Codetown ::: a software developer's community
$ scala searchjar.scala ServerInfo /opt/tomcat6/lib /opt/tomcat6/lib/catalina.jar org/apache/catalina/util/ServerInfo.classThe script can walk over one or more directory and search all jar files for you.
/opt/tomcat6/lib/catalina.jar org/apache/catalina/util/ServerInfo.properties
$ scala displayjar.scala /opt/tomcat6/lib/catalina.jar org/apache/catalina/util/ServerInfo.properties # Licensed to the Apache Software Foundation (ASF) under one or moreYou can even run the displayjar.scala with just a jar file, and it default to print out the Manifest file content.
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
server.info=Apache Tomcat/6.0.18
server.number=6.0.18.0
server.built=Jul 22 2008 02:00:36
Tags:
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.
Check out the Codetown Jobs group.

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 Vedagiri
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 Kumili
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 Linders
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 Krzaczyński
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 BhattacharyaSwitch to the Mobile Optimized View
© 2026 Created by Michael Levin.
Powered by