... Mnexium Integrations feel like one of the most important parts of the platform because they solve a different problem than memory. It also outlines the completion of the feature-set for the platform. I don't think any more features will offer any more utility. Memory

... Hello everyone, I built an open-source tool called LEAX. https://github.com/hooop/leax It s a CLI that acts as a companion to Valgrind to help understand memory leaks in C programs. It combines Valgrind analysis, GDB-based tracing, and Mistral AI to: identify the root cause of a leak explain why it happens suggest a possible fix The memory

... Mimic AI Desktop Assistant A privacy-first, AI-powered desktop assistant with voice synthesis, persistent memory, and local model inference. Mimic AI runs entirely on your local machine using Ollama for language models and supports Browser TTS and Qwen3-TTS for voice synthesis. Table of Contents Features System Requirements Installation Quick Start Architecture Overview Data Flow Component Interaction Configuration Usage Guide Troubleshooting Privacy & Security Features Core Capabilities Local AI Inference : Uses Ollama for running LLMs locally (Llama, Mistral ... ... Voice Synthesis : Browser TTS (no setup) and Qwen3-TTS (AI voice cloning) Persistent Memory

... stitch systems together. The browser extension was a patch. This is infrastructure. Here's what makes it different at the architecture level: The AI pulls what it needs, when it needs it (via semantic search across your memory) It writes back. Conversations, decisions, and new context get saved from within any chat It works outside the browser entirely (IDE, CLI, local models, anywhere) Every tool connects to the same memory

... captures more of an AI workload. There will always be new features & improvements we can make. This is one of those, we've always had and seen a need in the platform to direct and instruct our memory generation layer. This is what memory

... Today we re open-sourcing the core memory engine behind @Mnexium AI : CORE-MNX. GItHub NPM For us, this is a product decision and a philosophy decision. Memory

... Mnexium SDKs give you a complete memory infrastructure as a service. Install the package, pass your LLM provider key, and your AI remembers. Node ( https://www.npmjs.com/package/@m... ) Python ( https://pypi.org/project/mnexium/ ) Docs ( https://mnexium.com/docs ) Get Started Both SDKs are MIT licensed and available now. No sign-up required omit the API key and a trial key is auto-provisioned on your first request. JavaScript / TypeScript npm install @mnexium/sdk import { Mnexium } from '@mnexium/sdk'; const mnx = new Mnexium({ apiKey: 'mnx_...', openai ... ... apiKey: process.env.OPENAI_API_KEY }, }); const alice = mnx.subject('user_123'); // Chat with memory

... Built It Most automation workflows can call a model, but still need substantial glue code for memory, personalization, and structured data. The Mnexium connector makes those capabilities native in n8n. npm install n8n-nodes-mnexium Install once, then add the Mnexium node in n8n. What You Get Chat operations with memory

... name is chunhee from South Korea. I'm excited joning product hunt because I wanted to make my own product for a long time. I'm frontend web developer and also singer songwriter. I used to search my memos to make lyrics, but it was hard to find memos

... just made memories 100x better. group memories

... been building AI agents for a while and the memory problem kept bugging me. Every time a conversation ends, your agent forgets everything. The standard fix is cramming thousands of tokens of conversation history into every prompt. It's slow, expensive, and most of it isn't even relevant. So I looked at what's out there. Mem0, Zep, Supermemory. They all do basically the same thing: take text, embed it as blobs, throw it in a vector database ... ... body. Around 350 tokens each. When an agent needs to remember something, it searches by meaning or keyword and pulls back just the cards it needs. Not your entire history. The cards thing changes how agents actually work with memory

... thought I'd start by sharing some recent learnings that hopefully other builders will find useful. For context, we're building FanBase Copilot, an AI assistant for content creators that learns their voice and context over time. The memory layer is critical. It's what makes the AI actually useful after the first conversation. Our first approach seemed sound: store memories

