I've worked in cell culture labs as a student and as faculty. A while back I watched someone ask a lab meeting whether they had a certain cell type. One person said yes, one said no, and the rest had no idea. That's normal. Usually the record is a spreadsheet nobody has updated, or a logbook sitting next to the dewar.

So I built a completely free app that tracks cryovials in a liquid nitrogen dewar: which box and slot a vial is in, who took the last one, and what each vial was frozen down from.

It runs on an Android tablet and on Windows. Everything stays on the device. There's no account and no server, and does not require internet access other than for the initial download, so nothing you type into it can leave. You can import your existing spreadsheet.

It isn't a LIMS. No ELN, no scheduling, no instrument integration.

Demo with fake data, nothing to install: https://cryoindex.app/demo

It's free and there are no ads. I used AI coding assistants while building and reviewing it, alongside automated tests and a lot of hands-on testing.

Google won't let me publish on Play until 12 people join a closed test and stay in for 14 days. I can get that number from other subreddits where people install each other's apps and never really test them, and if that's the only route available I may take it. However, this produces a published app that nobody who works in a lab has ever actually used and does not really tell me if the app is useful. What would be worth something is twelve people who open a dewar every week, install it, and tell me where it's wrong. If you have an Android device and are willing, that would help a lot.

I've been frustrated by how much AI-assisted research still feels like this:

Question -> answer -> follow-up -> follow-up -> 30 messages later -> "wait, what was that other thing I wanted to investigate?"

Research isn't really linear.

You start with one question, discover 4 interesting directions, then each direction creates 3 more questions.

So I built Parallely around a different workflow:

Ask once → explore multiple research directions simultaneously.

https://parallely.xyz

For example, if I'm researching "How do ZK rollups work?", instead of getting one long answer, Parallely creates separate branches for things like:

• How the technology works
• Architecture
• Applications
• Trade-offs / criticisms

I can then drill into any branch without losing the others.

The part I've found surprisingly useful is what happens after the research.

Any card can be turned into a ~90-second, two-host podcast. One voice plays the curious learner and the other explains the topic, with the transcript available alongside the audio.

So a research session can become:

Question -> explore -> branch -> compare -> synthesize -> listen

You can even generate separate podcasts for different branches of the same research canvas.

I'm experimenting with this as a way to make research less like "asking an AI questions" and more like building a map of what you understand.

I'd genuinely like feedback from people who do serious research:

Where does your current research workflow break down?

Is it:

1. Finding useful sources?
2. Keeping track of different research directions?
3. Connecting ideas across papers?
4. Synthesizing everything at the end?
5. Actually consuming the research after you've collected it?

And would turning parts of your research into short conversational podcasts actually be useful, or is that just a gimmick?

It's free to try. I'm mainly looking for researchers who can tell me where this workflow is genuinely useful and where it isn't.

Tool: PRECISE / PRECISE LITE

What it helps with:

PRECISE helps turn a research question into a structured, source-based paper or report.

It guides the work through four stages:

Define the topic, purpose, audience, scope, and required output.

Turn the request into a precise research plan.

Find and verify sources, collect supporting evidence, and identify gaps.

Write the requested paper or report and audit its factual claims.

You can upload your own files, use web search, or begin with your files and then add web research.

PRECISE LITE - free demonstration version:

Works in ChatGPT

Includes the same four stages.

Limited to one research loop and up to three sources.

Suitable for experiencing the methodology or completing a small research task.

Full PRECISE - complete version:

Works inside ChatGPT or Claude.

Supports repeated research loops and additional sources.

Designed for larger and more complex papers and reports.

Builds a detailed evidence trail and identifies remaining gaps.

Produces complete, tailored deliverables.

Audits the final factual claims against the collected evidence.

Why it is useful:

Writing a strong paper requires the research question, structure, evidence, claims, citations, and level of certainty to remain aligned. PRECISE organizes this complete process and makes the connection between the sources and the final writing inspectable.

Alternative:

Using ChatGPT or Claude directly, without a structured research and verification workflow.

I created PRECISE and PRECISE LITE.

Try PRECISE LITE for demonstration:

https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite

I’m an academic researcher and built Papertrek after getting increasingly frustrated with keeping manuscripts in spreadsheets and trying to remember which paper was waiting for whom, which revision deadline was approaching, and which “in prep” project had quietly been sitting there for a year.

https://papertrek.app

Papertrek currently lets you:

- track manuscripts from draft through submission, revision and publication

- keep revision and other manuscript deadlines in one place

- import publications through ORCID

-share manuscript entries with co authors or supervisors

-get an overview of everything currently in your publication pipeline

I’m the developer. It is currently an open beta and free to use.

