Christopher Akiki
@cakiki.bsky.social
research scientist at ScaDS.AI Leipzig in nlp, ir, and ml. @hf.co fellow. @lichess.org team member. @kaggle.com datasets expert.
Ah very cool! Any chance you can share the code for that?
February 9, 2026 at 2:54 PM
Ah very cool! Any chance you can share the code for that?
This absolutely phenomenal. What framework did you use for the frontend? cc @lelandmcinnes.bsky.social
February 9, 2026 at 2:51 PM
This absolutely phenomenal. What framework did you use for the frontend? cc @lelandmcinnes.bsky.social
Reposted by Christopher Akiki
Any chess position with 8 pieces on the board and at least one pair of opposed pawns has been solved! Lichess can now tell you definitively if it's a win, loss or draw with no engine required. Read about the massive technological undertaking to accomplish this partial 8 piece tablebase on our blog:
February 7, 2026 at 2:53 PM
Any chess position with 8 pieces on the board and at least one pair of opposed pawns has been solved! Lichess can now tell you definitively if it's a win, loss or draw with no engine required. Read about the massive technological undertaking to accomplish this partial 8 piece tablebase on our blog:
github.com/cakiki/chess...
In case you want to play with the files, I made this kaitai parser for the op1 format.
In case you want to play with the files, I made this kaitai parser for the op1 format.
chess-file-formats/tablebases/op1 at main · cakiki/chess-file-formats
Parsing utilities for binary chess file and database formats - cakiki/chess-file-formats
github.com
February 7, 2026 at 1:45 PM
github.com/cakiki/chess...
In case you want to play with the files, I made this kaitai parser for the op1 format.
In case you want to play with the files, I made this kaitai parser for the op1 format.
Looking forward to your course on there!
January 31, 2026 at 12:00 PM
Looking forward to your course on there!
They're parameter sweeps of the UMAP algorithm, varying the number of neighbors (rows) and the spread of the data (columns).
January 23, 2026 at 3:35 PM
They're parameter sweeps of the UMAP algorithm, varying the number of neighbors (rows) and the spread of the data (columns).
UMAP connectivity plots of 3,627 chess openings from the @lichess.org datasets (huggingface.co/datasets/Lic...)
January 20, 2026 at 12:21 PM
UMAP connectivity plots of 3,627 chess openings from the @lichess.org datasets (huggingface.co/datasets/Lic...)
I think you'll appreciate it's actually chess opening positions from @lichess.org : huggingface.co/datasets/Lic...
Lichess/chess-openings · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
January 18, 2026 at 5:50 PM
I think you'll appreciate it's actually chess opening positions from @lichess.org : huggingface.co/datasets/Lic...
Which colormap do you think looks the nicest? I'm leaning toward plasma.
January 18, 2026 at 3:30 PM
Which colormap do you think looks the nicest? I'm leaning toward plasma.
I'm not 100% sure if DBLP (dblp.org) indexes non-English venues, but the plot itself is only authors, not papers.
dblp: computer science bibliography
The dblp computer science bibliography is the online reference for open bibliographic information on major computer science journals and proceedings.
dblp.org
January 16, 2026 at 8:19 PM
I'm not 100% sure if DBLP (dblp.org) indexes non-English venues, but the plot itself is only authors, not papers.
Scatterplot of 4 million computer science authors, laid out according to co-authorship connections. Large blob in the bottom left are all single authors; removing them lets the plot breathe more somehow.
The source of the data is the @dblp.org bibliography.
The source of the data is the @dblp.org bibliography.
January 16, 2026 at 4:31 PM
Scatterplot of 4 million computer science authors, laid out according to co-authorship connections. Large blob in the bottom left are all single authors; removing them lets the plot breathe more somehow.
The source of the data is the @dblp.org bibliography.
The source of the data is the @dblp.org bibliography.
Reposted by Christopher Akiki
Who is winning the open AI race?
Our new study Economies of Open Intelligence maps @hf.co 851k models' downloads 2020→2025.
1) Power rebalance: US tech ↓; China + community ↑
2) Models size & efficient ↑ (MoE, quant, multimodal)
3) Intermediary layers ↑ (adapters/quantizers)
4) Transparency ↓
/🧵
Our new study Economies of Open Intelligence maps @hf.co 851k models' downloads 2020→2025.
1) Power rebalance: US tech ↓; China + community ↑
2) Models size & efficient ↑ (MoE, quant, multimodal)
3) Intermediary layers ↑ (adapters/quantizers)
4) Transparency ↓
/🧵
November 26, 2025 at 4:03 PM
Who is winning the open AI race?
Our new study Economies of Open Intelligence maps @hf.co 851k models' downloads 2020→2025.
1) Power rebalance: US tech ↓; China + community ↑
2) Models size & efficient ↑ (MoE, quant, multimodal)
3) Intermediary layers ↑ (adapters/quantizers)
4) Transparency ↓
/🧵
Our new study Economies of Open Intelligence maps @hf.co 851k models' downloads 2020→2025.
1) Power rebalance: US tech ↓; China + community ↑
2) Models size & efficient ↑ (MoE, quant, multimodal)
3) Intermediary layers ↑ (adapters/quantizers)
4) Transparency ↓
/🧵
Reposted by Christopher Akiki
Researchers at Google DeepMind used our free puzzle database and reinforcement learning to train a model to generate creative chess puzzles.
