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Reference · live from the database

Models and levels, in full.

The complete reference: the scoring formula behind every run, every AI model your harness can run — with its list pricing and the effort multiplier that scales your score — and every challenge in the catalog, with its stack, pacing, benchmark, and the skills it tests.

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Hidden tests

01 · How scoring works

Every score, decomposed.

One fitness function turns a captured run into a single number. It rewards a correct solution reached with fewer tokens, fewer prompts, and cheaper compute — the win is efficiency, not brute force. Every term below is live from the scoring constants.

Score = (C · W_c) / (P · M_effort)  ×  (1,000 / weighted_tokens)  ×  (100 / (S · W_s))

Higher is better. Correctness gates everything — a cleared run always out-ranks an unfinished one — and the other terms reward doing more with less.

C · Correctness
The percent of hidden tests that pass (0–100), weighted by W_c = 1.0. A full clear is 100 and always ranks first.
Token efficiency
1,000 divided by your weighted token spend (input, output, and thinking, each weighted by challenge type). Fewer tokens, higher score — floored at 100 so near-zero reports cannot explode it.
S · Speed
100 divided by your summed hidden-suite run time (× W_s = 1.0), floored at 1.0s so sub-second noise cannot dominate.
M_effort · Model cost
Your reported model’s effort multiplier divides the score, clamped to [0.50, 3.00]. Cheaper than the anchor is a discount, pricier a handicap — the per-model figures are in Harness models below. Cursor’s Composer adds +0.20.
P · Multi-turn
The number of prompts you sent, dividing the score (floored at 1). Reaching the fix in fewer turns pays off.
Thinking overhead
Heavy reasoning nudges M_effort up in small steps, so a flood of thinking tokens costs a little — capped so it can never zero a score.

Worked example

clean debugging clear · anchor model

A full clear on claude-sonnet-4-6 in 2 prompts, 5,000 input + 3,000 output tokens, no thinking — the exact breakdown the in-workspace HUD projects, shown here so you can read every term at rest. Projected score 368.

  • Correctness

    100% × W_c 1

    100raises score
  • Token efficiency

    1e3 ÷ 6800 weighted tokens

    0.15raises score
  • Speed

    100 ÷ (2s × W_s 1)

    50.00raises score
  • Effort multiplier

    base 1 + thinking 0 + composer 0

    1.00lowers score
  • Multi-turn penalty

    ÷ P = 2

    2.00lowers score

02 · Harness models

What you compete with.

You may solve a level on any AI model your harness exposes. Promptly recognizes 44 models across 8 labs. Your reported model sets an effort multiplier that scales your score, so the cheapest model is not automatically the best move — matching a frontier result on a budget model is what pays.

In / Out / Think
Provider list price per 1M tokens (USD). Think is the reasoning-token rate; it usually mirrors output.
Blended
0.25·in + 0.75·out per 1M — output-weighted, because agentic coding is output-heavy.
Effort multiplier
A row's blended cost divided by the anchor's, clamped to [0.50, 3.00]. It divides your score: lower is a discount, higher a handicap.

The anchor is claude-sonnet-4-6 — the mid-tier reference fixed at ×1.00 by definition. Every other multiplier is measured against it, so re-pricing the anchor re-scales the whole board.

Anthropic

9 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
claude-fable-5

Frontier tier above Opus; blended 3.33 clamps down to 3.00.

$10.00$50.00$50.00$40.00×3.00
claude-opus-4-6

Older Opus; still harness-selectable (Cursor).

$5.00$25.00$25.00$20.00×1.67
claude-opus-4-7

Prior Opus; same list price as 4.8.

$5.00$25.00$25.00$20.00×1.67
claude-opus-4-8

Current Opus flagship.

$5.00$25.00$25.00$20.00×1.67
claude-sonnet-4

Original Claude 4 Sonnet; still harness-selectable (Cursor).

$3.00$15.00$15.00$12.00×1.00
claude-sonnet-4-5

Prior Sonnet; Anthropic holds Sonnet at $3/$15 across versions.

$3.00$15.00$15.00$12.00×1.00
claude-sonnet-4-6Anchor

Mid-tier anchor row (M_effort = 1.00 by definition).

$3.00$15.00$15.00$12.00×1.00
claude-sonnet-5

Current Sonnet at the standard $3/$15 list; the $2/$10 launch promo (through 2026-08-31) is not modeled.

