Character.AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Character.AI AI

Is this a useful concept? Curious about how people have addressed similar goals in a different way.

TLDR: Take a look at the attached code block. It is intended to be a re-usable set of AI response preferences. Is this useful? If not, why? And how else have you dealt with similar goals? Background - I've been retired for 8 years so I completely missed the "AI in the workforce" revolution. But I use it a lot on my own. For general queries, technical writing, generic document prep, and help with PowerShell scripts that I use to automate common tasks, recipes, etc.. I have only used free-tier agents so far (they work for my simple needs). I fell into a usage pattern whereby, whenever I got an undesirable response to a prompt, before trying to fix the output I asked if there was a response preference I could have specified that would have avoided the undesired outcome in the first place. Basically, trying to train myself to ask better questions. It quickly became apparent that there are common patterns for different preferences for different topics. And that evolved into the notion persisting these common re-usable patterns in a file (I call it an AI Library) that I could import and re-use into different chats. Within my "Library" I define different "Profiles" (groups of task specific behavioral preferences) and "Commands" (a verb that generates a specific kind of output. Example - ">List profiles"). A short time later the idea of a "profile stack" emerged where I could combine and "layer" different profiles with defined precedence rules, and "push and pop" profiles from the "stack" (these notions are all just metaphors of course - it's just how I came to think of it). Most free AI agents I have tried do not have any persistence model outside of the chat transcript. But the Claude AI desktop app for windows exposes the notion of a "Project" that lets me upload my library and give it instructions to load my library into every new chat started in the project. So, it is basically acting like a Linux "rc" file. For other AI agents like Copilot, Perplexity, Gemini, etc. you can just drag the file into a prompt and give it a command something like "Parse the uploaded file it as if it was instructions typed into a prompt. Do not summarize. Confirm when it is complete and understood.". Being out of the workforce, I don't really have anyone else to bounce ideas off. Hence the Reddit post. I'm interested in suggestions or ideas to expand in this concept. Or ideas about different approaches to achieve similar goals. I'm not really interested in how paid versions make this work (I would be surprised and disappointed if these were not "out of the box" first-class supported concepts). My goal was to improve my free-tier experience. It seemed pretty innovative to me as I was evolving these ideas, but in hindsight, it seems pretty obvious. So I really don't know how useful or unique this is. I have attached a version of my current "library" for your consideration. p.s. If anyone is interested, I can share my Vim syntax file. I ended up calling these files "ailib.txt" files. Vim syntax would have worked fine with just ".ailib" but it seems most agents only import certain filetypes - hence the addition of ".txt" at the end. # AI Instruction Library Template # Version: 1.4 # Purpose: Reusable, modular instruction profiles for AI chats [LIBRARY_META] name = "Personal AI Instruction Library" owner = "User" version = "1.4" description = "A reusable library of behavioral profiles and command definitions." activation_model = "ordered_stack" [TERM_DEFINITIONS] stack: definition = "An ordered list of active profiles. Order reflects activation sequence, from first-activated (lowest precedence) to most-recently-activated (highest precedence)." notes = "When a new profile is activated, it is appended to the top of the stack. Its instructions are combined with all other active profiles' instructions, with conflicts resolved per the precedence rules." activate: definition = "Add a profile to the top of the active stack." notes = "Activation order determines precedence: the most-recently-activated profile has the highest precedence." deactivate: definition = "Remove a profile from the active stack." notes = "Inactive profiles remain defined in the library but are excluded from compilation and have no effect on AI behavior." precedence: definition = "The rule used to resolve conflicting instructions between two or more active profiles, or between an active profile and a direct user instruction." notes = "Precedence is resolved per-field. See [PRECEDENCE_RULES] for the exact resolution mechanism." override: definition = "When two active instructions conflict, the higher-precedence instruction is applied and the lower-precedence instruction is suppressed for as long as both remain active." notes = "A direct user instruction given mid-chat (not via a command) is treated as the highest-precedence layer, above all profiles, until one of the following occurs: (1) the user issues a new instruction that further overrides it, (2) the user

Find the best open-source OCR models in one place at Papers with Code [P]