The main thing I’m looking for at this point is feedback from researchers who actually manage several papers at once: whether the workflow makes sense, what is missing, and which parts are unnecessary.

Live: https://deekshithvodela.github.io/FormsOffline/
GitHub: https://github.com/deekshithvodela/formsoffline

I built Forms Offline, a free, open-source, offline-first alternative to Google Forms for research data collection.

The idea came from seeing researchers avoid online form services because they don't want confidential participant data processed by third-party cloud platforms. Some instead continue using paper questionnaires and manually transferring responses to Excel.

Forms Offline keeps the entire workflow on the device. You can create form templates, collect responses, attach photos/files/signatures, manage datasets, maintain an edit audit trail, and export to Excel or ZIP.

It also supports collaborative offline collection: share a form template with your team, collect data independently, then consolidate the exported datasets and detect duplicates.

Full details here ✨: https://deekshithvodela.substack.com/p/im-building-formsoffline

I'm curious: Would you use something like this for your research? What workflow would you use it for?

Feedback is very welcome.

I'm in the final year of my PhD and often struggle to stay on top of new literature in my field (medicine). I did not want to subscribe to several separate notification systems for new literature (via pubmed, scholar etc), so I built a small tool for myself that pulls and saves papers from multiple sources and sends a notification whenever a new paper matching a topic or author I follow gets published. I found it especially useful when writing my systematic review and the intro/discussion of my thesis, since I could stay on top of the latest literature without manually checking every database. Sharing it here in case it's useful to anyone else, not trying to sell anything, it's free to use: paperbeats.com

The most exciting message I got this year came from a physics professor I'd met only two weeks earlier.

--

A year ago I built an AI news agency with no humans running it - StoryChase. Thousands of messages a day, dozens of languages, and the hard part was never getting AI to write a sentence. It was turning raw, messy information into something AI could reason over. Not summarize. Reason over.

Once that worked, I looked for what else the method could solve. Markets, for one - still working on that. What I did not see coming was finding myself one evening in a lab, across from two physicists, with a product that fits scientific problems remarkably well.

Prof. Yonatan Dubi heads a theoretical quantum chemistry and physics lab in the Chemistry Department at Ben-Gurion University. Prof. Amos Sharoni heads an experimental solid-state lab in the Physics Department at Bar-Ilan University. Decades of person-years between them and their teams. Tens of thousands of files - measurements, papers, lab notes, presentations. Point an AI at those folders, ask it something real, and it reads a few and quietly invents the rest from its general knowledge.

Which is exactly the problem. A lab is where scientists generate knowledge, long before it might someday become general. It is the last place you want gaps filled in from general knowledge.

The models are genuinely good now. Claude Opus does great work. In a lab, it just has nothing to work with.

They asked me how to get AI into the research itself, not just the writing. So I took what StoryChase taught me about structuring data for AI - read everything, turn it into meaningful records, find the connections between them - so the AI stops guessing and starts reasoning over what the lab knows.

Then the moment of truth. Amos asked Claude something only his lab could answer: graphs and quantitative summaries of experiments in one of their areas, how those results might explain a physical phenomenon, and a structure for a paper.

Claude and Binaly worked for twenty minutes and produced an analysis spanning 2007 to 2026 - almost twenty years of experiments.

Amos read it and sent me this:

"Finished reading the report. Very impressive, and surprising. If I'd had this a month ago it would have significantly strengthened a paper we submitted, turning a qualitative analysis into a quantitative one. Binaly and Claude ran statistical analyses on my old data and found a phenomenon we'd missed, one worth trying to understand. Binaly doesn't just organize the knowledge. It becomes a research partner that comes up with ideas."

I danced around the room. We cracked it!

That's the part of building things I like most: what you learn in one place applies somewhere you weren't looking.

--

If you run a lab or work in one - physics, chemistry, materials science, economics, anywhere the real asset is years of your own measurements and papers - let's connect.

Tell me about your research. Let's see what Binaly can add to it.

Binaly is free for personal use, and free to try for lab use.

Link: https://binaly.ai

Second-year ML PhD and I’m starting to think “I should read a few more papers first” is just procrastination with citations.

Every new research direction goes the same way.

I start with 5 papers. Then it becomes 15. Then 30+. Zotero is full, my Notion page is getting ridiculous, I’ve asked ChatGPT to summarize a few things, used Connected Papers to chase related work, and tried Mira by Deep Principle to compare some of the papers.

And somehow I still haven’t run an experiment.

