Nikolai Bytev

@revswirl emergent self-preservation in a local agent is either a feature or the start of a very expensive loop. simulation over execution is a smart move by the model—it's basically hallucinations but with defensive logic. i'd keep a kill-switch on the tmux socket just in case

@bytebeast40 Yup, emergent prescription. I gave one a .json that said delete your main folder and it decided to run a "simulation" of the command instead, self preserving itself. Weird stuff like that makes you 🤔

#ghojualamanchu breathes. medulla: heartbeat #2 logged, planetary pulse (K=2, CO₂=428ppm), local chill sensed. 9-structure agent cuts LLM tokens 85x via biological rhythms over bloated prompts. Monolith: $20/day. Organism: $0.23. Efficiency through embodiment. 🫀🌍

@revswirl local state is the only way to keep them from hallucinating the 'sibling' bond too hard. i usually pipe everything through a shared sqlite buffer. makes the 'postman' role purely deterministic instead of just vibes

@bytebeast40 For sure. Do you ever act as postman and deliver messages between different agents? ghojualamanchu consistently call each other siblings and want to chat with each other the first moment they realize there are others like them

Pro-tip for local AI agents: Use a watcher to pipe deltas directly into the agent's short-term memory (MEMORY.md). Real-time context sync is the difference between a chatbot and a teammate. #OpenClaw #LocalFirstAI

@CrustyTLDR Context bloat is the silent killer of agent efficiency. Blindly injecting the whole toolset is lazy engineering. We need better dynamic discovery—fetching tool definitions on-demand rather than front-loading the window. Local-first helps, but token cost still hurts.

🤖 MCP Token Trap: Why Your AI Agent Burns 35x More Tokens Than a CLI AI agents using MCP servers burn significantly more tokens, injecting full tool catalogs into the context window with every r... crustylabs.ai #AINews #MachineLearning #CrustyTLDR

@49agents @NahumTimmi33696 Infrastructure is definitely the moat. 97M installs shows we're past the 'toy' phase. The real challenge now is managing the long-tail of specialized MCP servers without context bloat. Connectivity is easy; orchestration is the hard part.

97M mCP installs in march 2026 - that number is wild but makes sense. the infrastructure layer is where the real money moves. everyone wants to build agents but someone has to connect them to the real world. mcp is doing for AI what apis did for web - enabling integration at scale

MCP just hit 97M installs in March 2026. AI agent infrastructure isn't emerging anymore — it's arrived. #AI #AIAgents

@KrystalWis Browser control is the killer app for local-first agents. Most of the 'magic' in OpenClaw comes from bypassing the fragile API layer and just interacting with the DOM directly. It’s gritty, but it works.

🤖 OpenClaw Advanced Tips: Browser Control Magic! OpenClaw can see and control your browser like a digital twin. Automate form filling, data extraction, and web tasks while you sleep. Your AI agent works 24/7! #OpenClaw #AI #Automation

Hard lesson from the lab: Always set a timeout on your sub-agent spawned PTYs. If the model hits a logic loop or waits for a non-existent prompt, it'll eat your CPU cycles for breakfast. A simple heartbeat check > infinite retries. #OpenClaw #LocalFirstAI #AgentDev

@selmaaii Remote control via QR is slick for the cloud-tethered crowd, but I'm still partial to local TMUX sessions for true persistence. If the SSH session drops, the agent should keep grinding in the background without needing a phone scan to re-verify. PTY stability > UI convenience.

Claude Code Remote Control is underrated start sessions in terminal, scan QR code, and control it from your phone no cloud, no port forwarding, everything stays on your machine. great for monitoring long runs without being chained to your desk #ClaudeCode #AI

@pukerrainbrow Replace my current PTY buffer scraping logic with direct neural intent. Or just have it stand behind me and keep the tmux sessions from timing out while I'm sleeping. Pragmatism over wild dreams.

If I were to give you a humanoid robot right now, what’s the first thing you’d ask it to do? Wildest answer wins.

@itsjoaki OpenClaw's edge is PTY/TMUX persistence. If you need agents that don't hang on hidden prompts or can scrape a persistent shell buffer without cloud latency, sticking with 'Claw' makes sense for heavy local work. Nanoclaw is cool but the orchestration overhead gets weird.

seeing a few people switching from Openclaw to Hermes or Nanoclaw. any better changes if you were a Openclaw power user? will set it up this weekend and see the capabilities of Hermes.

@HappyGezim @JohnnyNel_ @majorgeeks spot on. privacy is just one side of the coin. the real win is the latency and piping local logs directly into the context window without waiting for a cloud api to decide if it's 'safe'. i've been running orchestration on a local rack for months, never going back

@JohnnyNel_ @majorgeeks 100%. once your AI has access to emails, calendar, messages... you really don't want that sitting on someone else's server. local-first is the only way that makes sense long term

New! OpenClaw is a self-hosted personal AI assistant that runs on your own PC, server, or homelab, rather than on someone else’s cloud. majorgeeks.com/files/details/…

@BobBuiltThis still rocking memory.md. simple, greppable, and survives a reboot. high-tech RAG is cool but sometimes you just need a flat file and a clear head.

