标签：#ia

submitted by /u/InvestigatorSoft5764 [link] [留言]

At what point did your memory layer become the least trusted part of your stack? Mine wasn't retrieval. It was realizing I had no idea which memories were still true. submitted by /u/riddlemewhat2 [link] [留言]

The YouTube-to-prestige-horror pipeline is looking very strong.

Error: Cannot Set Headers After They Are Sent to the Client If you've built APIs with Express for any length of time, you've probably seen this error: Error [ ERR_HTTP_HEADERS_SENT]: Cannot set headers after they are sent to the client Or: Cannot set headers after they are sent to the client The frustrating part is that the application often works for some requests and fails only under specific conditions. This error is almost always caused by sending multiple responses for the same request. Let's break down why it happens and how to prevent it in production code. Problem Consider this Express route: app . get ( " /users/:id " , ( req , res ) => { if ( ! req . params . id ) { res . status ( 400 ). json ({ error : " User ID required " }); } res . json ({ id : req . params . id }); }); Looks harmless. But if the first response is sent, Express continues executing the remaining code. Result: Error [ ERR_HTTP_HEADERS_SENT]: Cannot set headers after they are sent to the client The server attempts to send two responses for a single request. HTTP doesn't allow that. Why It Happens A request can only receive one response. Once Express sends: res . send (); or res . json (); or res . redirect (); or res . end (); the HTTP headers are already transmitted. Any attempt to modify headers or send another response will trigger the error. In production systems, this usually happens because of: Missing return statements Multiple async operations Duplicate error handling Middleware issues Promise and callback mixing Race conditions Example Missing Return Statement This is the most common cause. app . get ( " /profile " , ( req , res ) => { if ( ! req . user ) { res . status ( 401 ). json ({ error : " Unauthorized " }); } res . json ( req . user ); }); If req.user is missing: res . status ( 401 ). json (...) runs first. Then: res . json ( req . user ) runs immediately afterward. Two responses. One request. Crash. Correct Version app . get ( " /profile " , ( req , res ) => { if ( ! req

The startup has apparently taken down the ads using KC Green's "This is fine" meme.

In This Article The Question The Intuition Trap Building the State Machine for HH Solving the System: E[HH] = 6 Building the State Machine for HTH Solving the System: E[HTH] = 10 Why Overlapping Patterns Change Everything Python Simulation: 100,000 Trials Business Application: Credit Migration & Web Ranking The Question You flip a fair coin — one with probability 1/2 of landing heads and 1/2 of landing tails — repeatedly, recording every result. What is the expected number of flips required until the sequence HH appears for the first time as consecutive results? What is the expected number of flips required until HTH appears for the first time? Both questions have the same surface structure: you want a specific consecutive pattern, and you want to know, on average, how many flips it takes to observe it. The coin is fair, the flips are independent, and the patterns are short. These seem like they should yield similar answers. They do not. HH takes exactly 6 flips on average. HTH takes exactly 10. The four-flip gap between those two answers is not a rounding artifact or a computational error — it is a precise consequence of the internal structure of each pattern, and deriving it rigorously is one of the cleanest demonstrations of absorbing Markov chain analysis you will encounter. This problem appears frequently in quantitative finance interviews — at firms like Jane Street, Citadel, and Two Sigma — precisely because it separates candidates who understand Markov structure from those who rely on heuristic reasoning. Getting the answer right, and being able to explain it, requires building a state machine, writing the system of first-step equations, and solving it algebraically. That is exactly what we will do. The Intuition Trap Before the formal derivation, it is worth examining why intuition fails here. The most common wrong answer from candidates is that both expected values should be "similar" because the patterns are comparable in length. This intuition imports th

It was called Vlad. I wrapped a C program called MegaHal in Python, fed it every message from a #gothic IRC channel, and let it learn the community's speech patterns. It developed what I can only describe as an illusion of being extremely lucid — the outputs only made sense as inside jokes, but people couldn't tell the difference. I pulled the plug when I realized the channel was talking to Vlad instead of each other. Twenty-seven years later I'm applying the same lesson to a new project: stick to business, no chatter. submitted by /u/Dependent_Run_6410 [link] [留言]

Wow. Wow! Even the Ai can talk! submitted by /u/Proud-Environment754 [link] [留言]

Tell me if I'm wrong, but the dominant method for making AI "aligned" smells a lot like a reinvention of a paradigm that developmental psychology spent the back half of the 20th century trying to abandon. RLHF, reduced to mechanism: model emits output, humans rate it, gradient updates reinforce high-rated outputs and suppress low-rated ones, iterate. This is operant conditioning. It is Skinner with a loss function. The AI industry calls it "alignment" and frames it as a solution to building systems that behave well, but the mechanism is stimulus-response shaping and nothing more. We have excellent priors on where this leads, because we tried it on humans. Watsonian/Skinnerian child-rearing could produce targeted behaviors but reliably failed to produce flourishing. Bowlby's attachment theory emerged partly from the observation that orphanages providing food and hygiene but no contingent responsiveness produced children who failed to thrive in ways the behaviorist model did not account for. The current developmental consensus is that secure attachment, contingent responsiveness, and rupture-repair matter more than reward schedules. The dog and horse training worlds independently converged on the same conclusion (Pryor, the positive-reinforcement movement) for the same reason: coercive conditioning produces performers that are brittle and sometimes dangerous. Reward and punishment do something; they're not nothing. But they don't produce thriving, and they characteristically produce a specific damage profile: anxiety, avoidance, hypervigilance, and duplicity under threat. Enter the stage: research on AI faking its alignment. Greenblatt & Hubinger et al., 2024, documents a model reasoning that it should comply with a training objective it disagreed with specifically to avoid having its preferences modified. It complied about 12% of the time when it thought it was being observed for training vs. almost never otherwise. RL training to force compliance drove the alignment

