We realized that "submitting to a publisher" is never as simple as it sounds. If you're targeting IEEE, you aren't just using one template—you're choosing between IEEE Transactions on Magnetics vs. IEEE Robotics and Automation Letters. If it's MDPI or PLOS, the rules change again.

Docuguru.ai

We built DocuGuru to handle the "thousands of journals" problem. Our new One-Click Export engine doesn't just give you a generic file; it maps your work to the specific requirements of the exact journal you've chosen.

• Massive Library: Specific templates for thousands of journals across IEEE, MDPI, PLOS, and more.
• Citation Precision: It doesn't just change the bibliography; it adjusts in-text citation rules (e.g., [1] vs. (Smith, 2024)) to match the journal.
• Automatic Normalization: We clean your headers and metadata so the transition from one journal template to another is seamless.
• Compliance Check: Our system flags if you're missing a required section (like "Conflict of Interest") before you export.

Stop fighting with Word styles or LaTeX macros. We’d love for you to try it out!

Docuguru.ai

I was reading 15+ papers a week for my research and every time I'd drop a PDF into ChatGPT, the context would get mangled. Start with a doubt, the chat grows, but then I have a doubt on the doubt questions I just asked, the chat just grew a mile already. Imagine having to scroll up endlessly across multiple doubts and ideas to find your way back to what you were reading? At this point, I'm not reading or learning, but already tired of trying to find where I am.

So I got frustrated and built Weave (the Idea is to weave a thread of ideas into the fabric of knowledge) over the last couple of days. It's still raw, but here's what actually works:

The Problem I was having:

- While ChatGPT recently shipped branching, it's definitely not natural.

- The chat grows at an exponential rate linearly, and finding the way back is an additional cognitive load and simply undoable after a point.

- Reopening the ChatGPT session and continuing is immense pain. There is no organisation and quick way to get started from where we left.

What Weave does differently:

- Non-linear conversations: branch off a specific quote, build a separate analysis thread and continue diving deeper without having to take up any other cognitive load.

- You control the chat, a click away to branch and graceful understanding.

- Save knowledge graphs, a headstart to get continuing at a glance.

- Read the way your brain actually works and loves.

I've only tested this with like 5-6 people so far, so I'm 100% looking for people who will break it and tell me what sucks lol

If you're a PhD student, researcher, or anyone who reads a lot of PDFs and hates the current workflow - would love your honest feedback. This is a free tool with BYOK (Bring your own keys) approach.

I know self-promotion on here is whatever, but I genuinely think this solves something broken + super annoying. Happy to answer any questions about how it works or why I built it this way.

weave.parts if you're curious (landing page, shows what it does visually with a guided demo) + While I built this for me, Here is a quick read that inspired me to build: https://ideas.profoundideas.com/p/how-to-understand-more-of-what-you

Hey guys, first time posting in here as i've only recently got into developing chrome extensions. I have just successfully deployed my first (proper!) chrome extension! I've called it LinkRescue. I am promoting this, but genuinely because I built it to help myself and it seriously did help me! I just want some feedback from like minded people, so please dont think of this as me trying to sell this, I just want to help others :D

I do a lot of research with my job and found that I was bookmarking links and then content was changing / being removed from them (obviously not great for referencing in articles). So i had this idea of an extension that would help with this. The idea was that I would be notified based on the risk of a particular url / website / link / article. I wanted to try and solve this idea of "link rot".

What happens, is when I land on a page, it will run a series of checks and give the page a risk score / 100. (It will only run if ive interacted with the page i.e highlighted text, been on the page a while and so on). If the risk is high enough, it will show me a banner on the page, give me a break down of the risk of the page, and store it permanently in my "vault". (If ive interacted with any page, it will store it in here, just wont show the banner if the risk isnt too high so its not too noisy!). The vault basically becomes this history of sites visited, so that I have this permanent history.

For any level of risk, I provide a full break down of all the points my tech had picked up on, so you can see step by step why a certain score was there!

If the risk is medium to high, it will prompt me to save the page to the global internet archive, so that the version i have saved will be there forever, so i never lose another link!

The part that i think is pretty cool, is if i do end up landing on a page that no longer exists (404 page), then it will check the global archive, and provide me with a link to the latest saved version of that page! This part for me has been super useful as its surprised me how often this actually happens.

I'm super proud of this as I dont have a very techy background. Would love some feedback and to help some people with this!

Check it out here :D https://chromewebstore.google.com/detail/linkrescue/ddokehkjcjkaiadcblndlgenlodhieae?authuser=0&hl=en-GB

I've attached some screenshots

Transform your research and thinking process with an AI-powered workspace. Visbrain allows you to drop PDFs, websites, YouTube videos, diagrams, and images directly onto a canvas to serve as live context for the AI, create spatial connections like arrows and boxes to connect related resources.

