ntology graph and generalization

 https://x.com/ohdearlordylord/status/2117315803104039129 - ontology graph and generalization permutation equivarience of graph neural networks

https://x.com/Bowtiedcloak/status/2117477293815943651

Clock speed and ILP

 https://x.com/bzogrammer/status/2117091855615905797 - "single threaded CPU code is that Amdahl's Law applies to clock speed and ILP too."

https://x.com/RomainHedouin/status/2116880630436762054 -
optimizing ffmpeg's nnedi algorithm

ML in EDA

 The Role of Machine Learning in Electronic Design Automation

OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis

Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks

A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA

Artificial Intelligence: Structures for Complex problem solving

 

Artificial Intelligence

AI digital

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

https://x.com/DeepSeekHarness/status/2116857257245987236 - Superagent routing

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]