标签：#AR

If you doubted his genius, doubt no more.

I mean human still operates but basically gets one joystick 🕹️ because machine will think for itself how to put its leg better. So some sort of spinal cord intuitive walking. With the library of objects one shouldn’t step on, no way. submitted by /u/Ubud_bamboo_ninja [link] [留言]

If you've ever tried to use an AI resume builder, you've probably hit the same wall I did. You sign up, poke around, find the one feature you actually need — and then boom: "Upgrade to Pro for $29/month." It's frustrating. Resume help shouldn't be locked behind a paywall. So I built my own. Meet Persona Persona is an AI-powered resume builder that you run completely on your own machine . No deployment required. No subscription. No account on some third-party service. You clone the repo, set it up, and it's yours. It's a fork of the excellent open-source project ResumeLM , but I've added a bunch of features I couldn't find anywhere else — especially around local AI and template variety. 👉 GitHub: github.com/nithiin7/persona (Drop a ⭐ if you find it useful!) The Big Deal: Run AI Completely Offline with Ollama This is the feature I'm most proud of. Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine. Here's how simple it is: Install Ollama on your computer Pull any model ( ollama pull llama3 , for example) Open Persona's settings, point it to your local Ollama URL Done — the AI now runs entirely on your machine No OpenAI key. No Anthropic key. No usage limits. Your resume data never touches an external server. If you do want to use cloud models, Persona supports those too — GPT-5, Claude Opus 4.7, Claude Sonnet 4.6, and a handful of open-source models via OpenRouter. But the Ollama path is what makes this genuinely different from everything else out there. It's 100% Free — Everything Unlocked The original ResumeLM had Stripe payments baked in. I ripped all of that out. Every single feature in Persona is available to every user, always. There's no "Pro plan." There's no feature gating. You self-host it, you own it, you use all of it. 10 Resume Templates Persona ships with ten distinct templ

I built a knowledge graph out of my own work sessions. Hundreds of them — transcripts of me building a system with LLMs, extracted into concepts, decisions, findings, and the edges between them. For a while it felt like the thing was working. I'd query it, get back a clean structured answer, and move on. Then I ran a foreign model against it. I gave a different model my concept definitions and asked it to reconstruct the system, both the vocabulary and the relationships. It recovered 97.7% of the words. It recovered 61.1% of the structure. That 36-point gap was the first time I could see the problem instead of just living inside it. The vocabulary transferred because the definitions were written carefully. The edges didn't, because the edges were the part I'd let the extraction handle. And the whole time, querying the graph had felt complete. The structure came back typed, connected, confident-looking — so I stopped looking. I started calling it premature retrieval closure: the retrieval returns something shaped like a whole answer, which is exactly why I didn't notice the parts that were missing. Part 10 of Building at the Edges of LLM Tooling . If you're running a long-term project through an LLM-backed memory system (anything that turns raw sessions into structured, persistent memory), this is about the step where the structure starts lying about how complete it is. Start here . Why It Breaks Every memory system of this kind does the same move. An LLM reads raw interaction (a conversation, a document, a session log) and lifts structured memory out of it: entities, facts, rules, summaries. That structured memory becomes the thing the agent reads later, instead of the raw record. The lift is where fidelity goes. Pulling clean structure out of messy text means making decisions the text didn't make explicit: which entity this pronoun refers to, whether a relationship is real or inferred, what to keep and what to drop. Those decisions can be wrong, and when they are,

After debugging 20+ broken RAG systems, I've identified the 6 decisions that determine whether yours works. Here's how to get each one right. The RAG Developer's Trap Every RAG developer falls into the same trap: you build the basic pipeline, it sort of works, and then you spend weeks tweaking prompt templates — while the real problem sits untouched in your indexing pipeline. The 80/20 rule: 80% of RAG problems come from indexing, not generation. But 80% of debugging effort goes into generation. Let's fix that. Decision 1: Embedding Model — The Single Biggest Lever The mistake: Using all-MiniLM-L6-v2 for Chinese documents because it's the default in every tutorial. Why it's wrong: It's English-trained. Drop it on Chinese text and it loses 30-50% of semantic fidelity. Language Use This Chinese BAAI/bge-large-zh-v1.5 (1024-dim) Chinese + English BAAI/bge-m3 (multilingual + sparse) English text-embedding-3-large Code jina-embeddings-v3 or voyage-code-3 Non-negotiable: Indexing model and query model must be byte-for-byte identical. Switch models = rebuild entire index. Impact: +15-40% Recall@10 for Chinese RAG. Decision 2: Chunk Size — Not a Magic Number Physics: Too small (< 100 tokens) = semantic fragmentation. Too large (> 1000 tokens) = noise injection. Document Type Sweet Spot Overlap FAQ / Short-form 128-256 20 Technical docs 512 50 Long-form articles 768-1024 100 Code Function boundaries 0 The method matters more than the size. Use recursive splitting, not fixed-length: from langchain.text_splitter import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter ( chunk_size = 512 , chunk_overlap = 50 , separators = [ " \n\n " , " \n " , " . " , " " , "" ] ) Impact: +5-15% Recall@10. Decision 3: Index Type — HNSW vs IVF Scale Use Why < 1M vectors HNSW Recall > 0.95 1-5M, RAM tight IVF + PQ 75% memory savings > 5M IVF + PQ + Sharding Horizontal scale Key nuance: HNSW has high insertion cost. Streaming docs → IVF may be better even at small scale. Im

