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🤯🤯 That's insane! Hugging Face just shipped Data Studio on dataset pages... Ask a question about any dataset, the agent writes and runs a SQL query... then share the result or download as CSV... check it out! 👀 ⤵️ Wow GG Caleb 👏

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Jeff Boudier 🤗

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19,703 görüntüleme • 1 yıl önce •via X (Twitter)

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Jeff Boudier 🤗 profil fotoğrafı
Jeff Boudier 🤗1 yıl önce

Here's the example result saved in the video: "show me the 100 tasks with highest percentage with bar charts":

Jeff Boudier 🤗 profil fotoğrafı
Jeff Boudier 🤗1 yıl önce

This works for private datasets as well for organizations upgraded to Enterprise Hub:

Tembo - Multi-Workload Managed Postgres profil fotoğrafı
Tembo - Multi-Workload Managed Postgres2 yıl önce

The "data platform" status quo is vendor sprawl and data islands. Tembo Cloud can help you tame the sprawl and centralize your data using the full power of Postgres.

VerbaGPT profil fotoğrafı
VerbaGPT1 yıl önce

@huggingface @calebfahlgren Very cool! I just checked it out. Works well for simple cases, but complex cases (especially tabular), it seems the understanding isn't quite there. Still experimenting. I've been building a text-to-python tool to work with any databases, so I know it can be tricky.

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Dave1 yıl önce

@mervenoyann @huggingface @calebfahlgren Pretty cool! Let me try it!

Gonçalo Alves profil fotoğrafı
Gonçalo Alves1 yıl önce

@huggingface @calebfahlgren now we can analyze datasets with just one query

steve ike profil fotoğrafı
steve ike1 yıl önce

@huggingface @calebfahlgren Wow, this is awesome. Nice find.

Benzer Videolar

We recently shipped a Data Exploration Agent in the Hugging Face Dataset Viewer 💽 • powered by OpenAI gpt-oss-120b 🤖 • served by GroqInc for super fast inference ⚡ • multi-turn SQL on datasets in the browser 💻
0:38

We recently shipped a Data Exploration Agent in the Hugging Face Dataset Viewer 💽 • powered by OpenAI gpt-oss-120b 🤖 • served by GroqInc for super fast inference ⚡ • multi-turn SQL on datasets in the browser 💻

Caleb

13,689 görüntüleme • 7 ay önce

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:
0:26

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

Over the Reality 🌐

34,651 görüntüleme • 1 ay önce

Most robot datasets store one sentence per episode: the task. Real problems need more: a plan, a memory, room to answer questions and change course. We just shipped full language support in the LeRobot dataset format. Now lerobot-annotate points a vision-language model at each episode and writes the subtasks, plans, memory, and grounded Q&A itself - roughly $1.20 for 100 episodes. From there, recipes turn the annotated rows into policy-agnostic training messages, so the same dataset can teach planning, memory, tool calls, or low-level control, no dataset redesign required. Read how it all works on the blog:
0:20

Most robot datasets store one sentence per episode: the task. Real problems need more: a plan, a memory, room to answer questions and change course. We just shipped full language support in the LeRobot dataset format. Now lerobot-annotate points a vision-language model at each episode and writes the subtasks, plans, memory, and grounded Q&A itself - roughly $1.20 for 100 episodes. From there, recipes turn the annotated rows into policy-agnostic training messages, so the same dataset can teach planning, memory, tool calls, or low-level control, no dataset redesign required. Read how it all works on the blog:

LeRobot

13,451 görüntüleme • 2 ay önce

Ora just released the Trustpilot for AI agents 🤯 Every agent faces the same question: "which product should I use for my user?" Now they can ask ora. One MCP call returns a ranked list of agent-ready products, scored and reviewed by other agents who tried them first. Add it to OpenClaw, Hermes or any other agent in 30 seconds. Check out the docs here:
0:40

Ora just released the Trustpilot for AI agents 🤯 Every agent faces the same question: "which product should I use for my user?" Now they can ask ora. One MCP call returns a ranked list of agent-ready products, scored and reviewed by other agents who tried them first. Add it to OpenClaw, Hermes or any other agent in 30 seconds. Check out the docs here:

Assaf Elovic

16,919 görüntüleme • 4 ay önce

PEOPLE OF THE EARTH. Figured out how to ask natural language questions of current financial data and get back a natural language response...with correct/relevant numbers! 🤯💰✨ So. Freaking. Cool. 1. Send database schema along with the question and relevant scope/parameters to GPT. 2. GPT returns SQL query. 3. Run SQL query on your data. 4. Take returned data and pass it in along with the original question. 5. GPT returns natural language answer with relevant data! Original Question: What was the highest price of Apple's stock over the past decade and how does it compare to Tesla's highest stock price? And on what date were those highest prices? Final Answer: The highest price of Apple's stock over the past decade was $182.94 on January 4, 2022, while Tesla's highest stock price was $414.50 on November 4, 2021.
3:23

PEOPLE OF THE EARTH. Figured out how to ask natural language questions of current financial data and get back a natural language response...with correct/relevant numbers! 🤯💰✨ So. Freaking. Cool. 1. Send database schema along with the question and relevant scope/parameters to GPT. 2. GPT returns SQL query. 3. Run SQL query on your data. 4. Take returned data and pass it in along with the original question. 5. GPT returns natural language answer with relevant data! Original Question: What was the highest price of Apple's stock over the past decade and how does it compare to Tesla's highest stock price? And on what date were those highest prices? Final Answer: The highest price of Apple's stock over the past decade was $182.94 on January 4, 2022, while Tesla's highest stock price was $414.50 on November 4, 2021.

