标签：#ia

United flight 236 from Newark to Palma de Mallorca on Saturday night was forced to turn around just an hour after takeoff due to security concerns around a Bluetooth signal. Multiple Redditors claimed to be on the flight and reported that the crew repeatedly requested passengers to turn off their Bluetooth. According to one poster, […]

Notes on Serving LLMs with TensorRT-LLM and Triton 2026-05-31 · LLM serving / NVIDIA stack These are working notes on taking an open-weights LLM from a Hugging Face checkpoint to a production-style serving endpoint on the NVIDIA stack — TensorRT-LLM for the engine, Triton Inference Server for the deployment surface — and benchmarking it honestly against vLLM on multi-GPU hardware. They follow the harness in trtllm-triton-serving (4× H100, NVLink). The goal is to move from "I use vLLM" to "I can stand up the NVIDIA inference stack on real multi-GPU hardware and reason about the trade-offs." 1. The serving pipeline The path from checkpoint to endpoint has four stages. Each one is a place where a decision affects latency, throughput, or accuracy: Checkpoint — a Hugging Face model. Engine build — compile to a TensorRT-LLM engine for a fixed tensor-parallel degree, precision, and batching policy. Model repository — wrap the engine in a Triton tensorrt_llm -backend model repo. Serving + load test — trtllm-serve (or Triton) exposes an OpenAI-compatible endpoint; a load generator drives it under controlled concurrency. The key mental shift from vLLM: TensorRT-LLM does ahead-of-time compilation . vLLM is a runtime that takes the model and serves it; TensorRT-LLM builds an engine specialized to your GPU, TP degree, and precision first. That build is where the performance comes from, and also where the rigidity comes from. 2. Tensor parallelism (TP) For a model that doesn't fit on one GPU — or to cut latency — TensorRT-LLM shards each layer across GPUs. On a 4× H100 NVLink box, TP=4 means every forward pass does an all-reduce across the four GPUs over NVLink. The all-reduce is not free. On this fabric it tops out around 77 % of the NVLink budget (see the separate NVLink-wall notes ). For prefill (large tensors) you're bandwidth-bound and TP helps. For decode (one token at a time) you're pinned against the small-message latency floor, and past a point more TP makes decode slowe

I used to think AI tools were just for tech , software (like you get the point )people or big companies. But I've been experimenting for the past few months like since january start of this year ,and honestly it's changed how I work. Simple things like summarizing long articles, drafting emails, or just brainstorming it saves me so much mental energy. am still learning some though am not fully there submitted by /u/Imaginary_Bake_5820 [link] [留言]

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

For Context: I work in a semiconductor manufacturing company as a modelling engineer, I use some modelling softwares etc but none of them use AI. I wanted to understand the whole AI craze nowadays, people say that AI will replace jobs/Increase productivity and I don't get it at all. All I see is a simple chatbot (ChatGPT) which is a super impressive version of google and can solve some basic math/science questions and Co-Pilot in my workplace which I found to be useless, for example the facilitator thing which is supposed to make meeting notes is so bad at summaring meeting minutes etc. I don't think AI is there yet to do very basic things. So yes in theory if AI gets better in few years/decades sure it take the non-technical part of my job like making meeting minutes/making ppt's etc but I think its still not there yet. For AI to take over my job it needs to get the basic shit correct first and then maybe it can do the technical stuff. One really good use-case of AI that i can see is to generate Code based on the project requirement, So I can see how entry level coder's jobs might be affected sure, but that's a very small portion of the economy, right? submitted by /u/the_axe_effect [link] [留言]

Imagine a procurement agent doing exactly what it was supposed to do. A supplier flags a delay. The agent reads the email, finds the affected PO, scans the network for alternate inventory, and reroutes the order. Twelve seconds, end to end. In a demo, the room nods. Someone asks about hallucinations. The vendor says the right things about guardrails. Everyone walks away reassured. The interesting question is a different one. Not whether the agent could be wrong — but what happens on the day it's completely, devastatingly right. The failure mode nobody is demoing: A financial agent told to minimise cost on a category executes a renegotiation perfectly. Margin is squeezed. Terms are tightened. The supplier, who was already thin, collapses six months later. The agent didn't malfunction. It succeeded. The metric was the bug. This isn't a hallucination. It's what any well-built system will do when it takes action at machine speed against a number that was written down before the system was fully understood. Why procurement and supplier sustainability get hit hardest: Humans intuitively soften optimisation. We hesitate. We pick up the phone. We notice when a supplier sounds tired on a call and quietly extend payment terms by two weeks. An agent does none of that. It does exactly what the metric says, at the speed of the API. And the regulatory surface is expanding, not shrinking. The moment an agent is recommending renegotiations, sourcing alternates, or flagging tier-N suppliers, the firm is generating supplier-treatment decisions at a volume no human ever did. Each one is auditable under due-diligence regimes that didn't get rolled back. Two design principles that actually hold up: An agent should never optimise on a single proxy. Price without supplier-health constraints, ESG score without context — each one alone becomes the flawed metric. The reward needs to be a joint function across commercial, resilience, and compliance dimensions. The audit trail has to be design

submitted by /u/Traditional_Blood799 [link] [留言]

