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🚨 CHINA IS ON FIRE! Tencent open-sourced TencentDB Agent Memory. Long-term memory for AI agents that runs 100% locally. ↳ No Pinecone or cloud APIs ↳ You don't need to repeat yourself every session ↳ 61% fewer tokens ↳ PersonaMem accuracy: 48% → 76% ↳ Runs on plain SQLite... show more

Kanika

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23,775 views • 1 month ago •via X (Twitter)

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no money for chatgpt pro or claude pro? this is for you🫵🏽 someone open-sourced 800M tokens/month of free AI inference on github. – Free LLMs, 2.3k stars on github, MIT licensed – no subscription. no rate-limit panic. no vendor lock – 14 providers stacked: google, groq, cerebras, openrouter, github models + 9 more – one openai-compatible endpoint. drop-in for any SDK – smart router with auto-failover, sticky sessions, encrypted key storage – runs locally on your machine it's a fresh repo, 2300 stars on github already.

no money for chatgpt pro or claude pro? this is for you🫵🏽 someone open-sourced 800M tokens/month of free AI inference on github. – Free LLMs, 2.3k stars on github, MIT licensed – no subscription. no rate-limit panic. no vendor lock – 14 providers stacked: google, groq, cerebras, openrouter, github models + 9 more – one openai-compatible endpoint. drop-in for any SDK – smart router with auto-failover, sticky sessions, encrypted key storage – runs locally on your machine it's a fresh repo, 2300 stars on github already.

m0h

239,584 views • 3 months ago

Excited to introduce Nativ 🚀 Run frontier open models locally on your Mac. No accounts, no subscriptions, no cloud. ⚡ Built on mlx-vlm — multimodal + fastest on Apple Silicon 🔒 100% private — every token generated on your machine 🛠 Plug your coding agents into a local endpoint 📊 Live telemetry — tokens/sec, memory, usage 🆓 100% open source, MIT licensed, free forever Your Mac is more capable than you think. Stop renting intelligence. Download for macOS 👇 Github repo 🌟

Excited to introduce Nativ 🚀 Run frontier open models locally on your Mac. No accounts, no subscriptions, no cloud. ⚡ Built on mlx-vlm — multimodal + fastest on Apple Silicon 🔒 100% private — every token generated on your machine 🛠 Plug your coding agents into a local endpoint 📊 Live telemetry — tokens/sec, memory, usage 🆓 100% open source, MIT licensed, free forever Your Mac is more capable than you think. Stop renting intelligence. Download for macOS 👇 Github repo 🌟

Prince Canuma

122,643 views • 1 month ago

We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

andy nguyen

28,980 views • 4 months ago

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

RetroChainer

16,971 views • 9 days ago

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 views • 13 days ago

🚨 SOMEONE JUST KILLED THE COACHING INDUSTRY a developer spent 22,000 hours building a Personal AI Operating System on top of Claude Code now anyone with a terminal can install it for FREE it knows your goals, remembers every decision you've made, and prepares your morning briefing while you sleep [ the numbers are insane ]: - hours of dev work in it: 22,000 - sessions logged: 6,000 - time saved per day: 2-3 hours - GitHub stars: 12,100 - skills built in: 45 - workflows wired up: 171 - safety hooks: 37 - cost to install: $0 [ the science is wild too ]: no embeddings, no vector databases, no AI magic you can't read every memory, decision, and context lives in plain markdown files you read it with cat, search it with ripgrep, version it with git 4 memory types compound over time: - work memory (active projects, open decisions) - knowledge memory (domain expertise, research) - people memory (contacts, companies, relationships) - learning memory (patterns, mistakes, what works for YOU) every complex task routes through a 7-step cycle: OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN privacy is enforced by CODE, not prompts a hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones [ the grift opportunity is even wilder ]: freelancers are already charging $500-2,000 per personal AI setup for executives, founders, and busy operators one person + one weekend = a consulting business that didn't exist 6 months ago every AI productivity app you're paying $30/month for is replaceable by 4 hours of setup work and this one repo REPO: 100% OPEN SOURCE, FREE

