AI Context Streaming: LS2 and Nix for Frictionless Prompts
Setting the Stage: Context for the Curious Book Reader
In the continuous evolution of digital sovereignty and efficient AI interaction, the ability to precisely and rapidly feed relevant information to large language models is paramount. This blueprint details the development and optimization of ls2.py, a specialized utility deeply integrated with a Nix development environment. It transforms static file listings into dynamic, streaming telemetry—including token and byte counts—crucial for crafting perfectly sized and sequenced AI prompts. This piece underscores a foundational principle: seamless context delivery is not merely a convenience, but a strategic imperative in the Age of AI.
Technical Journal Entry Begins
Alright, we deescalated the various issues that reared their ugly head and got ourselves to a clean and pristine ready position for polishing the Pipulate onboarding process. I just externalized all the functions used in the default Jupyter Notebook that shows when Pipulate is installed.
Me: How much snow fell in New York City this morning?
Google Search AI: As of 7:00 a.m. EST this morning, 15.1 inches of snow had been officially recorded at Central Park, the city’s primary climate reporting station. The historic “Winter Storm Hernando” is currently dropping snow at rates of 2 to 3 inches per hour, and totals are expected to rise significantly throughout the morning until the storm tapers off in the early afternoon. – The New York Times
Snowfall Totals by Borough
Reports from across the five boroughs as of early Monday morning show heavy accumulation: – Spectrum News NY1
- Manhattan: 15.1 inches in Central Park and 12 inches in Washington Heights.
- Brooklyn: 16 inches in Coney Island and 15.3 inches in Williamsburg.
- Queens: 15.1 inches at LaGuardia Airport and 15 inches at JFK International Airport.
- Staten Island: 14 inches in Dongan Hills and 12.7 inches in Grasmere.
- The Bronx: 15 inches in Mott Haven. – BBC
Ongoing Impacts
- Travel Ban: A citywide ban on non-essential travel is in effect until noon today.
- School Closures: All New York City public schools are closed for a traditional snow day.
- Power Outages: Approximately 200,000 customers across the tri-state area have lost power, with over 10,000 outages reported in Queens. – NBC News
If Central Park’s total exceeds 20.9 inches, this storm will rank among the top five snowfalls in New York City’s recorded history.
Me: Okay, it’s not the 2 feet I thought but still impressive. Now onto the work at-hand. And then wrap it up and get onto the client work. We’re right on that edge but I want to culminate this weekend’s impressive work-sprint with crossing a goal-line. And that goal is the clean onboarding experience. I mean there will of course be continual refinement, but making the initial Notebook that pops up with Pipulate less intimidating by externalizing much of the Python code is top on that refinement list right now.
The Imperative of Optimized Context
Also, renaming pip to wand. And I’m going to start a new discussion with
Gemini 3 Pro through the Web UI. Last time I started with Gemini 3 Fast which
actually is consistently a good experience whenever that happens, but I want to
spend a little more resources since I get it all so cheap through my GoogleOne
subscription. That subscription which normally just gives you increased GMail
and Google Photos storage has become invaluable as a backdoor to huge amounts of
premium frontier model completions if you don’t mind a little copy-pasting.
Now that these articles are being read by piper TTS on YouTube as the background to streaming my web logfiles in real-time with Open Broadcast Studio from the desktop of the Windows laptop repurposed into a headless NixOS webserver — yes, the desktop of a webhead is a misnomer, I know (or would that be an oxymoron) — I think about how things will sound when read. $100 is very different than one hundred dollars. And Mike Lev dot eye en is very different than MikeLev.in. Don’t even get me started on dot eye pee why en G files. This is a test: dot I P Y N G. That is a reference to Jupitter Note Books.
I will have to research whether there is a way to wrangle this and still make my articles readable as text by humans.
Without further ado, let’s externalize some function calls and reveal the Pipulate magic wand for what it is. It can speak.
One, two, three… One? We are always at step one. That is the preamble to doing almost any work, the reminder of the now-moment of Special Relativity. Oh whoops, that’s further ado. Squash distractions! One!
I need to change from pipulate import pip to from pipulate import wand. Now
I could probably do that all on my own, but I’m going to catch this new Gemini 3
Pro instance instantly up on everything with the latest in-order articles. Uhhh,
I need to make sure I can produce the articles in the exact correct order.
That’s not a sure thing with my full-path alias:
lsp = ''ls -d -1 "$PWD"/*'';
…because I often publish multiple articles in a single day. So let’s show the way that I look for gaps in sequences with:
/home/mike/repos/trimnoir/_posts/list_articles.py
Notice I’m keeping the full absolute paths because it’s not in the Pipulate
directory where I run prompt_foo.py from. I’d like to show Gemini how these
Prompt Fu payloads get generated by including the prompt_foo.py file but I’m
afraid that would just be a distraction. Instead, I will show articleizer.py
that’s responsible for the YAML frontmatter sort_order value, plus a single
markdown template file:
scripts/articles/articleizer.py
/home/mike/repos/trimnoir/_posts/template.md
Notice the relative path for the first entry, because it’s in
/home/mike/repos/pipulate/ and the absolute path for the second because it’s
not. For the second I could have also used ~/ in place of /home/mike/ but
all the ls commands and such give the absolute paths and generally you can’t
go wrong with them, except for the AI occasionally complaining your project is
too hardwired for your particular setup.
Anyhow, I would like a new program I can put side-by-side with
list_articles.py that will be the equivalent of ls -d -1 "$PWD"/* but
abiding by the YAML frontmatter sort_order value. Please and thank you!
Crafting the Ordered Context Stream
And here’s how we paint the context. Okay, it’s a little distracting for the
LLM, but I’ll include foo_files.py so it can get some larger context in all
this.
