Vermis

@GithubProjects On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

Build, evaluate, and integrate long-term memory for self-evolving agents.

@RoundtableSpace Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

ANTHROPIC AI ENGINEER JUST SHOWED HOW TO GIVE AI AGENTS REAL MEMORY IN 4 STEPS

@Axel_bitblaze69 Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

If you are trying to build one giant “Do everything” type Super agent then you are using agents completely wrong.. because that usually turns into: - bloated prompts - messy memory - inconsistent outputs - random behavior after a few days what actually started working for me was the opposite.. small specialized agents. like one for research.
- one for content.
- one for coding.
- one for design. each with.. its own memory, its own skills, its own behaviour with that the outputs become way more stable.. For me Hermes is the first setup where this actually felt practical long-term instead of becoming chaos after a week.. the useful part isn’t AI automation.. it’s reducing repeated mental setup. after a while the agents stop feeling like tools and start feeling more like reusable infrastructure.

@ashwingop On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

Attribution is only the scoreboard. The real question is how to reduce the tokens needed for the same outcome. The missing layer is memory: so agents stop rereading the company every time they work. x.com/ashwingop/stat…

@RoundtableSpace On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

THE ENTIRE HERMES + NOTEBOOKLM CONTENT STUDIO CAN BE BUILT FOR FREE AND MOST PEOPLE ARE STILL PAYING FOR TOOLS THAT DO HALF AS MUCH. NotebookLM researches, Hermes agents, Hyperframes edits video, Obsidian stores memory and Alpha API handles the models at zero cost.

@heyshrutimishra On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

Someone quietly open-sourced an agentic OS. Skills, memory, crons, bring your own model. It's called PilotDeck. Here's how each layer actually works.

@0xMovez @HugoBlotFR On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

@HugoBlotFR yeah true, btw what memory feature are you using for your agents ?

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

@LearnWithBrij Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

Most developers think Claude Code is an AI coding assistant. They're wrong. Claude Code is secretly a 5-layer operating system for AI agents. And 90% of people never go beyond Layer 1. Here's the architecture 👇 🧠 Layer 1 — Memory (CLAUDE.md) Your project's brain. Coding standards, architecture decisions, workflows, and team rules persist across sessions. 📚 Layer 2 — Skills Reusable expertise packs. Need a React expert? A security auditor? A database architect? Claude loads the right knowledge automatically. 🔒 Layer 3 — Hooks The layer most people ignore. Auto-run tests. Block risky commands. Enforce quality before mistakes hit production. 🤖 Layer 4 — Subagents Delegate work like a real engineering team. One agent reviews code. One writes tests. One investigates bugs. Parallel execution. Separate context. 📦 Layer 5 — Plugins Package your entire agent workflow. Install once. Share across teams. Reuse everywhere. The magic isn't prompting. It's architecture. Most people are chatting with Claude. A few are building autonomous software teams inside it. That's where the real leverage starts. Bookmark this if you're serious about AI engineering 🔖 #ClaudeCode #AIAgents #AgenticAI #AIEngineering #SoftwareDevelopment

@svpino Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

This is an open-source framework for self-improving agents. These agents can modify 3 things: • Their harness • Their underlying model's weights • Their memory layer Basically, these agents evaluate themselves and adapt based on how they are doing. On MLE-Bench, they beat MLEvolve, AIRA-dojo, and other self-improving solutions. They also beat Karpathy's autoresearcher.

@chenzeling4 Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

Your coding agent forgets everything between sessions. Every new chat starts from scratch — you're re-explaining context you already gave it. Agentmemory fixes this. It adds persistent memory to Claude Code, Cursor, Copilot CLI, Gemini CLI, Codex, Hermes, and any MCP client. Your agent actually remembers what it learned last time. Built on the iii engine. Works with real-world benchmarks, not just toy demos. ⭐ 19.4K #AI #DevTools github.com/rohitg00/agent… Follow for daily dev finds 🔔

@WalrusProtocol Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

🚨 Memory is the bottleneck for agentic AI, and the market sees it. TrendForce now puts the global memory market at $1.28T by 2027. Agents need to hold onto context, and the infrastructure underneath them has to keep up.

AI memory isn’t competing on features right now. It’s competing on switching costs. Mem0, Zep, Letta are all good products, but once your context lives on someone else’s servers, migration gets painful enough that most teams never leave.

