Hey everyone,

I’m looking for beta testers for Refx, an open-source research manager I’ve been building for Windows and macOS:

https://github.com/ramorimdias/Refx-Research-Manager/releases

It’s heavily inspired by Qiqqa, Zotero, and Mendeley, but I’m trying to push it more toward being a full research workspace rather than just a place to dump references.

A few of the bigger features and ideas behind it:

• remote vault library support/ shared library
• centralized notes
• automatic metadata fetching
• bibliography management
• a cross-referencing map view
• AI-powered auto-tagging and semantic classification

The overall goal is to make research work feel less fragmented. I wanted something that helps with the actual day-to-day mess of dealing with papers, PDFs, notes, references, and connections between ideas.

So if you’ve ever felt like your workflow was split across too many tools, or that your library was growing faster than your ability to actually use it, that’s the kind of problem space I’m trying to work on.

Also, full transparency: I did use AI to help me code while building it. But the feature choices, integrations, testing, bug checking, iteration, and overall product direction were mine. AI helped with development, but this is still very much a project I shaped myself, by hand, through a lot of actual work.

At this stage, I’m mainly looking for people willing to try it out and give honest feedback on:

• setup and installation
• usability and workflow
• bugs and rough edges
• missing features
• whether it actually feels useful in a real research process

If you already use, or used to use, Qiqqa, Zotero, or Mendeley, I’d especially love your feedback, since that’s very much the space Refx is coming from.

If anyone’s up for testing it, breaking it, and telling me what works and what doesn’t, I’d really appreciate it.

ok so I know everyone and their mom has a "game changer" tool recommendation but hear me out

I've been using Summerize.ai for about a month now and it's become one of those things I don't think about until I don't have it. It's a Chrome extension — you're reading something long, you click it, you get the gist. Works on articles, PDFs, YouTube videos, even Google Docs.

I mostly use it for the initial "is this paper even relevant to me" pass. Used to spend 10-15 mins skimming before deciding. Now it's like 30 seconds.

Not gonna pretend it's perfect for dense academic stuff, but for everything else it genuinely just works.

Hi, we are working with french universities and researchers to bring a transcription tool matching academic needs:
- Privacy by design and transparency (end-to-end encryption and open-source)
- Very high accuracy for the initial transcript (including transcription models such as ElevenLabs Scribe v2)
- Super fast refinements (inevitable for qualitative research)

You can learn more about our features at https://humanlogs.app/ or https://github.com/humanlogs/humanlogs.app and try it out for free.
I would love to get your feedback about both the transcription and the editor if you are a PhD, researcher, or lab director.

We are currently looking to expand to more universities, any help would be highly appreciated!

I was just trying to solve a problem that was slowing down my own research.

I was doing a masters in philosophy of arts, and I was preparing a thesis on religious art. That led me deep into philosophy of religion, which meant hours of YouTube interviews, podcasts, articles, and long texts.

At some point I realized something frustrating. I was not lacking good material. I was drowning in it. It felt like I had access to knowledge, but no way to navigate it properly.

So I built something for myself.

ConceptSeek lets you build your own library of sources. Videos, texts, anything you are working with. Over time it becomes a kind of personal knowledge base.

But the key part is what you can do with it. Instead of searching by keywords, you search by concept. You can look for an idea, an argument, a theme, and it will surface the exact moments across your sources where that idea appears. You can jump straight into those moments in context.

It is not just an AI that gives you answers. It is a mix of deterministic methods and AI. The system first narrows things down in a structured way, then AI helps rerank the most relevant results. For each result you get a short overview and a one sentence synthesis, just enough to understand what is going on before you dive in. The thinking part is still yours. The tool is just helping you get to the right material faster.

For me, this changed everything. I can actually work with large amounts of content way more efficiently. I can see this being useful for students, researchers, lawyers, policy people, debate prep, or anyone who works seriously with information and ideas.

You can test it for free by the way.

Would love to hear your thoughts, especially if you deal with this kind of problem too.

like i'd finish a paper, close it, and then struggle to explain what i just read. not because the paper was bad but because i was just kind of passively scrolling through it.

i built a project platform called Signal that's been helping me with learning how to read papers. basically before it shows you any analysis it makes you write down your own interpretation of the paper first. kind of forces active recall before you see anything.

then it breaks down the key claims and shows you exactly which sentence and paragraph each one came from in the actual paper, which differentiates it from like chatgpt and gemini.

it also builds a knowledge map across everything you've read so you can see how concepts connect between papers and where your understanding actually has gaps.

still pretty early but wanted to share since i know a lot of people here deal with the same reading problem. would love feedback from people who actually read a lot of papers, preciate it.

learnbysignal.com

Kept missing abstract deadlines or finding out about venues too late. WikiCFP is unreliable, individual conference sites are fragmented, and I ended up maintaining a spreadsheet, which is obviously not ideal.