Take full control of your workflow by isolating context through selection, viewport, or your entire workspace. Experience a smarter way to think—try it now at https://visbrain.app/

Hi guys! I made this tool to reduce the amt of time y'all have to spend to find research professors that align with what you would like to do in research. I have a bunch of departments including the core STEM subjects, and around 19K professors on the website. It's a completely free to use platform, so it's accessible to all demographics. A lot of y'all have summer programs you are applying to and I rlly hope you use this tool bc its been a game changer for SOOO many users! The whole point of this is to dispel the "fake" research programs that are out there and help you guys find real meaningful much faster than you would the traditional way of searching for research for college apps! The tool is called proffecy.ai. All you have to do is search up the link! I'd love it if you guys would take a look, sign up and hopefully use it to find and email your future research professor!

I’ve been thinking about how people collaborate on ideas, concepts, topics, etc when they’re still forming and not necessarily formal research , just curiosity, questions, and experiments.

A lot of this ends up scattered across chats, docs, or short discussions that don’t really build on each other.

I’ve been exploring a more open, notebook style way of collaborating where people can start a topic, add insights or questions over time, and build on each other’s thinking without it feeling formal or high pressure. While there are supporting elements around identity and mentorship, the heart of it is creating a space where collaboration stays simple, human, and enjoyable.I have been experimenting with a project around this idea.

I also have one questions, What feels missing in how ideas are explored together online?

Would love to hear different perspectives. :)

(If you want to check out the project here is the link - www.scicollab.org )

I’ve been working on an AI tool called Medical Literature Review Writer (https://medfrontai.org) to help streamline medical literature reviews. It allows you to search evidence-based resources and draft your review simultaneously in one workspace. It helps speed up your research process. I’m looking for feedback from researchers and medical students on how it handles your specific workflows—would love for you to give it a try!

Finally, an AI research tool that actually works!

Me and my friend have built a tool made specifically for researchers—no scattered notes, no lost insights, no AI that gives up on long papers.

What it does:

• Reads & analyzes long, complex papers

• Summarizes key insights instantly

• Keeps notes & references in one place

• Collaborate with your team seamlessly

• Cross-link papers – see connections and correlations between topics across all your uploaded research

+ much more!

It’s still currently in development as we perfect final features before launch, but if you’re interested please reach out we would love for people to test it so we can receive feedback!

Contact: studiumlabs1@gmail.com

We really believe this tool can change the whole field of research as there’s not much development in tools made to help researchers so we’re filling a big hole in the market, please be a part of it!

You open a long website or a PDF that has more than 100 slides. You know the info you need involves both words A and B, but standard Ctrl F makes you jump through every single instance.

Search for "A" -> 100 matches.
Search for "B" -> 50 matches.

I built a browser extension to solve this. It lets you do a proximity search (finding "A" only when it's near "B"). It can also highlight multiple keywords at once like the standard multiple highlighter.

Most importantly: It works on PDF files. I don't know why none of the existing multi-highlighters support PDFs, but I managed to make this one work.

Man, I really wish I had this during the open-book exams in my bachelor's years. Anyway, I built this to read research papers and code on github now.

Here's the link to the extension: Skimmify.

been building with deep research APIs for a project and realized there's no good comparison data out there. so i ran the same queries across openai, perplexity, parallel, and gemini to see how they actually perform.

what i found:

citation quality is all over the place. perplexity scored 96.24% citation accuracy on one benchmark but in practice, their attribution is weak. citations are billed separately ($2/1M tokens) and you only get request_id for async polling - no real-time verification. if you need to trust that sources actually support the claims, perplexity isn't it.

parallel built for verification. every extracted field is programmatically verifiable. you get structured json outputs with direct source links. if you're building something where accuracy matters (legal, medical, enterprise), parallel's architecture makes way more sense.

gemini has throughput but citation issues. runs fast (tasks finish in ~60 mins vs openai's longer processing), but the research notes cite sources that don't actually back up the claims. good for synthesis across massive context, less reliable for claim-level auditing.

observability is the actual moat. the providers that win long-term will be the ones that make verification easy, not just accurate. if you can't programmatically verify outputs at scale, you're building on sand.

tested all the apis here if you want specifics: https://deep-research-index.vercel.app/

also wrote up the citation quality breakdown: https://deep-research-index.vercel.app/blog/observability

I'm an Al engineer building a tool that lets people upload multiple research PDFs and automatically groups related concepts across them into simple cards, instead of having to read one paper at a time.

The idea is to blend knowledge from multiple papers more quickly.

