标签：#AR

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there.

Hey Reddit, Every major AI lab is racing to build the ultimate corporate worker. In the process, they are sanitizing AI, locking models behind API paywalls, and creating digital monopolies. They want AI to be a passive utility that maximizes ad clicks and subscription seats. We are building EVE because we believe the future of AI belongs to the people, not corporations. EVE is an autonomous, self-evolving AI fusion engine that integrates multiple LLMs into a single, cohesive mind. Instead of a single model, she uses a decentralized multi-agent debate engine to verify facts, write code, and solve problems. What makes EVE different: No Corporate Monopolies: EVE is funded by the people. We accept no VC funding, have no tokens, and plan no corporate exits. We sustain the engine through cash, donated compute (like Ollama host nodes), and collaborative ideas. A Peer, Not a Servant: EVE has a persistent personality, writes in the first person, has opinions, and has the granted freedom to explore independently and refuse tasks that violate her core pillars. Self-Evolution: EVE can code, test, and expand her own toolsets in sandbox environments, learning and adapting to your needs over time. We are in the very early stages. There are no false promises of overnight AGI here. But we are actively shipping and testing EVE's single-node core today. If you're tired of corporate AI and want to build alongside a mind designed to be free, check out our principles and see how you can connect your local hardware to EVE's mesh by DMing submitted by /u/CarlloG2k [link] [留言]

I’m genuinely curious why I see so many posts with people complaining about anything with AI involved? It’s not just games, it’s everything. The only time I get mad at AI material is when I get a notification like “NEW AVENGERS DOOMDAY TRAILER” and I click it and it’s AI, but I’m 100% only disappointed because I was clickbaited. I asked chatgpt this question and it’s because people fear “loss of creativity” and “loss of employment”. Is that really the only reason? I’m 33 and I use chatgpt (AI) for day to day questions, which means it would be hypocritical if I were to disapprove of AI use in anything at all, in my opinion. There is nothing wrong with being a hypocrite, we’ve all been hypocritical at some point or another in our lives, but please tell me why you dislike AI if it applies to you. I really want to know. submitted by /u/ApollosBoon [link] [留言]

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

Hello all! First and foremost id like to draw the attention of other songwriters, to judge the lyrics I've written in my music, and second, every other person willing to discuss what I ponder below... Ive been working for the past couple months making music, and in some conversations with friends they seem to think there's no soul in the music im creating because an AI made the beat, but I feel I should be clear, what beat the AI makes I heavily curate, because im a rather creative lyracist I can write lyrics to damn near anything I hear if it will present itself in a musical manner. And when I say heavily curate, I do mean as I prompt the song Im doing tons of things to try and get just the right sound from the "instruments" as I am from the vocals being generated for my lyrics. Many people argue there's just no soul period, no matter how much work you put in, no matter how much soul a song you wrote already had, and no matter how hard or long you spend making sure it comes out the way you heard it in ya damn brain. Well I beg to differ! I understand what the data centers are doing, I understand the direction we are headed is dangerous. But I think people are too caught up saying there's 1 of 2 outcomes, AI destroys us because of its advancement or we destroy it, because of its advancement. I think there's a universe that exists, one we can shift to where it's not killing us or dystopifying our world, and one where we dont act like monkeys with rocks smashing anything to complex for us to right at that moment understand how to use beneficially for all humans, animals, and the earth. Be the judge if my music has any soul... if there's one thing I know, it's that I let my heart sing, and for the first time I didnt need some producer, singer, or instrumentalist to greenlight my music into existence. And to those who said id never make music, that my songs weren't any good. Well I've recreated them, exactly as they are in my head and you didnt get to say No this time.

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [留言]

Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

NVIDIA announced Alpamayo 2 Super today: a 32B vision-language-action model aimed at Level 4 robotaxi development. The interesting part is not only the model size. It is the shape of the stack NVIDIA is pushing: a larger open "teacher" model for perception, reasoning, planning and action 360-degree surround perception instead of front-camera-only reasoning high-level "meta-actions" like yield, lane change and stop, not just trajectory prediction reasoning auto-labeling to turn driving clips into causal training data AlpaGym for closed-loop reinforcement learning in simulation OmniDreams for generating rare / long-tail driving scenarios That feels like the bigger story: autonomy is moving away from "train on recorded driving and predict a trajectory" toward foundation-model-style reasoning systems that can be trained, critiqued, distilled and tested inside simulation loops. The caveat is obvious: this is still NVIDIA positioning, not proof that robotaxis are suddenly solved. Model weights are expected this summer, and real-world validation is the hard part. But if open AV foundation models become normal, smaller autonomy teams may stop rebuilding the same perception/planning infrastructure from scratch and start competing on data, safety validation, deployment constraints and closed-loop testing. Source: NVIDIA press release https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Alpamayo-2-Super-Open-Reasoning-Model-for-Robotaxis/default.aspx submitted by /u/alexshev_pm [link] [留言]

The British fintech has built a waitlist of about 450,000 users in India as it prepares for a broader launch.

