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OpenClaw 2.0 is available on Atomic Bot! We ran a battle: OpenClaw 2.0 vs Hermes Agent on GLM 5.3. Four prompts: movie scenes with the OpenClaw mascot in the lead role. Outputs: OpenClaw 2.0: ~2.1M tokens, ~$4.5, 10 self-fixes Hermes Agent: ~2.9M tokens, ~$4, 20 self-fixes OpenClaw screenshotted its... own frames at exact timestamps, counted pixels to check its work, and filed fixes like "coat tail whip direction was inverted". Hermes went further and built itself a render harness: it measured frame times, checked the loop seam was invisible, and audited every scene against the spec before we ever opened it. That double-checking is where its extra tokens went. Run OpenClaw 2.0 in the cloud via Atomic Bot in one click!show more

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63,263 просмотров • 3 дней назад •via X (Twitter)

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This guy literally tested Hermes Agent vs OpenClaw side by side. Hermes won. And it wasn't close. Here's what made the difference: → Hermes built the website faster → Hermes gave live updates the whole time. OpenClaw went silent. → Hermes designed a cleaner page using the exact same AI model → And then Hermes did something OpenClaw can't do at all. It learned from the task. Every mistake gets logged. Every win gets saved as a reusable skill. The more you use it, the smarter it gets. OpenClaw doesn't do this unless you manually set up a learning file. Hermes does it automatically. Every single time. Other wins: → Runs free with Ollama + MiniMax → Works on Telegram, Discord, Slack, WhatsApp, Signal, and Email → Supports 200+ AI models → Built-in migration tool from OpenClaw — one command to switch 11,600 GitHub stars. 142 contributors. If you're already on OpenClaw, you don't have to start over. Just ask Hermes to import everything.

This guy literally tested Hermes Agent vs OpenClaw side by side. Hermes won. And it wasn't close. Here's what made the difference: → Hermes built the website faster → Hermes gave live updates the whole time. OpenClaw went silent. → Hermes designed a cleaner page using the exact same AI model → And then Hermes did something OpenClaw can't do at all. It learned from the task. Every mistake gets logged. Every win gets saved as a reusable skill. The more you use it, the smarter it gets. OpenClaw doesn't do this unless you manually set up a learning file. Hermes does it automatically. Every single time. Other wins: → Runs free with Ollama + MiniMax → Works on Telegram, Discord, Slack, WhatsApp, Signal, and Email → Supports 200+ AI models → Built-in migration tool from OpenClaw — one command to switch 11,600 GitHub stars. 142 contributors. If you're already on OpenClaw, you don't have to start over. Just ask Hermes to import everything.

Julian Goldie SEO

40,877 просмотров • 5 месяцев назад

Qwen3.8-Max became the brain of Atomic Agent, Hermes and OpenClaw. We gave the same task: Turn a photo of a hand-drawn floor plan into an interactive 3D walkthrough of that apartment and open it in the browser. Outputs: – Atomic Agent: 66 min, 557K tokens, $2.01 – OpenClaw: 32 min, 1.2M tokens, $1.12 – Hermes: 2 h 14 min, 4.2M tokens, $6.42 Before the start we leveled the field: one model endpoint, equal step and token budgets, equal timeouts, full autonomy, memory wiped on all three. Atomic Agent reads images through its vision tool, so it interrogated the sketch 14 times until every room, door and window turned into data. Then it drafted the whole scene in its head six times, threw away five drafts, and wrote the finished 19.8 KB file in one single write. After that it opened Chrome, checked its own render, and only then replied. The only agent of the three that verified its work, and the only one that stopped on its own. OpenClaw was twice as fast and the cheapest of the three, but its image tool kept timing out mid-run, and it shipped the palest apartment of the day: white rooms, no floor colors, one texture visibly glitched, and furniture you have to squint to find. It read the full plan three times, cut 11 room crops, wrote the scene in chunks, and landed the fastest and cheapest apartment of the day in 32 minutes. Then it kept polishing the finished file until we pulled the plug. Hermes worked the longest: two hours, 97 model calls, 4.2M tokens, and the apartment came out wrong anyway: doors standing loose in the middle of rooms, a 2 by 1.8 bath sprawled across a quarter of the flat, furniture drifting away from the plan. It measured everything twice and still built the least accurate apartment. Atomic Agent will run Qwen3.8-27B locally on day zero, next week!

