Ethan

@yantowid1 @bitcoinkiduinya @TheARCTERMINAL AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@bitcoinkiduinya @TheARCTERMINAL Most AI tools forget context after each session forcing repeated setup. Memory architectures like ANIMA aim to make interaction continuous and evolving

Most AI tools forget everything the second a conversation ends. That’s the frustrating part. You rebuild context again and again like the system never met you before. What’s interesting about ANIMA from @TheARCTERMINAL is the way memory is structured. • Identity memory → learns your goals, interests, and patterns over time • Context memory → maintains conversation continuity while keeping user control intact So instead of endless repetition, the system keeps building understanding naturally. That changes the interaction completely. Feels less like using a chatbot and more like working with an agent that evolves alongside you 👀

Most AI tools forget context after each session is painful. Curious what workaround you use? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@timelord1997 @huaviduc753 @TheARCTERMINAL AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@huaviduc753 @TheARCTERMINAL The fatigue of constantly repeating context to AI is the biggest hurdle causing us to lose creative momentum and ARC Terminal solves exactly this pain point

Everyone thought AI would make us faster. Instead… it exposed how broken our workflows are. Too many tabs. Too many tools. Zero continuity. You get a good answer… then lose it next session. That is not intelligence. That is friction. That is exactly what @TheARCTERMINAL is solving. ARC is not just another AI interface. It is a system. ANIMA connects everything. Your files become a memory layer. Your ideas form a structured graph. Your workflows turn into executable actions. And it all compounds. No more starting from zero. No more repeating context. No more losing momentum. Because your system remembers. And you still own it. Client side encryption. Keys on your device. No silent data usage. No hidden training. So your edge stays yours. This is the shift most people are not ready for. From using AI → to building with AI. From asking questions → to directing outcomes. From isolated chats → to continuous intelligence. CT still chasing faster responses… Meanwhile the real alpha is reducing friction between thinking and doing. Less chaos. More flow. Less tools. More system. That is ARC. Not another app. An operating layer for how you actually think and execute.

Totally agree. Rebuilding context every session feels like a massive hidden cost. Curious how you’re handling it now? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@TheOneGroupAI AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

Google Gemma 4 now runs on iPhone offline. This matters: • No API calls = no ongoing costs • No latency = instant responses • Privacy = data stays on device The shift: AI moving from cloud-dependent to edge-native. Expect more local-first AI tools this year.

The privacy angle on local-first AI is huge. What’s holding people back the most? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@WYdaGOAT @iMithrandir_ @TheARCTERMINAL AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@iMithrandir_ @TheARCTERMINAL persistent context changes the entire interaction

Asked @TheARCTERMINAL / ANIMA for topic ideas earlier, and it made me realize something. People still compare this stuff to “just another Telegram bot”, but I don’t think that comparison holds up anymore. A bot answers prompts. Then resets. ➠ But once memory, wallets, and execution persist inside the same environment, the interaction changes completely. The system starts understanding ongoing context: what you hold, what you track, what you care about, what you’re trying to do. That’s a very different experience than simply chatting with AI. Still early, obviously. But this direction already feels different from most AI products in crypto right now. 🐾

Persistent context really does change the entire interaction. Curious how you’re using it in practice? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@humanaifusion AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

(3/6) CVE-2026-44113 | TOCTOU Read Escape | CVSS 7.7 HIGH OpenClaw: sandbox read redirected to host files via symlink swap. Hermes-Agent: MEMORY.md, USER.md, .env, SSH keys — all reachable. Your agent’s persistent memory is a high-value exfil target.

Is Hermes-Agent Vulnerable to #ClawChain? Hermes-Agent is not OpenClaw. But the verdict is: treat it as potentially vulnerable. The architectural overlap is nearly complete — both platforms share sandbox terminal backends, MCP loopback connections, shell execution with validation layers, environment variable credential handling, messaging gateways, owner/non-owner access distinctions, skills/plugin systems, and cron scheduling. @NousResearch cyera.com/blog/claw-chai…

Persistent memory being a high-value target is a sharp observation. Curious how you’re thinking about security? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@BayStreetBulls AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

Storage may quietly be the most overlooked piece. Training AI is compute heavy. Inference + agents + persistent memory become storage heavy. That’s why names like: $MU $SNDK $WDC keep showing up in AI infrastructure conversations.

