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Yiming Jing ymjing

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pragma solidity 0.6.0;
contract time_capsule {
string public letter =
"Dear Riley, "
" "
"As you may or may not remember, we're writing this to you from the past. As of "
"today, you are just one month old and your whole bright future is ahead of you. "
"We're building this time capsule as a way to remind you of who you were when you "
"turned 18 and to give you a smile in 2038 on your birthday. "
" "
@ymjing
ymjing / Dockerfile
Created May 7, 2022 02:08
A minimal docker file for running Grasscutter
FROM azul/zulu-openjdk:17 AS builder
RUN apt-get update \
&& apt-get install -y --no-install-recommends git libprotobuf-dev \
&& git clone -b development --depth 1 https://github.com/Grasscutters/Grasscutter /work
WORKDIR /work
RUN export GRADLE_OPTS="-Dfile.encoding=utf-8" \
&& git submodule update --init \
@ymjing
ymjing / kubevirt-cr.yaml
Last active February 20, 2025 18:19
kubevirt-snp
---
apiVersion: kubevirt.io/v1
kind: KubeVirt
metadata:
name: kubevirt
namespace: kubevirt
spec:
certificateRotateStrategy: {}
configuration:
developerConfiguration:
@ymjing
ymjing / readme.md
Last active February 24, 2025 18:06
Kubevirt SEV-SNP Code Lab
@ymjing
ymjing / provable-privacy-doc.md
Created August 11, 2025 23:08
Provable Privacy Pitch

The Rising Tide of Enterprise AI and the Imperative for Provable Privacy in LLM Inference Systems

Executive Summary

As enterprises rapidly adopt Large Language Models (LLMs) to transform their operations, they face a critical dilemma: unleashing AI's full potential requires processing sensitive data, yet current cloud-based solutions lack verifiable privacy guarantees. This document presents the case for provable privacy—a paradigm shift from trust-based to technically demonstrable data protection—as the essential bridge between AI innovation and enterprise security requirements.

1. The Enterprise AI Revolution

Artificial Intelligence, particularly Large Language Models, has evolved from experimental technology to strategic imperative. Organizations across sectors are embedding LLMs into their core operations—from code generation and data analysis to customer service and decision support. This transformation is backed by substantial investment, with most enterprises planning significant increases