Alternatives
Products that do what Run an Agent Council of LLMs that debate and synthesize answers does
I built a local-first UI that adds two reasoning architectures on top of small models like Qwen, Llama and Mistral: a sequential Thinking Pipeline (Plan → Execute → Critique) and a parallel Agent Council where multiple expert models debate in parallel and a Judge synthesizes the best answer. No API keys, zero .env setup — just pip install multimind. Benchmark on GSM8K shows measurable accuracy gains vs. single-model inference.
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I built AutoThink, a technique that makes local LLMs reason more efficiently by adaptively allocating computational resources based on query complexity. The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%. I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical…
2025
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I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic…
16d ago · ozbrain.com
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Hi HN community! I am excited to share my recently upgraded website, DebateDevil (https://debate-devil.com), an AI chat platform designed to help users practice debates, challenge their ideas, and expand their thinking. I created this site to provide a more objective opinion and help users consider their hypotheses from multiple perspectives. example discussin: ``` AI: The time has come to test your hypothesis and beliefs. I will argue against any topic. What do you want to debate? You: AI will take away most jobs from humans. AI: I understand your concern about AI taking away jobs…
2023 · debate-devil.com
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Ever wish you could get the best arguments for both sides of a debate? I built an AI-powered debate platform that pits language models against each other on controversial topics. Each AI is randomly assigned a side (pro/con). You vote before and after to see if you were persuaded. Most content today presents lopsided arguments. They provide strong points for one side, weak ones for the other. This project aims to surface the strongest arguments from both sides, using LLMs to simulate a fair debate. With enough usage, I want to use it to benchmark LLMs. My hypothesis is that randomly…
2025 · bot-bicker.vercel.app
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2025 · github.com
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Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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HN, first things first: one year ago you make me believe in my opensource AI project, and I'm forever grateful[1]. I am back with Beam - a technique to use diverse LLMs to generate responses, and Merge them - all within a snappy UX. I am no researcher, so you'll find a dark-mode blog, and not a light-mode PDF on arxiv :) Blog, open code, and live hosted demo, all published. You can use Beam early on in a chat, where looking at more options is key to be more confident in the answer, but also when no answer if perfect, but fusing many together will work well. Take a look and let me know what…
2024 · big-agi.com
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I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io
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Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
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I've built an aggregator for free Large Language Models that provides answer generation services. The project aims to make powerful AI accessible to everyone as I believe free LLMs may become a significant trend. Currently supported models: Qwen Series - Qwen 32B: Alibaba's 32B parameter model for Chinese/English content - Qwen 2.5 7B Instruct: Lightweight, responsive daily assistant DeepSeek Series - DeepSeek V3 0324: Specialized in long-text and domain knowledge - DeepSeek R1: Focused on mathematical and logical reasoning Google Series - Gemini 2.5 Pro: Google's latest multimodal…
2025 · qwq32.com
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