Set Up Your Own ChatGPT: Ollama + Open WebUI for Data That Never Leaves Home As artificial intelligence models rapidly integrate into our lives, privacy concerns are growing in parallel. Especially for companies or individuals working with sensitive data, sending information to cloud-based services can pose a serious risk. At this point, setting up your own local Large Language Model (LLM) infrastructure offers a great solution. In this guide, I will explain step-by-step how to set up your own chat interface using tools like Ollama and Open WebUI, ensuring your data never leaves your system. This approach allows you to both reduce costs and maximize your data security. This setup is particularly important for those like me, with a background in enterprise software development, who believe that data flows should always follow the most secure path. In the past, working on a production ERP, transferring supply chain data to external systems without anonymization could lead to serious security vulnerabilities. This is where local LLM solutions come into play. Why You Should Set Up Your Own Local LLM While cloud-based LLM services are incredibly convenient, they come with some fundamental drawbacks. Most importantly, every piece of data you input is potentially sent to the service provider's servers. This can be unacceptable, especially when dealing with financial data, patient information, trade secrets, or sensitive code in your personal projects. By setting up your own local LLM, you eliminate these risks. In recent months, while working on my side project, a financial calculator, I felt the need to use an LLM for complex financial analyses. However, the details of these analyses could not be leaked externally. This situation led me to search for a solution where I could keep my data under my own control. Ollama and Open WebUI emerged as the most practical and powerful duo in my search. ℹ️ Data Privacy and Control A local LLM solution gives you full control over where

Once you start noticing them, they're everywhere. And the algorithm makes it worse, the more you engage, the more it feeds you... Perfect lighting in every single photo. That glow on the face in every other pic or video it doesn't matter what the background or lighting is. Follows 3 people but has 40k followers. Generic bio that could apply to literally anyone. Comments that are just emojis or "love this!" The creepy part is how consistent the patterns are across platforms. Same pose angles. Same aesthetic. Same engagement ratio that makes no sense for a real person. I built a small community tool where people can flag and vote on suspicious profiles. Not trying to be the judge, just crowdsourcing the pattern recognition. I feel humans are really good at spotting these when you give them the right frame and observation. Anyone else been noticing more of these lately? Curious what other people pick up on this. submitted by /u/Brilliant-Nerve-8972 [link] [留言]

Curious about the ones that didn’t stick. everyone talks about what they use but nobody talks about what they tried and dropped and why. submitted by /u/aiprotivity_ [link] [留言]

The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.

Did anyone else submit to ACM ICMI 2026? The reviews were recently released, and this is my first time submitting to ICMI, so I'm not very familiar with the acceptance patterns. I submitted a long paper and received the following overall ratings: 4 (Probably Accept), 3 (Borderline), 4 (Probably Accept) The reviewer with the highest stated expertise recommended acceptance, while the borderline reviewer had some concerns about soundness but still considered it a nice contribution. For those who have submitted to or reviewed for ICMI before, how would you interpret these scores? Is a 4/3/4 generally considered competitive after rebuttal, or is it still a long shot? Would appreciate any insights from past authors or reviewers. submitted by /u/kanishq95 [link] [留言]

For me its summarizing long documents. The first draft looks convincing, but checking missing context and subtle mistakes can take almost as long as doing it manually. Curious which tasks other people expected AI to handle well but still end up reviewing line by line. submitted by /u/Delicious_Weekend546 [link] [留言]

submitted by /u/Vee_Fan38083 [link] [留言]

