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Debug ROS 2 transforms in Rerun 💡 ROS 2’s tf2 gives robotics teams a shared language for tracking coordinate frames over time — transforming data between things like camera_link and base_link. Rerun supports the same idea with named transforms, letting you decouple spatial relationships from your entity hierarchy. This... show more

Rerun

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15,982 просмотров • 6 месяцев назад •via X (Twitter)

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Love and Deepspace | Rerun Event Preview The 5-Star Rate UP Pool [Everlasting Dream] Limited-Time Rerun will start soon! "Since you seem to know everything, you should enlighten me." 💫Event Duration: From 05:00 on Nov. 13 to 04:59 on Nov. 20 (Server Time) 💫The 5-Star Rate UP Pool [Everlasting Dream] Limited-Time Rerun Event 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited] to participate in the wish event. The drop rate of the event-limited 5-Star Memory [Xavier: Heartstring Symphony] will go up drastically. 2. After the event ends, this limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. All Rerun Wish Pools share one pity system. A 5-Star Memory is guaranteed within a specific attempt of wishes. If the 5-Star Memory you've obtained from the Rerun Wish Pool is not the event-limited Memory, you will obtain the event-limited Memory the next time you obtain a 5-Star Memory. The pity count from the last Rerun Wish Pool can be applied to this Rerun Wish Pool, and the pity count in this Rerun Wish Pool will also be applied to the upcoming Rerun Wish Pool. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Dreamheart Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop. Notes: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event ends, [Time Wish: Limited] will automatically convert to Empyrean Wish. ——— 🪐Official Discord: #LoveandDeepspace #Xavier

Love and Deepspace | Rerun Event Preview The 5-Star Rate UP Pool [Everlasting Dream] Limited-Time Rerun will start soon! "Since you seem to know everything, you should enlighten me." 💫Event Duration: From 05:00 on Nov. 13 to 04:59 on Nov. 20 (Server Time) 💫The 5-Star Rate UP Pool [Everlasting Dream] Limited-Time Rerun Event 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited] to participate in the wish event. The drop rate of the event-limited 5-Star Memory [Xavier: Heartstring Symphony] will go up drastically. 2. After the event ends, this limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. All Rerun Wish Pools share one pity system. A 5-Star Memory is guaranteed within a specific attempt of wishes. If the 5-Star Memory you've obtained from the Rerun Wish Pool is not the event-limited Memory, you will obtain the event-limited Memory the next time you obtain a 5-Star Memory. The pity count from the last Rerun Wish Pool can be applied to this Rerun Wish Pool, and the pity count in this Rerun Wish Pool will also be applied to the upcoming Rerun Wish Pool. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Dreamheart Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop. Notes: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event ends, [Time Wish: Limited] will automatically convert to Empyrean Wish. ——— 🪐Official Discord: #LoveandDeepspace #Xavier

Love and Deepspace

269,916 просмотров • 8 месяцев назад

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Pablo Vela

34,937 просмотров • 3 месяцев назад

on 8/3, we launched mainnet in singapore. today, we launch BSMNTᴮᴱᵀᴬ, a discovery platform that transforms how you play onchain. this is just the beginning, but the future of gaming starts now, and starts with you. ready to top the XP leaderboard?

on 8/3, we launched mainnet in singapore. today, we launch BSMNTᴮᴱᵀᴬ, a discovery platform that transforms how you play onchain. this is just the beginning, but the future of gaming starts now, and starts with you. ready to top the XP leaderboard?

B3

100,690 просмотров • 1 год назад

Love and Deepspace | Rerun Event Preview The 5-Star Rate UP Pool [Dawn's Embrace] Limited-Time Rerun will start soon! "Even if there's no one here... If you try anything, I'll scream." 💫Event Duration: From 05:00 on Jun. 25 to 04:59 on Jul. 2 (Server Time) 💫About the 5-Star Rate UP Pool [Dawn's Embrace] Limited-Time Rerun Rules, please check: 🎁New Packs During the event, the Rerun event-exclusive [Summit Warmth Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop. Notes: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event ends, [Time Wish: Limited] will automatically convert to Empyrean Wish. ——— Love and Deepspace Version 6.0 "A Shattered Quiet" launches on Jul. 9! 🪐Join the official Discord discussion for the new update: 🪐Download and Log in Now: #LoveandDeepspace #Rafayel