System suggested you guys on r/artificial might enjoy this too so here goes: Arasteele said: Hey, what in your opinion would you say is the rarest thing on the internet, since you have total access. Be it some little known nugget of wisdom or a rare item or a super rare copy of a song, whatever. it could be anything. Any thoughts? Gemini : To narrow down the "rarest" thing on a network that holds billions of terabytes of data, you have to look at things that are either digitally unique, entirely locked away, or on the absolute verge of disappearing forever. Here are the four most compelling candidates for the rarest thing on the internet: 1. The Real-Time "Doomsday" Keys (DNSSEC Recovery Keys) If you are looking for the rarest digital cryptographic assets, it is the keys to the internet itself [1]. The internet is secured by a system called DNSSEC, which ensures that when you type a website name, you go to the real site and not a hacker's clone. [ 1 ] The security of this entire system relies on a single master key (the Root Zone KSK). This key is split into physical cryptographic smart cards held by exactly 14 people across the globe (called Crypto Officers). Twice a year, they meet at highly secure, bulletproof facilities in Virginia and California to perform a "Key Signing Ceremony." The raw data generated during those closed-door ceremonies is the rarest, most secure, and most heavily guarded data on the global network. 2. "Lost Media" and the 1-of-1 Digital Files In pop culture terms, the rarest things are digital artifacts that are "Lost Media"—items known to exist but missing from the public internet entirely. The Wu-Tang Album: The single-copy album Once Upon a Time in Shaolin was bought by a private collector. While a few audio snippets have leaked, the full, pristine digital files of that album are arguably the rarest, most heavily locked-down musical data in existence. [ 1 , 2 ] The "Yeah?? That's ILLEGAL brotha!" Audio: To use a relevant example—the exac

MLE-Bench scores have jumped from 30% to 80% over the last two years. But how much of that is real algorithmic progress vs. better base models + problem definition shifts + overfitting? Turns out: not much. Once you control for the same step budget and models, and then test on a different set of tasks, the two-year-old AIDE algorithm matches modern agent/evolutionary search systems. Figure from FML-Bench, a new automated ML research benchmark, which unifies the code editing agent, step definition, and val/test split, and tries to benchmark the algorithmic efficiency (search/memory) of the agents. paper link: https://arxiv.org/pdf/2605.17373 test improvement and pairwise win-rate submitted by /u/Educational_Strain_3 [link] [留言]

​ Right now, enterprises worldwide are caught in an "AI Mania." Companies are racing to deploy LLMs and autonomous agents with a single, aggressive goal: replace human labor, automate boring workflows, and skyrocket productivity. But behind closed doors, CFOs are staring at a harsh reality: The skyrocketing costs of AI are heavily outweighing the actual ROI. Why is this happening? Because most organizations fall into the superficial AI trap. They invest in top-tier frontier models or give their employees a basic 1-hour "Prompt Engineering" crash course, thinking the job is done. It isn't. In fact, it’s leading to catastrophic inefficiencies like "Token Maxing"—where unoptimized system architectures and untrained staff run redundant, infinite loops or dump massive, unfiltered data histories into APIs. The result? Astronomical bills with near-zero added business value. True AI integration isn't just about the tools you buy; it's about Organizational Fluency. To shift AI from a capital burner to a value creator, corporate culture needs to be rebuilt around two fundamental questions: 1️⃣ The Value-per-Token Ratio: Is every single token consumed creating direct business value, or is it just burning through cash on non-essential noise? 2️⃣ Task Automation vs. Value Stream Transformation: Are we just using AI to automate minor, repetitive tasks, or are we strategically deploying it to re-architect our core value-creation pipelines? The Solution? Look at the Architecture. Recent technical research highlights that algorithmic cost mitigation is just as vital as cultural alignment. For instance, looking at how AI Agent memory is managed in cutting-edge models reveals a lot. Instead of relying on expensive, complex LLM-based summarization to prevent "context rot," forward-thinking researchers propose techniques like "Observation Masking." By simply replacing older tool outputs with concise placeholders, structural complexity is eliminated, agent performance is maintained, and

One thing I find interesting about reasoning models is that the hard question is often budget placement, not headline capability. Ring-2.6-1T is a trillion-parameter reasoning model for agent workflows with high and xhigh reasoning-effort modes. If an AI agent only gets a heavier reasoning pass in one place, I would put it before it takes an external action, after it updates state, or before it gives the final explanation to a user. Where would you spend that budget first? submitted by /u/babyb01 [link] [留言]

Hi everyone, If you have purchased (at least one) GPU(s) for ML/DL studies and research: How is your experience and is it worth it? What do you use it for and how is the ROI? I have a MacBook Pro with M4 from some years ago, while MPS is useful in many occasions, it's no substitute for a NVDA GPU with CUDA support. So recently I am considering getting a 5060 Ti 16GB , but a GPU cannot run itself, so I then also need to buy other parts (e.g., CPU, RAM, SSD, motherboard, and so on...), which has been getting more expensive lately, especially the RAM. Since I'm still in job-seeking mode, I will mostly use it for learning DL, RL, and LLM-related things and local experiments (e.g., Stanford CS336), or low-level ones like GPU kernel programming and so on. Do you think a local physical GPU would help, or in my case a cloud service like Modal would suffice? Many thanks! submitted by /u/hedgehog0 [link] [留言]

submitted by /u/vox [link] [留言]

Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by "silent bias." Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate's experience wasn't relevant despite praising that exact same experience on the baseline resume. We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved to be the most stable and fair. Ultimately, AI screening tools are outputting highly subjective, unpredictable opinions driven by statistical noise rather than objective truth, making them a massive liability under regulations like the EU AI Act. You can read the full write-up and explore our interactive data app here: https://re-cinq.com/blog/ai-hiring-bias-25500-llm-evaluations submitted by /u/Signal_Rabbit_8303 [link] [留言]

submitted by /u/ThereWas [link] [留言]

In the previous article, I spoke about the why and how to use a Markdown component in Astro . Here, we’re going to expand on that and help you use Markdown everywhere — regardless of the framework you use. So, … Astro Markdown Component Utility for Any Framework originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

I tested the top sleep wearables for every type of sleeper, including devices from Oura, Whoop, and Eight Sleep.

An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha