AI: OpenClaw on AWS Bedrock

sample-OpenClaw-on-AWS-with-Bedrock/README.md at main · aws-samples/sample-OpenClaw-on-AWS-with-Bedrock @GitHub

 OpenClaw on AWS Bedrock AgentCore: Secure and Serverless - DEV Community

Deploy OpenClaw on AWS EC2 with Amazon Bedrock Using Terraform — No API Keys Required - DEV Community

as mentioned here

NVIDIA's $1 Trillion Prediction, Anthropic Beats OpenAI, Tesla vs. TSMC & The CS Job Collapse | 240 - YouTube by Peter H. Diamandis - YouTube

key points from the video:

• NVIDIA Growth: NVIDIA expects AI demand to drive revenue to $1 trillion by 2027 (0:39), aiming to manage vast ecosystems including robots and data centers (07:00).
• OpenClaw & Software: OpenClaw is highlighted as an incredibly popular open-source project (0:07). NVIDIA is supporting it with NemoClaw to optimize enterprise workflows (12:01).
• Anthropic vs. OpenAI: Anthropic is recognized as a highly disruptive company challenging OpenAI for market share (0:46).
• Tesla's Terafab: Tesla announced Terafab, aiming to produce a massive number of chips for robotics (01:03:00).
• Energy Challenges: The U.S. faces a significant power shortfall for data centers, leading to renewed interest in nuclear energy (1:24:00).
• Human Impact: Discussions covered the collapse of traditional computer science roles (1:48:00) and Elon Musk's vision for Universal High Income (UHI) based on shared upside from automation (1:42:00).

AI: autoresearch by Andrej Karpathy

Andrej is struggling with making sense of latest LLM tools, like Claude Code and OpenClaw, like everyone else...

And he has some interesting ideas, too...

karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically

"The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. 

The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc."

The End of Coding: Andrej Karpathy on Agents, AutoResearch, and the Loopy Era of AI - YouTube @NoPriors podcast

also 

microgpt

SQL: Postgres patterns

good patterns or anti-patterns for db design?

this is quote common now, and for sure is taking more space... 

but it is often useful to have more data;

trouble is that common relational mode does allow update and delete...

and there is no storage optimization.

Life Altering Postgresql Patterns

• Use UUID primary keys
• Give everything created_at and updated_at
• on update restrict on delete restrict
• Use schemas
• Enum tables
• Name your tables singularly
• Mechanically name join tables
• Almost always soft delete
• Represent statuses as a log
• Mark special rows with a system_id
• Use views sparingly
• JSON Queries