🚨 SOMEONE JUST KILLED THE COACHING INDUSTRY a developer spent 22,000 hours building a Personal AI Operating System on top of Claude Code now anyone with a terminal can install it for FREE it knows your goals, remembers every decision you've made, and prepares your morning briefing while you sleep [ the numbers are insane ]: - hours of dev work in it: 22,000 - sessions logged: 6,000 - time saved per day: 2-3 hours - GitHub stars: 12,100 - skills built in: 45 - workflows wired up: 171 - safety hooks: 37 - cost to install: $0 [ the science is wild too ]: no embeddings, no vector databases, no AI magic you can't read every memory, decision, and context lives in plain markdown files you read it with cat, search it with ripgrep, version it with git 4 memory types compound over time: - work memory (active projects, open decisions) - knowledge memory (domain expertise, research) - people memory (contacts, companies, relationships) - learning memory (patterns, mistakes, what works for YOU) every complex task routes through a 7-step cycle: OBSERVE → THINK → PLAN → BUILD → EXECUTE → VERIFY → LEARN privacy is enforced by CODE, not prompts a hook called ContainmentGuard physically blocks sensitive data from being written outside designated zones [ the grift opportunity is even wilder ]: freelancers are already charging $500-2,000 per personal AI setup for executives, founders, and busy operators one person + one weekend = a consulting business that didn't exist 6 months ago every AI productivity app you're paying $30/month for is replaceable by 4 hours of setup work and this one repo REPO: 100% OPEN SOURCE, FREE

Ronin

614,458 views • 3 months ago

THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need

THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need

leopardracer

2,764,696 views • 3 months ago

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product

Gipp 🦅

60,173 views • 2 months ago

MICROSOFT TRIED TO BUY IT. HARVARD AND THREE US UNIVERSITIES RELEASED IT FOR FREE a team from Harvard, Michigan State, Minnesota, and Georgia ran the first hard study on how agent memory behaves over time. the finding is uncomfortab they call it the "experience-following property": when a new task looks like a memory the agent pulls up, its answer copies that memory's answer almost 1:1 (correlation up to 0.95). the agent isn't reasoning - it's imitating that breaks in two ways: > error propagation - one wrong memory repeats on every similar task after it > misaligned replay - "successful" past runs turn out low-value or misleading when reused so "just add more memory" quietly makes agents worse. what fixed it: > store only high-quality experiences, not everything > use future task outcomes as free labels to delete low-value memories > pair selective adding with utility-based deletion the payoff: ~10% absolute accuracy gains, a cleaner memory bank, and agents stable under distribution shift no company owns this. they dropped the whole thing open-source, for free bookmark this. the next edge in AI agents isn't more memory - it's cleaner memory

MICROSOFT TRIED TO BUY IT. HARVARD AND THREE US UNIVERSITIES RELEASED IT FOR FREE a team from Harvard, Michigan State, Minnesota, and Georgia ran the first hard study on how agent memory behaves over time. the finding is uncomfortab they call it the "experience-following property": when a new task looks like a memory the agent pulls up, its answer copies that memory's answer almost 1:1 (correlation up to 0.95). the agent isn't reasoning - it's imitating that breaks in two ways: > error propagation - one wrong memory repeats on every similar task after it > misaligned replay - "successful" past runs turn out low-value or misleading when reused so "just add more memory" quietly makes agents worse. what fixed it: > store only high-quality experiences, not everything > use future task outcomes as free labels to delete low-value memories > pair selective adding with utility-based deletion the payoff: ~10% absolute accuracy gains, a cleaner memory bank, and agents stable under distribution shift no company owns this. they dropped the whole thing open-source, for free bookmark this. the next edge in AI agents isn't more memory - it's cleaner memory