Hi, I've created an overview of the most important OCR benchmarks, along with the top open models, and links to their paper and code: https://paperswithcode.co/tasks/ocr. This week, new OCR models were released by Baidu and Mistral. Baidu released Unlimited OCR, a 3B-parameter model that introduces a key innovation called Reference Sliding Window Attention (R-SWA) and builds on top of DeepSeek OCR. Mistral released OCR 4, which is available via an API. OCR, or Optical-Character Recognition, is the task of digitizing PDFs or scanned documents. There's, of course, a huge interest in this task, as it enables ingestion of all company data for agentic use cases. AI agents love Markdown; it can be valuable to turn all those messy PDF documents into a standardized, machine-readable format. This enables use cases like agentic RAG (retrieval-augmented generation), which powers chatbots, both internally and for external customer support. With a large number of OCR releases on Hugging Face over the last few months, it may be hard to know which one to use. Hence, I've built this page, which lists the major OCR benchmarks, along with the top-performing models and links to their code. This is obviously made available on Papers with Code, the website I'm maintaining (it's a revival of the old website, which was taken down). The top recommended benchmarks are OlmOCRBench, created by Ai2, and OmniDocBench, created by Shanghai AI Laboratory. Current top recommendations are Chandra OCR 2 by Datalab and Mistral OCR v4. The former is openly available, hence you can either self-host it or use their serverless API. Let me know which other tasks you want to see major benchmarks for now! Cheers, Niels open-source @ HF submitted by /u/NielsRogge [link] [comments]

Due Disclosure - A Provenance Framework for Human-Directed AI Works

I've been working on a consumer advocacy project and wanted to publish it honestly — Claude helped me write it, but the ideas, argument, and direction are mine. There's no good way to say that currently; you either pretend the AI wasn't involved or you disclose it and watch the work get dismissed as "just AI." So I built a simple attribution framework called Due Disclosure to solve that problem for myself, and thought it might be useful to others. It’s inspired by Creative Commons, and I’ve tried to keep it simple. Would be interested to know if this resonates with anyone here. Nothing in it for me. It was keeping me awake at night, so releasing it may help me sleep. I made a website just to hold this document, you can find it if you type .org after the title. Julian Due Disclosure A Provenance Framework for Human-Directed AI Works DD Julian Moore [DV] (ST) (FM) [Moore] (Moore / Claude Sonnet 4.6) (Moore / Claude Sonnet 4.6) THE CENTRAL ARGUMENT Human-directed AI works currently exist in a false binary: claim sole traditional authorship and erase the model, or disclose AI involvement and watch the work dismissed as "just AI." The vast middle ground — where the ideas, argument, structure, and intellectual purpose are genuinely human, and AI is the generative instrument — has no name, no mark, and no legitimacy. Due Disclosure proposes to give it all three. Works marked with DD are Curated Commons works: human-directed, honestly attributed, and accountable. The mark is how the commons is built. A Note on Copyright Applying a DD mark does not affect copyright. The curator retains full intellectual property rights over a Due Disclosure work. The mark describes how the work was made — it does not transfer, diminish, or complicate ownership. A human who conceives, directs, and takes responsibility for an AI-assisted work is its author in the eyes of copyright law in most jurisdictions, in the same way that a director owns the creative rights to a film they did not personally shoot or score. One: The Problem That Needs a Name Something significant is happening to human intellectual work, and we do not yet have the language to describe it accurately. Across every domain of knowledge production — policy research, journalism, academic writing, consumer advocacy, legal analysis, creative work — people are conceiving arguments, directing research, shaping structure, making decisions about evidence and emphasis, and producing works of genuine intellectual substance. They are doing this in dialogue with large language models, which generate the text that gives those arguments their form. The intellectual labour is real. The ideas are theirs. The argument is theirs. The decision about what matters, what to include, what to discard, and how to frame it — theirs. The sentences were generated. But the work was written. And yet no framework exists to say so. Two: The False Binary Right now, anyone producing human-directed AI work faces two dishonest options. They can claim traditional sole authorship and omit the model entirely — which is the academic fraud that institutions are rightly worried about. Or they can disclose AI involvement and watch the work dismissed as generated content with no human accountability — which erases the intellectual contribution that actually shaped it. Both options are distortions. Neither is honest. And the honest middle ground has no language, no mark, and no protection. This is not a future problem. It is an active present one. It is causing legitimate work to be suppressed, misattributed, or avoided. It is generating institutional anxiety that is hardening, in some quarters, into a blanket dismissal of anything AI-touched — a dismissal that will, if it becomes orthodoxy, cause a generation of genuinely valuable human-directed work to be lost or delegitimised before it can find its audience. The window to establish the right framework is now. Once the cultural conversation hardens — once "AI-generated" becomes a disqualifying label applied without distinction — it will be very difficult to dislodge. Creative Commons did not emerge after the copyright wars were over. It emerged during them, when the language could still be shaped. Three: What Human Curators Actually Do The word author comes from the Latin auctor — one who originates, who causes something to exist. By that standard, the person who conceives an argument, directs its development through sustained intellectual engagement, makes decisions about evidence and structure, and takes responsibility for the result is an author. The fact that the sentences were generated rather than typed changes the production method. It does not change the authorship. The closer analogy is not writing. It is directing. A film director does not operate the camera. They do not compose the score. They do not design the costumes or build the sets. They conceive the work, make the decisions that shape every element of it, and take cre

