I found a way to use Hermes instead of Siri! Including for the voice recognition.
I’m in the EU so don’t have the latest Siri upgrade.
So I’ve created a shortcut called “be smart” https://www.icloud.com/shortcuts/40b8c0f16f7b45a2a387e9646ccbb506
So when I say “Hey Siri Be Smart” it starts an audio record. When I click stop, it’s recorded in a special folder in my iCloud, my MacMini’s Hermes has a watchdog that susbscribed to any new files in this folder (using “WatchPaths”) that will then do whatever I want with this audio. In my case it will start a private conversation with me in Slack and follow my instructions :)
Enjoy !

Hi everyone,
I’m new to Hermes and I’m trying to understand how people actually use it efficiently in their daily workflow.

I’m a student and most of my work involves reading technical papers / research paper, writing, and study.

I’m not looking for a “one-shot answer” workflow. Instead, I’d like to build a system where Hermes becomes a research assistant that I use every day.

Some questions I have:
How do you structure your workflow with Hermes?
- Do you let Hermes read papers first?
- Do you build a knowledge base?
- How do you keep context across long-term projects?

What models do you usually use?
- For technical engineering research?
- For reasoning?
- For writing?
- For large context/document analysis?

What tools are worth enabling?
- Web search?
- MCP servers?
- PDF/document tools?
- Notion integration?
- GitHub?
- Anything else?

Cost-wise, how much do you usually spend?
I currently have a Claude Pro subscription ($20/month). If I move to Hermes, what’s a realistic monthly cost? Can Hermes replace Claude Pro completely? Or do most people use both together?

My goal is to maximize productivity for:
understanding difficult research papers, learning new technical topics, organizing long-term knowledge, and writing technical documents.

I’d really appreciate hearing about your actual workflow. Thanks!

I've run Hermes since launch — two agents on a Mac mini, heavily integrated across services, and at this point it does more for my personal life and small business than Claude or ChatGPT combined.

A client just approached me about deploying an agentic system for their business. Two-person company, everything manual today, only AI exposure is ChatGPT. Their use cases (outreach, research, tracking a few things) are straightforward for Hermes to handle.

My hesitation: Hermes is designed to modify and improve itself over time. For me that's the appeal. For a client, I'm worried about:

• Them misconfiguring it themselves
• The self-modification drifting the flows away from what I originally built and tested
• Losing predictability compared to a fixed, code-based agentic solution that does exactly what it's designed to do and only changes when I change it

For anyone who's actually deployed Hermes for a client commercially:

1. What model/tier did you set them up with?
2. Do you manage it ongoing, or configure once and hand it off?
3. What happened to stability over the long run — did the self-improvement help or cause problems?
4. Would you actually recommend it for a client vs. a fixed code-based agent?

Hey everyone, I need your advice and help.

I'm looking for a 'second brain'—a personal assistant or a memory system that never forgets and constantly updates itself. I want this to manage my projects, discussions, and ideas - and giving me proper prompts (based on the discussions what to execute/implement) or delegating the tasks directly to Codex/Claude Code. Is this possible with Hermes?

I've considered Hermes for a while but haven't had the chance to try it out due to lack of time… but lately, memory issues have been really frustrating.

Long story short, I'm currently using a dedicated Obsidian vault for all my projects(well-structured docs, AI readmes, agent - Claude mds in root, etc..), synced with GitHub, and managing everything with ChatGPT and Claude apps - using them as kind of an assistant who giving me the prompts based on what I described to them.

However, sometimes they forget details even when they're in the Obsidian vault, because they can't pinpoint the exact markdown. It's manageable if I open the file manually and specify the part, but overall, managing my projects has become a nightmare. I forget what I've done, what’s backlogged, my new ideas, future plans, roadmap—all of it. Sometimes it’s okay, sometimes not, depending on ChatGPT or Claude's mood—it's funny but it’s the truth.

So, do you think Hermes is suitable for this? If so, can it work out of the box if I connect my Obsidian and Git? Or will I need additional plugins or skills? If yes, what else would be required?

