Subject: Request to resolve heating issue promptly

Hi [Landlord’s Name],

I’m following up on the heating issue I first reported on [date]. It has now been three weeks, and the heating still isn’t working. Being without reliable heat for this long has been difficult, and I need the repair to be treated as a priority.

Please confirm by [date/time] when a qualified technician will attend and when you expect the heating to be fully restored. If there will be any further delay, please arrange a safe temporary heating solution in the meantime.

I’m happy to coordinate access to help get this resolved quickly. My goal is simply to have working heating and a clear timeline for the repair.

Thank you for your prompt attention. I look forward to your confirmation.

Best, [Your Name] [Property Address / Unit Number]

import { useEffect, useState } from "react";

export function useDebouncedValue<T>(value: T, delayMs: number): T {
  const [debouncedValue, setDebouncedValue] = useState<T>(value);

  useEffect(() => {
    const timeoutId = setTimeout(() => {
      // The wrapper also supports values that are themselves functions.
      setDebouncedValue(() => value);
    }, delayMs);

    // Cancel the pending update when dependencies change or on unmount.
    return () => clearTimeout(timeoutId);
  }, [value, delayMs]);

  return debouncedValue;
}

Usage:

function SearchInput() {
  const [query, setQuery] = useState("");
  const debouncedQuery = useDebouncedValue(query, 300);

  useEffect(() => {
    // Run your search using debouncedQuery.
    console.log("Search:", debouncedQuery);
  }, [debouncedQuery]);

  return (
    <input
      value={query}
      onChange={(event) => setQuery(event.target.value)}
      placeholder="Search..."
    />
  );
}

The initial value is returned immediately. Subsequent changes are applied after delayMs without another change; pending timers are cleared on unmount.

About 33.2%, assuming “99% accurate” means both:

• 99% of drug users test positive.

• 99% of non-users test negative.

Imagine testing 20,000 people:

Of the 298 positive results, only 99 come from drug users:

[ P(\text{uses drug}\mid\text{positive})=\frac{99}{99+199}\approx \boxed{33.2\%}. ]

Why it surprises people: We tend to confuse “a user has a 99% chance of testing positive” with “someone who tests positive has a 99% chance of being a user.” Because drug use is rare, the much larger non-user population produces more false positives than true positives—even with a small false-positive rate. Ignoring that prevalence is called the base-rate fallacy.

If “99% accurate” means only overall accuracy, there isn’t enough information to calculate the probability.