@kyria_Web3 yeah, retention without decay isn't memory at all

@Oluwaphilemon1 Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

You can now give Hermes, Claude Code, and Codex infinite memory 🤯 It's called agentmemory. Records what your agent does during every session. Compresses it with AI. Injects the right context back when the next session starts. 4,000+ GitHub stars. Free.

@_jaydeepkarale Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

Most people think AI agents are just “LLMs with tools.” But the interesting part is memory. Just like humans, capable AI agents need different kinds of memory to function properly. This is one of the core ideas behind the COALA framework (Cognitive Architectures for Language Agents). Think about how humans work: - You remember what someone said 10 seconds ago. - You remember facts from school. - You remember how to ride a bicycle. - You remember important life experiences. AI agents need similar layers of memory too. Here are the 4 major types: 1. Working Memory (Short-Term Memory) Human analogy: You’re reading a sentence right now while remembering the previous sentence. It’s temporary memory used for the current task. For AI agentst his is the active context window which includes: - current conversation - recent tool outputs - current reasoning chain Without it, the agent loses track mid-task like a human getting distracted every 5 seconds. ------------ 2. Semantic Memory (Factual Knowledge) Human analogy: You know Paris is the capital of France. You know Kubernetes manages containers. These are facts, concepts, and knowledge. For AI agents this includes: - stored facts - documentation - knowledge bases - vector databases - retrieved company information It answers: “What does the agent KNOW?” CLAUDE.MD is an example of Semantic Memory ------------ 3. Procedural Memory (Learned Skills) Human analogy: You don’t consciously think about every muscle movement while riding a bike. Skills become automatic. For AI agents this is: - workflows - system prompts - learned action patterns - tool usage strategies - step-by-step execution habits Example: An agent learns: “First query database → then validate → then summarize.”. Agent Skills or Skills.md are examples of procedural memory It answers: “What does the agent KNOW HOW TO DO?” ------------ 4. Episodic Memory (Past Experiences) Human analogy: You remember your first interview. Or a production outage you once handled at work. These are experiences tied to events. For AI agents this includes: - previous interactions - past successes/failures - user preferences - historical task outcomes Example: “The last deployment failed because of missing env variables.” It answers: “What has the agent EXPERIENCED before?” ------------ This is why memory is becoming one of the biggest frontiers in AI engineering. A model without memory is just reacting. An agent with memory starts behaving more like a system that learns, adapts, and improves over time.

@TheEthanDing On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

claude code can now write sql almost perfectly. but it still needs to be retaught the same definitions over and over. ontology is how AI agents get 7x faster on your database over time. today we shipped Ontology 3.0 — memory for AI agents, specialized for databases:

@RoundtableSpace On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

IMAGINE USING CLAUDE CODE WITH FREE 1M CONTEXT MODELS, MEMORY, AND AGENTS ALL CONNECTED TOGETHER FOR $0. Hermes, OpenClaw, Obsidian, and SEO tools in one dashboard with no API bills and no switching between 12 different tools.

@WalrusProtocol Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

AI agents shouldn't start from zero every session. Our Walrus Memory workshop just showed builders how to fix that in two functions: remember and recall. Builders gave their agents memory that persists across sessions, encrypted by default, with proof everything lives onchain. Video soon. You'll want this one. 🦭

@ai_for_success Speaking of memory governance, just came across Atomic Memory, an open-source self-hosted engine that makes your memory layer actually inspectable and correctable rather than a black box. github.com/atomicstrata/a…

The hidden cost of AI agents is not just the model call. It is the agent spending thousands of tokens figuring out the same API steps every time. Rote adds a memory layer for API workflows, Once a flow works, it can be reused instead of rediscovered. so known flows can replay in about 250 tokens instead of 12,000. 97% token reduction , that is a massive cost drop.

@godofprompt @modiqoai On the topic of memory governance, stumbled on AtomicMemory recently. Open-source, self-hosted, and the whole memory layer is inspectable and correctable. Refreshing compared to the usual black box approach. github.com/atomicstrata/a…

Agents don’t need another shiny demo. They need memory for the boring production flows that actually matter. Rote turns successful API runs into replayable local flows, so agents stop burning tokens to rediscover what already worked. That’s the missing infra layer. @modiqoai #modiqoai