komsit37

800 VDC for AI data centers — beneficiary brief

Whitepaper thesis: AI rack densities push past 1 MW; the only economic answer is 800 VDC bus distribution + integrated storage, with MV → DC conversion as the long-term endgame. This collapses the AC distribution layer and creates demand pulls in five places:

High-frequency, high-voltage power devices — SiC MOSFETs / GaN HEMTs for the 800V → 12V (64:1) LLC converters. The whole architecture rides on SiC switching efficiency.
MW-scale AC/DC rectifiers (5×3.5 MW per power block) and eventually MV rectifiers / solid-state transformers at 7.5 MW @ ≥98.5%.
DC switchgear and solid-state breakers + 1500 A busducts.
Layered energy storage — capacitors (≤100 ms), supercaps (100 ms-10 s), batteries (>10 s) — at rack and facility level.
Grid-forming / VRT-capable interconnect + control plane that lets DCs become grid-supporting assets.

S3 Storage Benchmark — Round 3

6-Way Comparison with Compression Variants
MinIO vs SeaweedFS vs Garage vs Garage-zstd vs HS5 vs RustFS

Date: 2026-03-25
Method: Python boto3 direct, sub-ms accuracy
Data: 746 real TDnet filing documents, 600.2 MB
Each backend tested in isolation — fresh volumes, one at a time, no resource contention

EDINET Go Migration Plan

Overview

Migration of @gcp/doc-index/functions/src/edinet2/ to standalone Go application with dev container environment.

Tech Stack

Runtime: Go 1.21+
Web Framework: Gin
Database: PostgreSQL 15
Cache: Redis 7

Using with SublimeREPL

package controll install SublimeREPL
add python 3 build system Tools/Build System/New (optional)

{
  "cmd": ["python3", "-i", "-u", "$file"],
  "file_regex": "^[ ]File \"(...?)\", line ([0-9]*)",
  "selector": "source.python"

	<html>
	<head><meta charset="utf-8" /></head>
	<body>
	<div> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
	<script type="text/javascript">/**
	* plotly.js v3.3.0
	* Copyright 2012-2025, Plotly, Inc.
	* All rights reserved.
	* Licensed under the MIT license
	*/

	/need to manually copy json response to file and load

	/1. go to https://www.jitta.com/explore?country%5B0%5D=TH
	/2. open Network dev tool
	/3. copy response from link that looks like https://instantsearch.jitta.com/1/indexes/*/queries?x-algolia-agent=Algolia%20for%20vanilla%20JavaScript%20(lite)%203.24.8%3Breact-instantsearch%204.1.3%3BJS%20Helper%202.23.2&x-algolia-application-id=L6HU33HKS0&x-algolia-api-key=8fa6652dfc7f2225015b6d6613466a82

	readJsonResponse: {.j.k read1 `$(":jitta/", string x)}
	extractHits: {((x`results) 0)`hits}
	/rmCols: `objectID`role_jittstor
	/meaningfulColumns: {{x where not (x = `graphs) or ((string x) like "0") or ((string x) like "_") or (x in rmCols)} cols x}

	// see http://vuejs-templates.github.io/webpack for documentation.
	var path = require('path')

	module.exports = {
	build: {
	env: require('./prod.env'),
	index: path.resolve(__dirname, '../dist/index.html'),
	assetsRoot: path.resolve(__dirname, '../..'),
	assetsSubDirectory: 'app/dist/static',
	assetsPublicPath: '/',

	#put this file in ~/
	unbind C-b
	set -g prefix C-a
	bind C-a send-prefix

	set -sg escape-time 1 # quicker responses
	set -g base-index 1 # Numbering of windows
	setw -g pane-base-index 1 # Numbering of Panes

	bind \ split-window -h # Split panes horizontal

	case class DbOrg(id: Int, name: String, a: String, b: Int, c: Long)

	val q = quote {
	(id: Int, orgName: String) =>
	query[DbOrg].filter(_.id == id).filter(_.name == orgName).update
	}

	val d = DbOrg(1, "n", "a", 1, 2L)
	mirrorSource.run(q(lift(d.id), lift(d.name)))(d)

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console