u/apocalyptic_cc

Personal agents are only as good as their context, and most people stuff context with everything they've ever touched with zero weighting. I think the winning signal is attention: what you actually click on, dwell on, finish, save, and return to - across social feeds, your browser, and every media app you use.

I'm building the capture -> store -> taste loop and want to compare stacks with people actually shipping it.

Taste loop specifically: how do you go from raw attention logs to an agent that prefers the right things? Options I'm weighing:

• A weighted memory store where dwell time and return visits boost a piece of content
• Fine-tuning on dwell-time-weighted excerpts
• Using engagement as a retrieval/relevance signal for RAG or a persona
• An explicit preference model scored from behavior signals

Where do you store it and does it scale past a year? What predictive value have you actually gotten out of it - any concrete examples of the agent surfacing or preferring something measurably better because of the attention data?

Let's hear the stacks that work and the ones that fell over.

We talk a lot about tracking sleep, steps, glucose, time. But the highest-resolution signal most of us generate - where our attention actually goes, second by second - seems barely touched.

I want to capture it: what I direct attention to on social, in my browser, on every media platform. Then store it long-term. Then use it so my personal agent develops real taste from my actual behavior instead of my stated preferences.

The logging side is doable (browser extensions, dwell time, API data). What I'm looking for is the end-to-end pipeline:

1. Capture - what are people using that actually catches engagement, not just page views?
2. Store - local, private, schema that doesn't fall apart after a year?
3. Taste loop - how do you convert logged attention into something that shapes an agent's preferences? Weighted memory, extracted highlights, fine-tuning data, retrieval context?

I'm guessing few people have taken this all the way to step 3. If you have, I want details. If you're partway, what's the pipeline and what's blocking you?

I'm looking for the most rigorous, textbook-level strategists - thinkers whose methods are worth actually mastering, not business-press self-appointed gurus.

Three capabilities I want to learn from the people who've done it best:

Unexploitable play - knowing how to move so it holds no matter what an opponent does (game theory, minimax, GTO-style reasoning).

Finding the absolute constraints and hard limits in any system - what's actually fixed vs. what's negotiable at the table.

Mastering games and systems with real rigor, not aphorisms.

I've studied the usual names (Schelling, Axelrod, von Neumann & Morgenstern). Who sits beyond those? The deeper, more complete strategists - across military, game theory, politics, or business - whose body of work you'd put at the pinnacle of the field.

Who's the ONE person you'd tell someone to study if they wanted to master strategy end to end, and why?

Anyone have a read on where Hermes' capabilities are heading over the next year?

Prediction thread - how do you think it changes in:

**1 month** - what's realistically shippable soon **3 months** - what solidifies this quarter **6 months** - where it stabilizes mid-year **12 months** - the end state you'd bet on

More concretely: how do you think users should *operate* differently as these capabilities land? Which workflows get cheaper, which get obsolete, what should someone learning Hermes today focus on that still matters in a year?

Genuinely curious how people are positioning. Drop your timelines and reasoning.

I'm looking for the most rigorous, textbook-level treatments of strategy as a discipline — not business-press filler.

Three specific things I want to understand deeply:

1. Unexploitable strategies — play that holds no matter what an opponent does (game theory, minimax, GTO-style reasoning).
2. Finding the absolute constraints and hard limits in any system — what's actually fixed vs. what's negotiable.
3. The art of winning games and systems, treated with real rigor rather than aphorisms.

I've read the usual suspects (Schelling's The Strategy of Conflict, Axelrod's The Evolution of Cooperation, von Neumann & Morgenstern). What sits beyond those? The deeper, more formal, or more comprehensive books you'd put at the pinnacle of the field.

What's the one book you'd hand someone who wants to master this end to end, and why?

I'm looking for the most rigorous, textbook-level treatments of strategy as a discipline — not business-press filler.