NO1ennn

27,496 views • 11 days ago

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 views • 1 year ago

10 repos blowing up on GitHub this week that replace $1,500/month in AI tools 1. andrej-karpathy-skills → replaces paid Claude Code courses one CLAUDE.md file from Karpathy's LLM coding observations 48,965 stars. 7,939 stars TODAY 2. claude-mem → replaces paid context/memory tools auto-captures everything Claude does across sessions compresses with AI and injects into future sessions 59,373 stars. 1,907 stars today 3. voicebox → replaces ElevenLabs ($22/mo) open-source voice synthesis studio 18,963 stars. 887 stars today 4. open-agents → replaces paid agent platforms ($200/mo) open-source template for building cloud agents. by Vercel 3,105 stars. 735 stars today 5. cognee → replaces paid knowledge bases ($50/mo) AI agent memory engine in 6 lines of code 15,733 stars 6. magika → replaces paid file detection tools AI file content type detection. by Google 14,603 stars 7. GenericAgent → replaces paid agent infra ($100/mo) self-evolving agent. grows skill tree from 3.3K-line seed 6x less token consumption than standard agents 2,661 stars. 883 stars today 8. omi → replaces Rewind AI ($25/mo) AI that sees your screen + listens to conversations tells you what to do next 8,952 stars. 488 stars today 9. evolver → replaces manual agent optimization self-evolution engine for AI agents genome evolution protocol 3,074 stars. 866 stars today 10. wallet tracking + copy trading → Kreo tracks top Polymarket wallets. auto copies trades the only tool on this list i actually pay for because it makes more than it costs → total before: ~$1,500/month in AI subscriptions total now: $0 + Kreo like + bookmark you'll need this

10 repos blowing up on GitHub this week that replace $1,500/month in AI tools 1. andrej-karpathy-skills → replaces paid Claude Code courses one CLAUDE.md file from Karpathy's LLM coding observations 48,965 stars. 7,939 stars TODAY 2. claude-mem → replaces paid context/memory tools auto-captures everything Claude does across sessions compresses with AI and injects into future sessions 59,373 stars. 1,907 stars today 3. voicebox → replaces ElevenLabs ($22/mo) open-source voice synthesis studio 18,963 stars. 887 stars today 4. open-agents → replaces paid agent platforms ($200/mo) open-source template for building cloud agents. by Vercel 3,105 stars. 735 stars today 5. cognee → replaces paid knowledge bases ($50/mo) AI agent memory engine in 6 lines of code 15,733 stars 6. magika → replaces paid file detection tools AI file content type detection. by Google 14,603 stars 7. GenericAgent → replaces paid agent infra ($100/mo) self-evolving agent. grows skill tree from 3.3K-line seed 6x less token consumption than standard agents 2,661 stars. 883 stars today 8. omi → replaces Rewind AI ($25/mo) AI that sees your screen + listens to conversations tells you what to do next 8,952 stars. 488 stars today 9. evolver → replaces manual agent optimization self-evolution engine for AI agents genome evolution protocol 3,074 stars. 866 stars today 10. wallet tracking + copy trading → Kreo tracks top Polymarket wallets. auto copies trades the only tool on this list i actually pay for because it makes more than it costs → total before: ~$1,500/month in AI subscriptions total now: $0 + Kreo like + bookmark you'll need this

self.dll

361,846 views • 4 months ago

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

CyrilXBT

34,586 views • 2 months ago

Dhravya Shah sold two companies before he could legally drink, then raised $3 million alone at 19. This 73-minute interview is the clearest case I've heard for why AI memory is the next platform. Here's the part I can't stop thinking about: > everyone will have their own AI agent just like everyone has their own mobile phones > in that world the most important thing will be context about you > my entire infrastructure for 100,000 users can run on $5 per month > context infrastructure for agents will be as important as inference Most builders still bolt memory onto an agent as an afterthought. The ones who get this are building the layer everything else runs on. Read how memory becomes the loop your agents run on, below.

Dhravya Shah sold two companies before he could legally drink, then raised $3 million alone at 19. This 73-minute interview is the clearest case I've heard for why AI memory is the next platform. Here's the part I can't stop thinking about: > everyone will have their own AI agent just like everyone has their own mobile phones > in that world the most important thing will be context about you > my entire infrastructure for 100,000 users can run on $5 per month > context infrastructure for agents will be as important as inference Most builders still bolt memory onto an agent as an afterthought. The ones who get this are building the layer everything else runs on. Read how memory becomes the loop your agents run on, below.

Phosphen

371,490 views • 1 month ago

Paid AI trading bots just got cooked,, While everyone’s paying $99/mo for bots that still need babysitting, one open-source thing on GitHub is already running real trades for free. OpenClaw🦞 (June 2026 update) lets you spin up agents that: - Copy-trade big wallets automatically - Scan viral narratives 24/7 - Trade perps on Hyperliquid with zero fees - Run BTC DCA + ML strategy that improves itself All self-hosted. No subscription. Just fork it and go. Real edge right now: -- Transparent public challenge stacking BTC from 0 to 10 over 12 months -- Uses Claude/Gemini locally or cheap APIs -- Agents that actually pay for services and settle themselves Most people are still chasing closed SaaS dashboards. The ones who win will just run the open version tonight... I’m setting one up this weekend (copy-trader + perps agent). Will update you soonnnn....