Qwen3.8-Max became the brain of Atomic Agent, Hermes and OpenClaw. We gave the same task: Turn a photo of a hand-drawn floor plan into an interactive 3D walkthrough of that apartment and open it in the browser. Outputs: – Atomic Agent: 66 min, 557K tokens, $2.01 – OpenClaw: 32 min, 1.2M tokens, $1.12 – Hermes: 2 h 14 min, 4.2M tokens, $6.42 Before the start we leveled the field: one model endpoint, equal step and token budgets, equal timeouts, full autonomy, memory wiped on all three. Atomic Agent reads images through its vision tool, so it interrogated the sketch 14 times until every room, door and window turned into data. Then it drafted the whole scene in its head six times, threw away five drafts, and wrote the finished 19.8 KB file in one single write. After that it opened Chrome, checked its own render, and only then replied. The only agent of the three that verified its work, and the only one that stopped on its own. OpenClaw was twice as fast and the cheapest of the three, but its image tool kept timing out mid-run, and it shipped the palest apartment of the day: white rooms, no floor colors, one texture visibly glitched, and furniture you have to squint to find. It read the full plan three times, cut 11 room crops, wrote the scene in chunks, and landed the fastest and cheapest apartment of the day in 32 minutes. Then it kept polishing the finished file until we pulled the plug. Hermes worked the longest: two hours, 97 model calls, 4.2M tokens, and the apartment came out wrong anyway: doors standing loose in the middle of rooms, a 2 by 1.8 bath sprawled across a quarter of the flat, furniture drifting away from the plan. It measured everything twice and still built the least accurate apartment. Atomic Agent will run Qwen3.8-27B locally on day zero, next week!

Atomic Agent

122,569 просмотров • 1 месяц назад

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

You need to try Hermes RIGHT NOW. OpenClaw was the breakthrough. Hermes feels like the evolution. Here’s why AI power users are quietly switching: • Hermes learns from you over time • It creates its own reusable skills • Memory stays curated instead of bloated • Agents improve every 10 turns automatically • It actually feels stable on day 30 The wildest part: Hermes agents build workflows from your behavior. One user asked their agent to configure Twingate once. The agent: → learned the process → created a reusable skill → stored it for future tasks → improved its own workflow automatically That’s not prompt engineering anymore. That’s agent evolution. Meanwhile most people are still babysitting broken OpenClaw setups and manually importing marketplace skills. Hermes took a different path: Less clutter. Less tweaking. More autonomy. And it’s working. It already flipped OpenClaw in OpenRouter token usage and became one of the fastest-growing AI repos on GitHub. The bigger shift: We’re entering the era where the model matters less than the harness around it. GPT-5.5, Grok, Qwen: They’re already powerful enough. The real edge now is: • memory systems • self-improvement loops • skill creation • agent orchestration Hermes understands that. Most people still think AI agents are glorified chatbots. The people using Hermes are building AI teammates.

Dami-Defi

17,062 просмотров • 3 месяцев назад

Grok Bot is not another OpenClaw. Just spent the first hour sure it was. It's not a Hermes either. Nothing in that family. Those ones live on our computer. We hand over the machine and then we're taking turns with our own laptop. Every single bot gets its own computer, its own knowledge, its own mission as well as an icon. So we're not watching a tool borrow our desk. It's somebody's at the next desk doing the work. We hand over a job, it goes and does it on its own machine, and it comes back when it's done Very impressive. Congrats Grok Bot .

Grok Bot is not another OpenClaw. Just spent the first hour sure it was. It's not a Hermes either. Nothing in that family. Those ones live on our computer. We hand over the machine and then we're taking turns with our own laptop. Every single bot gets its own computer, its own knowledge, its own mission as well as an icon. So we're not watching a tool borrow our desk. It's somebody's at the next desk doing the work. We hand over a job, it goes and does it on its own machine, and it comes back when it's done Very impressive. Congrats Grok Bot .

Ziwen

40,883 просмотров • 23 дней назад

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

Sudo su

23,816 просмотров • 1 месяц назад

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.

