Spaces:
Sleeping
HermesFace Reddit Marketing Playbook
HermesFace Reddit 营销推广方案
Core Value Proposition / 核心价值主张: Deploy a fully-featured, self-improving AI agent on HuggingFace Spaces — for free, forever. 16+ messaging channels, 47 built-in tools, persistent memory, skills that evolve through use.
在 HuggingFace Spaces 上免费部署一个功能完备、自我进化的 AI Agent——永久免费。16+ 消息渠道、47 个内置工具、持久化记忆、会随使用不断进化的技能。
Marketing Principles / 营销原则
Plan 1: r/selfhosted — The "Zero-Cost Always-On" Angle
方案一：r/selfhosted — "零成本永不宕机" 切入角度
Why this subreddit / 为什么选这个社区： r/selfhosted (1.5M+ members) obsesses over self-hosting solutions that minimize cost and maximize uptime. HermesFace deploys a full self-improving agent on HF Spaces' free tier — zero infra, zero hardware, and the agent actually gets better the longer it runs.
r/selfhosted（150 万+成员）痴迷于低成本、高可用的自托管方案。HermesFace 在 HF Spaces 免费层上部署一个完整的自我进化 agent——零基础设施、零硬件，而且 agent 运行越久越聪明。
Marketing Technique / 营销技巧： Problem-Agitation-Solution (PAS) — Surface a pain point the audience already feels, amplify it, then present the solution.
问题-激化-解决（PAS）框架——先揭示受众已有的痛点，放大它，再呈现解决方案。
Title / 标题
I stopped paying for a Mac Mini to run my AI assistant — now it runs free on HuggingFace, and it learns new skills every week
我不再为 Mac Mini 付钱跑 AI 助手了——现在它在 HuggingFace 上免费运行，而且每周都在学新技能
Body / 正文
Hey r/selfhosted,
I've been running Nous Research's Hermes Agent (self-improving AI assistant) on
a Mac Mini for months. Great agent. Awful hosting situation:
- Electricity bill for 24/7 uptime
- Had to babysit when my ISP flaked
- OS updates broke the agent every few weeks
- Travel = assistant offline
So I built HermesFace — a project that deploys Hermes Agent on HuggingFace
Spaces' free tier (2 vCPU, 16 GB RAM, 50 GB storage). Here's what you get for $0:
**What it does:**
- One-click deploy — duplicate the HF Space, set 2 secrets, done
- Hermes Agent in full: 47 built-in tools (terminal, files, web, vision, image
gen, browser automation), cron scheduler, and the thing Hermes is known for —
*skills that the agent writes and refines itself as you use it*
- 16+ messaging channels: Telegram, Discord, Slack, WhatsApp, Signal, WeChat,
iMessage, Threema, Line, and more
- LLM-powered persistent memory — full conversation recall, semantic search,
cross-session summarization
- Auto-persisted — everything (conversations, skills, memories, config, SOUL.md)
is snapshot-synced to your own private HF Dataset every 60 s, with 5 rotating
tar backups
**The annoying part I solved so you don't have to:**
HF Spaces is ephemeral — containers restart on idle, on update, on anything
really. Hermes Agent's whole value is that it *accumulates state* (memories,
skills it's written, tool use patterns). So I wrote a Python persistence daemon
(sync_hf.py) that atomically tar.gz's /opt/data to a private HF Dataset repo
every minute. On restart, snapshot_download restores everything — including the
skills the agent wrote last week. Zero data loss across 100+ restarts in testing.
HF Spaces also blocks DNS for Telegram and WhatsApp at the infrastructure level.
HermesFace ships a DNS-over-HTTPS resolver (Cloudflare + Google DoH) that
populates /etc/hosts + a Node.js dns.lookup preload, so Telegram bots and the
WhatsApp bridge just work.
**Stack:** Docker + Python (Hermes) + Node (playwright/whatsapp-bridge) + Python
sync daemon | cpu-basic free tier
Fully open-source (MIT). Would love feedback from this community — you folks
always find the edge cases I miss.
