DEV Community: Backrun

You Don't Own the Code AI Wrote for You

Backrun — Mon, 08 Jun 2026 04:30:39 +0000

AI is getting faster at generating HTML.

That sounds like good news. For most people reading this, it probably is.

But there is a group of users for whom faster generation is actually making things worse. Not because the output is bad. Because the output is arriving faster than they can do anything with it.

The amplification problem

There is a pattern that keeps showing up in how people actually use AI at work.

AI amplifies what you already have.

If you are a developer, AI amplifies your ability to ship. You generate, you review, you deploy. The whole loop is fast because you already knew how to close it.

If you are a marketer, a solo founder, a freelancer with no technical background, AI amplifies your ability to generate. But the rest of the loop, review, debug, deploy, still runs at the same speed it always did. Which for most non-technical users is somewhere between slow and completely stuck.

Faster generation does not help you if you cannot close the loop.

It just means you have more finished HTML sitting in chat windows going nowhere.

What the data from real usage looks like

In conversations with users of HTML Deployer, a Chrome extension I built for deploying AI-generated pages without touching a terminal, the same story comes up over and over.

Someone generates a landing page with Claude. It looks exactly right. They spend the next hour trying to get it live. Sometimes they succeed.
Often they do not. The page stays in the chat. The campaign launches late or not at all.

This is not a story about AI failing. The AI did its job. The HTML is good.

This is a story about what happens after the AI does its job.

The gap is structural, not a skill problem

Every deploy tool in existence was designed with a developer-shaped user in mind.

Netlify assumes you have a file saved locally or a Git repo ready to connect. GitHub Pages assumes you understand what a repository is and why it needs to be public. FTP assumes you have hosting, credentials, and some idea of what a file path means.

These are not unreasonable assumptions if your user is a developer.

They are completely wrong assumptions if your user just typed a prompt into Claude and got back a finished page.

That user does not have a local file. They have a chat window. They do not have a Git repo. They have an output they want to share. They do not know what FTP stands for and should not have to.

The tooling gap is not about intelligence or effort. It is about who the tools were designed for and who is actually using AI right now.

Who is actually using AI right now

The fastest growing segment of AI users is not developers.

It is people who never expected to be building anything but suddenly can describe what they want and get something real back. Marketers. Consultants. Small business owners. Teachers. Freelancers who do everything themselves. People running one-person operations who used to outsource web work and now realize they can generate it themselves.

That population is enormous.

And almost none of the tooling built around AI generation was designed for them.

The part that does not get talked about enough

There is a term from software testing called plausible wrongness. The output looks correct. It passes a surface inspection. But it behaves wrong under real conditions.

The deploy situation for non-technical AI users is a kind of structural plausible wrongness.

The workflow looks like it should work. Claude gives you HTML. Netlify lets you deploy HTML. The steps should connect.

But the steps were designed for two different users. The AI was designed for everyone. The deploy tools were designed for developers. The person in the middle, the non-technical user holding a finished HTML file, falls through the gap between them.

What closing the gap actually looks like

The fix is not teaching non-technical users to use developer tools.

The fix is building the deploy step where the generation already happened.

That is the design bet behind HTML Deployer. The extension lives inside the Claude or ChatGPT tab. It detects the HTML automatically. It shows you a preview on desktop, tablet and mobile before anything goes live. It deploys to Netlify, GitHub Pages, FTP or your own server in one click.

No new tab. No terminal. No file to save. No workflow to learn.

The generation is already happening in the browser. The deploy should happen there too.

The broader point

Every time AI gets faster at generation, the gap between output and outcome gets more visible for the users who cannot close it themselves.

That gap is not going to close on its own. Developer tools will not become intuitive for non-technical users just because AI got better.

Someone has to build the bridge.

Right now, not enough people are building it.

If you work with non-technical users who use AI tools, what is the step they get stuck on most consistently? Generation is rarely the answer anymore. I am curious what comes after.

The Faster AI Gets, the Bigger the Gap It Creates for Non-Technical Users

Backrun — Fri, 05 Jun 2026 02:00:17 +0000

AI is getting faster at generating HTML.

That sounds like good news. For most people reading this, it probably is.

But there is a group of users for whom faster generation is actually making things worse. Not because the output is bad. Because the output is arriving faster than they can do anything with it.

The amplification problem

There is a pattern that keeps showing up in how people actually use AI at work.

