Today's Deep-Dive: Harper

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Before we jump into today's fascinating analysis of a really interesting privacy-first

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Okay, let's unpack this. Our mission today is a deep dive into Harper, an open-source

grammar checker.

It's a project created by the people at Automatic, you know, the company behind

GitHub. So, if you're like most people, you probably use writing tools, right?

But maybe you also worry about what happens to your words after you hit save.

Harper promises to be the solution.

We're going to try and understand how this tool tackles what we're calling the

triple threat of modern editing software.

Poor privacy, really bad performance, and unnecessary cost.

Yeah, and if we connect this to the bigger picture, I mean, this project signals a

pretty crucial shift.

The developers weren't just aiming for, like, feature parity with other tools. They

were aiming for a total paradigm shift toward user control.

Our stated goal is really clear. Harper is an offline, privacy-first grammar checker.

Fast, open source, rust-powered.

And that last phrase, rust-powered, that's kind of the secret ingredient that lets

them achieve everything else.

Absolutely. And the source material is fantastic because it doesn't just introduce

a new tool.

It really acts as a kind of postmortem on the failures of the tools we've, well,

basically been forced to use until now.

So why did the creators feel the necessity to build a new grammar checker from

scratch?

Well, it seems like it was born out of some profound professional frustration.

Let's start with the critique of the market leader, Grammarly.

The developers considered it fundamentally flawed, especially for professional,

high-volume use.

First, they called it too expensive and too overbearing.

Now, that's a bit subjective, sure, but the objector issues are maybe more critical.

The suggestions often lacked necessary context or, as the source materials bluntly

put it, were sometimes just plain wrong.

Right. And then we get to the core ethical problem, the thing that really dictates

how we feel about using these tools.

Exactly. It was definitively labeled a privacy nightmare.

I mean, when you use a cloud-based checker like Grammarly, everything you type,

every sensitive document, every internal memo, it gets sent off to their remote

servers.

And while companies might claim they don't sell the data, the source material

correctly points out the concern.

This data can be used for training large language models or maybe other proprietary

purposes we simply don't have disability into.

And that ties directly back to performance too, doesn't it?

Because even if you somehow trust the vendor completely, sending every single word

you write back and forth across the internet just to get a basic suggestion, that

must slow down the process considerably.

Oh, it absolutely does. That network round-trip time, you know, waiting for the

server to process and respond, it made revising work tedious and slow.

For someone trying to maintain that flow state while writing, that kind of lag is

just a constant disruption. It pulls you right out.

OK, so if the cloud-based solution is a privacy disaster zone, what about the major

open-source alternative language tool?

I mean, that should solve the privacy problem, shouldn't it? But the source

material suggests it has its own pretty massive issues.

Yeah, language tool is characterized as great, but, and it's a big but, only if you're

willing to dedicate an enormous amount of computing muscle to it.

Its resource demands are staggeringly high. It requires gigabytes of RAM, and crucially,

it forces the user to download this massive statistical package.

It's known as an anagram dataset, and it weighs in at around 16 gigabytes.

16 gigs, just for a grammar checker. Wow. We need to pause there for a second. What

exactly is an anagram dataset, and why does language tool need such a colossal file?

Right, that's essential context. An anagram dataset is basically a massive

statistical table. It's built from analyzing billions and billions of sentences.

It tells the software how likely certain words are to follow other words. So if you

see the dog ran, the anagram model confirms, yeah, that's a highly probable

sequence.

Language tool uses the statistical approach to catch errors. But to make those

predictions accurate across many languages, you need a gigantic corpus of data, and

that data is the 16 gigabyte download.

Okay, so language tool is privacy friendly because it runs locally, right on your

machine. But it's resource intensive because it's essentially hauling around this

library the size of a small country just to check sentence flow.

Precisely. And that sheer bulk translates directly into speed problems. The creator

found it too slow. It often took several seconds to lint even a moderate size

document.

Let's quickly clarify that term for our listeners. We keep using the word lint.

What does linting actually mean here?

Sure. Linting is essentially the software checking the quality and correctness of

your text or sometimes code.

So when we say lint time, we mean the time it takes the software to scan your

document, run all its rules against it, and then present the errors or suggestions.

And if that takes several seconds, yeah, it just kills your productivity, you lose

momentum.

Okay, that sets the stage perfectly for Harper then, because here's where the

comparison becomes really stark. Harper was explicitly engineered to be like the

Goldilocks solution, fast, small, and completely private.

Exactly. Harper's gains are, well, transformational seems like the right word.

First, the privacy promise is absolute. It is completely private. It functions

entirely offline.

Your machine is the beginning and the end of the data flow, period. But the

performance metrics, I mean, that's what really blows the competition out of the

water.

While LanguageTool takes several seconds and demands gigabytes of RAM, Harper

checks the same document in milliseconds. And regarding memory footprint, it uses

less than 150th of LanguageTool's memory requirements.

That's a staggering efficiency gain. But OK, this is the point where I have to be a

bit skeptical. Surely for a tool to be that fast and that small, it must compromise

on intelligence, right?

How can Harper possibly manage accuracy without that huge 16 gigabyte Engram file

and without cloud-based AI training?

