Distributed query engine providing simple and reliable data processing for any modality and scale (github.com/Eventual-Inc/Daft)
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A teleoperated robot episode doesn't start when the recording does - the operator is still setting up while the camera rolls.
The robot's own joint positions tell you which frames matter without decoding a single pixel. With this method, trimming all of DROID - 500 hours of robot data - took 32 seconds on a laptop.
daft.ai/blog/cutting-dead-fr…
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Hand-pose annotation is the plumbing between raw robot video and a trained model. daft-physical-ai packages it into a single Daft UDF.
This is how it works: track_hands in daft-physical-ai passes a Daft image column and gets a hand-pose column back. MediaPipe (CPU/2D) or WiLoR (GPU/3D MANO), same schema either way. Lazy, batched, distributed — Daft handles execution.
On EgoDex: detect=100%, PCK@.1/.2/.3 = 49/84/96.
Try it: pip install daft-physical-ai
Check more use cases here eventual.ai/blog/announcing-…
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Excited to announce: daft-physical-ai, a new Python data processing library for physical AI.
We're starting with two use cases: hand tracking and reward scoring - essential steps for making robot data useful for training.
daft.ai/blog/announcing-daft…
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Some founders find their calling at 30.
Sammy found his at 12.. and it led to Eventual.
Why is he the one to do this?
Number one: he loves robots.
He's a Transformers guy.
But even before that, at 12 years old,
he was building robotics.
He's much older than 12 now.
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Here's how: eventual.ai/blog/how-we-made…
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.@LeRobotHF is becoming the dominant open format for robot data.
We recently introduced a native LeRobot reader in Daft, and we've now made it up to 15× faster.
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We're excited to share that the Eventual team will be at AUTONOMOUS: The Future of Robotics & Physical AI
We're looking forward to meeting with some of the most talented builders and investors in the industry.
See you on July 16th at The Midway, San Francisco.
#PhysicalAI #Robotics #AUTONOMOUS2026
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When contributors roll through ... 🔥🔥🔥
🦾 @_BabTuna_ has been crushing PRs lately on daft so we invited him out to the office for lunch.
Come back any time!
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We trained this robot 🤖 to dance Bhangra 🕺🕺🕺through
@UFBots
Looks sooooooooo cool! #UltimateBotsStudio
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Flight Shuffle makes disk the data plane.
Each map task writes one combined Arrow IPC file to local disk. Every worker runs an Arrow Flight server that streams partitions over DoGet, so reduce tasks process batches as they land rather than buffering a whole partition first.
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Daft's previous shuffle relied heavily on Ray's durable object store. Every map-to-reduce transfer became an object reference the head node tracked at ~3 KB each.
Which means a 4096×4096 shuffle is 50 GB of references;
and 8192×8192 is 200 GB.
This would lead to head node OOMs.
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If a distributed query has to materialize more than a few terabytes of data, there's one operation that will dominate: the shuffle.
Shuffling data at scale has been a real bottleneck for Daft users, so we took the time to fix the root cause and rebuild the shuffle from scratch.
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WAM!!!
VLAs are dead, long live World Action Models
So declares @DrJimFan, the most credible researcher in robotics today.
daft.ai/blog/vlas-are-dead-l…
👆We just published a short blog where @ykdojo breaks down the video. It certainly helped me correct my mental model.
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sheesh, this one blew up
1/ It's now easier than ever to use @huggingface 🤗 Datasets at scale! Introducing Daft's native support for reading from and writing to Hugging Face.
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Everyone's talking about Apache Iceberg this week.
Snowflake just shipped v3 public preview. Supabase built Analytics Buckets on it. The entire data eng timeline is Iceberg.
Meanwhile @daftengine has been quietly shipping deep Iceberg support. Here's what you can do today:
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why did we choose arrow2...
this ones for the real ones... universalmind303 what would we do without you 😩
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arrow2 -> arrow-rs migration complete
122 PRs. 38,850 lines changed. 9 contributors.
We initially built Daft on top of arrow2 but the ecosystem centered on arrow-rs which is backed by the @TheASF and @ApacheDataFusio .
daft.ai/blog/daft-v074-arrow…
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Daft v0.7.4 just shipped.
THE GREAT ARROW-RS MIGRATION
122 PRs completing the arrow2 → arrow-rs migration. 38,850 lines changed.
Plus: full observability stack, Apache OpenDAL support, and Flight shuffle for Flotilla.
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Agents do not fail because they lack intelligence.
They fail because they lack institutional memory.
In the new episode of Zero Shot Espresso with @akoratana, we discussed context graphs and decision traces, and what it takes for AI systems to actually improve over time in enterprise environments.
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