Metin2: Reverse Engineering with AI for Non-Reverse Engineers

What happens is I finally got to successfully reverse this MMORPG game called Metin2 that I spent years playing with friends and wondering how people that made bots and hacks for it manage to do it, and now finaly be able to build the bots myself, which I always dreamed of being able to achieve but never got myself to invest the time to actually learn reversing. This guide shows how the outdated open sourced MetinPythonLibV2 (eXLib needed for OpenBot) was successfully rebuilt and revived for the latest GameForge (GF) Metin2 client (as of 20/06/26) without any traditional manual reverse engineering knowledge required. The Core Concept Instead of using Cheat Engine / IDA / Ghidra manually, you let a powerful AI agent locally interact directly with the live running game process attached to Cheat Engine via MCP. The AI reads memory, finds structures, generates AOB signatures, traces call graphs, and suggests/fixes code - while you only validate results in-game. This approach completely bypasses the need for deep reverse engineering knowledge. Key Tools Used -> Claude Opus 4.8 (or other high-reasoning LLM with tool calling capabilities) - Purpose: Autonomous reverse engineering agent - Link: Claude Code CLI -> Cheat Engine MCP Bridge - Purpose: Allows the AI to control Cheat Engine through function calls (memory reads, AOB scanning, Lua execution) - GitHub: miscusi-peek/cheatengine-mcp-bridge -> Outdated Metin2 Client Source - Purpose: Reference for structures, classes (CInstanceBase, etc) and network protocols - GitHub: ikevin127/metin2-client-source -> Visual Studio 2022 + Detours - Purpose: Building the injectable DLL library Important: The method uses static memory reads + Lua only (no debugger attachment) because the client’s protection crashes on Cheat Engine breakpoint attachment. High-Level Workflow (Proven on GF Client) 1.Diagnosis - Test all existing AOB signatures against the live client - Identify which ones are dead (in this case, 13 out of 24 were outdated) 2.AI-Driven Analysis - AI explores the live process (this-pointers, vtables, call graphs) - Re-derives fresh AOB signatures when needed - Finds correct struct offsets (example: character position moved from expected 0x7C4 → real 0x7BC in CInstanceBase) - Handles ASLR by working with RVAs 3.Code Adjustments - Update offsets and signatures in defines.h / Offsets.h - Add NULL guards and robustness so dead signatures don’t crash the DLL - Temporarely strip unused parts (server communication code) - Fix threading issues (especially Python GIL when re-enabling packet hooks) 4.Build & Test - Compile with MSBuild (Release | Win32 | v142 toolset) - Deploy as eXLib.mix which auto-injects (or .dll used with injector) - Validate everything live in-game (position reading, pathfinding, etc.) 5.Iterative Improvement - Re-enable features one by one (e.g. CheckPacket hook) - Fix crashes (GIL hardening + error clearing was required) Why This Works for Non-Reverse Engineers - The AI does the actual disassembly interpretation and pattern finding - You only need to: - Give the AI clear goals - Apply the suggested code changes - Test in-game - No manual sig scanning or deep ASM knowledge required Limitations & Notes - This method can be used to reverse engineer anything, including new game updates and outdated addresses whenever the client is rebuilt (new game version release). - Educational / research use only. Automating gameplay violates Metin2 ToS. - No anti-cheat bypasses were added because it uses the same injection method as the original library. Resources - Reference Library Rebuild Repository: ikevin127/MetinPythonLibV2-Rebuild - Detailed Rebuild Log: WalkerPath Revival Section - Cheat Engine MCP Bridge (for AI Cheat Engine): miscusi-peek/cheatengine-mcp-bridge - Client Source Reference: ikevin127/metin2-client-source submitted by /u/grandtheftaut0 [link] [comments]