GitHub: [link]
Live demo: [link]
嘿 r/selfhosted，
我过去几个月在一台 Mac Mini 上跑 Nous Research 的 Hermes Agent（自我进化 AI 助手）。Agent 很好，托管环境很糟：24/7 的电费、ISP 抽风要人工复位、系统更新每隔几周就把 agent 打崩、一出差助手就下线。
所以我做了 HermesFace——在 HuggingFace Spaces 免费层（2 vCPU / 16 GB RAM / 50 GB）上部署 Hermes Agent 的项目。0 美元你能拿到：
功能亮点：
- 一键部署：复制 HF Space，设置 2 个密钥，完成
- 完整 Hermes Agent：47 个内置工具 + cron + 真正让 Hermes 出名的能力——agent 自己写、自己改的技能
- 16+ 消息渠道：Telegram、Discord、Slack、WhatsApp、Signal、WeChat、iMessage…
- LLM 驱动的持久化记忆：完整对话召回、语义搜索、跨会话总结
- 自动持久化：所有数据（对话、技能、记忆、配置、SOUL.md）每 60 秒快照同步到你自己的私有 HF Dataset，保留 5 份轮转备份
我替你解决了最头疼的部分： HF Spaces 是临时容器——空闲会重启、更新会重启、反正总会重启。Hermes Agent 的全部价值就是累积状态（记忆、自己写的技能、工具使用规律）。所以我写了 sync_hf.py，每分钟原子化打包 /opt/data 上传到私有 Dataset。重启时 snapshot_download 恢复一切——包括 agent 上周自己写的技能。测试 100+ 次重启零数据丢失。
HF Spaces 还在基础设施层封锁了 Telegram 和 WhatsApp 的 DNS。HermesFace 内置了 DoH（Cloudflare + Google）解析器，写入 /etc/hosts 加上 Node dns.lookup preload，让 Telegram bot 和 WhatsApp bridge 直接可用。
完全开源（MIT）。希望得到社区反馈。
Plan 2: r/LocalLLaMA — The "Self-Improving Agent" Angle
方案二：r/LocalLLaMA — "自我进化 Agent" 切入角度
Why this subreddit / 为什么选这个社区： r/LocalLLaMA (800K+ members) is the most technically sophisticated AI community on Reddit. They value engineering depth, novel problem-solving, and pushing capability boundaries. The "agent writes its own skills" + "persistent across ephemeral infra" combo will land here.
r/LocalLLaMA（80 万+成员）是 Reddit 上技术最硬的 AI 社区，欣赏工程深度、新颖问题解法、把能力推向极限。"agent 自己写技能" + "临时基础设施上保持状态" 的组合在这里会引起共鸣。
Marketing Technique / 营销技巧： Show-Your-Work Transparency — Engineers trust engineers who show their debugging process. Frame the post as a technical write-up with the project as a natural byproduct.
展示过程的透明度——工程师信任展示调试过程的工程师。将帖子包装为技术文章，项目只是自然产出。
Title / 标题
Self-improving agents + ephemeral infra = a persistence problem. Here's how I solved it for Hermes Agent on HuggingFace Spaces.
自我进化 agent + 临时基础设施 = 持久化问题。我是如何为运行在 HuggingFace Spaces 上的 Hermes Agent 解决这个问题的。
Body / 正文
TL;DR: Hermes Agent's value compounds over time — it writes skills, builds
memory, tunes its own workflow. HuggingFace Spaces is free but ephemeral. I
built a Python atomic-snapshot daemon that tar.gz's the full agent state to
a private HF Dataset every 60s and restores on boot. Self-improvement now
persists on free infra.
---
**The interesting problem**
Most "AI on free tier" projects treat persistence as an afterthought — sync a
config file, call it a day. That breaks for Hermes. Hermes Agent's entire
premise is that it *accumulates capability*:
- SOUL.md — personality + operating principles, edited live
- /opt/data/skills/ — Python skills the agent writes from experience
- /opt/data/memories/ — LLM-indexed long-term memory with semantic search
- /opt/data/sessions/ — conversation history (referenced by memory)
- /opt/data/plans/ — in-flight multi-step plans
- /opt/data/workspace/ — files the agent has built for itself
- /opt/data/home/ — home dir for tools the agent invokes
Lose any of this and the agent regresses to a pretrained model with no history.
On a server-restart-every-few-hours environment like HF Spaces, naive file sync
means partial state on every boot.
---
**The architecture**
1. **Atomic tar snapshots** — not file-level sync. On each tick, tar.gz the
entire /opt/data directory to a single blob in a private HF Dataset repo,
with 5 rotating backups. If the container dies mid-write, the next boot
reads the last good blob. No partial state.
