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@viktoriiagolovtseva shared a post, 1ย month, 3ย weeks ago

Contract Review Template for the Legal Team: a Free Checklist

Whether youโ€™re reviewing a routine non-disclosure agreement or a complex commercial contract, your team likely follows roughly the same steps. Although many legal professionals donโ€™t require a predefined plan, in some cases, having a contract review template is very helpful.ย 

For example, if you have a large legal department, work in a legal firm with numerous clients, or need to scale best practices, in all these cases, process standardization is essential. A contract review template enables you to document all the crucial steps, ensuring that your processes are robust and repeatable.

In this guide, weโ€™ll share a free contract review template for legal teams. We also explain how to use it in Jira or Monday and how to add it to your tasks automatically.

Zrzut ekranu 2026-05-01 103840
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@viktoriiagolovtseva shared a post, 1ย month, 3ย weeks ago

Jira Automation Best Practices That Will Save You Time

Managing projects in Jira often involves many repetitive actions. Creating work items for recurring tasks, reassigning them for reviews, and syncing statuses with dev tools โ€“ these are just a few examples. With Jira automation, you can handle such routine actions more efficiently and reduce manual work. If you set up automation rules that work together as a whole, you will create a well-functioning system. This will help you streamline processes and optimize resources.

In this article, we explore hands-on Jira automation best practices that actually save time. Youโ€™ll learn how to set up effective rules, avoid common mistakes, and make automation work seamlessly for your teamโ€™s unique processes.

Zrzut ekranu 2026-05-01 090408
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@laura_garcia shared a post, 1ย month, 3ย weeks ago
Software Developer, RELIANOID

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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.