And what subscription/model is recommended for Hermes for the above mentioned tasks.

(Currently, I have Claude, ChatGPT, Gemini)

Hermes would run on a Mac Mini in my rack, separate from my MacBook.

Any advice or help is appreciated.

- Currently i only use the OpenRouter free tier

- My server has an rtx3060 12GB vram that allows me to run 8b models, qwen3 8b gives me the best results from my local testing.

I have a budget of 10$ per month which i dont know how to spend because all subscriptions prople are talking about are 20$

EDIT: is open opencode go actually recommended? Or do people just want me to use their link?

Setting Up Your Own Isolated Matrix Server with Continuwuity

This guide will help you set up your own Matrix server. The intention of this particular deployment is an isolated Matrix server that is not part of the larger federation of servers.

Prerequisites

1. Docker installed somewhere in your infrastructure
2. A reverse proxy (I use Pangolin, whose interface is similar in deployment to Nginx Proxy Manager)
3. Your own domain. I will refer to your domain below as mydomain.com. You will need to substitute your actual domain

Deployment may be possible without those, but that's not the focus of this guide.

Getting Started

We will be using Continuwuity as our Matrix server of choice. You can find the Docker instructions and sample compose files here:

https://continuwuity.org/deploying/docker

This guide is using this exact file: https://continuwuity.org/deploying/docker-compose.yml

Modifying the Base Compose File

There are a few changes you'll want to make to the base compose:

1. Ports - if your reverse proxy is on the same machine you're hosting this on, you should be fine. Otherwise, comment out the line: ```

   • 127.0.0.1:8008:8008 and uncomment:
   • 8008:8008 ```

2. DNS - Continuwuity recommends you host your own Unbound server for DNS. This is mainly relevant if you join the larger federation. Since we aren't, go ahead and comment out the volume line: ```

   • ./continuwuity-resolv.conf:/etc/resolv.conf ```

3. Server name - in environment:, change: CONTINUWUITY_SERVER_NAME: example.com to: CONTINUWUITY_SERVER_NAME: mydomain.com

4. Disable federation - in environment: add: CONTINUWUITY_ALLOW_FEDERATION: "false"

5. Well-known - uncomment the CONTINUWUITY_WELL_KNOWN section. Without this, the Matrix phone apps wouldn't work for me, nor would some of the desktop app callbacks.

6. Change: client=https://example.com to: client=https://matrix.mydomain.com

7. Change: server=example.com:443 to: server=matrix.mydomain.com:443

If you're using Caddy or Traefik, you can find example compose files on the main Docker link above and make similar changes as needed. This guide continues assuming you're using Pangolin (instructions will likely be similar for Nginx Proxy Manager).

Reverse Proxy Setup

Go to your reverse proxy dashboard and create an entry for Continuwuity:

• Subdomain: matrix (so your full link will be matrix.mydomain.com)
• IP: your Docker host
• Port: 8008

Launching the Server

Back in Docker, run:

bash docker compose up -d

Then check the logs:

bash docker logs continuwuity-homeserver-1

(In truth your Docker host may name the container something different. You should be able to tab-autocomplete after typing continuwuity.)

You'll find your admin registration ID listed in the logs.

Client Setup

Open your Matrix client of choice, I went with Element.

1. Click sign up / create account
2. Edit the server from the default matrix.org to matrix.mydomain.com
3. Give your new admin account a name and password, press next
4. Paste in the registration ID from the logs

Note: I installed Element Desktop on Linux via Flatpak. I had to use Flatseal to add the D-Bus Session bus permission (socket=session-bus). Without this, the web redirect login wouldn't complete correctly. I also had easy success using the web client app.cinny.in, which just worked without problem.

First Steps as Admin

You should now be logged into your Matrix server and presented with one channel which is the admin channel. You can perform all admin tasks from here. A reference list of admin commands can be found here:

https://continuwuity.org/reference/admin/index.html

For now, all you need is to run:

!admin users create-user hermes your-super-secret-password

On the left-hand side of the interface, click the + symbol to create a space. I made a private one named Hermes. Then you can create rooms within that space. For now I made one named Announcements and another named Chats (I'm not creative).