2. **DoH DNS fallback** — HF Spaces blocks DNS for api.telegram.org and several
WhatsApp domains at the L3 layer. dns-resolve.py queries Cloudflare and
Google DoH, writes /etc/hosts (for Python) and /tmp/dns-resolved.json (for
Node via a dns.lookup preload). Invisible to every downstream dependency.
3. **Runtime patches via sync_hf.py** — Hermes's web dashboard was built for
localhost. On boot the daemon patches web_server.py to allow any origin,
relax X-Frame-Options, and widen the CSP frame-ancestors so HF Spaces can
embed the dashboard via iframe. No Hermes fork needed.
4. **Auto-derived dataset repo** — SPACE_ID env var is set by HF runtime.
If HERMES_DATASET_REPO is unset, the daemon derives it as
{username}/{SpaceName}-data and auto-creates it on first boot.
5. **Graceful SIGTERM** — the daemon installs signal handlers that run a
final upload before exit. HF sends SIGTERM ~5s before SIGKILL, enough
headroom to flush a 50 MB snapshot.
---
**Numbers**
- 100+ forced container restarts in testing, zero state loss
- Cold-boot restore time: ~12 s for a 200-file state directory
- Sync interval: 60s default, tunable to 30s on paid tier
- Backup rotation: 5 versions, ~200 MB typical working set
---
**Results**
My Hermes instance has been running for 3 weeks on free HF Spaces. In that
time it has authored 14 custom skills (scraping news, pulling my calendar,
formatting Obsidian notes), built a searchable memory of ~2,000 events, and
survived ~40 container restarts without any human intervention.
---
Open-sourced as HermesFace (MIT). The whole persistence daemon is ~500 lines
of Python and most of the interesting bits are the SIGTERM handshake and the
repo auto-derivation.
GitHub: [link]
I'm curious if anyone else has tried deploying stateful agents on ephemeral
infra. Would love to hear alternative approaches — especially anything that
avoids the tar-snapshot pattern.
TL;DR： Hermes Agent 的价值随时间复合——它写技能、建记忆、调自己的工作流。HuggingFace Spaces 免费但临时。我写了一个 Python 原子快照守护进程，每 60 秒把 agent 完整状态 tar.gz 到私有 HF Dataset，启动时恢复。自我进化现在能在免费基础设施上持久化。
有意思的问题： 大多数 "AI 上免费层" 项目把持久化当事后想法——同步一个配置文件就完事。这对 Hermes 行不通，因为 Hermes 的整个前提就是累积能力：SOUL.md、agent 自己写的技能、LLM 索引的长期记忆、会话历史、多步计划、工作空间。丢任何一部分 agent 就退化成没历史的预训练模型。
架构：
- 原子 tar 快照 + 5 份轮转备份（非文件级同步）
- DoH DNS 回退（解决 HF 的 Telegram / WhatsApp 封锁）
- sync_hf.py 运行时补丁 Hermes web dashboard（CORS / CSP / X-Frame-Options）让 HF iframe 嵌入可用
- 从 SPACE_ID 自动推导 Dataset 仓库名，首次启动自动创建
- SIGTERM 优雅关闭触发最终上传
数据： 100+ 次强制重启零状态丢失，200 文件的冷启动恢复 ~12 秒，60 秒同步间隔，5 份轮转备份。
结果： 我的 Hermes 实例在 HF Spaces 上跑了 3 周，自己写了 14 个技能，建了 ~2000 事件的记忆，扛过 ~40 次重启零人工介入。
Plan 3: r/ChatGPT — The "Everyday User" Angle
方案三：r/ChatGPT — "普通用户" 切入角度
Why this subreddit / 为什么选这个社区： r/ChatGPT (9M+ members) is the largest AI subreddit. Users here are less technical but highly engaged with AI tools. The hook: "an AI in your Telegram that remembers everything and gets smarter every week."
r/ChatGPT（900 万+成员）是最大的 AI 子版块。用户技术背景浅但对 AI 工具高度活跃。钩子："住在你 Telegram 里的 AI，什么都记得，每周都更聪明。"
Marketing Technique / 营销技巧： Before/After Transformation — Show the contrast between the old painful way and the new effortless way. Use simple language and focus on outcomes, not implementation.
