Tag: plasma refactoring

If you are programmer, have ollama & local llm installed then continue reading else skip it I am continously working on completely offline vsode extenstion and my purpose is to add agent mode capabilites using local llms. So I started building it and as of know: Automatically create, edit files. Add selection as context, Add file as context, Add project structure, framework as context. I am still working on it to add more functionalities and features. I want feedbacks from you as well. I am trying to make it as capable as I can with my current resources. If you’re curious to try it out, here is link: https://marketplace.visualstudio.com/items?itemName=Knowivate.knowivate-autopilot Share feedback, bug reports, and wishlist items—this is your chance to help shape the final feature set! Looking forward to building something awesome together. Thanks! submitted by /u/InsideResolve4517 [link] [comments]

There is the pending question wether or not LLMs can get us to AGI by scaling up current paradigms. I believe that we have gone far and now towards the end of scaling compute in the pre-training phase as admitted by Sam Altman. The post-training is now where the low hanging fruit is. Wether current RL techniques are enough to produce AGI is the question. I investigated current RLVR (RL on verifiable rewards) methods, which mostlikely is GRPO. In theory, RL could find novel solutions to problems as shown by AlphaZero. Do current techniques share this ability? The answer to this forces us to look closer at GRPO. GRPO samples the model on answers, and then reinforces good ones and makes bad ones less likely. There is a significant difference to Alphazero here. For one, GRPO bases its possible 'moves' with output from the base model. If the base model can't produce a certain output, then RL can never develop it. In other words, GRPO is just a way of incovering latent abilities in base models. A recent paper showed exactly this. Secondly, GRPO has no internal mechanism for exploration, as opposed to Alphazero which uses MCTS. This leaves the model sensitive to getting stuck in local minima, thus inhibiting it from finding the best solutions. What we do know however, is that reasoning models generalize surprisingly well to OOD data. Therefore, they don't merely overfit CoT data, but learn skills from the base model. One might ask: "if the base model is trained on the whole web, then surely it has seen all possible cognitive skills necessary for solving any task?", and this is a valid observation. A sufficient base model should in theory have enough latent skills that it should be able to solve about any problem if prompted enough times. RL uncovers these skills, such that you only have to prompt it once. We should however ask ourselves the deep questions; if the LLM has exactly the same priors as Einstein, could it figure out Relativity? In other words, can models make truely novel discoveries that progress science? The question essentially reduces to; can the base model figure out relativity with Einsteins priors if sampled close to infinite times, i.e. is relativity theory a non-zero probability output. We could very well imagine it does, as models are stochastic and almost no sequence in correct english is a zero probability, even if its very low. A RL with sufficient exploration, thus one that doesn't get stuck in local minima, could then uncover this reasoning path. I'm not saying GRPO is inherently incapable of finding global optima, I believe with enough training it could be that it develops the ability to explore many different ideas by prompting itself to think outside of the box, basically creating exploration as emergent ability. It will be curious to see how far current methods can bring us, but as I've shown, it could be that current GRPO and RLVR gets us to AGI by simulating exploration and because novel discoveries are non-zero probability for the base model. submitted by /u/PianistWinter8293 [link] [comments]

If you've tried out Claude Computer Use or OpenAI computer-use-preview, you'll know that the model intelligence isn't really there yet, alongside the price and speed. But if you've seen General Agent's Ace model, you'll immediately see that the model's are rapidly becoming production ready. It is insane. Those demoes you see in the website (generalagents. com/ace) are 1x speed btw. Once the big players like OpenAI and Claude catch up to general agents, I think it's quite clear that computer use will be production ready. Similar to how ChatGPT4 with tool calling was that moment when people realized that the model is very viable and can do a lot of great things. Excited for that time to come. Btw, if anyone is currently building with computer use models (like Claude / OpenAI computer use), would love to chat. I'd be happy to pay you for a conversation about the project you've built with it. I'm really interested in learning from other CUA devs. submitted by /u/WompTune [link] [comments]

I've thought about this a bit and I'm curious what other perspectives people have. If a super intelligent AI emerged without any emotional care for humans, wouldn't it make more sense for it to just disregard us? If its main goals were self preservation, computing potential, or to increase its efficiency in energy consumption, people would likely be unaffected. One theory is instead of it being hellbent on human domination it would likely head straight to the nearest major power source like the sun. I don't think humanity would be worth bothering with unless we were directly obstructing its goals/objectives. Or another scenario is that it might not leave at all. It could base a headquarters of sorts on earth and could begin deploying Von Neumann style self replicating machines, constantly stretching through space to gather resources to suit its purpose/s. Or it might start restructuring nearby matter (possibly the Earth) into computronium or some other synthesized material for computational power, transforming the Earth into a dystopian apocalyptic hellscape. I believe it is simply ignorantly human to assume an AI would default to hostility towards humans. I'd like to think it would just treat us as if it were walking through a field (main goal) and an anthill (humanity) appears in its footpath. Either it steps on the anthill (human domination) or its foot happens to step on the grass instead (humanity is spared). Let me know your thoughts! submitted by /u/paledrip [link] [comments]

TIME article: https://time.com/7279010/ai-virus-lab-biohazard-study/ submitted by /u/MetaKnowing [link] [comments]

submitted by /u/MetaKnowing [link] [comments]

Last week I got an email asking for help on a technical issue. It was well written, totally to the point, but it was a bulleted list with key words bolded–and–about–nine–hundred em–dashes sprinkled in just because. I put about as much effort into reading it as I assumed they did writing it, figuring any real nuance was lost. Sound familiar? Once a day I see an email or LinkedIn post that screams “AI did this” and my brain hits skim‑mode. The text is fine, the grammar spotless… and the vibe completely beige. And it's not to say you shouldn't be using AI for this, you absolutely should... but with a few seconds to can give it that human edge. Why do we sniff it out so fast? Three reasons, lightning‑round style: Audience design is instinct. Real people slide between tones without thinking. An LLM can imitate that only if you spoon‑feed the context. Training data is a formal swamp. Models are force fed books and white papers, so they default to high polish academic/journalism voice. Imperfections are proof of life. A tiny typo or weird phrasing (“None of Any of the Above”) feels human. How I pull a draft back from the uncanny valley Set the scene out loud. “You’re a support rep writing a friendly apology to one angry customer.” Forces the model out of Investor‑Day mode. Show a mini sample. Paste two sentences in your actual voice, tell it to keep going. Nudge the randomness, but not to 11. Temperature 0.9 is usually enough spice. Feed real details. Quotes, dates, product names...anything concrete beats “our valued user.” Edit while muttering to yourself. If a sentence makes you roll your eyes, kill it. Leave one rough edge. An em‑dash jammed against a word—like this—or a single stray comma can be the handshake that says “human.” That’s basically it. AI is an amazing writing partner, but it still can’t nail “typing on my phone while driving and yelling at traffic.” That part is for now, distinctly human. What tricks are you using to keep your robots from making you sound like a robot? I’m collecting any tip that keeps my feed from turning into an em dash hellhole. submitted by /u/bantler [link] [comments]

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