Search: “agents”

These numbers were captured during a real feature implementation task in Next.js and Nest.js (adding a theme switching system across components). The structural predictability of UI/state refactoring

I'm guessing that many people have been waiting for this comparison. For clarity, both models are running at full FP16 KV-cache. Due to VRAM limitations, Muse Glimmer is running full 262,144 context,

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

Having a ‘Killer Application’ that everyone wants to use helps sell hardware, plain and simple. DeepSeek V4 Flash 0731 isn’t an app of course, but I think it’s going to be the major catalyst for getti

I'm playing with a conversational agent I made using either api/generate or api/chats. In both case I do ask him to not ask follow up question, to not act like an assistant, etc. Either from a system

Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus) Q6 — 1 GPU llama-server --model <MODEL_DIR>/Muse-Glimmer

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

Looking for V100 users to share your config and it's performance. GPU: Tesla V100 PCIE 32Gb Qwen3.6 27B Q4_K_M + Q8_0 MTP 128K context length Pi coding agent llama.cpp model preset: [*] spec-default =

I swear AA is not the bipartisan they so claim. An open source mode (Qwen 3.8 max) was number 1 on the agentic index, then they just so happen to launch 'v4.1.1' of their index in which they just adju

As you all know the model is 2.69B parameters with a 128K context window and purpose-built for multi-step agent workflows. What you are seeing is the Q4_K_M GGUF running on my own inference engine bui

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

It is one of the best local models ever released, in both 20B and 120B versions. I always come back to it, especially the 120B version. Its only competition is, in my opinion, Qwen 3.5 122B, but that

Released today, with emphasis on agentic capabilities. I really like their models for simple, high volume tasks ('summarize these gazillion documents') and their 8b-a1b was my go-to for certain tasks

I serve a free local model on my Mac Mini and route real agent work to it. To check I was not fooling myself, I set up a blind grader that replays frontier tasks locally and scores both. https://previ

🚀 We just built our first real-time implementation of Graph Engineering, inspired by our experience building graph tooling used by 4,000+ developers. 🔗 Repo: https://github.com/CodeGraphContext/grapha

We developed a leaderboard ranking frontier model security. There's no shortage of model capability rankings, but we didn't find anything comparable for model security. Yet security is becoming increa

What happened? I just asked GLM 5.2 one question, and in 3 minutes (one agent) it used up 15% of my 5 hour limit to produce a single answer. At this rate, I'll exhaust the entire 5 hour limit in just

I really love this model, I have been using the q4_k_l by Bartowski (I have heard QAT is quite the downgrade in some aspects) and it handles every task I throw at it easily. Agentic and coding perform

Following some recent commits in llama.cpp, preserve_thinking behavior for chat templates included in older DSV4 ggufs got broken. This makes the model pretty dumb in a coding agent context. Adding kw

By 'task oriented', I dont really mean agentic, I mean no deep coding ability, no need for conversation. More things like classification, identification, simple interaction with web apps and APIs, etc

I'm considering the 20 Pro plan, but the limits seem to change over time and are hard to compare with direct API pricing. For anyone using Pro currently, roughly how much coding agent usage do you get

I just managed to get it running on windows and this thing is fucking insane. I get around 550-720t/s depending on task at hand. Previously to get to such numbers i would have to do batching and agent

I have tried ollama locally on my gaming PC (5070 with 12bg of VRAM) It works pretty nice on qwen2.5-coder:7b (and 14b) So I decided to take a step further, and install an openWebUI instance on my hom

I apologize that this question has been asked in various flavors over time, but I couldn't find anything in the posts before that matches the options I have. I have a PC and a mac, both available in m

Guys, this is my current opencode.jsonc ``` { '$schema': 'https://opencode.ai/config.json', // Start in plan mode 'default_agent': 'plan', // Use OpenRouter as the provider for GLM 5.2 'model': 'openr

Nanocoder is an open coding agent for the terminal built by a community collective rather than a company. It allows users to bring their own model, keep code on their machine, and owe nothing to anyon

OpenAI is planning its biggest ChatGPT overhaul yet, aiming to turn it into a 'superapp' with coding tools and AI agents to boost revenue ahead of a potential stock market listing. The redesigned Chat

Nanocoder 1.27.0 is an agentic coding tool available in your terminal that runs on any AI model you choose, whether local models via Ollama or cloud providers like OpenAI and Anthropic. This release i

A peer-to-peer AI inference mesh is a decentralized network architecture where agents connect directly to discover peers and communicate through distributed protocols to share inference workloads . Su

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

Claude Opus 4.7 'thinks more' at higher effort levels, particularly on later turns in agentic settings. Anthropic introduced a new 'xhigh' (extra high) effort level between high and max to give users

A community member shared a project on r/ollama (posted in Spanish) announcing the creation of a custom FUSE (Filesystem in Userspace) implementation designed to work with Ollama, the popular local LL

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

This Reddit post contrasts the r/ChatGPT community's tendency to focus on AI failures and viral screenshots with a more pragmatic user base building real workflows on RunLobster, a managed cloud platf

A Reddit user exploring Ollama Cloud noted that, at the time of their post, only the Kimi model supported image (vision/multimodal) inputs among the available cloud models. Kimi K2.5 is a native multi

This Reddit post examines the true total cost of running OpenClaw, an open-source AI agent framework, across different deployment models. While OpenClaw itself is free and open-source, hosting costs r

This Reddit post from r/ollama likely discusses how to configure the Goose desktop application — an open-source, autonomous AI agent developed by Block — to work with Ollama Cloud as its LLM provider.

When using Ollama Pro's cloud models with OpenClaw, a local Ollama installation is not strictly required — OpenClaw is an AI agent execution layer that handles tools, memory, scheduling, and messa...

i was hoping they would release both today but the big one just dropped now , and it says its been listed since 5 hours ago on huggingface. I guess the real date for 27b is https://modelscope.cn/model

I just heard of this model. Seems to be a competitor to DeepSeek V4 Flash. About the same size and active parameters. Anyone tested it compared to V4 Flash? Link: https://huggingface.co/upstage/Solar-

Spent two nights getting deepseek-ai/DeepSeek-V4-Flash-0731 (284B MoE, 13B active, native FP4/FP8, 1M context) running production-grade on two DGX Sparks connected by one QSFP DAC cable. Everything —

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

Just downloaded the model, UD-Q5_K_XL quant, asked it to generate a long story to test out reasoning and speed with dflash (super fast btw, ~ 90 to 160 tok/s on a 5090 depending on task) and was surpr

I've been running Gemma 4 E4B with oMLX and I can't find any chat interfaces that directly send the audio file to the model instead of running the audio through a separate STT layer. I can confirm the

I did some testing this morning, and I was surprised to find that Muse Glimmer actually comfortably fits on a single RTX 3090 with full context + DFlash + mmproj at Q4_K_XL, unlike Qwen3.6-27B and Gem

Hey everyone, I've been experimenting with the new DeepSeek-V4-Flash-0731 release locally using the Unsloth Studio Q8_K_XL GGUF with OpenCode. Overall, it's been working really well, but I've noticed