Love and Deepspace | Rerun Event Preview The 5-Star Rate UP Pool [Dawn's Embrace] Limited-Time Rerun will start soon! "Even if there's no one here... If you try anything, I'll scream." 💫Event Duration: From 05:00 on Jun. 25 to 04:59 on Jul. 2 (Server Time) 💫About the 5-Star Rate UP Pool [Dawn's Embrace] Limited-Time Rerun Rules, please check: 🎁New Packs During the event, the Rerun event-exclusive [Summit Warmth Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop. Notes: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event ends, [Time Wish: Limited] will automatically convert to Empyrean Wish. ——— Love and Deepspace Version 6.0 "A Shattered Quiet" launches on Jul. 9! 🪐Join the official Discord discussion for the new update: 🪐Download and Log in Now: #LoveandDeepspace #Rafayel

Love and Deepspace

358,819 просмотров • 1 месяц назад

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Akshay 🚀

280,449 просмотров • 1 год назад

We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂

We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂

Pablo Vela

35,662 просмотров • 4 месяцев назад

Heres how to get started with the neatlogs MCP ! with this you can access your traces, detections, complete RCA and traige cards right inside Claude Code, Cursor, Windsurf, or whichever you like to build and you dont need to jump between your editor and a bunch of dashboards, you can just debug and fix your agents in one place. :)

Heres how to get started with the neatlogs MCP ! with this you can access your traces, detections, complete RCA and traige cards right inside Claude Code, Cursor, Windsurf, or whichever you like to build and you dont need to jump between your editor and a bunch of dashboards, you can just debug and fix your agents in one place. :)

simran sachdeva

24,742 просмотров • 1 месяц назад

Fortnite: Infinite NPC Spawn Glitch (Chewbacca) - Enter a Builds match and head to Canyon Crossing - Refer to the video below for the exact location - Build straight up until you find the debug trigger - Step in and out of the debug trigger to repeatedly spawn Chewbacca NPCs - Only one Chewbacca can follow you at a time, each drops the Chapter 5 Season 2 Mythic Bowcaster and occasionally some ammo FAQ: Yes this works in all Fortnite public Battle Royale games #Fortnite

Fortnite: Infinite NPC Spawn Glitch (Chewbacca) - Enter a Builds match and head to Canyon Crossing - Refer to the video below for the exact location - Build straight up until you find the debug trigger - Step in and out of the debug trigger to repeatedly spawn Chewbacca NPCs - Only one Chewbacca can follow you at a time, each drops the Chapter 5 Season 2 Mythic Bowcaster and occasionally some ammo FAQ: Yes this works in all Fortnite public Battle Royale games #Fortnite

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Love and Deepspace | 2024 Zayne's Birthday Event [Eternal Attachment] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 05:00 on Aug. 31 to 04:59 on Sept. 7 (Server Time) 🍰Destined Reunion: Zayne's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Zayne: Eternal Attachment] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Yearning Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Very Moment With U] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Zayne: Shining Blessing] and Daily Outfit [Zayne: Autumn Breeze] will be available in Hope Shop. You can use the event item [Verglas Bead] to redeem rewards. ——— 🪐Official Discord: #LoveandDeepspace #Zayne #HBDZayne

Love and Deepspace | 2024 Zayne's Birthday Event [Eternal Attachment] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 05:00 on Aug. 31 to 04:59 on Sept. 7 (Server Time) 🍰Destined Reunion: Zayne's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Zayne: Eternal Attachment] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Yearning Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Very Moment With U] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Zayne: Shining Blessing] and Daily Outfit [Zayne: Autumn Breeze] will be available in Hope Shop. You can use the event item [Verglas Bead] to redeem rewards. ——— 🪐Official Discord: #LoveandDeepspace #Zayne #HBDZayne

Love and Deepspace

187,156 просмотров • 11 месяцев назад

The 2-3 Zone That Forces Turnovers Most coaches think a 2-3 zone is passive — something you use to protect the paint. But what if your zone defense could attack instead of react? In this video, we’ll show you how Coach Mike Tartara’s Lockdown 2-3 Zone Defense transforms the traditional zone into a turnover-creating machine, using the same principles that Merrimack College used to become one of the best defensive programs in Division I basketball. The stats don’t lie: -Since going to the 2-3 zone 5 years ago, Coach Tartara’s program has consistently set school records for steals. -Merrimack annually ranks as one of the top turnover-forcing teams in Men’s D1 college basketball. Coach Tartara’s Cuyahoga Heights teams averaged 250+ steals per season after switching to this system — proof that you can win more games while playing zone defense that pressures opponents instead of sitting back. Then, watch Merrimack College execute the same rotations at the D1 level — showing how these high school principles produce elite-level results.