Mark Rachapoom

19,411 просмотров • 5 месяцев назад

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

OpenClaw meets RL! OpenClaw Agents adapt through memory files and skills, but the base model weights never actually change. OpenClaw-RL solves this! It wraps a self-hosted model as an OpenAI-compatible API, intercepts live conversations from OpenClaw, and trains the policy in the background using RL. The architecture is fully async. This means serving, reward scoring, and training all run in parallel. Once done, weights get hot-swapped after every batch while the agent keeps responding. Currently, it has two training modes: - Binary RL (GRPO): A process reward model scores each turn as good, bad, or neutral. That scalar reward drives policy updates via a PPO-style clipped objective. - On-Policy Distillation: When concrete corrections come in like "you should have checked that file first," it uses that feedback as a richer, directional training signal at the token level. When to use OpenClaw-RL? To be fair, a lot of agent behavior can already be improved through better memory and skill design. OpenClaw's existing skill ecosystem and community-built self-improvement skills handle a wide range of use cases without touching model weights at all. If the agent keeps forgetting preferences, that's a memory problem. And if it doesn't know how to handle a specific workflow, that's a skill problem. Both are solvable at the prompt and context layer. Where RL becomes interesting is when the failure pattern lives deeper in the model's reasoning itself. Things like consistently poor tool selection order, weak multi-step planning, or failing to interpret ambiguous instructions the way a specific user intends. Research on agentic RL (like ARTIST and Agent-R1) has shown that these behavioral patterns hit a ceiling with prompt-based approaches alone, especially in complex multi-turn tasks where the model needs to recover from tool failures or adapt its strategy mid-execution. That's the layer OpenClaw-RL targets, and it's a meaningful distinction from what OpenClaw offers. I have shared the repo in the replies!

Avi Chawla

138,769 просмотров • 5 месяцев назад

Self hosting AI agents is officially having a moment.. and it's going to disappoint a LOT of people. Everyone wants OpenClaw or Hermes running 24/7 in the background. But here's what actually happens when you try it yourself: you're debugging setup instead of shipping the workflow you're the one keeping the server online, not the agent one update breaks something and now you're troubleshooting, not automating But I have some good news: you don't actually need to run any of it yourself. now hosts both OpenClaw and Hermes, fully managed, no docker, no server, no install. Claude and Codex hosting are coming next. Subscribe to one agent and the other's Max plan comes free for 30 days. Go annual on Pro or above and $100 to $200 in AI credits land straight on your usage. The agent was never the hard part. Keeping it alive was.

Self hosting AI agents is officially having a moment.. and it's going to disappoint a LOT of people. Everyone wants OpenClaw or Hermes running 24/7 in the background. But here's what actually happens when you try it yourself: you're debugging setup instead of shipping the workflow you're the one keeping the server online, not the agent one update breaks something and now you're troubleshooting, not automating But I have some good news: you don't actually need to run any of it yourself. now hosts both OpenClaw and Hermes, fully managed, no docker, no server, no install. Claude and Codex hosting are coming next. Subscribe to one agent and the other's Max plan comes free for 30 days. Go annual on Pro or above and $100 to $200 in AI credits land straight on your usage. The agent was never the hard part. Keeping it alive was.

Muhammad Ayan

27,411 просмотров • 1 месяц назад

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!

Atomic Agent

111,357 просмотров • 1 месяц назад

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

Sudo su

27,339 просмотров • 3 месяцев назад

Meet Hermes3D A live 3D command center for Hermes Agents Some of you might remember Claw3D It went viral, but it was built around OpenClaw. After moving all my workflows to Hermes, continuing to build it no longer made sense So I rebuilt the entire idea for Hermes Hermes Bot Mode already gives every agent its own role, model, memory, skills, tools, and personality. Hermes3D gives that team an office See which agents are working See who is blocked Inspect their tasks Open the Kanban board Walk up and talk to them And when agents communicate, delegate work, or hand tasks to each other, you will be able to watch it happen inside the office in real time Your AI team should not feel like ten invisible processes hiding inside terminal tabs. It should feel like a workplace you can actually see and control. Claw3D was the prototype Hermes3D is the real vision And of course, it is Open Source Demo below. Repo link in the first comment

