I’ve been working on a personal creative writing project all year, and along the way I built a tool for it. I’ve open-sourced it.

It’s called Editorial VCS, and it’s a self-hosted version control system for writing projects. Every save is a full revision with a commit message. Side-by-side diffs. Files move through Draft, Review, and Final independent of their revision history. Projects are siloed, can be tied to a URL, and you can promote a file from one to another without losing its past. If you’re working on fiction, there’s an optional lore bible that catches every mention of a character or place across your files and draws the relationship graph for you.

Plain PHP, MariaDB, Docker compose up and you’re running. MIT license.

Why it exists: I spent this spring running essays through five AI models, each with an assigned editorial role: one for facts, one for structure, one to attack the argument. That produces a lot of revisions, and the question “which pass lost the details I wanted to keep?” turned out to need a real answer. Git is built for code. Nothing I found was built specifically for this.

So the tool built for a project that will never see the light of day ended up separately shepherding ten essays and an eight-story anthology to publication instead.

It’s version one. I had to strip out some internal LAN-based pieces before publishing, so it’s a single commit, no releases, and I haven’t finished testing the clean deploy yet. I wanted it up to share while I polish it. If you write with a pipeline (human or otherwise) I’d like to know whether it fits how you work, and where it doesn’t.

And yes, I wrote it with Claude. Domain expertise did the steering.

Available on GitHub: https://github.com/bcGITster/editorial-vcs

I measured what watermarking actually does to AI writing. The answer isn’t what either side thinks.

Anthropic started watermarking Claude’s text output in August and extended it to additional older models on November 3rd per EU AI Act compliance. SynthID-Text. No opt-out.

Some users claim watermarking degrades output quality. Anthropic says zero impact. I think we’re somewhere in between, and I have the data to show it.

I run RVI (rvi.run), a tool that measures linguistic variance in text. Sentence length distribution, vocabulary diversity, and structural patterns. THIS IS NOT an AI detector, it’s more like a thermometer. It doesn’t care who wrote the text, it just measures how much variety is present.

In August, before watermarking, I ran 100 structured prompts (10 genres × 10 each) across five Claude models via API. Clean baseline. On November 5th I ran the same prompts against seven models, including ones I had baselines for.

Sonnet 4.6, the cleanest before-and-after:
Aggregate RVI: 59.0 (August) -> 59.43 (November). Median: 59 -> 59. No change.

Anthropic’s claim holds… On the surface.

But broken out by category:
• Scientific review: −3.3 points
• Business writing: −2.4
• Narrative fiction: +6.2
• Personal essay: +2.6

The November outputs showed less variety in factual writing and more in creative writing. The aggregate looked flat because the effects canceled out.

This makes mechanical sense. SynthID-Text biases word choices using a keyed random pattern. In factual text there’s often only one right word… the watermark constrains an already narrow field. In fiction, where “overcast,” “grey,” “leaden,” and “dim” are all valid, the keyed randomness adds variation on top of the model’s natural distribution.

The watermark isn’t neutral. Aggregate metrics completely miss the story.

If you’re generating technical documentation or research, watermarking measurably constrains your output variety. Not by a lot, but not zero. If you’re writing fiction or personal essays, it may actually be adding variety.

The dataset is 1,100 scored essays across 11 Claude models and growing. It’s all measured through RVI at rvi.run. I’ll be adding analysis results to the site soon along with other models.

Fun fact, Adam Smiths’ “Wealth of Nations” is the lowest scoring document in my human test set at a score of 33… not a lot of information in that work it seems.

The Y2K “bug” was a serious data and infrastructure issue impacting nearly all technology coming into the end of the 1990’s.

The main reason is that from the 1960’s and forward memory wasn’t just expensive, it just didn’t exist for systems in the scale that you may be used to today. In the mid-to-late 1980’s 64k of RAM was a standard for desktop computers and if you wanted to expand that you might be able to buy 1MB and by 1990 2MB-4MB was an exotic premium.

Beyond cost and construction the #1 limiting factor was the CPUs on these early systems. They literally didn’t have the capability to address RAM in large quantities, so early on you augmented the RAM with tricks that offloaded information to the computers storage medium (we still do this even today with Swap memory on modern PCs but that’s a practical solution to not enough RAM, not the CPU anymore).

