wassname

"""General candidate-slot label placement for matplotlib: polygon-aware, short-leader, gist-ready.

The problem: matplotlib has no label placer that (a) tries every side of a marker and keeps the
nearest clear slot, (b) draws a leader line ONLY when the label had to move far, and (c) treats a
filled polygon (a convex-hull region) as something to avoid. adjustText (Phlya/adjustText) relaxes
by force and parks labels in local minima; textalloc (ckjellson/textalloc) does candidate placement
but only against points/lines/other-text, and always/never draws lines. This is a small placer that
does all three, generalised so ONE call handles both marker labels and region labels.

The generalisation (wassname's idea): every label owns a SET of 1..N candidate anchor points, and we

wassname

"""Minimal greedy non-overlapping label placement for plotly.

`place_labels` returns plotly annotation dicts that place text labels around
labelled scatter points without overlapping each other or nearby obstacle
markers. No maintained plotly-native label de-overlap exists as of 2025
(plotly.js issue #4674 open since 2020); this is textalloc / D3-Labeler in
miniature, with two deliberate differences:

  1. Determinism. The canvas is fixed (fig_w/fig_h/margins) and labels are
     placed in input order, so a given (points, obstacles) set always yields

wassname

"""
Bounded-thinking + force-answer judging for a weak reasoning model
==================================================================

Problem. A small reasoning model (e.g. qwen3.5-9b) used as a JUDGE deliberates to its
max-token budget on an ambiguous item and emits NO verdict. Your parser then silently
defaults to 0 / "tie", which is indistinguishable from a real "score = 0" -- so a
NON-conclusion gets laundered into a tie, and downstream a sign-test drops the item.
Two more traps: temperature=0 is OUT OF DISTRIBUTION for a thinking model (it can loop),
and OpenRouter reasoning-BUDGET knobs (effort=low/medium, reasoning_tokens=N) are IGNORED

wassname

---

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

wassname

"""minicache — anycache-spirit single-file disk cache.

Why not anycache: 
- in machine learning you can't has `model` so we have `exclude=["model", "tok"]``
- you sometimes want to cach `tensor` so we use cloudpickle and to compress if possible (TODO check it works)
- we add cloudpickle backend. Handles lambdas, closures, dataclasses, pandas,
  jax pytrees, torch tensors — everything stdlib pickle chokes on.
Why not just `functools.lru_cache + pickle.dump`: same arg-hashing problem.
- gzip on disk. ~3× smaller files. cloudpickle output streams cleanly
  through `gzip.open`, no extra tooling.

wassname

Principles

• don't use many tokens
• make it so a dumb summary LLM can easily 1) see problems 2) have clues to diagnose
• timing information

example of good log

• with single line should statement inline in log that make it clear how it should look, distinguish from subtle failures, and give principled clue for diagnosis
• table short have longest and least important lines last, so that humans can read it even with wrap around e.g short numeric columns first. long text columns last, notes or desc last
• use tabulate plain for token effecient, not logging each step or epoch but just table

wassname

    repetition_penalty (`float`, *optional*):

        The parameter for repetition penalty. 1.0 means no penalty. See [this
        paper](https://huggingface.co/papers/1909.05858) for more details.

https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py#L416

class RepetitionPenaltyLogitsProcessor(LogitsProcessor):

wassname

def classify_linear_sublayers(
    model,
    block_layers: list[str],
) -> dict[str, list[str]]:
    """Classify all Linear sublayers in each block by their residual-stream role.

    For a Linear layer with weight shape [out_features, in_features]:
      - residual_write : out_features == d_model  (writes TO residual stream)
      - residual_read  : in_features  == d_model  (reads FROM residual stream)

wassname

"""Reusable guided-rollout primitive: think → forced-close-think → JSON choice.

One rollout, three numbers. The same primitive backs:
  - calibrate()'s coherence + format + rep measurement
  - probe replay at edit time
  - post-keep probe regeneration
  - (future) DD eval (once _measure_logratios is ported to this)

Substrate:
  <user_prompt + schema_hint>

wassname

# Smoke test: demo on task 0 showing steered outputs at -1, 0, +1
smoke *ARGS:
    BEARTYPE=1 {{ PY }} ssteer_v3.py --quick {{ ARGS }} 2>&1 | tee /tmp/smoke.log | tail -80