How to Design a Distributed Logging Platform from Scratch

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Most applications can generate logs easily, but managing millions of log events across hundreds of machines turns logging into a distributed systems challenge. A well-designed logging platform must simultaneously handle high-speed ingestion and fast, flexible querying — two workloads that require careful architectural balance. The proposed architecture layers log collectors, an ingestion layer, a buffer queue, indexing nodes, storage nodes, and a query coordinator to separate concerns cleanly. Using an e-commerce platform as a case study, the design supports queries such as filtering errors by service, searching by status code, and aggregating failures over time. The article argues that building such a system from scratch is one of the most effective ways to understand how production-grade tools like Elasticsearch work under the hood.

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Same AI Model, Better Engineering: Backboard CLI Outscores Claude Code on Terminal-Bench

A team running Backboard CLI on Terminal-Bench 2.1 achieved a score of 85.4% using Claude Opus 4.8 via Amazon Bedrock, outperforming Claude Code's published score of 78.9% on the identical underlying model. The result suggests that the system architecture surrounding an AI model — including context management, tool selection, and failure handling — can significantly influence benchmark performance. Beyond accuracy, the Backboard run cost $280.72, roughly 49% less than the then-verified leaderboard leader, which scored 83.8% at a reported $552.67. The team argues this shifts the key question from which model performs best to which system delivers reliable results at sustainable cost. They also emphasize that well-engineered AI infrastructure should remain adaptable across models, reducing the need to rebuild entire stacks whenever a newer model emerges.

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ProgrammingDEV Community ·

AI, Blockchain, and Surveillance Converge in a Growing Digital Rights Crisis

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ProgrammingDEV Community ·

Venus Core Pool Governance Rated High Risk With Nine Attack Vectors Identified

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ProgrammingDEV Community ·

Developer builds read-only AI agent that audits AWS accounts in under two minutes

A developer has created an AI agent capable of auditing an AWS account for security and cost issues, identifying problems such as publicly exposed S3 buckets, open SSH ports, disabled GuardDuty, and wasted spend on idle Elastic IPs. The entire audit takes roughly two minutes to complete. The agent is deliberately restricted to a read-only IAM identity, meaning AWS itself blocks any write, delete, or modify actions before they can execute. The build relies on just one JSON configuration file and a Markdown checklist, requiring no Python frameworks or vector databases. The full source code has been published on GitHub for others to deploy against their own AWS accounts.

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