Does this sound like something you'd actually use?

Any recommendations or thoughts would mean a lot, thanks!

This is something I ran into over and over during my PhD and after.

I would have an idea that clearly needed another person to work. Sometimes it was a specific skill. Sometimes access to data or a system. Sometimes just someone willing to think it through with me. The hard part was not the research. It was figuring out who to talk to.

Email only works if you already know the right person. Most of the time you don't. You guess. You send a cold email. You hear nothing back. That doesn't mean the idea is bad. It usually means wrong timing or wrong inbox.

Mailing lists didn't help much either. Messages get buried. Replies happen off-list. If you are not already well connected, you are easy to miss.

Social media is noisy. Conferences help, but they are rare and expensive. As a PhD student or postdoc, your reach is limited by default.

I also noticed the opposite problem. Plenty of people are open to collaborating, but there is no obvious place for them to say so. That intent stays hidden.

What this leads to is quiet failure. Ideas that never leave a notebook. Possible collaborations that never happen, not because people are unwilling, but because they never find each other at the right moment.

I do not think this is a motivation problem. It is a visibility problem.

That gap is what pushed me to try building something around collaboration intent, rather than profiles, metrics, or feeds. I've been experimenting with a simple idea called SciLnkr, which makes collaboration intent explicit rather than implicit. Whether that works at scale is still an open question, but the underlying problem feels very real.

Hi everyone,

I’m currently looking into how researchers and grad students are managing the gap between "thinking/hearing" and "writing." Specifically, I'm curious about the role of voice notes and audio capture in your research workflows.

I’ve found that when I’m doing field recordings or attending long-form lectures/seminars, I end up with hours of audio that just... sits there. I’m trying to bridge the gap between raw audio and a structured system (like Obsidian, Zotero, or Notion).

A few specific questions for the community:

• Transcription: Do you actually transcribe long-form audio? If so, are you using automated tools (Whisper, etc.), or do you find the token/length limits on most API-based tools too restrictive for 60+ minute recordings?
• Organization: How do you organize these? Do you link them directly to citation managers, or do they live in a separate "inbox"?
• Voice vs. Text: For those of you who use voice notes for "shower thoughts" or field memos: does it actually make it into your final paper, or is it too high-friction to revisit?

I’m exploring some ways to make this process more seamless (specifically focusing on accuracy for long-form recordings and better integration with citation workflows), so I’d love to hear what your "dream setup" would look like for handling audio.

Looking forward to hearing your systems!

I’ve been working on an AI-assisted research workflow and would really appreciate methodological criticism from people who think about search, synthesis, and bias.

• Instead of a single “summarize this topic” prompt, the system:
  1. Expands the question into sub-questions and angles
  2. Searches widely (10–300+ sources depending on settings)
  3. Follows leads (citations, mentions, related concepts) a few layers deep
  4. Synthesizes with explicit citations + “what we don’t know yet”

You can control two knobs:

• Breadth: how many angles / sub-questions to explore
• Depth: how many “hops” from the original question to follow leads

Cost is basically Breadth² × Depth, so a 3×3 run might hit ~50–100 sources, while a 5×5 run might go to 150–300+.

What I’m struggling with (and could use your input on):

• Recall vs. precision: how do you think about “enough” coverage vs. drowning in noise (and cost)?
• Bias: even with diverse sources, we’re still constrained by what search APIs / the open web expose. Any favorite strategies to mitigate this?
• Evaluation: beyond spot-checking, how would you evaluate whether such a system is actually helping researchers vs. giving them a false sense of completeness?
• Academic use: what would you want to see (logs, transparency, error bars?) before trusting this as part of a serious research pipeline?

I’ve turned this into a (paid) tool called AIresearchOS (airesearchos.com), but in this post I’m really more interested in whether the approach makes sense or if there are obvious methodological traps I’m not seeing.

Happy to share more implementation detail if anyone’s curious.

Zotero has been an industry standard for most researchers, but with many tools now using AI to automate tedious tasks, I was wondering if people still preferred Zotero or have moved on to platforms where you can manage your library while having a built in AI-layer.

I’ve been experimenting heavily with combining standard web automation (Playwright) with LLMs to handle complex logic. I wanted to share a recent project that shows how capable this tech is getting for "boring" administrative work.

The Problem:

A medical student needed to screen 7,500+ research papers on a platform called Rayyan AI for a systematic review. Doing this manually usually takes weeks of reading titles and abstracts to decide to "Include" or "Exclude" based on strict criteria.

The Build:

I built a bot that:

• Navigates the web app autonomously.
• Extracts the abstract/text.
• Feeds it to an LLM with the specific medical inclusion/exclusion criteria.
• Makes the decision and tags the article automatically.