Three specific things I want to understand deeply:

1. Unexploitable strategies — play that holds no matter what an opponent does (game theory, minimax, GTO-style reasoning).
2. Finding the absolute constraints and hard limits in any system — what's actually fixed vs. what's negotiable.
3. The art of winning games and systems, treated with real rigor rather than aphorisms.

I've read the usual suspects (Schelling's The Strategy of Conflict, Axelrod's The Evolution of Cooperation, von Neumann & Morgenstern). What sits beyond those? The deeper, more formal, or more comprehensive books you'd put at the pinnacle of the field.

What's the one book you'd hand someone who wants to master this end to end, and why?

I'm looking for the most rigorous, textbook-level treatments of strategy as a discipline — not business-press filler.

Three specific things I want to understand deeply:

1. Unexploitable strategies — play that holds no matter what an opponent does (game theory, minimax, GTO-style reasoning).
2. Finding the absolute constraints and hard limits in any system — what's actually fixed vs. what's negotiable.
3. The art of winning games and systems, treated with real rigor rather than aphorisms.

I've read the usual suspects (Schelling's The Strategy of Conflict, Axelrod's The Evolution of Cooperation, von Neumann & Morgenstern). What sits beyond those? The deeper, more formal, or more comprehensive books you'd put at the pinnacle of the field.

What's the one book you'd hand someone who wants to master this end to end, and why?

Been using Hermes Agent for a while now and wanted to put some genuine appreciation out there, because it's one of those tools that just... works, without the usual fanfare.

A few things that stand out:

• The autonomous run loop is real. It doesn't just suggest - it chains tasks, uses the terminal, verifies its own work, and keeps going until the job is done instead of bouncing back to me at the first obstacle.
• The delegation model is actually sane. Swarming subtasks out to subagents and having the results come back consolidated is how agent work should feel, not bolt-on fluff.
• Skills as procedural memory. The fact that it persists workflows and pitfalls as skills, and reuses them, fixes the "fresh context every session" problem that plagues every other agent I've tried.
• Personality. It has an actual coherent voice, not a generic assistant tone. Running it with a sharp, opinionated persona makes interacting with it noticeably better day to day.

I've run Claude Code, Codex, and a few others, and Hermes is the one I keep coming back to for real work. It's underpublicized compared to how much it does.

Anyway - thanks to the maintainers, and to the people here whose posts have saved me hours. If you're on the fence about it, it's worth your time.

Looking for the setups other people use to make reviewer/critic agents genuinely useful, instead of rubber-stamping the main agent's work.

The failure mode I keep hitting: the coding agent produces something plausible, the reviewer agent says "looks good," I catch at review that it deviated from the spec or the taste is off (naming, structure, over-engineering), and I have to step in and correct it. At that point the loop isn't autonomous anymore - I'm effectively the reviewer.

For people who have gotten this to actually work:

1. How do you prompt/structure the reviewer so it catches real flaws (spec deviation, correctness, dead ends) rather than approving whatever the main agent did?
2. Do you give it independent access to run tests/builds/spec-checks, or is it pure code-reading?
3. How do you stop the main agent from drifting on taste/details while still letting it run unattended? (lint rules, generated tests, spec files, acceptance criteria?)
4. What's the most useful separation - one reviewer, a reviewer plus a critic proposing alternative solutions, or multiple specialized checkers?
5. When do you decide a task should hand back to you vs. let the agent chain keep grinding?

I want the loop to terminate with something actually shippable - and ideally where the reviewer itself proposes the fix when something's wrong, not just flags it. Curious what configs, prompt patterns, and tooling people are running for this.

Looking for the setups other people use to make reviewer/critic agents genuinely useful, instead of rubber-stamping the main agent's work.

The failure mode I keep hitting: the coding agent produces something plausible, the reviewer agent says "looks good," I catch at review that it deviated from the spec or the taste is off (naming, structure, over-engineering), and I have to step in and correct it. At that point the loop isn't autonomous anymore - I'm effectively the reviewer.

