Search: “llm”

Someone who has always loved looking at the space between different AI techniques, this time I went a little deeper into the memory trade-offs between RNNs, Transformers and SSMs. I found it interesti

Hi folks, LIVE PROJECT PAGE TLDR: Moral of the story. You need better CPU to do actual agentic coding doing real work... I've been on a mission to make my RTX5090 go brrr for past 2 months so much so

I know Qwen is a key player in the local LLM space and has consistently introduced truly impactful technologies—like n-gram in Qwen-Next and the recent Qwen 3.8 27B, which is an amazing local model. H

I set out to test portable Engrams and accidentally ended up testing compiled external memory instead. Am I onto something useful or reinventing a known idea? I've been building a small open research

I’m looking for advice as I’ve just started out the local llm journey. I’d like to try out coding. I downloaded LM Studio and Qwen3.8-27B-Q4, but I wonder if there are more optimal options for my hard

Vacations give you time to think and explore. I came across a few things and made a few discoveries of my own. It turns out that, as a programmer, I no longer need the smartest and most expensive AI m

First off, I'm super thankful for the value Ollama has given me Got me setup with my first local LLM setup (early OpenClaw days amirite!?) Then got me into their 20 plan and what really sold me here w

Upgraded my work LLM server from DSV4-Flash-0731 to the new Vision-Exp model and did this little test for fun to see quality comparison. Test was performed in Openweb UI connected to my vLLM container

SlopTV: a YouTube live stream where the chat writes the programming. You type 'capybara dj underwater rave', an LLM inflates it into a 400-word structured video prompt, one of my 5090s renders 15 seco

Hey, An LLM writing into durable memory is an unreliable author. It invents, mislabels, restates, and contradicts. Once one of those mistakes is saved, the agent can carry it into every conversation t

Post rédigé avec l'aide de GLM5.3 flash Contexte : formateur professionnel d'adultes en France, j'utilise des agents LLM au quotidien et le français qu'ils produisent est mon critère n°1, avant les be

Hi there. I have a Lenovo Yoga laptop (16GB LPDDR5, AMD Ryzen 7 8840HS, integrated GPU). I have downloaded Ollama and installed Qwen 2.5coder:3b llm. I have tried OpenClaw, and Qwen Code but it seems

Any LLM will hallucinate the world knowledge, even a 3T model. Use a 4B with a kiwix skill and local Wikipedia, 50gb and no more hallucinated world knowledge. Ask your coding agent to build your own,

Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below: How I chose the comparisons The basic question I’m trying

Not even sure if I'm allowed to post this, what with the 'completely/primarily LLM generated copy' rule (the post itself is fine, but the repo/model I'm sharing definitely is, whoops) and the whole li

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

TLDR: There is no one prompting trick that will result in Krea 2 Turbo giving you exactly the style you want and across the whole image. Instead, if you are trying to achieve styles without the use of

I've been thinking about building a pipeline to generate reasoning training data for LLMs, but I want to avoid the common failure mode of synthetic data where you just generate the same template with

Most graph-based LLM interfaces use a canvas as a visual layer over what is still a linear chat. I wanted the graph itself to determine what Ollama receives. ThoughtDAG has one rule: wires are the con

Cuda and Vulkan Benchmark: TensorSharp vs. llama.cpp I would like to share my latest open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like

In the past few months I’ve experimenting heavily and tortured my old 2016 Desktop PC to run the biggest Local LLM I can fit. I documented the whole process and research and I’ve published a repositor

I worked on this project (https://github.com/workofart/harness-training) for the past few months to reframe 'Agent-driven Self-improving Harness' to 'Harness Training'. The idea is simple, the harness

GPU Compass is an open-source tool that tracks and displays real-time GPU pricing information across over 20 cloud providers. The platform likely helps machine learning practitioners and researchers c

ClawBench is a benchmark of 153 everyday web tasks spanning 144 live platforms across 15 categories — from completing purchases and booking appointments to submitting job applications. Unlike existing

This Reddit post from r/MachineLearning discusses the concept of decomposing machine learning models into a graph database representation, treating a model's components — such as layers, weights, and

This Reddit post on r/MachineLearning is a short community survey seeking opinions and experiences related to AI-based music description — that is, the use of AI models to automatically generate natur

This Reddit post from r/MachineLearning announces a build-from-scratch Python book focused on the engineering infrastructure surrounding AI systems — the components beyond the model call itself, such

This r/MachineLearning thread discusses AI critic Gary Marcus's reaction to the Claude Code source leak, in which Anthropic accidentally included a 59.8 MB JavaScript source map file in version 2.1.88

I made a tiny site that let's you feel how fast a local LLM runs before buying the hardware. I made it for myself and a friend but thought it could be useful for others. Note that it works as an estim

I use Pi/OpenCode for coding tasks which still seems to be the most popular, but I'm wondering what you guys use for normal chat interface. It's been a few months since I've seen discussions about har

Hey everyone, I recently put together a local LLM machine using mostly second hand hardware. I wanted to experiment with local models for app development without spending a fortune on a new workstatio

Lipwise is a [yet another] dictation app for Windows, macOS and Linux. You hold a hotkey, talk, and it types into whatever app has focus but with a twist. Since most of us that speak to our computers

I’ve been building SonicBoom, a native tensor runtime written in C++/CUDA with a C ABI and no Python anywhere in the stack. I just tagged v0.0.1, and the first target is local LLM inference on consume

A conversational AI plush toy where nothing leaves my network. Everything runs on my LAN: STT: Whisper LLM: Hermes 3 Llama 3.1 8B (Q8_0) TTS: Kokoro Memory: per-person (LangGraph). It knows who it's t

It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu

I've been building ANCHOR, a web automation agent that runs entirely on Ollama. You describe a task in plain language ('Register Maria Silva, IT department, contractor, and save'), point it at a site,

Upgrade Question If we say the 'budget max' is 1700-1800, and this could include upgrading to a Taichi motherboard: With a new motherboard, no need to bifurcate 1. Would you add a second 5060Ti (16GB)

I've been wanting to test out LLM inference on FPGAs for a while now, but didn't have a big reason too since there were no frontier class models at 9B-27B scale (3.6 27B was great still but it didn't

I've come up with a meta-prompt for generating YuE2 prompts that seems to work pretty well. Copy-paste into an LLM (it should have internet searching capabilities, I mostly just use ChatGPT). Just to

There are quite often posts here complaining that Krea 2 is too predictable / different seeds give almost the same output / you need to type in extremely long prompts to get good images / there is no

Benchmarking an LLM here with a NVIDIA RTX 5070 12 GB VRAM here I had been working on a llama.cpp based expert streaming setup for Qwen3.8-Flash-Next 177B (UD-IQ3_XXS) on Windows. Benchmark is about 1

Generated with H3: Mock H3 version of the ending to Wargames(83) as a test of a custom node I've been working on w/ both Claude(Opus/Fable) and OAI's Sol/Astra. One click generation using only 4 movie

You may think that jev, OpenClaw or TurboQuant are super cool, but actually the coolest LLM invention happened two years ago As we all know, the best source of reliable information about LLMs is YouTu

Not an LLM, but the ternary findings should carry over, and we hadn't seen Sherry-style 3:4 weights run in a browser before. Disclosure: this is our work at Precisit, everything is MIT. What it is A 7

Hello everyone, I made a calculator for the theoretical HW roofline for decoding / prefill based on several parameters (LLM model architecture, quants, GPU, Memory, ..). It still a theoretical bound,

AI grad student here. With all the recent interest in Jev, I thought I would share something I built la few months ago that brings ML models and LLMs together in a different way than Jev does for diff