اكتشفت أداة رهيبة لطلاب الدكتوراه 👇
تقدر تقارن 10 أوراق علمية خلال ثواني!
ترفع الملفات، وهي ترتبها في جدول وتعمل تحليل مقارن شامل، وحتى تقدر تقارن كل قسم مثل النتائج 🔥
وفر وقتك واشتغل بذكاء:
👇👇
https://t.co/FvSbaFkfI0
What I hate about copyright and trademark law is that you're essentially forced by the law to send legal letters, takedown requests and eventually sue
If you don't, whatever rights you own are invalidated in court whenever you do really need to defend them by the judge because the infringer can prove "you didn't defend your rights earlier either"
Which absolutely does not work in 2026 because by sueing someone smaller than you in the internet era they will absolutely leverage that to the maximum possible for marketing purposes
"The big guy is sueing us underdogs"
Which I would do too if I was the underdog
And now everyone knows about your new competitor while before they didn't!
Aka Streissand effect
Need to work out the balance between speed, quality, and cost. Let me know if you have any tips to spare
My development bills for this last month were kind of crazy and I need a new office chair 😅
This is what it looks like to plan, build, test, and ship 10,000+ LOC in <2 hours to thousands of users using @moararesearch
The goal is 30k-40k LOC per day to keep up with @garrytan
Velocity isn’t the constraint
Quality and cost are
A few things I’m experimenting with ↓
3. Collapsing non-development work into this pipeline
Things like feature announcement emails being sent to our users that are generated during planning, revised as build occurs, reviewed during testing, and automatically sent once a PR into production is finished
@moararesearch@garrytan I’m leaning heavily toward the second
But it introduces a new problem
All of those agents rely on instruction files
And those go stale almost immediately as the codebase evolves
@maxsbob21@heyrimsha There's a lot of services for discovery, yes. Most research tools stop at helping you collect papers.
The hard part is everything that comes after: screening, cataloguing findings from full text, synthesizing, writing, and doing it all in a reproducible, organized way.
@MolBioMike@heyrimsha For discovery, we primarily build around results from Google Scholar, though you also have the option to search SS if you want. Candidly, we are more focused on everything after discovery: screening, cataloguing findings from full text, and synthesizing.
🚨 BREAKING: Someone built an AI that replaces 6 months of literature review with a single workspace.
It's called Moara.
You dump your research papers in. The AI organizes, analyzes, and synthesizes everything across hundreds of millions of academic sources.
You come back and there's a structured literature review waiting. Not a chatbot answer. Not a vague summary. The actual research synthesis.
A full research workspace that thinks like a scientist.
Works for medicine, engineering, social sciences, law, business, humanities — every discipline.
Here's what it does on its own:
→ Searches hundreds of millions of papers across PubMed, arXiv, Google Scholar, Semantic Scholar, ClinicalTrials .gov, Cochrane, and more in one shot
→ Screens and tags results automatically, pulling out methodologies, findings, and contributions from every paper
→ Lets you chat with your entire library at once, find themes, spot gaps, and compare findings across dozens of papers simultaneously
→ Integrates with Zotero, LibKey, RIS, and BibTeX so your existing research workflow doesn't break
→ Traces every AI insight back to its original source so nothing is hallucinated and everything is verifiable
→ Supports full team collaboration so entire research groups can screen and synthesize together
Try it here:
https://t.co/3n1sqOFU0x