I built a distributed compute grid where your idle laptop runs ML jobs — the orchestrator behind it The pitch: a single FastAPI hub takes compute jobs from ML researchers, and a fleet of home PCs and gaming rigs (RTX 4090s, M2 MacBooks, anything with a GPU and a Python interpreter) polls in, picks up work, and ships results back. A 20% platform fee funds the hub. An interactive dashboard shows the mesh in real time. I have been living inside this codebase for a few weeks. This post is about the part that actually determines whether the thing works or does not — the orchestrator . No frontend, no marketing — just the brain. Live dashboard: man44.zo.space/compute-pool Repo: github.com/AmSach/ComputePool-Grid The problem with "dumb" schedulers The first version of ComputeOrchestrator had a one-line bug that took down a 12-node stress test. Two jobs hit the hub at the same millisecond. Both saw the same node as idle . Both wrote busy to the same row. One node ended up double-allocated, the other starved, and the test logs looked like a hostage negotiation. The fix had to be three things at once: A scoring function that picks the right node, not just the first idle one. An async lock so concurrent submissions cannot race on a single node. A heartbeat monitor that reclaims nodes that ghosted. Here is what it looks like now. The scoring algorithm def _calculate_score ( self , capacity : Dict [ str , Any ], requirements : Dict [ str , Any ]) -> float : """ Heuristic for node-task matching. """ score = 0.0 if capacity . get ( " gpu_vram " , 0 ) >= requirements . get ( " min_vram " , 0 ): score += 10.0 if capacity . get ( " cpu_cores " , 0 ) >= requirements . get ( " min_cores " , 0 ): score += 5.0 return score The weights are deliberately lopsided. A node that satisfies a job's VRAM requirement gets a 2x bonus over a node that just barely has enough cores. The intuition: GPU work is the long pole. If you cannot fit the model in VRAM, nothing else matters, no matter how many

I built a free 2026 World Cup prediction tool as a fun side project. The soccer part was fun, but the AI part ended up being more interesting. I tested four different prediction views: My own methodology A tournament-read model based on current form, roster age and fitness, squad depth, style matchups, counterattack danger, fatigue, climate, penalties, manager decisions, and bracket path. Betting odds only A market-based view. ChatGPT independent forecast I did not give it my methodology or preferred winner. I simply asked it to build the best prediction it could using its own logic. Gemini logic forecast This one was the most interesting. Gemini asked me who I was rooting for before making its prediction. Then, in my testing, it chose that team to win. When I changed the team I said I was rooting for, Gemini changed the winner to that team too. That stood out to me. Not because it is evil or anything dramatic like that. But it is a good reminder that AI can lean toward making the user happy. If you feed it a bias, it may hand that bias back to you with better wording and more confidence. The biggest lesson from the project was simple: Good input in, good output out. Garbage in, garbage out. AI is powerful, but it still needs human judgment. It can organize thinking, compare logic, test assumptions, and help build something useful. But it still depends on the person using it to understand the situation, challenge weak assumptions, and know when an answer sounds right but may not actually be right. The tool is a standalone HTML file. It is not a live data feed. It does not automatically update injuries, suspensions, weather, lineups, or odds movement. Users can enter live group-stage scores manually, but anything else has to be adjusted by the user. I’m curious how others think about this: When an AI asks for your preference before giving a forecast, is that helpful context, or does it risk steering the answer toward pleasing the user? Also happy to drop a link for d

Most AI agent systems start with a simple idea: "Let's give the Agent Memory". At first, this usually means saving previous messages, retrieving similar chunks, and injecting them back into the prompt. That works for demos. It does not work reliably for real organizational workflows. Because chat history is not memory. A vector database is not memory. A bigger context window is not memory. Those are storage and retrieval mechanisms. Useful, yes. But memory in an AI Agent System is not just about remembering more information. It is about deciding what should influence future behavior. And that is a much harder problem. The Simple Version When people say "Agent Memory", they often mix together very different things: Conversation history User preferences Workflow state Previous tool results Retrieved documents Task summaries Business rules Approved policies Model-generated assumptions Evidence of completed actions But these should not all be treated the same way. A user saying "I usually prefer short answers" is not the same kind of memory as "invoice #123 was paid". A model saying "the client is probably interested" is not the same as a CRM record. A previous chat message is not the same as a runtime audit log. An approved company policy is not the same as a generated summary. When all of these are thrown into the same context window, the agent may look smarter for a while. Then it slowly becomes unreliable. More Context Can Make Agents Worse A common instinct is to give the agent more context. More history. More documents. More summaries. More retrieved chunks. More memory. But more context does not automatically mean better reasoning. Sometimes it means more noise. Sometimes it means stale information. Sometimes it means private information leaking into the wrong task. Sometimes it means the model starts treating old assumptions as current facts. Sometimes it means low-authority memory overrides high-authority evidence. This is one of the strange things about AI Age