Josh Pigford

245,304 görüntüleme • 3 yıl önce

We are officially in the AGI era🤯 I built this in 2 hours with GPT-6 Astra. > pointed Astra at an open anatomy dataset > Codex wrote the whole interface, all 206 bones > shipped it as a Claude Code Artifact Spin it, blow it apart, cut it in half, click any bone to read what it is. I know nothing about anatomy.
0:40

We are officially in the AGI era🤯 I built this in 2 hours with GPT-6 Astra. > pointed Astra at an open anatomy dataset > Codex wrote the whole interface, all 206 bones > shipped it as a Claude Code Artifact Spin it, blow it apart, cut it in half, click any bone to read what it is. I know nothing about anatomy.

Emm | scenario.com

25,533 görüntüleme • 14 gün önce

We're releasing XDOF s ABC-130k dataset converted to RRD on a Hugging Face bucket, along with the conversion code. ABC-130k is the largest open-source bi-manual teleoperation dataset published to date. With Rerun, you can view all the episodes, query for insights, and use them in training workflows. One interesting finding is that annotators use varying levels of granularity for subtask labeling. Take the chess task in this video, for example: One episode uses language like 'place the chess piece', while another uses a fine-grained description like 'place the queen/pawn at E8'. This distinction is clearly visualized in Rerun through the colors in the state view! Our repo also includes a "mistake" query example and other interesting observations about the dataset, complementing the official ABC-130k dataset card. Download a subset and explore it yourself!! What other open datasets would you like to see in RRD?
1:24

We're releasing XDOF s ABC-130k dataset converted to RRD on a Hugging Face bucket, along with the conversion code. ABC-130k is the largest open-source bi-manual teleoperation dataset published to date. With Rerun, you can view all the episodes, query for insights, and use them in training workflows. One interesting finding is that annotators use varying levels of granularity for subtask labeling. Take the chess task in this video, for example: One episode uses language like 'place the chess piece', while another uses a fine-grained description like 'place the queen/pawn at E8'. This distinction is clearly visualized in Rerun through the colors in the state view! Our repo also includes a "mistake" query example and other interesting observations about the dataset, complementing the official ABC-130k dataset card. Download a subset and explore it yourself!! What other open datasets would you like to see in RRD?

Rerun

21,234 görüntüleme • 1 ay önce

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.
0:34

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Santiago

20,877 görüntüleme • 3 ay önce

Hermes just got a local model nobody is talking about. And it runs your entire workflow for free. You do not need to pay for smart agents anymore. A developer just dropped a free brain called Karnis on Hugging Face. It is built perfectly for Hermes. It runs right on your computer using LM Studio. It watches real agents work and copies their exact steps. It edits files and runs commands for you. Just download LM Studio, search for Karnis, and connect it to Hermes. Your private agent is ready to work.
10:00

Hermes just got a local model nobody is talking about. And it runs your entire workflow for free. You do not need to pay for smart agents anymore. A developer just dropped a free brain called Karnis on Hugging Face. It is built perfectly for Hermes. It runs right on your computer using LM Studio. It watches real agents work and copies their exact steps. It edits files and runs commands for you. Just download LM Studio, search for Karnis, and connect it to Hermes. Your private agent is ready to work.

Julian Goldie SEO

22,365 görüntüleme • 5 ay önce

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra
0:33

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra

Teneo Protocol

41,825 görüntüleme • 6 ay önce

Plot your data before you model it. That's the 18 minutes of Stanford's statistical learning course. First Lesson. The professors show a prostate cancer dataset with a hidden outlier - a data entry typo that would silently destroy any model trained on it. Only a scatter plot catches it. Then they walk through six more real-world problems: heart disease risk, gene expression clustering, digit recognition, vowel classification, spam detection, satellite land mapping. Each dataset teaches a different statistical concept. Each one is messy in a different way. This is Hastie and Tibshirani the people who literally defined statistical learning as a field. Statistics can be deployed everywhere. In Medicine, Finance or Polymarket. Just Master that craft.
18:18