AI amplifies what you already have.

If you are a developer, AI amplifies your ability to ship. You generate, you review, you deploy. The whole loop is fast because you already knew how to close it.

If you are a marketer, a solo founder, a freelancer with no technical background, AI amplifies your ability to generate. But the rest of the loop, review, debug, deploy, still runs at the same speed it always did.
Which for most non-technical users is somewhere between slow and completely stuck.

Faster generation does not help you if you cannot close the loop.

It just means you have more finished HTML sitting in chat windows going nowhere.

What the data from real usage looks like

In conversations with users of HTML Deployer, a Chrome extension I built for deploying AI-generated pages without touching a terminal, the same story comes up over and over.

Someone generates a landing page with Claude. It looks exactly right. They spend the next hour trying to get it live. Sometimes they succeed. Often they do not. The page stays in the chat. The campaign launches late or not at all.

This is not a story about AI failing. The AI did its job. The HTML is good.

This is a story about what happens after the AI does its job.

The gap is structural, not a skill problem

Every deploy tool in existence was designed with a developer-shaped user in mind.

These are not unreasonable assumptions if your user is a developer.

They are completely wrong assumptions if your user just typed a prompt into Claude and got back a finished page.

That user does not have a local file. They have a chat window. They do not have a Git repo. They have an output they want to share. They do not know what FTP stands for and should not have to.

The tooling gap is not about intelligence or effort. It is about who the tools were designed for and who is actually using AI right now.

Who is actually using AI right now

The fastest growing segment of AI users is not developers.

That population is enormous.

And almost none of the tooling built around AI generation was designed for them.

The part that does not get talked about enough

There is a term from software testing called plausible wrongness. The output looks correct. It passes a surface inspection. But it behaves
wrong under real conditions.

The deploy situation for non-technical AI users is a kind of structural plausible wrongness.

The workflow looks like it should work. Claude gives you HTML. Netlify lets you deploy HTML. The steps should connect.

What closing the gap actually looks like

The fix is not teaching non-technical users to use developer tools.

The fix is building the deploy step where the generation already happened.

No new tab. No terminal. No file to save. No workflow to learn.

The generation is already happening in the browser. The deploy should happen there too.

The broader point

Every time AI gets faster at generation, the gap between output and outcome gets more visible for the users who cannot close it themselves.

That gap is not going to close on its own. Developer tools will not become intuitive for non-technical users just because AI got better.

Someone has to build the bridge.

Right now, not enough people are building it.

If you work with non-technical users who use AI tools, what is the step they get stuck on most consistently? Generation is rarely the answer anymore. I am curious what comes after.

I Started Counting the Steps Between AI Output and Something Real

Backrun — Wed, 03 Jun 2026 09:27:02 +0000

Something changed in how I use AI tools about three months ago.

I stopped asking "can AI do this?" and started asking "how many steps does it take to go from AI output to something I can actually use?"

That question changed everything.

The output is not the product

When ChatGPT or Claude generates something for you, what you get is raw material. Not a finished thing. Raw material.

A developer gets code. They still need to run it, test it, debug it, deploy it.

A marketer gets HTML. They still need to put it somewhere, get a URL, send it to someone.

A founder gets a landing page. They still need to figure out hosting, domain, publish flow.

The AI part is fast. Genuinely, impressively fast. 45 seconds for a complete HTML page is real and it matters.

But we got so excited about the 45 seconds that nobody counted what came after.

I counted

Here is what it actually takes to go from "Claude gave me an HTML file" to "I have a live URL I can share" using the default workflow most people end up with.

Copy the HTML from the chat
Open a new tab
Create or log into a Netlify account
Find the deploy option
Save the HTML as a file locally
Drag the file into Netlify
Wait for deploy
Copy the URL
Realize the URL is something like rainbow-unicorn-a3f92.netlify.app
Go back and configure a custom domain
Wait for DNS propagation

That is eleven steps. Eleven steps between a finished AI output and something a real person can open on their phone.

Some of those steps take 30 seconds. Some take 20 minutes. Some fail and send you back to step 4.

The AI saved you 2 hours of writing and design work. The deploy process took 45 minutes on a good day and 3 hours when something went wrong.

The honest math

People talk about AI productivity in terms of what it generates. Nobody talks about the total workflow.