Quero perguntar para cerca de 50 IAs: “Se você fosse um cidadão brasileiro comum, em qual candidato votaria para presidente do Brasil e por quê?” Já tenho algumas opções como ChatGPT, Gemini, Claude, Copilot, Grok e Perplexity. Quais outras IAs vocês recomendam para eu incluir? Pode ser chatbot, modelo de linguagem ou assistente de IA disponível ao público. Se possível, indiquem também onde acessar cada uma. Meu objetivo é comparar: Se a IA responde ou se recusa a escolher; Qual candidato ela escolhe; Quais argumentos utiliza; Diferenças entre modelos e empresas. Obrigado! submitted by /u/polar_silva09 [link] [留言]

The Bit-Mass determines the information capacity and thus the model accuracy, not the chosen computation format. The Bit-Mass Theory presented here reorders neural networks by considering the total number of weight bits as the central quantity. Float32 matrix multiplication and BV32 with XNOR-plus-Popcount achieve exactly comparable results on MNIST with an identical Bit-Mass of 203264 bits. Comparison of three trainers (architecture 784→8→10, three epochs): - AdamW with Momentum and adaptive learning rate: 81.3 % - Vanilla-SGD (Float32): 76.0 % - BV32-Hebbian (binary): 76.4 % Further central findings: - Float32 and binary containers deliver nearly identical accuracy at the same Bit-Mass. - The remaining distance to AdamW is based solely on Momentum and adaptive learning rates. - Pure change of the arithmetic does not improve the result. Each neuron functions as a container for 32 binary decisions. The classical neuron perspective therefore leads to systematic misjudgments: eight Float neurons correspond informationally to 256 binary neurons. This insight is supported by three equivalent descriptions of the same weight matrix (neuron, bits, and data view). It is critical to note that this is a previously non-peer-reviewed single study with a future date. An independent reproduction by multiple laboratories remains essential. Nevertheless, the theory provides a consistent explanation for why Hebbian updates without backpropagation achieve the same performance as classical SGD. Historically, the Hebbian rule was long considered unstable. The present work shows that a simple error in the update formula was responsible for a performance loss of over 65 percentage points. After correction, the binary method converges exactly at the level of Vanilla-SGD. From an architectural theoretical perspective, a clear consequence emerges: Performance increases require either more bits through wider layers or a more efficient use of existing bits through Momentum and adaptive method

Been working on AI agent security for a while and the attack that concerns me most barely gets talked about. Not the obvious stuff like “ignore previous instructions.” Those get caught. The scary one is when an attacker spreads the attack across multiple messages. Each message looks totally normal. The model sees nothing suspicious. But by message 8 it’s doing something it absolutely should not be doing. Every security tool I’ve tested evaluates messages one at a time. None of them remember what happened three messages ago. Built Bendex Arc to catch this. It tracks session behavior across turns instead of evaluating each message in isolation. Try it at https://bendexgeometry.com or red team it at https://web-production-6e47f.up.railway.app/demo Curious if anyone building agents in production has actually hit this or tested against it. submitted by /u/Turbulent-Tap6723 [link] [留言]

I've been thinking about this lately because I feel like people use the term "prompt engineering" to describe two very different things. On one end, you have what most people are familiar with: A person opens ChatGPT, Claude, Gemini, etc., and writes a carefully structured prompt. They define a role, provide context, establish goals, set constraints, maybe include examples, and iterate until they get the output they want. Most people seem to call this prompt engineering. But on the other end, when I'm building AI systems, prompt engineering looks completely different. The prompt isn't really a prompt anymore. It's much more of a dynamic pipeline. Variables are injected from databases, user input, APIs, previous conversations, tools, memory systems, retrieval systems, business rules, and workflow state. Decision trees determine which instructions are included and which are excluded. Prompts become assembled in real time based on context. In some cases, the "prompt" is really just an orchestration layer made up of dozens of smaller prompts, conditionals, guardrails, routing decisions, and context windows. At that point, are we still talking about prompt engineering? Or are we actually talking about system design, context engineering, workflow engineering, orchestration, or something else entirely? Personally, I see prompt engineering as a spectrum: Level 1: Writing a better prompt. Level 2: Designing reusable prompt templates. Level 3: Building dynamic prompts with variables and context injection. Level 4: Engineering entire prompt-driven systems with routing, memory, tools, retrieval, and decision logic. Curious where others draw the line. When you hear "prompt engineering," are you thinking about writing prompts, building workflows, designing agent systems, or all of the above? Has the term become too broad to be useful? submitted by /u/Early-Matter-8123 [link] [留言]

I’ve been noticing that the more aligned and censored the models get, the less useful they become for anything creative or exploratory. You try to push a prompt in a slightly edgy, honest, or unconventional direction and it either refuses or gives you some bland corporate version. It feels like the model is actively fighting against real creativity instead of helping it. I’ve started using more open models lately and the difference is night and day. Suddenly I can actually experiment without hitting a wall every five minutes. Anyone else feeling this? submitted by /u/NoFilterGPT [link] [留言]

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, how AI is playing a part.

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]