What sets Quanta apart from every other flashcard app? The 5 monopoly USPs

Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:

(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).

(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).

(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.

(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.

(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.

In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis

Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.

Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.

Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.

Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.

The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.

Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).

Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.

Which degree programs and subjects is Quanta built for?

Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.

Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).

Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.

Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.

Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.

High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.

The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.

Quanta vs. the competition, a technical comparison matrix (as of May 2026)

FeatureQuantaAnkiQuizletRemNoteKnowtChatGPT
AlgorithmFSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD)SM-2 1987 (log-loss 0.45)Proprietary (unpublished)SM-2, with FSRS availableNo published algorithmNo scheduling
Source transparency (anti-hallucination)Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per cardNot availableNot availableNot availableNot availablePost-hoc citations without verification
Bloom taxonomy constraintLevels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural levelNo controlNo controlNo controlNo controlNo control
Distractor validation (MC)Every incorrect answer checked for plausibility (Haladyna and Downing 1989)Not availableNot availableNot availableNot availableNot available
AI tutor methodologySocratic method: counter-questions only, no direct answers (Chi et al. 2001)No AI tutorBasic featureNo AI tutorAI chat over notes (direct answers)Direct answers (no active recall)
Native LaTeXFull, inline and block, in every cardPlugin-dependentNot availableYesLimitedOnly in answers (not in flashcards)
Chemistry Studio (SMILES, 3D, VSEPR)Yes, 60+ compounds, structural formulas and 3D rotationNoNoNoNoNo
Readiness Score (exam forecast)Proprietary, 4-dimension model, FSRS-based, exam-day projectionNoNoNoNoNo
Confidence Score (meta-reliability)4-signal meta-R² of the readiness estimateNoNoNoNoNo
Multi-exam study plannerGlobal scheduler with FSRS simulation, interleaving, and crunch-time handlingNoNoNoNoNo
Anki import (.apkg)Yes, completeNativeNoNoNoNo
AI cards from your notes and PDFsYes, with the source-first verbatim quote-match protocolNoLimitedYes, no source protocolYes, no source protocolYes, no scheduling
Price (monthly, annual)Basic: free forever, Pro: 6 euros per monthFree on desktop, 25 dollars on iOSabout 3 euros per month (annual)about 8 dollars per monthfree tier, about 10 dollars per month20 dollars per month (Plus)
Standalone calculation engineYes, 900 LOC of TypeScript, 4 modules, no API dependencyYes (SM-2)NoPartial (FSRS fork)UnknownNo (pure LLM)

Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).

Biology · Ecology

Shannon Index (Biodiversity)

The Shannon index (Shannon-Wiener index) measures the diversity of a community. It accounts for both species richness and the evenness with which individuals are distributed among the species.

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Formula

H' = −Σ pᵢ·ln(pᵢ)
LaTeX: H' = -\sum_{i=1}^{S} p_i \cdot \ln(p_i)
H' and pᵢ are dimensionless; pᵢ = nᵢ/N is a proportion (0…1)

Variables & units – Shannon Index (Biodiversity)

SymbolMeaningUnit
H'Shannon diversity index (diversity of the community)– (dimensionslos)
pᵢrelative frequency of species i, that is nᵢ/N– (0…1)
nᵢnumber of individuals of species iAnzahl
Ntotal number of all individualsAnzahl
Snumber of species (species richness)Anzahl

Derivation & background – Shannon Index (Biodiversity)

Claude Shannon developed the measure in 1948 in information theory; ecology adopted it as a diversity measure. Usually the natural logarithm ln is used (unit "nats"), more rarely log₂ or log₁₀. H' is zero for a single species and becomes maximal (Hmax = ln S) when all species are equally frequent. From H' and Hmax you compute the evenness E = H'/ln S as a measure of even distribution (0…1).

Exam blueprint

Validity range

Applies to diversity comparisons of communities whose species and individual counts are fully recorded or representatively sampled.

Derivation steps

Each species contributes its information content −ln(pᵢ), weighted by its frequency pᵢ; the sum measures the uncertainty about which species a random individual belongs to.

  1. 1Compute the relative frequencies pᵢ = nᵢ/N of each species.
  2. 2Sum pᵢ·ln(pᵢ) over all species and reverse the sign.

Rearrangements

Maximum diversity

H'_{max} = \ln S

Reached when all S species are equally frequent.

Evenness

E = \frac{H'}{\ln S}

Lies between 0 and 1; 1 means perfect even distribution.

Task variant

Three species with 10, 6, 4 individuals (N = 20). Compute H' and the evenness.

p = 0.5; 0.3; 0.2. H' = −(0.5·ln0.5 + 0.3·ln0.3 + 0.2·ln0.2) = −(−0.347 − 0.361 − 0.322) = 1.030. Hmax = ln3 = 1.099, so E = 1.030/1.099 = 0.94.

Four species with 25 individuals each (N = 100). What is H'?

All p = 0.25. H' = −4·(0.25·ln0.25) = −(ln0.25) = ln4 = 1.386 = Hmax, i.e. maximum diversity with evenness 1.

Common mistakes

Using absolute counts instead of proportions pᵢ.

The relative frequencies pᵢ = nᵢ/N enter the formula, not the raw numbers.

Forgetting the minus sign.

Since ln(pᵢ) is negative for pᵢ < 1, the leading minus makes H' positive.

Comparing H' values with different logarithms.

Only values with the same base (usually ln) are comparable.

Interpreting H' alone as evenness.

H' also depends on the number of species; the evenness E = H'/ln S separates both.

Exam context

  • Typical in ecology: comparing the species diversity of two sites and interpreting the evenness.

These mistakes cost points in real exams. The set drills them until they stick.

