Workflows Articles
Browse 1206 articles about Workflows.
12 Million Token Context Windows: What SubQ Means for AI Agent Workflows
SubQ's 12M token context window lets agents process entire codebases, legal contracts, and financial filings at once—at 5% the cost of Claude Opus.
What Is the Harness Maintenance Checklist? 5 Questions to Ask Before Every Model Update
Before updating your AI agent's model, audit what it reads, what it can touch, what its job is, what proof it provides, and whether it still delivers value.
AI Agent Harness Maintenance: Why Agents Break When Models Get Better
Agents can fail not because the model degraded but because it improved. Learn why harness maintenance is the most underrated skill in agentic AI development.
How to Use AI for Deep Research Reports: Local Models, Web Search, and Visual Output
Tools like Odysseus can run multi-round deep research using local models and produce formatted HTML reports with table of contents—entirely offline.
How to Use Claude Code /goal and Auto Mode Together for Fully Autonomous Workflows
Combine Claude Code's Auto Mode and /goal command to run tasks end-to-end without approvals or early stops. Here's the setup and when to use it.
Claude Code Ultra Code Mode Explained: When to Use /effort Max vs Dynamic Workflows
Ultra Code spawns parallel sub-agents for massive tasks while /effort max deepens single-agent reasoning. Learn which to use and when for best results.
How to Build an Expert AI Coding Workflow: Skills, Automations, Loops, and Cloud Agents
Top agentic coders use skills, automations, loops, and cloud agents to ship code 24/7. Here's the full workflow from beginner prompting to expert automation.
How to Use GLM 5.2 in Claude Code: Cheaper Agentic Workflows Without Sacrificing Quality
GLM 5.2 plugs into Claude Code via OpenRouter or Z.AI, cutting costs 5x vs Opus. Here's how to set it up and when to use it over frontier models.
How to Use a Multi-Model AI Coding Workflow: Fable for Planning, Composer for Execution, GPT for Review
Using different models for planning, implementation, and review cuts costs and speeds up delivery. Here's how to build a multi-model skill in Claude Code.
How to Run DeepSeek V4 Flash Locally on a MacBook or DGX Spark with Dwarf Star
Dwarf Star's selective quantization shrinks DeepSeek V4 Flash from 568GB to 81GB, letting you run a 284B-parameter model on consumer hardware. Here's how.
SSD Streaming for AI Models: How to Turn RAM from a Wall into a Dial
Dwarf Star's SSD streaming stores expert weights on disk and loads them on demand, eliminating the binary 'fits or doesn't run' problem for large local models.
AI Agent Harness Maintenance: Why Your Wrapper Breaks When the Model Gets Better
Agents break when models improve, not just when they fail. Learn the four principles of harness maintenance that keep AI workflows reliable over time.
Cross-Vendor AI Agent Review: Why Claude Should Review Codex's Code and Vice Versa
Using different AI models to review each other's work reduces internal bias and catches more bugs. Learn how to set up cross-vendor review in your workflows.
How to Audit Your AI Agent Harness: 5 Questions to Ask Before Every Model Update
Use this five-question audit to check your agent's sources, reach, job definition, proof requirements, and value before switching to a new model.
How to Build an AI Second Brain: 5 Levels from Basic Routing to Knowledge Graphs
Learn the five levels of AI second brain architecture—from simple folder routing to semantic search and knowledge graphs—and which level fits your needs.
How to Build an AI Workflow That Survives Sudden Model Access Loss
When a frontier model goes offline overnight, your workflows shouldn't stop. Learn how to build model-agnostic AI systems that survive access disruptions.
How to Use /goal and /loop in Claude Code for Autonomous Long-Running Workflows
Combine /goal and /loop in Claude Code to define completion criteria and set recurring schedules so your agent runs until the job is truly done.
Loop Engineering vs Harness Engineering: What's the Difference and Which Do You Need?
Loop engineering sets cadence and completion criteria. Harness engineering defines the full system around an agent. Learn when each approach wins.
How to Build a Portable AI Second Brain That Works Across Claude, Codex, and Hermes
Build your AI second brain as markdown files and folders so any agent harness can read it. Learn the routing rules, folder structure, and memory patterns.
Semantic Search vs Keyword Search for AI Agents: When Vector Databases Win
Understand when to use vector databases for semantic search versus simple keyword matching in AI agent memory systems, with real examples and trade-offs.