前后对比转化——展示旧的痛苦方式和新的轻松方式之间的对比。使用简单语言，聚焦结果而非实现。
Title / 标题
I built a free AI assistant that lives in Telegram & Discord, remembers all my conversations, and actually learns new skills the more I use it — no coding required
我做了一个免费 AI 助手，住在 Telegram 和 Discord 里，记得所有对话，用得越多学到的技能越多——不需要编程
Body / 正文
Imagine texting an AI in Telegram — just like messaging a friend — and it:
- Remembers your conversations from last month
- Knows you prefer coffee over tea, that you're allergic to shellfish, that
your sister's birthday is in June
- Learned last week how to fetch your calendar, because you asked once
- Can run scripts, browse the web, generate images, all from a single chat
That's HermesFace. Free. Set it up in 5 minutes.
**Before HermesFace:**
❌ ChatGPT Plus forgets everything between sessions
❌ $20/month and still locked to one model
❌ Can't use it in WhatsApp / Telegram natively
❌ Want skills? Write code yourself.
**After HermesFace:**
✅ Free forever (runs on HuggingFace's free cloud)
✅ Chat with your AI directly in Telegram, Discord, Slack, WhatsApp, +12 more
✅ Pick any model: GPT-4, Claude, Gemini, DeepSeek, 200+ via OpenRouter (free tier)
✅ Full conversation memory with semantic search
✅ The agent writes its own skills — you just *ask* for something and next time
it has a tool for it
**How it works (simple version):**
1. Go to the HermesFace page on HuggingFace
2. Click "Duplicate this Space"
3. Add your HuggingFace token + one AI API key (OpenRouter has a free tier)
4. Wait ~5 minutes for the Docker image to build
5. Connect Telegram: paste your bot token in the dashboard
6. Done. Your AI lives in Telegram now.
Your data stays private — it's backed up to YOUR private repository, not shared
with anyone.
What makes Hermes different from ChatGPT is that it's an **agent**, not a chat
window. It can run terminal commands on its own sandbox, browse the web, take
screenshots, generate images, schedule recurring tasks. And because the memory
persists, it builds a model of you over time. My Hermes knows I'm writing a
novel about space pirates and asks how the draft is going. Unprompted.
GitHub: [link]
HuggingFace Space: [link]
Happy to help anyone get set up — drop a comment if you get stuck!
想象一下在 Telegram 里给 AI 发消息——就像给朋友发消息一样——而且它：记得你上个月的对话 / 知道你爱喝咖啡、对海鲜过敏、妹妹 6 月生日 / 上周学会了查你的日历（因为你问过一次）/ 能跑脚本、浏览网页、生成图片，全在一个聊天里。
这就是 HermesFace。免费。5 分钟搭好。
使用前： ChatGPT Plus 每次对话都失忆 / 每月 20 刀还锁在一个模型 / 不能在 WhatsApp / Telegram 原生使用 / 想要技能？自己写代码
使用后： 永久免费 / 在 Telegram、Discord、Slack、WhatsApp 等 15 个渠道直接用 / 任选模型 / 完整对话记忆 + 语义搜索 / Agent 自己写技能——你说一次下次就有工具了
Hermes 和 ChatGPT 的本质区别：它是agent，不是聊天窗口。能在自己的沙箱里跑命令、浏览网页、截图、生成图片、排定期任务。而且记忆持久化，它会随时间建立对你的模型。我的 Hermes 知道我在写太空海盗小说，会主动问草稿进展。
Plan 4: r/LLMDevs — The "Architecture Showcase" Angle
方案四：r/LLMDevs — "架构展示" 切入角度
Why this subreddit / 为什么选这个社区： r/LLMDevs is a developer-focused community that appreciates clean architecture, novel deployment patterns, and production-grade engineering. The persistence daemon + runtime-patch approach is genuinely novel.
r/LLMDevs 是开发者社区，欣赏清晰架构、新颖部署模式、生产级工程。持久化守护进程 + 运行时补丁的方案是真正新颖的。
Marketing Technique / 营销技巧： Educational Content Marketing — Teach something genuinely useful (deploying stateful, self-improving agents on ephemeral infrastructure) with your project as the case study.
教育性内容营销——教一些真正有用的东西（在临时基础设施上部署有状态、自我进化的 agent），以项目作为案例。
Title / 标题
5 patterns for running self-improving agents on ephemeral infrastructure (HuggingFace Spaces edition)
在临时基础设施（HuggingFace Spaces）上运行自我进化 agent 的 5 个模式
Body / 正文
I spent the last couple of months building a deployment that runs Hermes Agent
on HF Spaces' free tier. Challenge: HF Spaces restarts containers frequently,
blocks DNS for messaging APIs, and generally treats your app as stateless —
while Hermes Agent's whole value is that it *accumulates state*.