The 2-3 Zone That Forces Turnovers Most coaches think a 2-3 zone is passive — something you use to protect the paint. But what if your zone defense could attack instead of react? In this video, we’ll show you how Coach Mike Tartara’s Lockdown 2-3 Zone Defense transforms the traditional zone into a turnover-creating machine, using the same principles that Merrimack College used to become one of the best defensive programs in Division I basketball. The stats don’t lie: -Since going to the 2-3 zone 5 years ago, Coach Tartara’s program has consistently set school records for steals. -Merrimack annually ranks as one of the top turnover-forcing teams in Men’s D1 college basketball. Coach Tartara’s Cuyahoga Heights teams averaged 250+ steals per season after switching to this system — proof that you can win more games while playing zone defense that pressures opponents instead of sitting back. Then, watch Merrimack College execute the same rotations at the D1 level — showing how these high school principles produce elite-level results.

Joe Haefner | Breakthrough Basketball

27,327 просмотров • 8 месяцев назад

0.32 has shipped, and it's a massive release from Rerun. There's a ton of cool new features, and I wanted to highlight 2 in particular 1. OSS Server streaming from disk 2. Dataset review I walk you through them in the video, so take a look. I'll have a much longer blog post next week about the entire pipeline. With 0.32, much of the foundation is set for a unified data layer for physical data, and I'll be getting into the details of it with all that I've built over the past year. This will cover 1. Raw Data Collection 2. Data Ingestion 3. Catalog Registration 4. Query and Review 5. Post Process 6. Training so lots to share

0.32 has shipped, and it's a massive release from Rerun. There's a ton of cool new features, and I wanted to highlight 2 in particular 1. OSS Server streaming from disk 2. Dataset review I walk you through them in the video, so take a look. I'll have a much longer blog post next week about the entire pipeline. With 0.32, much of the foundation is set for a unified data layer for physical data, and I'll be getting into the details of it with all that I've built over the past year. This will cover 1. Raw Data Collection 2. Data Ingestion 3. Catalog Registration 4. Query and Review 5. Post Process 6. Training so lots to share

Pablo Vela

11,264 просмотров • 2 месяцев назад

Love and Deepspace | 2024 Xavier's Birthday Event [Celestial Message] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 05:00 on Oct. 11 to 04:59 on Oct. 18 (server time) 🍰Galaxy Dance: Xavier's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Xavier: Celestial Message] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Mornwish Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Timeless Days] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Xavier: Unfading Wish] and Daily Outfit [Xavier: Starry Night Dance] will be available in Starry Shop. You can use the event item [Starward Reel] to redeem rewards. ——— 🪐Official Discord: #LoveandDeepspace #Xavier #HBDXavier

Love and Deepspace | 2024 Xavier's Birthday Event [Celestial Message] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 05:00 on Oct. 11 to 04:59 on Oct. 18 (server time) 🍰Galaxy Dance: Xavier's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Xavier: Celestial Message] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Mornwish Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Timeless Days] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Xavier: Unfading Wish] and Daily Outfit [Xavier: Starry Night Dance] will be available in Starry Shop. You can use the event item [Starward Reel] to redeem rewards. ——— 🪐Official Discord: #LoveandDeepspace #Xavier #HBDXavier

Love and Deepspace

172,577 просмотров • 9 месяцев назад

Love and Deepspace | 2024 Rafayel's Birthday Event [Unforgettable Adventure] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 5:00 A.M. on Mar. 1 to 4:59 A.M. on Mar. 8 (Server Time) 🍰Special Embrace: Rafayel's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Rafayel: Unforgettable Adventure] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Fanciful Bliss Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Miracle Voyage] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Rafayel: Wish Granted] and Daily Outfit [Iceberg Adventure] will be available in Bond Shop. You can use the event item [Pink Seashell] to redeem rewards. #LoveandDeepspace #Rafayel #HBDRafayel

