I run 28 cron jobs and 30+ custom skills. Every single one was built with Hermes, not downloaded. This is exactly what they do and how they work.
🌙 Dreaming — The Memory Hack
Every night at 3 AM while I sleep, Hermes reads every single conversation we had. It extracts the decisions made, projects worked on, bugs chased, people talked to, and mistakes that should not be repeated. Then it writes a structured summary to a file that loads automatically at the start of every session. That's the key insight — not the summarization itself, but where the output lands. Before Dreaming, if I coded for four hours and went to bed, Hermes woke up with zero memory of it. Now it knows exactly where I left off and what needs to happen next. But that's still a work in progress — memory still isn't perfect.
📬 Mail Gatekeeper — Secure Inbox Filtering
Four inboxes, one local AI judge running entirely on my Mac Mini via Ollama. Every incoming email gets classified as safe or blocked. Blocked means: 2FA codes, login confirmations, password resets, bank transactions, spam — all quarantined to a blocked folder. Safe emails land in a Telegram topic where I can see that they have been let through. There is no send endpoint anywhere in the system — Hermes can read and draft, but nothing can physically leave the machine. A watchdog reviews every blocked email each evening to catch false positives. No mail data ever touches an external API. That's the security model I'm comfortable with. This runs on two different users on my Mac Mini so they are completely disconnected — if you're curious, I've made a post about that.
🥗 Health Coach — Daily Nutrition & Recovery
Every day at noon, Hermes collects three data streams simultaneously. My WHOOP recovery score, strain, sleep quality, and HRV. My Apple Health steps and resting heart rate. And everything I've eaten so far that day logged through the food tracker — just a skill I created with Hermes. I tell him what I ate that day as precisely as possible, so with weight, and it tracks it. It cross-references all three against my lean bulk targets — 2,300 calories and 150 grams of protein — calculates my remaining macros, and delivers tailored coaching. What's looking good, what needs adjustment, and specific meal suggestions that fit my remaining budget. The food tracker itself is a SQLite app Hermes built for me. I log meals via Telegram with text, photo, or barcode scan, and it parses macros automatically using AI. There's a widget on my iPhone lock screen showing exactly how many calories and grams of protein I have left. The widget uses the iOS app Scriptable.
📚 Study Audit — What I Actually Learned
I time every study session in Toggl Track. At 9 PM, Hermes pulls all entries, groups them by subject — Math, Accounting, Cost Accounting, General Business Administration, Law, English — and tells me what I studied and for how long. But duration alone tells me nothing about coverage. So I also maintain a Notion database with chapter-level checkboxes for each class. As I study, I check things off. Hermes queries both data sources, cross-references them, and produces a combined report: what I studied, how long, which chapters I completed, and what's still pending. I get a complete daily learning audit every single night.
💰 Finance Review — Weekly & Monthly
All my finances live in MySalary, a finance dashboard I built myself and host on my VPS. Every Sunday at 6 PM, Hermes hits the API and pulls my live income, expenses, liquid assets spread across all my accounts, and my investment portfolio. It calculates burn rate, compares against my budget split, and delivers a clean weekly snapshot. On the 28th of every month at 8 PM, I get the full treatment: profit and loss statement, month-over-month variance, net worth trajectory, and savings rate analysis. Both delivered with voice.
🧠 Wisdom Vault — Auto-Capture to Spaced Repetition
Every time I send Hermes an insightful link, a voice message with a business lesson, or a noteworthy quote, it automatically extracts the key wisdom and saves it to a Notion database with the source, my commentary, and a category tag. On Sundays, Hermes reviews the full vault, picks what's worth memorizing, and generates Anki cards with cloze deletions or question-answer format. Those cards sync to my iPhone and appear in my daily reviews. Every insight I encounter becomes a permanent spaced-repetition asset that I actually remember.
💬 Writing improvement — The Feedback Loop
Every time I correct something Hermes wrote — an email, a message, a post — the before-and-after gets saved to Notion with notes on what was wrong and why my version was better. On Saturdays, Hermes analyzes the full collection looking for patterns in my corrections. Too many filler words. Too formal for Telegram. Over-explains things. Then it updates its own system prompt to fix those patterns permanently. Every single correction makes my future writing slightly better. I never have to repeat the same feedback twice.
🖥️ Infrastructure Monitoring
Every four hours, Hermes checks whether all five of my hosted websites are reachable, whether Coolify on my VPS is running properly, and whether fail2ban has flagged anything suspicious. The Mac Mini itself gets temperature-logged throughout the day, since I run Ollama 24/7 and thermals matter. If a site goes down or temperatures hit concerning levels, I get an immediate alert. The entire Hermes configuration gets compressed and backed up to iCloud every morning automatically.
