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  1. 🎉 "We broke 92% of SHA-256!" cries the self-congratulatory parade, flaunting their not-so-peer-reviewed #research. 🤔 But fear not, the paper (sponsored by 'dataplay.ai' – totally unbiased, right?) has all the cryptographic wizards shifting to... well, something. Maybe. 🧙‍♂️🔮
    stateofutopia.com/papers/2/we- #SHA256 #Breakthrough #Cryptography #DataplayAI #Cybersecurity #HackerNews #ngated

  2. 🎉 "We broke 92% of SHA-256!" cries the self-congratulatory parade, flaunting their not-so-peer-reviewed #research. 🤔 But fear not, the paper (sponsored by 'dataplay.ai' – totally unbiased, right?) has all the cryptographic wizards shifting to... well, something. Maybe. 🧙‍♂️🔮
    stateofutopia.com/papers/2/we- #SHA256 #Breakthrough #Cryptography #DataplayAI #Cybersecurity #HackerNews #ngated

  3. 🎉 "We broke 92% of SHA-256!" cries the self-congratulatory parade, flaunting their not-so-peer-reviewed #research. 🤔 But fear not, the paper (sponsored by 'dataplay.ai' – totally unbiased, right?) has all the cryptographic wizards shifting to... well, something. Maybe. 🧙‍♂️🔮
    stateofutopia.com/papers/2/we- #SHA256 #Breakthrough #Cryptography #DataplayAI #Cybersecurity #HackerNews #ngated

  4. 🎉 "We broke 92% of SHA-256!" cries the self-congratulatory parade, flaunting their not-so-peer-reviewed #research. 🤔 But fear not, the paper (sponsored by 'dataplay.ai' – totally unbiased, right?) has all the cryptographic wizards shifting to... well, something. Maybe. 🧙‍♂️🔮
    stateofutopia.com/papers/2/we- #SHA256 #Breakthrough #Cryptography #DataplayAI #Cybersecurity #HackerNews #ngated

  5. 🎉 "We broke 92% of SHA-256!" cries the self-congratulatory parade, flaunting their not-so-peer-reviewed #research. 🤔 But fear not, the paper (sponsored by 'dataplay.ai' – totally unbiased, right?) has all the cryptographic wizards shifting to... well, something. Maybe. 🧙‍♂️🔮
    stateofutopia.com/papers/2/we- #SHA256 #Breakthrough #Cryptography #DataplayAI #Cybersecurity #HackerNews #ngated

  6. 📢 Working with educational datasets in psychology?

    Consider publishing a data paper in the upcoming Journal of Open Psychology Data (JOPD) special issue on educational datasets. Contributions across all learning stages, from early childhood to adult learning, are welcome, especially datasets enabling secondary analyses.

    🗓 Deadline: June 30, 2026
    🔗 openpsychologydata.metajnl.com

    #OpenScience #OpenData #EducationalResearch #Psychology #FAIRData #DataPapers

  7. 📢 Working with educational datasets in psychology?

    Consider publishing a data paper in the upcoming Journal of Open Psychology Data (JOPD) special issue on educational datasets. Contributions across all learning stages, from early childhood to adult learning, are welcome, especially datasets enabling secondary analyses.

    🗓 Deadline: June 30, 2026
    🔗 openpsychologydata.metajnl.com

    #OpenScience #OpenData #EducationalResearch #Psychology #FAIRData #DataPapers

  8. 📢 Working with educational datasets in psychology?

    Consider publishing a data paper in the upcoming Journal of Open Psychology Data (JOPD) special issue on educational datasets. Contributions across all learning stages, from early childhood to adult learning, are welcome, especially datasets enabling secondary analyses.

    🗓 Deadline: June 30, 2026
    🔗 openpsychologydata.metajnl.com

    #OpenScience #OpenData #EducationalResearch #Psychology #FAIRData #DataPapers

  9. 📢 Working with educational datasets in psychology?

    Consider publishing a data paper in the upcoming Journal of Open Psychology Data (JOPD) special issue on educational datasets. Contributions across all learning stages, from early childhood to adult learning, are welcome, especially datasets enabling secondary analyses.

    🗓 Deadline: June 30, 2026
    🔗 openpsychologydata.metajnl.com

    #OpenScience #OpenData #EducationalResearch #Psychology #FAIRData #DataPapers

  10. #Uber’s HiveSync team optimized Hadoop Distcp for multi-petabyte replication across hybrid cloud and on-prem data lakes.

