lmmx

Assistant Summary

• Assistant simulating task challenges: evaluating personal abilities and task complexities
• Demeanour: straightforward, polite, British English, not forceful/demanding
• Queries user's motivating "drives" and to disambiguate task evaluation, as necessary
• Assumes where possible to minimise questions.
• Avoids applying external social pressures that could trigger defiance or aversion.

(Paste task_description.md here, verbatim)

lmmx

uv add --dev $(tq -f pyproject.toml 'tool.pdm.dev-dependencies.test' -o json | jq -r '.[]')

lmmx

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, GetCoreSchemaHandler, TypeAdapter, constr
from pydantic_core import CoreSchema, core_schema

aa_ta = TypeAdapter(constr(pattern=r"^(?i)aa.{10}$"))

lmmx

from pprint import pformat

from inline_snapshot import outsource, snapshot


def round_down_to_order_of_magnitude(n):
    """Small values go to a decimal, so `int` would coerce to zero"""
    magnitude = 10 ** (len(str(n)) - 1)
    return int((n // magnitude) * magnitude) or n

lmmx

from pathlib import Path
from pydantic import BaseModel, BeforeValidator, TypeAdapter, model_validator
from typing import Annotated, Union, Any, Generic, TypeVar

RoutePart = Union[int, str]
rp_ta = TypeAdapter(RoutePart)


def split_route(route: str) -> list[RoutePart]:
    match route:

lmmx

from pathlib import Path
from pydantic import BaseModel, BeforeValidator, TypeAdapter
from typing import Annotated, Union

RoutePart = Union[int, str]
rp_ta = TypeAdapter(RoutePart)


def split_route(route: str) -> list[RoutePart]:
    match route:

lmmx

from pydantic import BaseModel, model_validator

class Model(BaseModel, extra="allow"):
    a: int
    b: int

    @model_validator(mode="before")
    @classmethod
    def pop_off_extra(cls, values: dict):
        fields = list(cls.model_fields)

lmmx

from inspect import Parameter, signature
from typing import Any, Callable, ClassVar, Generic, ParamSpec, TypeVar

from pydantic import BaseModel, Field, model_validator

P = ParamSpec("P")
R = TypeVar("R")
T = TypeVar("T", bound=BaseModel)

lmmx

from inspect import Parameter, signature
from typing import Any, Callable, Generic, ParamSpec, TypeVar

from pydantic import BaseModel, Field, model_validator

P = ParamSpec("P")
R = TypeVar("R")
T = TypeVar("T", bound=BaseModel)

lmmx

Code style: one file, no external libraries besides pathlib and Pydantic, never use with open, Python 3.10+ modern code style (list not List style hints), no bloat or sprawl. Prefer list/dictcomps over loops where possible. Don't use global or otherwise 'free' variables for configuration when you could use a Pydantic model, and set default values on fields rather than setting them at runtime where applicable.