built a new feature into discrawl (store media), codex said it's done, then I used my codex review skill… https://
github.com/steipete/agent
-scripts/blob/main/skills/codex-review/SKILL.md
…
LLMS
-
Built discrawl store media feature, used codex review skill
By
–
-
Anticipation for GPT-5.6 Release
By
–
End game until GPT-5.6 ships next month and everyone forgets they said this.
-
ClickUp UI Reshapes Based on AI Model Choice: Claude, Gemini, ChatGPT
By
–
ClickUp is reshaping its Brain platform UI based on which model you pick. > With Claude Opus 4.7, the interface reads "Tell Claude what to do next." > With Gemini 3.1 Pro, it becomes "Let's ask Gemini." > With GPT 5.5, it says, “Let's ask ChatGPT.” The confirmed models are
-
Discussion on GPT-5.5 performance and marketing
By
–
OpenAI marketing is part of it. The other part is GPT-5.5 actually being good. Both can be true.
-
Codex autoreview and crabbox enable automated issue-to-fix
By
–
This is a game changer. With codex autoreview and crabbox I can now go from issue to fix almost fully automated. (yes it does burn lots of tokens)
-
CodexBar 0.26.0 introduces Kiro, Antigravity, OpenRouter, Kimi and improvements
By
–
CodexBar 0.26.0 is live Kiro, Antigravity, OpenRouter, Kimi calmer menus + keyboard nav better Codex/Claude limits and cost scoping named macOS assets, CLI + Homebrew fixes
-
New Technical Guide to Designing Multi-Agent AI Systems with MCP
By
–
New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI -
LLM Engineer’s Handbook for Model Development and Training
By
–
LLM Engineer's Handbook — Master the art of engineering Large Language Models LLMs from concept to production: http://
amzn.to/4dUQrv6 v/ @PacktDataML Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine with the help of hands-on -
Guide to Building Production-Ready AI Agent Systems
By
–
30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: http://
amzn.to/41ckg6z v/ @PacktDataML —
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
Use LangChain and -
Enhancing Generative AI Systems with RAG Integration
By
–
Unlocking Data with #GenerativeAI and RAG — Enhance Generative AI systems by integrating internal data with large language models using RAG: http://
amzn.to/3PtFHdv v/ @PacktDataML