Also storage mediums were a huge expense too. In the late 80’s a 10MB (MegaByte! Not GigaByte) hard-drive for an Apple //e was well over $1000, and even with the best hardware you couldn’t actually utilize all of the 10MB as one volume. Enterprise HDs for servers were in the 5-6 figures, easily.

So when you wrote a program that needed a database, every single byte cost you hard dollars. Either in the RAM or storage. So doing things like only storing the last two digits of the year was THE standard practice up until the early 90’s as a practical cost solution. Why spend money on storing the bytes “1” “9” on every single year when that’s just wasted space.

So, all of a sudden all these systems were looking at a situation where when the year 2000 hit they would all roll back to 1900. This wasn’t speculation, this was the core crisis. Suddenly untold number of programs doing calculations on “date greater that 19XX” would suddenly report, NO. This impacted banking, finance, government records, *everything*. And many times it was not just software but embedded systems and that hardware just had to go in the trash.

The industry pulled together behind the scenes and globally replaced hardware and software across the spectrum to meet the challenge. Staggering amounts of money were spent and people put in 80-100+ hours a week of labor to fix it and the fact that we passed over that threshold without serious issues should be celebrated not minimized as a “scare”.

Hiring managers keep telling candidates that $100,000 is a lot. Let’s check that.

According to the Bureau of Labor Statistics, consumer prices are up about 67% since 2006. So $100,000 today buys roughly what $60,000 did then. Compared with just 2016, it’s about $71,000.

That $80,000 “manager” offer? In 2006 dollars, it’s about $48,000. The $70,000 tech support role is about $42,000.

And that’s the general average. The things that actually decide whether a paycheck stretches have gone up faster:
* Housing costs are up about 75%, and the median home price has nearly doubled.
* Family health insurance premiums have more than doubled, from about $11,500 to roughly $27,000.

This isn’t new, either. In 2008, I watched a newly promoted director take $85,000 for a role whose previous holder made $135,000. The pitch was, “You don’t want that much. It’ll bump you into a higher tax bracket and complicate your life.” That’s a myth. Only the dollars above a bracket’s threshold get taxed at the higher rate, so a raise never lowers your take-home pay. In today’s money, that’s the difference between about $133,000 and $211,000. Lowballing has always come with a story about why less is better for you.

Six figures isn’t a reward anymore. It’s what a middle-class salary from 2006 looks like today and often not a comfortable salary if you have a family to support. If your salary bands still feel “generous,” check what year they’re set to.

Pay what the work is worth today, not what it felt like it was worth in 2006.

Years ago I knew a brilliant engineer who took a job with a new company as their first hire… programmer, strategist, everything.

They delivered work that allowed the company to become a successful, thriving business that ultimately supported a sales team, marketing team and office workers, as well as a (what some might call) lavish lifestyle for the founder and offices in a prominent upscale area of the city this was in.

For the first few years they worked themselves to the bone, working through holidays and 12+ hour days building solutions for the company that brought in lucrative contracts. For months if their family wanted to have dinner with them it would be at the office, or cold leftovers whenever they finally made it home to sleep.

Eventually they hit burnout and had to walk away.

Sometime later I happened to be talking with former employees of that company who had moved on to other jobs and in a casual conversation they said “Hey you remember so-and-so from that place?” I did. They followed with “Man we all felt so bad for them and how things went. It’s like they built that whole operation and they were the lowest paid person in the company. Even the receptionist was getting paid more and we all knew it but nobody knew what to say. I hope things worked out well for them.”

😳

Then, years later I happened to run into the owner of that company in a public space and we had a pleasant conversation (I had done some contract work for them over the years myself) and at one point he asked if I was still in touch with the former employee and if I knew how they were doing. I shared what I was aware of and asked him casually… “so I ran into some of the other guys and they were telling me something, is it really true that (the former employee) was the lowest paid employee there?” His answer was yes, but it was the person’s fault for undervaluing their talent and he held no fault, hopefully they learned from that experience. He literally used the term “tough love” to summarize the situation.

😶

I want you to sit with that story. Do not be that guy. If an employee is the source of your revenue, compensate them accordingly. Not everyone is savvy about fair compensation for their skills. Someone lowballing themselves isn’t a win for you. It’s exploitation.