Codetown ::: a software developer's community
Simple. If you have a commercial good or service that you'd like to advertise with us, the rate is $95 for 3 months for each ad. This includes jobs, blog posts, events, discussions and anything for which you charge a fee.
Just PayPal the payment to ads@codetown.us and post your ad. You can also mail a check to Cambridge Web Design, PO Box 1741, Winter Park, FL 32790-1741. We accept credit cards, too. Just send Michael Levin a message (mike@codetown.us) with your phone number and we'll chat on the phone.
Please invite some new members, if you please, and feel free to share Codetown's content on other social networks. We have pretty good volume at this point, depending on SEO. It seriously helps when you share and invite people...
If you are looking to post a job description head over to the Groups page. There you will find the Jobs group, where you can post your job as a discussion with a detailed description and salary, rate, or range. We ask you to disclose the compensation as a favor to the developers.
Other places you can advertise include the Events section. We can add a link to your site in the Reading List for the homepage of the Codetown website or one that will show up in the Reading Lists for specific groups.
Codetown content gets marketed, promoted and otherwise passed along by yours truly (in a way I hope is pleasant) to like-minded individuals more or less, depending on the content.
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.

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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