Lies My Calculator and Computer Told Me [pdf]

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • mpmath

    Python library for arbitrary-precision floating-point arithmetic

  • What you've done here is tell SymPy to use extra precision for the intermediate (and final) output. This doesn't truly fix the problem of cancellation and loss of precision, but for many practical purposes it can postpone the problem long enough to give you a useful result.

    Internally, SymPy uses mpmath (https://mpmath.org/) for representation of numbers to arbitrary precision. You could install and use the latter library directly, gaining extra precision without going through symbolic manipulation.

    All that being said, it's still good practice to avoid loss of precision at the outset. Arbitrary-precision calculations are slow compared to hardware-native floating point operations. Using the example from mpmath's homepage in iPython:

        In [1]: import mpmath as mp; import scipy as sp; import numpy as np

  • 0.30000000000000004

    Floating Point Math Examples

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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