Codetown ::: a software developer's community
At Measures for Justice (MFJ) our mission is to use data to transform how we measure, understand, and reform the criminal justice system in America. We collect, clean, code, standardize, and analyze data from criminal justice agencies to provide consistent, comparable, objective, and public performance measures across the whole criminal justice system, from arrest to post-conviction, on a county-by-county basis (see our Data Portal at https://measuresforjustice.org/portal/).
In 2017, MFJ educated the Florida legislature about how data transparency in criminal justice could be improved in that state. As a result, the state passed into law (Florida Statutes 900.05) a bill that mandates court clerks, state attorneys, jail administrators, public defenders, and the Department of Corrections to report data to the Florida Department of Law Enforcement (FDLE) on a monthly basis. MFJ is supporting the implementation of the new legislation through a pilot in the 6th Judicial Circuit (Pasco and Pinellas counties) that will embed at least one Data Fellow within the Clerk of Courts Office of each county.
See more details here: https://measuresforjustice.org/about/jobs/data-fellow.html
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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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DoorDash has launched a multimodal machine learning system that aligns product images, text, and user queries in a shared embedding space. Trained on 32 million labeled query-product pairs using contrastive learning, the system improves semantic search, product ranking, and advertising relevance. Embeddings also support other machine learning tasks across the marketplace.
By Leela Kumili
Stefan Dirnstorfer discusses the shift from DOM-based testing to visual UI agents. He explains why LLMs often fail at precision tasks - like spotting one-pixel shifts or broken road networks - and shares how advanced image registration and "Chain-of-Thought" vision processing are essential for reliable QA. Learn why combining generative AI with classical algorithms is the future of automation.
By Stefan Dirnstorfer
Celebrating its 23rd year, Devnexus 2026 was held from March 4-6, 2026 at the Georgia World Congress Center in Atlanta, Georgia. The event featured speakers from the Java community who delivered workshops and talks under tracks such as: AI Generative; AI in Practice; Core Java; Java Frameworks; and Security and Developer Tools.
By Michael RedlichAndres Almiray, a serial open-source contributor and the creator of JReleaser, discusses the project's state, noting that the tool is usable across any ecosystem, not just Java. He also touches on the Common House Foundation's mission.
By Andres Almiray
This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
By Amit Kumar Padhy
© 2026 Created by Michael Levin.
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