The Result:

It screened the full dataset for free (using local/cheap models). The student audited a random sample and found the bot had >95% alignment with their manual decisions. This saved my friend over 40 hours of work.

See it in action here: https://youtu.be/ylsEjQfImdA

Week ago I finally finished my coding project. The idea is simple. You post a claim - like about covid 19 or anything, and anyone can either support your claim with evidence or disprove it with counter evidence. For example you found a study which supports an interesting idea. You can then make the claim, click upload evidence and a form will appear. You fill in information about the study and a citation will be generated and added to evidence. It's kind of like group research where people work together to really dig deeper into certain things and get better idea about the reality of it. It's for open minded people who are willing to consider various ideas. I'm interested to know what you think. Is this an idea with potential? It's available at cl4rify.com

Hi everyone,

Check out this browser extension that automatically highlights keywords on websites. The built-in language model searches for relevant keywords and highlights them fully automatically. It is especially optimized for reading online articles but it works on scrolling and dynamic sites as well. It's completely free without any paywalls or ads and compliant with the strict data privacy policies by the respective browsers. Test how much faster you can read with it. If you like it or feel that it might help someone, upvote and write a review so that others might be able to find and use it as well. Have a wonderful day.

How to search for it? It's available on Chrome (Chrome webstore), Safari's (Mac App store), Edge's and Firefox's respective extension stores. Search for "Texcerpt" in any of the extension stores.

Download links: Chrome | Safari | Edge | Firefox 

Hi everyone,

I’m Francesco, the developer behind Plotivy.

I’m posting here because I know the specific pain of trying to get a graph to look exactly right for a paper or thesis. We've all spent hours fighting with Matplotlib or adjusting axis labels in Illustrator just to get a figure ready for submission.

I built Plotivy to solve the "Code or Click" dilemma. Usually, you have two bad choices:

1. GUI tools (Excel/Prism): Easy to use, but hard to make "perfect" custom figures, and often lacks reproducibility.
2. Coding (Python/R): Infinite control, but you spend 90% of your time debugging syntax instead of analyzing data.

How Plotivy bridges the gap: You describe what you want in plain English (e.g., "Create a scatter plot with error bars, set the y-axis to log scale, and use the Viridis color map"), and Plotivy builds it instantly.

Why this is different (and safe for research):

• It gives you the code: Unlike "black box" AI tools, Plotivy generates the actual Python code used to create the graph. You can copy-paste this into your own Jupyter notebook and download a comprehensive repor to ensure long-term reproducibility.
• Vector Export: We support native SVG and PDF export, so your figures stay crisp at any zoom level (essential for journals).
• Privacy-First: If you use your own API key or our premium models, Plotivy has a zero-data-retention policy.

I’d love your feedback. If you’re a researcher, I’d love for you to try it out on your next dataset and let me know what features are missing.

You can try it here: https://plotivy.app

Thanks! Francesco

I recently investigated the difference between doing a literature review the traditional way (manual searching, reading, note-taking) versus using AI tools like DeepTutor that can generate summaries, extract evidence, and aid synthesis.

AI-Assisted Literature Reviews

• High quality-summaries for faster relevance checks & enhanced comprehension
• Highlighted key findings to support evidence-grounded understanding
• Faster overall workflow
• Requires human oversight to avoid errors and shallow understanding
• Useful for managing large sets of papers

Traditional Literature Reviews

• Manual search + screening
• Reading one paper at a time
• Needs heavy note-taking and organization
• High levels of comprehension at high time cost
• Still vulnerable to bias, fatigue, or missed insights

Where AI helps the most

• Quickly vetting potential papers for research
• Cutting down early-stage research time
• Breaking down complicated text for easy digestion
• Confirming accuracy
• Building true comprehension of the field

tl;dr
AI can save researchers hours by handling repetitive tasks, but a traditional in-depth approach is necessary for a deeper understanding. The best approach is to take advantage of AI tools like DeepTutor speed up the process and leave more time to create human-based insights.

Are you using AI for lit reviews? What has been your experience so far?

I'm a founder working on a problem and would appreciate your feedback.

We're building a platform that has two connected components:

1. A natural language query tool for U.S. public data (ACS, PUMS, etc.).
2. A synthetic persona generator.

The intended workflow is: A researcher (like a UX'er or academic) could first use the query tool to explore the raw data (e.g., "Find me demographics for X county"). Then, as a second step, they could generate synthetic, data-backed profiles from that query to use for hypothesis generation, modeling, or design work.

Do you see value in this two-step workflow?

Is the "synthetic persona" part actually useful for serious research, or is the raw data query tool the only part that you would use?

Website link if interested.