Search: “engineering”

Peptide and hormone replacement therapy companies have been systematically posting to r/biohackers to manipulate AI-generated search answers on ChatGPT and Google through a practice called AI-engine o

Dreamverse is an AI video generation engine that produces 30 seconds of 1080p video in 4.5 seconds on a single GPU , making it significantly faster than existing systems like Sora. It provides a creat

Witchcraft is a Rust reimplementation of Stanford's XTR-Warp semantic search engine that uses a single-file SQLite database for storage, enabling client-side deployment. The system operates completely

Ollama's MLX engine runs on NVIDIA GPUs via CUDA v13 on Windows and Linux. Users have reported that Ollama crashes on RTX 3060 with cuda_v13 in versions 0.13.0 through 0.15.6, while deleting the cuda_

This Reddit post from r/StableDiffusion asks the community for recommendations on which prompt engineering techniques and AI image generation models (specifically comparing z-image and z-image turbo v

A Reddit post discussing performance issues with GLM-5.1 when running through Ollama Cloud. GLM-5.1 is Z.AI's next-generation flagship model for agentic engineering, with significantly stronger coding

A Reddit post from the r/ChatGPT community sharing a user-discovered technique for improving the quality of AI-generated images in ChatGPT, likely involving prompt engineering strategies or workflow a

Moss is a YC-backed high-performance runtime for real-time semantic search that delivers sub-10ms lookups, instant index updates, and zero infrastructure overhead, running where the agent lives — clou

OllamaGPT is a self-hosted, ChatGPT-style web interface that runs locally using Ollama as its backend LLM engine, requiring no API keys or external services. It is designed for developers and power us

Ollama is a local inference engine designed for running pre-built LLMs on your own machine, and it does not include fine-tuning capabilities — this distinction is correct. Unsloth (and its Unsloth Stu

A Reddit thread from r/ChatGPT exploring the user experience phenomenon where GPT responses can feel cognitively draining or over-engineered even when technically accurate — touching on issues like ve

GLM-5.1 is Z.AI's next-generation flagship model for agentic engineering, featuring significantly stronger coding capabilities than its predecessor and achieving state-of-the-art performance on SWE-Be

TurboOCR is a high-performance OCR project that combines PaddleOCR with NVIDIA TensorRT, implemented in C++ and CUDA, achieving throughput of 270–1,200 images per second using FP16 half-precision infe

GLM-5.1 is Z.ai's next-generation flagship model for agentic engineering, built on a 754-billion parameter Mixture-of-Experts architecture with 40 billion active parameters per token, a 200,000-tok...

Local AI tools such as Ollama, LM Studio, and Jan all rely on the same underlying inference engine (llama.cpp) and support compatible model formats (primarily GGUF), meaning a single downloaded mod...

Everything started with the sudden death of my old ASRock J1900. While looking for the perfect ITX replacement, I stumbled upon the Chinese CW-NAS-ADLN-K motherboard, which looked perfect on paper: In

I wanted to find out whether a huge text-only MoE could be given basic vision without retraining the language model itself. The short answer is yes. I froze DeepSeek V4 Flash and a 417M-parameter Moon

Hey all, I'm serving DSv4Flash 0731 on a cluster of 2x DGX Sparks but am running into constant issues with having almost no RAM (unified memory) left for the OS/cache and I'd love to hear the communit

Questions & Responses(in BOLD) below. Favorite question(s) moved to end of the thread with combined responses(removed duplicates). Be optimistic folks. I'm sure we're getting other models too apart fr

Just wanted to share my agentic coding benchmark run of DSv4F 0731 at both High and Low reasoning efforts (not Max)... I ran a 109-question subset of Aider Polyglot (the JS/C++/Python languages), base

I've been out of these forums for awhile but I figured I would provide a formal update on how this has been going now that it has some operation time under its belt, just to put the information out th

TL;DR: I spent 9 days developing a new quantization method for MLX models and measured 18 variants against each other on a single M5 Max MacBook Pro (128 GB). The result is the best-measuring MLX quan

Disclaimer, this was kinda written with AI (Gemma 4 again) but it also did really well here, it outputted what I wanted, when I asked it to refine stuff or improve on certain areas it did that without