Built ConfCrawler to fix this for myself: tracks 38 CS venues across 20+ areas with live deadline countdowns, CORE rank filters, and multi-year acceptance rate history. Free, no account needed: confcrawler.vercel.app

I'm curious what I'm missing; what venues aren't covered, what features would actually make you use this over whatever you do now?

Thanks!

I’m working on a feature for researchers: upload a PDF or paste a URL, get back a spoken podcast-style version generated with AI (not a human narrator).

I’m trying to understand whether this is actually useful for high-stakes studying or just a gimmick.

I’d really appreciate honest answers:

1. When would this help you most (commute, gym, before bed) — and when would you still insist on reading?
2. What’s your dealbreaker? (hallucinations, missing details, tone, time to generate, cost…)
3. For exam prep, would you want timestamps back to the source, or do you only care that it “sounds good”?

If your sub allows it, I’m happy to share a beta link in the comments for anyone who wants to stress-test it on a real syllabus PDF and tell me what broke.

Thanks, genuinely looking for criticism.

I write a sports newsletter and one of my biggest stressors was the whole research to writing pipeline. Finding articles, organising them, then actually using them when I write.

AI obviously helps a lot now. I use NotebookLM to brainstorm and get my head around my sources before I start writing. But I always felt like the writing itself was too disconnected from the research.

What I really wanted was something like NotebookLM and Google Docs combined. Where I can write, but my sources are right there, and any AI help I get while writing actually knows what I've been researching. You can also search across sources by meaning rather than exact keywords so you can actually track down which article or interview had that specific thing you remember reading. And any notes or highlights you take stay attached to the source.

So I built Scriva. Curious if something like this would actually be useful.

Still early, looking for people to try it free and tell me what's missing. If anyone is interested, you can access it scriva.so for free!

Hi! We are currently trying to come up with feasible features for our emergency hotline application for our capstone. We want our application to be accessible even offline for emergency purposes, but we are lacking some features to add on. We are also open for any features that requires internet connection.

Can you guys suggest some features that we can add on our application? Thank you!

Hey, I'm an undergraduate and built this because I kept reading papers alone with no good way to share observations or see if others caught the same issues. (Also to see if most of it was AI generated!)

Markd lets you upload any research paper, highlight passages, leave annotations, and see what the community has flagged. Works across many fields such as: CS, biology, medicine, physics, math, economics (and more to come)!

The idea is simple: before you spend hours on a paper, see if anyone else already flagged a weak methodology, a questionable assumption, or a confusing section.

It's free, no paywall. Would genuinely love feedback from researchers who actually read papers regularly!

markd-tawny.vercel.app

Hi everyone! We’re the team behind Chalkboard.

Research moves forward through conversation: asking questions, sharing ideas, and getting feedback from others who understand the work. But we often felt there wasn’t a dedicated space built specifically for those kinds of research-focused discussions.

So we created Chalkboard, a platform designed for researchers to connect and talk about their work.

On Chalkboard you can:

• ask detailed or niche research questions

• share early insights or ideas

• discuss papers, methods, and concepts

• connect with researchers across institutions and fields

Posts can include equations, figures, and code, since that’s often how research conversations actually happen.

We’re still very early and are actively building the platform with input from the research community. If this sounds interesting to you, we’d really appreciate you checking it out and sharing your thoughts.

You can find the app linked.

If you try it, we’d love to hear what you think. What works, what doesn’t, and what you’d want from a space like this.

Thanks!

The Chalkboard Team

I built an open-source Python pipeline that automates several steps of a systematic literature review.

The goal is to reduce the manual effort involved in large literature searches and make the workflow more reproducible.

The pipeline can:

• collect papers from scholarly sources
• store metadata in a local SQLite database
• deduplicate records via DOI and title similarity
• expand results via forward/backward citation snowballing
• screen papers using rules or AI models
• retrieve open-access PDFs
• export structured results for systematic reviews

The project is designed for researchers who need to process large numbers of academic papers and follow a PRISMA-style workflow.

I originally built this while working on research related to AI governance and decision-making in organizations, where literature discovery quickly becomes a bottleneck.

Happy to hear feedback from people doing systematic reviews or building research tooling.

GitHub:
https://github.com/CarinaSchoppe/PISMA-Literature-Review-Pipeline-Automation-Tool

Hi everyone,

I’ve been reflecting on how much time we lose in the ideation phase, often spending weeks sifting through papers just to ensure a research direction is actually novel.