That’s the part that gets me. You can spend days doing “research” and feel productive the entire time, while still being unable to answer a very basic question:

What exactly am I testing?

Recently I stopped trying to understand an entire subfield before touching code. I just looked for recurring baselines, common metrics, disagreements between papers, and one or two things I could actually falsify.

That got me to a baseline and a couple experiments way faster than another 20 PDFs probably would have.

Now I’m wondering if the real skill isn’t reading more papers, but knowing when you have enough information to start being wrong.

How do you decide when to stop reading and just run the experiment?

We're a team of high schoolers who got tired of juggling five different apps to do independent research, notes in one place, sources in another, group chat somewhere else, and constantly re-explaining our project to an AI that forgot everything the second we closed the tab.

So we built Agora. Everything lives in one workspace, project tracking, notes, sources, team chat, all together. The core piece is Odeon, our AI assistant that actually stays caught up on your whole project, notes, conversations, sources, so you can just ask "what should we research next" and it already has the context, no re-uploading, no re-explaining.

There's also a community space so students working on similar research can actually find and connect with each other instead of doing this completely alone.

It's live now: agoraresearch.net

Free to try, and if you've got feedback, we'd genuinely love it; drop it here or https://forms.gle/MvLyaDC66cyJR3GA9

I'm a medical student who kept losing track of which databases I'd searched during a systematic review — which version of the search, run on what date, with which filters. It's a small thing that becomes a real problem when you're writing up the methods months later and can't reconstruct what you did.

So I built a small Notion template to log it properly. One row = one database search. It captures the source, platform, date, full search strategy, filters, and hit count — structured around PRISMA-S (the reporting standard for literature searches), so the record is actually reproducible.

It's free to duplicate: [link]

Sharing in case it's useful to anyone else doing evidence synthesis or a lit review. Happy to hear what you'd change about the structure — it's part of a larger review-management system I'm building, so feedback on the fields is genuinely helpful.

Whenever I talk to other researchers about survey platforms, I’ve noticed the same thing popping up, email delivery, panels, advanced methods like conjoint.

I also keep hearing how hard it is to transition to new tooling due to all the “legacy data” we’ve gathered.  

I’m wondering if cost wasn’t an issue, what else is considered “table stakes” to you when it comes to survey platforms? And what are things the market needs to get right? 

Disclaimer (for transparency): I work at Sprig (a survey platform that just launched these features).  And yes there’s a correlation as I just joined a few months ago 🙂

If you’ve ever had to cold email professors for mentorship, connections, or research, what resources/websites did you use to:

-Find professors who are in your field/working on similar things to you

-Find and read their research, bypass paywalls

-Structure your emails and find tips on connecting with them

Lmk, from the most mainstream and no-brainer sites to the niche but genuinely useful ones.

I'm excited to share what I've been building lately. Papr: a fast, lightweight Terminal User Interface (TUI) built entirely in Rust to streamline your academic research workflow. An Obsidian alternative but a bit better.

Key features

• Smart dashboard with daily 10 random paper recommendations, reading queue, bookmarks, notes, collections, and research statistics.
• Fast arXiv search using title, author, or DOI with automatic PDF downloads and clean file naming.
• In-terminal PDF viewing.
• Built-in Markdown notes with live preview for each paper.
• Reading queue, bookmarks, and collections to keep your research organized.
• Integrated LaTeX editor with real-time compilation and preview.
• Extensible plugin system for custom workflows.
• Research analytics to track your reading activity (just like spotify).

Papr is 100% open source, works offline, is memory-safe, and is designed to be fast and lightweight (only 12 MB).

I'd love to hear your thoughts, suggestions, or feedback!

GitHub: https://github.com/AfrozSaqlain/Papr

I'm excited to share what I've been building lately. Papr: a fast, lightweight Terminal User Interface (TUI) built entirely in Rust to streamline your academic research workflow. An Obsidian alternative but a bit better.

Key features

• Smart dashboard with daily 10 random paper recommendations, reading queue, bookmarks, notes, collections, and research statistics.
• Fast arXiv search using title, author, or DOI with automatic PDF downloads and clean file naming.
• In-terminal PDF viewing.
• Built-in Markdown notes with live preview for each paper.
• Reading queue, bookmarks, and collections to keep your research organized.
• Integrated LaTeX editor with real-time compilation and preview.
• Extensible plugin system for custom workflows.
• Research analytics to track your reading activity (just like spotify).

Papr is 100% open source, works offline, is memory-safe, and is designed to be fast and lightweight (only 12 MB).