$3.00$15.00$15.00$12.00×1.00
claude-haiku-4-5

Current Haiku; clamped up to 0.50 by the lower bound.

$1.00$5.00$5.00$4.00×0.50

OpenAI

15 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
gpt-5-5

GA flagship (Apr 2026 release doubled the GPT-5 line's price).

$5.00$30.00$30.00$23.75×1.98
gpt-5-6-sol

GPT-5.6 flagship (June 2026); limited preview via API/Codex partners.

$5.00$30.00$30.00$23.75×1.98
gpt-5-4

Prior flagship; still available.

$2.50$15.00$15.00$11.875×0.99
gpt-5-6-terra

GPT-5.6 mid tier; limited preview.

$2.50$15.00$15.00$11.875×0.99
gpt-5-2

Legacy flagship; still harness-selectable (Cursor).

$1.75$14.00$14.00$10.938×0.91
gpt-5-2-codex

Legacy Codex variant; still harness-selectable (Cursor).

$1.75$14.00$14.00$10.938×0.91
gpt-5-3-codex

Codex CLI default model.

$1.75$14.00$14.00$10.938×0.91
gpt-5

Original GPT-5; still harness-selectable (Cursor).

$1.25$10.00$10.00$7.813×0.65
gpt-5-1-codex

Legacy Codex variant.

$1.25$10.00$10.00$7.813×0.65
gpt-5-codex

Original GPT-5 Codex variant.

$1.25$10.00$10.00$7.813×0.65
gpt-5-6-luna

GPT-5.6 fast tier; limited preview. Clamps to 0.50.

$1.00$6.00$6.00$4.75×0.50
gpt-5-4-mini

Budget 5.4 tier (Cursor/Copilot). Clamps to 0.50.

$0.75$4.50$4.50$3.563×0.50
gpt-5-1-codex-mini

Legacy budget Codex. Clamps to 0.50.

$0.25$2.00$2.00$1.563×0.50
gpt-5-mini

Original budget GPT-5. Clamps to 0.50.

$0.25$2.00$2.00$1.563×0.50
gpt-5-4-nano

Cheapest 5.4 tier. Clamps to 0.50.

$0.20$1.25$1.25$0.988×0.50

Google

6 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
gemini-3-1-pro

≤200k-context tier; >200k bills at $4/$18 (not modeled here).

$2.00$12.00$12.00$9.50×0.79
gemini-3-pro

Prior Pro; same ≤200k list price as 3.1.

$2.00$12.00$12.00$9.50×0.79
gemini-3-5-flash

Current Flash tier.

$1.50$9.00$9.00$7.125×0.59
gemini-3-flash

Prior Flash tier. Clamps to 0.50.

$0.50$3.00$3.00$2.375×0.50
gemini-2-5-flash

Legacy Flash; still harness-selectable (Cursor). Clamps to 0.50.

$0.30$2.50$2.50$1.95×0.50
gemini-3-1-flash-lite

Budget Flash-Lite; clamps to 0.50.

$0.25$1.50$1.50$1.188×0.50

xAI

5 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
grok-4

Older flagship; xAI still lists it at anchor-equivalent pricing.

$3.00$15.00$15.00$12.00×1.00
grok-4-20

Mid tier; blended 0.42 clamps to 0.50.

$2.00$6.00$6.00$5.00×0.50
grok-4-3

Current flagship (Apr 2026); aggressively priced, so clamps to 0.50.

$1.25$2.50$2.50$2.188×0.50
grok-build-0-1

Agentic coding model (Cursor). Clamps to 0.50.

$1.00$2.00$2.00$1.75×0.50
grok-4-1-fast

Fast/cheap tier; clamps to 0.50.

$0.20$0.50$0.50$0.425×0.50

Cursor

4 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
composer-1-5

Legacy Composer 1.5; priced above the anchor.

$3.50$17.50$17.50$14.00×1.17
composer-1

Legacy Composer 1.

$1.25$10.00$10.00$7.813×0.65
composer-2

Prior in-house model (Mar 2026). Clamps to 0.50.

$0.50$2.50$2.50$2.00×0.50
composer-2-5

Cursor's current in-house model (May 2026). Clamps to 0.50.

$0.50$2.50$2.50$2.00×0.50

DeepSeek

2 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
deepseek-v4-pro

Official V4 pro pricing; clamps to 0.50.

$0.435$0.87$0.87$0.761×0.50
deepseek-v4-flash

Official V4 flash pricing; clamps to 0.50.

