... 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

... 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

... 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

... launched, the app was paywalled. But the whole point was to inspire more people to travel with curiosity, not fewer. So we opened it up. What's now free: Build your world map automatically from your photos Resurface travel memories you forgot you had See how much of the planet you've actually explored Earn badges Planet Explorer, Globe Trotter, Planet Legend (top 0.01% of adults) Everything stays on your phone. No GPS. No accounts. No cloud. What's still ...

... 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

... impact your daily development reliable and ready for long-term use. We cleaned up the core. About 5,000 lines of deprecated AST v1 code are gone, the index system is unified, verbose logs reduced, and race conditions plus memory leaks in AI Session Explorer fixed. It runs smoother and gives you more control. We made visuals and interactions trustworthy. Git Graph text positioning and color consistency are corrected, terminal cursor alignment after layout transitions is fixed, and theme previews ... ... accurate. The details you see and touch every day are back to precise. We invested in long-term AI memory

... Mnexium AI Now supports all three major AI providers! OpenAI ChatGPT models Anthropic Claude Models Google Gemini Models NEW Why this matters: Your users can now seamlessly switch between providers while keeping their memory and context intact. Learn something with GPT-4 Recall it with Gemini Continue with Claude. Same user. Same memories

... Most AI products eventually hit the same problem They forget who the user is and the experience feels generic again. We ve been working on @Mnexium AI , a simple memory layer for AI apps that: remembers users across sessions recalls relevant context automatically keeps conversation history lean (rolling summaries) creates structured user profiles over time No vector DB setup, no custom retrieval logic it plugs into your existing LLM flow. mnx: { learn: true, // extract & store memories

... ArkTabs is getting a lot of improvements and new features. They'll be published in few days. These Include: Improved algorithm for sorting tabs. Schedule opening workspaces Memory cleanup Mute/Unmute And there are some major and subtle improvements and additions those make the experience amazing. You'll notice them and find them useful. Install from Chrome Web Store https://chromewebstore.google.co... Free Upgrade to PLUS https://nabeelhashmi.com/arktabs Thank ...

... Everyone went crazy in 2025 after AI Memory. There are atleast a dozen launches in the space on product hunt from june-december, but the problem seems far from solved. Are you using any of the current memory

... human, they re basically goldfish. Today, I m excited to share something I ve been building to fix that: GetProfile is now open source a drop-in LLM proxy that gives your AI agents user profiles and long-term memory. It s Apache-2.0 licensed, self-hostable, and designed to slot into existing OpenAI-style stacks with minimal changes. Why another memory

... everyone! We just launched Hito - a free AI assistant with memory, built on our own reasoning architecture. Why we built it: We were tired of AI that forgets everything the moment you close the tab. Explain your job, your preferences, your context - and the next day? Gone. Start over. What makes it different: Our own state-of-the-art reasoning models using cognitive nested reasoning Persistent memory