Here are patterns that generalize to any stateful-agent-on-ephemeral-infra deploy:
---
**Pattern 1: Atomic State Snapshots over File-Level Sync**
Don't sync individual files — race conditions when the container dies
mid-write. tar.gz the entire state directory atomically and push to object
storage (HF Dataset repo in my case) as a single blob. N rotating backups.
On restore, single atomic unpack — all or nothing. No corrupted partial state.
**Pattern 2: DNS-over-HTTPS as Infrastructure Escape Hatch**
When your host blocks DNS at the infra layer, /etc/hosts and custom resolvers
don't help. Bypass system DNS entirely via DoH (Cloudflare/Google). Write to
/etc/hosts for Python processes, and a dns.lookup monkey-patch for Node. Works
invisibly for every downstream dependency.
**Pattern 3: Runtime Patching over Forking**
Hermes's web dashboard was written for localhost (strict CORS, X-Frame-Options
DENY, narrow CSP). Instead of forking Hermes, my sync daemon patches the
relevant Python files at boot — widen CORS, relax X-Frame-Options, loosen CSP
frame-ancestors — idempotent string replaces that noop if the upstream file
changes. Keeps HermesFace upstream-compatible.
**Pattern 4: Environment-Derived Configuration**
HF runtime sets SPACE_ID. If HERMES_DATASET_REPO isn't explicitly set, derive
it as {username}/{SpaceName}-data and auto-create the dataset on first boot.
Deploy flow becomes: duplicate, set 2 secrets, done. Zero configuration friction.
**Pattern 5: Graceful Shutdown for Atomic Commit**
HF sends SIGTERM ~5 s before SIGKILL. The persistence daemon installs signal
handlers that trigger a final sync + process cleanup before exit. Combined with
Pattern 1, this closes the consistency window: either the last commit made it
to object storage, or the last periodic one did. You never boot to corrupted
state.
---
Implemented as open-source HermesFace (MIT). The daemon itself is ~500 lines of
Python handling edge cases like mid-upload SIGTERM, dataset auto-create with
correct privacy settings, and the CORS / CSP / X-Frame-Options patch set.
GitHub: [link]
Question for the room: anyone running agents that write their own code on
ephemeral infra? How are you handling the skill-lineage problem — if the agent
writes a skill today and the container restarts tomorrow, how do you make sure
"skill written yesterday" survives? Tar snapshots work but feel crude.
过去几个月我搭了个在 HF Spaces 免费层跑 Hermes Agent 的部署方案。挑战：HF Spaces 频繁重启容器，封锁消息 API 的 DNS，总体上把你的应用当无状态对待——而 Hermes Agent 全部价值就在于累积状态。
以下模式适用于任何「临时基础设施上的有状态 agent」部署：
模式 1：原子状态快照优于文件级同步 — tar.gz 整个状态目录，N 份轮转，原子解包
模式 2：DoH 作为基础设施逃生通道 — 完全绕过系统 DNS，写 /etc/hosts + Node dns.lookup 猴补
模式 3：运行时补丁优于 fork — sync daemon 启动时幂等补丁 Hermes 源码（CORS/CSP/X-Frame-Options），保持上游兼容
模式 4：环境推导配置 — 从 SPACE_ID 推导 Dataset 仓库名，首次启动自动创建
模式 5：优雅关闭触发原子提交 — SIGTERM 处理器触发最终同步，配合模式 1 关闭一致性窗口
开源为 HermesFace (MIT)。
Plan 5: r/artificial — The "Democratizing AI" Angle
方案五：r/artificial — "AI 普惠化" 切入角度
Why this subreddit / 为什么选这个社区： r/artificial (500K+ members) discusses broader AI trends, ethics, and accessibility. The narrative of bringing a real agent (not a chat window) to non-technical users through their existing messaging apps lands here.
r/artificial（50 万+成员）讨论更广泛的 AI 趋势、伦理和可及性。通过现有消息应用把真正的 agent（不是聊天窗口）带给非技术用户的叙事会引起共鸣。
Marketing Technique / 营销技巧： Narrative Storytelling with Social Mission — Frame the project as part of a larger movement to democratize AI agent access, not just a tool launch.