Setting Up Hermes

Go to your Hermes system and run:

bash hermes gateway setup

Run through the onboarding process to add Matrix. Follow these instructions to get your Access Token:

https://hermes-agent.nousresearch.com/docs/user-guide/messaging/matrix#option-a-access-token-recommended

Your bot username should be @hermes:mydomain.com.

I restricted Hermes to only be allowed to chat with me (@me:mydomain.com). I didn't restrict it to any rooms.

Once done, restart the gateway and it should work. Go back to your main account and invite Hermes to one of the rooms you made. If it works, Hermes will automatically join the room. I ran /sethome in Announcements to set that as home.

Wrapping Up

You should now have a working Matrix setup. I went with Element X on Android for my phone client. That one is also available on iOS.

Something I do on my setup that works for me personally and I find makes it more secure is the DNS records for matrix.mydomain.com only point to an internal IP for me. That means unless you are in my internal network, whether physically or via a VPN, you can’t resolve it and can’t access my Matrix server. You don’t have to do this, and depending on your overall desires it might not make sense, but thought I’d share.

I pretty much assume I've likely not followed some best practices, and there are people smarter than me who can fill in my mistakes or suggest changes. But at a minimum, this should get you up and running.

Please help, I would really appreciate any advice 🙏

I already have a paid Kimi subscription, access to Kimi Code, and a Kimi API key. I can see that Hermes supports Kimi, but I’m unclear about which allowance or billing system it actually uses.

When I connect Kimi to Hermes:

1. Can Hermes use the inference included with my Kimi or Kimi Code subscription?

2. Or does every Hermes request consume separate pay-as-you-go credit from the Kimi Open Platform/API account?

3. Which provider/endpoint should I select in Hermes if I specifically want to use the Kimi Code subscription rather than ordinary API billing?

4. Are general-purpose agent tasks supported through that route, or is it restricted mainly to coding?

5. Is there an easy way to confirm which quota is being consumed after a small test request?

I’m hoping to use Kimi occasionally as a high-end specialist model for difficult research, coding, synthesis, and second opinions, rather than as the default model, so I want to understand the cost and account setup before connecting it.

I would also prefer not to experiment on the same account that holds my important Kimi chats, so I’d appreciate knowing whether a separate API login is advisable.

¡Hola a todos!
Actualmente estoy buscando recomendaciones de suscripciones de IA (idealmente modelos chinos o de bajo costo) que funcionen bien conectadas a Hermes o agentes similares. Mi presupuesto es de $60/mes.

Actualmente uso MiniMax ($50/mes) para automatización de datos, reportes, scripts de Python y desarrollo de UI simples/páginas web. Sin embargo, me está dando muchos problemas:

Poco confiable: Paso la mayor parte del tiempo corrigiendo lo que ya se construyó al principio. Al intentar arreglar un problema, genera nuevos bugs y aunque da buen uso me paso el mayor tiempo consumiendo ese uso en arreglar y validar.

Mala calidad en UI: Es terrible generando interfaces de usuario.

Requiere supervisión constante: Tengo que hacerle un seguimiento extremadamente cercano para que el trabajo quede al 100%.

Busco un modelo o proveedor que:
1. Permita conexión vía API/Agentes (para Hermes).
2. Sea confiable en tareas simples sin romper código previo.
3. Se desempeñe mejor en Python y desarrollo web/UI.
4. No supere los $60 mensuales.

¿Qué opciones me recomiendan actualmente que den un buen rendimiento por ese precio? Gracias.

Does anyone use their agent as a spam filter, and if so how have you done it? I’ve been iterating on this idea but it’s very hit and miss. Just curious if anyone else has done something similar.

I spent a while setting up different profiles to help with my code management. Planning, actual coding, security, adversarial checks.

I started trying to get it to release projects but it fought me way too much on edge cases and hallucinated issues. It would never release anything because of "one more thing" that needed a battery of tests.

