标签：#cia

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]

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]

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]