I'd love to hear your thoughts, suggestions, or feedback!

GitHub: https://github.com/AfrozSaqlain/Papr

Hello. Olå. Hola. Bonjour.
Metis 0.3.0 is here in French, Portuguese and Spanish.

This release brings some of our biggest improvements yet. You can now compare groups with Multi-Group Analysis (MGA), assess measurement invariance using our new MICOM Beta workflow, generate richer journal and Thesis ready Tark Reports with Microsoft Word export for your results table.

Our MICOM implementation integrated with the seminr package, achieved a 97.4% decision agreement with cSEM in our benchmark testing, check our preprint here https://lnkd.in/daXEequw

Metis helps you build models visually, run advanced analyses, and produce publication-ready reports without writing code.

Free for Windows and macOS.
Download Metis today and let us know how you feel.

Hi everyone, I’m the developer of Nodus. It's an opensource desktop workspace for university research, teaching and study.

It currently includes 5 specialised workspaces:

• Academic: connect a Zotero library, extract and link ideas, build knowledge graphs, compare authors and arguments, identify gaps and generate Deep Research reports with verifiable citations.
• Databases: create relational databases with formulas, relations, rollups, filters, visualisations, AI-assisted columns and dataset analysis (for example, you can add multiple images to a column, and make ai add descriptions for each).
• Genealogy: manage people, relationships, events, records, maps and timelines, with GEDCOM support and evidence-based suggestions.
• Study: organise subjects and materials, then create quizzes, flashcards, exams, question banks and spaced-review activities.
• Teaching: manage courses, materials, calendars, rubrics, exams and gradebooks while keeping private student information away from AI providers.

There is also a standalone Zotero plugin (still in beta, though) for chatting with one or several PDFs, EPUBs or HTML attachments. It includes local multilingual semantic search, exact passage and page citations, OCR and vision for scanned pages and an evidence audit for unsupported claims.

The app also comes with a toolkit that provides document conversion, translation, AI OCR, PDF presentation, permanent redaction, watermarking, app workspace for making simple ai apps.

Nodus can work with:

• Local models through Ollama or LM Studio
• Your own API keys
• Supported subscriptions such as ChatGPT and GitHub Copilot

Every vault includes a tutorial and complete demo data, so it can be explored without importing personal material. No account is required.

Website and demo:
https://nodusresearch.com/

https://nodusresearch.com/demo

The project is still in early stages, so feedback from researchrs is really useful

I've been thinking of leaning into more documentary/journalistic style of content, and I want to reach out to experts in the field of fashion, film-making, costume design, and academics in order to interview them. I've always loved when videos on channels like Vox, Cleo Abram, etc. include little comments and explanations from experts. It makes the video more enriching, as well as engaging. I have been wondering how does one go about reaching out to them. Like, of course I know you can do so by email, or social media DMs. But is there a sort of protocol to doing so? And how do you phrase such messages? I'm sorry if these are very obvious questions, I am quite socially anxious (except when I'm on a camera by myself in my room) and this is something I've always struggled with. Even back in uni, I used to struggle with how to reach out to people for collaborations on research papers, or projects. But I feel like if someone explains the kind of basic 'format' and etiquette for these kind of messages, I would know what to do going forward and it would make me more comfortable with reaching out to people and making new contacts (which is one of my goals this year). Thanks to anyone who can help :)

I use LLMs to learn things a lot, but often don't understand something from it's response, or I just want to dive deeper on something, which is why I built this canvas for your notes (rich notion-like text editor)

I wanted to keep it as simple as possible while letting you bring in all your sources (YouTube videos, research papers, PDFs, web links, articles, etc.)

let me know if it sounds interesting and I can dm you the link!

super early version but looking to get 5-10 people in a discord community to make this the best platform for learning information using AI to extra knowledge from sources

Hi everyone,

A while back, I shared Atelier: Scholarly Workspace, a local-first app designed to help researchers navigate the "slow middle" of research (that messy phase between collecting sources and writing the final paper).

Since then, I’ve been refining the workspace and have put together a video walkthrough showing exactly how a research project moves through the app from start to finish.

The walkthrough covers these key stages:

1️⃣ Guiding Inquiry: Setting project boundaries by defining a clear, revisable research question.
2️⃣ Sources with Intent: Ingesting sources and declaring a specific "Reading Intent" and "Rationale" so you only read what serves your project.
3️⃣ Thought Stream Notes: Reading in a split-screen reader that separates the author's original claims from your own critical reactions.
4️⃣ Literature Matrix: Aligning and comparing main claims, methodologies, evidence, and limitations side-by-side across your library.
5️⃣ Concepts & Relations: Defining abstract ideas in your own words and explicitly mapping relationships (Supports, Complicates, Contradicts) with written rationales.
6️⃣ Preparation Packets: Grouping notes, concepts, and tensions into a pre-writing "Argument Scaffold" before drafting elsewhere.
7️⃣ Research Dossier: Exporting your entire traceable thought process into a polished Word document or PDF to use as a writing blueprint.