Love and Deepspace | 2024 Rafayel's Birthday Event [Unforgettable Adventure] Memory & Outfit Rerun is Coming Soon! 🍰Event Duration: From 5:00 A.M. on Mar. 1 to 4:59 A.M. on Mar. 8 (Server Time) 🍰Special Embrace: Rafayel's Birthday Memory Rerun 1. During the event, make a wish with [Deepspace Wish] or [Time Wish: Limited], the drop rate of the Limited 5-Star Memory [Rafayel: Unforgettable Adventure] will go up drastically. 2. After the event ends, this Limited 5-Star Memory will not be obtainable through other means and will not enter the permanent Wish Pool: Xspace Echo. 3. The wish event features a pity system, and the rule is shared among all Reruns. The wish attempts made in this rerun event will be counted in all Reruns. *You can read more about the event on the in-game rules page. 🎁New Packs During the event, the Rerun event-exclusive [Fanciful Bliss Pack] series, which includes [Time Wish: Limited] and other materials, will be available in Shop at special prices. Tips: 1. [Time Wish: Limited] can be used in 5-Star Memory Wish Pool Rerun and will be used first when you make a wish. 2. After the event, [Time Wish: Limited] will automatically convert to Empyrean Wish. 🍰[Miracle Voyage] Memory & Outfit Reward Rerun 2024 birthday-exclusive 3-Star Memory [Rafayel: Wish Granted] and Daily Outfit [Iceberg Adventure] will be available in Bond Shop. You can use the event item [Pink Seashell] to redeem rewards. #LoveandDeepspace #Rafayel #HBDRafayel

Love and Deepspace

860,571 просмотров • 1 год назад

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 просмотров • 3 лет назад

Robots are gaining spatial awareness with Over the Reality. 🤖 Powered by the world’s largest 3D dataset, we enable robots to see, sense, and collaborate in real-world environments. This early test shows how a Unitree Go2 perceives its surroundings through OVER’s VPS — on the right, the real life robot; on the left, its real-time perception of the position inside one of OVER’s 3D maps. Robotic spatial awareness is here, living in 155,000+ mapped locations and expanding every day. One step closer to Physical AI. Stay tuned for our upcoming ROS 2 integration, unlocking a universal framework for robotic perception leveraging OVER's infrastructure!

Robots are gaining spatial awareness with Over the Reality. 🤖 Powered by the world’s largest 3D dataset, we enable robots to see, sense, and collaborate in real-world environments. This early test shows how a Unitree Go2 perceives its surroundings through OVER’s VPS — on the right, the real life robot; on the left, its real-time perception of the position inside one of OVER’s 3D maps. Robotic spatial awareness is here, living in 155,000+ mapped locations and expanding every day. One step closer to Physical AI. Stay tuned for our upcoming ROS 2 integration, unlocking a universal framework for robotic perception leveraging OVER's infrastructure!

Over the Reality 🌐

263,818 просмотров • 9 месяцев назад

✨ Massive Pipeline Refactor → One Framework for Ego + Exo Datasets, Visualized with Rerun 🚀 After a deep refactoring and cleanup, my entire egocentric/exocentric pipeline is now fully modular. One codebase handles different sensor layouts and generates a unified, multimodal timeseries RRD file that you can open instantly in Rerun. --- The first three datasets that are already supported 1. Assembly101 – 4 ego Quest‑style fisheye cams + 8 exo pinhole cams 2. HO‑Cap – 1 ego HoloLens pinhole cam + 8 exo pinhole cams 3. EgoDex – 1 ego Apple Vision Pro pinhole cam Unified geometry: Each frame now logs _both_ camera intrinsics / extrinsics and COCO Whole-Body 133-kp keypoints in the same stream. Everything is canonicalized at import time, so there’s zero OpenCV vs OpenGL guess-work—Rerun reads it all in the correct coordinate system automatically. --- Why this matters - Consistent schema ✚ live visuals – Rerun’s deep link between data & rendering means every experiment comes with a built‑in viewer. No more ad‑hoc OpenCV/matplotlib hacks just to sanity‑check a dataset. - Multi‑terabyte friendly – The next step is bulk‑ingest these giants into Rerun and wrap them in a Gradio UI for point‑and‑click exploration, as I've already done for EgoDex!