Meet Hermes3D A live 3D command center for Hermes Agents Some of you might remember Claw3D It went viral, but it was built around OpenClaw. After moving all my workflows to Hermes, continuing to build it no longer made sense So I rebuilt the entire idea for Hermes Hermes Bot Mode already gives every agent its own role, model, memory, skills, tools, and personality. Hermes3D gives that team an office See which agents are working See who is blocked Inspect their tasks Open the Kanban board Walk up and talk to them And when agents communicate, delegate work, or hand tasks to each other, you will be able to watch it happen inside the office in real time Your AI team should not feel like ten invisible processes hiding inside terminal tabs. It should feel like a workplace you can actually see and control. Claw3D was the prototype Hermes3D is the real vision And of course, it is Open Source Demo below. Repo link in the first comment

Luke The Dev

54,600 просмотров • 15 дней назад

Qwen 3.8 27B on Atomic Agent was 2x faster than on Hermes and Prime agents! Outputs: Atomic: 5,535 lines, 19 modules, 2h35min Hermes: 10,424 lines, 109 modules, 4h15min Prime: 12,226 lines, 129 modules, 4h42min We gave three agents the same tasks, rebuild these games: ✦ Hole Io ✦ Tower Bloxx ✦ Subway Surfers Results: ✦ Atomic Agent gave us a real city going down the hole, skyscrapers tipping in, cars on the crossings, shops eaten one by one. In the tower every block swings in on its cable and lands where you drop it. Its runner is the cleanest of the three, though the world behind the boy stands still while he runs. ✦ Hermes dropped its hole onto a bare brown field with a few crates, and the hole grew while nothing ever fell in. Its tower buildings hang in the air with no cable at all, the logs reporting 12/12 tests passing on a game it had just broken. Its runner is an empty corridor with almost nothing in it. ✦ Prime built the prettiest town of the three, and five seconds in the whole thing tore off the ground into a tornado spinning around the hole, cars and houses circling forever and never falling in. Its tower lags on every drop, colours flickering on the buildings, and the lag is what brings the whole thing down by the fifth floor. In its runner the coins move at exactly the boy's speed, so the gap never closes and you can never pick one up. Run Qwen 3.8 27B in Atomic Agent!

Qwen 3.8 27B on Atomic Agent was 2x faster than on Hermes and Prime agents! Outputs: Atomic: 5,535 lines, 19 modules, 2h35min Hermes: 10,424 lines, 109 modules, 4h15min Prime: 12,226 lines, 129 modules, 4h42min We gave three agents the same tasks, rebuild these games: ✦ Hole Io ✦ Tower Bloxx ✦ Subway Surfers Results: ✦ Atomic Agent gave us a real city going down the hole, skyscrapers tipping in, cars on the crossings, shops eaten one by one. In the tower every block swings in on its cable and lands where you drop it. Its runner is the cleanest of the three, though the world behind the boy stands still while he runs. ✦ Hermes dropped its hole onto a bare brown field with a few crates, and the hole grew while nothing ever fell in. Its tower buildings hang in the air with no cable at all, the logs reporting 12/12 tests passing on a game it had just broken. Its runner is an empty corridor with almost nothing in it. ✦ Prime built the prettiest town of the three, and five seconds in the whole thing tore off the ground into a tornado spinning around the hole, cars and houses circling forever and never falling in. Its tower lags on every drop, colours flickering on the buildings, and the lag is what brings the whole thing down by the fifth floor. In its runner the coins move at exactly the boy's speed, so the gap never closes and you can never pick one up. Run Qwen 3.8 27B in Atomic Agent!

Atomic Agent

93,468 просмотров • 20 дней назад

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.

Atomic Agent

19,638 просмотров • 2 дней назад

ex-Apple engineer gave Grok 4.6 - 30 minutes to redesign a live app, then showed the GrokBot run that merged its own PRs: "GrokBot runs 20-30 agents in parallel while Hermes and OpenClaw run one. Same weights, 20x the throughput - and the agents keep improving from their own runs." Grok 4.6 runs 5x cheaper on input and 8x on output - $2 and $6 per million. his verdict: this is an easy Opus replacement worth more than any $500 course on agentic engineering watch today, then read the article below on building a self-running agent stack

ex-Apple engineer gave Grok 4.6 - 30 minutes to redesign a live app, then showed the GrokBot run that merged its own PRs: "GrokBot runs 20-30 agents in parallel while Hermes and OpenClaw run one. Same weights, 20x the throughput - and the agents keep improving from their own runs." Grok 4.6 runs 5x cheaper on input and 8x on output - $2 and $6 per million. his verdict: this is an easy Opus replacement worth more than any $500 course on agentic engineering watch today, then read the article below on building a self-running agent stack

