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).
Thin Lens Equation
The lens equation links focal length, object distance and image distance of a thin lens, the fundamental equation of optical imaging.
Free · no credit card · in your study plan in 2 minutes
Formula
\frac{1}{f} = \frac{1}{g} + \frac{1}{b}Variables & units – Thin Lens Equation
| Symbol | Meaning | Unit |
|---|---|---|
| f | Focal length of the lens | m oder cm |
| g | Object distance (object to lens) | m oder cm |
| b | Image distance (lens to image) | m oder cm |
Derivation & background – Thin Lens Equation
The equation follows geometrically from similar triangles for the parallel, central and focal rays. Sign convention (thin lens): g > 0 for real objects, b > 0 for real images behind the lens, b < 0 for virtual images (magnifying glass: g < f), f > 0 for converging lenses, f < 0 for diverging lenses. The magnification is B/G = b/g.
Exam blueprint
Validity range
Holds for thin lenses and paraxial rays. Sign convention: g > 0 for real objects, b > 0 for real images, b < 0 for virtual images, f < 0 for diverging lenses.
Derivation steps
From similar triangles for the parallel and focal rays at the lens centre.
- 1Similar triangles give B/G = b/g and B/G = (b−f)/f.
- 2Equating and rearranging leads to 1/f = 1/g + 1/b.
Rearrangements
Image distance from focal length and object distance
For g < f, b becomes negative; the image is virtual (magnifying glass).
Focal length from g and b
This is how the focal length is determined experimentally (Bessel method as an alternative).
Magnification
Image size to object size behaves like image distance to object distance.
Task variant
Converging lens f = 10 cm, object at g = 15 cm. Where does the image form?
1/b = 1/10 − 1/15 = 1/30 → b = 30 cm, real, inverted, magnified (b/g = 2).
Object at g = 5 cm in front of a lens with f = 10 cm. What results?
1/b = 1/10 − 1/5 = −1/10 → b = −10 cm: a virtual, upright, magnified image, the magnifying glass.
Common mistakes
Forgetting the reciprocal at the end and reporting 1/b as b.
First compute 1/b, then invert.
Ignoring the sign convention.
b < 0 means a virtual image, f < 0 a diverging lens; the signs carry the physics.
Substituting g and b in different units.
Use the same unit (cm or m) for all distances.
Expecting an image at g = f.
For g = f the image lies at infinity; the rays emerge parallel.
Exam context
- Typical: ray construction plus calculation, case analysis g > 2f, f < g < 2f, g < f, and application to the eye and camera.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Geometrical optics
Refraction explains the lens, the lens equation its imaging.
Worked example
Converging lens f = 10 cm, object at g = 30 cm: 1/b = 1/10 − 1/30 = 2/30, so b = 15 cm, a real, inverted image.
Applications
Camera and lens design, fitting spectacles, microscope and telescope, projectors, the eye (accommodation)
Quanta exam set
Curated exam set for "Thin Lens Equation":
Question (front)
Which formula describes Thin Lens Equation?
Answer in your set
Question (front)
How do you rearrange 1/f = 1/g + 1/b for Image distance from focal length and object distance?
Answer in your set
Question (front)
Which common mistake happens with Thin Lens Equation?
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 Thin Lens Equation
How do you calculate with the lens equation 1/f = 1/g + 1/b?+
Solve for the required reciprocal and only take the reciprocal at the end. Example: converging lens with f = 10 cm, object at g = 30 cm. Then 1/b = 1/f − 1/g = 1/10 − 1/30 = 3/30 − 1/30 = 2/30 = 1/15, so b = 15 cm, where a real, inverted image forms. It is safest to work with fractions over a common denominator instead of rounded decimals. All three distances must be in the same unit (all cm or all m). The classic slip: computing 1/b = 0.0667 and writing it down as b, forgetting the reciprocal.
What sign convention applies to the lens equation?+
In the school convention for thin lenses: the object distance g is positive when the object stands really in front of the lens (the normal case). The image distance b is positive for real images behind the lens, which can be caught on a screen. A negative b means a virtual image on the object side: it appears when looking through the lens, as with a magnifying glass. The focal length f is positive for converging lenses and negative for diverging lenses, which fundamentally produce only virtual, reduced images. If you carry the signs consistently, the image type comes for free: the numerical result itself says whether the image is real or virtual; you only have to interpret it correctly.
What happens when the object is inside the focal length?+
Then the converging lens works as a magnifying glass. Mathematically the image distance becomes negative: at g = 5 cm and f = 10 cm, 1/b = 1/10 − 1/5 = −1/10, so b = −10 cm. The negative sign means there is no real image that could be caught on a screen. Instead a virtual, upright and magnified image forms on the object side; the rays leaving the lens diverge, and the eye extends them backwards to an apparent image. The magnification |b|/g = 10/5 = 2 shows twice the size. Exactly the three cases g > 2f (reduced), 2f > g > f (magnified, real) and g < f (magnifier) are the standard material of exam problems.
How is the magnification related to the lens equation?+
The magnification follows from the same similar triangles as the lens equation: B/G = b/g; image size to object size behaves like image distance to object distance. Example: a 2 cm object at g = 15 cm in front of a lens with f = 10 cm gives b = 30 cm from the lens equation; the magnification is b/g = 2, so the image is 4 cm tall, real and inverted. Special cases help with orientation: at g = 2f, b = 2f and the image is exactly the same size, which is also a quick experimental way to find the focal length. For g → ∞ (distant objects) the image moves into the focal plane, the principle of every camera: the sensor sits roughly at b ≈ f.
Does the lens equation also hold for diverging lenses and glasses?+
Yes, with a negative focal length. A diverging lens with f = −10 cm and an object at g = 20 cm gives 1/b = −1/10 − 1/20 = −3/20, so b ≈ −6.7 cm: always a virtual, upright, reduced image, wherever the object stands. Opticians calculate with the refractive power D = 1/f in dioptres (f in metres) instead of focal lengths: glasses of −2 dpt have f = −0.5 m and correct short-sightedness by pushing the prematurely focused image back onto the retina; long-sighted people wear converging lenses with positive dioptres. The dioptre measure is practical: for lenses placed closely one behind the other the powers simply add, D_total = D₁ + D₂.
Retain Thin Lens Equation for exams
Create a curated FSRS exam set for 1/f = 1/g + 1/b: 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 Thin Lens Equation?
Here is how to work through a typical Thin Lens Equation (1/f = 1/g + 1/b) task step by step:
- 1
Task
Converging lens f = 10 cm, object at g = 15 cm. Where does the image form?
Solution path
1/b = 1/10 − 1/15 = 1/30 → b = 30 cm, real, inverted, magnified (b/g = 2).
- 2
Task
Object at g = 5 cm in front of a lens with f = 10 cm. What results?
Solution path
1/b = 1/10 − 1/5 = −1/10 → b = −10 cm: a virtual, upright, magnified image, the magnifying glass.
1/f = 1/g + 1/b · 10 cards ready
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