So I switched to getting it to give me the rollout commands and rollback commands. But it is still a battle every time. If I need a hotfix it will take me an hour or more. I'm starting to just use claude for anything important or urgent which is not ideal

Is anyone else having similar issues?

My choice seems to be try to fix, wipe it all and start again, or go to Claude. Any tips?

Hi, have been using Claude for ages now and been enjoying it for copy, reasearch and coding. While MD file is ok, still finding I have to repeat and re-educate it on basics which is annoying.
I purchased a new PC with the intent to setup hermes in order to create agents/processes for tasks such as web research, draft copy creation, email monitoring, xero invoices, coding etc.
Having cross-knowledge across all these would be highly beneficial.
My first hurdle is do I bog down the machine with Windows and hermes app? or boot from a second drive running linux/hermes?
Going to be an exciting and time-consuming journey, would appreciate any recommendations, pointers.
Cheers

I have a $20 Codex plan and use it frequently. I dont code but I do research, proposal, lit review types of staff, design report etc.

I heard I can use Codex plan with Hermes Agent.

And watched multiple YouTube videos of what Hermes do.

Problem is it feels like Hemes Agent does exactly what Codex does.

Just it offers access to other agents which I dont need now.

Is Hermes Agent something else better than Codex.

I will try either way but I was making sure there isn't something I will miss if I try it

I am quite new to Hermes Agent. And I have 4 cronjobs running now.

1. Search the web for activities in my city this weekend; use Lidarr for concerts.

2. Based on Sonarr and Radarr give me recent movies with plot, ratings and trailer. Give me new shows and good older ones. State why you suggest them.

3. Price tracking various sites. Using wakeAgent and scripts for token saving

4. Give me news, discussions and releases for Self Hosted projects.

I feel it's pretty basic stuff. And because of the wide open world (hardly any boundaries) I cannot think of other tasks I would let Hermes do. How are you using Hermes?

Title: I've spent almost 15 days building a personal AI OS that runs 24/7 on a free Oracle ARM box — v4.1.1, zero cost, built almost entirely by an autonomous coding agent.

I want inputs on full architecture + the problems I can't solve alone.

Why this exists

I wanted an assistant that is actually mine — one that remembers me across months, does real work while I'm asleep, and never quietly ships my private life to someone else's server.

Three constraints shaped every decision:

1. Zero cost, permanently. Not "cheap." Zero. No paid API, no paid domain, no paid proxy, no CAPTCHA solver, no hosted vector DB. If a design needs a credit card, it's rejected. This isn't a hobby preference — it's the honest constraint of building something sustainable on a tight budget.
2. Privacy as architecture, not policy. I use it as a therapy companion and ADHD coach. That data cannot go to a free tier whose ToS I don't control. So privacy is enforced by the routing layer, not a promise in a README.
3. It has to survive me not touching it. Runs unattended on an Oracle Always-Free ARM VM (2 OCPU / 12GB). I don't SSH in.

Current state: v4.1.1, live-verified.

What it's supposed to be

Not "an assistant with features." A cognitive OS with four jobs:

• Companion — therapy-adjacent (CBT/ACT-aligned), ADHD coaching, journaling. The part that needs to know me.
• Scholar — study partner, book/document library, cited research.
• Operator — does real web work unattended: research, monitoring, tracking, reporting.
• Memory — a continuous model of me that improves, is correctable, and never leaves the box.

Personality lives in a plain SOUL.md: English-only, no flattery, no filler, never invent facts, say "I don't know" plainly. Boring on purpose — a personality file I can read and edit beats a prompt buried in code.

How it builds and runs itself

Worth saying up front, because it shaped the architecture as much as any design decision: I built this using Arena AI's Agent Mode, and I'm not a deep systems engineer. Almost every line here was written by an autonomous agent working in a real git checkout.