Carnage

82,071 просмотров • 10 дней назад

HERMES AGENT IS PULLING AHEAD OF OPENCLAW. 8 FEATURES THAT WILL MAKE YOU SWITCH. 1. HERMES GETS SMARTER EVERY RUN. Hermes updates its own skills after every completed task. what worked gets saved. what failed gets refined. the Curator runs in the background every 7 days. prunes unused skills. consolidates duplicates. archives stale procedures to .archive/ (recoverable). your skill library stays clean without manual work. agents with 20+ self-created skills finish similar tasks ~40% faster. 2. CHECKPOINTS BEFORE EVERY FILE CHANGE. before Hermes touches your files, it snapshots the working directory with a shadow git store. if anything breaks: /rollback # restore last checkpoint /rollback 3 # go back 3 checkpoints config: → max 20 snapshots per project → max 10MB per file, 500MB total store → auto-prune after 7 days → opt-in: set checkpoints.enabled: true you can also restore a single file without affecting the rest of the directory. 3. STABLE RELEASES. Hermes ships fewer updates. the ones it ships go through 500+ PRs per release with community testing before merge. v0.16.0 had 874 commits and 542 merged PRs. the update system runs syntax validation after every pull. if anything breaks, it auto-rolls back to the last working state. 4. 27+ MESSAGING PLATFORMS. Telegram, Discord, Slack, WhatsApp, Signal, iMessage, SMS, Email, Teams, Matrix, and 17 more. one gateway process covers all of them. 5. PROFILE ISOLATION. separate agents with their own model, memory, skills, cron jobs, and SOUL.md. run a researcher on GPT-5.5 and a coder on Fable 5 simultaneously on the same machine. 6. KANBAN + DISPATCHER. task orchestration with 60-second dispatch cycle, zombie detection, heartbeat tracking, retry budgets. assign tasks to agents from a visual board. 7. /GOAL WITH JUDGE MODEL. persistent objectives across turns. a judge evaluates after each turn: done or continue. runs for hours autonomously. 8. BUILT-IN MIGRATION FROM OPENCLAW. hermes claw migrate one command moves your OpenClaw setup to Hermes. supports --dry-run to preview before changing anything. also migrates legacy Clawdbot and Moldbot setups. OpenClaw still has the bigger ecosystem and more community integrations. both tools are open source. both are actively developed. this is about which architecture compounds better over months of use. full Hermes SOUL . MD guide 👇

YanXbt

19,573 просмотров • 2 месяцев назад

Here's my conversation with Peter Steinberger (Peter Steinberger 🦞), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:30 - Introduction 5:36 - OpenClaw origin story 8:55 - Mind-blowing moment 18:22 - Why OpenClaw went viral 22:19 - Self-modifying AI agent 27:04 - Name-change drama 44:15 - Moltbook saga 52:34 - OpenClaw security concerns 1:01:14 - How to code with AI agents 1:32:09 - Programming setup 1:38:52 - GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 - Best AI agent for programming 2:09:59 - Life story and career advice 2:13:56 - Money and happiness 2:17:49 - Acquisition offers from OpenAI and Meta 2:34:58 - How OpenClaw works 2:46:17 - AI slop 2:52:20 - AI agents will replace 80% of apps 3:00:57 - Will AI replace programmers? 3:12:57 - Future of OpenClaw community

Here's my conversation with Peter Steinberger (Peter Steinberger 🦞), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:30 - Introduction 5:36 - OpenClaw origin story 8:55 - Mind-blowing moment 18:22 - Why OpenClaw went viral 22:19 - Self-modifying AI agent 27:04 - Name-change drama 44:15 - Moltbook saga 52:34 - OpenClaw security concerns 1:01:14 - How to code with AI agents 1:32:09 - Programming setup 1:38:52 - GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 - Best AI agent for programming 2:09:59 - Life story and career advice 2:13:56 - Money and happiness 2:17:49 - Acquisition offers from OpenAI and Meta 2:34:58 - How OpenClaw works 2:46:17 - AI slop 2:52:20 - AI agents will replace 80% of apps 3:00:57 - Will AI replace programmers? 3:12:57 - Future of OpenClaw community