➡️ Read more on this by Tom Zahavy from the DeepMind discovery team: lichess.org/@/tomas135/b...
➡️ Read more on this by Tom Zahavy from the DeepMind discovery team: lichess.org/@/tomas135/b...
AI-Generated Chess Puzzles
A new research by the Discovery team at @GoogleDeepMind using RL and generative models to discover creative chess puzzles
lichess.org
November 11, 2025 at 2:42 PM
Researchers at Google DeepMind used our free puzzle database and reinforcement learning to train a model to generate creative chess puzzles.
➡️ Read more on this by Tom Zahavy from the DeepMind discovery team: lichess.org/@/tomas135/b...
➡️ Read more on this by Tom Zahavy from the DeepMind discovery team: lichess.org/@/tomas135/b...
Find the data here: huggingface.co/datasets/chr...
christopher/wikipedia-colors · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
November 4, 2025 at 12:05 PM
Find the data here: huggingface.co/datasets/chr...
Three different ways to represent colo(u)r. Work in progress, inspired by an old post by Kat Zhang / The Poet Engineer.
![Three scatterplots of colorful points.
titles = ['Color Space', 'Text Space', 'Image Space']
subtitles = ['Embeddings of color features', 'Text embedding of color names', 'Image embeddings of color swatches']](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:s62ilc26irrbo2dndmdzboqi/bafkreieocq6xfp5xzela4ge3f4nidi4krh7ufkxftg423pjwmipz53zmpm@jpeg)
November 4, 2025 at 12:05 PM
Three different ways to represent colo(u)r. Work in progress, inspired by an old post by Kat Zhang / The Poet Engineer.
- Dataset: huggingface.co/datasets/Hug...
- Embeddings: huggingface.co/datasets/air... (H/T @loubnabnl.hf.co for recommending this)
- Embeddings: huggingface.co/datasets/air... (H/T @loubnabnl.hf.co for recommending this)
October 31, 2025 at 2:52 PM
- Dataset: huggingface.co/datasets/Hug...
- Embeddings: huggingface.co/datasets/air... (H/T @loubnabnl.hf.co for recommending this)
- Embeddings: huggingface.co/datasets/air... (H/T @loubnabnl.hf.co for recommending this)
- Sasha's talk: ted.com/talks/sasha_...
- Tools: datamapplot and EVōC by @lelandmcinnes.bsky.social and colleagues at the Tutte Institute and openTSNE by @pavlinpolicar.bsky.social
- Tools: datamapplot and EVōC by @lelandmcinnes.bsky.social and colleagues at the Tutte Institute and openTSNE by @pavlinpolicar.bsky.social
We’re doing AI all wrong. Here’s how to get it right
Artificial intelligence is changing everything — but at what cost? AI sustainability expert Sasha Luccioni exposes how tech companies' massive data centers are burning through energy and wrecking the ...
ted.com
October 31, 2025 at 2:52 PM
- Sasha's talk: ted.com/talks/sasha_...
- Tools: datamapplot and EVōC by @lelandmcinnes.bsky.social and colleagues at the Tutte Institute and openTSNE by @pavlinpolicar.bsky.social
- Tools: datamapplot and EVōC by @lelandmcinnes.bsky.social and colleagues at the Tutte Institute and openTSNE by @pavlinpolicar.bsky.social
I made this annotated scatter plot of 1 million FineWeb-Edu documents for @sashamtl.bsky.social's new TED talk.
October 31, 2025 at 2:52 PM
I made this annotated scatter plot of 1 million FineWeb-Edu documents for @sashamtl.bsky.social's new TED talk.
Reposted by Christopher Akiki
When the fish left the river:
October 28, 2025 at 12:01 AM
When the fish left the river:
Also really love how organic the plot looks with "inferno" (left) and "viridis" (right).
October 27, 2025 at 10:42 AM
Also really love how organic the plot looks with "inferno" (left) and "viridis" (right).
Update: the color map in this post is misleading. See the quoted post for context.
bsky.app/profile/caki...
bsky.app/profile/caki...
Thanks to @jamesabednar.bsky.social I realized I had used the wrong background color for the colormap I had chosen. This is another version of the plot (different embeddings) with the corrected background.
October 26, 2025 at 4:07 PM
Update: the color map in this post is misleading. See the quoted post for context.
bsky.app/profile/caki...
bsky.app/profile/caki...
Thanks to @jamesabednar.bsky.social I realized I had used the wrong background color for the colormap I had chosen. This is another version of the plot (different embeddings) with the corrected background.
October 26, 2025 at 4:06 PM
Thanks to @jamesabednar.bsky.social I realized I had used the wrong background color for the colormap I had chosen. This is another version of the plot (different embeddings) with the corrected background.
Glad you like it! Nothing yet, only a very messy jupyter notebook for now. I will share the code and data pipelines once they're all cleaned up.
October 26, 2025 at 3:05 PM
Glad you like it! Nothing yet, only a very messy jupyter notebook for now. I will share the code and data pipelines once they're all cleaned up.
Huge props to @lelandmcinnes.bsky.social for optimizing the hammer bundling code in datashader and to Barrett Lyon's inspring work at the Opte Project.
October 26, 2025 at 1:39 PM
Huge props to @lelandmcinnes.bsky.social for optimizing the hammer bundling code in datashader and to Barrett Lyon's inspring work at the Opte Project.