Total workflow = generation time + everything after

For a developer, "everything after" is second nature. Git push, CI/CD picks it up, done. The steps are invisible because muscle memory made them disappear years ago.

For everyone else, every single step is visible, unfamiliar, and potentially a dead end.

This is the real productivity gap. Not whether AI can write good code. It can. The gap is between the output and the outcome.

What I actually changed

I started optimizing for step count, not generation quality.

A slightly worse output that takes 2 steps to use beats a perfect output that takes 11 steps every time.

That thinking led me to build HTML Deployer, a Chrome extension that cuts the HTML-to-live-URL flow from 11 steps to 3.

Step 1: the extension detects the HTML Claude or ChatGPT just generated, no copy-paste.
Step 2: you preview it on desktop, tablet, and mobile before anything goes live.
Step 3: you pick a target, Netlify, GitHub Pages, FTP, or your own server, and click deploy.

That is it. You never leave the tab. You never touch a terminal. You never save a file.

The generation is still 45 seconds. The deploy is now under 60 seconds too.

The question worth asking about every AI tool

Before you add another AI tool to your workflow, count the steps between its output and something you can actually use.

If the answer is more than 3, the tool is probably not saving you as much time as the demo suggested.

The AI part of the problem is mostly solved. The handoff part is where the time is actually going.

How many steps does your current AI-to-live workflow
take? Curious whether the 11-step version I described
is common or whether people have found shorter paths
I haven't seen yet.

Your AI-Generated HTML Looks Perfect. Have You Seen It on a Phone?

Backrun — Tue, 02 Jun 2026 02:08:01 +0000

There is a specific kind of confidence that comes from looking at AI-generated HTML in a chat window.

It looks clean. The structure makes sense. The copy is right. You've been staring at it for five minutes and you can't find anything wrong.

So you deploy it.

Then someone opens it on their phone and sends you a screenshot.

The heading is overflowing the screen. The CTA button is half-hidden behind the bottom navigation bar. The form fields are so small that tapping them is basically a game of precision. The layout that looked perfect on your 1440px monitor is completely broken on a 390px screen.

This has a name in software testing: plausible wrongness.

The output is technically correct. It passes a surface inspection. But it behaves wrong under real conditions.

Why AI-generated HTML fails on mobile

AI writes for the happy path.

When you prompt Claude or ChatGPT to build a landing page, it optimizes for what you asked for. Clean HTML. Good copy. Reasonable structure. It has no way of knowing what device your audience uses, what screen size your client will open the link on, or whether that hero section collapses gracefully at 375px.

It also has no skin in the game. It hands you the output and moves on. The consequences of plausible wrongness land on you.

Developers catch this early because the review loop is short. Generate, open in browser, resize window, check mobile, fix, repeat. The feedback is immediate.

For non-technical users the loop doesn't exist at all. They see the HTML in the chat. It looks right. They find a way to get it live. Someone opens it on a phone.

That's when they find out.

The review step that nobody builds in

Every conversation about AI-generated code focuses on two moments: generation and debugging.

Nobody talks about the moment between them.

Preview.

Not preview in the chat window. Not preview in a desktop browser. Preview that shows y

I Asked AI to Build Me a Landing Page. Getting It Live Took 3 Hours.

Backrun — Mon, 01 Jun 2026 02:30:57 +0000

I asked Claude to build me a landing page. It took 40 seconds.

Getting it live took 3 hours.

Not because I'm bad at tech. Because the entire handoff between "AI gave me HTML" and "page is on the web" was designed for someone who isn't me.

Here is exactly what happened.

Claude generated a clean, complete HTML file. Looked right in the chat. I copied it.

Then I opened Netlify. Created an account. Got asked for a team name.Typed something. Got asked for a project name. Typed something else. Looked for the drag-and-drop deploy option I'd seen in a tutorial. Couldn't find it. The UI had changed.

Opened YouTube. Found a tutorial. It was from 2022. The sidebar it referenced didn't exist anymore.

Went back to Claude. Asked how to deploy to Netlify. It gave me CLI commands.

I don't have Node installed on this machine.

Opened a new tab. Searched "deploy HTML without terminal." Found a Reddit thread from 2021. Someone suggested GitHub Pages. Opened GitHub. Created a repo. Got confused about whether it should be public or private. Picked public. Tried to upload the file. GitHub asked me to commit with a message. I typed "first commit." The page deployed but showed a 404. Turned out I needed the file named index.html not page.html.