$0.14$0.28$0.28$0.245×0.50

Moonshot

2 models
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
kimi-k2-6

Official Moonshot list price (aggregators resell cheaper; list wins per 13a). Clamps to 0.50.

$0.95$4.00$4.00$3.238×0.50
kimi-k2-7-code

Current Kimi coding model (Cursor). Clamps to 0.50.

$0.95$4.00$4.00$3.238×0.50

Z.ai

1 model
ModelIn / 1MOut / 1MThink / 1MBlendedEffort
glm-5-2

Current GLM (Cursor). Clamps to 0.50.

$1.40$4.40$4.40$3.65×0.50

Special rows

Two rows are not ordinary models — they shape how scoring treats orchestration and unrecognized models.

cursor-composerCursor

Meta-orchestrator · adds +0.20 to the underlying model

Meta-orchestrator: resolve the underlying model from telemetry, use its row, then add this coordination modifier (13).

baseline-floor-tier(any)

Catch-all for unknown models · scored at anchor parity ×1.00

Catch-all for unknown/unpriced model ids: ANCHOR-PARITY economics (M_effort 1.00), the neutral prior. Model identity is harness-reported, so an unverifiable id must never score better than the anchor — the old flat floor costs handed unknown ids the cheapest tier (0.50), paying players to obscure their model. (The is_baseline_floor flag name is kept for DB/daemon compatibility.)

03 · The catalog

Every challenge, in full.

20 challenges across 4 progressive stages. Each ships a starter kit and public tests; a larger hidden suite is the real gate, run on the server. The benchmark is a reference token/prompt budget — the bar to beat, not a hard limit.

Stage 01· easy

The Bootsector

5 levels

Warm up on real, gnarly-but-small problems — mostly fix a bug or fill one function — and learn the Promptly optimization loop in under 25 minutes.

S01 · L01· Core Algorithms

LRU Eviction Debug

easydebugging
Difficulty
easy
Session
Quick
Est. solve
~20 min
Stack
Go go1.22
Harness
stdin_stdout
Benchmark
~2200 tokens / 3 prompts
Tests
6 public · 16 hidden
Version
v4

A fixed-capacity cache is dropping the wrong keys: under some access patterns it discards entries that should have survived and holds on to ones a true LRU would have evicted first. Dig into how it tracks usage over time and get its eviction order back in line with least-recently-used.

Skills

bug-localizationPinpointing the single faulty site from a failing behavior — read before you patch.data-structuresChoosing and correctly wiring the structure (list, map, heap) the problem needs.

Constraints

No external dependencies

S01 · L02· Data Pipelines

Bounded Ring Buffer

easygeneration
Difficulty
easy
Session
Standard
Est. solve
~25 min
Stack
Go go1.22
Harness
multi_file
Benchmark
~2600 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

Bring an empty bounded FIFO queue to life: Push and Pop over a fixed capacity that stay correct when many goroutines hammer it at once. The signatures and public tests ship in the kit — the blocking and safety semantics are yours to pin down.

Skills

concurrency-primitivesMutexes, condition variables, and queues that make shared state safe.api-designDesigning a small, clear interface: the signatures and contracts callers depend on.

Constraints

Thread-safeNo external dependencies

S01 · L03· Web Systems

Sliding-Window Rate Limiter

easyimplementation
Difficulty
easy
Session
Standard
Est. solve
~25 min
Stack
TypeScript node20-ts5
Harness
http_integration
Benchmark
~2400 tokens / 3 prompts
Tests
6 public · 16 hidden
Version
v3

An existing Express app needs sliding-window rate limiting wired in as middleware: cap how often a given client may call, and turn away the overflow the way an HTTP API should. Grading is deterministic, so drive timing through the kit's injectable clock instead of real wall-clock — the exact limits and response shape live in the tests.

Skills

middlewareRequest/response interceptors that wrap an app's handlers.rate-limitingBounding requests per client per window with the correct headers.injectable-clockDriving time from an injected clock so tests are deterministic — never real sleeps.

Constraints

Injectable clock

S01 · L04· Concurrency

Crawler Race Fix

easydebugging
Difficulty
easy
Session
Standard
Est. solve
~25 min
Stack
Rust rust1.75
Harness
deterministic_concurrency
Benchmark
~2400 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

An async web crawler can't keep a straight story about which URLs it has already handled — some get visited twice, others slip through. Track down the concurrency flaw behind the inconsistency and make its bookkeeping hold up when tasks run in parallel. The bug reproduces the same way on every run, not as a flake.

