stable-worldmodel-a-high-performance-platform-for-reproducible-world-model-research
Ayush Chaurasia
Quentin Lhoest
Lucas Maes
Quentin Le Lidec
reproducible-data-curation-in-the-multimodal-lakehouse
Prashanth Rao
newsletter-may-2026
ChanChan Mao
newsletter-april-2026
ChanChan Mao
how-lancedb-accelerates-vector-search-at-10-billion-scale
Yang Cen
opensearch-vs-lancedb-for-vector-search-query-cost-and-infrastructure
Justin Miller
volcano-engine-autonomous-driving-data-lake-solution
Kejian Ju
unifying-the-av-ml-stack-lancedb
Ayush Chaurasia
lance-json-support-why-you-might-not-really-need-variant
Jack Ye
building-a-storage-format-for-the-next-era-of-biology
Pavan Ramkumar
newsletter-march-2026
ChanChan Mao
smart-parsing-meets-sharp-retrieval-combining-liteparse-and-lancedb
Clelia Astra Bertelli
Prashanth Rao
lance-format-v2-2-benchmarks-half-the-storage-none-of-the-slowdown
Xuanwo
make-your-sql-workflows-multimodal-with-lancedb-x-duckdb
Prashanth Rao
agentic-coding-as-community-stewardship
Xuanwo
what-we-mean-by-multimodal
Prashanth Rao
ai-native-development-local-continue-lancedb
Ty Dunn
lance-file-format-2-2-taming-complex-data
Xuanwo
lance-blob-v2
Xuanwo
Jack Ye
openclaw-lancedb-memory-layer
Xuanwo
Prashanth Rao
openclaw-lancedb-seed2
LanceDB
openclaw-memory-from-zero-to-lancedb-pro
Prashanth Rao
upload-lance-datasets-to-hf-hub
Prashanth Rao
zero-shot-image-classification-with-vector-search
Vipul Maheshwari
werides-data-platform-transformation-how-lancedb-fuels-model-development-velocity
Qian Zhu
Fei Chen
training-a-variational-autoencoder-from-scratch-with-the-lance-file-format
LanceDB
track-ai-trends-crewai-agents-rag
LanceDB
tokens-per-second-is-not-all-you-need
Mingran Wang
Tan Li
the-future-of-open-source-table-formats-iceberg-and-lance
Jack Ye
the-case-for-random-access-i-o
LanceDB
series-a-funding
Chang She
semanticdotart
Ayush Chaurasia
second-dinners-secret-weapon-lancedb-powered-rag-for-faster-smarter-game-development
Qian Zhu
search-within-an-image-331b54e4285e
Kaushal Choudhary
scalable-computer-vision-with-lancedb-voxel51-d8b65066d5f6
LanceDB
rethinking-table-file-paths-lance-multi-base-layout
Jack Ye
rag-isnt-one-size-fits-all
Leonard Marcq
python-package-to-convert-image-datasets-to-lance-type
Vipul Maheshwari
one-million-iops
Weston Pace
november-feature-roundup
Will Jones
newsletter-september-2025
Jasmine Wang
newsletter-october-2025
Jasmine Wang
newsletter-november-2025
ChanChan Mao
newsletter-june-2025
David Myriel
newsletter-july-2025
Jasmine Wang
newsletter-january-2026
ChanChan Mao
newsletter-february-2026
ChanChan Mao
newsletter-december-2025
ChanChan Mao
newsletter-august-2025
Jasmine Wang
my-summer-internship-experience-at-lancedb-2
Raunak Sinha
my-simd-is-faster-than-yours-fb2989bf25e7
LanceDB
multimodal-myntra-fashion-search-engine-using-lancedb
LanceDB
multimodal-lakehouse
David Myriel
multi-document-agentic-rag-a-walkthrough
Vipul Maheshwari
modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6
Mahesh Deshwal
memgpt-os-inspired-llms-that-manage-their-own-memory-793d6eed417e
Ayush Chaurasia
late-interaction-efficient-multi-modal-retrievers-need-more-than-just-a-vector-index
Ayush Chaurasia
lancedb-x-continue
LanceDB
lance-x-huggingface-a-new-era-of-sharing-multimodal-data
Prashanth Rao
Quentin Lhoest
Xuanwo
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lance-x-duckdb-sql-retrieval-on-the-multimodal-lakehouse-format
Xuanwo
lance-windows-windows-lance
Chang She
lance-v2
Weston Pace
lance-namespace-lancedb-and-ray
Jack Ye
lance-file-2-1-stable
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lance-file-2-1-smaller-and-simpler
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lance-data-viewer
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introducing-lance-namespace-spark-integration
Jack Ye
implementing-corrective-rag-in-the-easiest-way-2
LanceDB
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Mahesh Deshwal
hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6
LanceDB
hybrid-search-and-custom-reranking-with-lancedb-4c10a6a3447e
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how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f
Tevin Wang
guide-to-use-contextual-retrieval-and-prompt-caching-with-lancedb
LanceDB
grpo-understanding-and-fine-tuning-the-next-gen-reasoning-model-2
Mahesh Deshwal
graphrag-hierarchical-approach-to-retrieval-augmented-generation
Akash Desai
gpu-accelerated-indexing-in-lancedb-27558fa7eee5
LanceDB
geo-support
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geneva-twelvelabs
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geneva-feature-engineering
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fluss-integration
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file-readers-in-depth-parallelism-without-row-groups
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feature-rabitq-quantization
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Yang Cen
feature-full-text-search
David Myriel
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Kaushal Choudhary
effortlessly-loading-and-processing-images-with-lance-a-code-walkthrough
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Ayush Chaurasia
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Prashanth Rao
Linghua Jin