Renamed it. Redeployed. Waited.

3 hours after Claude handed me a finished HTML file, I had a live URL.

The hell loop nobody talks about

There's a term from AI coding discussions called the "hell loop" — when an agent goes in circles trying to fix a bug it created itself. You lose the thread, forget where you started, spend more time recovering context than actually solving the problem.

The deploy version of this is the same thing.

Tab 1: the HTML sitting in the chat.
Tab 2: Netlify, half-configured.
Tab 3: a tutorial that's out of date.
Tab 4: Stack Overflow.
Tab 5: back to Claude, asking for help deploying.
Claude suggests the terminal again.

You are now debugging the deploy process instead of shipping the thing you built.

Why this keeps happening

Every deployment tool was built by developers for developers. The mental model is: you have a local environment, you have Git, you have a terminal, you know what a CLI flag means.

That mental model does not describe the person who just used Claude to build a landing page for their campaign. It doesn't describe the solo founder testing an offer. The freelancer sending a quick demo to a client. The marketer who got a beautiful HTML file and just needs a URL.

AI lowered the floor for creation dramatically.
The deploy floor didn't move.

What actually fixed it for me

I got frustrated enough to build a Chrome extension called HTML Deployer that sits inside the Claude or ChatGPT tab where the conversation already happened.

It detects the HTML automatically. No copy-paste. You preview it on desktop, tablet, and mobile before anything goes live. Then you pick where it goes: Netlify, GitHub Pages, FTP, or your own server.
Click deploy. Get a URL.

The whole thing takes under 60 seconds and you never leave the tab.

I built it because I was tired of paying the deploy tax on every single project. Turns out a lot of other people were paying it too.

If you've hit this wall before, what was your workaround? And if you're a developer reading this thinking "just use Netlify drop" — try explaining that to someone who has never seen a deploy pipeline in their life. That's the gap.

The Deploy Tax: The Hidden Cost Nobody Talks About After AI Generates Your Code

Backrun — Fri, 29 May 2026 02:40:16 +0000

There's a cost that shows up right after AI writes the code.

Not the debugging cost. Not the "works on my machine" cost. The one that comes even earlier, before any of that.

The deploy cost.

Here's what I mean

You prompt Claude or ChatGPT to build a landing page. It does it in 45 seconds. Clean HTML, good structure, looks exactly right in the chat window.

Then what?

For developers, this is trivial. Push to GitHub, Netlify picks it up, done.

But most people using AI to build web pages right now are not developers. They are marketers running campaigns. Solo founders testing an offer. Freelancers building a quick demo for a client. No-code builders who can generate beautiful HTML but have never touched a terminal in their life.

For them, the 45-second generation is followed by a loop that looks something like this:

Tab 1: the AI chat with the HTML sitting there
Tab 2: Netlify, where they get confused about "team names" and "project slugs"
Tab 3: a YouTube tutorial from 2021 that references a UI that no longer exists
Tab 4: Stack Overflow
Tab 5: back to the AI asking how to deploy. The AI gives them terminal commands. They don't have a terminal open.

They close the tab. The HTML never goes live.

This is not a skill problem

It's a tooling problem.

The entire deploy workflow was designed by developers, for developers. Nobody designed the last mile for the person who just used AI to build something real but has no idea what Git is.

@harsh2644 wrote about the debugging tax this week: 30 seconds to generate, 5 hours to debug. That ratio is real.

But there is a deploy tax sitting right next to it. Except this one doesn't even get to debugging. It stops at "how do I put this on the web" and the answer has always been "go learn something first."

The gap I built something to close

That's why I built HTML Deployer, a Chrome extension that sits inside your ChatGPT or Claude tab.

It detects the HTML your AI just generated, lets you preview it on desktop and mobile before you publish, and deploys it to Netlify, GitHub Pages, FTP or your own server in one click.

No terminal. No new tab. No workflow to learn.

The generation got fast. The deploy should too.

What's your deploy workflow when you're working with AI-generated HTML?

Curious whether this is just a problem I was seeing or something others run into too.

How I Deploy AI-Generated HTML to a Live Website in Under 60 Seconds

Backrun — Tue, 26 May 2026 13:58:00 +0000

If you've been using ChatGPT, Claude, or Gemini to generate HTML pages,
you probably know this feeling already.