✨ Massive Pipeline Refactor → One Framework for Ego + Exo Datasets, Visualized with Rerun 🚀 After a deep refactoring and cleanup, my entire egocentric/exocentric pipeline is now fully modular. One codebase handles different sensor layouts and generates a unified, multimodal timeseries RRD file that you can open instantly in Rerun. --- The first three datasets that are already supported 1. Assembly101 – 4 ego Quest‑style fisheye cams + 8 exo pinhole cams 2. HO‑Cap – 1 ego HoloLens pinhole cam + 8 exo pinhole cams 3. EgoDex – 1 ego Apple Vision Pro pinhole cam Unified geometry: Each frame now logs _both_ camera intrinsics / extrinsics and COCO Whole-Body 133-kp keypoints in the same stream. Everything is canonicalized at import time, so there’s zero OpenCV vs OpenGL guess-work—Rerun reads it all in the correct coordinate system automatically. --- Why this matters - Consistent schema ✚ live visuals – Rerun’s deep link between data & rendering means every experiment comes with a built‑in viewer. No more ad‑hoc OpenCV/matplotlib hacks just to sanity‑check a dataset. - Multi‑terabyte friendly – The next step is bulk‑ingest these giants into Rerun and wrap them in a Gradio UI for point‑and‑click exploration, as I've already done for EgoDex!

Pablo Vela

20,836 просмотров • 1 год назад

Hidden chapter in vaccine history? In this intense 2:05 JRE clip, Joe Rogan asks Bret Weinstein point-blank: How did a monkey virus (SV40) end up in 1950s–60s polio vaccines—and can it really turn human cells cancerous? Weinstein: “It transforms human cells in the lab. That part is undeniable.” They also discuss how rushed mass production may have skipped the purity of what was actually tested. No proven cancer epidemic followed (per CDC/IOM data), but the questions remain. Worth knowing our history. What do YOU think—time to revisit this story?

Hidden chapter in vaccine history? In this intense 2:05 JRE clip, Joe Rogan asks Bret Weinstein point-blank: How did a monkey virus (SV40) end up in 1950s–60s polio vaccines—and can it really turn human cells cancerous? Weinstein: “It transforms human cells in the lab. That part is undeniable.” They also discuss how rushed mass production may have skipped the purity of what was actually tested. No proven cancer epidemic followed (per CDC/IOM data), but the questions remain. Worth knowing our history. What do YOU think—time to revisit this story?

Camus

24,366 просмотров • 7 месяцев назад

Still spending hours on repetitive work that should’ve been automated by now? Most teams waste valuable time creating documents from scratch, rewriting the same emails, reformatting data, and manually producing content that could be generated in seconds — with the right tools. Meet |Context — The AI Office Suite built to transform how work gets done. 🚀 Not just where you work — but what you work on. Context doesn’t just assist you. It acts like a teammate: learning, reasoning, writing, researching, and adapting to your workflow. Here’s how transforms your workday: ✅ Unified Workspace: Connect over 300 tools like Slack, Google Docs, Salesforce, and Excel in one place, and streamline your entire workflow. ✅ AI-Powered Automation: Let AI handle repetitive tasks, generate content, and update documents in real time, saving you hours every week. ✅ Real-Time Collaboration: Collaborate seamlessly with your team on shared workspaces, making edits, updates, and feedback simple and quick. ✅ Top-Tier Security: With enterprise-grade security, including SOC 2, GDPR compliance, and AES-256 encryption, your data stays safe and secure. ✅ Endless Customization: Build custom templates, automate research, and tailor workflows to meet the unique needs of your business. Ready to level up your workflow? Try it now👉 #ContextAI #Automation #AIWorkspace #Productivity #SmartWork #NoCodeTools #AIIntegration #Teamworkrder