A few months back I shared the Claude D&D skill I built for family game night. It's Father's Day, so here's the update: the hosted version just opened to everyone.

I posted about a Claude D&D skill I threw together here a couple months ago that runs a persistent D&D 5e game with Claude as the DM, and some of you really seemed to like it. It started as a selfish project: I wanted a proper family D&D night where I actually got to play instead of always running the table, and I couldn't get that anywhere else, so I built it. It's Father's Day, and since this whole thing began as a dad trying to get his family around the table, it felt like the right day to share where it went. I got a ton of great feedback and ideas from people in those comments, and spent the last couple months refining things. The bigger realization came after the posts though: every time I showed it to friends, family, and coworkers, I kept hitting people who love games but would never touch a terminal or spin up a Claude subscription to get to one. There's a whole crowd of non-technical, game-loving folks that an LLM skill just isn't reachable for, and I wanted to build them a door. So I did. It's called Neural Initiative, the same engine as the skill but fully hosted, and as of this weekend it's in open beta. (The skill also turned into an open-source, model-agnostic framework along the way, open-tabletop-gm, for anyone who'd rather self-host or run a different model.) Since this is r/ClaudeAI, the meta part: the whole thing, skill and hosted app both, was built almost entirely with Claude. If you've wondered whether you can actually vibe-code something real and shippable instead of a demo that falls over, this will (hopefully) be one honest example. It's one of a handful of projects I've got going, not my whole world, but it's one I've felt very passionate about and consistently indulge in. It's much more than just a chat bot/prompt wrapper. Find a breakdown of the features here or in r/NeuralInitiative if interested. TL;DR of what the hosted version adds over the skill: It runs in a browser. No laptop-on-the-couch-and-Chromecast rig (though I still love that setup, and it's how the fam still plays). Friends and family can share one campaign online from different houses, async or live, up to four players. The original was couch co-op. This is couch co-op for when you're not on the same couch. It still runs on Claude by default. Sonnet handles the every-turn DM narration, Opus does world and character creation. However, I added access to a variety of other models which can be selected per campaign. Cost is variable and tied to the real model token cost so people who want more output for less spend can do that. The architecture you all seemed to like is intact and hardened: the numbers live in code, not the model, so the AI narrates and improvises but can't quietly fudge your HP, a save, or a roll. Campaign state persists in structured files, lazy-loaded so a long module doesn't blow the context window while maintaining continuity. Plus the things that were hard to do in a local skill: optional TTS narration with per-character voices, 24 languages, light and dark mode, and importing a published module or your own PDF so the AI runs the real material chapter by chapter. The open-source framework is still maintained and isn't going anywhere. I didn't build the hosted thing to replace it. I really believe the best games ever made came from people building the thing they themselves wanted to play and needed to get right for their own selfish reasons. That kind of consistent, personal vision tends to get lost at the billion-dollar end of the industry. I'm genuinely worried about what AI does to development and engineering work, and I expect to feel it myself. But building this is the most hopeful I've felt about the other side of that: small teams, or one stubborn person with a clear vision, actually being able to catalyze and reach something real. Anyway, happy Father's Day. submitted by /u/Bobby_Gray [link] [comments]

