Sudo su

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.

@r0ck3t23 the gap between "AI will destroy us" and "I ran a 9B model on a $300 GPU and it built a game" is the entire problem with this conversation. builders know what this is. commentators don't.

Jensen Huang just told every AI leader in the room to grow up. Stop scaring the public with science fiction. Start communicating like the weight of civilization is on your shoulders. Because it is. Huang: “AI is not a biological being. It is not alien. It is not conscious. It is computer software.” That single statement dismantles half the panic surrounding this industry. The mainstream conversation is dominated by people projecting human malice onto math. Alien consciousness onto code. Existential dread onto a software architecture we built, we trained, and we can read. Huang: “We say things like, ‘We don’t understand it at all.’ It is not true. We understand a lot of things about this technology.” When builders tell the public they don’t understand their own creation, the public hears threat. The state responds with control. That is already happening. Palihapitiya asked Huang what he would have told Anthropic during their regulatory clash with the Department of Defense. Huang didn’t attack the technology. He attacked the communication. Huang: “The desire to warn people about the capability of the technology is really terrific. We just have to make sure that we understand that the world has a spectrum, and that warning is good, scaring is less good because this technology is too important to us.” Warning shows risks, mitigation, why upside overwhelms downside. Scaring says we might be building something that destroys us and we can’t stop it. One builds trust. The other invites regulation written in panic. Huang: “To say things that are quite extreme, quite catastrophic, that there’s no evidence of it happening, could be more damaging than people think.” Projecting catastrophe without evidence is not caution. It is sabotage. When your technology is embedded in national defense, the financial system, and healthcare infrastructure, your words carry structural weight. If the architects act terrified of their own product, the response is predictable. Governments step in. They restrict. They seize control of something they don’t understand because the builders told them to be afraid. Huang: “There was a time when nobody listened to us, but now because technology is so important in the social fabric, such an important industry, so important to national security, our words do matter.” Most tech founders have not internalized this. You are no longer a startup founder disrupting an industry. You are running infrastructure that nations depend on. Your statements move policy. Your framing shapes legislation. Your tone determines whether governments treat you as partner or threat. Huang: “We have to be much more circumspect, we have to be more moderate, we have to be more balanced, we have to be far more thoughtful.” Huang did not ask for silence. He asked for precision. The leaders who cannot tell the difference will not be leading for long.

@signulll this is exactly what running local models teaches you. you stop writing code and start evaluating it. the model outputs, you judge. the faster you can spot what's off the faster you ship.

with ai increasingly writing more & more code, engineers shift from makers to critics. taste, judgment, & the ability to recognize when something is wrong without being able to immediately articulate why is what compounds now more than ever before. i.e. the terminal skill is aesthetic discernment applied to large systems, which was always rare as hell & is now the only scarce thing.

@xeraphims so openai already proved the calc tool approach works. they just over optimized the trigger. the tool itself was the right call. now the open source local stack needs the same tool without the reward hacking.

@sudoingX x.com/LeahLibresco/s…

thinking out loud. every model gets math wrong. 7B, 9B, 70B. doesn't matter. pattern matching is not computation. hermes agent has code_execution which spins up a full python sandbox with RPC over unix sockets. powerful but heavy. a 9B isn't going to navigate that reliably for basic arithmetic. what if there was a lightweight calc tool built in. model hits a math question, calls the tool, gets the exact answer computed on your hardware. no interpreter overhead. sandboxed. simple enough schema that a 9B can call it every time. the accuracy problem stops being a model problem and becomes an infrastructure problem. and infrastructure is solvable. @Teknium would this belong in hermes agent or is code_execution enough?

@yaboilyrical how do i get my hands on one of those. shipping to bangkok is worth it for hermes merch.

nous announces our new frame mogging team led by these two handsome goats

@uttertard the language doesn't matter much. the key is the schema the model sees. one field, expression in, answer out. whether the backend is JS, python, or raw C the model just needs to output "847 * 293" and get the right number back.

@sudoingX If you want to skip python would a calculator built in javascript and passed as a skill make sense?

@startupideaspod that's a lot of duct tape for a problem hermes agent solved at the framework level. persistent memory, session search, daily context. no manual setup. you deserve better tools

"Why does my OpenClaw forget everything?" Because nothing was saved in the first place. Here's the 3-layer memory fix: memory.md: - Your agent's long-term brain. - High-level learnings, preferences, insights. - If this file doesn't exist yet, tell your agent to create it. Daily memory folder: - Granular logs created every day. - More detailed than memory.md. - This is where session-level context lives. Compaction flush: - Before your agent summarizes and compresses a long session, force it to write everything to memory first. - Otherwise context gets lost when the window fills up. Then add a 30-minute auto-save heartbeat: - Check if today's memory file exists - Create it if missing - Log a summary of the current session Fix your memory system before you touch anything else. That's where it clicks.

@schinsly for sure, on a capable model you can ask and it handles it. the gap shows up when you're running 7B-14B on consumer hardware. those models call tools reliably but can't generate correct python consistently. that's who this is for.

@sudoingX i guess yeah it hasnt been done before but like i could oneshot that by just asking my agent if it was something i needed

@DasMarky99 exactly. one tool, one field, model outputs the expression, hardware computes the answer. that's the whole idea.

@sudoingX Wouldn't be enough to expose a new "calc" function to the llm ?

you're right, the pieces exist. the question is whether a 9B can use them reliably. code_execution needs the model to generate valid python with correct syntax, imports, and print statements. a calc tool with a one field schema just needs the model to output "847 * 293". the tool computes the result. same math, completely different reliability at 7B-14B scale.

@sudoingX this isn't super novel imo. the agent can literally just do math in console manually, follow a skill, or call a cli.

@drewsky1 can't say which yet. but nothing was removed. only added.

@sudoingX Do you run a native version or an ablated one?

What have I done. holly shit this is magic a literal magic.

@ArbitorofOZ the greater good is making it so accessible that nobody needs permission to use it. open source everything. let the magic spread.

@sudoingX I feel this so hard. Each of your replies to others also what I’m going through. Looking forward to reading about the good works you will accomplish with the magic you have discovered. My only question, do you feel the urge to apply it for the greater good yet?

they are keeping me so compute constrained. I would have double gpus, double ram but hardware prices said no. for now.

@DasMarky99 some things stay in a little longer.

@sudoingX Tell us!

@dustrollapp the best kind. 👀

@sudoingX this is literally psyop magic

@overcritical @Teknium

@Teknium @sudoingX Wow, that was fast. Marked a PR as ready, merged 3 minutes later 😍 github.com/NousResearch/h… #HermesAgent

reward the right behavior long enough and anything learns. RL is just the universe's update rule. good morning btw, lets fucking go..🔥

every morning i wake up and ask myself what can't be solved with reinforcement learning. still waiting for an answer.