Working with Agents in EDA

 https://skycadeda.com/blog/automating-eda-tool-config-python/

from-ai-assisted-eda-to-ai-mediated-engineering

help LLM write better code faster

https://research.nvidia.com/labs/electronic-design-automation/publication/#1

collaborating with agents

 https://x.com/m4lkxx/status/2114338456750997922 - Evidence ladder

SPAR -> MATS -> a frontier lab fellowship (AI generated(

https://kjfeng.me/blog/industry-job-search.html
 This sequence illustrates the standard progression pathway for early-career researchers looking to break into the AI Safety ecosystem. It charts a trajectory from part-time remote research to prestigious, full-time independent fellowships, and finally into direct industry or lab-based roles. [1]

A consensus among users on platforms like LessWrong breaks down what each stage of this talent pipeline represents: [1]

1. SPAR (The Stepping Stone)

What it is: SPAR (Supervised Program for Alignment Research) is a 3-month, part-time, fully remote research initiative. [1]
The Role: You work in small teams (typically 10 hours a week) under the guidance of an established mentor. [1, 2]
Purpose: It serves as a low-risk "Level 3" trial program. It allows you to build a portfolio, learn the basics of ML upskilling/interpretability, and secure a recommendation letter without leaving your day job or university degree. [1, 2, 3]
2. MATS (The Accelerator)
What it is: MATS (Machine Intelligence Academy for Trustworthy Safety) is a highly competitive, 10-to-12-week, full-time, fully funded research fellowship. [1, 2, 3]
The Role: Based in hubs like Berkeley, CA or London, UK, fellows receive an intensive stipend, heavy compute allocations, and direct mentorship from researchers at top institutions. [1, 2]
Purpose: With a single-digit acceptance rate (typically 4–7%), MATS acts as a primary talent filtration and acceleration engine for the community. It turns promising scholars into independent researchers who routinely publish at top ML conferences. [1, 2, 3]
3. A Frontier Lab Fellowship (The Destination)
What it is: A full-time, long-term employment track or specialized internal fellowship at an elite, safety-oriented AI institution (e.g., Anthropic Fellows, OpenAI, Google DeepMind, or Apollo Research). [1, 2, 3]
The Role: You are embedded directly inside an organization to work on critical, cutting-edge evaluations, frontier deployment safety, or structural security-level (SL5) architecture. [1, 2]
Purpose: This is the culmination of the training pipeline—transitioning from an aspiring trainee to an impactful, full-time machine learning professional directly mitigating catastrophic AI risks. [1, 2]

Volume of a sphere in higher dimension

 

Source: Chatpgt and https://x.com/atulit_gaur/status/2113910174016835804

sousho dictionary

 https://x.com/NekoJitaBlog/status/2113277672768094215

kanjikana

Age of Ideas

 When the meetings are recorded, can it be in a database so that is queryable or comes up with solutions to prior problems or new areas of exploration?

Can Handoff heavy be managed well if about 300 claude agents manage the design?

Knowing exactly what to remember and what to forget and when to be "Fifty first dates"

Centaur model

https://readable-intelligence.com/

https://felixrieseberg.com/how-to-be-more-ambitious-a-tutorial-for-humans-with-models/

 

A Human History of Emotion: How the Way We Feel Built the World We Know

Emotional regimes
Emotional labor
Emotional refuges