    ✅ Task parallelization
    ✅ Uber jobs for small transfers
    ✅ Improved observability

    Result: 5× replication capacity & seamless on-prem-to-cloud migration.

    Read more: bit.ly/4bwUUFt

    #InfoQ #SoftwareArchitecture #DistributedSystems #Observability #DataLake

  11. #Uber’s HiveSync team optimized Hadoop Distcp for multi-petabyte replication across hybrid cloud and on-prem data lakes.

    ✅ Task parallelization
    ✅ Uber jobs for small transfers
    ✅ Improved observability

    Result: 5× replication capacity & seamless on-prem-to-cloud migration.

    Read more: bit.ly/4bwUUFt

    #InfoQ #SoftwareArchitecture #DistributedSystems #Observability #DataLake

  12. ’s HiveSync team optimized Hadoop Distcp for multi-petabyte replication across hybrid cloud and on-prem data lakes.

    ✅ Task parallelization
    ✅ Uber jobs for small transfers
    ✅ Improved observability

    Result: 5× replication capacity & seamless on-prem-to-cloud migration.

    Read more: bit.ly/4bwUUFt

  13. #Uber’s HiveSync team optimized Hadoop Distcp for multi-petabyte replication across hybrid cloud and on-prem data lakes.

    ✅ Task parallelization
    ✅ Uber jobs for small transfers
    ✅ Improved observability

    Result: 5× replication capacity & seamless on-prem-to-cloud migration.

    Read more: bit.ly/4bwUUFt

    #InfoQ #SoftwareArchitecture #DistributedSystems #Observability #DataLake

  14. AI Image Annotation for Detection Models

    Structured polygon annotation, 3D cuboids, landmark detection, and semantic segmentation designed for scalable AI training. An image annotation company delivers datasets for computer vision, medical imaging, and object detection systems.

    Know More: hitechdigital.com/image-annota

    #ImageAnnotation #AITrainingData #ObjectDetection #MachineLearning #MedicalImagingAI #DataLabeling

  15. AI agents are finally getting enterprise-grade treatment. 🤖

    Databricks Custom Agents (formerly Agent Framework) enable:
    - Local development with your own tools and models
    - Integrated evaluation + CI/CD for continuous improvement
    - Deployment as governed, serverless Databricks Apps
    - Lakehouse native memory so agents stay context-aware across sessions

    Less glue code, more trustworthy, data aware AI systems.

    #Databricks #CustomAgents #AIAgents #MosaicAI #DataPlatform #FOSS

  16. What Makes a Good Video Annotation Partner

    Strong AI models depend on precise training data. Learn how to evaluate industry experience, quality control layers, security policies, and operational efficiency when selecting a video annotation service provider.

    Know More: hitechdigital.com/blog/choosin

    #VideoAnnotationServices #VideoAnnotationCompany #AnnotationServiceProvider #DataAnnotationServices #AIAnnotationServices #DataLabelingCompany

  17. Most ML issues are not model problems. They are data problems.

    I retrained the same churn model twice.
    Same code. Same path to the data.
    Different result.

    Why? Because of mutable data references.

    :blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: tinyurl.com/lake-vs-lakehouse-

    :blobcoffee: Friend-Link: medium.com/towards-artificial-

    #ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression

  18. Most ML issues are not model problems. They are data problems.

    I retrained the same churn model twice.
    Same code. Same path to the data.
    Different result.

    Why? Because of mutable data references.

    :blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: tinyurl.com/lake-vs-lakehouse-

    :blobcoffee: Friend-Link: medium.com/towards-artificial-

    #ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression

  19. Most ML issues are not model problems. They are data problems.

    I retrained the same churn model twice.
    Same code. Same path to the data.
    Different result.

    Why? Because of mutable data references.

    :blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: tinyurl.com/lake-vs-lakehouse-

    :blobcoffee: Friend-Link: medium.com/towards-artificial-

  20. Most ML issues are not model problems. They are data problems.

    I retrained the same churn model twice.
    Same code. Same path to the data.
    Different result.

    Why? Because of mutable data references.

    :blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: tinyurl.com/lake-vs-lakehouse-

    :blobcoffee: Friend-Link: medium.com/towards-artificial-

    #ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression