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@kaptain shared a link, 3 weeks, 1 day ago
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Kubernetes Was Overkill. We Moved to Docker Compose and Saved 60 Hours.

A small team rolled back their Kubernetes move after six months in the weeds. The setup tanked productivity, bloated infra costs, and turned simple deploys into a slog. They ditched it, brought back Docker Compose, and chopped deploy time from 45 minutes to 4. That one change freed up 60+ engineerin.. read more  

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@kaptain shared a link, 3 weeks, 1 day ago
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Bryan Cantrill: How Kubernetes Broke the AWS Cloud Monopoly

Bryan Cantrill says Kubernetes didn’t just organize containers, it cracked open the cloud market. By letting teams provision infrastructure without locking into provider APIs, it broke AWS’s first-mover grip. That shift putcloud neutralityon the table, and suddenly multi-cloud wasn’t just a buzzword.. read more  

Bryan Cantrill: How Kubernetes Broke the AWS Cloud Monopoly
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@kala shared a link, 3 weeks, 1 day ago
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8 plots that explain the state of open models

Starting 2026, Chinese companies are dominating the open AI model scene, with Qwen leading in adoption metrics. Despite the rise of new entrants like Z.ai, MiniMax, Kimi Moonshot, and others, Qwen's position seems secure. DeepSeek's large models are showing potential to compete with Qwen, but the Ch.. read more  

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@kala shared a link, 3 weeks, 1 day ago
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Build an AI-powered website assistant with Amazon Bedrock

AWS spun up a serverless RAG-based support assistant usingAmazon BedrockandBedrock Knowledge Bases. It pulls in docs via a web crawler and S3, then stuffs embeddings intoAmazon OpenSearch Serverless. Access is role-aware, locked down withCognito. Everything spins up clean withAWS CDK... read more  

Build an AI-powered website assistant with Amazon Bedrock
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@kala shared a link, 3 weeks, 1 day ago
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Towards Generalizable and Efficient Large-Scale Generative Recommenders

Authors discuss their approach to scaling generative recommendation models from O(1M) to O(1B) parameters for Netflix tasks, improving training stability, computational efficiency, and evaluation methodology. They address challenges in alignment, cold-start adaptation, and deployment, proposing syst.. read more  

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@kala shared a link, 3 weeks, 1 day ago
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Where good ideas come from (for coding agents)

A new way to build agents treats prompting ascontext navigation, steering the LLM through ideas like a pilot, not tossing it prompts and hoping for magic. It maps neatly onto Steven Johnson’s seven patterns of innovation. For coding agents to actually pull their weight, users need to bring more than.. read more  

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@kala shared a link, 3 weeks, 1 day ago
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Agentic AI, MCP, and spec-driven development: Top blog posts of 2025

AI speeds up dev - but it’s a double-edged keyboard. It sneaks in subtle bugs and brittle logic that break under pressure. To keep things sane, teams are fighting back withguardrail patterns,AI-aware linters, andtest suites hardened for hallucinated code... read more  

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@devopslinks shared a link, 3 weeks, 1 day ago
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Weaponizing the AWS CLI for Persistence

Researchers pulled off a slick persistence trick usingAWS CLI aliases. They chained dynamic alias renaming with command execution to swipe credentials, without breaking expected CLI behavior. No red flags. Perfect fit forautomated environmentslike CI/CD pipelines. Backdoors, no AWS CLI tampering req.. read more  

Weaponizing the AWS CLI for Persistence
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@devopslinks shared a link, 3 weeks, 1 day ago
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Cloud Workload Threats - Runtime Attacks in 2026

Cloud-native breaches keep slipping through the cracks, not because no one’s watching, but because they’re watching the wrong things. Static checks and posture tools can’t catch what happens in motion. That’s where most attacks live now: at runtime. Think app-layer exploits, poisoned dependencies, s.. read more  

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@devopslinks shared a link, 3 weeks, 1 day ago
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21 Lessons From 14 Years at Google

A seasoned Google engineer drops 21 sharp principles for scaling engineering beyond just writing code. Think:clarity beats cleverness,users over egos,alignment over being “right.”The core message? Build systems humans can work with - especially under stress. Favorites: kill pointless work, treat pro.. read more  

21 Lessons From 14 Years at Google
Magika is an open-source file type identification engine developed by Google that uses machine learning instead of traditional signature-based heuristics. Unlike classic tools such as file, which rely on magic bytes and handcrafted rules, Magika analyzes file content holistically using a trained model to infer the true file type.

It is designed to be both highly accurate and extremely fast, capable of classifying files in milliseconds. Magika excels at detecting edge cases where file extensions are incorrect, intentionally spoofed, or absent altogether. This makes it particularly valuable for security scanning, malware analysis, digital forensics, and large-scale content ingestion pipelines.

Magika supports hundreds of file formats, including programming languages, configuration files, documents, archives, executables, media formats, and data files. It is available as a Python library, a CLI, and integrates cleanly into automated workflows. The project is maintained by Google and released under an open-source license, making it suitable for both enterprise and research use.

Magika is commonly used in scenarios such as:

- Secure file uploads and content validation
- Malware detection and sandboxing pipelines
- Code repository scanning
- Data lake ingestion and classification
- Digital forensics and incident response