hit limits on power, uptime, and security, and is organized around four primitives: Firecracker microVM sandboxes, an in-house MCP gateway, YAML-defined playbooks, and multiple invocation surfaces (Slack, GitHub, cron, CLI, conversational interfaces).
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
Mashrul Haque writes about using git worktrees to run multiple LLM coding agents in parallel, each on its own branch and isolated directory, eliminating the bottleneck of sequential agent sessions. He demonstrates a 5-agent workflow with Claude Code on a .NET/Blazor project, reporting roughly 5x throughput gains over single-agent sequential work.
- Boris Cherny, Creator and Head of Claude Code at Anthropic, calls worktrees his number one productivity tip and runs 3'' 5 simultaneously
- Claude Code ships a built-in `--worktree` (`-w`) flag that auto-creates an isolated worktree per session
- Boris Cherny, Creator and Head of Claude Code at Anthropic, calls worktrees his number one productivity tip and runs 3'' 5 simultaneously
- Claude Code ships a built-in `--worktree` (`-w`) flag that auto-creates an isolated worktree per session
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
