标签：#ia

submitted by /u/ThereWas [link] [留言]

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

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]

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

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]

TLDR TypeScript has 7 primitive types: string , number , boolean , null , undefined , bigint , and symbol . You use them to tell TypeScript what kind of value a variable holds. You write them in lowercase. TypeScript can often figure out the type for you. But knowing how each one works is key to writing safe and clear code. What Are Primitive Types? Primitive types are the simplest building blocks in TypeScript. Every piece of data in your program starts with one. They hold a single value. They are not objects. You cannot add methods or properties to them directly. TypeScript has 7 primitive types in total: Type What It Holds string Text like names, messages, or IDs number Any number: integers, decimals, negatives boolean Only true or false null An intentional empty value undefined A value that was never assigned bigint Very large whole numbers symbol A unique identifier value This article covers all 7. You will use string , number , and boolean the most in everyday TypeScript code. How to Use the string Type A string holds text. Use it for names, messages, emails, URLs, and any other text data. Basic string annotation let firstName : string = " Alice " ; let greeting : string = " Hello, world! " ; let empty : string = "" ; Three ways to write strings TypeScript supports the same three string styles as JavaScript: let single : string = ' Single quotes work fine ' ; let double : string = " Double quotes work too " ; let template : string = `Template literals with ${ firstName } ` ; Template literals (backticks) let you insert values inside a string with ${} . TypeScript checks the types of those inserted values too. let age : number = 30 ; let message : string = `I am ${ age } years old` ; // TypeScript checks that 'age' is compatible here What TypeScript catches with strings let name : string = " Alice " ; name = 42 ; // Error: Type 'number' is not assignable to type 'string'. name = true ; // Error: Type 'boolean' is not assignable to type 'string'. Once a variable

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.

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 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

Among WordPress maintenance tools, InfiniteWP is one of the most established names. Released by Revmakx in 2011, the tool has been operated continuously for over a decade. It enjoys deep loyalty from agencies that have invested years building operational know-how around it . We at WP Maintenance Manager take a different approach, and our comparison pages outline where the two diverge. But before talking about differences, the strengths of InfiniteWP deserve to be stated honestly . Here are the five points where InfiniteWP fits an agency particularly well. 1. Over a decade of operational track record InfiniteWP's biggest structural advantage is trust built across more than a decade of continuous operation . Released in 2011 — one of the oldest tools in the space A large base of long-time English-speaking users with shared operational patterns Well-defined upgrade paths from older versions Backward compatibility with existing workflows and scripts has been maintained for years For agencies already invested in InfiniteWP, switching tools means more than "migration work" — it means rebuilding the operational know-how accumulated over years . Continuing to use a tool with proven track record is, in itself, a strength that long-running platforms have. The temporal depth that newer tools simply cannot replicate is a meaningful selection reason for conservative industries — those reluctant to substantially change established workflows. 2. Self-hosted — full control of the dashboard InfiniteWP is self-hosted by default , letting you place the dashboard on your own server (a cloud-hosted version is available separately). Host on infrastructure you own Complete data ownership No dependency on external SaaS Arbitrary customization possible When the constraint is "client data must not sit in a third-party SaaS" or "our security policy doesn't permit SaaS," InfiniteWP's self-hosted architecture is a direct answer. If your team has experience operating PHP / WordPress infrastructu

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]

Bluesky will be getting "communities," which will function as smaller spaces where you can "go deeper and hang out with people who care about the same stuff" sometime this year, according to head of product Alex Benzer. They will be built on the decentralized AT Protocol that underpins Bluesky, with Benzer saying that "it's a […]

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]

The regulation also imposes new safety expectations on 'AI chatbot services.'

Asked it: "what did I waste on takeaways last month?" Answer: "You spent £340 on takeaways in May — 22% more than April. Want a £200 cap with a nudge near the limit?" £340. In one month. The "22% more than April" detail was the real knife. This is from my own app (Expenzez — it reads uploaded statements on-device, no bank login), so yes, I built my own roaster. But the broader point stands: AI answering questions from YOUR actual numbers beats generic budgeting advice by a mile. Best/worst thing an AI has told you about your own data? submitted by /u/biszaal [link] [留言]

CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform

Lawsuit: "Police let an error-prone AI system stand in for an investigation."