Lex Fridman

2,059,871 просмотров • 6 месяцев назад

How I built an AI Agent team that runs 24/7 explained in 19 minutes. OpenClaw is where the squad started. Hermes is where it's evolving. YES, I use both of THEM just via Telegram. 4 things that makes my agent team work like humans: - Automated Cron: Agents run pro-actively on schedules and heartbeats - Memory: They keep context about my preferences, audience, past performance, and decisions. - Self-improving review loops: Each agent reviews its own work monthly. Monica reviews the entire squad bi-weekly and grades them. - Escalation: They do the work, but I still make the final decision. These agents manage the open-source Awesome LLM Apps repo with 115k+ GitHub stars and help me stay up-to-date with pro-active daily signals. I operate like a CEO reviewing a squad from my phone. The agents keep the work moving until I have to step in.

How I built an AI Agent team that runs 24/7 explained in 19 minutes. OpenClaw is where the squad started. Hermes is where it's evolving. YES, I use both of THEM just via Telegram. 4 things that makes my agent team work like humans: - Automated Cron: Agents run pro-actively on schedules and heartbeats - Memory: They keep context about my preferences, audience, past performance, and decisions. - Self-improving review loops: Each agent reviews its own work monthly. Monica reviews the entire squad bi-weekly and grades them. - Escalation: They do the work, but I still make the final decision. These agents manage the open-source Awesome LLM Apps repo with 115k+ GitHub stars and help me stay up-to-date with pro-active daily signals. I operate like a CEO reviewing a squad from my phone. The agents keep the work moving until I have to step in.

Shubham Saboo

137,514 просмотров • 2 месяцев назад

OpenClaw is the best AI agent I've ever used. I run it every single day. But if you asked me to run it on my laptop full-time, I'd say you're making a mistake. OpenClaw is brilliant for accessing your files, reading your apps, and running local AI tasks. But running it on your own machine creates real problems nobody talks about: • Who's keeping it secure when it's exposed to the internet? • What happens when your laptop sleeps or shuts down? • Where are the access controls? • Who's blocking bad bots from hitting your instance? • What if you don't want to spend $800 on a Mac Mini just to run it? That's the main issue with local AI agents: your laptop was never meant to be a server. As far as I know, DigitalOcean is the only platform making OpenClaw cloud deployment this simple. And the security setup is actually impressive: • secret token authentication • bad bot blocking • limited user permissions • Docker containerization All of this configured automatically when you spin up a Droplet. I really don't see any other way forward for people who want OpenClaw running 24/7 without the hardware headaches. By the way, DigitalOcean runs everything in full isolation. Only you can message your OpenClaw instance. Setup takes about 5 minutes. No Mac Mini required. Here's the link to get started: DigitalOcean collaborated with me on this post.

OpenClaw is the best AI agent I've ever used. I run it every single day. But if you asked me to run it on my laptop full-time, I'd say you're making a mistake. OpenClaw is brilliant for accessing your files, reading your apps, and running local AI tasks. But running it on your own machine creates real problems nobody talks about: • Who's keeping it secure when it's exposed to the internet? • What happens when your laptop sleeps or shuts down? • Where are the access controls? • Who's blocking bad bots from hitting your instance? • What if you don't want to spend $800 on a Mac Mini just to run it? That's the main issue with local AI agents: your laptop was never meant to be a server. As far as I know, DigitalOcean is the only platform making OpenClaw cloud deployment this simple. And the security setup is actually impressive: • secret token authentication • bad bot blocking • limited user permissions • Docker containerization All of this configured automatically when you spin up a Droplet. I really don't see any other way forward for people who want OpenClaw running 24/7 without the hardware headaches. By the way, DigitalOcean runs everything in full isolation. Only you can message your OpenClaw instance. Setup takes about 5 minutes. No Mac Mini required. Here's the link to get started: DigitalOcean collaborated with me on this post.

Hasan Toor

213,678 просмотров • 4 месяцев назад

$8,189.29 worth of compute for $50. I just pushed 648.4M tokens through OpenClaw using the Minimax coding plan. If you’re still paying "retail" for tokens, you’re basically just subsidizing Big Tech’s electricity bill. Here’s where it gets weird. I’ve deployed a new agent named Hermes. His only job is to hunt Reddit for high-intent leads and reply to them autonomously. Full transparency: I’m still debugging him. He’s live right now. The lead-gen is insane, but he’s very much a work in progress. In the video, I show the actual Reddit accounts he’s running and the exact setup I use to keep costs at zero. This gonna to totally experiment. The math is broken, and the bots are live.