That's the critical question, you're right. And it's answered by the choice of

technology. Instead of relying on that brute force statistics approach, the Ennegrams,

Harper relies on carefully optimized, hand-crafted rules, dictionaries, and highly

efficient algorithms. And crucially, these are all built using a programming

language called Rust.

OK, we've mentioned Rust powered several times now. Why is Rust the key here,

especially for that staggering efficiency gain, particularly regarding memory use?

Well, Rust is known for a couple of things. Memory safety and extreme speed. And

the memory savings compared to language tool, which is Java-based, are enormous.

It really comes down to how the languages handle memory management. You see, Java

relies on something called a garbage collector, or GC.

It's a process that runs in the background to automatically find and clean up

memory that's no longer being used.

But that GC itself requires constant runtime resources and adds significant memory

overhead. It's always kind of there.

Rust, on the other hand, manages memory differently. It figures it out at compile

time when the program is built.

It guarantees memory safety without needing a runtime garbage collector.

And this mechanism, this absence of a GC, is why Harper can deliver the same or

similar results with almost no memory overhead.

That's how it achieves that astonishing reduction, down to less than a hundred and

fiftieth of language tool's requirements.

Ah, okay. That distinction, getting rid of the garbage collector overhead, that's

the missing technical piece I needed.

That helps understand the efficiency gains. It sounds like a design choice that

prioritizes performance above everything else.

Exactly. And that focus on efficiency also leads directly to its portability.

The source notes that Harper is small enough to load via WebAssembly, or Wasm.

Right, Wasm. For our listeners, what's the practical implication? What's the so-what

of WebAssembly?

Think of Wasm as like a tiny, standardized virtual machine.

It lets pre-compiled code run almost instantly inside any modern web browser or

application environment, so the practical implication is huge.

You don't need to install anything complex. You don't need an API key. You

certainly don't need a server.

It makes Harper truly ubiquitous. You could integrate a grammar checker that runs

at new native speeds right into a browser application almost instantly.

It really reinforces that promise of quick, local, and private processing.

And finally, it is truly open source. It's under the Apache 2.0 license, available

at writewithharper.com.

Okay, so now let's talk about the practical application side. What does this all

mean for the person actually sitting down and writing?

Well, the ecosystem looks pretty robust. Currently, the project is focused on

English support only. That's important to note.

However, the core architecture was explicitly designed from the ground up to be extensible

for other languages, and the project clearly states they welcome contributions.

So expanding its linguistic reach seems like it's simply a matter of community

effort, building on what's already a very solid, very fast foundation.

And that commitment to performance, it isn't just a launch feature, is it? It

sounds like an ongoing philosophy.

It absolutely is. It seems like the defining feature, really. The development team

is so serious about maintaining peak performance that they state they consider it

long-lint times bugs

that signals they are constantly profiling and optimizing the code. They treat any

slowdown not as a minor issue, but as a critical defect that must be addressed

immediately. That's a strong commitment.

And looking at the health of the project, the community metrics, they speak volumes,

don't they? 8.1k stars on GitHub, 202 forks, 57 contributors.

For an open source project, 8,100 stars indicate significant developer trust and viability.

It shows this is well past the side project stage. It looks like a major ecosystem

tool now.

Yeah, definitely. And that level of community engagement has translated directly

into real-world usability. They have extensive documentation for integrating Harper

into, well, virtually every major professional text editor you can think of.

They list support for Visual Studio Code, NeoVim, Helix, Emacs, and Zed. Pretty

comprehensive.

Why is integration into specialized tools like NeoVim and Helix such a big deal?

What does that tell us?

Well, it demonstrates its incredible lightness, its efficiency. NeoVim and Helix

are terminal-based, very power-user-focused environments. They're often preferred

by developers specifically because they're fast and use very few resources.

The fact that Harper integrates seamlessly there proves that it's truly lightweight

enough for even the most resource-conscious environments. It really solidifies its

place as a professional, ready-to-use tool, not just some proof of concept.

OK, so to summarize the key takeaway for you, the listener, Archer seems to

successfully solve that triple threat we talked about with modern editing software,

the lack of privacy, the high cost or resource usage, and the poor performance.

And it does this by leveraging the speed and memory efficiency of modern languages

like Rust, combined with the transparent, collaborative power of the open-source

model. It really could be a blueprint for how critical software like this can be

built in the future.

Yeah, and this whole thing raises an important question, I think, for you to

consider. As AI models and editing tools become more ubiquitous, more powerful, but

also demand more and more of your data to function, how much value do you

personally place on processing your sensitive documents you're writing locally for

that guaranteed absolute privacy?

How does that compare to the perceived convenience, or maybe the occasional smarter

suggestion offered by those massive cloud-based services? That trade-off, you know,

between privacy and perhaps features or intelligence, that's a calculation we all

probably need to be ready to make more consciously going forward.

That's a fascinating dilemma to mull over. Thank you for diving deep with us today.

And remember, this deep dive was supported by SafeServer, your partner for hosting

project at www.safeserver.de. We'll catch you next time for the next Deep Dive.

project at www.safeserver.de. We'll catch you next time for the next Deep Dive.