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
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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After the Datadog Agent grew from 428 MiB to 1.22 GiB over a period of 5 years, Datadog engineers set out to reduce its binary size. They discovered that most Go binary bloat comes from hidden dependencies, disabled linker optimizations, and subtle behaviors in the Go compiler and linker.
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Artificial intelligence is rapidly transforming how software vulnerabilities are detected, but questions about who governs the risks AI exposes, and how those risks are acted on, are becoming increasingly urgent, according to a new blog post by GitLab.
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Patrick Debois discusses the evolution of software engineering in the age of AI. He shares four key patterns: transitioning from producer to manager, focusing on intent over implementation through spec-driven development, moving from delivery to discovery, and managing agentic knowledge. He explains how these shifts redefine seniority, team roles, and the future of the DevOps workflow.
By Patrick DeboisSwitch to the Mobile Optimized View
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