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    Roboflow says users can label data with Astra

    Roboflow claims labeling time and cost are going “almost to $0,” unblocking millions of vision products.

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    6 Sources, 23d ago, first seen 23d ago

    TLDR

    Roboflow says Astra can be used to label data in its platform. The company claims that labeling time and cost are falling to nearly zero, removing a barrier for millions of vision products.

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    61.7K

    6 Sources, first seen 23d ago

    681 likes

    Combined views

    61.7K

    6 Sources, first seen 23d ago

    681 likes
    17 comments
    586 saves
    80 reposts

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    17 comments
    586 saves
    80 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    6 Sources

    @roboflowyou can label data in roboflow with astra millions of vision products are now unblocked by labeling time/cost going almost to $0 crazy next 12 months ahead
    @peteskomorochRT @roboflow: you can label data in roboflow with astra millions of vision products are now unblocked by labeling time/cost going almost t…
    @josephofiowaThis thread is the future of how to build purpose-built realtime vision models Use a large, slow, 1T+ parameter model that knows a lot about a lot to train your purpose-built, fast, small model to run fast / on edge for a focused task eg 1️⃣ Add roboflow MCP: claude mcp add -s user roboflow \ --transport http https://mcp.roboflow.com/mcp 2️⃣ Tell it to collect/label/train: "hey, LLM, use Astra build a dataset on roboflow and label all my video / images to train an RF-DETR model for me" 3️⃣ Deploy: tell your LLM to use your new owned model that runs 30+ FPS on a cloud endpoint or run it on consumer/edge hardware
    @vanstriendanielThe real tokenmaxxing move: use frontier agents to build small classifiers for large-scale data curation. Small classifiers already help curate the data used to train LLMs. I wanted to see how far an agent could take me in building one. I used Astra, SetFit and @huggingface Jobs to turn 200 agent-labelled examples into a reusable document-purpose classifier, reviewing the categories and tricky cases along the way. It classified 191,724 FinePDFs-Edu documents for ~$0.70 in inference compute, versus an estimated $13–26 to label the same document excerpts with low-cost batch LLMs. Training experiments added ~$2.90 in compute. Workflow, mistakes, reusable model and a prompt to try on your own data: https://danielvanstrien.xyz/posts/2026/agents-data-curation/index.html
    @huggingfaceRT @vanstriendaniel: The real tokenmaxxing move: use frontier agents to build small classifiers for large-scale data curation. Small class…

    6 Sources

    @roboflowyou can label data in roboflow with astra millions of vision products are now unblocked by labeling time/cost going almost to $0 crazy next 12 months ahead
    @peteskomorochRT @roboflow: you can label data in roboflow with astra millions of vision products are now unblocked by labeling time/cost going almost t…
    @josephofiowaThis thread is the future of how to build purpose-built realtime vision models Use a large, slow, 1T+ parameter model that knows a lot about a lot to train your purpose-built, fast, small model to run fast / on edge for a focused task eg 1️⃣ Add roboflow MCP: claude mcp add -s user roboflow \ --transport http https://mcp.roboflow.com/mcp 2️⃣ Tell it to collect/label/train: "hey, LLM, use Astra build a dataset on roboflow and label all my video / images to train an RF-DETR model for me" 3️⃣ Deploy: tell your LLM to use your new owned model that runs 30+ FPS on a cloud endpoint or run it on consumer/edge hardware
    @vanstriendanielThe real tokenmaxxing move: use frontier agents to build small classifiers for large-scale data curation. Small classifiers already help curate the data used to train LLMs. I wanted to see how far an agent could take me in building one. I used Astra, SetFit and @huggingface Jobs to turn 200 agent-labelled examples into a reusable document-purpose classifier, reviewing the categories and tricky cases along the way. It classified 191,724 FinePDFs-Edu documents for ~$0.70 in inference compute, versus an estimated $13–26 to label the same document excerpts with low-cost batch LLMs. Training experiments added ~$2.90 in compute. Workflow, mistakes, reusable model and a prompt to try on your own data: https://danielvanstrien.xyz/posts/2026/agents-data-curation/index.html
    @huggingfaceRT @vanstriendaniel: The real tokenmaxxing move: use frontier agents to build small classifiers for large-scale data curation. Small class…