Give it a machine learning directory and AutoCog will create predict.py and cog.yaml until it successfully runs a prediction
Cog lets you create a Docker image from a machine learning repository with very little code. But wouldn’t it be better if you didn’t have to write any code? Enter AutoCog!
Inspired by tools like Auto-GPT and BabyAGI, AutoCog uses GPT-4 to not only write code, but to run and fix the code. The algorithm is roughly:
Give AutoCog a machine learning repository
Order the files in the repository based on how important they are to Cog
Pass as many of them as the GPT-4 context window allows into GPT-4
The Prehistory of A.I. Slop - Longreads
The Prehistory of A.I. Slop
x.AI plays catch-up with Grok Build, its first terminal-based coding agent
xAI launches an “early beta” of Grok Build.
OpenAI made Codex smart enough that the bottleneck moved. Most people haven't noticed where it went.
Gemini vs. ChatGPT for Coding: A Developer's Guide
Domain-specific AI agents at scale: CustomGPT.ai serves 10,000+ customers with Pinecone | Pinecone
Beyond BYOK: Why governance matters for AI agents