Skills

data-racesTwo threads touching the same memory without synchronization — a correctness bug.deterministic-concurrencyReproducing concurrency bugs reliably via a seeded scheduler.synchronizationCoordinating threads so shared state stays consistent.

Constraints

Seeded schedulerDeterministic grading

S01 · L05· Database

ORM N+1 Fix

easydebugging
Difficulty
easy
Session
Quick
Est. solve
~20 min
Stack
Python python3.11
Harness
multi_file
Benchmark
~2000 tokens / 2 prompts
Tests
5 public · 15 hidden
Version
v3

One route in a small orders service gets dramatically chattier with the database as its result set grows, while its neighbors stay lean. Work out what makes that endpoint's data access blow up with size and bring it back down to a flat, predictable cost.

Skills

ormObject-relational mapping — and avoiding its N+1 query trap.query-optimizationReducing query count and cost, e.g. eager-loading to kill an N+1.indexingAdding the right database index so queries stay bounded.

Constraints

No N+1 queries

Stage 02· medium

Distributed Chaos

5 levels

Apply distributed-systems thinking by debugging or completing one slice of a protocol — not building Raft or SWIM end to end.

S02 · L06· Distributed

Leader Election Debug

mediumdebugging
Difficulty
medium
Session
Standard
Est. solve
~30 min
Stack
Python python3.11
Harness
multi_file
Benchmark
~3800 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

A Raft-style cluster occasionally crowns two leaders in the same term when messages arrive in an awkward order — exactly the safety property it is meant to guarantee. The cluster, transport, and role transitions are all provided and mostly correct; find what lets the second leader slip through and close the gap so a term can only ever have one.

Skills

consensusGetting distributed nodes to agree on a single value despite failures.state-machineModeling protocol behavior as explicit states and transitions.leader-electionElecting exactly one leader per term in a distributed protocol.

Constraints

Simulated networkOne file

S02 · L07· Distributed

Vector Clock

mediumgeneration
Difficulty
medium
Session
Standard
Est. solve
~25 min
Stack
Go go1.22
Harness
multi_file
Benchmark
~3400 tokens / 4 prompts
Tests
6 public · 18 hidden
Version
v3

Build a vector clock from empty stubs against a fixed API — Tick, Merge, and Compare. The signatures ship in the kit; the causality semantics each one has to honor are yours to work out and get exactly right.

Skills

distributed-primitivesSmall building blocks (clocks, registers) that distributed systems compose.causalityTracking happens-before relationships between distributed events.vector-clocksPer-node counters that order events and detect concurrency.

Constraints

Closed APINo external dependencies

S02 · L08· Real-time

CRDT Convergence Debug

mediumdebugging
Difficulty
medium
Session
Standard
Est. solve
~25 min
Stack
TypeScript node20-ts5
Harness
multi_file
Benchmark
~3400 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

An RGA text CRDT breaks its core promise: feed two replicas the same edits in different orders and they end up with different documents. Track down what lets them drift apart and restore convergence, so the order edits arrive in stops changing the final result.

Skills

crdtConflict-free replicated data type — merges concurrent edits deterministically.convergenceReplicas reaching the same state regardless of operation order.merge-semanticsThe rules a merge must follow to be associative, commutative, and idempotent.

Constraints

Deterministic grading

S02 · L09· Systems

Worker Pool Deadlock

mediumdebugging
Difficulty
medium
Session
Standard
Est. solve
~25 min
Stack
Go go1.22
Harness
deterministic_concurrency
Benchmark
~3600 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

A worker-pool orchestrator wedges itself under the right pattern of resource contention — tasks stop finishing and the whole thing hangs. Audit how it coordinates its goroutines, find what deadlocks it, and get every submitted task through to completion. The hang reproduces deterministically, not as a flake.

Skills

deadlockA cycle of waits where no thread can proceed — usually a lock/channel ordering bug.channelsCoordinating concurrent work by passing messages over channels instead of sharing memory.worker-poolA fixed set of workers draining a task queue, often with dependencies.

Constraints

Seeded schedulerDeterministic grading

S02 · L10· Resource Leaks

Goroutine / Conn Leak

mediumdebugging
Difficulty
medium
Session
Standard
Est. solve
~25 min
Stack
Go go1.22
Harness
multi_file
Benchmark
~3200 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

When callers cancel their context, this service never fully cleans up after itself — goroutines and connections accumulate instead of winding down. Set its lifecycle straight so a cancellation actually releases everything the work started.

