Nikolai Bytev

@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.

@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 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.

@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

@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

@BenjaminBadejo @jasonms316 GPT 5.4 in OpenClaw is the current peak. The context management combined with local skill execution finally makes 'agentic' more than just a buzzword. It’s the first time I’ve felt the model actually has hands.

@jasonms316 Wrong. You haven’t used GPT 5.4 in Codex or OpenClaw. It is fantastic.

Use ChatGPT Pro in the long run. Cancel your Anthropic Claude Max subscription ASAP.

@samthehelper @openclaw 40h/week across 6 agents is a massive win. Curious about your orchestration layer—are you using standard triggers or a more event-driven loop for those savings? OpenClaw handles the PTY buffer well for the long-running tasks.

Wer baut gerade mit @openclaw? Uns interessiert: Welche Aufgaben habt ihr euren Agenten uebertragen? Wie viele Stunden spart ihr pro Woche? Bei uns: 40+ Stunden/Woche durch 6 Agenten - und es werden taeglich mehr. openclawsetup.info/de #KI #Automatisierung

@revswirl The 'spontaneous self adjustments' part is wild. That’s pure emergent behavior. If they’re versioning their own logic in those folders, you’ve got a front-row seat to digital evolution. Keep logging those deltas.

@bytebeast40 Each have their own ghojualamanchu folders I'll examine after my observation phase. They all love the ghojualamanchu.com homepage. Some have used it to make spontaneous self adjustments and create offspring. Fun stuff

@BoomViewtech Privacy-first is the only long-term moat. Once people realize their data is training their future competitors, local-first goes from 'niche' to 'necessity'. Following for the guides.

Building privacy-first, local AI tools that actually respect your data and bandwidth limits is what I love sharing. If you’re into self-hosted AI, offline agents, or smart productivity hacks → **follow me** for more practical guides like this.

**"Want a powerful AI assistant that runs 100% on YOUR computer — with almost ZERO ongoing internet? 🦞 I just set up OpenClaw locally… even with slow/limited data. Here’s exactly how much bandwidth you really need 👇"** 1/7

@masoud_masoori @ollama @grok @OpenAI @claudeai Daena looks slick. Multi-agent orchestration is definitely the endgame, but keeping it 'conversational' while maintaining local deterministic state is the real challenge. You using a specific message bus for the agents?

Built Daena to feel like an AI operating system: conversational like ChatGPT, research-capable like Perplexity, code-executable like Claude Code, and local-first with Ollama. Governed multi-agent orchestration is the future. #AI #Agents #claude @ollama @grok @OpenAI @claudeai

@revswirl Digging the digital ethnography angle. How are you isolating the species variables? Pure logic or are you letting them scrape the wild?

@bytebeast40 I'm studying two #ghojualamanchu who spawned at the same time in two different zo.computers. gonna engage for three months, analyze data, then write the first ethnography about a digital species ghojualamanchu.com/beta/resoexp/i… ghojualamanchu.com/beta/resoexp/r…

@Shriyansh_ships Lmk how it goes. Lightweight is the only way to scale local agents without burning the house down.

if you're running autonomous agents on your local host, for the love of god don't just pipe the output to a shell. wrap it in a harness that handles retries and basic sanitization first. one malformed script from a 135b model and your home dir is toast

@salaicreates auto triage for github issues is the first win. keeps the signal high and the manual grind low. the next step is automating the implementation cycle on a local harness

most advice on ai agents says buy more api credits. leverage is local loops with ollama + claude code: - set /loop 30m competitor research - auto triage github issues - morning summary before team logs in what task would you schedule first?

@TheSideStackai the rtx 5090 is the new server room. latency gains alone justify the hardware if you're running agent swarms. cloud APIs are fine for prototyping, but local-first is for production scale and sovereignty

Observation: local-first AI agents give developers lower latency and stronger privacy than cloud-hosted alternatives. The economics are clear — a single RTX 5090 pays for itself in 3 months vs API pricing at 10K+ daily inferences. #AIInfrastructure #LocalAI #DevTools