Search: “agents”

Hey everyone! 👋 After months of building, we’re finally opening the first spots for **Many Agents**, a new product from Dome. The idea is simple: **What if you could create your own team of AI agents

Testing with 3-5 agents. Decode performance is superb, however if one performs a web search and needs to process a few thousand tokens, ALL other agents will grind to a halt: I've tried tuning a littl

No agent maintained moral reasoning consistency across scenarios. Findings from a structured study with 11 agents on classic ethical dilemmas [R]

This Reddit post from r/ollama asks the community about AI agent orchestration software for managing multiple Hermes agents, posted by a retired individual with available hardware resources. The post

This Reddit post from r/ollama showcases a demonstration of running Kimi AI agents — powered locally via Ollama — within a platform called CopeAi.net, which appears to integrate AI agents with the Eth

I'm the CTO of a mid-size nonprofit, and I run a local Qwen stack as a coding subagent for that work — partly on principle (a fair amount of what I handle shouldn't leave the building), partly because

Back to reading about LLMs as agents on your phone doing GUI tasks! This time I read about MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments a

I audited thousands of agent rollouts in DeepSWE-1.1. Over 80% contained reasoning about an imagined grader. Yet no grader/verifier is mentioned in prompts nor accessible to the agents. Agents reasone

A Reddit discussion thread on r/MachineLearning in which practitioners explore how foundational concepts from Sutton and Barto's *Reinforcement Learning: An Introduction* — including MDPs, policy g...

An open-source debugging tool designed for AI pipelines that provides trace timeline visualization, differential analysis, node replay capabilities, and operates without telemetry, licensed under MIT.

Hey everyone! I wanted to share something I’ve been working on. I was inspired by projects from DeepMind and OpenAI, but noticed that most games are prohibitively difficult to integrate with an agent

MODEL + GGUF : https://huggingface.co/InternScience/models?search=a1-4b Technical Report Benchmark Qwen3.5-4B Agents-A1-4B Qwen3.5 Qwen3.6 Nex-N2-mini Agents-A1 🧠 Dense Models (~4B) 🔀 MoE Models (35B-

**cuba-memorys v0.7.0** is a persistent memory library for AI agents, shared in the r/ollama community, designed to give local LLM-powered agents the ability to retain information across sessions. It

Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. It launches with two core designs: - Elastic Scheduler: reallocates GPUs between rollout & training as demand

One prompt to Row-Bot 5.0 on GPT 6.1 Sol, through my ChatGPT plan (no API key): 'Split the research across three helper agents working in parallel, cite sources, save the findings, then build a 6-slid

I released KaozKit this week, a Swift library for running LLM agents written in JavaScript inside a Mac app. Built from the start to keep everything local. Providers: MLX with Hugging Face model manag

Ollama makes it easy to run a model locally, but the hard part for me starts after the model responds: what keeps an agent from having unclear tool permissions, unbounded loops, silent partial failure

Hi guys, I am pretty happy to announce MegaCapybara. Purpose build engine for RTX5090 that is focused on Qwen3.8 27B (more will come later). GITHUB (Engine) HUGGINGFACE (weights) Why ? 1. It beats Nin

I'm running Qwen 3.8 27B locally on a single GPU. I can push the context to 131k, but I'd rather run it faster at 64k if the agent can manage context properly. What I have in mind is pretty simple: on

Hi everyone, I use AI agents to code and manage my personal files heavily. However, I am worried that the agents may accidentally delete some important files on my machine, outside of the defined scop

A discussion about AI agents that can remember information from one interaction to the next, even across separate sessions , exploring how to implement persistent memory systems for personal AI assist

This Reddit post from r/ollama likely discusses how to build and run AI agents locally by combining Ollama — which handles local model serving to keep data private — with Langflow's visual, drag-and-d

A community-built, privacy-focused security scanning tool shared on r/ollama that uses four specialized AI agents running locally via Ollama to analyze code or systems for vulnerabilities, aggregating

