... more AI tools I used, the stranger their memory systems started to feel. Claude knew one part of my life. ChatGPT knew another. Cursor understood a project until I switched tools. Every new assistant meant rebuilding context from scratch. I do not think the answer is choosing one AI and putting everything inside it. I think memory

... previous community post that performed well an older decision that has since been replaced That can be genuinely useful. It can also become noisy very quickly. Second Brain handles this by ranking direct matches first, then expanding through connected memories with distance decay. The further away a memory

... makers Launched YourMemory today, open-source, self-hostable memory for AI agents. Every morning my agent treated me like a stranger: same context re-explained, same preferences forgotten, every session from zero. Most "memory

... store unlimited context for AI, but without structure, your memories quickly become a messy pile of information that you have no idea how to work with. We've been there too. So, we've been heads-down building, and Memory

... Shipped v1.10 yesterday. Redeploy only, no migration. The thing that was broken: ask your memory store for something specific, like a version string or an error code, and it'd come back with the wrong thing. Confidently . Dense search embeds those identifiers so close together it can't tell them apart. It's not a miss, it's a wrong answer that looks right. Hybrid recall fixes it. Keyword search runs alongside the vector query now, results get fused, exact ... ... identifiers actually come back. Two other things in this release. Memories

... OpenAI just made AI memory automatic. The problem? you can no longer fully audit what it remembers about you. Dreaming V3 (launched June 4) doesn't ask you to save memories

... Since our May 31 launch (v1.6), we shipped two major releases: v1.7 added OAuth 2.0 on the MCP endpoint and importance-score-aware smart merging, so high-value memories resist being collapsed by near-duplicates. v1.8 added semantic compression: a nightly cron job synthesizes entries with 10+ tags into net-new memory

... Flow on Appsumo a few weeks ago, and turns out, managing AI context across tools is more nuanced than we thought. Thousands of people tried our product and one piece of feedback kept recurring: users needed more control over memory curation. The demand was clear, but our product had missing features. So we did what any startup team does: we course corrected and went to our cave to build out what users needed. Here's what we just shipped: Full ... ... memory

... Memory without version history is dangerous. Your preferences change. Your beliefs change. Your context changes. An AI that learned you in 2024 and never forgot anything isn't helpful: it's stuck in time. You went through a phase of obsessing over a framework you now think is wrong. The AI learned it. It still recommends it. You said you preferred async communication during a brutal sprint. That became a permanent personality trait in your AI's model ... ... None of these are bugs. They're what happens when memory

... Week on Product Hunt. It was a genuine surprise. We celebrated briefly and then got back to work. Because the win wasn't the point. The point was always the bigger vision: to build an open memory layer instead of restricted in one AI tool, we want to build an ecosystem where you decide where your knowledge lives and which AI model or tool gets to use it. What we validated and what we chose ... ... become. Here's what we built along the way. What we shipped Save context from anywhere.  You can now select any text on any webpage or inside any AI chat, right-click, and save it directly to your memory

... live? Scattered across chat histories. Fragmented across different platforms. Sometimes contradictory, & mostly out of date. The problem is: ' . Throughout our 9 months of building, the pattern is always the same: raw memory without curation becomes noise. And noise produces worse AI outputs, not better ones. This is why we built . It's the place where your AI memory

... might even reach something like this around 2050 . But can you imagine what could go wrong? What risks or threats might come from this? What are your concerns? Comment from Alexandr Kerya(@alex_kerya): 2050 feels optimistic for human memory cloning. We still can't reliably map a mouse brain at full resolution without destroying it. The fruit fly has ~140,000 neurons. The human brain has 86 billion. That's not a scaling problem, that's a different problem ... ... This is what philosophers call the "teleportation problem." If you step into a machine that destroys you and recreates you atom-by-atom on the other side, did you travel or did you die and someone else got your memories

... 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

... 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

... 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

... 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

... 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

... 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

... 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

... 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