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20x Faster TRL Fine-tuning with RapidFire AI

RapidFire AI just dropped a scheduling engine built for chaos - and control. It shards datasets on the fly, reallocates as needed, and runs multipleTRL fine-tuning configs at once, even on a single GPU. No magic, just clever orchestration. It plugs into TRL withdrop-in wrappers, spreads training acr.. read more  

20x Faster TRL Fine-tuning with RapidFire AI
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Code execution with MCP: building more efficient AI agents

Code is taking over MCP workflows - and fast. With theModel Context Protocol, agents don’t just call tools. They load them on demand. Filter data. Track state like any decent program would. That shift slashes context bloat - up to 98% fewer tokens. It also trims latency and scales cleaner across tho.. read more  

Code execution with MCP: building more efficient AI agents
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Hacking Gemini: A Multi-Layered Approach

A researcher found a multi-layer sanitization gap inGoogle Gemini. It let attackers pull off indirect prompt injections to leak Workspace data - think Gmail, Drive, Calendar - using Markdown image renders across Gemini andColab export chains. The trick? Sneaking through cracks between HTML and Markd.. read more  

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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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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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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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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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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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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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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
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