Todd Mostak

Accelerating SQL on GPU at Nvidia @RAPIDSai, former founder and CEO of @heavy_ai.

[Todd Mostak @ToddMostak]

Truly awesome to see Jensen in his GTC keynote present @nvidia's joint partnership with @IBM to use cuDF to accelerate open source Velox and the Presto SQL engine in watsonx, showcasing how @Nestle achieves 5X faster queries for 83% less cost.

Amazing collaboration between the Nvidia and IBM teams to make this a reality, and we're just getting started.

[Todd Mostak @ToddMostak]

@firstadopter @wallstengine Basically that Rubin is looking to be much more cost competitive against TPUv8, perhaps retaking the lead.

[Todd Mostak @ToddMostak]

@dedene @thsottiaux This ^

[Todd Mostak @ToddMostak]

If it was already difficult to know what is real, we are quickly going to converge on a world where representation replaces reality.

Generated with Sora 2 Pro.

[Todd Mostak @ToddMostak]

Sora 2 Pro is really good. I asked it to recreate the scene of the 2010 Tahrir Square protests and it pulled together quite a convincing scene, complete with anti-Mubarak chants calling for the fall of the regime.

[Todd Mostak @ToddMostak]

Sora 2 is just wild…

[Todd Mostak @ToddMostak]

@springertimo @willdepue This.

[Todd Mostak @ToddMostak]

@Alibaba_Qwen are you guys still planning to release Qwen3-32B-Base? It seems to be the only Qwen 3 model variant aside from the 235B version without a base model.

[Todd Mostak @ToddMostak]

@JagersbergKnut @Dan_Jeffries1 I think there’s an argument that China “wastes” far more resources with easy money for whatever the Party’s current goals are (ie electric car or chip production), but out of the waste emerges an actual edge, if just by the law of large numbers.

[HEAVY.AI @heavy_ai]

We recently expanded our support for the Uber H3 hierarchical geospatial index to HeavyDB, allowing users to easily aggregate, visualize, and join data using the H3 grid.

Check out our new blog to learn more and see examples of this powerful new capability in action.

[Todd Mostak @ToddMostak]

Enabling real-time geospatial joins at scale has always been a key goal of ours at @heavy_ai. Check out our blog benchmarking geo join performance in GPU-accelerated HeavyDB against Postgres, DuckDB, Snowflake, BigQuery, and Redshift.

[Todd Mostak @ToddMostak]

Post here: https://t.co/nd8wyUMScZ

[Todd Mostak @ToddMostak]

Enabling real-time geospatial joins at scale has always been a key goal of ours at @heavy_ai. Check out our blog benchmarking geo join performance in GPU-accelerated HeavyDB against Postgres, DuckDB, Snowflake, BigQuery, and Redshift.

[Todd Mostak @ToddMostak]

@BelugaClyde @Fandango Same, bought two tickets for my sons and one for myself expecting they would both get Jetpack promo codes. Now we only have one code. Would have bought separately if we had known.

@Fandango if you care about your loyal customers you need to make this right.

[Todd Mostak @ToddMostak]

@elitedoorworks @musk_news13 @grok It could also be 25 I suppose but that breaks the symmetry of all numbers on the top half of the circle multiples by 5 equaling their counterpart on the bottom half.

[Todd Mostak @ToddMostak]

@elitedoorworks @musk_news13 @grok It’s 1. For each pair of numbers on the opposite sides of the circle, the smaller number multiplied by 5 equals the larger number. Here ?X5=5 so it’s 1.

[Todd Mostak @ToddMostak]

@USGS in conjunction with @UCSD recently released LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM) datasets for the areas impacted by the devastating Palisades and Eaton fires.

First we ingested each dataset into @heavy_ai (roughly 1.2 billion records each), and then pulled @OvertureMaps building footprint data for the area. We then joined the Overture building footprints to the the DEM data (essentially ground elevation) and DSM data (which includes the height of man-made structures) to get a map of which structures were destroyed by the fire, generally the purple structures on the map below, which are computed to have near-zero structure height post-fire.

The entire query, with two 1B+ row geospatial joins between the elevation and buildings datasets, takes less than 100ms on an @nvidia Grace Hopper GH200 GPU on @Vultr cloud, meaning the outputs can be interactively visualized. We are currently diving deeper into the data, and augmenting it with imagery to try to build a burn risk model based on the location of vegetation relative to structures. Our hope is that approaches like this that can fuse multiple geospatial datasets can help mitigate risks of future wildfires.

[HEAVY.AI @heavy_ai]

We're proud to announce a new partnership with @Ookla, a leader in network intelligence. With Ookla's wide array of unique crowdsourced and drive test data, paired with @heavy_ai, organizations can optimize, plan, and pinpoint issues in their networks faster than ever before.

[HEAVY.AI @heavy_ai]

Interactively explore over 20 billion records of ship AIS data in our new demo of https://t.co/mOwqFIkAzu on the @nvidia Grace Hopper Superchip, running in @Vultr Cloud.

See our blog for more info: https://t.co/4qObJlhp8l

[Todd Mostak @ToddMostak]

@zied_houidi Wonder if you could programmatically generate the scenarios but then have an LLM turn them into word problems to add some entropy to the prompts?