2
KH
Verdict · three-score model

Judgment detail

One signal, fully reasoned: what goal it was meant to move, how good the work is on its own terms, and whether it was the highest-leverage use of capacity.

System-blocked
Kimaitime entryconfidence 79% 1d ago

Manual QA regression · Hytech

6h logged across two days re-running the full WhatsApp QR flow by hand after every deploy. Note: 'no automated e2e yet, doing it manually each release'.

AAAhmed Amer· Junior AI Engineer
duration h: 6project: Hytechactivity: Manual QA

What the engine inferred

Inferred role
Junior AI Engineer
Inferred goal
QA automation ramp

The three scores

never a single number
45
Output value
40
Goal alignment
30
Leverage fit
45
Output
40
Alignment
30
Leverage

Dimension breakdown

how output value was earned
Correctness60

The manual QA itself is done diligently — but by hand.

Craft & clarity40

Repetitive, copy-paste process with no leverage.

Reliability impact35

Catches bugs but doesn't prevent them or scale.

Judgment trace

question → finding
  1. 1

    What goal was this meant to move?

    Reliability, indirectly — but 6h of manual regression every release is a system smell.

  2. 2

    How good is the work on its own terms?

    Diligent but low-leverage; this is exactly what !407 is starting to automate.

  3. 3

    Was this the highest-leverage use of capacity?

    No — and it's not Amer's fault. The system lacks e2e automation, so a junior burns 6h/release.

Narrative

Score the system, not Amer. Six hours of by-hand regression per release is a process failure, not a performance one. The good news is the fix is already in flight (!407). The story here is: finish the e2e harness so this time disappears.

Action ladder

how far the engine will go
Surface
Recommend
Prepare
Act
Recommended action

Treat !407 as priority-zero for the junior. Every automated flow erases hours of this manual work across the whole team.

Execute

Executing runs the recommended action; the engine logs the outcome against the goal.