Climb

Test Prep · iOS

An AI tutor who understands your student. Built around mastery, not engagement.

What it does

Three capabilities, done well

Climb pairs an adaptive practice engine with an AI tutor who remembers your student across sessions. The application is structured as a sherpa-guided climb to a 1340 target on the SAT® exam — the metaphor mirrors a real Mt. Everest base-camp trek the founder is taking with his son in 2027.

Adaptive practice

Targets weak topics, not random questions

A Beta-posterior mastery model tracks what your student knows per topic. Leitner spaced-repetition surfaces cards he is about to forget. Bootstrap propagation transfers learning across difficulty levels, so a correct answer at the Hard tier also raises confidence at the Medium tier.

Example: a missed Easy comma-splice yesterday returns today. A correct answer at the Hard difficulty also raises confidence on the Medium.

Wren — the AI tutor

Memory across sessions, three teaching modes

Wren reads your student's recent attempts and per-topic mastery before saying anything. She anchors explanations in concrete past successes — not generic test-prep advice. Three modes: Explain (default), Diagnose ("walk me through how you got there" — only when warranted), and Reassure & Reset (when he is in a wrong-answer streak, she pauses teaching and acknowledges the struggle rather than piling on).

Example — "You got the parallel-structure version yesterday. Same move here. The trap is that the four options all have similar wording. Pick the one whose verb form matches the first item, not the one that sounds smoothest."

The climb

Real progression, real mountain

Mastery growth maps to altitude on Mt. Everest — Kathmandu at the start, Everest Base Camp once fundamentals are secure, Camps 1–4 and the summit at advanced mastery. Every answer writes a real altitude time-series. The route is the same one the founder is walking with his son in 2027 as a graduation trip.

No fanfare at stage transitions. No achievement sounds. The mountain is real; the metaphor stays earned.

What it doesn't do

The differentiator

Most apps in this space optimize for time-on-app. The 2023 meta-analysis on educational gamification found that the standard points-badges-leaderboards trio does not measurably improve felt competence — the most-borrowed mechanic in ed-tech does not move the outcome it claims to.

No XP
No streaks
No daily quests
No badges
No levels
No character classes
No leaderboards
No engagement notifications
No subscription trap
No engagement-loop manipulation

Every mechanic from mobile-game design that could be exploiting your student — we considered each one, audited it against the research, and explicitly removed it. Each removal is documented in the research below. The frame is consistent: every reward loop must be denominated in real mastery gains, not presence. If a mechanic rewards opening the application, it is wrong. If it rewards getting better, it stays.

Why these design choices

The research base

Climb's design is grounded in the educational-psychology literature on what actually moves learning outcomes. Every claim below links to the underlying work.

One-on-one tutoring is the gold standard. Climb's Wren approximates it.

Bloom (1984) found that one-on-one tutoring produces a two-standard-deviation improvement over conventional classroom instruction. The mechanism is not more time — it is that the tutor models the student's mental state, identifies the specific misconception, and intervenes against that misconception directly. Wren does this through persistent memory, mastery-state queries, and diagnostic questions that ask the student to surface his reasoning before the explanation lands.

Bloom, B. (1984). The 2 Sigma Problem. Overview · Carnegie Learning's Cognitive Tutor lineage: Anderson, Corbett, Koedinger & Pelletier — Cognitive Tutors: Lessons Learned.

Mild confusion correlates with learning gain. The tutor's job is not to dissolve it.

Kapur's productive-failure work shows that students who struggle with a problem before receiving instruction outperform students given direct instruction first — by effect sizes equivalent to two to three years of typical schooling on transfer tasks. Wren's diagnostic mode is engineered around this finding. When she asks "walk me through how you got there," she sustains productive disequilibrium long enough for the student to construct the right concept himself, rather than resolving the confusion immediately.

Kapur, M. — Productive Failure (overview). Roediger & Karpicke on the testing effect: The Power of Testing Memory (2006).

Spaced retrieval beats massed cramming, by substantial margins.

Bjork's "desirable difficulties" framework, supported by decades of cognitive-science replication, shows that spaced retrieval practice produces durable learning while massed cramming produces short-term recognition that does not survive the actual test. Climb's Leitner queue is a working spaced-repetition implementation; the adaptive sampler weights against recency to enforce spacing.

Bjork & Bjork — Making Things Hard on Yourself, But in a Good Way. Background: Desirable difficulty.

Extrinsic rewards can crowd out intrinsic motivation. So we do not add them.

Lepper, Greene & Nisbett (1973) demonstrated the over-justification effect: bolting extrinsic rewards (XP, badges, points) onto an activity in which a student might develop intrinsic interest reduces that intrinsic motivation when the rewards are removed. The implication for an app preparing students for the SAT® exam is direct: if we want a student to develop genuine interest in mastering the material, layering XP and badges on top is the most counterproductive thing we can do. Self-determination theory (Deci & Ryan) names the actual drivers of sustained motivation — autonomy, competence, relatedness — none of which are points.

Lepper, Greene & Nisbett (1973): Undermining children's intrinsic interest with extrinsic reward · Ryan & Deci (2000): Self-Determination Theory and the Facilitation of Intrinsic Motivation.

The standard gamification trio does not measurably improve learning competence.

A 2023 meta-analysis of educational gamification found that points, badges, and leaderboards — the most-borrowed mechanics in ed-tech — do not measurably improve felt competence. What does move competence: appropriate-difficulty challenges with visible feedback. That is precisely what Beta-posterior mastery, Leitner spacing, and Wren's anchored explanations deliver, without badges.

