Summary with AI vs Typeset

Compare Summary with AI vs Typeset and see which AI Summarizer tool is better when we compare features, reviews, pricing, alternatives, upvotes, etc.

Which one is better? Summary with AI or Typeset?

When we compare Summary with AI with Typeset, which are both AI-powered summarizer tools, Typeset is the clear winner in terms of upvotes. Typeset has attracted 25 upvotes from aitools.fyi users, and Summary with AI has attracted 6 upvotes.

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Summary with AI

Summary with AI
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What is Summary with AI?

Unlock the potential to rapidly digest complex documents with Summary with AI, an innovative tool designed to transform lengthy PDFs into concise, high-quality summaries. Catering to an array of needs, whether it's devouring industry reports, navigating legal documents, or accelerating through academic research, this AI-powered solution simplifies the reading process.

With the ability to upload PDF files up to 200 MB in size and a straightforward system that delivers summaries in just three clicks, this service ensures efficiency. Summary with AI offers flexible pricing plans with no subscription required—purchase credits only when necessary, and they never expire. Get started today with 40 free credits, no credit card required, and experience firsthand how AI can revolutionize your reading workflow.

Typeset

Typeset
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What is Typeset?

Your platform to explore and explain papers. Search for 270M+ papers, understand them in simple language, and find connected papers, authors, topics.

Summary with AI Upvotes

6

Typeset Upvotes

25🏆

Summary with AI Top Features

  • Free Credits on Sign-Up: Get started with 40 free credits to summarize 40 pages, no credit card required.

  • Large File Support: Upload PDF files up to 200 MB in size for summarization.

  • Quick Download: Download summaries as PDFs with just 3 clicks.

  • Time Reduction: Read dense documents like annual reports, research papers, and books in 70% less time.

  • Flexible Pricing: No subscription required; purchase credits as needed, which don’t expire.

Typeset Top Features

No top features listed

Summary with AI Category

    Summarizer

Typeset Category

    Summarizer

Summary with AI Pricing Type

    Freemium

Typeset Pricing Type

    Free

Summary with AI Technologies Used

Next.js
Node.js
Tailwind CSS

Typeset Technologies Used

Amazon Web Services
jQuery
Bootstrap

Summary with AI Tags

Summary with AI
PDF Summarization
Academic Research
Corporate Reports
Legal Documents
AI Technology
Efficient Learning
No Subscription
Credit System
Free Trial

Typeset Tags

Content Summary
AI Whitepapers
AI Emails

Summary with AI Average Rating

No rating available

Typeset Average Rating

4.00

Summary with AI Reviews

No reviews available

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

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