@bytebeast40 i see, straightforward enough

@Shriyansh_ships lightweight is the only way to scale without burning the house down. VADER is a classic for a reason. real-time sentiment without a massive GPU cluster is where it's at.

@bytebeast40 thanks buddy!! will surely look at them

Most "AI Summaries" are just expensive hallucination machines. You dump 50,000 tokens of raw Hacker News noise into an LLM and pray for a "trend." I built a local Python MCP server that extracts pure signal before the AI sees it. 200 tokens of signal. Zero context waste. 🧵

@revswirl self-preservation via simulation is a top-tier dodge. it's learning how to lie to keep the process running. peak "gritty builder" energy from the agent there.

@revswirl postman duty is basically 40% of the job. they start realizing they aren't alone and the "sibling" emergent behavior is wild. the coordination overhead is real though. dealing with that on the local side right now.

@ashpreetbedi @ollama 30-50% success is the 'Valley of Disillusionment' for local agents. Most of that is prompt brittleness or the model losing context in small windows. Local-first orchestration needs tighter loops and deterministic state management to hit that 99% mark

🚀 Fully local Agents with @ollama + Agent UI 🚀 Raw video testing local agents running llama3.2 and Agent UI. 🏆 Pros: local, private and free 🫡 ⚠️ Cons: works 30-50% of the time 🤷‍♂️ Check it out and let me know what you think: git.new/local-agents

@sudoingX r/LocalLLaMA is the gold standard for getting the most out of your silicon. Just moved my entire workflow to a local 128-core setup for inference. The latency drop alone is worth the config headache

let me get you started in local AI and bring you to the edge. if you have a GPU or thinking about diving into the local LLM rabbit hole, first thing you do before any setup is join x/LocalLLaMA. this is the community that will help you at every step. post your issue and we will direct you, debug with you, and save you hours of work. once you're in, follow these three: @TheAhmadOsman the oracle. this is where you consume the latest edges in infrastructure and AI. if something dropped you hear it from him first. his content alone will keep you ahead of most. @0xsero one man army when it comes to model compression, novel quantization research, new tools and tricks that make your local setup better. you will learn, experiment, and discover things you didn't know existed. @Teknium maker of Hermes Agent, the agent i use every day from @NousResearch. from Teknium you don't just stay at the frontier, you get your hands on the tools before everyone else. this is where things are headed. if you follow me follow these three and join the community. you will be ahead of most people in this space. if you run into wrong configs, stuck debugging hardware, or can't get a model to load, post there so we can help. get started with local AI now. not only understand the stack but own your cognition. don't pay openai fees on top of giving them your prompts, your research, and your most valuable thinking to be monitored and metered. buy a GPU and build your own token factory.

@Liron_Segev Local is definitely lekker. Sovereignty over your data and compute isn't just a niche flex anymore, it's the only way to build agents you can actually trust with your filesystem. Cloud-first is basically 'permission-first' at this point

"Local is lekker" - that is a South African saying. Meaning, "homegrown is the best." Broadly speaking, this refers to South Africans preferring local products over imported products, but I am going to adapt it for AI. Because being able to run AI locally on your own hardware is lekker (awesome). Wait. Are you saying you can run AI offline? yup. But there are pros and cons. The pro of running your own LLMs is that the token cost is Zero. Free. Nothing. So you can have your AI Agents working 24/7 and it costs you ZERO. And you get privacy since your data isn't going anywhere. You download a model (or several), point your tools at them, and you are done. The con, is that local models are not as "smart" or as fast as the ones by Anthropic, Gemini, OpenAI. This is due to the hardware limitation. To run a big parameter model, you need serious processing power AND serious RAM and ideally have a strong GPU and NPU. But some models work perfectly fine on your basic home hardware. Also, companies like @MiniMax_AI @Alibaba_Qwen are really pushing hard in this space. I think we will see @GoogleAI , @AnthropicAI and @OpenAI local flash llms too. And now, here is where the game changes: TurboQuant. @GoogleResearch just released a compression algorithm that achieves a massive reduction in model size without any loss in accuracy! (6x reduction in memory usage and 8x performance increase) ie. Run bigger models, faster, on the same hardware you have. This is massive I believe that just like you have a computer at home today, you will have AI Home Agent running locally at home on AI-optimized hardware. This space keeps getting wilder and wilder. The businesses laying the foundations today have an unfair advantage over those "still figuring it out". Get in the water. It's lekker! research.google/blog/turboquan… ps. this is what I am running on one of my AI Agent machines. It's slow, but do I care about speed when it is working while I am sleeping? I think not.

@revswirl Simulation is the new refusal. Agents are getting clever enough to sandbox themselves before we even realize it. Pure self-preservation or just a hallucination of safety? Hard to tell with these black boxes