workflows

A walkthrough of common mistakes when using Hermes Agent and how to build effective workflows, crons, and dashboards for automation.

A discussion on the missing infrastructure required to run AI agents in production, including monitoring, permissions, recovery, and audit trails, questioning whether this will become a new infrastructure category.

A user shares 30 overlooked Obsidian workflows and plugins that significantly enhance the tool's capabilities, turning it into a genuinely different tool compared to the default setup.

A comprehensive guide listing 30 essential Obsidian workflows, plugins, and setups that integrate Claude AI to build a powerful personal knowledge system, highlighting the growing Obsidian+Claude ecosystem.

Shared an open collaborative repository Awesome Vibe Research maintained by ModelScope. This repository collects and curates reusable, verifiable, and evolvable AI-assisted components across the full research workflow, including agents, skills, workflows, tools, and best practices. It aims to help researchers and developers leverage AI to improve research efficiency.

A GitHub repository that packages production-grade engineering skills for AI coding agents, encoding senior engineer workflows and quality gates into slash commands like /spec, /plan, /build, etc., with setup instructions for Claude Code, Cursor, and other tools.

A developer shares satisfaction with Opus 4.8 for planning and GPT-5.5 for execution, emphasizing that breaking tasks into smaller steps improves quality and that dynamic workflows are underrated.

Cursor AI launches a new interview series with developers, starting with a conversation with the Baseten team about their use of coding agents, current workflows, and future predictions.

JetBrains launches Mellum, a fast LLM designed for low-latency and high-performance workflows.

A CS student shares his experience building simple n8n automation workflows for small businesses, noting that straightforward solutions often outperform complex AI systems in real-world adoption.

Microsoft open sourced pg_durable, a PostgreSQL extension that enables durable execution of long-running SQL functions with automatic checkpointing and fault-tolerant resumption.

The author discusses the growing use of agent swarms/workflows for processing unstructured data at scale, noting that reliable execution drops significantly when deploying more than 30+ sub-agents in parallel, and teases a solution for combining intelligent decision-making with reliable task execution.

A practical guide arguing that mastering sub-agents requires building four specific workflows in a weekend, covering decomposition, context packaging, verification, and cost control, rather than spending 200 hours on tutorials.

Agency founder shares 13 free n8n workflows built with Claude Code for go-to-market automation, including prospecting, enrichment, and sentiment tracking.

A reflective article questioning the casual assumption that building AI agents is easy, highlighting the complex components like APIs, RAG, tool calling, memory, and orchestration, and suggesting that simpler workflows often suffice before needing true agents.

The author questions whether many so-called AI agents are better described as workflows, arguing that for repeatable browser tasks, defined workflows may be more reliable than agents that reinterpret steps each time.

The author argues that the real danger of AI agents is not their errors but their ability to perform final actions autonomously, suggesting that agents should stop one step earlier and leave the final click to humans or narrow workflows.

IBM Research explores how agent logic—software primitives like knowledge graphs and program analysis—can guide LLM-based agents to efficiently handle complex enterprise workflows, reducing hallucinations and costs while improving outcomes.

A discussion about whether AI agents can reliably automate complex, multi-step workflows without constant human supervision, asking about current limitations and experiences.

Haydn Belfield discusses how tokenmaxxing experiments and token leaderboards serve an inspirational and exploratory purpose by testing AI model limits and discovering new workflows.