The AI Product Ladder (and why most apps are stuck on Rung 1)
Most "AI features" are stuck on Rung 1: a single LLM call. Here's the three-rung AI product ladder and why Rung 3 means going agent-native.
Read the full articleYou might also wanna read
RunLLM: AI Support Engineer for Resolving Complex Issues
Built on 10 years of UC Berkeley research, RunLLM reads logs, code and docs to resolve complex support issues. Saves 30%+ eng time, cuts MTT
Product Hunt·11mo agoRunLLM: AI Tool for Resolving Support Issues with UC Berkeley Research
Built on 10 years of UC Berkeley research, RunLLM reads logs, code and docs to resolve complex support issues. Saves 30%+ eng time, cuts MTT
Product Hunt·11mo ago
AI Agent Scopes and Tool Lifecycles in Production-Grade Systems
Building production-grade AI agents requires more than just wiring LLM calls to tools
How LLM Tool Calling Actually Works: Build an Agent From Scratch in 160 Lines of Python
No LangChain. No CrewAI. Just the loop that every agent framework is hiding from you. Continue reading on Towards AI »
LAI #134: Your First LLM App on AWS for Under a Dollar
Loop engineering with Claude Code, plus a Towards AI enterprise launch, vLLM on L40S, and context windows as memory management Good morning,
Guide to Using OnPrem.LLM Agent Pipeline for Autonomous AI Task Execution
A tool for running on-premises large language models on non-public data
Comments
Sign in to join the conversation.
No comments yet. Be the first.