AI Code Agents "snake oil sales" vs boot.dev

code-gen agents are useful; but they are also dangerous;

it is like driving a high-speed / racing car of motorbike:
you need to constantly "steer" or they will crash.
DraganSr: Bicycle for mind => AI EV superbike
it is not like manual-powered human-coding "bicycle for mind" anymore.

but this is not what AI model promoters are "selling";
they are selling modern "snake oil";
why "snake oil?" 
because that is/was a useful substance, but what was sold was "fake substitutes" that got a bad name.

Snake oil - Wikipedia

The creator of Claude Code just revealed his workflow, and developers are losing their minds | VentureBeat

Boris Cherny on X: "I'm Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit. My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to" / X

Microsoft begs for mercy - YouTube (and Google is cheating!)

So, feed "best ideas that survived" one year of experimentation
generated good TOY VERSION!

That is EXACTLY what you get from code agents if you are just "vibe coding".
That is it.

New snake oil.

Excepted better from Google engineers!

Jaana Dogan ヤナ ドガン (@rakyll) / X

the real deal?

real CODE skills!
to KNIW what to ask AI agent to do, and to be able to understand and drive results!

Learn backend development the smart way | Boot.dev

An AI assistant that uses the socratic method to deepen your understanding, not hand out answers

AI storage tool: Amazon S3 Vectors

Amazon S3 Vectors

Purpose-built storage for vectors

"Cost-optimized AI-ready storage with native support for storing and querying vectors at scale,
reducing total costs by up to 90%

Amazon S3 Vectors is the first cloud object store with native support to store and query vectors, delivering purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context of AI agents as well as semantic search results of your S3 data.

Designed to provide the same elasticity, scale, and durability as Amazon S3, S3 Vectors lets you store up to billions of vectors and search data with sub-second query performance. It's ideal for applications that need to build and maintain vector indexes at scale so you can organize and search through massive amounts of information."

Working with S3 Vectors and vector buckets - Amazon Simple Storage Service

• Vector buckets – A new bucket type that's purpose-built to store and query vectors.
• Vector indexes – Within a vector bucket, you can organize your vector data within vector indexes. You perform similarity queries on your vector data within vector indexes.
• Vectors – You store vectors in your vector index. For similarity search and AI applications, vectors are created as vector embeddings which are numerical representations that preserve semantic relationships between content (such as text, images, or audio) so similar items are positioned closer together.

Using S3 Vectors with OpenSearch Service - Amazon Simple Storage Service