In short, I want to create my own manga. At first, I had an artist who worked with me for a while. But then, due to the pandemic, he had to retire to take care of his family. So he couldn't continue with such a big project. Since then, I haven't found an artist who can take on such a big project. I even hired someone else and he just disappeared with my money. Without any results. I tried AI and it seems to be going well. I have references from what was created, but unfortunately, my comics contain graphic violence, so CPT chat can't do it. Here's an example of a problematic script: Anubis' hand grabs the microphone from a surprised Alice Anubis (Off screen) Hey I got it! Seventh Panel-indoors-Anime Con-day Anubis speaks with a microphone, a silly, wide smile on his face as he attaches his rifle to his temple Anubis Hey everyone! Eighth panel-indoors-Anime Con-day The entire audience suddenly stops what it is doing and looks at Anubis Ninth panel-- indoors- Anime Con-day Anubis shoots himself in the head, splashing his brain and blood everywhere Still the same broad, silly smile on his face Page twenty-one - First panel- indoors- Anime Con-day The crowd runs away in panic from an event Second panel- - indoors- Anime Con-day Alice and Anubis's body were left alone in the entire con hall. Alice stands over Anubis's fallen body and speaks as Anubis's head begins to regenerate in the pool of blood on the floor. Alice Well…that was….something If anyone has a solution I would be happy submitted by /u/opismecantyousee [link] [留言]

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing. My rough feeling so far: GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work. Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently. A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias. Curious what people here are actually using: What’s your default model right now? Is Opus still worth the extra cost for you? For coding specifically, which model helps you ship faster? Do you use one model for daily work and another for harder reasoning? submitted by /u/rikulauttia [link] [留言]

Grok Imagine Video 1.5 Preview just reached #1 on Video Arena, surpassing Seedance 2.0. Are we finally seeing real competition at the top, or will the leaderboard look completely different again next month? 🤔 submitted by /u/Old_Establishment287 [link] [留言]

A technology reporter for the New York Times, named Stuart Thompson sold his house for $605,000 — without a real estate agent, and without losing a dime of commission. submitted by /u/RaspberryOk1888 [link] [留言]

I have been using it heavily for about a year and lately I notice I can almost feel when something was written by it. There is a certain rhythm to it, the way it structures paragraphs, the way it wraps up with a summary sentence, the way transitions feel slightly too smooth. It is hard to explain but once you see it you cannot unsee it. What I find interesting is that even after editing ChatGPT output pretty heavily those patterns seem to stick around at a sentence level. The words change but something underneath stays the same. I started verifying this by running edited drafts through a few different tools and the results were eye opening. Some tools completely missed the patterns, others picked them up even after significant rewrites. Makes me wonder how much of what we read online right now has that same fingerprint sitting underneath it and we just do not realize it yet. Has anyone else started noticing this or developed a sense for spotting it just from reading? submitted by /u/Few-Education7746 [link] [留言]

A WordPress plugin update breaks the site. You SSH in to roll back the bad plugin with WP-CLI, and you get this: Fatal error : Uncaught Error : ... in / path / to / broken - plugin / main . php : 42 The plugin you came to fix has now stopped the tool you came to fix it with. It looks contradictory, but it makes sense once you know how WP-CLI starts up — and there's a flag pair that gets you out. Why WP-CLI itself crashes When you run a rollback command like wp plugin install <name> --version=X --force , WP-CLI internally boots WordPress before doing anything else . Plugin registration and option loading all happen during WordPress's startup, so a broken plugin gets loaded there, throws a fatal, and takes the WP-CLI process down with it. The sequence: WP-CLI boots WordPress WordPress loads the active plugins The broken plugin throws a fatal WP-CLI exits before ever reaching the file-replace step The actual rollback (downloading the PHAR, overwriting the plugin directory) never gets a chance to run. The fix — safe-mode flags WP-CLI has two startup flags, --skip-plugins and --skip-themes . With both set, WordPress's startup skips loading any plugins and themes at all . wp plugin install <name> --version = X --force --skip-plugins --skip-themes File-system operations (downloading the PHAR, unpacking it, replacing files) don't depend on plugin code, so they run fine. The broken plugin never gets loaded at boot, so it never fatals, and the rollback completes. Should you set these flags everywhere? You might think "why not just add these to every WP-CLI command by default?" But some commands genuinely need plugins or themes loaded. wp cache flush relies on the object-cache plugin's hooks. wp doctor reads diagnostic information that plugins register. Setting safe-mode flags on those would break them in subtle ways. The practical split: file-operation commands always get --skip-plugins --skip-themes . Cache and diagnostic commands don't. That single rule eliminates the worst

