Public data on which coding agents actually use the Hugging Face Hub, refreshed monthly. Claude Code now sends a majority of everything the Hub can attribute to a named agent. In May, it led on 4 days out of 30. In July, all 30. huggingface.co/datasets/hug...

A film catalogue tells you what a film is about, not what happens inside it. So: 1,864 public-domain Prelinger films, described every ~60 seconds by an open 2B video model. 23,148 searchable moments. Search "typing on a computer keyboard", land on the second it happens.

New recipe: timestamped video captions on @hf.co Jobs. Point it at a bucket of videos → parquet dataset out: scene descriptions + second-precise <start – end> events. ~$0.05 per hour of footage on a single A10G (Marlin-2B on vLLM). huggingface.co/datasets/uv-...

Small open models are getting genuinely good at document parsing: OvisOCR2 (0.9B, Apache 2.0) is claiming SOTA on OmniDocBench v1.6. Day-1 recipe: OCR a whole HF image dataset — digitised newspapers, archives, zines — to markdown with one command. huggingface.co/datasets/uv-scripts/ocr

Small open models are getting genuinely good at document parsing: OvisOCR2 (0.9B, Apache 2.0) is claiming SOTA on OmniDocBench v1.6. Day-1 recipe: OCR a whole HF image dataset — digitised newspapers, archives, zines — to markdown with one command. huggingface.co/datasets/uv-scripts/ocr

Reading the Archive by Machine: An OCR Benchmark for Historians, 1612–1921 Here is version 1 of a working paper on the new OCR tools that are transforming digital history. working-papers-in-critical-search.github.io/paper-004-oc...

I don't have it yet, but there's an econ history paper in these transcripts or transcripts like them (that we can now turn into textual data at the cost of pennies per hour)

Daniel van Strien@danielvanstrien.bsky.social · 4w ago

Open ASR models with speaker diarization are now fast and cheap: I diarized 174 hours of Apollo 11 mission audio (the real July 1969 NASA tapes) for $9.46 with a 0.9B open model. Search it, hear any moment on the original tape: huggingface.co/spaces/davan...

Open ASR models with speaker diarization are now fast and cheap: I diarized 174 hours of Apollo 11 mission audio (the real July 1969 NASA tapes) for $9.46 with a 0.9B open model. Search it, hear any moment on the original tape: huggingface.co/spaces/davan...

I ran 10 newer OCR models on @ai2.bsky.social's olmOCR-bench "old scans" subset. The ranking flips depending on what you actually want.

Coding agents are real users of the @hf.co Hub! They're searching for models, building and pushing datasets, training models on Jobs, spinning up Spaces... Now there's public data: each agent's share of Hub traffic, updated monthly 👇

The latest @commoncrawl.bsky.social crawl indexes way more than HTML pages. 20.9M PDFs. Plus calendars, BibTeX, markdown... The whole index now lives in a @hf.co Bucket, so I pulled this with one SQL query straight over it (new S3 API). 2.1B rows, nothing downloaded, $0 to read.

You can now use 100s of tools with @hf.co Buckets, thanks to the new S3 API! Usually just one or two lines to change. huggingface.co/docs/hub/sto...

Fairly benchmarking OCR models is hard! Ran a few newer OCR models on @ai2.bsky.social's olmOCR-bench "old scans" subset. Worth knowing: the score "punishes" models for extracting too much (letterheads, stamps, etc.)

If libraries, archives and museums pooled their (labelled) data, they could build state-of-the-art open models for the things they actually care about! I tried a small version: one open model (NuExtract-3, 4B) fine-tuned to read archival index cards across several collections.

Excited to share this. @neel2112.bsky.social, @mariaa.bsky.social, and I analyzed 500K anonymous ChatGPT convos (shared w/ consent from WildChat) to see if people were generating fiction. We found tons of stories, fanfiction & erotica. Many users iterated on the same stories for days and weeks.

Say, for example, if we had 3 examples of labeled city directory data from every state in the US, we could easily train a generic city directory parser! (I’m actually working on this, and I’ll have it done relatively soon, but @danielvanstrien.bsky.social’s point holds in so many other cases!)

Daniel van Strien@danielvanstrien.bsky.social · last mo.

If libraries, archives and museums pooled their (labelled) data, they could build state-of-the-art open models for the things they actually care about! I tried a small version: one open model (NuExtract-3, 4B) fine-tuned to read archival index cards across several collections.

