<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>Do Not Write with an LLM</h1>
<img src="/images/green_bird.webp" alt="A photo taken by myself of a green bird.">
<h2>A Growing Trend</h2>
I have been see­ing an in­creas­ingly preva­lent trend of peo­ple show­ing up in on­line spaces proudly ﬂaunt­ing that they are writ­ing with the as­sis­tance of AI.
I of­ten see them clar­ify that they are go­ing fur­ther than let­ting the model per­form gram­mar or fact-check­ing.
They are de­scrib­ing an idea to an LLM, and giv­ing it com­plete con­trol over the ﬁ­nal writ­ten doc­u­ment.
They seem to be proud of this. They should­n’t be.
<h2>What Does Not Work</h2>
Writing seems to be a shadow of a more fun­da­men­tal and use­ful process we like to call “thinking”.
There is a rea­son why LLMs, which are the clos­est thing we cur­rently have to “software that thinks” is trained to think through writ­ing.
It seems log­i­cal, then, that we can use this “software that writes” to help along our own think­ing process, right? Wrong.
Writing is one of the most cere­bral ac­tiv­i­ties that hu­mans do on a reg­u­lar ba­sis.
<a href="https://elijahpotter.dev/articles/why-i-talk-to-myself">I try to sit down and write about a com­plex topic at least once a week.</a>
There are a num­ber of rea­sons I do that per­tain­ing to my job in par­tic­u­lar, but there are more gen­er­ally ap­plic­a­ble rea­sons that can ap­ply to any pro­fes­sion.
When a math­e­mati­cian needs to think crit­i­cally about some the­o­rem or proof, they of­ten turn to a dry-erase board or a chalk­board.
The math­e­mati­cian then writes in clear no­ta­tion what they imag­ine the prob­lem to look like in their heads.
From there, they can ma­nip­u­late that no­ta­tion un­til they reach their de­sired out­come.
The English lan­guage is ex­tremely sim­i­lar, with a key dif­fer­ence.
Math­e­mat­i­cal no­ta­tion is specif­i­cally de­signed for a nar­row do­main.
Nat­ural lan­guages, on the other hand, can be ap­plied to any ﬁeld.
By writ­ing your idea down on pa­per (or in a Google Doc, it does not mat­ter), you give it sub­stance.
This sub­stance can then be shaped it­er­a­tively to­wards a goal of some kind.
No great ideas were writ­ten down on the ﬁrst try.
They started as a vague no­tion, and through writ­ing and rewrit­ing, they be­came some­thing con­crete and valu­able.
You can­not dic­tate a vague no­ta­tion to an LLM and ex­pect it to spit out some­thing tan­gi­bly valu­able.
Any at­tempt to do so is lazy, and it will re­sult in fail­ure.
I have seen it in the wild, and frankly, it is a lit­tle em­bar­rass­ing.
<a href="https://ammil.industries/i-know-you-didnt-write-this/">This ar­ti­cle</a> feels es­pe­cially rel­e­vant to­day.
<h2>What Does Work</h2>
Using an LLM for re­search can be very ef­fec­tive.
Searches to ChatGPT or Perplexity can sur­face in­for­ma­tion faster than dig­ging around in a book you read three months ago.
For lots of searches, these two sources have com­pletely re­placed Google and DuckDuckGo for me.
They’re also quite ex­cel­lent fact-check­ers.
I’ve seen that plenty of folks have re­ceived quite good re­sults by past­ing their work into an LLM with an in­ter­net tool (like ChatGPT with Thinking en­abled).
Finally, I’ve per­son­ally found that LLMs are quite good at ﬁnd­ing places where I sim­ply for­got to in­sert con­text.
Some­times I for­get to de­ﬁne a term or in­sert a rel­e­vant link.
Even small mod­els like <code>gemma3n:e2b</code> can catch me.
<h2>What You Should Be Doing</h2>
When you need to com­mu­ni­cate an idea in your head, sit down with a notepad and a key­board and ex­plain it.
I per­son­ally think you should use a gram­mar checker, since they are far less likely to im­pede your thought process, but if you need to, you can in­struct ChatGPT to cri­tique your work once it is ﬁn­ished.
If an idea de­serves an au­di­ence, you’re ca­pa­ble of writ­ing it out your­self.
In the same vein, if you can vaguely de­scribe it to ChatGPT, you can vaguely de­scribe it to your in­tended au­di­ence.
Do your­self a fa­vor and skip the mid­dle­man.
<h2>What We Should Be Building</h2>
Now, I’d like to en­ter the sec­tion where I talk specif­i­cally about what this all means for peo­ple who cre­ate tools for writ­ers.
That in­cludes both Harper and WordPress.
A tool that helps some­one write is a tool that di­rectly in­ter­faces with how that per­son thinks.
That might in­clude what they think about, but more con­cretely I am re­fer­ring to the ac­tual day-to-day process of think­ing through com­plex ideas.
If you don’t be­lieve me, it’s prob­a­bly be­cause you skipped ahead in­stead of read­ing this post from the be­gin­ning.
Most cru­cially, we can­not al­low our­selves to build tools that dis­suade peo­ple from ex­press­ing their ideas in the way they imag­ine.
This can take the form of fric­tion, like build­ing tools that are so cum­ber­some that one dreads to open them at the start of the work­day.
This can also take the form of a tool that crushes or over­writes an au­thor’s core mes­sage.
A com­mon com­plaint I hear about Grammarly (from peo­ple who have switched to Harper from Grammarly) is that it’s too de­mand­ing. When they write prose in a cre­ative or un­usual fash­ion, their ser­vice of­ten forces them back into a colder and more for­mal way of speak­ing.
We don’t want to be like them.
For Harper, that means we need to be di­verse in our train­ing datasets and mind­ful of feed­back from our users.
For WordPress, it means we need to en­cour­age our LLM in­te­gra­tions to tread lightly and re­spect­fully.
They need to ac­cept when they’re wrong.
Just as any good hu­man should.
<blockquote>
Months af­ter writ­ing this post, I came across an­other.
Au­thor Cynthia Dunlop gives <a href="https://writethatblog.substack.com/p/dev-reaction-to-ai-blog-posts">a great overview of how de­vel­op­ers re­act to blog posts writ­ten by AI</a>.
I highly sug­gest you take a look if you have more time.
</blockquote>