Still spending hours on repetitive work that should’ve been automated by now? Most teams waste valuable time creating documents from scratch, rewriting the same emails, reformatting data, and manually producing content that could be generated in seconds — with the right tools. Meet |Context — The AI Office Suite built to transform how work gets done. 🚀 Not just where you work — but what you work on. Context doesn’t just assist you. It acts like a teammate: learning, reasoning, writing, researching, and adapting to your workflow. Here’s how transforms your workday: ✅ Unified Workspace: Connect over 300 tools like Slack, Google Docs, Salesforce, and Excel in one place, and streamline your entire workflow. ✅ AI-Powered Automation: Let AI handle repetitive tasks, generate content, and update documents in real time, saving you hours every week. ✅ Real-Time Collaboration: Collaborate seamlessly with your team on shared workspaces, making edits, updates, and feedback simple and quick. ✅ Top-Tier Security: With enterprise-grade security, including SOC 2, GDPR compliance, and AES-256 encryption, your data stays safe and secure. ✅ Endless Customization: Build custom templates, automate research, and tailor workflows to meet the unique needs of your business. Ready to level up your workflow? Try it now👉 #ContextAI #Automation #AIWorkspace #Productivity #SmartWork #NoCodeTools #AIIntegration #Teamworkrder

Manish Kumar Shah

33,734 просмотров • 1 год назад

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!

Pablo Vela

22,880 просмотров • 1 месяц назад

I've been wanting proper Gaussian Splatting support in Rerun for the longest time. I had previously vibe-coded a custom view that kind of worked before, but it had some wonky bugs and was still too rough around the edges. This was originally done back in the Codex 5.4 era, and as you can imagine alot has changed. So I decided to revisit the implementation because of the following: - Rerun 0.34 release brought us MCP support for the rerun viewer. This means an agent can take screenshots, interact with the viewer, rotate 3d spatial components, and open/close views. Basically, anything you can do in the rerun viewer, an agent can now do, closing the loop on debugging. - Significantly stronger models have been released from both Anthropic and OpenAI. Fabel and Codex 5.6 Sol should make this implementation both cleaner and more performant, as well as find the previous bugs 5.4 couldn't - Harnesses have gotten much, much better since I first worked on this. HTML artifacts + /goal have made implementation + review much easier. I actively have Fabel in the driver's seat while 5.6 does mechanical code work + review after the fact. Thanks to the combination above, I was able to improve the custom Rust view to be lots better and fix most of the wonky bugs. Along with this everything should now use proper GPU rendering with no CPU fallbacks. I also wanted to make sure I could visualize training so I went ahead and integrated it with the wonderful brush since it works on Mac as well. With this proved out, we're also working on getting official Gaussian Splatting support as a proper archetype on the normal rerun distribution. This should be a great foundation to build all of the other esoteric representations (2DGS/4DGS/Triangle Splatting/whatever else you want)! I can finally log the full splat training run and move forward and backwards in time to see how the optimization went at different timesteps! Given powerful agents, the rerun mcp client, and a good harness, there's literally never been a better time to build.

I've been wanting proper Gaussian Splatting support in Rerun for the longest time. I had previously vibe-coded a custom view that kind of worked before, but it had some wonky bugs and was still too rough around the edges. This was originally done back in the Codex 5.4 era, and as you can imagine alot has changed. So I decided to revisit the implementation because of the following: - Rerun 0.34 release brought us MCP support for the rerun viewer. This means an agent can take screenshots, interact with the viewer, rotate 3d spatial components, and open/close views. Basically, anything you can do in the rerun viewer, an agent can now do, closing the loop on debugging. - Significantly stronger models have been released from both Anthropic and OpenAI. Fabel and Codex 5.6 Sol should make this implementation both cleaner and more performant, as well as find the previous bugs 5.4 couldn't - Harnesses have gotten much, much better since I first worked on this. HTML artifacts + /goal have made implementation + review much easier. I actively have Fabel in the driver's seat while 5.6 does mechanical code work + review after the fact. Thanks to the combination above, I was able to improve the custom Rust view to be lots better and fix most of the wonky bugs. Along with this everything should now use proper GPU rendering with no CPU fallbacks. I also wanted to make sure I could visualize training so I went ahead and integrated it with the wonderful brush since it works on Mac as well. With this proved out, we're also working on getting official Gaussian Splatting support as a proper archetype on the normal rerun distribution. This should be a great foundation to build all of the other esoteric representations (2DGS/4DGS/Triangle Splatting/whatever else you want)! I can finally log the full splat training run and move forward and backwards in time to see how the optimization went at different timesteps! Given powerful agents, the rerun mcp client, and a good harness, there's literally never been a better time to build.

Pablo Vela

18,675 просмотров • 24 дней назад