$8,189.29 worth of compute for $50. I just pushed 648.4M tokens through OpenClaw using the Minimax coding plan. If you’re still paying "retail" for tokens, you’re basically just subsidizing Big Tech’s electricity bill. Here’s where it gets weird. I’ve deployed a new agent named Hermes. His only job is to hunt Reddit for high-intent leads and reply to them autonomously. Full transparency: I’m still debugging him. He’s live right now. The lead-gen is insane, but he’s very much a work in progress. In the video, I show the actual Reddit accounts he’s running and the exact setup I use to keep costs at zero. This gonna to totally experiment. The math is broken, and the bots are live.

Ziwen

17,614 просмотров • 6 месяцев назад

If you do not set up your OpenClaw correctly, it's going to SUCK It will do tasks poorly, not remember anything, and not be proactive Here are the 5 things you need to do right away to turn your OpenClaw into AGI (demos of each in the video below) 1. Brain dump: your OpenClaw won't know how to accomplish your goals for you, if it doesn't know your goals If you haven't yet, you want to tell your OpenClaw: • Your interests • Your career • Your goals • Your ambitions • Anything personal Do this, and your Claw will have the context to be SUPER powerful for you 2. Connect your tools Your OpenClaw can basically use almost every tool you use on your computer You just need to ask it to Ask your OpenClaw to connect to any tools you use daily, and it will just figure out how to do it. Then create a skill for it I have it check my Things 3 todo list every morning and complete any tasks it can 3. Build a Mission Control Your mission control is just a hub for your OpenClaw to build custom tooling Ask your OpenClaw to build a Mission Control using NextJS Then anytime your bot doesn't have the tool available to do a task, have it build it in your Mission Control 4. Mission Statement Your OpenClaw needs a mission statement This is the one sentence statement that will be the north star for every single thing it does It should be based on your goals and ambitions. My Claw's mission statement is "“An autonomous organization of AI agents that does work for me and produces value 24/7” Make your own and have your Claw put it at the top of your Mission Control. Now every task your Claw does will take you closer to this statement 5. Make it proactive Your OpenClaw won't be proactive unless you set those expectations with it Tell your Claw you want it to do a task every night at 2am that brings you one step closer to your mission statement Now every morning you'll wake up and it will do something that surprises you and helps bring you closer to your goals Do these 5 things and your OpenClaw will be 10000x more powerful

If you do not set up your OpenClaw correctly, it's going to SUCK It will do tasks poorly, not remember anything, and not be proactive Here are the 5 things you need to do right away to turn your OpenClaw into AGI (demos of each in the video below) 1. Brain dump: your OpenClaw won't know how to accomplish your goals for you, if it doesn't know your goals If you haven't yet, you want to tell your OpenClaw: • Your interests • Your career • Your goals • Your ambitions • Anything personal Do this, and your Claw will have the context to be SUPER powerful for you 2. Connect your tools Your OpenClaw can basically use almost every tool you use on your computer You just need to ask it to Ask your OpenClaw to connect to any tools you use daily, and it will just figure out how to do it. Then create a skill for it I have it check my Things 3 todo list every morning and complete any tasks it can 3. Build a Mission Control Your mission control is just a hub for your OpenClaw to build custom tooling Ask your OpenClaw to build a Mission Control using NextJS Then anytime your bot doesn't have the tool available to do a task, have it build it in your Mission Control 4. Mission Statement Your OpenClaw needs a mission statement This is the one sentence statement that will be the north star for every single thing it does It should be based on your goals and ambitions. My Claw's mission statement is "“An autonomous organization of AI agents that does work for me and produces value 24/7” Make your own and have your Claw put it at the top of your Mission Control. Now every task your Claw does will take you closer to this statement 5. Make it proactive Your OpenClaw won't be proactive unless you set those expectations with it Tell your Claw you want it to do a task every night at 2am that brings you one step closer to your mission statement Now every morning you'll wake up and it will do something that surprises you and helps bring you closer to your goals Do these 5 things and your OpenClaw will be 10000x more powerful

Alex Finn

321,352 просмотров • 6 месяцев назад