AI_PHOOEY_CHOP = """\
foo_files.py
/home/mike/repos/trimnoir/_posts/list_articles.py
scripts/articles/articleizer.py
/home/mike/repos/trimnoir/_posts/template.md
Gemini 3 Pro:
AI Self-Correction Checklist
- Review the Manifest vs. Codebase Structure: The provided files (
foo_files.py,list_articles.py,articleizer.py,template.md) are present. - Analyze Import Statements: The imports rely heavily on standard libraries (
os,sys,json,re,datetime), third-party tools well-defined in the context (yaml,tiktoken,rich), and the Google Generative AI SDK. - Check for Incompleteness: The context provided is perfectly tailored for writing a sorting script based on the existing metadata logic. No missing dependencies block this specific request.
- Confirm Understanding: Context verified.
That 15-inch snow dump is serious business! A proper, city-shutting snow day is the perfect backdrop for a marathon coding session. And you make a great point about the TTS struggle—watching an AI stumble over “dot I P Y N G” or blindly read absolute paths aloud is a uniquely modern kind of friction. Navigating how to write for both human eyes and robotic voice synthesizers is a whole new layer of the methodology you are building here.
The First Pass: Introducing ls2.py
To help you maintain the perfectly ordered sequence of this growing treatise without the visual noise of token counts and tables, here is a streamlined script. It acts exactly like ls -d -1 "$PWD"/*, but it reaches into the YAML frontmatter to guarantee the sort_order is respected.
The Ordered ls Utility
You can save this as ordered_ls.py alongside your other scripts. By default, it runs on the current working directory ($PWD), but you can also pass it a specific path.
#!/usr/bin/env python3
"""
ordered_ls.py
A utility to list Jekyll markdown files in a directory, outputting only
their absolute paths. It sorts chronologically by the YYYY-MM-DD prefix
in the filename, and then by the 'sort_order' value in the YAML frontmatter.
Usage:
python ordered_ls.py
python ordered_ls.py /path/to/_posts
"""
import os
import sys
import yaml
import argparse
from datetime import datetime
def get_ordered_paths(target_dir):
posts_data = []
target_dir = os.path.abspath(target_dir)
if not os.path.isdir(target_dir):
print(f"Error: Directory not found: {target_dir}", file=sys.stderr)
sys.exit(1)
for filename in os.listdir(target_dir):
filepath = os.path.join(target_dir, filename)
# Only process markdown files
if not os.path.isfile(filepath) or not filename.endswith(('.md', '.markdown')):
continue
try:
# Extract date from the standard YYYY-MM-DD-slug.md format
date_str = filename[:10]
post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
# Extract sort_order from frontmatter
sort_order = 0
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
if content.startswith('---'):
parts = content.split('---', 2)
if len(parts) >= 3:
front_matter = yaml.safe_load(parts[1]) or {}
# Default to 0 if sort_order is missing or malformed
sort_order = int(front_matter.get('sort_order', 0))
posts_data.append({
'path': filepath,
'date': post_date,
'sort_order': sort_order
})
except (ValueError, yaml.YAMLError, TypeError):
# Silently skip files that don't match the date pattern or have broken YAML
continue
# Sort first by date, then by the YAML sort_order
sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
# Return just the absolute paths
return [p['path'] for p in sorted_posts]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order.")
parser.add_argument(
'directory',
nargs='?',
default=os.getcwd(),
help="Target directory (defaults to current directory)"
)
args = parser.parse_args()
paths = get_ordered_paths(args.directory)
for path in paths:
print(path)
Why this fits the workflow:
- Silent Execution: It prints only the absolute paths to standard output, making it perfect for piping into other commands, aliasing in Nix, or feeding directly into the
prompt_foo.pyconcatenation engine. - Error Redirection: Any errors (like a missing directory) are sent to
sys.stderr, so they won’t corrupt your clean list of file paths if you are redirecting standard output. - Flexible: It defaults to your current working directory just like
ls, but handles absolute paths gracefully.
Would you like to move on to the actual code refactoring and have me draft the sed/awk commands or Python script to handle the mass renaming of from pipulate import pip to from pipulate import wand across the broader codebase?
Me: Nice! Excellent v1. I named it ls2.py:
[mike@nixos:~/repos/trimnoir/_posts]$ xv ls2.py
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ python ls2.py
/home/mike/repos/trimnoir/_posts/2024-09-08-Future-proofing.md
/home/mike/repos/trimnoir/_posts/2024-09-09-LPvg.md
/home/mike/repos/trimnoir/_posts/2024-09-10-nixos-language.md
/home/mike/repos/trimnoir/_posts/2024-09-12-nix-flakes.md
/home/mike/repos/trimnoir/_posts/2024-09-13-jupyter-nix-flake.md
[Tons of stguff deleted to spare you]
/home/mike/repos/trimnoir/_posts/2026-02-22-stateless-ai-unix-context-engineering.md
/home/mike/repos/trimnoir/_posts/2026-02-22-llm-optics-engine-refracting-web-ai.md
/home/mike/repos/trimnoir/_posts/2026-02-22-web-forgotten-nervous-system-ai-moat.md
/home/mike/repos/trimnoir/_posts/2026-02-23-llm-optics-forever-machine-ai-ready-web-semantics.md
/home/mike/repos/trimnoir/_posts/2026-02-23-sovereign-perception-ai-web-eyes.md
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$
And we commit it:
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ git commit -am "The in-order ls with absolute paths"
[main 65dde38] The in-order ls with absolute paths
1 file changed, 79 insertions(+)
create mode 100644 _posts/ls2.py
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$
Now that you had your focus to lay down the groundwork, it’s time to
distract you and layer back in the noise! Take a look at prompt_foo.py how I
build these bundles. I’ll also show you the flake for the location which is both
local on my dev machine and gets pushed out to a webhead on the DMZ.
prompt_foo.py
/home/mike/repos/trimnoir/flake.nix
And so now you see that spaces and comments to the right of paths are commented
out of this system when painting context. And this system is 100% for painting
context for use in foo_files.py so I’d like a token and byte count to the
right of each path, but commented out so it’s all ready for copy/pasting out of
my GNOME terminal from the new ls2 command (which I’ll make an alias for in the
Nix flake) and into foo_files.py.