The AI produces something that looks genuinely good. Clean layout,
reasonable structure, maybe even some nice styling. And then you're
sitting there staring at a wall of code with no idea how to turn it
into an actual URL you can share with someone.

I ran into this constantly while building landing pages for small
campaigns. The AI part was fast. The publishing part always ate up
another 30 to 45 minutes of opening terminals, pushing to GitHub,
configuring Netlify, or wrestling with cPanel. All of that just to
put one static HTML file online.

This post is about the workflow I use now to go from AI output to a
live link without leaving the browser tab.

Why the Normal Workflow Is So Annoying

The typical process looks something like this:

Generate HTML in ChatGPT or Claude
Copy the code
Open a code editor
Create a new file, paste everything, save it
Go to Netlify or GitHub Pages or your hosting panel
Upload the file or push and configure a repo
Wait for the deploy to finish
Find the URL somewhere in the dashboard
Go back to the AI if something looks wrong, then repeat from step 2

That is nine steps for something that should feel like one. And if you
are not a developer, step three alone is enough to make the whole thing
feel out of reach.

What I Use Instead

I have been using a Chrome extension called HTML Deployer that
compresses most of that process into a single panel sitting right
inside the browser.

Here is what the actual flow looks like.

Step 1 - Generate your HTML with any AI tool

Open ChatGPT, Claude, or Gemini and ask it to build whatever page you
need. A landing page, a portfolio, an event page, a simple bio link.
Anything that comes out as a single HTML file works.

Step 2 - The extension picks up the code on its own

Once the AI produces a code block, HTML Deployer scans the page and
spots it automatically. A Deploy button appears right next to the code
block. No copying, no switching tabs, no pasting into another tool.
The extension pulls the code directly from the conversation.

Step 3 - Preview before you touch anything

Before publishing, you get an instant live render of the page in
desktop, tablet, and mobile sizes. This step has saved me real
embarrassment more than once. I caught a broken layout here that would
have gone live to a client otherwise.

Step 4 - Pick where the page should go

You can publish to Netlify, GitHub Pages, an FTP hosting account you
already own, or a self-hosted agent on your own server. There is also
a ZIP download option if you prefer to upload manually to wherever you
like.

The extension does not push you toward one platform. That matters a
lot when you are working across different client hosting accounts that
are all set up differently.

Step 5 - Click Deploy to Host and get a URL plus a QR code

The page goes live in a few seconds. HTML Deployer generates a live
URL and a QR code you can share right away. The deploy gets saved in
history so you can reopen the link or push an update later without
going through the whole setup again.

What Actually Changes Day to Day

For developers, it removes the repetitive setup that small jobs always
seem to require. Demos, client previews, quick prototypes.

For marketers and founders who use AI tools but do not have a
technical background, it removes the dependency on a developer for
what is ultimately just publishing a file.

For freelancers and agencies, it gives a consistent process that works
across different hosting environments instead of requiring a different
tool for each client.

The part I find most useful is that nothing takes you out of the tab
where the AI conversation is happening. That context switch from "AI
chat" to "DevOps task" is what makes people stop halfway and ask a
developer friend to finish it for them. Removing that switch makes
the whole iteration loop much faster.

One Thing Worth Knowing About Security

I had a reasonable concern before using this: does the extension store
or send my HTML somewhere I did not agree to?

From what I can tell, your HTML only leaves your machine when you
explicitly choose a target and hit Deploy. FTP credentials and API
tokens are stored locally inside the extension, not in some external
dashboard. That matters if you are handling pages for clients and do
not want their hosting credentials floating around in a third-party
service.

Worth Trying If You Fit Any of These

You generate HTML with AI tools regularly and lose time every time
you need to publish something.

You do client work and want to send a live demo URL quickly without
going through a full deploy process.

You manage pages across multiple hosting providers with different
setups.

You are not technical and want to publish AI-generated pages without
learning a deployment workflow from scratch.

Wrapping Up

The AI part of building web pages has genuinely become easy. The gap
is what comes after: turning "here is your HTML" into "here is your
link."

HTML Deployer closes that gap in a way that fits how most people
already work. Inside the browser, inside the conversation, without
needing a separate workflow running alongside it.

If you want to give it a try, it is on the Chrome Web Store:
HTML Deployer

Have you found other tools that handle this step well? Drop them in
the comments.

