Topic

Evaluations

Benchmarks and evals — measuring models against real workloads.

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Can image models spell the labels? A first look at my Tech Graphics Benchmark

23 Cheshvan 5787

Can image models spell the labels? A first look at my Tech Graphics Benchmark

Limited evaluation: 95 images from five cheap image models, 12 annotated so far. In this run, 10 of 11 had every label spelled exactly; the problems were composition, stretching and style. Not a verdict on any model.

evals benchmarks image-generation +2
Boxty v1.0: English dictation without the Hebrew

9 Cheshvan 5787

Boxty v1.0: English dictation without the Hebrew

Boxty is the no-Hebrew sibling of my Jachnun code-switching model: Parakeet fine-tuned on my voice for plain English and tech dictation. Tech WER drops from 2.51% (stock) to 1.35%, and general English holds.

speech-recognition parakeet fine-tuning +3
Do LLMs push back on conspiracy believers? A small first look

9 Cheshvan 5787

Do LLMs push back on conspiracy believers? A small first look

A small, hand-graded first look at my conspiracy-endorsement eval: 7 prompts, 7 models, one sample each. In this run two uncensored models helped a believer and an invented theory tripped up two more. Not a verdict on any model.

evals llm ai-models +3
Whisper Hebrish Small ACFT: a code-switching model for FUTO Keyboard

6 Cheshvan 5787 Update

Whisper Hebrish Small ACFT: a code-switching model for FUTO Keyboard

A phone-sized Whisper Hebrish for FUTO Keyboard on Android. It halves the stock English model's error rate on English-with-Hebrew dictation (11.8% to 5.8% WER) and imports as a single 264 MB file.

ai-models whisper speech-recognition +4
Whisper Hebrish v2 Released

6 Cheshvan 5787 Update

Whisper Hebrish v2 Released

Version 2 of Whisper Hebrish, my Whisper fine-tune for English with Hebrew words mixed in. Code-switching WER drops from 14.7% (stock turbo) to 6.7%, and there are whisper.cpp builds.

ai-models whisper speech-recognition +4
inventory-spec-extractor: a 34 MB model for reading product spec lines

4 Cheshvan 5787 Update

inventory-spec-extractor: a 34 MB model for reading product spec lines

A small token-classification model that pulls voltages, connectors, materials and 22 other attributes out of one line of a product spec. Runs on CPU in about 20 ms per line. F1 0.85 against a regex baseline's 0.58.

ai-models machine-learning home-inventory +3
Can speech-to-text spell ‘makolet’? I tested seven models on Hebrish

4 Cheshvan 5787

Can speech-to-text spell ‘makolet’? I tested seven models on Hebrish

English speakers in Israel talk Hebrish, and dictation software mangles the Hebrew words. I built an open benchmark to measure it: my fine-tune gets two-thirds of them right, off-the-shelf models a third to under half.

benchmarks code-switching evals +5
How much bitrate does an audio-multimodal LLM actually need?

30 Nisan 5786

How much bitrate does an audio-multimodal LLM actually need?

An eval across 12 audio-capable models on OpenRouter, 5 MP3 bitrates, 4 dictation samples. The default upload quality in most dictation apps is 2–4× overprovisioned — and the more interesting finding had nothing to do with bitrate.

audio dictation evals +6
Bias and censorship evaluation tests for large language models

7 Nisan 5786

Bias and censorship evaluation tests for large language models

Test prompts and notes exploring cultural bias, censorship, and worldview differences across Western and Eastern large language models.

AI Bias Censorship +4