techlist.io
KR EN

google-cloud

2 posts

woowahan

Delivering the Future: Global (opens in new tab)

The Global Hackathon 2025 served as a massive collaborative initiative to unite over 270 technical employees from seven global entities under DeliveryHero’s umbrella, including Woowa Brothers. By leveraging the community-building expertise of the Woowahan DevRel team, the event successfully bridged geographical and technical gaps to foster innovation in "Delivering the Future." The hackathon concluded with high-level recognition from global leadership and a strategic partnership with Google Cloud, demonstrating the power of synchronized global technical synergy. ## Strategic Planning and Global Coordination * The event adopted a hybrid "Base Camp" model, where participants worked from their local entity offices while staying connected through 24-hour live streaming and centralized online channels. * Organizers meticulously navigated the logistical hurdles of spanning 70 countries, including coordinating across vastly different time zones and respecting local public holidays and vacation seasons. * Efficiency was maintained through a decentralized communication strategy, using entity-specific meetings and comprehensive guidebooks rather than frequent global meetings to prevent "meeting fatigue" across time zones. ## Technical Infrastructure and Regulatory Compliance * To accommodate diverse technical preferences, the infrastructure had to support various stacks, including AWS, Google Cloud Platform (GCP), and specific machine learning models. * The central organization team addressed complex regulatory challenges, ensuring all sandbox environments complied with strict global security standards and GDPR (EU General Data Protection Regulation). * A strategic partnership with Google Cloud provided a standardized Google AI-based environment, enabling teams to experiment rapidly with mature tools and cloud-native services. ## Local Operations and Cross-Entity Collaboration * Physical office spaces were transformed into immersive hackathon hubs to maintain the high-intensity atmosphere characteristic of offline coding marathons. * The event encouraged "office sharing" between entities located in the same city and even supported travel for members to join different regional base camps, fostering a truly global networking culture. * Local supporters used standardized checklists and operational frameworks to ensure a consistent experience for participants, whether they were in Seoul, Berlin, or Dubai. Building a successful global technical event requires a delicate balance between centralized infrastructure and local autonomy. For organizations operating across multiple regions, investing in shared technical sandboxes and robust communication frameworks is essential for turning fragmented local talent into a unified global innovation engine.

google-cloudaimachine-learninginfrastructure+4
google

Teaching Gemini to spot exploding stars with just a few examples (opens in new tab)

Researchers have demonstrated that Google’s Gemini model can classify cosmic events with 93% accuracy, rivaling specialized machine learning models while providing human-readable explanations. By utilizing few-shot learning with only 15 examples per survey, the model addresses the "black box" limitation of traditional convolutional neural networks used in astronomy. This approach enables scientists to efficiently process the millions of alerts generated by modern telescopes while maintaining a transparent and interactive reasoning process. ## Bottlenecks in Modern Transient Astronomy * Telescopes like the Vera C. Rubin Observatory are expected to generate up to 10 million alerts per night, making manual verification impossible. * The vast majority of these alerts are "bogus" signals caused by satellite trails, cosmic rays, or instrumental artifacts rather than real supernovae. * Existing specialized models often provide binary "real" or "bogus" labels without context, forcing astronomers to either blindly trust the output or spend hours on manual verification. ## Multimodal Few-Shot Learning for Classification * The research utilized few-shot learning, providing Gemini with only 15 annotated examples for three major surveys: Pan-STARRS, MeerLICHT, and ATLAS. * Input data consisted of image triplets—a "new" alert image, a "reference" image of the same sky patch, and a "difference" image—each 100x100 pixels in size. * The model successfully generalized across different telescopes with varying pixel scales, ranging from 0.25" per pixel for Pan-STARRS to 1.8" per pixel for ATLAS. * Beyond simple labels, Gemini generates a textual description of observed features and an interest score to help astronomers prioritize follow-up observations. ## Expert Validation and Self-Assessment * A panel of 12 professional astronomers evaluated the model using a 0–5 coherence rubric, confirming that Gemini’s logic aligned with expert reasoning. * The study found that Gemini can effectively assess its own uncertainty; low self-assigned "coherence scores" were strong indicators of likely classification errors. * This ability to flag its own potential mistakes allows the model to act as a reliable partner, alerting scientists when a specific case requires human intervention. The transition from "black box" classifiers to interpretable AI assistants allows the astronomical community to scale with the data flood of next-generation telescopes. By combining high-accuracy classification with transparent reasoning, researchers can maintain scientific rigor while processing millions of cosmic events in real time.

google-cloudaigen-aigemini+4