Alternatives
Products that do what EasyLlama – Sexual Harassment Training Made Easy (Required in NY/CA/IL) does
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Hi Hackers, Excited to share a macOS app I've been working on: https://recurse.chat/ for chatting with local AI. While it's amazing that you can run AI models locally quite easily these days (through llama.cpp / llamafile / ollama / llm CLI etc.), I missed feature complete chat interfaces. Tools like LMStudio are super powerful, but there's a learning curve to it. I'd like to hit a middleground of simplicity and customizability for advanced users. Here's what separates RecurseChat out from similar apps: - UX designed for you to use local AI as a daily driver.…
2024 · recurse.chat
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Hey HN, I've been working on https://pornpen.ai, a site for generating adult images. Please only visit the site if you are 18+ and willing to look at NSFW images. This site is an experiment using newer text-to-image models. I explicitly removed the ability to specify custom text to avoid harmful imagery from being generated. New tags will be added once the prompt-engineering algorithm is fine-tuned further. If the servers are overloaded, take a look at the feed and search pages to look through past results. For comments/suggestions/feedback please visit…
2022 · pornpen.ai
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2020 · colab.research.google.com
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I made a free tool that stuns LLMs with invisible Unicode characters. *Use cases:* Anti-plagiarism, text obfuscation against LLM scrapers, or just for fun! Even just one word's worth of “gibberified” text is enough to block most LLMs from responding coherently.
Nov 2025 · gibberifier.com
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Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com
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2013 · pornblur.com
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Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com
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I trained the 65b model on my texts so I can talk to myself. It's pretty useless as an assistant, and will only do stuff you convince it to, but I guess it's technically uncensored? I'll leave it up for a bit if you want to chat with it. I posted this to Reddit and had several hundred people talking to it. Salient points from that discussion: LLAMA 1 65b Rank 128 5 epochs Batch size 1, 256 cutoff Trained in the Oobabooga suite using bitsandbytes 4-bit quantization for the lora Loss around 1.5 seems to give the most coherent results Trained on raw text dumps that is then parsed by a crappy…
2023 · airic.serveo.net
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I’ve been playing around with local LLMs for the past couple of months and decided to build something that can run on an iPhone. It’s a universal app built with SwiftUI and the excellent ggml library. The model is an SFT fine tuned and 4 bit quantised version of the RedPajama-INCITE-Chat-3B-v1 OSS LLM. It works reasonably well on recent-ish (~3 year old) iPhones, iPads and Macs. It was launched on the App Store yesterday[1] and Product Hunt today[2]. It seems to be reasonably ok at natural language interactions, but given its size, does pretty badly at coding and reasoning. Also, it…
2023
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I’d originally launched my app: Private LLM[1][2] on HN around 10 months ago, with a single RedPajama Chat 3B model. The app has come a long way since then. About a month ago, I added support for 4-bit OmniQuant quantized Mixtral 8x7B Instruct model, and it seems to outperform Q4 models at inference speed and Q8 models at text generation quality, while consuming only about 24GB of RAM[3] at 8k context length. The trick is: a) to use a better quantization algorithm and b) to use unquantized embeddings and the MoE gates (the overhead is quite small). Other notable features include many more…
2024
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I built an open source web app that generates cover letters using local AI models (Ollama, LM Studio, vLLM, etc.) so your resume and job application data never leaves your machine. No placeholders. No typing. Letters are ready to copy and paste. The workflow is: 1. Upload your resume (PDF) - it gets parsed and cached in your browser. 2. Paste the job description 3. Get a personalized cover letter in ~5 seconds It connects to any OpenAI-compatible local LLM endpoint. I use it with Ollama + llama3.2, but it works with any local model server. Key features: - 100% local and private depending on…
Dec 2025 · github.com
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Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift
8d ago · github.com
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2014 · nuttit.com
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2023 · github.com
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