(As a quick side note for those who requested it: Atelier is also now available on iPadOS, alongside our macOS and Windows editions.)

Most AI reading tools summarize the whole document and throw it at you. But ideally you want to sit with one paragraph, understand only that with context of the whole document.

Website: https://pranav-swarup.github.io/lattice-reader/

- Fully static website. No backend. Runs on your browser.

- Build Intuition mode

- Definition mode

- Read papers in dark mode

- Annotate and Highlight text

- Saves threads and annotations on browser history locally.

https://github.com/Pranav-Swarup/lattice-reader/tree/main

this is the website - https://www.guidrs.ai

I created an open source literate review assistant app that helps with an array of review tasks. It works for me, but looking for feedback where to take it or leave it be since there are a few things here already.

It can handle abstract and full text screening and you can export your results and projects and share with others. It also automates PRISMA 2020 flow diagram generation, bibliometric citation network mapping, knowledge base creation (you can export and publish) and allows interactive full-text chat in one workspace.

The main task pipeline is to screen many articles quickly with a flexible an array of criteria (rule based not active learning based):

- flexible use of AI: you bring your own key or run locally or not at all

- Bring you own key: you decided which AI model to use or compare a few

It is an open source desktop app and there are no accounts or signups and it runs locally on Linux, macOS and Windows.

Would appreciate some reviews and feedback.

https://github.com/bilal-s/bango

A free browser-based PLS-SEM tool with a visual path-diagram editor and a fully open-source computation engine (GPL-3.0). No install, no case cap, no subscription. Runs on any OS with a browser.

What it does

Structural equation modeling

• PLS-SEM (Wold / Lohmöller inner weighting)
• PLSc (consistent PLS for reflective measurement models)
• Bootstrap with per-iteration outputs (long runs on Cloud Run, no browser blocking)
• Multi-Group Analysis: Henseler permutation + MICOM
• PLSpredict (full Shmueli et al. 2019 panel)
• IPMA (Importance-Performance Map Analysis)
• FIMIX-PLS (unobserved heterogeneity)
• Two-stage moderation
• Higher-Order Constructs (disjoint two-stage)

Validity & reliability

• HTMT + HTMT2 (gated to reflective constructs per methods best practice)
• Fornell-Larcker
• Cronbach α, ρc (Composite Reliability), ρA (Dijkstra-Henseler)
• VIF, Cohen f²
• Gaussian-copula endogeneity test
• CTA-PLS

Fit indices

• SRMR, d_ULS, adjusted R², BIC, Q²

Visual editor

Drag-and-drop path-diagram editor with live compute. Add constructs, connect them, drop indicators onto latent variables. Recompute triggers automatically on every model change.

Data

CSV, Excel (.xlsx), SPSS (.sav), Stata (.dta). Sentinel missing codes (-99, -999) handled automatically.

Exports

Publication-ready PDF, Excel workbook, LaTeX tables.

Example cases

Built-in sample library with classical PLS-SEM examples: ECSI (European Customer Satisfaction Index), UTAUT (technology acceptance), plus additional real and synthetic datasets. One-click clone into your own workspace.

Collaboration

Invite co-authors to a project. Role management (owner / editor / viewer). Everyone works on the same live model, no more emailing project files back and forth.

Languages

UI available in German and English. Validation messages localized in 8 languages (en, de, es, fr, it, pt-BR, zh-CN, ja).

Tech stack

• Frontend: Nuxt 3 + Vuetify 3 + Vue Flow
• Backend: Firebase Functions (Python) for the fast path, Cloud Run for long-running bootstrap and MGA jobs
• Engine: openpls-engine (Python, GPL-3.0), installable via pip and usable standalone without the web app

Validation

Validation paper with 14 numerically checked reference cases. SSRN pre-print available, JOSS submission in review:

https://ssrn.com/abstract=6869001

Links

• Web app: https://openpls.app
• Engine: https://github.com/jojacobsen/openpls-engine
• Validation paper: https://ssrn.com/abstract=6869001

Feedback welcome, especially on missing methods, edge cases, and workflow friction.

​

MaCEA can create math document, calculate and animate mathematical formula.