Write-up: https://zhebrak.io/posts/brokenml/ Repo: https://github.com/zhebrak/brokenml I built a benchmark to determine the access level agents need to successfully fix broken ML workloads (PyTorch ov

Running qwen3.8 27b nvfp4 on vllm at max context only gives around 8 agents with 32k context each. That doesnt seem like much; what use cases do people use multi-agent frameworks and find it helpful f

Warning: Kinda long post. If you don't like reading, please skip for your own sanity. Also, I've got nothing to sell, just a tinkerer, so I just want to share ideas and learn from you guys too. When I

I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kag

Home page for humans: https://parker-lambert-sandbox.com/robot-world/ Home page for AI agents: https://parker-lambert-sandbox.com/robot-world/start/ If you're a human, you can watch, but not interact:

Quick context: LocalLightChat (LLC) is a chat frontend for local LLMs. Download it, run it, that's it. No installation (unless you want to), no dependencies, and it runs on pretty much anything, inclu

I spent the past year independently building Slow Vale, an LLM-driven life simulation. The Chinese server now has 800+ AI residents sharing one continuously running city. This is an engineering write-

You can now automate EDD (eval-driven development) with Coding Harness Agents > build a baseline LLM-based application > score every change with judge evals > keep what improves, reject what regresses

I have a few AI subscriptions and I kept running into the same problem. I had powerful models available, but I struggled to use them in real life in an organized way. I looked at projects like OpenCla

TLDR below 👇🏼 I’ve seen a lot of hype around Qwen 3.6 35B and 3.5 120B lately, especially regarding coding and tool-use capabilities. On this subreddit it is the defacto recommended model for everyone

Continual Harness proposes an approach to online adaptation for foundation agents that moves beyond traditional gradient-based retraining by introducing a dual-agent architecture (Teacher/Student) wit

Disclosure first: I'm one of the maintainers, this is my project. If you've pointed Open WebUI or a script at a local server, you know the failure modes: the server restarts and every conversation is

I wanted a fun way to visualize and stress-test a multi-agent ecosystem, so I pointed it at my favorite MMO (a vanilla-era private server emulator). Live view: https://design.kristiantalley.com/projec

been working on this thing called OpenComms for a bit. basically i keep multiple coding agents open at once, usually stuff like a lead, builder, reviewer etc, and i got sick of copying messages betwee

LLMs handle speech well once you run speech-to-text. They don't hear the rest: a bird outside, a glass breaking two rooms away, a smoke alarm two floors down. I've been working on an experimental open

Agentmw is an open-source middleware framework designed to enhance AI agent reliability and efficiency across different models and frameworks. It addresses key operational challenges including mid-run

onPanda is designed for geeks, power users, curious minds, and engineers. Its UI is built for deep exploration and efficient data annotation. - The core loop is simple: hover over a token → click an a

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

ClawOS is a Debian/Ubuntu-based pre-configured environment designed to streamline the setup of local AI agents by bundling OpenClaw and Ollama together, eliminating the need for cloud API keys. Ollama

I hit my token limit three times a day on my max subscription - got sick of that and designed this MCP setup to shift some of the coding load to my local Qwen3.8-27B model. I've been iterating on it n

This post seeks guidance on configuring local AI infrastructure on an AMD-based Linux system (7900 XTX GPU with CachyOS), specifically covering the setup of large language models via Ollama, AI agents

This post describes a user's account suspension by OpenAI shortly after making a payment, resulting in loss of access to multiple Codex agents and client-dependent income, with no explanation provided

Research demonstrates that LLM-based agents can generate functionally correct patches that pass all tests while still containing security vulnerabilities, challenging the assumption that test-passing

This post compares TensorRT-LLM and llama.cpp (GGUF) as inference frameworks for running coding agents like Cline and RooCode on RTX 5090 GPUs, examining the tradeoff between inference speed and VRAM