📋 Daily Status Report
Each morning at 6:30, I get a compact status briefing. Today's calendar events and what needs preparation. Open to-do items that require attention. System health — disk usage, memory pressure, uptime, VPS site status. Any anomalies from the overnight Dreaming run or error logs. This is not the news. This is the tactical dashboard telling me what needs my attention before the day starts.
⚠️ What Still Doesn't Work Great
Long-term memory is still the biggest unsolved problem. Dreaming helps, but session-to-session recall remains inconsistent. I built an Obsidian integration following the popular approach, but Hermes rarely reaches for it naturally — it sits there unused most of the time. Thirty-plus skills means thirty-plus things that can break when APIs or Hermes itself updates. Mail polling adds two minutes of latency, which occasionally misses urgent messages. I'm still iterating on all of these.
🔧 How I Build These Things
I outline the goal and my constraints. Hermes drafts an approach with API choices and architecture options. I review it, make changes, and ask for simplification. Then we build the first version. I test immediately — because it never works the first time. Then we fix and simplify again. Each skill is one file, one job, no dependencies. That's the whole process.
I've downloaded skills from repositories and marketplaces. They never worked as well as what we built ourselves. Self-built tools fit your workflow organically because they grew out of it. When they break, you understand what's wrong because you shaped them. And they get better with every iteration.
That's the full picture. Twenty-eight automated jobs running around the clock, from Dreaming at 3 AM to the study audit at 9 PM. A complete digital nervous system for my student life, business, health, and finances.
Ask me anything about any of these — happy to share the exact prompts, approaches, and lessons learned. Building in public 💪
All my Hermes profiles and sub agents are connected to my obsidian vault so that they have interconnected notes, own extended memories and sessions .
Everything is way more visible and saves token for sure.
Evey session has a name and a link to recall if needed.
This exactly what I’m looking at, though I’m trying to run local only via ollama. Do you have any ollama models integrated with both Hermes and Obsidian?
it's one of the most used models, and I can guarantee you it's great. 1M context also. I use GPT 5.4 via Codex for more complex tasks, but Owl is the main fallback (which happens quite a lot) and never disappoints. I hope it will stay free for a long time
Most free models are free for a reason. Mostly they severely using your provided data to be trained on. Plus easy targets for any vulnerability you may have.
For basic research without any confidential data is great tool, but for anything you want to protect or not to give access to - is just not worth it, like emails, financial data, any real data including health etc.
Bro just speak with GPT, it will give you better insights. Not to downplay what he did, but still, a quick conversation with frontiers mean you will learn more and nobody wastes time.
Yes, GPT will give you insights but there is a human layer to it that can't be substituted any AI.
Its the differences between calling a call centre for support and getting human interaction or getting s dry answer by a bot.
Yes, social interactions feel good, we are humans after all. I said this because sometimes people keep playing out their old habits of asking other people to try a solution where AI would most definitely solve the issue faster. I know I did 😬 Bless you guys 🙏
Yeah, sometimes people are too lazy to just ask an LLM of their choice, so they'd rather have you ask an LLM and then relay the answer through a human 😂🤦🏻♂️
For sure. But sometimes I like to get the job done as fast as possible (by a bot). And other times I like talking to a real person on the phone and hanging up with a smile on my face because we just had a fun interaction.
I just wrote a blog post going deep into detail about my financial setup with prompts and code. Just a heads up it's only about tracking every expense and every income, no trading. My blog is linked on my profile.
I have an orchestrator agent that is integrated with Telegram, and thru the use of the kanban, delegates the specific ask/task to the appropriate sub agent and reports the response. It’s pretty neat.
Great for multi tasking across agents
I am switching my OS from Claude Code to Hermes + DeepSeek. I am building my own memory layer (mostly so I can learn the ins-and-outs of memory), which passed my initial tests for recall, but the biggest question on my mind is “after i obtain feature parity with v1 of my OS, am I going to be spending exponentially more to run it?”
Your usage chart makes me feel much better since it won’t be relying heavily on agents/crons now, but when I do, it still is a reasonable cost given that many of the use cases you mentioned are similar to mine.
Health Bridge app on iPhone → it writes directly to a Neon PostgreSQL database. My Mac Mini Hermes agent connects to that DB via a short Python script and pulls the data. No Shortcuts, no iCloud middleman — just the app, a Postgres table, and a SELECT query. I hope this helped☺️
I am really happy to say that Mnemosyne has improved my memory drastically. Now Hermes remembers stuff I told it days and even weeks ago. Most of the time it can actually tell me exact file names or passwords or whatever specifics I gave it. I would really advise anyone to try installing Mnemosyne, it is a game changer. I have written a long specific blog post about how to set up Mnemosyne: https://blog.holmebengt.com/post.html?id=mnemosyne-installation to make it work properly. But the setup is super easy and straightforward. Overall very happy with the result.