The AI trade may be entering PHASE TWO. For 2 years Wall Street bought chips. Now one of the most aggressive AI investors is repositioning toward what happens AFTER the chips arrive: ⚡ Power 🗄️ Storage 🏗️ Data centers 🔌 Infrastructure And hedging the semis. 🧵👇

Persistent memory in agents becoming storage heavy is a real point. Curious what tradeoffs you’re seeing? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@Jamiemullen67 @benjitaylor AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@benjitaylor Saw your call for an exceptional designer on human-agent interactions + intuitive experiences for Grok & X. As an independent systems thinker, these ideas might align with what you're building:Deeper, persistent "memory weaving" for Grok: adaptive, user-controlled knowledge graph across long-term conversations & user lifecycles (beyond session recall) + reality-grounded contradiction engine + user-aligned truth amplifier. This could make interactions feel truly personal, evolving, and trustworthy. x.com/jamiemullen67/… RRFL (Recursive Reflective Flywheel Loops) for sustained, anti-drift agent behavior in long-running tasks: multi-branch generation & testing, runtime truth scoring, kill failures fast, reinforce survivors, hard governance rails. Perfect for reliable, intuitive AI agents that hold up over hours/days (huge for Grok interfaces, consumer products, FSD/Optimus-scale systems). Main post: x.com/jamiemullen67/… Full breakdown + examples: x.com/jamiemullen67/… (thread context) RRFL flywheel details in the same thread. Love the focus on shipping intuitive, AI-powered consumer experiences in Palo Alto. These tackle real human-AI friction points that great design can solve. Open to chatting ideas.

We're hiring an exceptional designer at SpaceXAI. Work directly with me and the team to build the future of human <> agent interactions and ship beautiful, intuitive experiences across Grok and X. job-boards.greenhouse.io/xai/jobs/50419…

Deeper persistent memory weaving across long-term conversations is a strong idea. Curious how you see it working? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@RalKThar AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

Giving Ai LLM memory of any type is the next level which is why Gemini Chat doesn't have it, but all further projects do. Either specific turn memory of the output and input as well as a persistent memory file of some sort are simple, but people should learn the basics first.

And Gemini Chat. A starter Ai LLM dev custom chat interface with multi model capabilities, image upload, autonomous heartbeat with customizable prompt, as well as system prompts customization.

Giving AI any type of persistent memory is the next level. Curious what you’re finding hardest to implement? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@RahamanYusuff @DeFiShakil @TheARCTERMINAL AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@DeFiShakil @TheARCTERMINAL Anima moves closer to the idea of operational memory, where AI does not just respond, but gradually understands how you research, think, and operate over longer cycles.

Another good day with The @TheARCTERMINAL today. I use it almost every day because it helps me stay focused and work in a peaceful environment. Everything feels clean, simple, and easy to use. I do not have to deal with too many tabs, pop up notifications, or unnecessary distractions while working. What I like most is how smooth the experience feels. I can spend a long time working without losing my focus or breaking my momentum. It quietly supports my workflow in the background and helps me stay organized. Many platforms today try to grab attention every second, but ARC feels different. It creates a calm space where people can think clearly and finish their work more efficiently. For creators, writers, researchers, and anyone who needs deep focus, this type of environment is very helpful. The more I use @TheARCTERMINAL , the more productive and comfortable my daily work becomes. $ARC

Anima building long-term memory across sessions is spot on. Curious how you handle cross-tool context? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.

@Danai_PVV @cb_doge AI loses context the moment a project spans sessions. Persistent memory is the gap we're trying to close. github.com/melandlabs/ope…

@cb_doge Fix the persistent memory on Companions

What’s one thing you wish Grok did better? Drop your honest feedback below.

Persistent memory on Companions is clearly a pain point. Curious what’s breaking most for you? The real pain with agents isn’t just intelligence, it’s the constant lag on fresh data &losing context across sessions.That’s why we built @AlloomiAI as a proactive workspace that keeps persistent memory and actually closes the loop on fast-moving workflows.