If libraries, archives and museums pooled their (labelled) data, they could build state-of-the-art open models for the things they actually care about! I tried a small version: one open model (NuExtract-3, 4B) fine-tuned to read archival index cards across several collections.

Ran the same 650M model across 7 languages: German, Polish, Finnish, Russian, Serbian, Estonian, Swedish via @europeana.bsky.social newspapers. Still some challenging layouts, but pretty mind-blowing to me that a 650M model is doing this when a year ago 72B VLMs failed very often.

Daniel van Strien@danielvanstrien.bsky.social · last mo.

I think VLM-based OCR might finally be close to working on historic newspapers! Many models I've tried before failed i.e. hallucinations, repetition loops, context overflow. Surya OCR 2 (a 650M model!) does a very good job!

I think VLM-based OCR might finally be close to working on historic newspapers! Many models I've tried before failed i.e. hallucinations, repetition loops, context overflow. Surya OCR 2 (a 650M model!) does a very good job!

I've been offline more than usual because of paper and grant deadlines plus work travel, but I'm back with my first real newsletter today! Click for rambling about infrastructure, benchmarks, Babylonian tablets, and more.

Dispatch: Humanistic AI, OCR, and Hugging Face

In which I reflect on the a recent trip to Chicago, overview the state of OCR for humanities data, and fangirl over Hugging Face infrastructure.

field-notes.leaflet.pub

Can the new DiffusionGemma model help fix broken OCR? In theory, denoising tokens in parallel could work better for OCR correction since context is seen upfront? Pointed it at 19th-century newspaper OCR. It corrected better than the autoregressive baseline — at ~8x the speed.

Can the new DiffusionGemma model help fix broken OCR? In theory, denoising tokens in parallel could work better for OCR correction since context is seen upfront? Pointed it at 19th-century newspaper OCR. It corrected better than the autoregressive baseline — at ~8x the speed.

Got a digitised collection that needs OCR? uv-scripts is a set of single-file Python scripts that OCR a whole image dataset to markdown in one command — 20+ open VLMs to pick from, nothing to install but uv. github.com/davanstrien/...

Got a digitised collection that needs OCR? uv-scripts is a set of single-file Python scripts that OCR a whole image dataset to markdown in one command — 20+ open VLMs to pick from, nothing to install but uv. github.com/davanstrien/...

What could a rich ecosystem of small GLAM AI models enable? IMO: cheaper, better-fitted, more robust models. Example: I extended an existing @natlibscot.bsky.social archival card detector to 4 collections to make a more generic index card detector. Took an hour or two and minimal $

Derived datasets are bigger on Hugging Face Hub than people realise. ~73% of analysed datasets on the Hub are derivatives of something else, i.e. cleaned, translated, extended, etc. Built an explorer that infers the missing lineage from content: huggingface.co/spaces/davan...

Built a useful GLAM model this week: flags blank index cards before expensive OCR. Cheap, scales, adapts. IMO the technical barrier is now very low, the main one is knowing how. So I wrote this up as a chapter: danielvanstrien.xyz/ai-patterns-for-glam/patterns/index-card-classifier.html

You can now run SQL over 2.19 BILLION web pages — zero download. @commoncrawl.bsky.social April 2026 crawl + URL index are on Hugging Face Storage Buckets. DuckDB reads it straight over hf:// — I counted all 2.19B in ~35s. Or point your own agent at it 👇 huggingface.co/spaces/davan...

The 'why' behind yesterday's demo: libraries don't (just) want searchable text from catalogue cards. They want structured records they can ingest into existing systems. Short blog + a live demo for this library use case: danielvanstrien.xyz/posts/2026/s...

Daniel van Strien@danielvanstrien.bsky.social · 3mo ago

NuExtract3 (4B, Apache-2.0) does OCR *and* structured extraction. Point it at a dataset of scanned index cards + a JSON schema → clean catalog JSON One command on huggingface Jobs. (or skip the schema for plain Markdown OCR) Script + dataset 👇

NuExtract3 (4B, Apache-2.0) does OCR *and* structured extraction. Point it at a dataset of scanned index cards + a JSON schema → clean catalog JSON One command on huggingface Jobs. (or skip the schema for plain Markdown OCR) Script + dataset 👇