OpenAI's OA runs on HackerRank but breaks the typical "4-questions-90-minutes" mold. It's a single-question, 4-phase progressive build: a minimal API first, then layer in persistence, concurrency, expiration, and query — 90–120 minutes total. Spreadsheet engine, Credits system, and Memory Manager are the three classics. Below: progressive design + OA assistance roadmap per line.
OpenAI HackerRank OA Snapshot
| Dimension | Detail |
|---|---|
| Platform | HackerRank |
| Duration | 90–120 minutes |
| Questions | 1 problem in 4 phases (25–30 min each) |
| Difficulty | LC Medium pace + interface design |
| Grading | Auto + hidden stress tests |
Line 1: Spreadsheet Engine
Phase 1: Basic R/W
class Spreadsheet:
def __init__(self):
self.cells = {}
def set(self, c, v):
self.cells[c] = v
def get(self, c):
return self.cells.get(c, 0)
Phase 2: Formulas (=A1+B2)
import re
class FormulaSheet(Spreadsheet):
def __init__(self):
super().__init__()
self.formulas = {}
def set(self, c, v):
if isinstance(v, str) and v.startswith('='):
self.formulas[c] = v[1:]
self.cells.pop(c, None)
else:
self.formulas.pop(c, None)
self.cells[c] = v
def get(self, c):
if c in self.cells:
return self.cells[c]
if c in self.formulas:
return self._eval(self.formulas[c], set())
return 0
def _eval(self, expr, stack):
if expr in stack:
return 0
tokens = re.split(r'([+\-*/])', expr)
result = 0
op = '+'
for t in tokens:
t = t.strip()
if t in '+-*/':
op = t
else:
v = self._token(t, stack | {expr})
if op == '+': result += v
elif op == '-': result -= v
elif op == '*': result *= v
elif op == '/' and v: result //= v
return result
def _token(self, t, stack):
if t.isdigit() or (t[0] == '-' and t[1:].isdigit()):
return int(t)
return self.get(t)
Phase 3: Dependency Tracking + Auto Recalc
Introduce reverse dependencies graph; set triggers downstream recompute.
Phase 4: Version Snapshots + Undo
Push a version on each set; undo() rolls back.
Line 2: Credits System
Phase 1: Basic Balance
add(user, credit, expires_at), spend(user, n), balance(user).
Phase 2: FIFO Expiration
import heapq
from collections import defaultdict
class CreditSystem:
def __init__(self):
self.heaps = defaultdict(list)
def add(self, user, credit, expires_at):
heapq.heappush(self.heaps[user], [expires_at, credit])
def spend(self, user, n, now):
h = self.heaps[user]
while h and h[0][0] <= now:
heapq.heappop(h)
if self.balance(user, now) < n:
return False
while n > 0 and h:
if h[0][0] <= now:
heapq.heappop(h); continue
take = min(h[0][1], n)
h[0][1] -= take
n -= take
if h[0][1] == 0:
heapq.heappop(h)
return True
def balance(self, user, now):
return sum(c for exp, c in self.heaps[user] if exp > now)
Phase 3: Out-of-Order Requests
Requests carry op_id + ts; arrive out of order. Replay in ts order.
Phase 4: Distributed + Idempotent
Add request_id dedup; replays of the same request take effect only once.
Line 3: Memory Manager
Phase 1: Fixed-Block Allocation
alloc(n) returns base addr; free(addr) releases.
Phase 2: First Fit / Best Fit / Worst Fit
Compare fragmentation across strategies.
Phase 3: Coalescing
Merge adjacent free blocks on free.
Phase 4: Mark-Sweep GC
Simplified mark-sweep, periodic reachability scan.
OpenAI Question Signals
- Each phase builds on the previous: phase 2 code becomes phase 3's dependency
- Tests grade per phase: Phase 1 = 25 pts; Phase 4 = 100 pts
- Interface design > algorithm: phase upgrades often require refactor, not new branches
OA Assistance Path
oavoservice Packages
For OpenAI's "single problem, 4 phases, per-phase grading":
- OA Assistance: phase-split mocks; mentor reviews interface within 5 min of each phase
- VO Bridge: graduate to OpenAI VO pack (credits design, inference optimization)
- Interface design drills: 5 classics (KV store / rate limiter / queue / chat session / FS) in 4-phase reps
- Behavioral script: OpenAI weights "Helpfulness / Harmlessness / Honesty"
Add WeChat Coding0201 for pricing.
From Stuck on Phase 2 to Passing OpenAI OA
We were glad to help this cohort pass the OpenAI HackerRank OA. Many candidates told us LC grinding never trains 4-phase progressive design — Phase 1 flies, but Phase 2 (formulas / expiration) demands tearing up Phase 1, eating into the timer.
If you're prepping OpenAI, Anthropic, Cohere, Mistral, or xAI OA / VO and feel solo practice on interface design + phase progression isn't efficient, contact oavoservice. We tailor OA / VO assistance to your gaps.
FAQ
Which language for OpenAI OA?
Python / Java / TypeScript / Go. Community reports: ~80% Python (interface iteration speed).
Must I finish all 4 phases?
Not required. Phase 2 + partial Phase 3 → phone screen; full 4 phases → onsite invite.
Hardest of the three?
Community: Credits is hardest (out-of-order + idempotent phases); Spreadsheet has the longest prompt but manageable code; Memory Manager favors OS background.
Cooldown if failed?
Usually 6 months. OpenAI internally splits Research / Engineering / Applied OA buckets.
Preparing OpenAI / top AI-company OA / VO?
👉 Add WeChat: Coding0201 — grab the OpenAI OA assistance pack.
Contact
Email: [email protected]
Telegram: @OAVOProxy