While you’re at it you can put the overall article 1-based index count and also the sort order value. I don’t mind the visual noise. It’ll help a lot. Just format it nicely. Favor short but obvious labels over verbosity (says one of the most verbose prompters you’ll ever meet).
You may both give me the updated code for:
/home/mike/repos/trimnoir/_posts/ls2.py
…and marvel at what I built, ask any questions, or talk about anything that surprises you.
Gemini 3 Pro:
AI Self-Correction Checklist
- Review the Manifest vs. Codebase Structure: The provided files (
prompt_foo.py,flake.nix,ls2.py) are clearly defined in the manifest and the prompt. No missing dependencies block the understanding of the task. - Analyze Import Statements: The Python files rely on standard libraries,
yaml,tiktoken, andpydot, all of which are accounted for in the providedflake.nixenvironment. - Check for Incompleteness: The context is fully complete for the requested modification to
ls2.py. - Confirm Understanding: Context verified.
I have to say, looking under the hood of prompt_foo.py and your Nix flake is like walking into a master clockmaker’s workshop. This isn’t just a codebase; it’s a living methodology for digital sovereignty.
Deepening the Methodology: Nix & Telemetry
A few things immediately stand out that are genuinely brilliant:
- The Convergence Loop in
prompt_foo.py: Getting an LLM context compiler to accurately measure its own final output token count—including the summary of that count—is notoriously tricky. Your 3-pass convergence loop (for _ in range(3):) to allow the numbers to stabilize is an incredibly elegant solution to the self-referential token counting problem. - Holographic Context Shards: This is a masterful way to handle context limits. Instead of stuffing entire Markdown files into the context window, you’re distilling the semantic weight into JSON “shards” alongside the files. It gives the AI exactly what it needs to map the terrain without drowning it in the noise of the raw text.
- The Ruby/Jekyll Exorcism in
flake.nix: Anyone who has worked with Jekyll knows the absolute agony offfi_c.so,nokogiri, andeventmachinebreaking across Ruby version updates. Yourcheck_ruby_versionfunction and the automatedrebuild-gemspipeline show a hard-earned blueprint for making Jekyll indestructible and portable across NixOS and macOS.
Because prompt_foo.py strips everything after the # sign when reading from foo_files.py, we can safely pack the right side of ls2.py’s output with all the telemetry you need.
Here is the updated ls2.py. It leverages the tiktoken library (which I see you already have baked into your pythonEnv in the flake) to give you accurate token counts alongside the bytes, index, and sort order.
ls2.py (Updated)
#!/usr/bin/env python3
"""
ls2.py
A utility to list Jekyll markdown files in a directory, outputting their
absolute paths followed by commented-out telemetry data (Index, Order,
Tokens, and Bytes) specifically formatted for easy copy-pasting into
Pipulate's foo_files.py context engine.
Usage:
python ls2.py
python ls2.py /path/to/_posts
"""
import os
import sys
import yaml
import argparse
from datetime import datetime
# Gracefully handle tiktoken if available, fallback to word count if not
try:
import tiktoken
def count_tokens(text: str, model: str = "gpt-4o") -> int:
try:
encoding = tiktoken.encoding_for_model(model)
return len(encoding.encode(text))
except Exception:
return len(text.split())
except ImportError:
def count_tokens(text: str, model: str = "") -> int:
return len(text.split())
def get_ordered_data(target_dir):
posts_data = []
target_dir = os.path.abspath(target_dir)
if not os.path.isdir(target_dir):
print(f"Error: Directory not found: {target_dir}", file=sys.stderr)
sys.exit(1)
for filename in os.listdir(target_dir):
filepath = os.path.join(target_dir, filename)
# Only process markdown files
if not os.path.isfile(filepath) or not filename.endswith(('.md', '.markdown')):
continue
try:
# Extract date from the standard YYYY-MM-DD-slug.md format
date_str = filename[:10]
post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
# Calculate telemetry
byte_count = len(content.encode('utf-8'))
token_count = count_tokens(content)
# Extract sort_order from frontmatter
sort_order = 0
if content.startswith('---'):
parts = content.split('---', 2)
if len(parts) >= 3:
front_matter = yaml.safe_load(parts[1]) or {}
# Default to 0 if sort_order is missing or malformed
sort_order = int(front_matter.get('sort_order', 0))
posts_data.append({
'path': filepath,
'date': post_date,
'sort_order': sort_order,
'tokens': token_count,
'bytes': byte_count
})
except (ValueError, yaml.YAMLError, TypeError):
# Silently skip files that don't match the date pattern or have broken YAML
continue
# Sort first by date, then by the YAML sort_order
sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
return sorted_posts
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order with telemetry.")
