June 2009 Blog Posts (3)

New Computer Guide

What to look for when buying a new computer

The following guide will help you to answer the questions of what you need, what do I upgrade, and more.



The first thing you need to consider is what you will be doing with the computer. Will you need to store a lot of video, pictures, and music? Will this be a media center PC? Or will this computer just be a basic email, websurfing, and word processing… Continue

Added by Tim Stevesi on June 18, 2009 at 1:00pm — No Comments

Getting Started with Arduino





I just went to the Maker Faire in the Bay Area with my friend Michael Hauser. The expo was filled with Arduino products! Getting Started with Arduino (Make: Projects)

coincidentally… Continue

Added by Michael Levin on June 14, 2009 at 5:03pm — 1 Comment

Hi from JavaOne!

Hello from the largest dev conf in the world, J1! If you're here, too, please pingme @mikelevin on Twitter. There's a group here especially for J1 - http://www.codetown.us/group/javaone09



If you're here at J1 and want to share your experiences with Codetown, please upload photos, videos, blogposts, etc and tag them "javaone09"



I'll do my… Continue

Added by Michael Levin on June 1, 2009 at 7:32pm — No Comments

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Happy 10th year, JCertif!

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.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

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

GitHub Acknowledges Recent Outages, Cites Scaling Challenges and Architectural Weaknesses

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

Presentation: Dynamic Moments: Weaving LLMs into Deep Personalization at DoorDash

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

Article: Redesigning Banking PDF Table Extraction: A Layered Approach with Java

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 Introduces Project Think: A Durable Runtime for AI Agents

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

Designing Memory for AI Agents: Inside Linkedin’s Cognitive Memory Agent

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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