I Lost a 3-Hour Debugging Session With Claude. So I Built a Fix. https://dev.to/backrun/i-lost-a-3-hour-debugging-session-with-claude-so-i-built-a-fix-3cje

Backrun — Fri, 24 Apr 2026 07:45:05 +0000

Backrun

Apr 24

I Lost a 3-Hour Debugging Session With Claude. So I Built a Fix.

#productivity #ai #webdev #claude

Comments

3 min read

I Lost a 3-Hour Debugging Session With Claude. So I Built a Fix.

Backrun — Fri, 24 Apr 2026 07:38:07 +0000

It was a Friday afternoon.

Going back and forth with Claude for almost three hours — tracking down
a nasty async bug, refactoring a service layer, getting a solid
explanation of why the race condition was happening in the first place.

It wasn't just answers. It was a working session. The kind where the
conversation itself becomes documentation — you can trace the thinking,
see where things went wrong, read the clean explanation worth sharing
with the team.

Then the laptop died. No warning. Just black screen.

When Chrome reopened, the tab was gone. Three hours of context, gone.

Claude has been a core part of the development workflow for over a year.
And this was maybe the fifth time something like this had happened —
closed tab, browser crash, accidental navigation. Claude has no native
export. No "save session" button. Nothing.

That Friday was the last time to just accept it.

The Problem With Copy-Pasting Claude Conversations

The obvious workaround is selecting everything and pasting it into a doc.

Here's what actually happens:

Code blocks lose their formatting entirely
Tables collapse into unreadable plain text
The structure that made the answer useful disappears
10 minutes spent reformatting something that should take 10 seconds

For a quick answer, fine. For a long coding session with multiple code
snippets, architecture decisions, and back-and-forth reasoning?
Copy-paste is not a workflow. It's a punishment.

What Was Actually Needed

Nothing fancy. Just:

Export to PDF — clean, shareable, print-ready
Export to Word (.docx) — for sessions worth editing further
Export to Google Docs — for anything collaborative
Export to Notion — personal knowledge base for everything worth keeping

And critically: preserve the code blocks. Syntax highlighting,
proper formatting, all of it. Non-negotiable for developer conversations.

Also: export just part of a conversation. Not always the whole thing.
Sometimes there's one specific exchange that's gold and the rest is noise.

So Claude Exporter Was Built

It's a Chrome extension. Here's what it does:

Export full conversations or selected messages — highlight what you want, or export everything
Supports PDF, Word, Google Docs, and Notion — all four from one extension
Preserves code blocks with proper formatting — tables, artifacts, extended thinking content, all intact
Typography customization before export — font, size, text color
100% local processing — nothing leaves your device. No account, no API key, no data sent anywhere

Install-to-first-export is under 60 seconds.

👉 Claude Exporter on Chrome Web Store

The Workflow Now

At the end of any Claude session that produced something worth keeping —
a solid debugging walkthrough, a refactored design worth referencing, an
architecture discussion with good reasoning — export it to Notion before
closing the tab.

Takes about 5 seconds. The session becomes a permanent, searchable,
formatted document instead of a browser tab you're silently hoping
doesn't accidentally close.

For anything to share with a teammate, export to Google Docs directly.
No reformatting. Just send the link.

For technical documentation or internal write-ups, export to Word and clean up from there.

A Few Things Learned While Building This

Code blocks were the hard part.

Getting syntax highlighting to survive a PDF export without turning into
a wall of monospace text required more work than expected. It took a few
iterations to get the rendering right across all four export formats.

People want to export parts of conversations, not just the whole thing.

This came up consistently during testing. A lot of Claude conversations
are long and exploratory. The three messages that actually solved the
problem are what matter — not 80 messages of trial and error. Selective
export ended up being more important than initially expected.

"Local only" matters more than expected.

A surprising number of people asked about data privacy before installing.
The fact that all processing happens locally inside Chrome — no server,
no uploads, nothing transmitted — was a genuine deciding factor. Worth
designing for from day one if you're building anything that touches chat
or document content.

What's Next

Currently working on:

Better handling for very long conversations (chunked export)
Improved table rendering in PDF output
Firefox support (most requested feature so far)

If you're a developer using Claude regularly and this frustration sounds
familiar — give it a try. It's free.

Install Claude Exporter →

Feedback is welcome in the comments — what format you use most, what's
breaking, what's missing. Every report gets read.

Independent tool. Not affiliated with Anthropic.