Custom Cultivation Program
Not more class hours —
a cultivation system built around one learner
Integration Track
A custom cultivation system with the growth archive as its spine: theory · math · practice · project, running alongside (not instead of) your current program.
Step one is always the precision diagnostic
Fully credited toward the program. It turns "unmeasured" into "named". If it ends here, the data is still yours.
Four layers
Published, bilingual system texts; every chapter requires a self-explanation artifact
- ●Mathematical Thinking · Problem Understanding & Modeling60m
- ●Mathematical Thinking · Logical Reasoning & Proof60m
- ●Mathematical Thinking · Pattern Recognition & Abstraction60m
- +8 …
"One math problem → one AI project" five-segment cards + diagnostic-driven interleaving
- ●Precision Diagnostic (four domains + proof + time profile)90m
- ○Five-Segment Card · Sequences → PageRank (open sample)45m
- ●Five-Segment Card · Derivative → Gradient Descent45m
- +8 …
From knowing math to building agents: data literacy and engineering scaffolds
- ●How to Ask AI Without Losing Your Skills45m
- ●Python Data Literacy (toolchain → EDA)60m
- ●Agent Engineering Starter (hands-on scaffold)60m
- +2 …
Graded project pool → original capstone → versioned portfolio
- ●First L3 Real Project90m
- ●Semester Mini-Capstone → Portfolio v190m
- ●Hatch · Train a Net That Reads Digits120m
The AI protocol — where we differ from most AI courses
- ▸Solve before tool: core math is hand-worked (no AI) and archived first; the AI-assisted version comes after — the diff is verifiable learning evidence
- ▸All formative assessments are no-AI (RCT evidence: unguarded AI tutoring erodes independent skill)
- ▸The first lesson is not a tool — it is how to ask AI without losing your skills
- ▸Every project ships with an AI-use declaration in the archive
What we measure, and how
- ▸Completed ≠ passed: passing requires a delayed retention check (2-4 weeks) plus a transfer item
- ▸Cross-domain formative assessment every 4-6 weeks; semester re-diagnostic with a versioned ability profile
- ▸The parent monthly report is human-verified: retention and profile deltas, not completion percentages
Weekly commitment
About 4.5-5.5 hours/week in term (hard cap 6), scheduled explicitly around school and your current program. Under pressure we cut new material first and protect review; exam weeks, this program yields.
Division of labor with your current program
| Advancing the curriculum | Your program | We do not duplicate |
| Integration (proof · cross-topic · multi-representation) | First casualty of large-group pacing | Our mainline |
| AI trajectory (math → algorithms → projects → portfolio) | Outside its structure | Our destination |
| Growth archive (ability profile · versioned work · verifiable record) | A report card | Our spine |
Choose your depth
- ✓All four layers unlocked (theory · math · practice · project)
- ✓Weekly orchestration reviewed and signed by a learning designer (48h response)
- ✓Monthly 30-min 1:1 planning call (12/yr) + 2 human capstone reviews
- ✓Parent monthly report, human-verified: retention & profile deltas, not completion %
- ✓Assessment cadence: no-AI cross-domain check every 4-6 weeks + semester re-diagnostic
- ✓Growth archive: points · badges · versioned portfolio
- ✓Everything in Core
- ✓Weekly 60-min 1:1 mentorship ×40 + weekly async work review
- ✓8h capstone intensive + 4h application-season portfolio curation
- ✓Competition module included (AMC→AIME ladder as an external check of integration)
- ✓Demo Day showcase (annual student exhibition)
- ✓Capacity: ≤6 students per mentor
The mentor tier is application-only; submit interest and we schedule a conversation.
寻找全程私教的旗舰路线? 旗舰私教线 Founder Track →
Not sure? Start with the Starter Blueprint: a plan plus 4 weeks actually executed, fully credited toward an annual tier within 90 days. ask your advisor
The System · Math → AI → ML / Neural Nets
Five layers, each gated by mastery evidence — never by attendance. Fifth generation of the system, evolved from the first book series.
① Math foundation
Mathematical thinking · proof line · interleaved practice
From competition inequalities to convergence arguments, all archived
② Bridge cards
Sequences→PageRank · derivative→gradient descent · matrix→forward pass · chain rule→backprop · probability→Bayes
Every card is solve-before-tool; the hand-worked page archives first
③ ML / neural-net track
Loss & generalization → backprop theory → why architectures exist
A from-scratch NumPy net, then a real training loop dissected line by line
④ Hatch project (talent-routed)
Digit-reading net / the music line: Fourier → note recognition → a net that hears instruments
Hand-worked version first; the AI-assisted one is a NEW version — the diff is the evidence. Talent picks the track, not just the résumé
⑤ Portfolio → competitions & camps
AMC/AIME ladder · proof-camp and research-program applications
Targets every October, applications in winter; the portfolio IS the evidence behind every recommendation
Competition-band goes straight to the ML track; anyone else learns exactly which three cards come first.
What you buy is what your child builds
Project-based learning is the core deliverable: at least one real project per semester, hatched into the versioned portfolio. The full catalog is public, maturity honestly labeled.
Browse every project →Commitments & terms
- ·30-day full refund, no questions (less the delivered blueprint portion)
- ·Mid-year exit: pro-rata refund of unstarted semesters
- ·Nothing included in a tier sits behind a points gate; points unlock honors only
- ·Lesson packages already purchased on the diagnostic line fully credit toward an upgrade within 90 days
- ·Need-based aid available (Access Scholarship, up to 75%); applying never affects participation, and credits stack on top of aid
The guardian account manages and pays; the student gets their own learning view and portfolio. Payments are hosted by Stripe and never touch this site.
Every pedagogical claim on this page is evidence-graded (mastery learning, deliberate practice, spaced retrieval, interleaving are all high-evidence); the portfolio's admissions value is a reasonable inference — we make no individual-outcome promises.
program: pathfinder