2023 meta-analysis of educational gamification — Educational Technology Research and Development.

Push notifications reduce learning performance. So Climb does not send any.

A 2022 study found that mobile push notifications, even non-engagement-oriented ones, measurably reduce learning performance. Climb sends no push notifications. The spaced-repetition queue creates a natural daily rhythm based on memory science; we do not need to interrupt the student to drive return visits.

Push notifications and learning performance — Computers & Education: Open, 2022.

For parents

What you'll want to know

Read carefully. Fact-check anything. The answers below are honest, including the limitations.

How is this different from Khan Academy, Magoosh, or UWorld?

Khan, Magoosh, and UWorld have excellent content libraries — Climb does not try to replace them on content depth. What Climb adds is personalized AI tutoring those platforms cannot deliver at scale: a tutor who remembers your student's specific struggles across sessions and adapts every explanation to what he already knows. Khan's points-and-badges layer is one of the elements Climb deliberately does not replicate, for the reasons in the research above.

How much screen time will my student spend in this?

Exactly as much as is mastery-productive, and no more. The application does not engineer engagement. There is no streak shaming him into opening it daily, and no notification pulling him back in the evening. The Leitner queue surfaces cards on a memory-science cadence; when the queue is empty, "0 cards due today" is shown as a feature, not a failure. Typical use is thirty to sixty minutes per session, a few sessions per week.

Is my student's data safe?

Yes. Study progress, answers, and chat history with the tutor are stored only on the device — none of that ever reaches our servers. AI requests flow from the device through our Cloudflare Worker proxy to Anthropic; the proxy is a passthrough and does not log or store the content of the requests. On the server side we hold the bare minimum to run the subscription: an App Attest device identifier (Apple's mechanism that proves a request comes from a real Climb install), the current subscription state, and a per-device monthly usage counter. No telemetry. No analytics. No third parties beyond Apple (which handles billing) and Anthropic (which serves the AI). Full privacy policy.

What does the AI tutor actually do, in plain terms?

Three modes. Explain — when your student gets a question wrong on something he mostly knows, Wren names the trap and the move in three to five sentences. Diagnose — when the wrong answer suggests a deeper misconception, Wren offers: "If you want, walk me through how you got there — otherwise I can show you the move directly." When he opts in, his answer reveals the specific misconception, and Wren teaches against that directly. Reassure & Reset — when he has missed three questions in a row, Wren stops teaching, acknowledges the struggle, and offers to switch to a topic he is strong in, to rebuild confidence. Mode selection is gated on numerical mastery thresholds and recent-attempt patterns, not on intuition.

Is this a subscription?

Yes — Climb Monthly is $9.99/month, billed by Apple. There is no annual lock-in; cancel anytime through your iOS Settings → Apple Account → Subscriptions and you keep access until the end of the current billing period. The subscription includes Wren AI tutoring up to a $25/month usage cap (intentionally generous — typical study usage runs well under that). The adaptive practice engine, the question bank, and lesson content all work the same; the subscription gates the AI tutor specifically.

Who is this not for?

Climb works best for students with at least an 1100 floor who can mostly self-direct. If your student needs structured tutoring through fundamentals (reading comprehension, basic algebra), this is not the right tool yet — the application assumes the substrate is in place and works on closing the gap to a target score. To be direct: Climb is not a replacement for a human tutor when one is needed, and it is not a substitute for the accountability scaffolding some students need to study at all.

How early-stage is this?

Early. Climb is currently on TestFlight (iOS beta), not the App Store. The founder built it for one user — his own son — and is opening it up as that work proves out. If you would like access, get in touch and we will discuss whether it is a fit for your student before adding you to the test group.

The story

Built for one student first

The founder's son, Westley, scored 1140 on his first SAT® exam. Target: 1340 by September 2026. Eighteen weeks of runway.

Westley plays Fortnite, Apex, Genshin Impact, and Baldur's Gate 3 — his attention pattern is game-trained. He expects rich feedback loops and felt progress. The temptation was to graft those mechanics onto the application: XP for every correct answer, streaks for daily use, character-class progression, all of it.

The honest move was different. The research is clear that those mechanics improve return-visit metrics but do not measurably improve learning. Westley deserves a tool that actually teaches him — not one that engineers his attention to look like it is teaching him.

So Climb went the other direction. Real adaptive practice. A real AI tutor with memory. A real mountain — because the founder and Westley are walking the Everest Base Camp trek in 2027 as Westley's high-school graduation gift, and the application's progression mirrors the literal route they will walk together. The climb is real.

If it works for Westley, it can work for other students. That is the path.

About

From KyrosWorks

KyrosWorks is a one-person operation building AI-native software for the kind of problems people reliably forget to solve, sustain, or get right by hand. Climb is one of those products. Open Road Ace — a millisecond-accurate pacing application for open-road time-trial racing — is another.

Each KyrosWorks product begins with a problem the founder personally has, is validated with users who share that problem, and ships when it works under the conditions where it actually fails. That is the methodology.

KyrosWorks → Open Road Ace →

Get TestFlight access

Climb is on TestFlight, not the App Store yet. Send a note about your student (current SAT® score, target, timeline, anything else worth knowing) and we will discuss whether it is a fit before adding you to the test group.

will.hays@gmail.com

In the interest of transparency: as of May 2026, Westley is the only confirmed test user. The project is early. We would rather work with a small group of motivated students than a large group of curious ones.