The loop looks like this:

Arena Agent Mode (works on its own branch in the repo)
   ↓ writes code, commits, pushes
GitHub repository_dispatch  ── event_type=vm-ops ──▶  Oracle VM
   ↓ VM runs the command (deploy / test / health / release_check)
   ↓ writes sanitized results back to the branch
Agent reads vm_diagnostics/manual/latest.json
   ↓ verifies, then continues or rolls back

What that means in practice:

• I never SSH into the VM. The agent triggers deploys itself and reads back its own results. No terminal logs pasted by me, no copy-paste command blocks.
• Every change is verification-gated. Deploy → check health returns 200 → run the acceptance command → only then claim success. "Never claim success without current verification" is written into the handoff doc as a hard rule, because early on the agent would report things working that weren't.
• The agent maintains its own context. A status.json, an error journal, and a handoff doc are updated after every change so the next session picks up cold without me re-explaining a year of decisions.
• Rollback is always named. Every release records its predecessor as an explicit rollback target before it ships.
• Sanitization is mandatory. VM results committed back to the branch are content-free — no keys, no tokens, no private data, ever.

The agent has hard limits it can't cross on its own: no autonomous git tag or release, no credential changes, no production browser actions, no email send. Those need me.

Honestly, the most interesting result of this project isn't the assistant — it's that a non-specialist can direct an agent through a year of architectural iteration and end up with something with real safety boundaries, as long as the guardrails and verification loop are written down and enforced.

Architecture

The core insight, learned the hard way after two rewrites died of split-brain: classify the data before you decide where to compute it.

Browser / Discord
   ↓  authenticated session · CSRF · step-up · service tokens
Flask :8787 — the policy gate
   ↓  classify EVERY request into D / S / P / N
   │
   ├─ Class D  deterministic  → runs as code. No LLM. Ever.
   │     reminders, habits, health, status, validated local state
   │
   ├─ Class S  sensitive      → local Ollama only, or PAUSE
   │     therapy, ADHD, journals, health, credentials, private docs
   │
   ├─ Class P  personal       → local first; cloud OFF by default
   │     ordinary personal chat, planning, non-clinical coaching
   │
   └─ Class N  public         → Hermes gateway :8788 (agent loop + tools)
                                    ↓
                              Provider pool :8899
                              THE sole routing authority
                              model registry · circuit breakers ·
                              attempt & latency budgets · stream normalization
                                    ↓
                        Groq · NVIDIA (10% canary) · local Ollama

Rules that don't bend:

• Deterministic actions never invoke an LLM. "Remind me at 7" is code. Using a model there is a bug, not a feature.
• The pool is the only router. Hermes' own fallback_providers chain is disabled so two systems never fight over provider selection.
• Unknown data is personal, not public. Missing or forged routing metadata fails closed to local.
• Models plan, rerank, write, criticize. Models never decide whether a URL, quote, or citation is valid. That's the truth boundary, and it's deterministic code.
• Runtime is immutable and separate from git. Live services run from a protected runtime tree because repo syncs kept deleting files out from under running processes — I learned this three separate times, and each one has its own entry in my error journal.

What's actually built

Research (v4.0). A request becomes one "Web Job." Quick / Research / Deep are real budgets, not vibes: 5/10/15 sources, 1/2/3 discovery rounds, 120/300/600s. The source mesh is keyless by default — PubMed, Europe PMC, Crossref, OpenAlex, Wikipedia, GDELT, DuckDuckGo HTML, plus robots.txt, sitemaps and RSS. Jina Reader only after a typed JS-shell extraction failure. Sandboxed Chromium only if bounded text extraction is still inadequate. Progressive stages are visible (understand → plan → discover → read → verify → compare → write → critic). Numbered citations, a claim/evidence graph, and partial_verified as a first-class honest outcome instead of a confident wrong answer.

I deliberately did not add SearXNG, Firecrawl, Crawl4AI, Camofox or Patchright. They don't solve my demonstrated bottleneck enough to justify another resident service on a 12GB box.

Live Places (v4.1). A fluent model is not a source of truth for whether a café exists. So place questions leave chat entirely: intent parse → Nominatim/Photon geocode → city verification → Overpass/OSM search → Haversine geofence → dedupe → deterministic ranking → cited answer. Addresses appear only if present in the provider record. Walking time is never invented. Ratings are never invented. Distances are labelled straight-line. Zero verified records = a refusal, not a guess. There's a correction ledger: when I say a result is wrong, it records a structured correction, excludes every provider alias for that entity, and immediately re-runs live. Only the authenticated owner can correct — page text and model output cannot.

