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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
GitHub - SillyTavern/SillyTavern: LLM Frontend for Power ...
doener · 2026-06-15 · via Hacker News - Newest: "LLM"


SillyTavern provides a single unified interface for many LLM APIs (KoboldAI/CPP, Horde, NovelAI, Ooba, Tabby, OpenAI, OpenRouter, Claude, Mistral and more), a mobile-friendly layout, Visual Novel Mode, Automatic1111 & ComfyUI API image generation integration, TTS, WorldInfo (lorebooks), customizable UI, auto-translate, more prompt options than you'd ever want or need, and endless growth potential via third-party extensions.

We have a Documentation website to answer most of your questions and help you get started.

What is SillyTavern?

SillyTavern (or ST for short) is a locally installed user interface that allows you to interact with text generation LLMs, image generation engines, and TTS voice models.

Beginning in February 2023 as a fork of TavernAI 1.2.8, SillyTavern now has over 300 contributors and 3 years of independent development under its belt, and continues to serve as a leading software for savvy AI hobbyists.

Our Vision

  1. We aim to empower users with as much utility and control over their LLM prompts as possible. The steep learning curve is part of the fun!
  2. We do not provide any online or hosted services, nor programmatically track any user data.
  3. SillyTavern is a passion project brought to you by a dedicated community of LLM enthusiasts, and will always be free and open sourced.

Do I need a powerful PC to run SillyTavern?

The hardware requirements are minimal: it will run on anything that can run NodeJS 20 or higher. If you intend to do LLM inference on your local machine, we recommend a 3000-series NVIDIA graphics card with at least 6GB of VRAM, but actual requirements may vary depending on the model and backend you choose to use.

Questions or suggestions?

Discord server

Join our Discord community! Get support, share favorite characters and prompts.

Or get in touch with the developers directly:

I like your project! How do I contribute?

  1. Send pull requests. Learn how to contribute: CONTRIBUTING.md
  2. Send feature suggestions and issue reports using the provided templates.
  3. Read this entire readme file and check the documentation website first, to avoid sending duplicate issues.

Screenshots

image image

Installation

For detailed installation instructions, please visit our documentation:

License and credits

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

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