How splitting a passive preamp's output into independent tube-buffered Class D channels solves the home audiophile's fundamental problem
I bought a Douk Audio T4+ because it looked good on a desk. Vacuum tube glowing through a metal housing, a VU meter with a bouncing needle, a solid aluminum volume knob — all for under $100. The intended use was simple: DAC into the AUX input, volume knob for level control, headphone output. A compact tube-flavored headphone amp with some visual charm.
With Formal Apparatus
The word ākāśa shows up in classical Indian philosophy as one of the five gross elements — the spatial medium through which sound propagates. It's a physical concept in Sāṃkhya and Vedānta, roughly analogous to the Western notion of aether: a substrate, not an information store.
Blavatsky changed this. In the late 19th century, she appropriated ākāśa for Theosophy and repackaged it as something fundamentally different — not a physical medium but an information-theoretic one. The Akashic Record, in her formulation, became a non-physical compendium encoding every event, thought, and intention across all of existence. An append-only universal ledger with perfect recall.
🔗 tinyurl.com/tube-cascade-mod
How cascading independent tube buffer components creates superior harmonic complexity
How I applied mathematical optimization to solve the audiophile value problem
Note: Pricing as of June 2025
| Latency Comparison Numbers | |
| -------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| import concurrent.futures | |
| import multiprocessing | |
| import sys | |
| import uuid | |
| def globalize(func): | |
| def result(*args, **kwargs): | |
| return func(*args, **kwargs) | |
| result.__name__ = result.__qualname__ = uuid.uuid4().hex | |
| setattr(sys.modules[result.__module__], result.__name__, result) |
| #yum install haproxy | |
| #configure haproxy-cloudera.cfg | |
| #haproxy -f /etc/haproxy/haproxy-cloudera.cfg | |
| #http://seawolf-3.vpc.wonderland.com:1936/ | |
| #https://cbonte.github.io/haproxy-dconv/ | |
| global | |
| daemon | |
| nbproc 1 | |
| maxconn 100000 |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.
In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.
Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j
| #! /usr/bin/env python | |
| from boto.ses.connection import SESConnection | |
| import os | |
| import sys | |
| import subprocess | |
| import socket | |
| TMPFILE = '/var/run/postgresql/last-wal-archive-error-file.tmp' | |
| if __name__ == '__main__': |