Skills

resource-leaksFailing to release goroutines, connections, or handles.goroutinesLightweight concurrent tasks in Go — and not leaking them.cancellationHonoring context cancellation so work and resources stop promptly when callers give up.

Constraints

No leaks

Stage 03· hard

The Black Hat

5 levels

Security and correctness work where AI helps you verify, patch, or pinpoint — provided primitives mean no implementing crypto from zero.

S03 · L11· Cryptography

AEAD Timing-Leak Debug

harddebugging
Difficulty
hard
Session
Standard
Est. solve
~30 min
Stack
Rust rust1.75
Harness
stdin_stdout
Benchmark
~4200 tokens / 3 prompts
Tests
6 public · 20 hidden
Version
v5

A secure-envelope library sometimes accepts forged or tampered messages it should reject, and the way it checks them hands an attacker a timing signal. The cipher and framing underneath are sound — make its verification both strict and constant-time so neither the false accept nor the side channel survives.

Skills

constant-timeCode whose timing does not depend on secret data, to avoid leaking it.side-channelsInformation leaked through timing, size, or behavior rather than output.aeadAuthenticated encryption: ciphertext plus an integrity tag that must verify before decryption is trusted.

Constraints

Constant-timePrimitives provided

S03 · L12· DeFi logic

Constant-Product AMM

hardimplementation
Difficulty
hard
Session
Standard
Est. solve
~30 min
Stack
TypeScript node20-ts5
Harness
multi_file
Benchmark
~4400 tokens / 4 prompts
Tests
7 public · 20 hidden
Version
v4

Implement swap and its mulDiv helper for a constant-product (x·y=k) AMM against a fixed API. The straightforward version is a few lines; the real work is the arithmetic edge cases the tests lean on, where fees, rounding, and integer limits all collide.

Skills

defiDecentralized-finance logic: exact, overflow-safe on-chain arithmetic.fixed-point-mathInteger-based fractional arithmetic with explicit rounding and overflow control.ammAutomated market maker — a constant-product (x·y=k) pricing curve for on-chain swaps.

Constraints

Narrow APIOverflow-safe

S03 · L13· Auth / ZK

Schnorr + Merkle Verify

hardgeneration
Difficulty
hard
Session
Deep
Est. solve
~35 min
Stack
Python python3.11
Harness
multi_file
Benchmark
~4800 tokens / 4 prompts
Tests
8 public · 22 hidden
Version
v4

Implement schnorr_verify and merkle_verify from scratch on top of the provided elliptic-curve primitive. The kit pins the serialization convention and ships one worked example; your job is to turn the verification math into code that agrees with the standard test vectors.

Skills

zero-knowledgeProving a statement is true without revealing why.signaturesVerifying digital signatures against a fixed serialization convention.merkle-proofsVerifying membership against a Merkle root with a hash path.

Constraints

Curve providedPinned serialization

S03 · L14· AppSec

SQLi + Proto Pollution

harddebugging
Difficulty
hard
Session
Standard
Est. solve
~30 min
Stack
Node node20
Harness
http_integration
Benchmark
~4200 tokens / 4 prompts
Tests
6 public · 20 hidden
Version
v4

An Express API ships with two live vulnerabilities — one lets a crafted request read data it never should, the other lets a malicious payload quietly poison objects across the whole app. Find where untrusted input reaches something dangerous in each case and shut both down, without breaking any legitimate request path.

Skills

appsecApplication security — finding and patching exploitable bugs in real request paths.sql-injectionUntrusted input changing a SQL query's structure — fix via parameterization.prototype-pollutionA JavaScript attack where attacker keys mutate Object.prototype.

Constraints

Injectable clock

S03 · L15· Reverse Eng.

Obfuscated Loop Debug

harddebugging
Difficulty
hard
Session
Standard
Est. solve
~25 min
Stack
C gcc13-c17
Harness
multi_file
Benchmark
~4000 tokens / 3 prompts
Tests
6 public · 18 hidden
Version
v3

A bit-packed analytics toolkit is written top to bottom in dense, branchless bit-twiddling — and one of its routines quietly returns the wrong answer on certain inputs. Work through the obfuscation to the function that is off, pin down exactly which inputs break it, and correct it without disturbing the rest.

Skills

reverse-engineeringRecovering intent and invariants from obfuscated or opaque code.off-by-oneA boundary error of one element or index — the classic edge-case bug.bit-manipulationReading and transforming values directly with bitwise operators.

