Recall vs SciSpace (formerly Typeset)

Explore the showdown between Recall vs SciSpace (formerly Typeset) and find out which AI Summarizer tool wins. We analyze upvotes, features, reviews, pricing, alternatives, and more.

In a face-off between Recall and SciSpace (formerly Typeset), which one takes the crown?

When we contrast Recall with SciSpace (formerly Typeset), both of which are exceptional AI-operated summarizer tools, and place them side by side, we can spot several crucial similarities and divergences. The upvote count favors SciSpace (formerly Typeset), making it the clear winner. SciSpace (formerly Typeset) has 24 upvotes, and Recall has 10 upvotes.

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Recall

Recall

What is Recall?

Recall is a personal AI knowledge base and summarizer that saves articles, YouTube videos, podcasts, PDFs, and notes in one place. It turns each saved item into a summarized card, tags it automatically, and links related ideas in a knowledge graph you can browse or chat with. You pick the AI model for questions, including ChatGPT, Claude, or Gemini, and answers can draw on your saved library, the open web, or both.

Where NotebookLM locks you into one notebook per project and Obsidian expects you to build every link by hand, Recall treats lifelong learning as one growing library. It captures web content in a click, summarizes it, connects it without manual filing, and adds spaced repetition quizzes so saved material actually sticks. The trade-off is less team workspace structure than Notion and less local-first control than Obsidian.

Recall fits researchers, founders, students, and heavy readers who save more than they can reread. Browser extensions for Chrome and Firefox, plus iOS and Android apps, make it easy to clip on the go and pick up in the web app later.

SciSpace (formerly Typeset)

SciSpace (formerly Typeset)

What is SciSpace (formerly Typeset)?

SciSpace, formerly Typeset, is a research workspace where you search a large academic paper index, chat with PDFs, and draft literature reviews with cited sources. The typeset.io URL now lands on scispace.com, which bundles an AI agent, literature review search, paraphraser, citation generator, data extraction, and AI detection in one interface.

General AI chat tools answer from the open web. SciSpace centers the workflow on papers: you can run systematic reviews across 280M+ indexed works, highlight passages for explanations, and move from reading into AI Writer drafts with references. Biomedical Agent, enterprise recruiting, and Chrome or mobile apps extend the same stack for labs and R&D teams.

Graduate students, PhD researchers, and pharma or biotech analysts use SciSpace to screen literature faster, unpack dense PDFs, and produce summaries or manuscripts with traceable citations. The platform reports adoption by 9.6 million researchers and lists SOC 2 compliance on its site.

Recall Upvotes

10

SciSpace (formerly Typeset) Upvotes

24🏆

Recall Top Features

  • Free tier includes 10 AI summaries per month plus unlimited saves of articles, videos, and PDFs

  • Plus plan at $10 per month (billed yearly) unlocks unlimited AI summaries and automatic smart tags

  • Chat across your saved library, the open web, or both using ChatGPT, Claude, Gemini, or other models

  • Spaced repetition quizzes generated from saved cards to reinforce long-term memory

  • Chrome, Firefox, iOS, and Android apps plus API and MCP access to connect external AI tools

  • Augmented browsing resurfaces related saved cards while you read new pages on the web

SciSpace (formerly Typeset) Top Features

  • Search and review literature across 280M+ indexed research papers

  • Chat with PDF highlights for plain-language explanations of dense sections

  • SciSpace Agent plus Biomedical Agent for guided research tasks

  • Built-in paraphraser, citation generator, AI detector, and data extraction tools

  • AI Writer and templates for drafting papers with cited sources

  • Chrome extension and mobile app for reading outside the browser tab

  • Premium plans with a 24-hour money-back guarantee on subscriptions

Recall Category

    Summarizer

SciSpace (formerly Typeset) Category

    Summarizer

Recall Pricing Type

    Freemium

SciSpace (formerly Typeset) Pricing Type

    Freemium

Recall Technologies Used

Next.js
Material UI
Amazon CloudFront
Font Awesome
Discord

SciSpace (formerly Typeset) Technologies Used

Next.js
Google Analytics
Google Tag Manager
Facebook Pixel
Python
Ruby
Discord
Webpack
Tailwind CSS

Recall Tags

Knowledge Base
YouTube Summarizer
Spaced Repetition
Knowledge Graph
Podcast Summarizer
Read Later
Personal Notes
Online Content

SciSpace (formerly Typeset) Tags

Paper Discovery
Chat with PDF
Research Agent
Citation Tools
Academic Writing
Paper Search
Biomedical Research
Content Summary

Recall Average Rating

No rating available

SciSpace (formerly Typeset) Average Rating

4.00

Recall Reviews

No reviews available

SciSpace (formerly Typeset) Reviews

Sara Sara
The simulation model validated experimental J-V and external quantum efficiency (EQE) to demonstrate an improvement in perovskite (PSK) solar cell (PSC) efficiency. The effect of interface properties at the electron transport layer (ETL)/PSK and PSK/hole transport layer (HTL) was investigated using the Solar Cell Capacitance Simulator (SCAPS). The interfaces between ETL, PSK, and HTL were identified as critical factors in determining high open-circuit voltage (Voc) and FF. In this study, the impact of two types of interfaces, ETL/PSK and PSK/HTL, were investigated. Lowering the defect density at both interfaces to 102 cm−2 reduced interface recombination and increased Voc and FF.The absorber layer defect density and n/i interface of perovskite solar cells were investigated using the Solar Cell Capacitance Simulator-1D (SCAPS-1D) at various cell thicknesses. The planar p-i-n structure was defined as PEDOT:PSS/Perovskite/CdS, and its performance was calculated. With a defect density of <1014 cm−3 and an absorber layer thickness of >400 nm, power conversion efficiency can exceed 25%. The study assumed a 0.6 eV Gaussian defect energy level beneath the perovskite's conduction band, which has a characteristic energy of 0.1 eV. These conditions produced the same result on the n/i interface. These findings place constraints on numerical simulations of the correlation between defect mechanism and performance
By Rishit