Checksum 2.0, coming soon..

After "The Loss", of which we shall not speak, I didn't think I'd ever see checksum's code again.

Then came AI.

A job which would have taken me a painful month or more and I wouldn't even bother starting has been reduced to a couple of days with my local AI, along with help from the odd frontier model when the local agent was busy.

In short, I can see the code again. w00h00!1

Yes decades have passed, and it still works great, but the bottom line is, I backed the loser, Blake2; politics or something got involved and SHA256 won and got baked into our actual hardware, meaning using it to hash files is FAST AF.

Replacing Blake2s with SHA256 seems like a no-brainer now, and something I've been thinking checksum should be doing, for quite a while2.

I'm a bit sad to see the cryptographically-superior (I remember thinking at the time) Blake go, but happy to shave heaps of CPU energy and time off hashing files; checksum's hash.dll, while still faster than Windows built-in bcrypt.dll (in the two algorithms they share; MD5 and SHA1), can't begin to keep up with modern hardware-accelerated computations of SHA256 (and only SHA256, btw)..

10.15GB test file:

old checksum hash64.dll (SHA1) = 13.9s (now faster than MD5 on my modern hardware ~ 16.5s)
Windows cryptutil (SHA256) = 9.5s
the new bcrypt.dll routines in checksum (SHA256) = 6.7s

Basically half. I want to insert this efficiency into the brain of checksum. And I'm working on it right now, also Open Sourcing the code, so you can have fun with it.

I have also got back simple checksum, checksum's wee brother, and he is the first to take the plunge; the SHA256 hashing is working great. There's still a few things to do yet, but expect a taster binary soon.

checksum itself will take a little longer to whip into SHA256 shape. Also I still need to wipe and upgrade the actual server all this lives on3, and also it's Summer Holidays right now and the garden has many time-sensitive needs, as do the kids, so..

Now you at least know the plan.

For now..

;o)

	1.7.5.21



	*	Incorporated a facility to upgrade legacy MD5 & SHA1 hashes to SHA256.



		I used my local Qwen 27B to come up with the basic structure, which I

		then tore apart. Hey, it was better than the bollocks Qwen 35B-A3B

		assured me was good code, it was not. We humans think of things AI

		simply doesn't. But once you have that, AI can be useful and is cheaper

		than a paid intern; I used Gemini to help me track down the bugs and

		find inefficiencies. So far; good work.



		The command-line switch is simply "upgrade", and I've created a couple

		of Windows registry scripts you can use to add and remove extra Explorer

		context commands for upgrading hashes (one to add the entries and

		another to remove the entries if/when you no longer need them).



		There is no facility to upgrade Blake2s hashes as they look identical

		to SHA256 hashes, which we skip during hash upgrade. Sorry Blake2 users!

		That's a can of worms I wasn't gonna open. Fortunately most folk use the

		fastest algorithm which was always either MD5 or SHA1, until SHA256 came

		along and got integrated into hardware.



		** If you do use Blake2s, I would simply verify all hash files then

		   delete and recreate with SHA256; similar security but *much* faster.



		During the upgrade, old hash files are interrogated; their hashes

		verified; and on success, the algorithm is recomputed using SHA256.



		checksum can upgrade all MD5 and SHA1 hashes to SHA256 as-it-goes,

		meaning	all .md5, .sha1, .sfv and UNIX MD5* files get re-computed and

		their extensions switched to .sha2. All the usual formats are supported.



		 * whatever is set in your ini, e.g. "md5sums.txt"



		If there are any issues during the upgrade process, a log will be

		created in the working directory (unless you have a fixed log path

		configured). You can work on files, directories, trees and volumes. The

		final dialog has useful information.



		As I run the upgrade over my own hash files I realise it's also a fine

		excuse to /check/ all my files, which is happening automatically as

		checksum works, a two-for-one!



		NOTE: RESUME IS SUPPORTED. If checksum gets interrupted during an

		upgrade operation, it leaves behind a temp file. If that temp file is

		present when you begin an upgrade, checksum picks up from where it left

		off. This is especially useful for BIG hash files spanning TBs of data.



		It also creates a .bak of your checksum file for the moments it's

		replacing your hash file, for extra redundancy in case something

		*unforseen* happens. I'm fairly sure I have your old hash data covered.







