This paper from PayPal and NVIDIA quietly kills the myth that agentic AI needs giant models to work. PayPal just published a research paper showing that their biggest performance win did not come from a better prompt, a bigger model, or a clever orchestration trick. It came from
PROMPT ENGINEERING
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Claude Code Short Course: Learn to Use It Well
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Lots of buzz on Claude Code here today. This short course, created with Anthropic, is the best way to learn to use it well. Please enjoy it! https://t.co/DvEZ8NWq12
— Andrew Ng (@AndrewYNg) 29 décembre 2025Lots of buzz on Claude Code here today. This short course, created with Anthropic, is the best way to learn to use it well. Please enjoy it!
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Side Chat with Codex Agent for Parallel Interactions
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A thought, would it be worth having a 'side chat' to your codex agent while it is working away? I know you can have multiple terminals at the same time but it is a bit more complex to manage. If it is working for 20 mins, I wouldn't mind speaking to a sub agent to ask it stuff
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AI Goal Tracking Systems Guide
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Ultimate Guide to AI Goal Tracking Systems Transforms how I achieve goals and boost productivity • Automate tasks • Gain personalized insights • Manage goals effectively Click below to read more: https://
godofprompt.ai/blog/ultimate-
guide-to-ai-goal-tracking-systems
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LLM Inference Visualizer: Interactive Tool for Message Flow
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LLM Inference Visualizer Made by the LangChain Community Interactive tool visualizing LLM inference through drag-and-drop messages. Uses LangChain's message types to show how context, system prompts, and tool calling workflows shape model outputs in real-time. Watch the
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Personal LLM-as-a-Judge: Continuous Learning System
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The best early application of continuous learning is your personal LLM-as-a-judge. Imagine an LLM that knows exactly how you code, write emails, what you consider to be 'good' legal document or what is a good design – anything that you normally give feedback to LLMs on is
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Prompt engineering is about psychology, not precision
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I used to think prompt engineering was about precision. It's actually about psychology. You're not instructing a computer. You're activating patterns in a language model trained on billions of human decisions, consequences, and stakes. Treat it like that.
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Framing consequences for high-stakes LLM responses
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Think about it: When you write an email to a friend → casual
When you write an email that could get you fired → every word matters LLMs learned from both types of text. By framing consequences, you're telling the model: "use the high-stakes mode." -
Why LLMs Respond to Stakes in Text
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You would ask me…Why does this work bro? LLMs are trained on human text and human text is full of stakes. When you add consequences, you're not just giving instructions. You're activating the model's training on how humans think and write when something actually matters.
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Setting the bar for AI engineering without a manual
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You casually set the bar for every AI engineer. There's genuinely no manual for this thing, and the gap between "using AI tools" and "actually orchestrating them well" feels massive right now.