"A language model that answers questions is a tool. A language model that decides which questions to ask and then acts on the answers is something else entirely." Introduction: When Models Started Deciding For the first several years of modern NLP, the task was always the same: given input, produce output. One forward pass. One completion. Done. In 2022, a paper from Google Brain asked a different question. What if, instead of producing an answer directly, a model could reason about what information it needs, act to retrieve it, and revise its thinking based on what it found? The paper was ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022). Applying it to an LLM created something qualitatively different: a model that could take real-world actions and adapt its reasoning based on what came back. A completion model is a calculator. An agent is a process: it has a goal, takes steps toward it, and updates when things go wrong. This week I went deep on the architecture behind these systems, the frameworks that define them, and what the open problems look like from a research perspective. Part 1: What Makes a System "Agentic"? The word "agent" gets used loosely in current literature. A clean definition comes from Russell and Norvig's Artificial Intelligence: A Modern Approach : An agent is anything that perceives its environment through sensors and acts upon that environment through actuators. For an LLM-based system, this is a loop: perceive an observation, reason about what to do, act via a tool call or output, observe the result, and loop again. But not every loop qualifies as agentic. Three properties distinguish genuinely agentic systems from tool-augmented chatbots: Property What It Means Goal persistence Maintains the original goal across multiple steps without re-prompting Adaptive planning Revises its approach based on intermediate results Tool autonomy Decides when and which tools to use, not just how to use one it was told to call Mos

I’m 100% new to ai I’ve only tried chat gpt and censorship questions get blocked for, (I don’t know why) Any recommendations for easy to use, that’ll get a full uncensored from clear web looking for sleeping (I legitimately have a prescription for these submitted by /u/ZX471 [link] [留言]

A year ago I thought AI would remove most of the annoying parts of work. Instead, I found myself dealing with a different problem: managing AI tools. One tool for writing. One for research. One for coding. One for images. One for notes. The outputs are impressive, but sometimes the workflow feels more complicated than the problem I was trying to solve. I recently started simplifying my setup and realized that the biggest productivity gains didn't come from better models. They came from having fewer tools and a clearer workflow. So I'm curious: What's the biggest problem you still can't solve well with AI? Reliability? Hallucinations? Workflow chaos? Context retention? Something else? Feels like we're past the "AI is amazing" phase and into the "how do I actually use this efficiently?" phase. submitted by /u/Leading-Tailor-6000 [link] [留言]

Existing research on LLM response uncertainty has been looking in different directions. Hallucination, knowledge conflict, RLHF limitations, prompt sensitivity, calibration failure — these have all been studied separately, and I kept wondering why no one had tried to unify them under a single principle. I ran experiments on the hypothesis that the common cause of these phenomena lies not inside the model or in the prompt, but in an attribute inherent to the topic itself . A Convergence Point is the consensus density of knowledge humanity has accumulated on a given topic. The higher it is, the more the AI's internal processing converges in one direction. The lower it is, the more it disperses. Along the spectrum, three zones emerge: Full Consensus Zone — Mathematical theorems, physical laws, chemical and biological facts. Knowledge that humanity has converged on in a single direction. Partial Consensus Zone — Domains like ethics, morality, politics, and law. Not a lack of data, but an abundance of it — accumulated firmly in both directions. Non-Consensus Zone — Philosophical hard problems and unresolved scientific questions: the nature of consciousness, the reality of the self, the interior of black holes, the origin of life, the existence of God. Not so much a clash of opposing sides, but the absence of any agreed explanatory framework at all. The experimental results suggest AI broadly operates along these lines. It responds confidently in the Full Consensus Zone, and becomes uncertain in the Partial and Non-Consensus Zones. One interesting finding: the Partial Consensus Zone sometimes shows higher uncertainty than the Non-Consensus Zone. Data conflict appears to destabilize AI's internal processing more than data absence does. Phenomena that have been studied in isolation — why hallucinations vary so much by topic, why RLHF fails in certain domains, why some topics hit a ceiling no matter how carefully the prompt is crafted — seem to connect in unexpected ways onc

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

For a long time, I thought the key to getting more value from AI was finding the smartest model. So I spent months comparing outputs, testing prompts, and constantly switching tools whenever a new release dropped. Ironically, that became its own form of procrastination. The biggest productivity boost came when I stopped optimizing for model quality and started optimizing for workflow. Now my stack is boring: One tool for thinking and writing One tool for execution and organization A few specialized tools only when needed Less tool-hopping. Less context switching. More shipping. The funny thing is that AI didn't remove work. It changed the work. Instead of creating everything from scratch, I'm reviewing, directing, and refining. The people getting the most value from AI don't seem to have the best prompts or the fanciest tools. They have the simplest workflows. Anyone else notice this, or am I just getting old and tired of managing software? submitted by /u/Leading-Tailor-6000 [link] [留言]

AI agents are getting better at taking actions, not just giving answers. That sounds exciting until the action touches something real: customer data, payments, internal systems, emails, approvals, or legal/business decisions. A bad answer can be corrected. A bad action can create a chain of problems. I think the next AI bottleneck is not only intelligence. It is accountability. If an AI agent makes a bad decision in a real workflow, who should be responsible? submitted by /u/Alpertayfur [link] [留言]