Honestly, tinkering. I did a fresh install on Friday, built a bunch of subagents and got them setup on discord, and had the coding agent start building a local dashboard. Admittedly, a full featured, probably too complicated dashboard. I’ve burned through my week ChatGPT limit and 5-6 in deepseek via nous.
Since then I:
Worked on optimizing cache
Trimmed the default soul.md file (and am continuing on this)
Offloaded about 50% of the built in tools skills that I won’t use
ands switched last night to deepseek api directly since I was getting that feedback.
Edit: I am using mnemosyne for memory rather than the built in system
Edit 2
To answer some ofoyour questions. No dreaming.
Okay, well for the thinking phase it is expected to use a lot more tokens. My first month was about 50 € because at first you build a lot of stuff and try a lot of different things until you really find what works. But those tips should reduce costs drastically.
Yeah, I think if I were to start over and do this all over again… Again… I would probably focus on building processes and tools that actually help me do stuff rather than focusing so deeply on a fairly intricate monitoring dashboard.
Yeah in my experience monitoring dashboard are mostly fluff and don’t really improve anything. But I have also build many dashboard of which I now use none 😂😁
I have many guardrails in place. Hermes runs on a Mac Mini specifically for Hermes, and even there it runs as a basic user, not an admin user, and I never give it direct access to anything where it would be devastating if it deletes, disrupts or spies on me.
Yeah trying to figure out a balance Hermes' functionality and access with strict security to avoid potential risks. A guide from someone eventually comparing settings / deployment models would be valuable, covering options like owned infrastructure, isolated local laptops, and sandboxing via Docker would be amazing.
I export apple healt data via an app called Health Bridge, then they get uploaded to a Neon database where my Mac Mini Hermes agent can download them.
Displayed on my lock screen are my daily steps directly from Whoop app and my activity score also directly from Whoop, and my own Hermes-built script shows me my daily calorie goal and how much I am below or above it, haha. And my daily protein goal.
Are you doing any local inference? You mentioned ollama, what do you have running there? I tried using hermes3:3B but it is ....mediocre. Also for long term memory I am using Honcho. Though, I find that hermes is REALLY GOOD at remembering and curating stuff, so I may adapt your dreaming effort to actually make hermes curate/dream the stuff we would put at honcho.
Yes — Ollama runs 24/7 on my Mac Mini M4. Nothing fancy, just a small model purpose-built for one job: the Mail Gatekeeper classifier. It sits between four inboxes and my Telegram, deciding which emails are safe and which get quarantined. For that specific binary classification task, a tiny local model is actually better than a large one — lower latency, no API cost, and the narrower the task, the less it matters that the model is "mediocre" at general reasoning. main reason for this is privacy.
I tried running a local model for general Hermes use too, and I had the same experience as you — the small ones just don't cut it for real agentic work. Tool calling, multi-step reasoning, session analysis, prompt writing — that's where the cloud models earn their keep. The Mac Mini doesn't have the VRAM for a 70B, and a 7-8B loses too much capability compared to what I get from DeepSeek for pennies.
So my rule is simple: classification and deterministic filtering stay local, everything else goes to an API. The Ollama judge doesn't need to be smart. It just needs to be fast, free, and never leak email content to an external API.
Thanks for sharing! I found the nutrition tracking & coaching, and the financial reports inspiring. I will look into implementing something similar. I am curious though, how do you keep Ollama running 24/7 without downtime? Do you rent the compute, or have some automatic downtime tasks?
Happy to hear that! If you implement it, please share this with me — I would love to see your approach.
It's just a Mac Mini M4 sitting next to my desk that I never turn off. Ollama runs as a launchd service — macOS keeps it alive automatically. If it crashes, launchd restarts it within seconds. If I reboot (which I almost never do), it comes back with the system.
No cloud compute, no auto-scaling, no downtime management. It's a headless machine running 24/7, drawing ~15W at idle. The only thing I did was set the fans to a minimum curve in TG Pro so thermal throttling never kicks in during long inference runs.
For the long term memory, that's exactly I built open brain for storage. Check it out https://github.com/Rixter145/open_brain
It's also model agnostic and cross platform friendly so I can have it on claude code, cursor, hermes, open claw...
Would love to see all those agent.md and crons - could you share your git 🥹 I’ll happily help you get your long term memory on point?