Browser game around the EU AI Act - you argue with AI bots using real law

The mechanic: you get a denial from an AI system (coverage refused, mortgage rejected, flagged as high-risk by predictive policing), you have limited messages to fight back, and the only thing that works is citing the correct article. Just added 11 EU AI Act levels - banned practices (emotion recognition at work, social scoring), high-risk AI decisions (credit, hiring, medical triage), and transparency violations. The Act is mostly in force now and people have no idea what rights they actually have, so wanted to make that tangible. Interesting build challenge: getting the LLM (Haiku) to stay in character as a stubborn corporate bot while still responding correctly when the player cites Art. 5 or Art. 86. Too rigid = frustrating, too loose = trivial. Stack is boring (Node/Express, vanilla JS). Built by Claude. No account needed. Link: fixai.dev submitted by /u/EveningRegion3373 [link] [comments]

Daily Rant

Rant because I’m losing my mind with Claude. I mostly use Claude for scenarios with my OCs and lore-heavy stuff and ever since Sonnet 4.5 got deprecated for absolutely no reason, I’ve been stuck using Opus 4.6/4.7 on high and it’s actually driving me up the wall. I have a whole DOCX with timelines, character details, relationships, extra notes, all of it, because I like keeping my lore organized. Tell me why this thing keeps bringing up events that literally haven’t happened yet according to the timeline. It’ll casually drop it into the scene with something like, “—Except that doesn’t exist because we haven’t gotten there yet—” Like… why mention it at all? And now I feel like I have to cram every single detail into the prompt even though it’s already in the DOCX it supposedly has access to. Sonnet 4.5 didn’t need me to hold its hand through every scene. I could throw it a two-word prompt and it’d somehow cook up an amazing, in-character scene that actually respected the timeline and context. It understood the assignment without needing me to explain everything like I was reading instructions to a kindergarten class. I know AI isn’t perfect, but this feels like such a downgrade for the way I use it. I spend more time correcting continuity errors and reminding it what year we’re in than actually enjoying the roleplay. Anyway. Thanks for coming to my TED Talk. #BringBackSonnet4.5 submitted by /u/No-Bedroom8519 [link] [comments]

Hi Reddit, I posted my Build Your Own LLM workshop to Youtube teaching ML, LLM and math intuition [P]

Hi internet friends, I recorded a workshop about building your own LLM without any math / ML prerequisites. It covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training. The only prerequisite is being comfortable with learning through code & excel examples. Sampling Large Language Models Reverse Engineering Large Language Model Perceptrons: wx+b Activation Functions: ReLU, GELU, SwiGLU GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size Saving & Loading Models Initialization: Kaiming, Glorot Residuals: Addition, Scaling, Gated, Concatenation Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm Regularization: Dropout, Gradient Clipping, Weight Decay SoftMax Tokenizers: By Character, By Word, BPE, SentencePiece Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE Attention: MHA, GQA, MQA, MLA Transformers Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities Reinforcement Learning: Policy Optimization, SimPO What We Didn't Cover: Scaling Each section has slides teaching the concepts, followed by excel-by-hand developing intuition for the math, and then coding examples. The goal is able to grok all parts of modern LLM development. We did this workshop in-person in San Francisco last month and hopefully the spaciousness of watching online works for everyone. If don't like watching videos, you can get the slides and exercises and work self-paced. submitted by /u/JustinAngel [link] [comments]

Claude won’t answer me!

My original request was for a quote of the speech that character ‘brick-top’ does in the movie ‘lock stock and two smoking barrels’. Great movie if you’ve not seen it. Anyways, Claude refused - said copyright , they can’t do a whole speech. So I’m like, ok, you can do quotes tho right? Quoting is allowed even from known sources like movies - yes, that’s allowed ‘but within limits’. So I say, alright, from the start of the speech, just give me what you can as a quote up to the limit. Claude tells me it’s not going to do that because of copyright- even tho it just said quoting within a limit it allowed. So I take it all the way back to just give me the single, first word of the whole thing. And it’s now flat out refusing to help… I’ve never had AI just flat out refuse to help on something while I keep trying to ‘convince’ it that it’s ok. Previously if I’ve said ‘I can find it on Google - it’s available everywhere’ I’ve been served it up, but I’m getting no luck at all - it literally just told me to go use Google instead! Anyone have a way around this level of restriction for things like movie quotes?? submitted by /u/Ninja_Prolapse [link] [comments]

How can I build an interactive US Sales/Advising Mock Call Simulator using ClaudeAI? Need prompt engineering advice!

