Flowing
Shape academic writing grounded in your references
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Flowing - Ground your academic writing in your own papers.

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Flowing is a desktop writing tool that keeps your research library in the writing loop. As you write, it highlights key terms, recalls relevant passages from your PDFs, and lets you ask, polish, and continue writing with suggestions grounded in your own papers, helping deliver better accuracy and alignment than generic AI assistants like ChatGPT. Research writing isn't just about generating fluent text. It's about staying faithful to your evidence. That's exactly what Flowing is designed for.

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Maker
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Hi Product Hunt! 👋

I'm Jim, an independent developer, and I'm really excited to finally share Flowing with you.

Over the years, I've noticed a few recurring pain points in research writing.

You know you've seen the evidence before.

You might even remember the paper—but not where the relevant paragraph, figure, or experiment is.

So you leave your editor, open PDFs, search again, and break your writing flow—sometimes only to realize you still can't find the exact passage you needed.

So I started building Flowing around one simple idea:

Keep your research library inside the writing process.

Instead of leaving your editor to search through dozens of PDFs, Flowing automatically detects high-quality keywords you're writing about and surfaces the most relevant snippet from your own paper library. You can preview the snippet thumbnail or return to the original source with one click.

Moreover, thanks to those retrieved snippets and your manuscript as context, Flowing's continuation and polishing suggestions stay much closer to your intended narrative than what you'd typically get from general-purpose AI assistants like ChatGPT. We even benchmarked this on a real chemistry PhD manuscript, where the difference became surprisingly clear. (Here is the detail if you are interested in: 📝)

Flowing is now publicly available. 🎉

To celebrate the launch, we're offering an Early Bird program. Join our Discord () to receive an invite code, and you'll get 2 years free, plus 50% off for life after that.

If you're already using ChatGPT, Claude, or Gemini for research writing, I'd genuinely love to hear how you currently keep your reference library in the loop—or whether that's still a pain point for you.

Thanks so much for checking out Flowing! 🙌

Would love to see a way to flag passages I'm not sure I agree with so the tool warns me when I lean on them, since right now it surfaces sources without telling me whether my own framing matches the cited claim. That kind of tension check would make it way more useful during drafting.

Maker

 Thanks! I really like this idea.

Our current goal is to make it much easier to find the right evidence while you're writing. But as you pointed out, retrieving a relevant paper is only part of the problem—it's equally important to know whether the evidence actually supports the point you're making.

A workflow that helps researchers spot mismatches between their draft and the cited evidence would be incredibly valuable. We'll definitely keep exploring ideas along these lines.

Thanks for the thoughtful feedback!

Congrats on the Lanuch. I wish I had something like flowing during my Management days. This is really need in academics here working with word and docs is just time consuming. Can we collaborate with peers? does it support multiple projects at a time?

Maker

 I'm really glad it resonates with your experience. 😊

At the moment, Flowing is primarily designed for individual researchers, so real-time collaboration isn't supported yet. It's definitely something we've heard interest in, though.

Yes, you can absolutely work on multiple projects. You can create separate documents and switch between different paper libraries depending on what you're writing.

Right now, our main focus is helping researchers stay grounded in their own literature while writing. Once that workflow is solid, we'll continue exploring features that make research collaboration easier as well.

Thanks again for the thoughtful questions!

 Awesome, I will recommend it to students. Real time collab is tricky, but needed as we have mostly group projects in academics.

📚 I've known Jim for quite a while—we actually built Paperly, a research reading tool, together a few years ago.

One thing I've always admired is that he's stayed focused on research productivity. It's a space that genuinely benefits from continuous exploration, and Flowing feels like a natural continuation of that journey.

Rather than simply adding AI to writing, it tackles something every researcher has experienced: remembering where you've seen the evidence you need. Helping researchers write with the knowledge they've already collected is a really thoughtful direction, and I'm excited to see where Flowing goes.

Congrats on the launch! 🚀

Maker

 Thank you so much—it really means a lot coming from someone who built Paperly with me.

Looking back, I think Flowing actually grew out of the same question we were asking back then: how can researchers spend less time searching for information and more time thinking and writing?

This time the focus shifted from reading to writing, but the goal is still the same—helping researchers make better use of the knowledge they've already collected.

Thanks again for all the support over the years! 😊

Does it work well with really large libraries, like several thousand PDFs?

Maker

 Yes! We've tested Flowing with libraries of 5,000+ PDFs (in the chemistry field), and that's actually quite close to the scale we designed it for.

Both automatic keyword detection—which surfaces relevant snippets as you write—and manual keyword search continue to work well even with libraries of that size.

Can it recognize handwritten notes or annotations inside PDFs too?

Maker

 Good question. Actually not yet. At the moment, Flowing focuses on the text content of your PDFs rather than handwritten notes or annotations. It's definitely an interesting direction, though, especially for researchers who annotate papers extensively. Thanks for the suggestion!

the tension-check idea in the comments below is the right instinct, and I'd push it one step further: once a suggestion gets accepted and lightly edited into the manuscript, is there any record that this sentence originated from an AI continuation grounded in source X, versus purely the researcher's own synthesis? asking because academic integrity policies are increasingly asking authors to disclose AI-assisted passages, and "draft -> check evidence -> light edit -> keep" as you described it is exactly the kind of workflow that gets fuzzy to reconstruct after the fact if someone asks "which parts did the AI actually write."

Maker

 That's another excellent point, and I hadn't thought about it from exactly that perspective.

You're right that once a suggestion has been accepted and lightly edited, it can become difficult to reconstruct which parts originally came from the AI. We don't currently keep that kind of provenance history, but I think this is definitely something we could support in the future by maintaining a traceable history of AI-assisted edits.

Thanks for bringing this up—it's a really thoughtful suggestion.

the part I'd worry about is subtle misattribution - if it's pulling a passage from your own PDFs to support a claim, does it ever surface a source that's tangentially related but not actually saying what the draft implies it says? that's the kind of error that's easy to miss during a deadline crunch and expensive to catch after submission

Maker

 That's an excellent question and I'm really glad you brought it up.

It's something we thought about a lot when designing this feature.For Continue Writing and Paragraph Polishing, we'd rather show no related snippet than one that's only tangentially related. During generation, we put a lot of effort into both retrieval relevance and evidence alignment, because a loosely related snippet can create exactly the kind of false confidence you described. We only surface snippets when we're confident they can genuinely support the generated suggestion.

Thanks for raising this—it was exactly the kind of failure mode we wanted to avoid.

How often do people actually accept Flowing's suggestions compared to starting from scratch?

Maker

 We’re tracking this now (accept / edit-then-keep / dismiss vs typing on your own), but it’s still too early for a reliable %.

In practice it’s rarely all-or-nothing: most useful cases look like draft → check evidence → light edit → keep. “Start from scratch” usually means the suggestion didn’t fit that spot in the manuscript, not that people avoid AI entirely.

Happy to share more once the data is actually meaningful.

Does it support citation managers like Zotero or Mendeley out of the box?

Maker

 Not a direct Zotero/Mendeley plugin today — Flowing is built for the writing loop, not bibliography management.

Most people use both: Zotero/Mendeley for citations, Flowing for drafting with PDF snippets you can verify.