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)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-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 available | No published algorithm | No 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 card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (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).
Kepler Third Law
The squares of the orbital periods behave like the cubes of the semi-major axes; the quotient T²/a³ is the same for all bodies orbiting the same central body.
Free · no credit card · in your study plan in 2 minutes
Formula
\frac{T^2}{a^3} = \frac{4\pi^2}{G \cdot M}Variables & units – Kepler Third Law
| Symbol | Meaning | Unit |
|---|---|---|
| T | Orbital period of the celestial body | s |
| a | Semi-major axis of the orbit | m |
| G | Gravitational constant (6.674×10⁻¹¹) | N·m²/kg² |
| M | Mass of the central body | kg |
Derivation & background – Kepler Third Law
Johannes Kepler found the law empirically in 1618 from Tycho Brahe observational data. Newton later derived it from his law of gravitation: for circular orbits, gravitational force = centripetal force gives G·M·m/r² = m·ω²·r and thus T²/r³ = 4π²/(G·M). Practically important: measuring T and a of a satellite or moon yields the mass of the central body; this is how solar and planetary masses were determined.
Exam blueprint
Validity range
Applies to orbits around the same central body when its mass M far exceeds that of the satellite. a is the semi-major axis of the ellipse; for circular orbits a is the orbital radius.
Derivation steps
Gravity provides exactly the centripetal force of the circular orbit.
- 1Force balance: G·M·m/r² = m·ω²·r with ω = 2π/T.
- 2Solving gives T² = 4π²·r³/(G·M), hence T²/r³ = 4π²/(G·M) = constant.
Rearrangements
Mass of the central body
This is how solar and planetary masses are determined from orbital data.
Orbital period
Larger orbits mean disproportionately longer periods.
Semi-major axis
Yields, for example, the altitude of geostationary satellites.
Task variant
Jupiter needs 11.86 years per solar orbit. What is its semi-major axis in AU?
In Earth-orbit units a³ = T²: a³ = 11.86² = 140.7, so a = ∛140.7 ≈ 5.2 AU.
The Moon orbits Earth in 27.32 days at a = 3.844×10⁸ m. Determine the Earth mass.
T = 2.36×10⁶ s. M = 4π²a³/(G·T²) = 39.48 × 5.68×10²⁵/(6.674×10⁻¹¹ × 5.57×10¹²) ≈ 6.0×10²⁴ kg.
Common mistakes
Using a satellite altitude above ground as the orbital radius.
Add the Earth radius (6,371 km) to the altitude: r = R_E + h.
Substituting periods in days or years into the SI form.
The form with G and M needs T in seconds; AU-year units only for pure ratios in the solar system.
Comparing orbits around different central bodies.
T²/a³ is constant only for the same central body; the constant contains its mass.
Exam context
- Typical: computing the geostationary orbit altitude, determining central masses from moon or satellite data and checking planetary data via the ratio T²/a³.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Gravity and orbits
Follows from the law of gravitation plus circular motion, the tool of celestial mechanics.
Worked example
Mars: a = 1.524 AU. In Earth-orbit units T = √(a³) = √(1.524³) = √3.54 ≈ 1.88 years. Geostationary satellites (T = 1 sidereal day) orbit at a ≈ 42,160 km.
Applications
Satellite orbits (GPS, geostationary), mass determination of stars and planets, exoplanet detection, spaceflight mission planning
Quanta exam set
Curated exam set for "Kepler Third Law":
Question (front)
Which formula describes Kepler Third Law?
Answer in your set
Question (front)
How do you rearrange T²/a³ = 4π²/(G·M) for Mass of the central body?
Answer in your set
Question (front)
Which common mistake happens with Kepler Third Law?
Answer in your set
+ 7 more cards: units, variables, derivation, example, exam task
These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Physics formulas
Frequently asked questions about Kepler Third Law
How do you apply Kepler third law in practice?+
There are two calculation routes. Within the solar system you use the pure ratio: in the units AU and years, T² = a³ holds for the Sun, because the Earth orbit sets both constants to 1. Mars with a = 1.524 AU therefore has T = √(1.524³) ≈ 1.88 years. For satellites or other central bodies you need the SI form T² = 4π²·a³/(G·M) with T in seconds, a in metres and M in kilograms. Choose the route by the question: ratios between planets work without G and M, absolute quantities such as a satellite altitude demand the full formula.