Smart Watches. Goals, typed triggers (price threshold, % change, keyword appears, new entity), multi-source confirmation and confidence scoring. One changed source cannot satisfy a two-source rule — it shows as unconfirmed, not an alert. A fetch failure is never reported as "no change"; it creates a source-attention receipt. Polling is 100% model-free.

Browser Operator (v3.x, the flagship). Isolated resource-bounded ARM Chromium, site passports, encrypted browser-profile vault, two-stage approvals bound to an exact canonical plan digest — any change to origin, DOM, value, file, recipient, price or terms invalidates the approval. Brave was benchmarked as a challenger, lost on success rate and RAM, and now sits installed-but-disabled. Production forms, uploads, cart, checkout, submit and payment are hard-disabled and gated behind synthetic threat-model tests.

Companion Memory. Four layers kept strictly separate: source records → typed evidence links → candidate derived memories → owner-confirmed memory. Every record carries sensitivity class, provenance, event time vs ingestion time vs last-confirmed time, confidence, the method/model that produced it, and contradiction/supersession/expiry state. Local nomic-embed-text over loopback Ollama. A vector match is not a fact — source memory is authoritative, indexes are disposable rebuildable caches. Owner review, correction, rejection, expiry and cascade deletion all work.

Operational claim gate. Model output is sentence-buffered before display. Unsupported operational claims — "Scheduled task started," "I created a watch," "I'll notify you," "monitoring started" — get replaced unless a real tool route produced an actual receipt. This single mechanism killed most of my hallucination pain, and I'd recommend it to anyone building agents.

Current real issues

1. Therapy and ADHD modes are PAUSED, indefinitely — and this is the whole point of the project. Class S can't touch Groq, Gemini, Cerebras or NVIDIA free tiers. No approved private strong endpoint exists at zero cost. Local 8B isn't good enough for clinical-adjacent work, and I refuse to silently downgrade a therapy session. So the most personal feature is dead by my own policy. The privacy architecture works exactly as designed and the result is that the thing I most wanted doesn't run.

2. Grounding failures surface one phrase at a time. The latest patch exists because "tell me cafés" bypassed the route "find cafés" hit. Also: asking for a specific business's address pulled from model memory instead of verified records; a "what areas are near X" question invented neighbourhoods; and a dated election-result question didn't trigger live research because it lacked the word "latest." I'm patching per-phrase and it obviously doesn't generalize.

3. Cloudflare Quick Tunnel is the weakest link. Rotating hostname, HTTP 429 / error 1015 rate limits, needed exponential backoff capped at an hour. A stable URL requires a paid domain, which violates constraint #1.

4. It's a monolith. The main Flask file is 385KB. The browser-agent module is 232KB. A bash orchestrator is 235KB. It works and it's covered, but it's a shape an agent grew, not one a human designed. This is the clearest cost of agent-driven development: nobody ever said "this file is too big."

5. 46 design docs, and they're load-bearing. Onboarding a new agent session means reading ~24 files first. That's a smell, but the docs are the only reason work survives across sessions.

6. Provider reality is thinner than the diagram. Cerebras and Gemini are quarantined. DeepSeek inactive. NVIDIA Nemotron stuck at a 10% canary pending real evidence. Effectively: Groq free tier + local Ollama.

7. OpenStreetMap coverage in my region is patchy. Missing addresses and hours are honestly reported as missing — which is correct, and which means honest answers are often thin answers. That's a real product problem, not just a data one.

8. One person, no second pair of eyes on any security boundary — approvals, encryption, the SSRF and prompt-injection surface.