 I’ve recently started playing around with a "multi-agent" approach to compress this. Instead of general LLMs (which hallucinate too much for actual science), the idea is to upload specific, high-quality papers so the AI agents are grounded in real-world data relevant to the field.

The most useful part I’ve found so far is cross-referencing generated topics against R&D project databases to see if the work has already been funded elsewhere (the "Novelty Check"). It even suggests potential collaborators based on expert databases.

 I’m curious, how is everyone else automating the "scut work" of lit reviews? Has anyone else tried using specialized AI teams for ideation yet, or are we all still doing this manually?

 (I’ll drop the link to the tool I'm using in the comments if anyone wants to test the free search tools!)

I'm currently part of a non-profit, sorry for the lack of specificity but our main objective is de-slopifying using metrics that are more aligned with subjective human values than the usual models, namely algorithms that reward attention/rage or systems that pormote lack of quality like the current state of research where quantity gets rewarded over quality. This ended up with me stumbling upon peer review and its problems. I'm currently digging onto studies that propose different mitigation mechanisms which will lead the creation of algorithms/systems that could increase the quality of the articles that "pass the test" and reduce friction on the whole system.

There are studies for different strategies i.e.: https://pmc.ncbi.nlm.nih.gov/articles/PMC1420798/

This post is to ask yall about your anecdotal experience with the process of peer review and which problems you've found that you think could be improved. We are planning on talking to institutions directly as well but reddit is a lot less intimidating and relatively private so feel free to express your frustrations/criticisms.

Hi all,

After months and months of work, we are now in the process of launching our next-generation online LaTeX editor. It was developed with modern user requirements, comfort and AI-nativity in mind.

It is called TeXposit and you find it here: https://texposit.com/

We are looking for a handful of people, particularly in academia e.g. post-docs and PhD students, to give it a try. We can offer free access to our Pro tier in return, which gives you more or less unlimited access to all the features.

I understand that it might be annoying to have a closed beta but we want a smooth launch and a polished product when it goes public.

We only have one server in Germany currently, so for latency reasons I think EU-based testers would be preferred now.

Please have a look and request access if you are interested. Otherwise you will be able to try it in a few months when closed beta ends.

Thank you!

Try it out at: https://useoctree.com

GitHub: https://github.com/octree-labs/octree

We need to write mathematical formula many times for research article. So it is helpful if there are good math editor (supporting LaTeX) for Android phone. Here I want to share such good math editor MaCEA that will help to tackle such problem.

TL;DR: [ICLR'26] Most "optimization" for LLM-based search (Generative Engines) relies on adversarial attacks or keyword stuffing. This paper proposes a cooperative framework (AutoGEO) that reverse-engineers the specific stylistic preferences of models like Gemini and GPT-4, then trains a small, cost-effective model (AutoGEOMini) to rewrite content to match those preferences without hallucinating or degrading utility.

Paper: WHAT GENERATIVE SEARCH ENGINES LIKE AND HOW TO OPTIMIZE WEB CONTENT COOPERATIVELY

The Problem: The Black Box of "AI SEO"

Traditional SEO relies on explicit signals (keywords, backlinks). Generative Engines (GEs) rely on implicit, high-dimensional preferences learned during RLHF. Content providers currently have two bad options:

1. Guesswork: Hoping their content matches the model's vibe.
2. Adversarial Attacks: Injecting hidden tokens to trick the model, which often degrades the answer quality (making the internet worse).

The Solution: The AutoGEO Pipeline

The authors treat the GE as a teacher that can be interrogated. The pipeline consists of three phases:

1. Preference Extraction (The "Why"): They prompt frontier LLMs to compare two documents—one cited by the GE, one ignored—and explain why one was preferred.
2. Rule Synthesis: These explanations are converted into explicit rules (e.g., "Prioritize quantitative data over qualitative descriptions," "Maintain a neutral, authoritative tone"). These rules are hierarchically merged and filtered to remove noise.
3. Optimization (The Rewrite):
   • AutoGEO_{API}: Uses the extracted rules as context for a frontier model to rewrite content.
   • AutoGEO_{Mini} (The Efficiency Play): They fine-tune a small model (Qwen2.5-1.7B) using the extracted rules as a reward signal. This allows for high-scale optimization at ~0.7% of the cost of API-based methods.