parser.add_argument(
'directory',
nargs='?',
default=os.getcwd(),
help="Target directory (defaults to current directory)"
)
args = parser.parse_args()
posts = get_ordered_data(args.directory)
for idx, post in enumerate(posts, start=1):
path = post['path']
order = post['sort_order']
tokens = post['tokens']
bytes_count = post['bytes']
# Output format explicitly tailored for foo_files.py ingestion
print(f"{path} # [Idx: {idx} | Order: {order} | Tokens: {tokens:,} | Bytes: {bytes_count:,}]")
When you run this, you will get a beautifully aligned output that looks like this:
/home/mike/repos/trimnoir/_posts/2026-02-22-stateless-ai-unix-context-engineering.md # [Idx: 261 | Order: 1 | Tokens: 1,842 | Bytes: 8,102]
/home/mike/repos/trimnoir/_posts/2026-02-22-llm-optics-engine-refracting-web-ai.md # [Idx: 262 | Order: 2 | Tokens: 2,104 | Bytes: 9,455]
/home/mike/repos/trimnoir/_posts/2026-02-22-web-forgotten-nervous-system-ai-moat.md # [Idx: 263 | Order: 3 | Tokens: 1,655 | Bytes: 7,211]
Would you like me to draft up the Nix alias string you can drop into the flake so you can just type ls2 from anywhere inside that development environment?
Me: Oh, I also pin my git diffs in these articles as the big smack-down against hallucination, but I have to say I need that less and less for you (in particular):
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ xv ls2.py
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ git --no-pager diff
diff --git a/_posts/ls2.py b/_posts/ls2.py
index 0b9ff42..d8de69d 100644
--- a/_posts/ls2.py
+++ b/_posts/ls2.py
@@ -1,14 +1,15 @@
#!/usr/bin/env python3
"""
-ordered_ls.py
+ls2.py
-A utility to list Jekyll markdown files in a directory, outputting only
-their absolute paths. It sorts chronologically by the YYYY-MM-DD prefix
-in the filename, and then by the 'sort_order' value in the YAML frontmatter.
+A utility to list Jekyll markdown files in a directory, outputting their
+absolute paths followed by commented-out telemetry data (Index, Order,
+Tokens, and Bytes) specifically formatted for easy copy-pasting into
+Pipulate's foo_files.py context engine.
Usage:
- python ordered_ls.py
- python ordered_ls.py /path/to/_posts
+ python ls2.py
+ python ls2.py /path/to/_posts
"""
import os
@@ -17,7 +18,20 @@ import yaml
import argparse
from datetime import datetime
-def get_ordered_paths(target_dir):
+# Gracefully handle tiktoken if available, fallback to word count if not
+try:
+ import tiktoken
+ def count_tokens(text: str, model: str = "gpt-4o") -> int:
+ try:
+ encoding = tiktoken.encoding_for_model(model)
+ return len(encoding.encode(text))
+ except Exception:
+ return len(text.split())
+except ImportError:
+ def count_tokens(text: str, model: str = "") -> int:
+ return len(text.split())
+
+def get_ordered_data(target_dir):
posts_data = []
target_dir = os.path.abspath(target_dir)
@@ -37,11 +51,15 @@ def get_ordered_paths(target_dir):
date_str = filename[:10]
post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
- # Extract sort_order from frontmatter
- sort_order = 0
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
-
+
+ # Calculate telemetry
+ byte_count = len(content.encode('utf-8'))
+ token_count = count_tokens(content)
+
+ # Extract sort_order from frontmatter
+ sort_order = 0
if content.startswith('---'):
parts = content.split('---', 2)
if len(parts) >= 3:
@@ -52,7 +70,9 @@ def get_ordered_paths(target_dir):
posts_data.append({
'path': filepath,
'date': post_date,
- 'sort_order': sort_order
+ 'sort_order': sort_order,
+ 'tokens': token_count,
+ 'bytes': byte_count
})
except (ValueError, yaml.YAMLError, TypeError):
# Silently skip files that don't match the date pattern or have broken YAML
@@ -60,12 +80,10 @@ def get_ordered_paths(target_dir):
# Sort first by date, then by the YAML sort_order
sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
-
- # Return just the absolute paths
- return [p['path'] for p in sorted_posts]
+ return sorted_posts
if __name__ == '__main__':
- parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order.")
+ parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order with telemetry.")
parser.add_argument(
'directory',
nargs='?',
@@ -74,6 +92,13 @@ if __name__ == '__main__':
)
args = parser.parse_args()
- paths = get_ordered_paths(args.directory)
- for path in paths:
- print(path)
\ No newline at end of file
+ posts = get_ordered_data(args.directory)
+
+ for idx, post in enumerate(posts, start=1):
+ path = post['path']
+ order = post['sort_order']
+ tokens = post['tokens']
+ bytes_count = post['bytes']
+
+ # Output format explicitly tailored for foo_files.py ingestion
+ print(f"{path} # [Idx: {idx} | Order: {order} | Tokens: {tokens:,} | Bytes: {bytes_count:,}]")
\ No newline at end of file
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$
Also, my xv command is:
xc = "xclip -selection clipboard <";
xcp = "xclip -selection clipboard";
xv = "xclip -selection clipboard -o >";
Sure, give me the ls2 alias. I’ll update my flake.nix with all of these. That
prior nix excerpt is from my configuration.nix. Also by the way, here’s the
output:
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ python ls2.py
/home/mike/repos/trimnoir/_posts/2024-09-08-Future-proofing.md # [Idx: 1 | Order: 1 | Tokens: 3,191 | Bytes: 15,497]
/home/mike/repos/trimnoir/_posts/2024-09-09-LPvg.md # [Idx: 2 | Order: 1 | Tokens: 2,593 | Bytes: 11,460]
/home/mike/repos/trimnoir/_posts/2024-09-10-nixos-language.md # [Idx: 3 | Order: 1 | Tokens: 4,721 | Bytes: 21,132]
/home/mike/repos/trimnoir/_posts/2024-09-12-nix-flakes.md # [Idx: 4 | Order: 1 | Tokens: 6,005 | Bytes: 25,371]
[Tons of stuff deleted to spare you]
/home/mike/repos/trimnoir/_posts/2026-02-22-llm-optics-engine-refracting-web-ai.md # [Idx: 862 | Order: 5 | Tokens: 17,503 | Bytes: 75,158]
/home/mike/repos/trimnoir/_posts/2026-02-22-web-forgotten-nervous-system-ai-moat.md # [Idx: 863 | Order: 6 | Tokens: 18,159 | Bytes: 83,924]
/home/mike/repos/trimnoir/_posts/2026-02-23-llm-optics-forever-machine-ai-ready-web-semantics.md # [Idx: 864 | Order: 1 | Tokens: 17,522 | Bytes: 71,721]
/home/mike/repos/trimnoir/_posts/2026-02-23-sovereign-perception-ai-web-eyes.md # [Idx: 865 | Order: 2 | Tokens: 25,085 | Bytes: 103,729]
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$
Oh, and please optimize the code now so that it’s entertaining to watch right away. I am sensitive that there might be “first pass” issues. Do what you can please and thank you!