Plot your data before you model it. That's the 18 minutes of Stanford's statistical learning course. First Lesson. The professors show a prostate cancer dataset with a hidden outlier - a data entry typo that would silently destroy any model trained on it. Only a scatter plot catches it. Then they walk through six more real-world problems: heart disease risk, gene expression clustering, digit recognition, vowel classification, spam detection, satellite land mapping. Each dataset teaches a different statistical concept. Each one is messy in a different way. This is Hastie and Tibshirani the people who literally defined statistical learning as a field. Statistics can be deployed everywhere. In Medicine, Finance or Polymarket. Just Master that craft.

gemchanger

174,961 görüntüleme • 6 ay önce

Launching in beta: Beautify any website. All vibe-coded landing pages look the same 👀 Drop a URL into Moda and our agent can restyle it, keeping the content intact. Then, edit manually or with our agent. Publish it with one click or export out.
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Launching in beta: Beautify any website. All vibe-coded landing pages look the same 👀 Drop a URL into Moda and our agent can restyle it, keeping the content intact. Then, edit manually or with our agent. Publish it with one click or export out.

Anvisha

44,181 görüntüleme • 2 ay önce

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏
0:35

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏

Gabriele Farei

10,565 görüntüleme • 5 ay önce

Built a 2.5MB image classifier that runs in the browser in an evening with Claude Code. In 2022, I hand-labelled ~1,900 pages from historical books for a project that never really shipped. The dataset sat on Hugging Face for 3 years. This week I pointed Claude Code at it: "make me a browser-deployable model which can run using transformers.js." A few hours later: trained, quantised, running entirely client-side. It finds illustrated pages in digitised books, i.e., engravings, photographs, maps, diagrams etc. Paste a IIIF manifest, see where they appear across hundreds of pages. No server required, so perfect for institutions that can't afford to pay to host big models! Old data work paying off!
0:28

Built a 2.5MB image classifier that runs in the browser in an evening with Claude Code. In 2022, I hand-labelled ~1,900 pages from historical books for a project that never really shipped. The dataset sat on Hugging Face for 3 years. This week I pointed Claude Code at it: "make me a browser-deployable model which can run using transformers.js." A few hours later: trained, quantised, running entirely client-side. It finds illustrated pages in digitised books, i.e., engravings, photographs, maps, diagrams etc. Paste a IIIF manifest, see where they appear across hundreds of pages. No server required, so perfect for institutions that can't afford to pay to host big models! Old data work paying off!

Daniel van Strien

47,657 görüntüleme • 9 ay önce

WTF 🤯🤯🤯 Running your own ChatGPT or LLM on your laptop just takes minutes! You just need to - Install moved to: @lmstudio - Download a model - That's it! You can even run a local server to make API requests Same requests as with OpenAI API You only need to change the URL
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WTF 🤯🤯🤯 Running your own ChatGPT or LLM on your laptop just takes minutes! You just need to - Install moved to: @lmstudio - Download a model - That's it! You can even run a local server to make API requests Same requests as with OpenAI API You only need to change the URL

Rodrigo Rocco 👨‍💻📈📗 from JobBoardSearch 🔎

383,546 görüntüleme • 2 yıl önce

So dang enchanted with the Gemini 3.0 Flash support in Browserbase, especially for tedious data tasks! Check out the video below - Gemini navigates to the USGS website for seismic data, filters on 3D Multichannel Seismic datasets, adds all of the options for California, and then downloads as a CSV list. ❤️ You could even then have the data science agent in Colaboratory post-process and visualize the surveys!
1:22

So dang enchanted with the Gemini 3.0 Flash support in Browserbase, especially for tedious data tasks! Check out the video below - Gemini navigates to the USGS website for seismic data, filters on 3D Multichannel Seismic datasets, adds all of the options for California, and then downloads as a CSV list. ❤️ You could even then have the data science agent in Colaboratory post-process and visualize the surveys!

👩‍💻 Paige Bailey

25,681 görüntüleme • 8 ay önce

The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open access 25M papers, 450M+ visually indexed pages. Total 175TB+. All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.
0:20

The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open access 25M papers, 450M+ visually indexed pages. Total 175TB+. All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.

Davit

238,113 görüntüleme • 9 ay önce

shipped this incredible AI Agent in PostSyncer 🤓 it generates posts from pretty much anything: • upload an image → AI creates the post • paste a link → AI analyzes and writes • drop a PDF → AI extracts insights • share an article → AI crafts the copy so many possibilities the AI does a crazy good job at analyzing and creating content from whatever you throw at it it even created this post, THAT'S SOOO META 🤯 PostSyncer is becoming the best social media tool out there
0:31

shipped this incredible AI Agent in PostSyncer 🤓 it generates posts from pretty much anything: • upload an image → AI creates the post • paste a link → AI analyzes and writes • drop a PDF → AI extracts insights • share an article → AI crafts the copy so many possibilities the AI does a crazy good job at analyzing and creating content from whatever you throw at it it even created this post, THAT'S SOOO META 🤯 PostSyncer is becoming the best social media tool out there

Tibo

12,537 görüntüleme • 9 ay önce