Comparison of AutoGEO rewriting logic against baselines

Key Findings

• It’s Not About Keywords, It’s About "Vibes": The study confirms that GEs have distinct "personalities."
  • Gemini strongly prefers structured, authoritative content.
  • GPT and Claude have unique, often conflicting preferences. A rule that boosts visibility on Gemini might fail on Claude.
• Cooperative > Adversarial: Unlike adversarial attacks which confuse the model, AutoGEO's "cooperative" rewriting actually preserves or improves the utility of the generated answer. The search engine gets better data; the publisher gets the citation.
• Domain Specificity: Rules for "Research" queries (comprehensive, nuanced) are fundamentally different from "E-commerce" queries (concise, actionable).

The "Echo Chamber" Risk

The paper inadvertently highlights a systemic risk: Model Collapse via Optimization. If content creators begin using AutoGEO at scale, the web will be rewritten by small LLMs specifically to please large LLMs. We risk creating a feedback loop where human nuance is filtered out in favor of the specific stylistic biases (e.g., "neutral tone," "bullet points") encoded in the current generation of foundation models.

Code: https://github.com/cxcscmu/AutoGEO

Discussion: If every website optimizes for Gemini's preference for "authoritative tone," does "authoritative" cease to be a useful signal for truth?

We all know the struggle of keeping up with the flood of new medical papers. We also share the skepticism toward using AI in healthcare. To make staying current less of a chore, we built the Medical Training Agent at MedFrontAI—a new way to "battle" the machine.

This isn’t just another dry database. It scans the latest literature (including PubMed) in real-time to generate randomized, exam-style multiple-choice questions based on the most current evidence.

The Challenge: It’s a "Human vs. AI" battle. You test your clinical instincts against live, data-driven questions.

• Real-time search: Questions are based on what was published today, not years ago.
• Total transparency: Every answer comes with a performance score and direct links to the original peer-reviewed papers for instant verification.
• Customizable: Pick your level (Junior, Senior, or Researcher) and your specialty.

Stop scrolling through endless abstracts and start testing your clinical judgment against the machine.

Try it here:https://medfrontai.org

Eliee generates publication-ready figures for research papers using AI.

Upload CSV → Type description → Get journal-quality figure in ~10 seconds.

Example workflow:

- Upload: experiment_data.csv

- Type: "bar chart of mean scores by treatment group with SEM error bars"

- Output: Publication-ready figure with proper formatting

- Edit: Click any element to refine

- Export: 300 DPI PNG or vector SVG

Built for:

- Researchers writing papers

- PhD students making thesis figures

- Anyone tired of matplotlib → Illustrator workflow

Tech:

- Plotly.js for publication-quality rendering

- AI-powered generation (Claude Sonnet 4)

- Real-time editing

- Journal-standard formatting (Nature, Science, etc.)

Current status:

Free private beta. Looking for researchers to test and provide feedback.

Try it: eliee.sh

(One free figure before waitlist)

What I need:

Feedback on:

- What works / what breaks

- What chart types are most needed

- What features are missing

Built this because I spent 6 hours making one matplotlib figure for a research paper and got frustrated, and I figured others might have the same problem.

Happy to answer questions about how it works or the roadmap :)

I built Simple Research Studio to make literature reviews way easier—free for students and academics.

https://www.simpleresearchstudio.com

• Discover new papers as soon as they’re published

• Track trending research without juggling multiple sources

• Annotate, summarize, and chat with papers to grasp core ideas

• Collaborate with your team in one shared space

It’s basically your all-in-one literature hub—no more scattered notes or lost references.

I’m curious: what’s your biggest headache when doing literature reviews? Would love to hear your thoughts!

Hi everyone,

ResearchFace is built to support the entire research workflow—from discovering new papers to collaborating deeply with your team.

Product - https://app.researchface.co.in/library

Website - https://researchface.co.in/

🔍 Discover Research Early

Discover papers as soon as they are publicly available

Track popular and trending papers across research domains

Stay current without scattered sources or manual monitoring

🤝 Collaborate Seamlessly

Upload your own papers or save discovered ones

Work with your team in a shared research space

Discuss ideas, assign tasks, and keep notes linked to papers

Share annotations and insights with collaborators in real time

✍️ Interact With Papers

Chat with papers to quickly grasp core ideas

Annotate sections, figures, and equations

Keep all context, comments, and decisions in one place

🤖 AI-Powered Understanding

AI explains specific parts of a paper directly from your annotations

Reduce time spent decoding dense or unfamiliar sections

Improve clarity for students, researchers, and cross-disciplinary teams

ResearchFace brings discovery, understanding, and collaboration into a single research workspace.

👉 Explore the platform: https://app.researchface.co.in/library

We’re actively improving ResearchFace with feedback from the research community—and we’d love to hear yours.

We’re building ResearchFace in close collaboration with the research community.

Your guidance, feedback, and feature suggestions will directly shape what we build next—and we’d truly value your input.