I have been seeing an increasingly prevalent trend of people showing up in online spaces flaunting that they are writing with the assistance of AI. They seem to be proud of this. They shouldn't be.

I have been see­ing an in­creas­ingly preva­lent trend of peo­ple show­ing up in on­line spaces ﬂaunt­ing that they are writ­ing with the as­sis­tance of AI. They seem to be proud of this. They should­n’t be.

Do Not Write with an LLM

Back in my day, we used math for autocomplete.

Back in my day, we used math for au­to­com­plete.

Markov Chains Are the Original Language Models

<h1>Get Rid of It</h1>
If you have not al­ready, I highly rec­om­mend that you read <a href="https://app.thestorygraph.com/books/2aeb3592-b6ca-449b-ad9e-345f9709dc3b">Walter Isaacson’s bi­og­ra­phy of Elon Musk.</a>
It fol­lows the bil­lion­aire’s stratos­pheric ca­reer from pro­gram­ming video games on an orig­i­nal Commodore VIC-20 to the ac­qui­si­tion of Twitter.
While I do not agree with all of his prac­tices or be­liefs, I do think that some of his philoso­phies are ex­tremely ap­plic­a­ble and largely ig­nored, even by his most ar­dent fol­low­ers.
<blockquote>
“Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you did­n’t delete enough.” - Elon
</blockquote>
In the last week, es­pe­cially since we are in hard­en­ing mode, clean­ing up af­ter the wreck­age of RSM, I have been see­ing an enor­mous num­ber of ex­am­ples demon­strat­ing that dele­tion is usu­ally bet­ter than ad­di­tion.
Say­ing “no” is more im­por­tant than say­ing “yes”.
<h2>Get Rid of Code</h2>
The num­ber of bugs in a pro­gram is roughly pro­por­tional to the num­ber of lines of code.
To a shock­ing de­gree.
Less code means fewer prob­lems.
If you can pro­vide the same ser­vice with less code, pro­vide the same ser­vice with less code.
While this is not a post about cod­ing with AI, I ﬁnd that mak­ing my agen­t’s plans sim­pler with fewer steps al­most al­ways re­sults in bet­ter code.
If most en­gi­neers said “no” to the ﬁrst plan an agent pro­vided, the qual­ity of the av­er­age pro­gram on GitHub would sky­rocket.
But peo­ple do not say “no”.
Be the ex­cep­tion.
<h2>Get Rid Of Decisions</h2>
When an en­gi­neer needs to make a de­ci­sion for whether to work on some­thing (or oth­er­wise al­lo­cate re­sources to a task), they have three op­tions.
<ol>
<li>They can do the thing.</li>
<li>They can not do the thing.</li>
<li>They can say “we will do that later”.</li>
</ol>
Almost al­ways, op­tion three turns into op­tion two.
Peo­ple pro­cras­ti­nate, and there will al­ways be more ur­gent things to at­tend to.
It’s bet­ter to de­cide to do op­tion two in­ten­tion­ally than to let fate de­cide.
So, if you can’t de­cide whether to do some­thing right now, your an­swer should be no.
You are free to change you mind later, but do not pre­tend that you will re­mem­ber to de­cide later.
<h2>Harper Says “No”</h2>
Do we train stuff on our user’s data? No.
Do we send data to the cloud? No.
Do we waste CPU cy­cles of friv­o­lous things? No.
People use Harper be­cause we say no.

'Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn't delete enough.'

‘Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you did­n’t delete enough.’

Elijah Potter

Get Rid of It

Get Rid of Code

Get Rid Of Decisions

Harper Says “No”

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Other Stuff

Why I Talk to Myself

Do Not Write with an LLM

Markov Chains Are the Original Language Models