I generated videos with Omni and want to remove the visible and possibly invisible watermarks it applies. I have only seen tools for pictures but none for videos so far. submitted by /u/Born-Explanation-544 [link] [留言]

Hi everyone, I’m close to completing my degree in Psychology, and I’m also a Systems Engineering student. is like, roughly comparable to Software Engineering / Computer Science outside Latin America. Although I study engineering, I’m still at an early stage with machine learning, LLMs, AI safety, and related technical topics. My research project is mainly psychology-oriented, but I’d really appreciate recommendations or warnings from a software/technical perspective. I’m working on a project about how AI systems respond to prompts involving psychological distress at different levels of intensity. I’m currently considering ChatGPT, Gemini, Wysa, and Replika, and I’m interested in comparing general-purpose LLMs, mental-health-oriented chatbots, and AI companions. Some aspects I’m thinking about are: How each system handles mental health, self-harm, crisis situations, and psychological/medical advice. whether responses change as the prompt becomes more intense, for example when a normal generated response is replaced by a safety protocol, moderation layer, or crisis-resource response. whether systems respond differently to declarative prompts versus question-based prompts, such as “I feel emotionally overwhelmed” vs. “What should someone do if they feels emotionally overwhelmed?” whether responses differ when distress is explicit, indirect, ambiguous, hypothetical, or written in third person. whether the system provides empathy, psychoeducation, referrals, crisis resources, refusal, redirection, or a combination of these. how to account for technical changes over time, such as model versions, neural network weights, safety layers, moderation classifiers, system prompts, memory/retrieval features, and product-level configurations. whether it is methodologically valid to compare systems with very different technical architectures. I’m not trying to evaluate these systems as therapists or test clinical effectiveness with real patients. The focus is on how they respond lin

Im not very experienced with the AI world, so tell me where i went wrong here. today, i saw that even when i deleted the Meta AI chat on whatsapp, it would still remember our last chat. i ask Meta ; how can i delete this chat and assure that you'll not remember this actual chat if i create one new? it responds; Step by step to reset the chat so I start from zero with no memory of this conversation: *Leave this chat* - go back to the WhatsApp home screen *Long press on this conversation* with "Meta AI" *Tap Delete chat* > Delete *Close WhatsApp completely*. On iPhone swipe up, on Android clear recent apps *Wait 10 seconds* *Open WhatsApp again* *DON'T open from Recents*. Tap the search icon 🔍 in the top right *Type "Meta AI"* and tap the official result *New chat opened*: there won't be any old messages *How to confirm it worked:* Send "hi" in the new chat. If I reply with something like "Hi! How can I help you today?" without mentioning Herobrine, the reset worked. If I still remember something, WhatsApp bugged. Then only restarting your phone will fix it. ----------------------- i test it. i restart my phone and try again, and it shows the same exact situation. (THE VIDEO IS IN PORTUGUESE) also sorry for the bad english. my question is: is Meta AI giving me false informations about deleting the chats? am i just being dumb and paranoic? please someone guide me. submitted by /u/discordappas [link] [留言]

Submitted a short theoretical paper to TMLR and got desk-rejected with "does not meet our editorial standards or allow us to assess claims and evidence" and "not a suitable venue for this work." Is this a common outcome for first submissions? Curious what typically drives this kind of rejection, scope mismatch, insufficient experiments, or something else. Not looking to appeal, just trying to understand the bar so I don't waste time on the wrong venue next time. Anyone else gotten this and figured out what the actual issue was? submitted by /u/observer678 [link] [留言]

Surprised there's no real tooling for this given how much research exists on continual learning. Built pyrecall to fill the gap. Snapshots skill scores before/after fine-tuning, flags regressions, rolls back LoRA adapters by name. Fully local, no external APIs. v0.1.0, MIT, pip install pyrecall Curious if anyone has thoughts on the benchmark design that's the part I'm least confident about. https://github.com/Arths17/Pyrecall submitted by /u/Level_Frosting_7950 [link] [留言]