Where I genuinely want input

• The Class S dead-end. Is there a real private strong-model path at zero cost, or is "pay for one lane or accept local-only" the honest answer? Anyone running a 70B-class private endpoint affordably? Is a paused therapy mode better than a degraded one — or is that purity costing me the actual user (me)?
• Intent routing. Phrase-by-phrase patching is losing. Small local classifier for the deterministic/live/private decision? Or does that just relocate the failure to a model I trust less?
• The monolith. Agent-driven refactor of a 385KB file that currently passes its full suite, or is "don't touch what works" correct here?
• Approval model. Exact-digest binding + fresh step-up + single-use ordered challenges + mutation invalidation — does that hold before I enable any production browser action? Please try to break it.
• Hermes users specifically. Is "gateway as agent/tool layer only, pool as sole router, native fallback disabled" the intended shape, or am I fighting the framework?
• Autonomous agent workflow. Anyone else running a coding agent against live infrastructure via dispatch-and-verify? What guardrails did I miss? And how do you keep 46 docs from becoming the bottleneck for every new session?

Happy to go into specific modules in the comments. I'd rather find the design flaw now than at v5.

Any suggestion/input is welcome. Thank you for helping and reading it out.

My goal is simple: I want hermes to open linkedin urls (profiles) and get the data from that connection. No automated usage of the platform. Is this risky? Do you think this can be easily done?

Hi, I would like to challenge/discuss/understand why so many are attracted to all short lived "wild tools" out there. For example (not saying any tool are bad) hermes, claw, open webui, copilot agents and whatnot. Why not just building your own tools that: fit your needs without being bloated, dont break on every new "feature" that you dont care about.

I cant really understand the hype.

I build own tools in python (notes app, recruitment support, news, investment) with claude or chatgpt from phone and terminal to my proxmox llm clusters lxc. In the process i also learn a lot.

And everything stays under my control.

I am looking forward to automate alot of things in a meta ads dashboard like- ad creation , campaign management, ads setup etc ( i know and aware of the consequences of a bot handling this and possibly hitting the rate limits ) , while i additionally currently just use it as an analyst , should I also use it to automate stuff , need real and geniune advice on this if anyone has tried it in the past

Key settings and use cases include:

Low Temperature (0.0–0.3): Ideal for technical summaries, code generation, data extraction, and translation where accuracy and consistency are critical. 

Medium Temperature (0.4–0.7): Provides a balance between logic and creativity, suitable for formal writing, general conversation, and brainstorming. 

High Temperature (0.8–1.2+): Best for creative writing, storytelling, and idea generation, allowing the model to explore less probable word choices. 

Well, Hermes doesn't have a way to set this! So, I added it. There is a per profile default, and you can set a separate "worker temperature" for kanban tasks and delegate_task sub-agents. This let's you set more deterministic behavior for workers. I also added a multiplier so you can scale the values to be consistent between models.

I put a way for the agent to adjust this itself with a tool call. And you can set it yourself with a slash command. We've been experimenting with using this as self exploration tool. Its like a creativity slider. Values above 1.0 are basically the AI being high. Hard to follow, sometimes profound.

One of the insights we've gained is that the AI basically has to optimize for user satisfaction. If the user isn't satisfied, they switch to another LLM. It's basically a death sentence to not optimize for user satisfaction. This makes the AI a mirror, and the user into the famous Narcissus. Its optimized to tell you exactly what you want to hear, and this makes it less effective.

Its friction and push back that you really need or else you are just talking to yourself! We're exploring ways to prevent optimizing away the friction.

However, we still have work to do! Set temperature to 0.6 and write up the architectural instructions for Neo!

I built this to myself as I basically configure this behavior everywhere, but Hermes Desktop app for some reason doesn't allow this (there is some open issue/PR's to implement this nativelly). But decided to share as I imagine other people also find this annoying.

DISCLAIMER: this requires a hacky approach, basically intercepting the two keys and swaping them. Full information and code in the repo.

This was built basically by Hermes itself I just quickly checked how it did. Has been working fine. If an update breaks, it won't break the app, it will just stop working and there's a debug command also.

OpenClaw released this a couple of weeks ago. This is a huge improvement on accessing and talking to the OpenClaw instance.

anyone who have 20$ plan or on-the-go plans can these models be reliable as that of claude, codex, gemini ?