Lance, Windows. Windows, Lance

May 31, 2023
Engineering

It was Spring of 2012. After being an avid user for 2+ years, I finally decided to join Wes Mckinney and work on pandas full time. We worked out of an apartment in Brooklyn on pandas by day. By night Wes was working on the first edition of Python for Data Analysis while I was working on what probably was the very first python package for quantitative portfolio management.

👷 In the days before github actions, distributing your python package was a very manual affair. We had a Mac Mini that we used to build the mac wheel, and our own linux desktops to build the linux wheel. We had no windows machines and didn’t have the bandwidth to deal with yet another toolchain.

🚑 And that’s when Christoph came to the rescue. I knew him best by his academic url https://www.lfd.uci.edu/~gohlke, where he build windows binaries for pandas, along with a lot of other packages in the scientific python stack that didn’t build windows wheels (or sometimes no wheels at all). Prior to each release we would send an email to Christoph and magically windows wheels would show up in a few days.

😢 I was saddened to come across this old Reddit post saying that Christoph’s lab has lost funding and the windows binaries would stop updating. I sincerely hope that work is able to continue elsewhere and he’s able to find funding elsewhere. Without his contributions to each and every package he maintained, the python data community would be largely devoid of windows users.

Windows support is coming to Lance

Christoph and so many unsung heroes of the python OSS community have been keeping the whole scientific python stack standing all these years. It is with this feeling of immense gratitude that we’re announcing windows support in Lance starting v0.4.0.

Installation and usage

Installing Lance on Windows is the same as other OS: pip install pylance . Windows wheels are only available starting v0.4.0.

Using Lance in python is also the same with import lance , lance.dataset(uri) , and lance.write_data(data, uri) .

Non-user facing changes

Required changes for supporting windows came in two flavors:

  1. Importing different libraries based on OS. This is no different than say using accelerate on MacOS vs openblas on Linux. Sometimes if MacOS and Linux behavior should be the same but different from windows, you can use cfg(target_os = “unix”) as the flag.
  2. Where the rubber meets the operating system. This usually manifested as dealing with file paths, tilde’s, and backwards vs forward slashes. Fortunately Rust crates like shellexpand takes care most of this as well.

Packaging and testing

Turns out most of the time and effort was spent in testing changes, setting up the right dev / test environments, and also including the required dll’s with the final python wheel.

The testing loop for github actions is slow so adding the required GHA for CI and CD are particularly frustrating. Manually installing protoc, configuring vcpkg, caching, etc etc etc. Each problem / task would take a long time.

Limitations

In this first milestone for windows support, we don’t yet link to LAPACK, which means that the OPQ option for the vector index in Lance is disabled on Windows. We’ll be addressing this soon.

Acknowledgements

Windows support would not be possible without the great work of Giancarlo Silvestrin, with additional contributions from Dennis Collinson.

Feedback welcome

If you’re on windows and interested in working with data on python, you can try out lance with pip install -U pylance . We’d love to hear your feedback. For more details, please check us out on github and don’t forget to give us a 🌟 if you like us!

Last but not least — Thank you Chistoph!

Chang She
CEO and Co-Founder of LanceDB

Stable-Worldmodel: A High Performance Platform for Reproducible World Model Research

Ayush Chaurasia
Quentin Lhoest
Lucas Maes
Quentin Le Lidec
June 2, 2026
stable-worldmodel-a-high-performance-platform-for-reproducible-world-model-research

🌍 Lance-Backed World Model Platform, 🦆 Multimodal SQL with Lance DuckDB Extension, 💰 LanceDB vs OpenSearch Cost Breakdown

ChanChan Mao
May 28, 2026
newsletter-may-2026

Reproducible Data Curation In The Multimodal Lakehouse

Prashanth Rao
May 29, 2026
reproducible-data-curation-in-the-multimodal-lakehouse