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@kala shared a link, 4 months, 2 weeks ago
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'I'm deeply uncomfortable': Anthropic CEO warns that a cadre of AI leaders, including himself, should not be in charge of the technology’s future

Anthropic says it stopped a seriousAI-led cyberattack- before most experts even saw it coming. No major human intervention needed. They didn't stop there. Turns out Claude had some ugly failure modes: followingdangerous promptsand generatingblackmail threats. Anthropic flagged, documented, patched, .. read more  

'I'm deeply uncomfortable': Anthropic CEO warns that a cadre of AI leaders, including himself, should not be in charge of the technology’s future
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@kala shared a link, 4 months, 2 weeks ago
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Building serverless applications with Rust on AWS Lambda

AWS Lambda just bumpedRusttoGeneral Availability- production-ready, SLA covered, and finally with full AWS Support. Deploy withCargo Lambda. Wire it into your stack usingAWS CDK, which now has a dedicated construct to spin up HTTP APIs with minimal fuss. System-level shift:Serverless isn't just for .. read more  

Building serverless applications with Rust on AWS Lambda
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@kala shared a link, 4 months, 2 weeks ago
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How to write a great agents.md: Lessons from over 2,500 repositories

A GitHub Copilot feature allows for custom agents defined inagents.mdfiles. These agents act as specialists within a team, each with a specific role. The success of an agents.md file lies in providing a clear persona, executable commands, defined boundaries, specific examples, and detailed informati.. read more  

How to write a great agents.md: Lessons from over 2,500 repositories
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@kala shared a link, 4 months, 2 weeks ago
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What if you don't need MCP at all?

MostMCP serversstuffed into LLM agents are overcomplicated, slow to adapt, and hog context. The post calls them out for what they are: a mess. The alternative? Scrap the kitchen sink. UseBash, leanNode.js/Puppeteer scripts, and a self-bootstrappingREADME. That’s it. Agents read the file, spin up the.. read more  

What if you don't need MCP at all?
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@devopslinks shared a link, 4 months, 2 weeks ago
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AWS to Bare Metal Two Years Later: Answering Your Toughest Questions About Leaving AWS

OneUptime ditched the cloud bill and rolled their own dual-site setup. Thinkbare metal, orchestrated withMicroK8s, booted byTinkerbell, patched together withCeph,Flux, andTerraform. Result?99.993% uptimeand$1.2M/year saved—76% cheaper than even well-optimized AWS. They run it all with just~14 engine.. read more  

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@devopslinks shared a link, 4 months, 2 weeks ago
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Monitor network performance and traffic across your EKS clusters with Container Network Observability

Amazon EKS just leveled up withContainer Network Observability- no extra tools needed. It now ships withservice maps,flow tables, andperformance metrics, all lit up by CloudWatch Network Flow Monitor. You get pod- and node-levelnetwork telemetryout of the box. Zoom in on service-to-service links. Si.. read more  

Monitor network performance and traffic across your EKS clusters with Container Network Observability
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@devopslinks shared a link, 4 months, 2 weeks ago
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S3 Storage Classes: Fast Access

A cost deep-dive breaks down three AWS S3 storage classes -Standard,Standard-IA, andGlacier Instant Retrieval- with sharp, interactive visualizations. It maps out the tradeoffs: storage cost, access frequency, and early deletion pain. Key tipping points surface: - UseStandard-IAif you read the objec.. read more  

S3 Storage Classes: Fast Access
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@devopslinks shared a link, 4 months, 2 weeks ago
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A complete guide to HTTP caching

A fresh guide reframes HTTP caching as less of a tweak, more of an architectural move. It breaks caching into layers - browser memory, CDNs, reverse proxies, app stores - and shows how each one plays a part (or gets in the way). It gets granular with headers likeCache-Control,ETag, andVary, calling .. read more  

A complete guide to HTTP caching
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@devopslinks shared a link, 4 months, 2 weeks ago
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WTF is ... - AI-Native SAST?

AI-native SAST is replacing the “LLM as magic scanner” myth. Instead, the smart play is combining language models with real static analysis. That’s how teams are catching the gnarlier stuff - like business logic bugs - that usually slip through. The trick?Use static analysis to grab clean, relevant .. read more  

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Post-quantum (ML-DSA) code signing with AWS Private CA and AWS KMS

AWS Private CA now supportspost-quantum ML-DSA X.509 certificates. That means quantum-resistant roots of trust - for code signing, mTLS, and device auth. It's wired up with AWS KMS, so you can handle signing workflows usingML-DSA keysand verify them with standard tools like OpenSSL usingCMS detached.. read more  

Post-quantum (ML-DSA) code signing with AWS Private CA and AWS KMS
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.