Join 📚 Kevin's Highlights
A batch of the best highlights from what Kevin's read, .
**Try to be clear about any context or details that are important to you. Think about:**
• **Subject:**
person, animal, character, location, object, etc.
• **Medium:**
photo, painting, illustration, sculpture, doodle, tapestry, etc.
• **Environment:**
indoors, outdoors, on the moon, in Narnia, underwater, the Emerald City, etc.
• **Lighting:**
soft, ambient, overcast, neon, studio lights, etc
• **Color:**
vibrant, muted, bright, monochromatic, colorful, black and white, pastel, etc.
• **Mood:**
Sedate, calm, raucous, energetic, etc.
• **Composition:**
Portrait, headshot, closeup, birds-eye view, etc.
Basic Prompts
midjourney.com
**Are you aware of the risks?**
You need to know about the reasonably foreseeable risks and impact of your AI product before putting it on the market.
If something goes wrong – maybe it fails or yields biased results – you can’t just blame a third-party developer of the technology.
And you can’t say you’re not responsible because that technology is a “black box” you can’t understand or didn’t know how to test.
Keep Your AI Claims in Check
Federal Trade Commission
The data engineering lifecycle
shifts the conversation
away from technology and
toward the data itself and the end goals that it must serve.
The stages of the data engineering lifecycle are as follows:
• Generation • Storage • Ingestion • Transformation • Serving
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
Readwise