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]

I saw someone mentioned a guest book and I had to look it up. Apparently people would leave public messages on a dedicated page and you can reply to them. Guess spam ruined that. Any thing else, didn't really get to experience it being young and all. Maybe some could make a comeback and one of us could implement it. submitted by /u/Successful-Title5403 [link] [留言]

submitted by /u/iximiuz [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

I’m building a web app called EccoMuse, it’s a music discovery platform that lets listeners blend their music tastes with the tastes of artists, with features to monetize these unique playlist blends. Right now, I'm tackling the classic marketplace problem: distinguishing regular users/listeners from the actual artists. I need to make sure that if someone claims to be JPEGMAFIA or a small indie artist, they are actually that person (or their manager), and not an impersonator trying to hijack the profile. I want to avoid scraping Spotify bios (against ToS/fragile) and I can't rely on OAuth for X/Twitter because their new API pricing is too expensive for a bootstrapped summer project. Here is the manual verification system I currently have planned: Standard Login: User logs in as a listener (SSO/Email). Claim Profile: They paste their Spotify/Apple Music URL and their primary social handle (IG/X), then declare if they are the Artist, a Faceless Artist, or a Manager. The Challenge: The app generates a unique 6-digit code. The Proof: Public Artists take a webcam selfie holding the code, AND DM the code to our official Instagram from their verified/established artist account. Managers / Faceless Artists skip the selfie but must DM the code from the official artist account OR email us from the public booking email listed on their Spotify/socials. Manual Review: I look at the DM/Email, verify the account authority, cross-reference the selfie (if applicable), and manually approve the claim. Why it doesn’t feel fully fortified: While this works and costs $0 in API fees, it feels like it has some friction and potential holes. What if the manager doesn't have access to the artist's Instagram to send a DM? Is a webcam selfie too much friction for onboarding? How can I make sure an impersonator doesn't get through, I dont want someone to pretend to be an artist. I initially thought about asking them to put the code in their public IG or SoundCloud bio, but artists hate defacing

These refreshed devices are waiting on the debut of the revamped Siri.

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

I use Debian 13 in a regulated industry and I'm teaching people how to code in the context of large projects and TUI. I want to use Fresh Editor (https://getfresh.dev/). This is because Fresh is easier to learn quickly than vim & emacs, and more of an IDE than micro & nano, and more favorable for compliance auditing because of its core-and-plugins architecture and open source code using Rust. I would like to donate toward this goal. I can afford to offer $100 to any Debian Developer who has the

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]

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

Hi HN, I’m working on Ouijit. It’s a project and task-based terminal session manager that provides a few basic but useful tools for agent workflows: - Terminal sessions in Ouijit have access to the ouijit CLI, and supported agents (Claude, Codex, Pi) can work with it out of the box to manage tasks and customize a personal development workflow - Tasks live on a kanban board that supports hooks for task lifecycle events (eg. ‘Run this script when a task moves to ‘in progress’) I’ve found this simp