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If You Like Hacks(2021)

TV

Based on real preference data from thousands of voters, here's what fans of Hacks also love. Use these connections to discover your next favorite.

39 connections found

Other versions:2021 2016

39 connections

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1

The White Lotus(2021)

63x

127 voters

2

55x

50 voters

3

Abbott Elementary(2021)

53x

50 voters

4

50x

66 voters

5

45x

74 voters

6

Only Murders in the Building(2021)

36x

65 voters

7

Ted Lasso(2020)

34x

75 voters

8

32x

54 voters

9

Succession(2018)

31x

89 voters

10

Dead to Me(2019)

23x

65 voters

11

The Marvelous Mrs. Maisel(2017)

22x

78 voters

12

22x

57 voters

13

Severance(2022)

18x

56 voters

14

15x

65 voters

15

Freaks and Geeks(1999)

7.5x

50 voters

16

6.3x

54 voters

17

The Good Place(2016)

4.9x

55 voters

18

Arrested Development(2003)

4.4x

60 voters

19

Parks and Recreation(2009)

3.9x

66 voters

20

Modern Family(2009)

3.6x

51 voters

21

The Office(2005)

3.1x

63 voters

22

I'm Alan Partridge(1997)

0.0x

3 voters

23

George Lopez(2002)

0.0x

3 voters

24

Big Brother(2000)

0.0x

3 voters

25

Melrose Place(1992)

0.0x

3 voters

26

Ghost Whisperer(2005)

0.0x

6 voters

27

0.0x

3 voters

28

Insatiable(2018)

0.0x

8 voters

29

Fuller House(2016)

0.0x

3 voters

30

Up All Night(2011)

0.0x

3 voters

31

16 and Pregnant(2009)

0.0x

3 voters

32

Last Man Standing(2011)

0.0x

3 voters

33

SpongeBob SquarePants(1999)

0.0x

3 voters

34

Hagane No Renkinjutsushi(2003)

0.0x

3 voters

35

Home Movies(1999)

0.0x

3 voters

36

0.0x

12 voters

37

0.0x

4 voters

38

Two and a Half Men(2003)

0.0x

11 voters

39

Smallville(2001)

0.0x

7 voters

How does this work?

These recommendations are based on real voting data, not algorithms. When thousands of people vote on their favorites, patterns emerge — people who love Hacks consistently tend to also love certain other things. The "strength" score shows how much more likely fans of Hacks are to enjoy each recommendation compared to the average person.

Unlike algorithmic recommendations, these connections come from actual human preferences. A high strength score means the connection is genuine — not just because two items share a category, but because the same people genuinely love both.

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