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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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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GitHub has publicly addressed a series of recent availability and performance issues that disrupted services across its platform, attributing the incidents to rapid growth, architectural coupling, and limitations in handling system load.
By Craig Risi
Sudeep Das and Pradeep Muthukrishnan explain the shift from static merchandising to dynamic, moment-aware personalization at DoorDash. They share how LLMs generate natural-language "consumer profiles" and content blueprints, while traditional deep learning handles last-mile ranking. This hybrid approach allows the platform to adapt to short-lived user intent and massive catalog abundance.
By Sudeep Das, Pradeep Muthukrishnan
PDF table extraction often looks easy until it fails in production. Real bank statements can be messy, with scanned pages, shifting layouts, merged cells, and wrapped rows that break standard Java parsers. This article shares how we redesigned the approach using stream parsing, lattice/OCR, validation, scoring, and selective ML to make extraction more reliable in real banking systems.
By Mehuli Mukherjee
Cloudflare's Project Think introduces a new framework for AI agents, shifting from stateless orchestration to a durable actor-based infrastructure. It features a kernel-like runtime enabling agents to manage memory and run code securely. Innovations include Fibers for checkpointing progress and a Session API for relational conversations, enhancing agent efficiency and resilience.
By Patrick Farry
LinkedIn introduces Cognitive Memory Agent (CMA), generative AI infrastructure layer enabling stateful, context-aware systems. It provides persistent memory across episodic, semantic, and procedural layers, supporting multi-agent coordination, retrieval, and lifecycle management. CMA addresses LLM statelessness and enables production-grade personalization and long-term context in AI applications.
By Leela Kumili
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