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"""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.

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"""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>

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---

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

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# This is a blocklist to block samsung smart tv's sending meta data at home.
# Please help to collect domains!
# It could be that the TV does not receive any more updates or other services no longer work. Please report such an incident.

abtauthprd.samsungcloudsolution.com
acr0.samsungcloudsolution.com
ad.samsungadhub.com
ads.samsungads.com
amauthprd.samsungcloudsolution.com
api-hub.samsungyosemite.com

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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

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    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):

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# 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

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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)

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GPU + uv in Claude Code sandbox (Linux/NVIDIA)

Problem: Claude Code's sandbox (bubblewrap) blocks GPU devices and the uv package cache.

Solution: bwrap wrapper at ~/.local/bin/bwrap

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PEFT Adapter papers

Orthogonal methods: OFT, BOFT, HRA, ROAD, GOFT (Givens), OFTv2 Low-rank methods: LoRA, AdaLoRA, LoHa, LoKr, RandLoRA, VBLoRA, FourierFT, DeLoRA Scaling methods: IA3, VeRA Prompt-based: Prompt Tuning, Prefix Tuning, P-Tuning, Adaption Prompt, Multitask Prompt Tuning, CPT Specialized: MiSS, SHiRA, C3A, LN Tuning, Poly, XLoRA

---