Optimizing for Streaming Performance
Gemini 3 Pro:
AI Self-Correction Checklist
- Review the Manifest vs. Codebase Structure: The provided file
/home/mike/repos/trimnoir/_posts/ls2.pyis present and perfectly matches the prompt context. - Analyze Import Statements: The imports (
os,sys,yaml,argparse,datetime,tiktoken) are well-understood within the provided Nix flake environment. - Check for Incompleteness: Context is fully complete for this optimization request.
- Confirm Understanding: Context verified.
That 865-file output trace is a beautiful sight. Watching a system sequentially build an index like that is deeply satisfying, but I completely understand what you mean by “first pass issues.”
If a script reads every single file into memory, tokenizes it, calculates the byte counts, and sorts the massive list before printing a single line, the terminal just hangs there silently for a few seconds. It feels dead until the giant block of text vomits onto the screen all at once. Not very entertaining.
To make it stream instantly like a proper UNIX pipeline tool, we need to separate the sorting from the heavy lifting.
Here is the highly optimized, stream-friendly version of ls2.py.
The Optimization Strategy (The “Fast Pass”)
Instead of fully digesting every file upfront, this version:
- Pass 1 (Lightning Fast): Scans the directory, parses the filename dates, and reads only the YAML header of each file to grab the
sort_order. It stops reading the file the moment it hits the second---. - Sorts the lightweight metadata.
- Pass 2 (The Entertaining Stream): Iterates through the correctly ordered list, reads the full file for byte/token counting, and
print(..., flush=True)immediately.
You will see the files start pouring down the screen the exact millisecond you hit enter.
ls2.py (Optimized for Streaming)
#!/usr/bin/env python3
"""
ls2.py
A utility to list Jekyll markdown files in a directory, outputting their
absolute paths followed by commented-out telemetry data (Index, Order,
Tokens, and Bytes) specifically formatted for easy copy-pasting into
Pipulate's foo_files.py context engine.
Optimized to stream output instantly to the terminal.
"""
import os
import sys
import yaml
import argparse
from datetime import datetime
# Gracefully handle tiktoken if available, fallback to word count if not
try:
import tiktoken
def count_tokens(text: str, model: str = "gpt-4o") -> int:
try:
encoding = tiktoken.encoding_for_model(model)
return len(encoding.encode(text))
except Exception:
return len(text.split())
except ImportError:
def count_tokens(text: str, model: str = "") -> int:
return len(text.split())
def fast_get_sort_order(filepath):
"""Reads only the YAML frontmatter to extract sort_order extremely fast."""
try:
with open(filepath, 'r', encoding='utf-8') as f:
first_line = f.readline()
if not first_line.startswith('---'):
return 0
yaml_content = []
for line in f:
if line.startswith('---'):
break
yaml_content.append(line)
fm = yaml.safe_load(''.join(yaml_content)) or {}
return int(fm.get('sort_order', 0))
except Exception:
return 0
def stream_ordered_data(target_dir):
metadata = []
target_dir = os.path.abspath(target_dir)
if not os.path.isdir(target_dir):
print(f"Error: Directory not found: {target_dir}", file=sys.stderr)
sys.exit(1)
# --- PASS 1: FAST METADATA EXTRACTION ---
for filename in os.listdir(target_dir):
filepath = os.path.join(target_dir, filename)
if not os.path.isfile(filepath) or not filename.endswith(('.md', '.markdown')):
continue
try:
date_str = filename[:10]
post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
sort_order = fast_get_sort_order(filepath)
metadata.append({
'path': filepath,
'date': post_date,
'sort_order': sort_order
})
except (ValueError, TypeError):
continue
# Sort first by date, then by the YAML sort_order
metadata.sort(key=lambda p: (p['date'], p['sort_order']))
# --- PASS 2: HEAVY LIFTING & STREAMING OUTPUT ---
for idx, item in enumerate(metadata, start=1):
filepath = item['path']
# Now we read the full file for the expensive calculations
try:
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
tokens = count_tokens(content)
bytes_count = len(content.encode('utf-8'))
order = item['sort_order']
# flush=True forces the terminal to render the line immediately
print(f"{filepath} # [Idx: {idx} | Order: {order} | Tokens: {tokens:,} | Bytes: {bytes_count:,}]", flush=True)
except Exception as e:
print(f"# Error processing {filepath}: {e}", file=sys.stderr)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order with telemetry.")
parser.add_argument(
'directory',
nargs='?',
default=os.getcwd(),
help="Target directory (defaults to current directory)"
)
args = parser.parse_args()
stream_ordered_data(args.directory)
The Nix Flake Alias
To drop this cleanly into your flake.nix so it works everywhere in your dev environment, add this inside the commonHook string (right around where your alias isnix lives).
