Maximize interview readiness

The AI-native practice system for product and technical judgment.

One connected loop. Live interviews, Hatch coaching, scoring, autopsies, study plans, code, and canvas practice.
Real reps, not flashcards. Practice the artifacts, tradeoffs, and communication that real loops actually score.
Hatch, your AI coach. Reads your work, catches blind spots, and helps human judgment compound.

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hackproduct.com/product-sense

LIVE

SQL · Window Functions · Rep 0247

For each customer, show their 3rd most recent order.

-- your query
SELECT customer_id, order_id, order_date
FROM (
  SELECT *,
    ROW_NUMBER() OVER (
      PARTITION BY customer_id
      ORDER BY order_date DESC
    ) AS rn
  FROM orders
) ranked
WHERE rn = 3;

✓ CorrectEdge case missedTie-breaking

▸ Hatch · SQL Coach

Window function: nailed it. Watch ties. Two orders on the same day will both rank 3, so pick row_number vs rank deliberately.

▸ Rubric

Correctness

8.8

Efficiency

7.6

Edge cases

6.2

Readability

9.1

▸ Overall

7.9 / 10

Coding · Strings · Rep 1184

Find the longest substring without repeating characters.

def longest_unique(s: str) -> int:
    seen = {}
    left = best = 0
    for right, ch in enumerate(s):
        if ch in seen and seen[ch] >= left:
            left = seen[ch] + 1
        seen[ch] = right
        best = max(best, right - left + 1)
    return best

✓ All 47 tests passO(n) timeO(min(n,Σ)) space

▸ Hatch · Coding Coach

Sliding window with hashmap, clean. Now walk me through your dry-run with "abba". That's where most candidates trip.

▸ Rubric

Correctness

Complexity

9.2

Communication

7.0

Tests

8.4

▸ Overall

8.7 / 10

System Design · Rep 0411

Design a real-time leaderboard for 50M players.

Client

Mobile / Web

API Gateway

Rate limit

Score Service

Stateless

Redis ZSET

Sorted set

Postgres

Source of truth

WebSocket Pub

Live updates

✓ Functional req coveredReplication gapNumbers asked

▸ Hatch · Systems Coach

Redis ZSET, good pick. But you skipped the replication step. What happens if your primary dies mid-tournament?

▸ Rubric

Requirements

9.0

Scale math

7.2

Trade-offs

8.5

Reliability

5.8

▸ Overall

7.6 / 10

Data Modeling · E-commerce · Rep 0822

Model an order with discounts applied at line vs cart level.

users

id PK

tier

created_at

orders

id PK

user_id FK

total

status

line_items

order_id FK

sku

qty

price

3NFDiscount table?Soft-deletes

▸ Hatch · Modeling Coach

Schema reads cleanly. But your line_items has price. Is that snapshot or live? Spell out the temporal contract.

▸ Rubric

Normalization

9.2

Indexes

7.0

Temporal sense

6.5

Evolvability

8.0

▸ Overall

7.7 / 10

Product Sense · Rep 0593

SaaS trial conversion dropped 22% in a week. Pricing was tested. Where do you look first?

1. Frame: drop = symptom, not the question.

2. Slice: pricing-page traffic vs activation.

3. Cohort: new signups vs returning trials.

4. Timeline: anchor on the deploy.

↵ Submitted · awaiting grade

StructureHypothesis-drivenNo metric tree

▸ Hatch · PM Coach

Strong frame. You separated symptom from cause. Skipped one move: anchor on the timeline first. Deploys narrow the surface area fast.

▸ Rubric

Framing

9.2

Trade-offs

7.8

Structure

8.4

Risk

7.1

▸ Overall

8.3 / 10

ML / AI Engineering · Rep 0316

Your RAG pipeline returns wrong docs 14% of the time. Where do you start?

# evaluation harness
from rag import retrieve, score

def audit(eval_set):
    miss = []
    for q, gold in eval_set:
        docs = retrieve(q, k=5)
        if gold not in [d.id for d in docs]:
            miss.append((q, docs, gold))
    return miss   # debug retrieval, not LLM

Isolated retrievalNo k-sweepEmbedding audit

▸ Hatch · ML Coach

Right instinct: separate retrieval from generation. Now is it the embeddings, the chunking, or the reranker? Run k=1,3,5,10.

▸ Rubric

Diagnostic mindset

8.8

Eval design

7.6

Trade-offs

7.2

Production sense

8.0

▸ Overall

7.9 / 10

Practice for interview loops at

Prep breaks when practice, blind spots, and feedback stay fragmented.

HackProduct brings the rep, rubric, artifact, and coach into one AI-native loop.

Abstract scattered practice tiles for interview disciplines.

Solo Grind

Scattered drills make practice feel busy, but keep the signal fragmented.

Abstract tangled path with hidden evaluation gaps.

Blind Spots

You can repeat reps for hours and still miss the evidence interviewers score.

Abstract organized feedback loop with rubric signals.

Calibrated Feedback

Hatch converges each attempt, rubric, artifact, and next rep into coaching.

Your coach arrives as the loop comes together.

Hatch turns interview practice into a guided system: calibrated reps, live judgment, and feedback that compounds.

Trained on interviews

Rubrics, loops, and real practice patterns shape every nudge.

AI native

Hatch reads the rep, not just the answer key.

Calibrated coaching

Feedback lands where the next attempt can use it.

Every practice surface converges into one system.

Autopsies, lessons, live loops, challenges, scoring, and Hatch feedback stay connected so each rep knows what came before it.

Live interviews with Hatch, your AI interview agent.

Hatch is trained on interview rubrics, interview loops, and product and technical practice patterns, so voice, chat, code, canvas work, scoring, and coaching happen in one company-calibrated session.

MetaProduct Sense

GoogleCoding

StripeSystem Design

AirbnbProduct Strategy

UberData Modeling

DoorDashExecution

Round 2 of 5Stripe system design loop

Live with Hatch

1Company brief

2Live round

3Artifact work

4Rubric score

5Next loop

Hatch voice ready

Hatch

Start with the tradeoff. What would you protect first in this payments flow?

You

I would preserve trust signals before optimizing conversion, then test the riskier paths.

Hatch

Good. Now defend the failure mode and sketch the recovery path.

Chat

Voice

Coding

Canvas

coding.pyHatch is watching complexity

def rank_candidates(events):
    trust = score_signal(events.checkout)
    speed = p95_latency(events.route)
    return blend(trust, speed, weight=0.72)

canvasDraw product and system flows

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Live interview loops across product, system design, data, SQL, and coding
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$199/ year

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