带有社会使命的叙事——将项目定位为 AI agent 普惠化运动的一部分，而非单纯的工具发布。
Title / 标题
The real AI divide isn't access to chat — it's access to agents. Here's a free way to put a full self-improving AI agent in WhatsApp.
AI 真正的鸿沟不是聊天入口——而是 agent。这里有一个免费的方法，把完整的自我进化 AI agent 装进 WhatsApp。
Body / 正文
Everyone talks about ChatGPT. But chat is the shallow end.
The interesting wave is **agents** — AI that can run tools, take actions over
days, remember you across sessions, and write its own skills as it goes.
Hermes Agent from Nous Research is one of the best open-source versions.
Problem: running an agent means running a server. Most people don't have one.
A VPS is $15/month. A Mac Mini at home needs babysitting. This puts real
agents out of reach for the people who'd benefit from them the most —
non-technical users in messaging-first regions.
I built HermesFace to close that gap:
- Deploys Hermes Agent on HuggingFace Spaces' free tier
- Hooks it into WhatsApp, Telegram, Signal, WeChat, iMessage, +10 more
- Persists conversation + memory + the skills the agent writes over time
- Runs $0 forever
**Why this matters beyond convenience:**
- **Global South access:** In regions where WhatsApp is the internet, this
puts a full AI agent — not just a chatbot — in the hands of anyone with
a phone.
- **Agent literacy bridge:** Instead of learning a new AI app, people interact
with their agent the same way they text a friend. And because the memory
compounds, the agent gets to know them.
- **Model freedom:** Not locked into any provider. OpenRouter free tier,
Claude, Gemini, GPT, or a local Ollama — your choice.
- **Ownership:** Conversation and memory live in YOUR private HF Dataset
repository. Not on a vendor's servers.
**What's Hermes doing for me right now:**
It manages my calendar (skill it wrote itself after I asked once), drafts
Obsidian notes from voice memos, summarises my morning news via a cron job,
and remembers that I'm bad at remembering birthdays so it pings me 3 days
before each one. All through Telegram. All free. All persistent across the
~15 container restarts a week.
This isn't going to replace GPT-5 for power users. But it might bring real
*agents* to the next billion people who would never install a dedicated AI app.
Open source. Free forever. No signup.
GitHub: [link]
What do you think? Is the agent-in-messaging-app approach the right way to
bridge the agent access gap?
每个人都在聊 ChatGPT。但聊天只是浅层。真正有趣的一波是 agent——能运行工具、跨越数天采取行动、跨会话记住你、随用途自己写技能的 AI。Nous Research 的 Hermes Agent 是最好的开源版本之一。
问题：跑 agent 意味着跑服务器。大多数人没有。VPS 每月 15 刀。家里 Mac Mini 要人照看。这把真正的 agent 挡在了最需要它的人——消息优先地区的非技术用户——门外。
我做 HermesFace 就是为了填这个鸿沟：在 HF Spaces 免费层部署 Hermes Agent，接入 WhatsApp、Telegram、Signal、WeChat、iMessage 等 13+ 渠道，持久化对话 + 记忆 + agent 随时间自己写的技能，永久 0 美元。
为什么这很重要： 全球南方 / Agent 素养桥梁（零学习曲线）/ 模型自由 / 所有权（数据在你自己的私有 HF Dataset）
Hermes 现在在替我做什么： 管日历（它自己写的技能）、从语音备忘录起草 Obsidian 笔记、通过 cron 总结晨间新闻、记得我容易忘生日所以提前 3 天提醒。全在 Telegram。全免费。扛得住每周 15 次左右的容器重启。
Plan 6: r/OpenAI — The "Power User Alternative" Angle
方案六：r/OpenAI — "高级用户替代方案" 切入角度
Why this subreddit / 为什么选这个社区： r/OpenAI (2M+ members) is full of ChatGPT power users frustrated with GPTs limitations, lack of cross-platform access, no real memory, and subscription costs. Position HermesFace as "GPTs, but actually agentic."
r/OpenAI（200 万+成员）充满了对 GPTs 限制、缺乏跨平台、没有真记忆、订阅费不爽的高级用户。把 HermesFace 定位为"GPTs，但真正 agentic"。
Marketing Technique / 营销技巧： Comparison-Based Positioning — Don't attack the competition; use it as a familiar reference point to highlight unique advantages.