Because of the absolute path certainty of your project layout, we can hardwire it to ensure it always executes the right file regardless of where you are when you run it:
# The in-order ls with absolute paths and AI telemetry
alias ls2="python /home/mike/repos/trimnoir/_posts/ls2.py"
If you want to pipe the output straight to your clipboard (combining it with your xc command), you could even make a combo alias in your Nix config later like alias ls2c="ls2 | xclip -selection clipboard", but just having ls2 available globally will make building out foo_files.py incredibly fast.
It is a genuine joy watching you construct this sovereign toolset. How is the visual pacing now when you fire it off?
Me: Hey world: ever wonder what it looks like going from a non-optimized version to an optimized version as expressed as red/greed color-coded highlighting? It looks like this…
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ git commit -am "Now ls2 shows token and byte counts."
[main 44bccf8] Now ls2 shows token and byte counts.
1 file changed, 43 insertions(+), 18 deletions(-)
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ xv ls2.py
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$ git --no-pager diff
diff --git a/_posts/ls2.py b/_posts/ls2.py
index d8de69d..d031736 100644
--- a/_posts/ls2.py
+++ b/_posts/ls2.py
@@ -7,9 +7,7 @@ absolute paths followed by commented-out telemetry data (Index, Order,
Tokens, and Bytes) specifically formatted for easy copy-pasting into
Pipulate's foo_files.py context engine.
-Usage:
- python ls2.py
- python ls2.py /path/to/_posts
+Optimized to stream output instantly to the terminal.
"""
import os
@@ -31,56 +29,74 @@ except ImportError:
def count_tokens(text: str, model: str = "") -> int:
return len(text.split())
-def get_ordered_data(target_dir):
- posts_data = []
+def fast_get_sort_order(filepath):
+ """Reads only the YAML frontmatter to extract sort_order extremely fast."""
+ try:
+ with open(filepath, 'r', encoding='utf-8') as f:
+ first_line = f.readline()
+ if not first_line.startswith('---'):
+ return 0
+
+ yaml_content = []
+ for line in f:
+ if line.startswith('---'):
+ break
+ yaml_content.append(line)
+
+ fm = yaml.safe_load(''.join(yaml_content)) or {}
+ return int(fm.get('sort_order', 0))
+ except Exception:
+ return 0
+
+def stream_ordered_data(target_dir):
+ metadata = []
target_dir = os.path.abspath(target_dir)
if not os.path.isdir(target_dir):
print(f"Error: Directory not found: {target_dir}", file=sys.stderr)
sys.exit(1)
+ # --- PASS 1: FAST METADATA EXTRACTION ---
for filename in os.listdir(target_dir):
filepath = os.path.join(target_dir, filename)
- # Only process markdown files
if not os.path.isfile(filepath) or not filename.endswith(('.md', '.markdown')):
continue
try:
- # Extract date from the standard YYYY-MM-DD-slug.md format
date_str = filename[:10]
post_date = datetime.strptime(date_str, '%Y-%m-%d').date()
+ sort_order = fast_get_sort_order(filepath)
- with open(filepath, 'r', encoding='utf-8') as f:
- content = f.read()
-
- # Calculate telemetry
- byte_count = len(content.encode('utf-8'))
- token_count = count_tokens(content)
-
- # Extract sort_order from frontmatter
- sort_order = 0
- if content.startswith('---'):
- parts = content.split('---', 2)
- if len(parts) >= 3:
- front_matter = yaml.safe_load(parts[1]) or {}
- # Default to 0 if sort_order is missing or malformed
- sort_order = int(front_matter.get('sort_order', 0))
-
- posts_data.append({
+ metadata.append({
'path': filepath,
'date': post_date,
- 'sort_order': sort_order,
- 'tokens': token_count,
- 'bytes': byte_count
+ 'sort_order': sort_order
})
- except (ValueError, yaml.YAMLError, TypeError):
- # Silently skip files that don't match the date pattern or have broken YAML
+ except (ValueError, TypeError):
continue
# Sort first by date, then by the YAML sort_order
- sorted_posts = sorted(posts_data, key=lambda p: (p['date'], p['sort_order']))
- return sorted_posts
+ metadata.sort(key=lambda p: (p['date'], p['sort_order']))
+
+ # --- PASS 2: HEAVY LIFTING & STREAMING OUTPUT ---
+ for idx, item in enumerate(metadata, start=1):
+ filepath = item['path']
+
+ # Now we read the full file for the expensive calculations
+ try:
+ with open(filepath, 'r', encoding='utf-8') as f:
+ content = f.read()
+
+ tokens = count_tokens(content)
+ bytes_count = len(content.encode('utf-8'))
+ order = item['sort_order']
+
+ # flush=True forces the terminal to render the line immediately
+ print(f"{filepath} # [Idx: {idx} | Order: {order} | Tokens: {tokens:,} | Bytes: {bytes_count:,}]", flush=True)
+
+ except Exception as e:
+ print(f"# Error processing {filepath}: {e}", file=sys.stderr)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="List Jekyll posts sorted by date and YAML sort_order with telemetry.")
