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Getting Started With Android
Interested in Android programming? Here's a nice, recent article at Lifehacker outlining good, free resources to get you going. http://lifehacker.com/i-want-to-write-android-apps-where-do-i-start...;
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Happy 10th year, JCertif!
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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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InfoQ Reading List
AWS Introduces ECS Managed Instances for Containerized Applications
AWS recently announced Amazon ECS Managed Instances, a new feature in ECS designed to simplify the deployment of containerized applications on EC2 instances. The service automatically manages instance provisioning, scaling, and maintenance, thereby reducing the operational overhead associated with maintaining container infrastructure.
By Renato LosioClaude Sonnet 4.5 Tops SWE-Bench Verified, Extends Coding Focus Beyond 30 Hours
Anthropic's Claude Sonnet 4.5, its most advanced coding model, excels in task performance and safety, achieving a 98.7% safety score and improving real-world coding capabilities. Enhanced reasoning skills allow for sustained multi-step tasks, with notable user gains reported. This drop-in replacement demonstrates a powerful balance of capability and security for users.
By Hien LuuPlanetScale Extends Database Platform to PostgreSQL
PlanetScale has announced the general availability of its managed sharded Postgres service, built for performance and reliability on AWS or Google Cloud. The launch extends PlanetScale's offerings to PostgreSQL users, adding to the company's existing popular MySQL-based platform built on top of Vitess.
By Renato LosioGoogle DeepMind Introduces CodeMender, an AI Agent for Automated Code Repair
Google DeepMind has introduced CodeMender, a new AI-driven agent designed to detect, fix, and secure software vulnerabilities automatically. The project builds on recent advances in reasoning models and program analysis, aiming to reduce the time developers spend identifying and patching security issues.
By Robert KrzaczyńskiPresentation: Deploy MultiModal RAG Systems with vLLM
Stephen Batifol discusses building and optimizing self-hosted, multimodal RAG systems. He breaks down vector search, nearest neighbor indexes (FLAT, IVF, HNSW), and the critical role of choosing the right embedding model. He then explains vLLM inference optimization (paged attention, quantization) and uses Mistral's Pixtral to detail multimodal large language model architecture.
By Stephen Batifol
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