	1.7.4.10



	*	Thanks to the wonders of AI, I have resurrected checksum!



		Not saying it wasn't a lot of work, but it became /doable/, which it

		previously was not, at least by me alone in the time I get.



		So, thank you AI!



		This also means all the things that I've considered checksum should or

		shouldn't be doing, in the last ten years, have a chance to get done, or

		not done! But, you know, DONE! Expect a few changes, features and fixes

		in the next wee while.



		I'll get a beta up ASAP so you can help me track down all the new bugs!



		Here goes..





	*	Removed hash.dll dependency.

	

		Still the fastest MD5 hashing, but there is no good reason to use MD5.



	*	Replaced Blake2s hashing with SHA256 hashing.



		We are using Windows' built-in bcrypt.dll for hashing now, so it should

		keep up-to-date with any underlying hardware improvements, but no BLAKE.



		checksum's routines have faster throughput than Windows' cryptutil and 

		of course utilize your underlying hardware's SHA256 capabilities, 

		meaning SHA256 is now *way* faster than even MD5 ever was.



	~	As SHA2 is now the default hashing method, if you require MD5, you will

		need to ask for it by either setting that as your default inside

		checksum.ini, choosing that in the options dialog, or adding a "5" to

		your command-line switches.



		The switch for SHA2 hashing is "2", but if it's your default, you won't

		need to add it unless selecting SHA2 as the algorithm for applicable

		operations, e.g. "a2" for verifying only SHA2 hashes.



	~	As well as making checksum more lightweight, it makes checksum much more

		responsive during hashing operations (the old DLL was so fast partly 

		because it hogged your CPU and ignored all input except Pause/Break). 

		You will find things like moving and toggling the tooltip a /lot/ 

		easier.



		Pause/Break still shows the abort dialog.



	*	SHA256 is now the default hashing algorithm.



	*	The HTML logging is now HTML5 (previously it was XHTML4.0).



	*	Added UP handling inside hash files, so ..\some.file will now work as

		expected. You can hash FitGirl releases directly!



	*	Removed the shaky, undocumented tar.md5 support. Oh, you didn't know

		about that? Hah!



 

My Agent

Okay, so I've been watching this Agentic AI business for a while now (my newsletters can't stop going on about it), and frankly, not too interested; the idea of letting some AI loose on my own workflows is not currently appealing.

But then a perennial thought of mine came to mind again today, a job I've been putting off for well, years because it would take ALL DAY, and is actually not that important, though it would be nice.

This sounds like exactly the sort of thing I could trust an AI agent to do, I figured. The job?

Sort my movie collection.

It's got a sorta sort going on, there are folders like "Action" and "Horror", but not much more. I started a folder of "Directors", which is a great way to categorise movies, but then you need to make shortcuts back into the genre folder and what you really want is to have ALL the movies inside there, and then create shortcuts to the movies in all sorts of other folders like "Genre" (inside there being Action, Horror, Comedy, etc., and maybe inside there, Dark Comedy, Mime, Slapstick, whatever!), "Actors", "Year", and so on.

Media managers aren't my bag. Nor are expensive "frontier" subscriptions.

TLDR: The bugger just did it in three minutes. Locally. On Windows.

I don't know if writing about it while my mind is still blown is such a good idea (there will be many tuts like this I reckon; there can never be enough), but here goes;

How I organised my entire movie collection just the way I've always wanted it, in three minutes, excluding setup time, on Windows..

Oh wait a minute, what setup? I hear you say. Well, actually not a lot.

You only need three things, and you might already have most of them;

Hold it, before that (oh wait, this is sounding like stone soup) you will need a build environment. Install the latest Python and Git, then install Microsoft Visual Studio 2022 (NOT ANY OTHER YEAR!3). These are all click-and-go installers these days; now you can build and run all that "AI stuff". Three things..

1. Something to run your AI model, provide an API for interacting with the model, etc. (llama.cpp is what I'm using, compiles in a couple of commands, loads of cute apps use this as their backend, but I prefer to interact directly).

2. An AI model (I'm using QWEN 3.6, which is available on huggingface and gagging to get into a harness)

3. A harness, which as the name suggest, cradles your AI model, keeping it safe-ish and possibly warm. The model runs inside the harness, which keeps it in check, stops it falling off the rails and makes the whole looping-learning-doing thing happen. I'm using OpenCode. This is what enables your AI model to interact with the outside world, and with real things, like your files.