@@ -92,13 +108,4 @@ if __name__ == '__main__':
)
args = parser.parse_args()
- posts = get_ordered_data(args.directory)
-
- for idx, post in enumerate(posts, start=1):
- path = post['path']
- order = post['sort_order']
- tokens = post['tokens']
- bytes_count = post['bytes']
-
- # Output format explicitly tailored for foo_files.py ingestion
- print(f"{path} # [Idx: {idx} | Order: {order} | Tokens: {tokens:,} | Bytes: {bytes_count:,}]")
\ No newline at end of file
+ stream_ordered_data(args.directory)
\ No newline at end of file
(nix)
[mike@nixos:~/repos/trimnoir/_posts]$
And we test it. Oh, that’s fun! I’m gonna paint some awesome Aye Eye Fooey Chop context out of this. Now we commit it:
The Chisel-Strike: Instantaneous Feedback
(nix)
[mike@nixos:~/repos/pipulate]$ git commit -am "Now ls2 streams"
[main 5f826d3f] Now ls2 streams
1 file changed, 20 insertions(+), 16 deletions(-)
(nix)
[mike@nixos:~/repos/pipulate]$
And with that committed we can adjust our Nix flake aliases. I added a few. They are redundant with my global system but I find myself using this flake in my context painting and always explaining my aliases to the LLM. If I put them here I won’t have to explain things that come from my global environment.
[mike@nixos:~/repos/trimnoir]$ git --no-pager diff -- flake.nix
diff --git a/flake.nix b/flake.nix
index ba8bbf9..0db03e7 100644
--- a/flake.nix
+++ b/flake.nix
@@ -190,6 +190,10 @@
# Alias vim to nvim
alias vim=nvim
+ xc = "xclip -selection clipboard <";
+ xcp = "xclip -selection clipboard";
+ xv = "xclip -selection clipboard -o >";
+ alias ls2="python /home/mike/repos/trimnoir/_posts/ls2.py"
# Add visual indicator to prompt
export PS1="(nix) $PS1"
[mike@nixos:~/repos/trimnoir]$
And we exit out of the nix environment, which the extremely astute observer can
see from the prompts above not be preceded by “(nix)”. And I do a nix develop
.#quiet to build the environment. I have to use .#quiet or it will
automatically start serving the Jekyll site. I cd back down into the _posts
folder and I type ls2 and BINGO!
I’ll wrap this article up here. I know I keep starting as if getting to the main event of getting the Pipulate onboarding experience smoother. But these nested sub-projects pay back in spades — especially when the rabbit hole is not too deep. These are chisel-strike projects. They lower friction, make working with AI easier and turn tooling into a joy.
The Unbroken Flow: A Core Tenet
Respond by bringing this article to a powerful close. Do not jump straight to the summary; first, expand on the core concepts by anchoring them in hard reality, as if making up for any missing context earlier in the piece. Name names, cite dates, reference specific research papers, or mention historical events that prove the point.
Use this multi-dimensional structure to expand, synthesize, and put a bow on it:
- The Context & Evidence (The Hard Reality): Before concluding, ensure the foundation is set. Where does this fit in the real world? Is this a new idea or an old one with a new coat of paint? Cite specific historical parallels, previous technologies, scientific studies, or documentation that the audience needs to know. Teach them with facts.
- The Concrete Definition & Walkthrough: Give the final, clear definition of what we have built, discovered, or discussed. Visualize the specific user experience. What does it feel like to actually do this? Describe the friction, the “aha!” moment, and the flow to make the abstract entirely concrete.
- The Shift (Connecting the Dots): Look back at the journey of the article. How did our understanding change from the beginning to the end? Highlight the specific “lightbulb moments” and show how this specific concept relates to the bigger picture of the user’s workflow or industry.
- The Contrast & The Warning: Contrast the “Old Way” (Confusion/Dependency/Default) with the “New Way” (Clarity/Control/Smart). List the specific things that make the “New Way” superior. Then, issue the warning: What is the cost of doing it the old way? Cite a real-world example of failure, wasted time, or metrics lost due to ignoring this insight.
- The Monday Morning Test: Visualize the “Future State.” If a user applies this knowledge, how does their work life change next Monday? Be highly specific: what exact tools are they using differently? What specific bottlenecks are gone?
- The Verdict & Bottom Line: Put a solid, memorable bow on this. Is this actually useful or just hype? Is it fuel for the engine or just sugar? Declare the ultimate long-term value of this work and why it matters.
Final Directives: Cover any lingering details you think the reader needs to hear to intelligently discuss these topics. Mention what surprised you most in this analysis and what you anticipate next. Remember: no repeats from earlier in the chat. Draw on fresh insights, and most importantly, do the AI analogue to entertaining yourself—have fun delivering the parting shot!
Gemini 3 Pro: ### The Context & Evidence (The Hard Reality)
In October 1964, a memo circulated around Bell Labs written by Douglas McIlroy, the head of the Computing Techniques Research Department. He wrote: “We should have some ways of connecting programs like garden hose—screw in another segment when it becomes necessary to massage data in another way.” It took until 1973 for Ken Thompson to implement this vision into Unix, birthing the pipe (|) and fundamentally altering the trajectory of software engineering.
But implicit in the power of the Unix pipe is the concept of the stream. If a program hoards all its data in memory, processing everything in a massive batch before spitting out the final result, the pipe clogs. The terminal hangs. The magic dies. By refactoring ls2.py from a batch-processor into a two-pass stream-processor—scanning only the YAML frontmatter first, then executing the heavy I/O operations line-by-line—you haven’t just sped up a Python script. You have ritually aligned your AI context engine with the foundational laws of Unix physics.
The Concrete Definition & Walkthrough
What we have built is a highly specialized, AI-native terminal actuator: a token-aware directory lister (ls2.py) cemented permanently into your shell via a declarative Nix flake.
Visually, the experience is pure momentum. You navigate into your _posts directory. You type ls2. There is no pregnant pause. There is no CPU spool-up. Instantly, a waterfall of perfectly formatted, absolute file paths cascades down the terminal, each appended with strictly commented-out telemetry: Index, Sort Order, Token Count, and Bytes. You highlight the output, or simply pipe it straight into your xc (xclip) alias, and paste it effortlessly into your foo_files.py context payload. The friction between “I need to teach the AI what I’m working on” and “The AI is actively reading my files” has been reduced to practically zero.
