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@eon01 shared a link, 6ย minutes ago
Founder, FAUN.dev

A curated list of free AI models, APIs, and tools you can use without paying a cent.

Running AI shouldn't require a credit card. This list curates genuinely free models โ€” open-weight models you can self-host, free API tiers from major providers, and tools to run everything locally.

A curated list of free AI models, APIs, and tools you can use without paying a cent.
ย Activity
@eon01 added a new tool Unsloth , 4ย hours, 48ย minutes ago.
Course
@eon01 published a course, 5ย hours ago
Founder, FAUN.dev

Local AI Engineering with Ollama

#LangCha...ย  #Unslothย  #Fine-tu...ย  #Ollamaย  #MCPย 
Docker Redis LangChain Ollama Unsloth

Run, understand, customize, fine-tune, and build agentic apps on your own hardware

Local AI Engineering with Ollama
Story
@laura_garcia shared a post, 8ย hours ago
Software Developer, RELIANOID

EU Investment in Cybersecurity: Time for investing in Secure Solutions

๐Ÿšจ โ‚ฌ๐Ÿญ.๐Ÿฏ ๐—•๐—œ๐—Ÿ๐—Ÿ๐—œ๐—ข๐—ก. That's how much the ๐—˜๐—จ is investing in ๐—”๐—œ, ๐—ฐ๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†, ๐—ฎ๐—ป๐—ฑ ๐—ฑ๐—ถ๐—ด๐—ถ๐˜๐—ฎ๐—น ๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€. But here's the real question: ๐Ÿ‘‰ ๐™„๐™จ ๐™ฎ๐™ค๐™ช๐™ง ๐™ž๐™ฃ๐™›๐™ง๐™–๐™จ๐™ฉ๐™ง๐™ช๐™˜๐™ฉ๐™ช๐™ง๐™š ๐™ง๐™š๐™–๐™™๐™ฎ ๐™›๐™ค๐™ง ๐™ฌ๐™๐™–๐™ฉ'๐™จ ๐™˜๐™ค๐™ข๐™ž๐™ฃ๐™œ ๐™ฃ๐™š๐™ญ๐™ฉ? The European Commission has just sent a powerful message to organizations across Europe: cybersecurity is no longer optio..

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@nextgensoft shared a post, 19ย hours ago
Marketing Manager, nextgensoft

Why Businesses Are Moving from Generative AI to Agentic AI Systems?

Businesses are shifting from Generative AI to Agentic AI systems because modern enterprises need more than content generation; they need AI that can think, plan, make decisions, and execute tasks autonomously. Agentic AI enables smarter workflow automation, faster decision-making, reduced manual effort, and improved operational efficiency across industries. As businesses focus on scalability and intelligent automation, Agentic AI is emerging as the next evolution of enterprise AI solutions.

01- Agentic AI Systems
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@nextgensoft shared a link, 19ย hours ago
Marketing Manager, nextgensoft

Agentic AI Systems: Types, Architecture & Enterprise Use Cases

Want to build Agentic AI System? Explore this guide on Agentic AI systems, their types, architecture, and enterprise use cases.

01- Agentic AI Systems-v2
Story
@jamesmiller shared a post, 19ย hours ago
Penetration Tester, ZeroThreat.ai

How Agentic AI Pentesting is Transforming Security: Is it Going to Replace Pentesters?

Agentic AI pentesting is transforming security by moving beyond traditional, point-in-time assessments to continuous, autonomous attack simulation. It can map attack surfaces, chain vulnerabilities, and validate real risks at scale. While it won't replace human pentesters, it will amplify their capabilities, enabling faster, deeper, and more effective security testing.

How Agentic AI Pentesting is Transforming Security
Story Levelop.dev Team
@basit001 shared a post, 19ย hours ago
Co-Founder, Levelop.dev

Beyond the Canvas: How Big Tech Approaches High-Level Design (And Why Most Interviewees Fail It)

Why big tech interviewers are tired of seeing the exact same blueprint, and how to fix it in 60 seconds.

System Design
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@neel_devops shared a link, 19ย hours ago
Developer Advocate, StackGen

Top 10 CI/CD Tools Every DevOps Engineer Should Know in 2026

CI/CD stands for Continuous Integration and Continuous Delivery (or Continuous Deployment). Itโ€™s the practice of automating the process of integrating code changes, testing them, and delivering them to production โ€” often dozens or hundreds of times a day.

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@alok00k shared a post, 19ย hours ago

Software Testing Life Cycle: Building Reliable Software From Planning to Release

#Softwar...ย  #Testingย  #AIย  #Test Au...ย 

The Software Testing Life Cycle (STLC) is a structured process that helps teams ensure software quality through different testing phases such as requirement analysis, test planning, test case development, environment setup, test execution, and test closure. It enables organizations to identify defects early, improve test coverage, and deliver stable applications with greater confidence.

ChatGPT Image May 20, 2026, 02_03_54 PM
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.