Yes, I realise OpenCode is supposed to be setup with a "project" and you are supposed to do it in some coding project's folder, but like I said, I couldn't care less about that stuff; I just want my movies organised.

The steps:

* In a shell:

cd C:\Path\to\your\AI-stuff
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

You are now inside llama.cpp's directory and we can set about building llama.cpp itself..

This is all one line, though it will be split up here on the web. Here we are just setting an environmental variable to enable CUDA compilation, or else your AI will run VERY slowly.:

"C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin\cmake.exe" -B build -DGGML_CUDA=ON

My MS build tools directory wasn't in the PATH, so I'm assuming yours isn't either, hence full paths; change if required; then run:

"C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin\cmake.exe" --build build --config Release -j

* Go do something else until its done.


Once it's finished compiling, we can test llama.cpp is working:

C:\path\to\bin\Release\llama-cli.exe -m "C:\path\to\your\model.gguf" -p "Tell me a joke" -ngl 999 -n 32

If all goes to plan you should see a lame joke. This will make you laugh, not because the joke is funny, but because you have just unlocked local AI chat. No API fees, no monthly subscriptions; pick a model and go2.

There are a few cute desktop apps for this local LLM malarkey, but llama.cpp is probably all you need. It has a CLI and web interface right off the bat, so you can go chat, Chat-GPT-Style, right away:

http://127.0.0.1:8080

You will want to use better parameters for running your actual model, and maybe a script to enable you to choose your model on-the-fly (pick a model and go, what I just said).

Gemini came up with this which works great (save as a .bat file somewhere):

@echo off
setlocal enabledelayedexpansion

:: ----------------------------------------------------
:: Configuration Pathways
:: ----------------------------------------------------
set "SERVER_EXE=I:\AI\llama.cpp\build\bin\Release\llama-server.exe"
set "MODEL_DIR=I:\AI\models\LLavacheckpoints"

:: Verify directory exists
if not exist "%MODEL_DIR%" (
    echo [ERROR] Model directory not found: %MODEL_DIR%
    pause
    exit /b
)

:MENU
cls
echo ====================================================
echo               Available GGUF Models
echo ====================================================
echo.

:: Loop through directory and index GGUF files
set "count=0"
for %%F in ("%MODEL_DIR%\*.gguf") do (
    set /a count+=1
    set "model[!count!]=%%~nxF"
    set "model_path[!count!]=%%~fF"
    echo  [!count!] %%~nxF
)

if %count%==0 (
    echo [ERROR] No .gguf models found in %MODEL_DIR%
    pause
    exit /b
)

echo.
echo ====================================================
echo.

:: Prompt User for Choice
set "choice="
set /p "choice=Select a model number to launch (1-%count%): "

:: Validate Input
if not defined choice goto MENU
if %choice% lss 1 goto MENU
if %choice% gtr %count% goto MENU

:: Resolve selection variables
set "SELECTED_MODEL_NAME=!model[%choice%]!"
set "SELECTED_MODEL_PATH=!model_path[%choice%]!"

cls
echo ====================================================
echo  Launching llama-server
echo  Model: %SELECTED_MODEL_NAME%
echo ====================================================
echo.


"%SERVER_EXE%" -m "%SELECTED_MODEL_PATH%" --host 127.0.0.1 --port 8080 -ngl 999 -c 65536 -np 1 --keep 12000 --timeout 600 --fit off --cache-type-k q8_0 --cache-type-v q8_0

:: pause
exit

You will want to edit in your own paths and whatever hardware constraints (this is for an RTX3060/12GB4). Pass --help to llama-server.exe to see all the available options.

This is great and all, but to fully unlock the capabilities of the latest "Agentic" models, you are going to want to put that model in a harness..

Enter OpenCode.

This is simple to install (using Node.js):

npm install -g opencode-ai

This assumes you have Python installed. Now head here:

C:\Users\<YOU>\.config\opencode

And open opencode.jsonc in a text editor. Paste in something like this:

{
  "$schema": "https://opencode.ai/config.json",
  "model": "llamacpp/qwen3.6",
  "provider": {
    "llamacpp": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Llama.cpp (Local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "qwen3.6": {
          "name": "Qwen 3.6"
        }
      }
    }
  }
}

You could also specify multiple models, which can be chosen with the -m flag or by using /models inside OpenCode..