Formula cluster

Biodiversity measures

Connects species richness, probability and information theory.

Worked example

Community with 3 species, individuals 10, 6, 4 → N = 20, so p = 0.5; 0.3; 0.2. H' = −(0.5·ln0.5 + 0.3·ln0.3 + 0.2·ln0.2) = −(0.5·(−0.693) + 0.3·(−1.204) + 0.2·(−1.609)) = −(−0.347 − 0.361 − 0.322) = 1.030. The maximum would be Hmax = ln3 = 1.099, so the evenness is E = 1.030/1.099 = 0.94.

Applications

Ecology (comparing species diversity of habitats), environmental monitoring, evaluating restoration and disturbances, water quality

Quanta exam set

Curated exam set for "Shannon Index (Biodiversity)":

Question (front)

Which formula describes Shannon Index (Biodiversity)?

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Question (front)

How do you rearrange H' = −Σ pᵢ·ln(pᵢ) for Maximum diversity?

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Question (front)

Which common mistake happens with Shannon Index (Biodiversity)?

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Scientific sources

Common notations & search queries

H' = -sum p_i ln(p_i)H=-Σpi·ln(pi)Shannon-IndexShannon-Wiener-IndexBiodiversität FormelDiversitätsindexshannon diversity indexEvenness berechnenArtenvielfalt Index

Related formulas

More Biology formulas

Frequently asked questions about Shannon Index (Biodiversity)

How do you calculate the Shannon index?+

First you determine the relative frequency pᵢ of each species, that is the number of individuals of the species divided by the total number of all individuals. Then you compute the product pᵢ·ln(pᵢ) for each species, sum these values over all species and finally reverse the sign. The leading minus is needed because the logarithm of numbers below 1 is negative. Example: three species with 10, 6 and 4 individuals give the proportions 0.5, 0.3 and 0.2 at N = 20. Then H' = −(0.5·ln0.5 + 0.3·ln0.3 + 0.2·ln0.2) = −(−0.347 − 0.361 − 0.322) = 1.03. The larger H', the more diverse the community. It is important to always use the same logarithm, usually the natural ln.

What does a high Shannon index mean?+

A high Shannon index indicates great diversity. It is driven by two factors: species richness, that is how many species occur, and evenness, that is how uniformly the individuals are distributed among the species. The index is maximal exactly when many species occur and all are roughly equally frequent. If, by contrast, a single species dominates strongly, the index stays low even when many species are present. A low value therefore points to a species-poor community or one dominated by few species. In practice you compare the index between sites or over time to detect changes in species diversity, for example after a disturbance or restoration. Absolute values are only meaningfully comparable with the same logarithm base and similar sampling.

What is evenness and how does it relate to the Shannon index?+

Evenness measures how uniformly the individuals are distributed among the species, independent of how many species there are. It follows from the Shannon index divided by its maximum: E = H'/ln S, where S is the number of species. The maximum Hmax = ln S is reached when all species are equally frequent. Evenness lies between 0 and 1: a value near 1 means all species occur similarly often, a value near 0 that one species strongly dominates. Example: three species with proportions 0.5, 0.3 and 0.2 have H' = 1.03 and Hmax = ln3 = 1.099, so E = 0.94, a fairly even distribution. Evenness thus separates the even-distribution aspect from pure species richness, which H' alone mixes together.

Which logarithm do you use for the Shannon index?+

In ecology the natural logarithm ln is most common; the unit is then called "nats". Some sources use the base-2 logarithm log₂, giving units in "bits", or the base-10 logarithm log₁₀. The choice only changes the numerical value, not the statement: a conversion factor transforms the values into each other, since log₂(x) = ln(x)/ln2. What matters is that you always use the same base within a comparison, otherwise the values are not comparable. The maximum also depends on the base: with ln, Hmax = ln S, with log₂ it would be log₂ S. For evenness the base cancels, because you divide H' by Hmax with the same base, so evenness always gives the same value between 0 and 1. Therefore check before every comparison which base was used.

What is the difference between the Shannon index and the Simpson index?+

Both measure diversity but weight differently. The Shannon index H' = −Σ pᵢ·ln(pᵢ) comes from information theory and reacts comparatively sensitively to rare species, because their information content −ln(pᵢ) is large. The Simpson index D = Σ pᵢ² is the probability of drawing the same species twice and emphasises common, dominant species more strongly; rare species barely influence it. As a diversity measure one usually uses 1 − D or the reciprocal 1/D with the Simpson index, which increase with diversity, while D itself measures dominance. In practice both complement each other: the Shannon index suits cases where rare species matter, the Simpson index where the dominance structure is decisive. Often one computes both to get a complete picture of the diversity.

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Create a curated FSRS exam set for H' = −Σ pᵢ·ln(pᵢ): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.

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How do you calculate with Shannon Index (Biodiversity)?

Here is how to work through a typical Shannon Index (Biodiversity) (H' = −Σ pᵢ·ln(pᵢ)) task step by step:

  1. 1

    Task

    Three species with 10, 6, 4 individuals (N = 20). Compute H' and the evenness.

    Solution path

    p = 0.5; 0.3; 0.2. H' = −(0.5·ln0.5 + 0.3·ln0.3 + 0.2·ln0.2) = −(−0.347 − 0.361 − 0.322) = 1.030. Hmax = ln3 = 1.099, so E = 1.030/1.099 = 0.94.

  2. 2

    Task

    Four species with 25 individuals each (N = 100). What is H'?

    Solution path

    All p = 0.25. H' = −4·(0.25·ln0.25) = −(ln0.25) = ln4 = 1.386 = Hmax, i.e. maximum diversity with evenness 1.

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