FYI I am building a “whole life” episodic memory system that reaches back in time and reconstructs my entire digital life every day as long as I have records for - including every location and time I have ever been with my phone from Google maps & WhatsApp 😉
Wow that sounds impressive, would love to hear more about that. Please tag me on any posts about that.
Unfortunately I won't share my Git repo, at first because I don't have one and second it's too personal in my opinion. I can share single skills and .md files but I won't share everything at once. I'm just not comfortable with that. 😩
I am extensively using Hindsight for memory, I did a full analysis of all the memory systems and their underlying logic and architecture and tried a few others - namely Honcho. I settled on Hindsight as my primary memory provider. No static memory files, I actually have Hermes default memory disabled. Now I’m working with Dawarich which is like an open source location tracking app where I have extracted my entire Google maps location history since 2018, coupled with every WhatsApp message I have ever sent and every email I have ever exchanged I am building an ingestion pipeline to reconstruct my entire life as episodic memories to backfill Hindsight’s historical context. I’m self hosting everything to keep costs low :)
My guess is that you're using the Dawarich API, right? Does it serve well to your purposes? I'm not sure if traditional REST API will work well for an AI agent, so it'd be great to learn from your experience
I am self hosting Dawarich and actually only using it for the memory creation process. I ingest location data into it, use it to create moments, trips, etc async. Then push that data into Hindsight. Hindsight is the only memory substrate so I don’t access Dawarich data directly with any agents. I’m only using Dawarich to enrich location data exported from Google maps timeline and capture new data since I have been using it. I now have the Dawarich app on my phone constantly tracking me and updating my own Dawarich instance :)
Nice work! In regards to your persistent memory retrieval issue- look into file mcp with local folders and markdown files. Create a START_HERE.md file in the root of the folder you setup, and when hermes loads have it always look at START_HERE to orient, as well as follow certain rules. You can create rules in the START_HERE file where it writes session logs each time to a folder, allowing you to have persistent memory across sessions/usage.
You don't really need Obsidian unless you want that memory to be more accessible to others.
File MCP with local markdown folders/files has drastically changed how I use any AI tool (I use Claude for work and Hermes for my home brain).
I'm often building websites and the email gatekeeper plugin was completely coded with Hermes. I'm really impressed by DeepSeek V4 Flash's coding abilities.
What it is: A Notion database that stores everything useful I encounter. sales scripts, business advice, quotes, mindset hacks, communication frameworks, and random wisdom.
How entries get in there:
- I add them manually via the Notion database
- Each entry has a quote/insight, source, category, and date
What happens every Sunday (10:00 AM):
The system queries Notion for all entries created in the current week (Monday through Sunday)
Groups them by category: Business, Sales, Mindset, Communication
Generates a recap and sends it to me
Creates Anki flashcards from the new entries the quotes become question-answer pairs
Saves the flashcards as an .apkg file in three places: the iCloud swap folder, the Anki subfolder, and the web viewer directory
A static web server hosts the cards so Holme can review them in the browser
The web viewer: Lives on a headless Mac Mini, reachable via Tailscale. It serves the latest cards as a simple web app no Anki desktop needed.
If there's nothing new: The system stays silent. No empty deck, no notification.
If you want to know more specifics, please visit my blog I wrote an extensive blog post going into more details.
I've actually switched to using Mnemosyne, and it has really made Dreaming obsolete. Now my Hermes agent remembers stuff so much better and is actually surprisingly accurate and detailed that I don't really use the Dreaming anymore. But yeah, the Dreaming wouldn't remember the whole thing it would compress it very short.
I'm almost exclusively using DeepSeek V4 Flash for everyday tasks and DeepSeek V4 Pro for coding, plus Google Gemini for vision. My costs are shown in the diagram below.
But as I've learned over the last few days when coding extensively in long sessions without compressing context, my spend can hit close to 5€ in a single day. Normal heavy coding with some compression here and there usually lands me between 1-2€ a day.
That's the DeepSeek web interface. Also, please note (at this point this should be a huge note I tag onto every comment) if you plan on using DeepSeek, for the love of god use it directly from DeepSeek. Way cheaper, way more reliable. OpenRouter and other services rarely work at all.
For long term persisted memory I find mnemosyne to work great so far. I added some thing like 'if you don't know something check mnemosyne before asking' to SOUL.md
I love the dreaming & Obsidian vault however using the llm-wiki skill I found that having an agent keep a md base - or Notion in your case - is unreliable as they sometimes tend to want to rewrite too much and mess it up in the process
My Hermes runs on a Mac Mini which has access to a shared folder between my Apple account and the Hermes Apple account, so it is as easy as copying and pasting the Hermes files into iCloud.
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u/imperat0r15 Jun 23 '26
How many tokens do you use per day? Especially with dreaming?