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Michael Levin's Blog – November 2011 Archive (6)
GIS in the Cloud - the ESRI Example
Great article here...
"Has your organization realized the potential of cloud computing yet? For users of geographic information system (GIS) technology, the cloud opens a number of new uses like weather forecasting, sales analysis, population…
ContinueAdded by Michael Levin on November 29, 2011 at 10:00am — No Comments
Codetown's Facebook page
Hey, everyone:
We have a Facebook page: http://www.facebook.com/codetown - if you haven't joined yet, please do. We'll post events, links to articles and discussions on Codetown and also links to articles outside the group, like this:
So, once you like the Facebook page, you'll get a stream of updates from…
Added by Michael Levin on November 24, 2011 at 9:33am — No Comments
Opening Call for Proposal - RubyConf India 2012
This just in from Satish N Kota of the Bangalore RUG:
"Hello Folks,
Lets begin the begin and in that spirit, call for talk proposals is
open for RubyConf India 2012. Interested folks can head over to:…
Added by Michael Levin on November 24, 2011 at 9:21am — No Comments
Mike Levin ::: Java Champion
http://blogs.oracle.com/java/entry/new_java_champion_michael_levin
Added by Michael Levin on November 21, 2011 at 12:40pm — 4 Comments
***Free*** Hudson book
Interested in Hudson? (or in finding out what it is) - check out this free book! Thanks to Frans Thamura all the way from Java,…
Added by Michael Levin on November 15, 2011 at 8:40am — No Comments
No Flash for Mobile
Added by Michael Levin on November 10, 2011 at 10:00am — No Comments
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2008
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Notes
Welcome to Codetown!
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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InfoQ Reading List
Article: Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs
Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.
By Saranya VedagiriUber Moves from Static Limits to Priority-Aware Load Control for Distributed Storage
Uber engineers detailed how they evolved their storage platform from static rate limiting to a priority-aware load management system. The approach protects Docstore and Schemaless, Uber’s MySQL-based distributed databases, by colocating control with storage, prioritizing critical traffic, and dynamically shedding load under overload conditions.
By Leela KumiliBuilding Software Organisations Where People Can Thrive
Continuous learning, adaptability, and strong support networks are the foundations for thriving teams, Matthew Card mentioned. Trust is built through consistent, fair leadership and addressing toxic behaviour, bias, and microaggressions early. By fostering growth, psychological safety, and accountability, people-first leadership drives resilience, collaboration, and performance.
By Ben LindersGoogle DeepMind Introduces ATLAS Scaling Laws for Multilingual Language Models
Google DeepMind researchers have introduced ATLAS, a set of scaling laws for multilingual language models that formalize how model size, training data volume, and language mixtures interact as the number of supported languages increases.
By Robert KrzaczyńskiPresentation: Foundation Models for Ranking: Challenges, Successes, and Lessons Learned
Moumita Bhattacharya discusses the evolution of Netflix’s ranking systems, from the multi-model architecture to a Unified Contextual Recommender (UniCoRn). She explains how they built a task-agnostic User Foundation Model to capture long-term member preferences. Learn how they solve system challenges like high-throughput inference and the tradeoff between relevance and personalization.
By Moumita Bhattacharya
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