Paid AI trading bots just got cooked,, While everyone’s paying $99/mo for bots that still need babysitting, one open-source thing on GitHub is already running real trades for free. OpenClaw🦞 (June 2026 update) lets you spin up agents that: - Copy-trade big wallets automatically - Scan viral narratives 24/7 - Trade perps on Hyperliquid with zero fees - Run BTC DCA + ML strategy that improves itself All self-hosted. No subscription. Just fork it and go. Real edge right now: -- Transparent public challenge stacking BTC from 0 to 10 over 12 months -- Uses Claude/Gemini locally or cheap APIs -- Agents that actually pay for services and settle themselves Most people are still chasing closed SaaS dashboards. The ones who win will just run the open version tonight... I’m setting one up this weekend (copy-trader + perps agent). Will update you soonnnn....

Rifat Ahmed

54,312 views • 2 months ago

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

22,720 views • 1 month ago

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,850 views • 1 year ago

THE CHINESE INSTALLED 6 AI AGENTS RUNNING 24/7 FOR $11 A MONTH. THE BOX UNDER ONE DESK REPLACED $1,200 IN CLOUD SUBSCRIPTIONS. The Minisforum MS-S1 Max ships with 128GB of unified memory shared between CPU and GPU. Qwen3-Coder 30B runs at 40 to 50 tokens per second. 6 agents run simultaneously without anyone watching the meter. An inbox sorter drafts replies every 15 minutes. A research monitor watches 30+ feeds and lands a digest by 7 AM, a job that costs $15 to $20 a day on cloud APIs. The same stack on cloud subscriptions ran $800 to $1,200 a month. The hardware costs $3,000 once. Break-even hits month 3 to 4. The real shift is behavior, not savings. A meeting prep agent never would have justified per-token cost. Running it free overnight made it obvious.

THE CHINESE INSTALLED 6 AI AGENTS RUNNING 24/7 FOR $11 A MONTH. THE BOX UNDER ONE DESK REPLACED $1,200 IN CLOUD SUBSCRIPTIONS. The Minisforum MS-S1 Max ships with 128GB of unified memory shared between CPU and GPU. Qwen3-Coder 30B runs at 40 to 50 tokens per second. 6 agents run simultaneously without anyone watching the meter. An inbox sorter drafts replies every 15 minutes. A research monitor watches 30+ feeds and lands a digest by 7 AM, a job that costs $15 to $20 a day on cloud APIs. The same stack on cloud subscriptions ran $800 to $1,200 a month. The hardware costs $3,000 once. Break-even hits month 3 to 4. The real shift is behavior, not savings. A meeting prep agent never would have justified per-token cost. Running it free overnight made it obvious.

Lummox

14,093 views • 2 months ago

Kimi K3 is the most powerful open-source AI model ever, and you can use it 100% free right now. Go to Sign in. Switch the dropdown from K2.6 to K3. That's it. No credit card. No API setup. On my own tests, it beat GPT 5.6 and Opus 4.8. It builds games. 3D worlds. Full websites. Even videos. It goes fully open source in July 2026. Use it off-peak hours and you get even more free tokens. Most people will never even try it. The ones who do get frontier-level AI for $0.

Kimi K3 is the most powerful open-source AI model ever, and you can use it 100% free right now. Go to Sign in. Switch the dropdown from K2.6 to K3. That's it. No credit card. No API setup. On my own tests, it beat GPT 5.6 and Opus 4.8. It builds games. 3D worlds. Full websites. Even videos. It goes fully open source in July 2026. Use it off-peak hours and you get even more free tokens. Most people will never even try it. The ones who do get frontier-level AI for $0.

Julian Goldie SEO

144,837 views • 1 month ago

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

Molt.id

78,109 views • 6 months ago

how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

how to set up hermes agent step by step. built-in memory, 40+ tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40+ tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged imran to come on The Startup Ideas Podcast (SIP) 🧃 and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

GREG ISENBERG

619,380 views • 4 months ago