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aientity · one source so far

pydantic

for ai founders

Steadynpm
Signal score
0
Live items
1
Trajectory
↓ Cooling
7-day est. ~0

Why this scored 0

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+12.2 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+0.0 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−29.0 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite-16.8 → clamped to 0

Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology

Outlook

medium confidence · estimate, not a guarantee

7-day

~0

range 0–12

14-day

~0

range 0–14

30-day

~0

range 0–19

Signal history

7-day window (free)
026

projected trajectory (estimate, not a guarantee)

The evidence

The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.

  1. 1
    31

    Python dataclasses - intercept field assignment

    intercept field assignment · for python devs

    stackoverflowSteady 3 · pythondevtools82d ago
  2. 2
    21

    How to express a conditionally required parameter in a Pydantic-validated tool/API schema, without losing the upfront schema validation?

    Shows how to enforce conditional required fields without breaking schema validation. · for python backend developers

    stackoverflowSteadydevtools11d ago
  3. 3
    19

    The time I screwed up a conference talk (And the talk on Pydantic I would have given)

    Missed Pydantic talk underscores need for clear preparation when presenting Python libraries. · for python developers, devrel teams

    devtoSteadydevtools21d ago
  4. 4
    0

    @pydantic/genai-prices — Calculate prices for calling LLM inference APIs

    LLM price calculator · for ai founders

    npmSteadyai−23 saturated102d ago