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A viral post shares a 15-minute setup combining Claude Code with tools like graphify (folder-to-knowledge-graph, 126k GitHub stars and YC-backed), googleworkspace CLI, Playwright MCP, and jev-guard to build a self-checking personal agent with memory, scheduling, and safety guards.
A fintech talent lead built an AI sourcing agent that trains on 60,000 past ATS applicants to predict interview and hiring outcomes, autonomously generating and testing search hypotheses to surface top candidates. Over a month it found 30 qualified candidates and 8 extended offers, though the author notes it inherits bias from past hiring patterns and struggles with low-data roles.
A developer praises @yetone for generously open-sourcing the development workflow behind Magpie, calling it a 'secret manual' for AI-assisted engineering and urging engineers who use Claude Code / Codex to watch the accompanying video about the process.
A thread sharing Anthropic's Claude Code 'advisor' trick: run Sonnet for coding, Haiku for parallel exploration, and Opus as an on-call advisor to get better results with far fewer tokens, along with a paste-ready configuration prompt.
GitHub's blog walks beginners through the GitHub Copilot app's built-in diff, terminal, and browser panels, showing how developers can review, run, and preview agent-generated code in one place without tab hopping.
This article introduces an open-source project called lemo-opuscar, which uses Claude Opus 5.5 to render videos frame-by-frame via code, add soundtracks, and mix, creating 39 different video styles without relying on traditional video generation models.
The article discusses methods to evaluate the anonymous AI model Space Bunny for agent workflows, focusing on stateful testing, error recovery, and consistency to ensure reliability.
The article introduces Stageflow, a configurable pipeline platform for multi-stage agent workflows that supports local execution, CI integration, and MCP server connectivity.
They built a reference implementation of an end-to-end agent workflow in TypeScript using Mastra, featuring 6 scoped agents, an orchestrator, and 3 feedback loops for automating code generation, validation, and monitoring tasks.
A developer shares their experience using the quantized Qwen 3.8 27B model on a 4060Ti GPU to autonomously write and merge a feature branch, demonstrating significant progress in local AI for coding tasks.
The author describes using Mastra's new preview deployment feature to let their coding agent automatically deploy changes to a sandbox, test them via API and UI, and then open a PR, closing the verification gap.
A team benchmarked routing different stages of an AI agent workflow to different models versus sending every request to Claude Opus 5 across 89 Terminal-Bench 2.1 tasks, and found surprising results.
The author created an open-source tool called awman that implements Claude's dynamic workflows concept, allowing multiple agents/models to collaborate on tasks with features like leader-designed workflows, shared context, and automated remediation.
A plugin distills Fable's problem-solving approach into a structured loop with verification agents, improving model reliability on research tasks and error detection, especially for weaker models.
Someone has organized the entire agent workflow of Fable 5 into 9 images, which can be directly fed to Claude for use, making it convenient and efficient.
Hermes Agent 0.18.0 Runtime is now officially live, with new /moa and /learn instructions, improving combined model calling and learning context capabilities.
The author introduces Dagu as a lightweight alternative to Temporal for dynamic workflow orchestration, along with a promotional plug for the Northflank platform.
Argues that the key skill for product managers in the AI era is loop engineering, not prompt engineering. Describes how to create reusable, self-improving loops for AI agents to maintain quality and avoid drift.
Maka has released Autonomous Task Loop v1, enabling a persistent agent loop: preflight → runtime → SelfCheck → FeedbackObservation → Decision. It supports self-checking, budget control, and state recovery, giving Maka's desktop AI workstation the foundational ability to run ongoing tasks.
This article proposes a 14-step roadmap from single agent to self-evolving system, emphasizing that base engineering (models, tools, permissions, context) is the key to determining the quality of loop output, and details practical methods for building an efficient base such as CLAUDE.md, sub-agents, skills, hooks, and state files.