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "llamacpp": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Llama.cpp (Local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "qwen3.6": { "name": "Qwen 3.6" },
        "llama3": { "name": "Llama 3" },
        "mistral": { "name": "Mistral" }
      }
    }
  }
}

But this is purely academic, as llama.cpp won't care what OpenCode thinks the model is, the tokens will all be the same, as far as I can gather. But unless you specify something, you won't be able to launch OpenCode. Second run, that's different, it will fallback to its previous config. But first run... *rolls eyes*

Okay, now you can launch OpenCode, and it can communicate with your AI model. All that's left is to get it to DO something. This is where the magic happens..

I had no clue how to go about prompting an AI agent, but another AI got me up-to-speed pretty quickly and suggested this:

Write and execute a Python script to organize this movie directory based on the following specific structural and metadata rules:

1. Scan the current directory and group loose files by their shared base name (for example, "A Night to Remember.1958.BR"). All matching auxiliary files (such as .srt, .hash, .nfo) must be kept and processed together with the main video file (.mkv, .mp4, .avi). Treat existing dedicated movie folders as single items.

2. Query an unofficial IMDb API or scrape data using the shared base name to retrieve the Director, Cast (Actors), Release Date, Writers, and Genres for each film.

3. Move Phase: Move the grouped items into a structured folder hierarchy: "\Directors\Director Name\". 
   - For loose files sharing a base name, move all of them directly into the target director's folder as loose files. Do not wrap them in a separate subfolder.
   - For existing movie folders, move the entire folder inside the director's folder.

4. Shortcut Phase: Create virtual taxonomies by generating root folders for "\Actors", "\Dates", "\Writers", and "\Genres".

5. Inside those virtual taxonomy folders, use Windows PowerShell COM scripting via the "WScript.Shell" object to programmatically create native Windows Shortcuts (.lnk) pointing directly back to the movie's new physical location under the "\Directors" path. 

6. Handle multi-value attributes (like multiple actors or genres) by creating a separate shortcut in every corresponding category folder.

I saved that to a file inside my movie directory named Sort Movies.ai. Then I open a shell in that directory and simply do:

opencode --prompt "Read the rules in @Sort Movies.ai and generate and execute the Python script now."

This, I have to say, was an amazing experience1.

Watching an AI "think" through your task is fun. And watching it hit a wall (no matches in OMDB as file name contains encoding info) and reason as to the issue and its solution, is quite remarkable; the way it hits walls and figures out solutions on-the-fly is informative as well as entertaining.

But the kicker, for me, was once it had finished, in under three minutes, it's actually done a remarkable job in the test folder (yeah, I'm not stupid!), with only a couple of stray files (which it has seen and plans to fix) and, weirdly, duplicates of all shortcuts (which again, it has seen and plans to fix, though I could also do that in a few seconds myself, if required; a minor issue). I chose the "fix" option and a few seconds later the folder is pretty much exactly how I would expect it to look after me spending a half hour on the task..

Actors\Kevin Bacon and Actors\Jack Nicholson both contain shortcuts to Directors\Rob Reiner\A Few Good Men 1992.1080p.BluRay.x265.AAC.mp4, as does Writers\Aaron Sorkin, as does Genres\Drama, as does, well, others too. YES! This is what I wanted! THANK YOU!

All in all, a job I've been putting off for years because it would take all day, can be done in minutes, with the right tools. The "real" folder is considerably larger, but I'm feeling pretty confident about smacking this bitch up and walking away, doing something else while my unpaid assistant does grunt work for no more than some green electricity (did I hear "B00Yah!"?).

Curiously, my later attempts to refine this script.ai, to catch the things it fixed perfectly well on its second pass (you know, with a mind to the larger, invariably more varied data set), created convoluted sessions that burned tokens like crazy (at a whopping, $0.00/token, no less) and did not produce the desired result, unless I did a wee bit back-and-forth afterwards. Yes! You can keep working with your agent to refine the results and get the job, in the end, done.

This tells me a) I have a lot to learn, and as with recent image models, it's probably best let AI write these prompts in future and b) people must waste a shit-ton of money5.

;o)