Cold email styles baked into the prompt = huge time saver.
@godofprompt
-
Chain-of-Thought prompting compared to modular code
By
–
The analogy to modular code hit hard. Imagine building an entire app in one function. That’s what we’re doing with CoT.
-
Treating AI Prompts as Structured Products
By
–
The real unlock is treating prompts as products, versioned, structured, and use case specific.
-
The Role of Tool Use and APIs in AI Agent Value
By
–
Tool use is where agents stop being interesting and start being valuable.
APIs are their superpowers. -
The importance of context engineering for AI agents
By
–
Context engineering might quietly be the most important concept here. If your agent has bad context, it doesn’t matter how smart the model is.
-
OdysseyBench for Evaluating AI Agent Capabilities
By
–
OdysseyBench doesn’t just evaluate agents. It interrogates them. If your agent passes OdysseyBench, it's not just good it's real-world ready. Otherwise? You're still benchmarking illusions.
-
A 3-Stage Framework for Benchmarking AI Agent Performance
By
–
Want to run your own benchmark? Start with a 3-stage eval: • 1-app tasks debug basic tool calls
• 2- and 3-apps test memory + planning
• Compare long-context vs RAG summaries Log: • Pass rate
• Token usage
• Fail type per task -
5 Design Shifts for Building Better AI Agents
By
–
Want to build better agents? OdysseyBench implies 5 core design shifts: 1. Plan before acting (return explicit step list)
2. Add file/tool validation steps (pre-checks)
3. Use chunked memory, not full transcripts
4. Log task-specific failures (missing file, missing write)
5.