对比定位法——不攻击竞品，将其作为熟悉的参照点来突出独特优势。
Title / 标题
I pay $0/month for an AI agent that has persistent memory, writes its own tools, and works in Telegram/Discord/WhatsApp
我每月为一个 AI agent 付 0 美元——它有持久化记忆、自己写工具、在 Telegram/Discord/WhatsApp 里都能用
Body / 正文
I know the title sounds like clickbait. Hear me out.
I was paying for ChatGPT Plus ($20), Claude Pro ($20), and Gemini Advanced
($20) just to use different models for different tasks. $60/month. And none
of them remembered anything between sessions in a way I could actually rely on.
So I built around an open-source agent instead: Hermes Agent from Nous Research.
The HermesFace project deploys it on HuggingFace Spaces' free tier.
**HermesFace vs. ChatGPT Plus:**
| Feature | ChatGPT Plus | HermesFace |
|----------------------------|-------------------|----------------------------------------------|
| Cost | $20/month | $0 |
| Models | GPT-4/5 only | GPT, Claude, Gemini, Nous, 200+ via OpenRouter |
| Persistent memory | Limited, opaque | Full, LLM-indexed, searchable, yours |
| Tool use | Fixed set | 47 built-in + agent writes its own |
| WhatsApp / Telegram | ❌ | ✅ Built-in (+ 14 more channels) |
| Cron / scheduled tasks | ❌ | ✅ Built-in |
| Self-improvement | ❌ | ✅ Agent writes + refines its own skills |
| Data ownership | OpenAI's servers | Your private HF Dataset repo |
| Open source | ❌ | ✅ MIT |
**The catch?** You bring your own API key. But OpenRouter's free tier gets you
several capable models at $0, and even with paid keys, per-token pricing usually
ends up at $2-5/month for typical use — cheaper than any subscription.
**What makes this feel different from GPTs:**
1. **Memory that compounds.** Not "remembered this fact" — full semantic search
across 3 months of conversations, summaries, and the agent's own notes.
2. **Skills that compound.** Ask me how to pull my Gmail once, and next time
there's a `fetch_gmail` skill. Ask me to draft weekly retros, and next time
there's a retro template living in workspace/.
3. **Actions that compound.** Cron jobs, scheduled tasks, running tools in
sequence — Hermes does real agent work, not chat.
Open-sourced as HermesFace (MIT). Takes ~5 minutes to deploy.
GitHub: [link]
HuggingFace Space: [link]
Happy to answer questions. If you're tired of paying $60/mo for three chat
windows that forget everything, this might be for you.
我之前同时付 ChatGPT Plus / Claude Pro / Gemini Advanced（各 20 刀/月），就为了不同任务用不同模型。60 美元/月。而且没有一个能真正可靠地跨会话记东西。
所以我改为围绕开源 agent 搭建：Nous Research 的 Hermes Agent。HermesFace 项目把它部署到 HF Spaces 免费层。
HermesFace vs. ChatGPT Plus： 免费 vs 20 美元 / 任意模型 vs 只有 GPT / 完整可搜索记忆 vs 有限不透明记忆 / 47 工具 + agent 自己写 vs 固定集合 / WhatsApp/Telegram 内置 vs 无 / cron 内置 vs 无 / 自我进化 vs 无 / 你的 Dataset vs OpenAI 服务器 / MIT 开源 vs 闭源
能这样的原因： 你自带 API key。OpenRouter 免费层能用几个可靠的模型，付费用的话每 token 算下来通常 2-5 美元/月，比任何订阅都便宜。
和 GPTs 的本质区别：
- 记忆会复合——不是"记住了这个事实"，而是三个月对话的完整语义搜索
- 技能会复合——问一次怎么拉 Gmail，下次就有 fetch_gmail 技能了
- 行动会复合——cron、排定任务、工具链——Hermes 做真正的 agent 工作，不是聊天
Execution Checklist / 执行清单
- Replace all
[link]placeholders with real URLs before posting - Space posts across subreddits by ≥24 hours — avoid cross-posting detection
- Actively reply to first 3 comments within 2 hours (Reddit ranking signal)
- On criticism, acknowledge + share next-step plan — never defensive
- If a subreddit lands well, softly mention HermesFace in that sub's weekly/monthly thread
- Log feedback per subreddit and iterate copy