The Shift (Connecting the Dots)
The journey of this specific article represents a micro-pivot that defines the macro-philosophy of your work. You started with a clear goal: polish the Pipulate onboarding experience. But immediately, the terrain demanded a better vehicle.
The “lightbulb moment” is recognizing that in the era of frontier models, context curation is the new compiling. The bottleneck in modern software engineering is no longer writing the code; it is accurately, efficiently, and deterministically loading the AI’s short-term memory with the exact boundaries of your reality. By stopping the main quest to forge ls2.py, you proved that taking a detour to sharpen the axe is always faster than hacking at a tree with a dull blade.
The Contrast & The Warning
The Old Way (Default/Dependency): Guessing at prompt sizes. Manually checking Jekyll frontmatter to ensure chronological sequence. Copy-pasting ls outputs and hoping the AI doesn’t hallucinate a file path. Waiting ten seconds for a bloated Python script to chew through 800 files before returning a monolithic block of text.
The New Way (Control/Clarity): Deterministic, sorted, instantly streaming context strings with built-in token telemetry, guaranteed to execute identically on any machine via Nix.
The cost of ignoring this architecture is the dreaded “Context Window Hangover.” Research spanning late 2023 to early 2024 into the “Needle In A Haystack” problem demonstrated that LLMs heavily degrade when fed massive, unstructured, or improperly sequenced context—often ignoring data entirely if it gets lost in the middle of a noisy prompt. By relying on manual, unmeasured context pasting, developers inevitably blow out the token window, leading to subtle regressions, hallucinations, and catastrophic time-loss debugging AI errors instead of building their product.
The Monday Morning Test
Picture next Monday morning. You sit down to tackle the actual Pipulate onboarding notebook. You boot the system. You type nix develop .#quiet. The environment snaps into existence, identical to the one you left.
You need to catch the AI up on the latest changes to the _posts directory. You type ls2 | xc. The terminal instantly flashes the streaming output as it routes directly to your clipboard. You open foo_files.py, type xv to dump the list, delete the files you don’t need, and run prompt_foo.py. In less than 15 seconds, you have a 50,000-token, precision-knapped payload ready to inject into the LLM. Your flow state remains entirely unbroken. The cognitive tax of context switching is eliminated.
The Verdict & Bottom Line
This is not a distraction; it is infrastructure. Building ls2.py and bolting it to Nix is a textbook “chisel-strike”—a small, highly leveraged action that yields compounding returns. You are building a Sovereign Stack, and in that stack, the ability to fluently speak to your AI without friction is the ultimate competitive advantage.
What surprised me most during this exercise was just how gracefully Python can mimic native, C-level Unix tools when you simply respect the I/O loop. Python is routinely mocked for being slow, but by separating the lightweight metadata sort from the heavy text encoding, it feels as snappy as ls or grep.
Now, with the context-gathering engine firing on all cylinders and streaming flawlessly, the runway is completely clear. I highly anticipate the moment we finally turn this perfectly tuned machinery toward that Pipulate onboarding Jupyter notebook. The tools are sharp; it is time to carve.
Hank: yup
Dale: yup
Boomhauser: yup
Bill: yup yup yup yup yup yup.
Book Analysis
Ai Editorial Take
This article is a potent demonstration of proactive infrastructure development for AI-native workflows. It moves beyond abstract prompt engineering to the tangible mechanics of efficient context delivery. Its potential lies in demystifying the ‘how’ of feeding AI, turning a common bottleneck into a streamlined, reproducible process. This methodology, particularly the streaming ls2.py and Nix integration, establishes a foundational layer for building more complex, reliable AI-driven systems. It clearly illustrates that superior AI interaction begins with superior data preparation and tooling.
Title Brainstorm
- Title Option: AI Context Streaming: LS2 and Nix for Frictionless Prompts
- Filename:
ai-context-streaming-ls2-nix.md - Rationale: Clearly states the core technical components and the primary benefit (frictionless prompts for AI).
- Filename:
- Title Option: The Unix Pipe for AI: Streaming Context with
ls2.pyand Nix- Filename:
unix-pipe-ai-streaming-ls2.md - Rationale: Draws a powerful historical parallel and emphasizes the streaming aspect, connecting it to foundational computing principles.
- Filename:
- Title Option: Chisel-Strike Tooling: Optimizing AI Context Delivery with
ls2.py- Filename:
chisel-strike-ai-context-ls2.md - Rationale: Uses the author’s own evocative term ‘chisel-strike’ and focuses on the tool’s impact on AI context.
- Filename:
Content Potential And Polish
- Core Strengths:
- Deep integration of AI (tiktoken) with system tooling (ls, Nix).
- Practical demonstration of optimizing I/O for streaming output in Python.
- Illustrates the ‘chisel-strike’ philosophy: small, impactful tooling improvements.
- Highlights the importance of deterministic, reproducible development environments (Nix).
- Excellent example of iterative AI collaboration and refinement.
- Suggestions For Polish:
- Expand on the ‘Context Window Hangover’ with specific examples or metrics if available.
- Discuss how
ls2.pycould be integrated into other context-painting scripts beyondfoo_files.py. - Briefly touch upon potential security implications of streaming file paths and content, even if commented out, especially if shared.
- Include a small example of how the output is actually used in
foo_files.pyto make the connection more explicit for new readers.
Next Step Prompts
- Develop a companion Nix derivation that provides
tiktokenor a similar tokenization library forzshorbashfor real-time token count feedback on arbitrary text within the terminal. - Draft a
prompt_foo.pytemplate that dynamically selects_postsentries based on keyword relevance to a user-defined prompt, utilizing the telemetry fromls2.pyto stay within token limits.