... This short demo shows how Mnexium works as a memory and context layer for AI apps. Mnexium sits between your app and the LLM to provide: Persistent memory

... Most AI platforms treat memory like a walled garden. What you tell ChatGPT stays in ChatGPT. What Claude learns about you stays in Claude. People frame this as a "memory

... Most AI memory systems treat all memories

... coolest parts of my job is getting a front-row seat to how @marianaprazeres thinks about AI. Memory feels table stakes in AI right now. But for @Meet-Ting , it s not just a log of the past - it s a living system that shapes how people schedule, work, and want to spend their week. It s not just logistics - it s patterns around energy, priorities, and relationships over time. Here are a few things we learned while designing ... ... testing agent memory

... Memory in Action We just shipped something we're really excited about: a fully interactive demo where you can experience AI with persistent memory

... When people talk about AI memory, it s usually framed from the developer s side. How do we store it? How do we retrieve it? How do we keep context alive? This is where @Mnexium AI started as well since that ecosystem is important. But the initial vision and goal was very different and yet to be executed on. What if users owned their memories

... their podcasts to synthesize a solution for your problem. Just call them and ask. Notably, you can now call the verified and authorized Callables of: Stephen Wolfram PhD: Founder of Mathematica/Wolfram Alpha, on Lex Pod 4+ times. (Callable memory: hundreds of Q&A, interviews, blogs, and theories). Lenny Rachitsky: The #1 business writer on Substack. (Callable memory

... When building AI agents with long-term memory, debugging is a challenge. You know something was remembered but: When was it created? What replaced it? Why is it being recalled now? Why was it created as a memory

... infrastructure first. This means every AI dev eventually will end up building this infra to provide the best user experience needs for their agent and app. What rolling your own really means: Vector DBs + embeddings + tuning Extracting memories from conversations (and resolving conflicts) Designing user profile schemas and keeping them in sync Managing long chat history + summarization pipelines Juggling different formats across OpenAI, Claude, etc. Hosting, scaling, backups, monitoring Timeline: 2 4 weeks (plus ongoing maintenance) Cost: engineering time + infra ... ... vector DB fees With @Mnexium AI , memory

... just shipped multi-provider support in @Mnexium AI so you can change LLMs without resetting conversations, user context or memories. The problem When teams switch providers, they usually lose everything: conversation history user preferences long-term memory

... founder's dashboard component had 47 useState hooks. Forty-seven. Every change risked breaking something unrelated because there was no separation of concerns. The AI just kept adding "one more function" to existing files until they became unmaintainable monsters. Memory Leaks That Crashed Production A trading dashboard client came to me because their app crashed after 2-3 hours of use. Users were complaining. They thought it was a backend issue. It wasn't. The app had WebSocket subscriptions that ... ... never got cleaned up. Every time a user navigated between pages, new subscriptions were created but old ones kept running. After a few hours, the browser was holding 2GB of memory

... different tasks. This isn't just theory, it's backed by real-world benchmarks: leads in coding (80.9% on SWE-bench) and long-form writing with careful reasoning excels at conversational tasks, creative work, and maintains memory across sessions dominates with 1M-token context windows, making it ideal for analyzing lengthy documents (Writer's enterprise LLM) offers cost-effective performance for business workflows with specialized variants for healthcare, finance, and creative tasks Despite these clear differences ... ... - ? This is exactly what we have done with Pluto - Plurality Network 's ontology agent that supports switching between 30+ AI agents. 1. Go to our memory

... memory across 30+ agents Switching between different AI models should not force you to endlessly repeat your information again and again. We decided 2025 would be the last year that people spend 5+ hours/week in endless context repetition loops. Hence emerged AI Context Flow, #1 product of the day with ~2000 users now ! But 2026 is going to be even better. Today, I'm excited to announce our biggest update yet: Pluto (can you guess the wordplay here? ) What ... ... integrated agents in Plurality's Memory