Write a polite but firm email to a landlord who has not fixed the heating for three weeks. I want the repair, not a fight.
Qwen 3.8 Max
Subject: Urgent: Heating repair needed
Dear [Landlord/Property Manager’s Name],
I hope you’re well. I’m writing to follow up on the heating issue at [address/unit number], which I first reported on [date]. It has now been three weeks, and the heating is still not working properly.
I understand that repairs can sometimes take time, but the lack of heat is making it difficult to comfortably and safely use the home, especially given the weather. I would appreciate it if you could arrange for the repair to be completed as soon as possible.
Could you please let me know by [specific date, e.g., the end of the week] what the next step will be and when a repair person will attend? If there is a delay, please also let me know the reason and the expected timeline.
I’d like to resolve this quickly and amicably, and I’m happy to cooperate with access or scheduling if needed. Please let me know if you need anything further from me.
Thank you for your attention to this matter.
Best regards, [Your Name] [Phone Number] [Address/Unit Number]
Asked Nov 1, 2026. Unedited.
Write a React hook that debounces a value and cancels on unmount. Include the TypeScript types.
Qwen 3.8 Max
import { useEffect, useState } from "react";
export function useDebouncedValue<T>(value: T, delayMs: number = 300): T {
const [debouncedValue, setDebouncedValue] = useState<T>(value);
useEffect(() => {
const timeoutId: ReturnType<typeof setTimeout> = setTimeout(() => {
setDebouncedValue(value);
}, delayMs);
// Cancels the pending debounce when:
// - `value` changes
// - `delayMs` changes
// - the component unmounts
return () => {
clearTimeout(timeoutId);
};
}, [value, delayMs]);
return debouncedValue;
}Usage:
import { useState } from "react";
import { useDebouncedValue } from "./useDebouncedValue";
export function SearchInput() {
const [search, setSearch] = useState("");
const debouncedSearch = useDebouncedValue(search, 500);
return (
<div>
<input
value={search}
onChange={(event) => setSearch(event.target.value)}
/>
<p>Debounced value: {debouncedSearch}</p>
</div>
);
}Asked Nov 1, 2026. Unedited.
A drug test is 99% accurate, and 0.5% of people use the drug. Someone tests positive. What is the probability they use it, and why does the answer surprise people?
Qwen 3.8 Max
Assuming “99% accurate” means:
99% sensitivity: a user tests positive with probability 0.99
99% specificity: a non-user tests negative with probability 0.99
and the prevalence is 0.5%, then:
[ P(\text{user} \mid \text{positive}) = \frac{P(\text{positive} \mid \text{user})P(\text{user})} {P(\text{positive} \mid \text{user})P(\text{user}) + P(\text{positive} \mid \text{non-user})P(\text{non-user})} ]
[ = \frac{0.99 \times 0.005} {0.99 \times 0.005 + 0.01 \times 0.995} ]
[ = \frac{0.00495}{0.00495 + 0.00995} \approx 0.332 ]
So the probability they actually use the drug is about 33%.
Why this surprises people
People often hear “99% accurate” and think a positive result must mean there is a 99% chance the person uses the drug. But that confuses:
[ P(\text{positive} \mid \text{user}) ]
with:
[ P(\text{user} \mid \text{positive}) ]
The key issue is the base rate: only 0.5% of people use the drug, so there are many more non-users than users. Even a small false-positive rate among the large non-user population creates many false positives.
Natural-frequency explanation
Imagine testing 100,000 people:
Drug users: [ 0.5\% \times 100{,}000 = 500 ] True positives among users: [ 99\% \times 500 = 495 ]
Non-users: [ 99{,}500 ] False positives among non-users: [ 1\% \times 99{,}500 = 995 ]
Total positive tests:
[ 495 + 995 = 1{,}490 ]
Of those, actual users are:
[ \frac{495}{1{,}490} \approx 33\% ]
So despite the test being highly accurate, a positive result only implies about a one-in-three chance of actual drug use because the drug is rare in the population. This is a classic example of base-rate neglect.
Asked Nov 1, 2026. Unedited.
What it’s good at
Ranked against 42 models, from public leaderboards and our own blind tests where we have them. How we rank
- #5of 42 for writing
- #8of 42 for everyday chat
- #10of 42 for coding
The facts
Qwen 3.8 Max is the largest model in Alibaba’s Qwen 3.8 family. It reads images as well as text.
Scores from artificialanalysis.ai and lmarena.ai.
- Made by
- Alibaba
- API price, per 1M tokens
- $2 in, $6 out
- Speed
- 41 tokens a second
- Artificial Analysis Index
- 45.4
- LMArena text
- 1,484
- LMArena coding
- 1,503
- Updated
- Oct 26, 2026