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@fidelissecurity shared a post, 2 weeks ago

Marketing, Fidelis Security

What Is CNAPP? A Complete Guide to Cloud-Native Application Protection Platforms

Learn what CNAPP (Cloud-Native Application Protection Platform) is, how it works, its key components, benefits, use cases, and why it is essential for securing modern cloud-native applications.

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@sancharini shared a post, 2 weeks ago

Making Test Management Tools Actually Work: From Tracking to Real Insight

Understand why test management tools fail and how leading teams make them work. Practical strategies for tracking, insights, and data-driven testing decisions.

Test Management Tools That Work: From Tracking to Insight

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@alicemgray86 shared a post, 2 weeks ago

Damco Solutions

AS400 Modernization Services in 2026: From Green Screen to Modern UX

For years, the green screen became the unofficial symbol of stability inside enterprise IT.

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@elsie-rainee shared a post, 2 weeks ago

Full Stack Engineer, WPWeb Infotech

Artificial Intelligence vs Machine Learning: What's the Difference?

#Machine... #Artific... #Artific...

Confused by AI vs machine learning? This guide breaks down what sets them apart, how they work together, and what it means for you.

Artificial Intelligence vs Machine Learning

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@laura_garcia shared a post, 2 weeks ago

Software Developer, RELIANOID

DevOpsDays Taipei

We're heading to DevOpsDays Taipei 2026! Join us on June 25–26 in Taipei to explore the latest in DevOps, AI, platform engineering, cloud-native technologies, and security alongside hundreds of technology professionals from across Asia. Learn more about the event and meet RELIANOID there. 👇 https://..

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@laura_garcia shared a post, 2 weeks, 1 day ago

Software Developer, RELIANOID

GLOBAL DATA SEGREGATION & PRIVACY POLICY

Data privacy, sovereignty, and compliance matter. Learn how RELIANOID manages global data governance, regional privacy requirements, and secure data handling practices across Europe, the United States, and Asia. Read our Global Data Segregation & Privacy Policy. https://www.relianoid.com/security-co..

Story Palark Team

@shurup shared a post, 2 weeks, 3 days ago

@palark

9 new CNCF projects from 2025: OpenTofu, kgateway, Cozystack, and others

#open so... #Contain... #Cloud N... #cncf #kuberne...

Followingthe recent overviewof newly added CNCF projects in 2025, the next batch of Open Source tools for Cloud Native needs include: - KitOpsfor packaging AI/ML models into all-in-one bundles and deploying them. - OpenTofu, a Terraform fork created by the community. - kagent, a framework for buildi..

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@laura_garcia shared a post, 2 weeks, 3 days ago

Software Developer, RELIANOID

Load Balancing IYC BLUE with RELIANOID

⚓ How do you ensure a yacht and fleet management platform stays available 24/7, even across challenging maritime networks? Discover how 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 delivers 𝘩𝘪𝘨𝘩 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘪𝘭𝘪𝘵𝘺, 𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺, 𝘢𝘯𝘥 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 for 𝙄𝙔𝘾 𝘽𝙇𝙐𝙀 with intelligent load balancing, SSL offloading, API routing, and resilient failover. R..

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@varbear shared a link, 2 weeks, 5 days ago

FAUN.dev()

Using local LLMs for agentic coding

Alex Ewerlöf walks through running open-weight models likeGemma 4locally for agentic coding via LM Studio, wiring them into Copilot and Pi as custom endpoints, with the practical traps around context length, KV-cache quantization, and cold-start prompt processing... read more

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@varbear shared a link, 2 weeks, 5 days ago

FAUN.dev()

I built a Go microservices framework in 2017.

Aafaq Zahid open-sourced Keel, a Go microservices framework he extracted from eight years of production systems... read more

Unsloth is an open-source toolkit for training and fine-tuning large language models faster and with less memory than a standard Hugging Face stack. Its core library replaces PyTorch's default autograd with custom backpropagation kernels written in OpenAI's Triton language, which is where most of its speed and memory savings come from. It supports LoRA, QLoRA, full fine-tuning, reinforcement learning, pretraining, and 4-bit, 16-bit, and FP8 training, across more than 500 text, vision, audio, and embedding models.

The practical draw is hardware reach. QLoRA workflows in Unsloth let you fine-tune an 8B model on a single 12 GB consumer GPU, and the project headlines roughly 2x faster training with about 70 percent less VRAM versus baseline implementations, though the exact figures vary by model, GPU, and config. A 2026 update added faster mixture-of-experts training, with models like Qwen3-30B-A3B fine-tunable on about 17.5 GB of VRAM. It runs on NVIDIA (including Blackwell and DGX Spark), AMD, and Intel GPUs, with free Colab and Kaggle notebooks for trying it without local hardware.

It fits cleanly into the local-AI workflow. Unsloth integrates with Hugging Face transformers and TRL, and uses llama.cpp to save and run models, exporting to GGUF for Ollama or LM Studio as well as safetensors. As of 2026 it also ships Unsloth Studio, a local no-code GUI that covers the full lifecycle from dataset creation to training to running and comparing GGUF and safetensors models, with tool-calling, web search, and an OpenAI-compatible API, all running offline on Mac and Windows, with the core library under the Apache 2.0 license.