I've been working on an open-source workflow automation platform over the past few months, with Ollama as one of the first-class providers rather than treating it as an afterthought. The goal wasn't t

This was my Bachelor's Final Project: implementing YOLO26n inference completely from scratch using ARM64 Assembly Language and C, without relying on existing inference frameworks. The goal was to unde

If you run local agentic coding harnesses (Aider, Claude Code, etc.), prompt evaluation usually eats up most of your execution time. Every turn re-evaluates thousands of identical prefix tokens_system

Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast. So, we post-trained Gemma 4 E2B post-t

dam bois we eating good this week ngl, The velocity of the open_weight ecosystem right now is hitting a point where proprietary, closed-source APIs are losing their leverage on compute intelligence. W

This Reddit post from r/ChatGPT discusses a technique for enabling OpenAI's Codex (or similar AI coding agents) to autonomously replicate a user's workflow by observing and learning from their actions

A Reddit user shares their project of building an AI-powered alchemy game inspired by titles like Little Alchemy, using ChatGPT to dynamically generate the result of every possible element combination

This r/MachineLearning discussion centers on Google's TurboQuant, a training-free KV cache compression algorithm released in March 2026 that compresses cache storage from 16 bits down to 3 bits with m

This Reddit post shares an educational PyTorch repository designed to teach distributed training techniques from the ground up, covering Data Parallelism (DP), Fully Sharded Data Parallel (FSDP), Tens

This r/MachineLearning post presents educational PyTorch implementations of FlashAttention versions 1 through 4, designed to highlight the key algorithmic differences across each iteration rather than

A research paper (arXiv:2601.11557) proposes replacing the dominant 'HNSW + float32 + cosine similarity' vector database stack with an information-theoretic alternative that uses Maximally Informat...

A bug was identified in NVIDIA's cuBLAS library where `cublasSgemmStridedBatched` dispatches the same suboptimal `cutlass_80_simt_sgemm_128x32_8x5` kernel for every batched FP32 workload from 256×...

A r/MachineLearning discussion explores the challenge of finding S3-compatible object storage that can both saturate H100 GPU bandwidth and avoid AWS's egress fees during high-throughput AI trainin...

A beginner-friendly tutorial demonstrating how to build a ~3.2M parameter LLM from scratch using Mary Shelley's *Frankenstein* as the sole training corpus, designed to run on Kaggle's free GPU in u...

Yesteday a paper showed a gap that allows to see 100% of the reasoning tokens form ALL Claude and GPT models Stealing Reasoning Traces from Proprietary LLM APIs. check it out, they have published lots

One of the reasons I got into local LLMs was the possibility of getting answers using my own documents and books (a few hundreds) instead of having to search through them manually. However since I'm n

Here's how it works in the app: Framework: Runs on LiteRT-LM (like Google's AI Edge). Models: Downloads either the E2B (~2.5 GB) or E4B (~3.6 GB) INT4 quantized models directly from ungated litert-com

Heeeey all! I just completed some fun tests with Muse Glimmer, I thought I'd let you know. In fact, the summary below was written by Muse itself! I ran a 2× DGX Spark cluster and got Meta's day-old Mu

aetherbird has done some great work getting Ling-3.0 to work in llama.cpp. The architecture is generally identical to deepseekv2. I recently added a microscopic 40 line PR to his that adds support for

Wowee!! Just when you thought it couldn't get better for open weight models, we probably have had our best period yet!?!?! Models that rival the closed frontier, Opus level models on non-insane hardwa

I've been comparing Cline / Kilo / Qwen Code lately since they all handle long-task state differently. Cline: has Focus Chain, a markdown file kept outside the conversation that gets reinjected on a c

TL;DR: I am building a Windows-first local AI setup for people who want to try local LLMs without spending days choosing models, setting up Ollama, Docker, WSL, Open WebUI, agents, and tool permission

Not the biggest or shiniest, but it's mine From gaming machine inference on the original llama models, to a 4x RTX 6000 Pro Max Q + 4x 3090s local AI cluster. Pictures are in reverse chronological ord