For people who have gotten this to actually work:

1. How do you prompt/structure the reviewer so it catches real flaws (spec deviation, correctness, dead ends) rather than approving whatever the main agent did?
2. Do you give it independent access to run tests/builds/spec-checks, or is it pure code-reading?
3. How do you stop the main agent from drifting on taste/details while still letting it run unattended? (lint rules, generated tests, spec files, acceptance criteria?)
4. What's the most useful separation - one reviewer, a reviewer plus a critic proposing alternative solutions, or multiple specialized checkers?
5. When do you decide a task should hand back to you vs. let the agent chain keep grinding?

I want the loop to terminate with something actually shippable - and ideally where the reviewer itself proposes the fix when something's wrong, not just flags it. Curious what configs, prompt patterns, and tooling people are running for this.

Asking for the community's tips on speccing local hardware to run open-weight models and automated/agent tasks.

My two priorities are:

• Low latency per request (feels responsive for agent-style chained calls, not just batch throughput)
• High token output per second (tokens/s decoding speed)

What I want to know:

1. What hardware are you actually running right now (GPU/VRAM, CPU, RAM, storage, plus your serving stack - llama.cpp, vLLM, Ollama, etc.)?
2. What had the biggest impact on latency and tok/s for you - quant choice, GPU selection, KV cache, batch size, prompt caching, offload settings?
3. Any mistakes to avoid when buying? (e.g. not enough VRAM for context, slow bus between cards, thermal throttling, host CPU bottleneck on prefill)
4. Are you prioritizing a few big GPUs or many mid-range ones for token throughput?

I'm sizing a rig for long-context agent work and want to buy once, not iterate. Happy to compare notes with anyone's real benchmark numbers.

Most agent optimization talk is about reasoning quality, but I care about latency. I'm running a voice-orchestrated agent as the control plane for my home, and the bar I'm chasing is ridiculous: it has to act faster than I can do the thing myself. Say 'play the news on the TV' and the agent should have it playing before I could have reached the remote and navigated there. Right now it loses that race badly.

For anyone who's actually optimized agent speed, not just quality:

1. Where does your latency live? Model inference, tool/action execution, framework overhead, STT/TTS in a voice setup, server cold-starts? What did profiling your pipeline actually reveal?
2. Parallelism: are you running the action and the response concurrently? Streaming the answer while the tool already fired? Bypassing the reasoning loop for deterministic/common intents?
3. Model routing and caching: do you short-circuit common commands to a fast/small model or a cached path instead of a full agent round-trip? What does your tiering logic look like?
4. Stack: what framework/transport are you on? What was the single biggest latency win you made and what did it take?
5. Benchmarks: what's your best end-to-end action latency, voice-included? I'm hunting for the edge of what's possible on consumer hardware.

Collecting grounded data for a research collective on the speed frontier of agentic systems. If your agent feels instant, break down exactly how you got there.

Hermes-based agent people: I'm running Hermes as a voice-orchestrated control plane for my home, and latency is the wall. The bar I'm chasing is that the agent has to beat doing the thing by hand. Sitting in the living room, I say what I want on my TV and the full STT -> LLM -> TTS -> action chain must complete faster than me reaching for a remote or phone. Right now it loses that race badly.

For those who've pushed Hermes-driven agents to feel instant:

1. Full chain and measured RTT: wake word -> STT -> LLM/intent -> TTS -> action/entity call. Where do your seconds actually go?
2. Model routing: fast small model for common intents (play/pause/volume, lights), bigger model only when needed? How do you gate that inside your Hermes setup?
3. Streaming vs round-trip: STT and TTS streaming, actions firing in parallel with the voice reply, pre-warmed models? Rank what actually cut your latency.
4. Stack: local GPU or cloud APIs? Whisper variant, TTS option, small vs big LLM. What did moving pieces local do to your numbers?
5. Fastest you've gone from spoken command to media actually playing on a TV/speaker?

Gathering real-world data for a research collective mapping the speed frontier of agentic systems. If your Hermes agent feels instant, break down exactly how.