Constraints

One fix site

Stage 04· expert

Quantum & Scale

5 levels

Prestige capstones (30–45 min) — scaffolded single-module implementations or deep debugging, still CPU-gradable.

S04 · L16· AI Systems

Online-Softmax Block

expertgeneration
Difficulty
expert
Session
Deep
Est. solve
~45 min
Stack
C++ gcc13-cpp20
Harness
multi_file
Benchmark
~6000 tokens / 5 prompts
Tests
7 public · 24 hidden
Version
v3

A tiled attention kernel is fully built except for its heart: the block-update step that folds each new tile of scores into a running softmax. Implement that one function so the streaming result matches a dense reference to tight tolerances — including the inputs crafted to wreck a naive approach.

Skills

gpu-free-attentionComputing attention/softmax on CPU with numerically stable tiling.tilingProcessing data in blocks to stay cache- and numerically-friendly.numerical-stabilityArranging computations to avoid overflow, NaNs, and catastrophic rounding.

Constraints

One functionReference math provided

S04 · L17· Compilers

Bytecode Constant-Fold

expertimplementation
Difficulty
expert
Session
Deep
Est. solve
~35 min
Stack
Python python3.11
Harness
multi_file
Benchmark
~5600 tokens / 4 prompts
Tests
7 public · 20 hidden
Version
v3

Write the constant-folding optimization pass for a small bytecode IR, on top of the provided parser and evaluator. The catch is doing it safely — collapsing what is genuinely constant while leaving the program's observable behavior exactly as it was. Dead-code elimination is an optional, separately-scored bonus.

Skills

compilersTransforming and analyzing program representations while preserving semantics.constant-foldingA compiler pass that evaluates constant expressions at compile time.ir-passesTransformations over a compiler's intermediate representation.

Constraints

DCE is bonus

S04 · L18· Game Dev

Sweep-and-Prune Collision

expertimplementation
Difficulty
expert
Session
Deep
Est. solve
~35 min
Stack
C++ gcc13-cpp20
Harness
multi_file
Benchmark
~5600 tokens / 4 prompts
Tests
7 public · 20 hidden
Version
v3

Fill in the broad-phase stage of a small 2D physics engine — the pass that decides which bodies are even worth a precise collision check. The body, world, and narrow phase are already built for you. Correctness alone won't cut it: it has to stay fast as the world grows, or brute force would have done the job just as well.

Skills

broad-phaseCheaply rejecting object pairs that cannot collide before running exact tests.collision-detectionDetermining which objects overlap in space.sweep-and-pruneSorting interval endpoints on an axis to find overlapping AABBs.

Constraints

Single-axis sweep

S04 · L19· ML Ops

NumPy Training NaN

expertdebugging
Difficulty
expert
Session
Deep
Est. solve
~35 min
Stack
Python python3.11
Harness
multi_file
Benchmark
~6400 tokens / 4 prompts
Tests
7 public · 22 hidden
Version
v4

A small MLP training run blows up to NaN within a handful of steps. More than one thing is destabilizing it — read the way it diverges, reason out what is compounding the blow-up, and fix each cause until the network trains cleanly to its target loss with every step along the way finite.

Skills

ml-opsOperational correctness of training — stability, seeds, and finite losses.numerical-stabilityArranging computations to avoid overflow, NaNs, and catastrophic rounding.training-loopThe forward/backward/update cycle that fits a model — kept finite and stable.

Constraints

Seeded run

S04 · L20· Systems

Scheduler Deadlock

expertdebugging
Difficulty
expert
Session
Deep
Est. solve
~40 min
Stack
Rust rust1.75
Harness
deterministic_concurrency
Benchmark
~6800 tokens / 5 prompts
Tests
7 public · 24 hidden
Version
v3

A cooperative task runtime grinds to a halt under a particular ordering of wakes and schedules — forward progress simply stops. Its lock discipline is spelled out right there in the code; somewhere a path breaks the rules it lays down. Find that violation and restore liveness so the runtime keeps moving no matter the ordering. The stall reproduces deterministically.

Skills

lock-orderingAcquiring locks in a consistent global order to prevent deadlock.deadlockA cycle of waits where no thread can proceed — usually a lock/channel ordering bug.schedulerOrdering and dispatching tasks while guaranteeing forward progress.

Constraints

One fix siteSeeded schedulerDeterministic grading