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Liz arrived at PyBay about an hour before her scheduled talk, ready to present an expanded version of a Pydantic talk she had given at smaller meetups. But she had missed an email asking speakers to check in two hours before their slots. By the time she reached the check-in table, her slot had gone to an alternate. Her account connects that painful conference mistake with the subject of her planned talk: how software handles invalid input—and how people might respond when plans fail.
What happened when Liz missed the speaker check-in?
Liz had spoken at PyBay before and was excited to return with an expanded Pydantic presentation. She prepared slides and a Jupyter notebook, rehearsed, and planned to arrive roughly an hour before her scheduled slot. She did not see the email saying speakers needed to check in two hours in advance.
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At the check-in table, she learned that an alternate had received her slot. Liz later found the message unread in her inbox and noticed a missed call. She attributed the call issue to unreliable phone signal while crossing the Bay Bridge. Those details come from Liz’s own account, rather than independently verified conference records.
Liz says the organizers had adopted a policy in response to earlier problems and that she understood their decision. A friend was with her, and an organizer offered comfort. Instead of leaving, Liz stayed for another talk and reflected on her disappointment and embarrassment, as well as the kindness she encountered. One lesson she took away was to check times carefully—and to try to offer herself the compassion others had shown her.
#1 Best Overall
What was the Pydantic talk about?
Liz’s planned talk, “Seeking validation,” introduced Pydantic through a practical idea: a data model can do more than reject inputs that do not already have the expected Python type. In the talk’s description, Pydantic can parse some inputs into the target type. For example, in lax mode, the string "14" may be converted to the integer 14. If the input cannot be parsed, Pydantic can raise a ValidationError.
Liz attributes the principle “don’t just validate, parse it” to software engineer Alexis King. The point is not that every input should be accepted: conversion works only when the input can be made to satisfy the model’s requirements. When it cannot, structured validation errors can explain what went wrong.
How did the proposed examples explain Pydantic?
Python model definitions and parsing
The examples in Liz’s post define a Pug model with constrained fields, then show how an input string can be coerced to an integer. The model gives Python code a declared shape for its data, while parsing and validation determine whether incoming values fit that shape or can be converted to it.
Schema, validation, and serialization
The talk describes Pydantic as generating a core schema for a model and using pydantic-core for validation and serialization. Liz characterizes pydantic-core as a high-performance engine implemented in Rust. The post does not provide an independently reproduced benchmark, so this description should not be read as a quantified performance result.
Other examples inspect the generated core schema and the model’s validator and serializer objects. Together, they illustrate how a Python-facing model definition connects to the machinery that processes data.
Useful errors rather than silent failure
Liz’s examples also supply an invalid name and an age that cannot be parsed, producing structured validation errors. That contrast helps clarify the boundary: an acceptable value may pass as-is or after conversion; an unacceptable one can fail with information about the field and problem instead of proceeding as though the data were valid.
Rank #4
Why use validation before an API request?
As a real-world example, Liz’s talk points to Pydantic models in the Vonage Python SDK and describes catching invalid input before making an API call. Local validation can make errors visible at the point where application data is prepared, rather than waiting for a downstream service to reject a request.
The article warns that its sample tests make real API calls and may incur costs. Do not run those tests without first checking the current code and service behavior.
Why connect a missed check-in with validation?
The connection is a matter of framing, not equivalence: the conference mistake was a human scheduling failure, while Pydantic handles data according to rules set in code. Still, Liz’s story and proposed talk share a useful theme. Systems need clear expectations and meaningful responses when something does not meet them; people, too, may need room to acknowledge a failure and recover with help from others. Liz summed up the unexpected overlap with the line, “I guess that’s actually a really meta demonstration of Pydantic.”
Read Liz’s first-person account and the full proposed tutorial on DEV Community.
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