How do you calculate the altitude of a geostationary satellite?+
A geostationary satellite appears fixed above one point of the equator, so its period is one sidereal day T = 86,164 s. Rearrange the law for the semi-major axis: a³ = G·M·T²/(4π²). With G·M_Earth = 3.986×10¹⁴ m³/s² you get a³ = 3.986×10¹⁴ × 7.42×10⁹/39.48 ≈ 7.50×10²², so a ≈ 42,160 km from the Earth centre. Subtracting the Earth radius of 6,371 km leaves an orbital altitude of about 35,790 km above the equator. Television and weather satellites sit there. The most common mistakes are not subtracting the Earth radius or using the 24-hour solar day instead of the sidereal day.
How do you determine the mass of a celestial body with Kepler 3?+
Rearrange the SI form for the central mass: M = 4π²·a³/(G·T²). You only need the orbital data of one satellite. Example, Earth mass from the lunar orbit: a = 3.844×10⁸ m and T = 27.32 days = 2.36×10⁶ s give M = 39.48 × 5.68×10²⁵/(6.674×10⁻¹¹ × 5.57×10¹²) ≈ 6.0×10²⁴ kg, in good agreement with the literature value 5.97×10²⁴ kg. The solar mass (from the Earth orbit), the Jupiter mass (from the Galilean moons) and the masses of exoplanet systems were determined by the same pattern. Important: the mass of the small satellite cancels; you always obtain the mass of the central body.
Why is T²/a³ the same for all planets?+
Because the constant contains only properties of the central body. Newton derivation shows it: the gravitational force G·M·m/r² provides the centripetal force m·ω²·r of the orbit. The planet mass m cancels, and with ω = 2π/T what remains is T²/r³ = 4π²/(G·M). The right side holds only natural constants and the solar mass M, so Mercury through Neptune yield the same numerical value although their periods range from 88 days to 165 years. Exactly this told Newton that his theory of gravity was right: it reproduces Kepler empirically found law. Around another central body, such as Earth or Jupiter, the same structure holds with a different M and therefore a different constant.
What is the semi-major axis of an elliptical orbit?+
According to Kepler first law, planetary orbits are ellipses with the Sun at one focus. The semi-major axis a is half the length of the longest axis of the ellipse and at the same time the mean of the smallest and largest solar distances: a = (r_perihelion + r_aphelion)/2. Exactly this a enters the third law, not the current distance, which changes constantly. For the nearly circular Earth orbit a is practically the orbital radius (1 AU = 1.496×10¹¹ m). Remarkably, the period depends only on a, not on the eccentricity. A comet with the same semi-major axis as a planet takes exactly as long per orbit, no matter how elongated its path is.
Retain Kepler Third Law for exams
Create a curated FSRS exam set for T²/a³ = 4π²/(G·M): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
Free · curated formula set · LaTeX · FSRS spaced repetition
How do you calculate with Kepler Third Law?
Here is how to work through a typical Kepler Third Law (T²/a³ = 4π²/(G·M)) task step by step:
- 1
Task
Jupiter needs 11.86 years per solar orbit. What is its semi-major axis in AU?
Solution path
In Earth-orbit units a³ = T²: a³ = 11.86² = 140.7, so a = ∛140.7 ≈ 5.2 AU.
- 2
Task
The Moon orbits Earth in 27.32 days at a = 3.844×10⁸ m. Determine the Earth mass.
Solution path
T = 2.36×10⁶ s. M = 4π²a³/(G·T²) = 39.48 × 5.68×10²⁵/(6.674×10⁻¹¹ × 5.57×10¹²) ≈ 6.0×10²⁴ kg.
T²/a³ = 4π²/(G·M) · 10 cards ready
Study as an exam set