Markov Chains Are the Original Language Models

<h1>Why I Talk to Myself</h1>
<img src="/images/bike_sidewalk.webp" alt="My bike broke down. Luckily, I had my camera">
Each week I sit down and reflect on what I've learned. I think about which of my efforts have helped the Harper endeavor, and which have harmed it. It's not easy, but I think it's one of the best habits I've built. More accurately, it's one of the best habits <a href="https://ma.tt">Matt</a> has encouraged me to build.
When I really think about it, I'm reminded of the original <a href="https://blog.afterthedeadline.com/">After the Deadline blog</a>, which coincidentally also had a category called "Talking to Myself". I wonder if Raphael Mudge (AtD's author) had a similar motivation.
I talk to myself to find the weaknesses in Harper's mission and focus my attention on what matters. It is an efficient way to get from <a href="https://ma.tt/2025/05/sharing-levels/">layer one to layer five</a>. It can help get other maintainers on the same page and build a body of knowledge that can be linked to from anywhere.
Not worrying about keeping information locked-down is one of the more beautiful parts of working in public. Open source code would be nothing without similarly open source knowledge. I talk to myself online so my users can understand my intentions.
In many senses, we are not unlike moths. We are drawn to the lights of distraction, hype, and procrastination. For me, setting up checkpoints to stop and reflect has been an effective deterrent from these lights. In fact, I think each minute for which I stop and think has paid itself back three times over in time savings. If I can better allocate my time and energy, I can avoid doing redundant work pursuing pointless goals.
<h2>Bonus Points</h2>
A <a href="./markov_chains_are_the_original_language_models">few</a> of my <a href="./local-first_software_is_easier_to_scale">articles</a> have become popular on sites like Hacker News and Reddit. We've gotten quite a few Harper installations from links on these articles. I would never rely on this kind of blogging as a source of advertising, but it is nice to see it help spread the word.

It's not easy, but I think it's one of the best habits I've ever built.

It's not easy, but I think it's one of the best habits I've ever built.

Why I Talk to Myself

<h1>Local-First Software is Easier to Scale</h1>
<img src="/images/flatirons.webp" alt="The Flatirons">
The title of this post is somewhat misleading. Local-first software rarely needs to be scaled at all.
<a href="https://writewithharper.com">Harper</a> recently received a massive increase in both traffic and user count.
How?
By making it to the front page of Hacker News.
If you couldn't tell by the extent to which I talk about this, I'm thrilled.
This amounted to a tremendous amount of free advertising, if nothing else.
I've been thinking a lot about the wondrous benefits of working at the edge.
That's just one term of many that people use to describe Harper: "edge-first".
Others include "local-first" or simply "on-device".
They are all just jargon for a simple idea: We run all the relevant grammar checking code as physically close to the users as possible.
That way, we can serve up suggestions faster than anyone else.
To achieve our goal, we make sure that everything is well-optimized and lean.
Harper barely uses any CPU or memory at all.
What does all this mean in the context of the recent uptick in user acquisition? Imagine, for a moment, we are LanguageTool.
Our software is written in Java, with questionable attendance to the efficiency of our code.
Since our software requires a big server to run, we have a certain number of servers on standby to handle a certain amount of expected load.
If suddenly the number of requests we received were to double (as it did for the Chrome extension), we would be big trouble.
To avoid increasing the latency on requests (or dropping requests all together), we would need to scale up the number of running servers.
This not only takes hiring an expert in cloud architecture, but also additional funding to pay the AWS (or whatever) bill at the end of the month.
Because Harper runs at the edge (no server required), we don't have to worry about that.
In fact, I only noticed that our user count had spiked after I visited Hacker News the morning after.
No hiccups at all.
Lots of cloud providers like to brag about being able to scale with their users. I like to brag about not having to scale at all.

The title of this post is somewhat misleading. Local-first software rarely needs to be scaled at all.

The title of this post is somewhat misleading. Local-first software rarely needs to be scaled at all.

Local-First Software Is Easier to Scale

<h1>Andrew Dunbar Becomes Harper's First Committer</h1>
I am pleased to say, with much fanfare, that Andrew Dunbar (known by online moniker <a href="https://github.com/hippietrail">hippietrail</a>) is becoming Harper's first committer.
Andrew has been doing stellar work for the project for more than a year now.
His work on our grammar engine has been phenomenal.
Odds are, if you have opened a ticket on GitHub or dropped on comment on Discord, he was the first one you spoke to.
He clearly understands and embodies Harper's mission and has demonstrated responsibility for the code he submits.
So today, we are making him Harper's first external committer.
<a href="https://writewithharper.com/docs/contributors/committer">That means a lot of different things</a>, but primarily it means that he has the permission to merge his own and other's PRs.

I am pleased to say, with much fanfare, that Andrew Dunbar (known by online moniker [hippietrail](https://github.com/hippietrail)) is becoming Harper's first committer.

I am pleased to say, with much fanfare, that Andrew Dunbar (known by online moniker <a href="https://github.com/hippietrail">hippietrail</a>) is becoming Harper's first committer.

Elijah Potter

Andrew Dunbar Becomes Harper's First Committer

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Other Stuff

Markov Chains Are the Original Language Models

Why I Talk to Myself

Local-First Software Is Easier to Scale