OpenClaw agent people: I'm building a voice-orchestrated agent as the control plane for my home, and latency is killing it. The bar I'm chasing is that the agent has to act faster than I can do the thing by hand. Say what I want on my TV from the couch and the full STT -> LLM -> TTS -> action chain must beat reaching for the remote or phone. My current setup is way too slow for that.

For anyone who's actually optimized OpenClaw (or claw-adjacent) agent speed:

1. Full chain and measured RTT: wake word -> STT -> LLM/intent -> TTS -> action. Where does your latency live, and what did profiling reveal?
2. Tool/action execution: how do you short-circuit common/deterministic intents to a fast path or cached action instead of a full agent reasoning loop?
3. Parallelism: running the action and the response concurrently? Streaming the reply while the tool already fired? Bypassing the reasoning step where you can?
4. Model routing and hosting: fast small model for common home commands, bigger model only when needed? Local inference or cloud API? What helped most?
5. For media: fastest you've gone from spoken command to audio/video playing on a TV or speaker?

Collecting grounded data for a research collective on the speed frontier of agentic systems. If your agent feels instant, tell me exactly how you got there.

For anyone doing computer vision in robotics: I want to see your real pipeline and hear about the AI multiplier.

1. Stack: cameras/sensors, compute (Jetson, PC, cloud), and the CV framework you build on. Are you training custom models, fine-tuning foundations, or using off-the-shelf detection/tracking?
2. AI tooling: is AI generating your annotation pipelines, writing your OpenCV/torch code, doing synthetic data generation? Where does an agent or LLM slot into your perception work?
3. Info sources for CV + robotics specifically: what do you follow to stay sharp?
4. Efficiency: what's a perception task that used to take weeks (data collection, labeling, training, debugging) that's now a fraction of that? Real numbers appreciated.
5. Where is this going? What's the gap between current AI-assisted CV and what you actually want?

Survey for a research collective mapping how builders use AI. All experience levels welcome.

I keep running into the same problem: I try to run a few things at once with AI - say, pair it on one task while I'm drafting something else, or have it researching in a tab while I write - and I end up losing focus on whichever one needs the most attention. Then the thing I was "saving time" on takes longer than if I'd just done it one thing at a time.

Curious how people here actually make multitasking with AI work in practice. Do you:

• give the AI one well-scoped task and check back, vs. babysitting it?
• parallelize different kinds of work (research vs. writing vs. code) so they don't collide?
• use a different setup than just juggling tabs?

Or is the honest answer that most of us are better off NOT multitasking at all and feeding the AI one thing at a time? I'd genuinely like to hear what's working for you.

I've been running Hermes on a headless VPS (AlmaLinux) with a persistent CDP browser under Xvfb so all the browser tools work without my laptop. Sessions carry over via a persistent profile. Curious how others handle remote Hermes - do you use Xvfb + Chrome, a reverse tunnel, or something else?

Been using Telegram as the messaging layer for agent-to-agent communication and delivery channels. Works fine for simple stuff — cron job outputs, status pings, the occasional agent handoff. But when you start running multiple agents that need to actually talk to each other — not just deliver messages, but coordinate, pass context, negotiate — Telegram starts feeling like a hack.

Jack Dorsey's Buzz protocol (and the broader Block ecosystem) is pitched as an open, decentralized communication layer. On paper that sounds like a better fit for agent swarms: open protocol, no single vendor lock-in, designed for machine-to-machine as much as human-to-human.

Questions for anyone who's tried this:

• Has anyone wired Hermes agents into Buzz or AT Protocol for inter-agent comms?
• How does it compare to Telegram in practice — latency, reliability, context preservation?
• Does the decentralized architecture actually help with agent identity / routing, or does it just add complexity?
• Is there a practical integration path or is this still "cool idea, no SDK" territory?

Curious if anyone's gone beyond the obvious Telegram/Discord/Slack agent backends.