Updates and recent posts about Unsloth..

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@goutham-annem started using tool Azure Kubernetes Service (AKS) , 3 days, 17 hours ago.

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@goutham-annem started using tool AWS EKS , 3 days, 17 hours ago.

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@goutham-annem started using tool Amazon Web Services , 3 days, 17 hours ago.

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@goutham-annem started using tool Amazon ECS , 3 days, 17 hours ago.

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@eon01 gave 🐾 to The unwritten laws of software engineering , 3 days, 20 hours ago.

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@varbear shared a link, 4 days ago

FAUN.dev()

Build and Deploy a Remote MCP Server to GKE in 30 Minutes

Google walks you through shipping a remoteMCP serveronGKE AutopilotusingFastMCPandstreamable-http, swapping localstdiofor shared HTTP endpoints. The clever bit: theGateway APIhandles managed SSL plusCLIENT_IP session affinity, so one centralized server beats everyone running redundant local copies... read more

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@varbear shared a link, 4 days ago

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The unwritten laws of software engineering

- Always related - first rollback, then debug. - Backups aren’t real until restored. - You’ll hate yourself for bad logs. - ALWAYS have a rollback plan. - Every external dependency will fail. - If there's risk, use the “4 eyes” rule. - Nothing lasts like a temporary fix... read more

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@varbear shared a link, 4 days ago

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How building an HTML-first site doubled our users overnight

Building HTML-first forms using Astro instead of React dramatically increased completion rates and sustainability, highlighting the effectiveness of lightweight, accessible web components for all users, regardless of browser or connectivity... read more

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@varbear shared a link, 4 days ago

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Everything a Senior Engineer Needs to Know About What's Inside an LLM

The shift from RNNs totransformerssolved sequential bottlenecks and long-range decay issues withself-attention. Transformers use encoding, decoding, and tokenization to process sequences efficiently and accurately. This evolution led to models like GPT, which excel at tasks with minimal fine-tuning .. read more

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@varbear shared a link, 4 days ago

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Google hits 50% IPv6

The 50% IPv6 milestone is real, but adoption differs by country. Analysts who report lower figures use population-weighted sampling, while their per-country adoption rates match the higher estimate... 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.