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).
Uniformly Accelerated Motion (Distance-Time Law)
The distance-time law gives the distance travelled under constant acceleration; the distance grows quadratically with time.
Free · no credit card · in your study plan in 2 minutes
Formula
s = \frac{1}{2} a t^2 + v_0 tVariables & units – Uniformly Accelerated Motion (Distance-Time Law)
| Symbol | Meaning | Unit |
|---|---|---|
| s | Distance travelled | m |
| a | Constant acceleration | m/s² |
| t | Time | s |
| v₀ | Initial velocity | m/s |
Derivation & background – Uniformly Accelerated Motion (Distance-Time Law)
Around 1604, Galileo Galilei found with inclined-plane experiments that the distance grows quadratically with time. Mathematically s(t) is the integral of the velocity v(t) = a·t + v₀. Without the time, the time-free equation v² = v₀² + 2·a·s helps. Free fall is the special case a = g.
Exam blueprint
Validity range
Holds only for constant acceleration along a straight line. For varying acceleration (e.g. with air resistance) integration is needed; pure changes of direction are described by circular motion.
Derivation steps
The distance is the area under the v-t diagram, a trapezoid for constant acceleration.
- 1Velocity: v(t) = a·t + v₀ (a straight line in the v-t diagram).
- 2Area under the line up to t: rectangle v₀·t plus triangle ½·a·t², together s = ½at² + v₀t.
Rearrangements
Time from distance (for v₀ = 0)
Only for a start from rest; otherwise solve the quadratic equation.
Acceleration from distance and time (v₀ = 0)
This is how driving tests and inclined-plane experiments are evaluated.
Time-free equation
Links speed and distance when the time is not given.
Task variant
A car accelerates from rest over 100 m at a = 2 m/s². How long does it take?
t = √(2s/a) = √(200/2) = √100 = 10 s.
A sled covers 45 m from rest in 3 s. Find a.
a = 2s/t² = 2·45/9 = 10 m/s².
Common mistakes
Forgetting the factor ½ or not squaring t.
s grows quadratically: double the time means four times the distance.
Dropping the v₀t term although an initial velocity is given.
Only for a start from rest does s = ½at² hold.
Mixing speeds in km/h with distances in metres.
Divide km/h by 3.6 before substituting.
Exam context
- Standard in acceleration, braking and overtaking problems, often combined with v = a·t + v₀ or the time-free equation.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Kinematics of straight-line motion
Distance law, velocity law and force law interlock.
Worked example
A car starts from rest (v₀ = 0) with a = 2 m/s². After t = 5 s: s = ½ × 2 × 5² = 25 m.
Applications
Acceleration and braking distance calculations, accident analysis, runway design, driver assistance systems
Quanta exam set
Curated exam set for "Uniformly Accelerated Motion (Distance-Time Law)":
Question (front)
Which formula describes Uniformly Accelerated Motion (Distance-Time Law)?
Answer in your set
Question (front)
How do you rearrange s = ½at² + v₀t for Time from distance (for v₀ = 0)?
Answer in your set
Question (front)
Which common mistake happens with Uniformly Accelerated Motion (Distance-Time 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 Uniformly Accelerated Motion (Distance-Time Law)
How do you calculate the distance under uniform acceleration?+
Insert acceleration, time and initial velocity into s = ½at² + v₀t. Example: a car starts from rest (v₀ = 0) with a = 2 m/s²; after t = 5 s, s = ½ × 2 × 25 = 25 m. If the body already moves at v₀, the term v₀t is added: with v₀ = 10 m/s it would be 25 + 50 = 75 m. Units matter: time in seconds, acceleration in m/s², velocity in m/s; divide km/h by 3.6 first. The quadratic term means the body covers more distance in the second second than in the first, so the distance-time diagram is a parabola.
When may you use s = ½at² without the v₀t term?+
Only when the body starts from rest, meaning v₀ = 0. That is the case when pulling away at traffic lights, in free fall from your hand, or at a rocket launch. As soon as an initial velocity exists, a braking car or a train speeding up, you must use the full form s = ½at² + v₀t, otherwise the entire uniform-motion part is missing. When braking, additionally insert a as negative: a car with v₀ = 20 m/s and a = −5 m/s² stops after t = 4 s and covers s = ½·(−5)·16 + 20·4 = −40 + 80 = 40 m of braking distance.
What do you do when the time is not given?+
Then the time-free equation v² = v₀² + 2·a·s helps, linking speed and distance directly. It arises by inserting t = (v−v₀)/a from the velocity law into the distance law. Braking example: a car travels at v₀ = 27.8 m/s (100 km/h) and brakes at a = −7 m/s² to a standstill (v = 0). Then 0 = 27.8² + 2·(−7)·s, so s = 772.8/14 ≈ 55 m. This equation also explains why braking distance grows quadratically with speed: twice the speed, four times the braking distance. In exams it is the standard route whenever speeds at the end of a stretch are asked for.
How are the distance-time and velocity-time laws related?+
They describe the same motion on two levels: v(t) = a·t + v₀ is the derivative of s(t) = ½at² + v₀t. Intuitively, the distance is the area under the v-t diagram. For constant acceleration this area is a trapezoid made of a rectangle (v₀·t) and a triangle (½·a·t·t = ½at²), exactly the two terms of the distance law. Conversely, the slope of the s-t parabola at any moment is the instantaneous velocity, and the slope of the v-t line is the acceleration. If you master this diagram logic, you can solve many problems graphically without rearranging a single formula, often the fastest route in exams.
Why does the distance quadruple when the time doubles?+
Starting from rest, the time appears squared: s = ½at². Substituting 2t gives ½a·(2t)² = ½a·4t², four times as much. Physically this is because the body not only travels longer but is also faster at the end: it accumulates far more distance in the second half of the time than in the first. Concretely, a uniformly accelerating body covers distances in the ratio 1 : 3 : 5 : 7 in successive equal time intervals (odd numbers); Galileo already measured this pattern on his inclined plane. For exams this is a quick sanity check: ratio problems can often be solved via the square law without any number crunching.
Retain Uniformly Accelerated Motion (Distance-Time Law) for exams
Create a curated FSRS exam set for s = ½at² + v₀t: 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 Uniformly Accelerated Motion (Distance-Time Law)?
Here is how to work through a typical Uniformly Accelerated Motion (Distance-Time Law) (s = ½at² + v₀t) task step by step:
- 1
Task
A car accelerates from rest over 100 m at a = 2 m/s². How long does it take?
Solution path
t = √(2s/a) = √(200/2) = √100 = 10 s.
- 2
Task
A sled covers 45 m from rest in 3 s. Find a.
Solution path
a = 2s/t² = 2·45/9 = 10 m/s².
s = ½at² + v₀t · 10 cards ready
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