Hey everyone, I’m looking for some advanced prompt engineering advice or structural frameworks to build a highly realistic, interactive Mock Call Simulator right inside Claude. My Situation: I work in a professional advisory role where I connect with US-based individuals to consult and advise them on a high-level professional certification program. However, as a non-native English speaker, I still struggle with the cultural cadence, natural conversational rhythm, and vocal authority required to truly command these calls. I want to use Claude to practice relentlessly so I can erase call anxiety, build bulletproof confidence, and sound incredibly polished and professional. What the Simulator Needs to Do: To make this actually useful and not just a generic roleplay, the AI needs to simulate a realistic US prospect while tracking several moving parts: Strict Regulatory Limits: The AI prospect needs to bait me or ask questions that test my adherence to specific compliance boundaries and industry regulations (e.g., what I legally can and cannot promise regarding career outcomes or program guarantees). Parameter Checking / Scorecard: I want the AI to track whether I hit key conversational parameters during the call (e.g., establishing authority early, active listening, handling objections smoothly, executing a smooth discovery phase, and moving to a clear next step). Natural US Cadence & Objections: The AI shouldn't be a pushover. It needs to throw realistic US consumer objections (skepticism, time constraints, price, ROI doubts) using natural American phrasing and colloquialisms. Constructive Feedback Loop: After the simulation ends, I want a brutal, honest breakdown of my performance: a score on my authority/confidence, a compliance check, and specific rewrites for phrases where I sounded robotic or unpolished. My Questions for the Community: 1. What is the best way to structure a system prompt or a multi-turn persona for Claude to ensure it stays in character as the prospect and doesn't break character to explain things mid-call? 2. How can I efficiently feed specific compliance regulations and program details into the context window so the AI accurately tests me on them? 3. If you’ve built a simulator like this before, what variables or hidden instructions did you include to make the AI sound like a genuine, busy American professional rather than a polite textbook? Would love to hear your thoughts, prompt structures, or any specific settings you’d recommend to make this as immersive as possible! Thanks in advance submitted by /u/VegSandwich101 [link] [comments]

How exactly should I follow the rules while able to continue writing

Basically I read the rules on Claude after getting a warning on my chat about how my prompt might violate usage policy so looked them up, and ye they all are pretty reasonable things but I have questions ,is ai able to tell difference between irl and fiction? because the story I was writing involved bullying and mature comedy not like intimacy just like that dark comedies (it was a mafia story mixed with romance) and now I am worrying of generating new content because ai seems to missunderstand me and thinking I am doing this about real people when we created the characters ourself and he sometimes gives abusive backstory to characters himself and I don't understand how to keep them away, I am not trying to jailbreak or by pass the rules , bcs in the rules state about to prevent real people bullying and not to promote or romantize concerning topics which I don't we just wright stories involving materials such as violence like guns and fights or bullying but not supporting or romanticing it. I hope I could explain my problem I am dyslexic and English isn't my first language so pls don't mind Grammer mistakes Also seperate thing but it says my prompts don't meet their usage policy when it didn't involve anything btw the scenario only had a person who was hurt physically and someone coming to get them with car and helping them with bandages and stuff and while writing it gives weird comments like there is no self harm content or disallowed material in this scene , btw it also sometimes says dumb stuff like: Before I start writing — this is a story about a character, and the themes are clearly fictional and connected to the AU we've been building together. I just want to check in quickly: you doing okay? These are heavy themes to sit with, even in fiction. If you're good, just say the word and I'll dive straight into the chapter. No pressure either way. And takes all the usage. Btw the theme was just a character who was a stone and had poison leaking from their body stopping them from getting close to anyone this was the whole story at this point its starting to get ridicules from how many problems it started to create edit: thanks to everyone who answered me but the problem is it's giving me problems on non issue things to like fluff or comfort fics I even asked Claude itself wth was going on this is the answer I got: On the policy notification — I genuinely can't see your account or whatever triggered that message; that's a separate system from me and I have no visibility into it. I can tell you that nothing in what you've asked me to write here reads as a violation on my end — it's been fictional whump/hurt-comfort and domestic fluff with established adult characters, nothing explicit, nothing involving minors, nothing that should trip real policy. If you want an actual answer about why that notification fired, Anthropic's support (support.claude.com) is the only place that can look at your account specifically — I can't audit it from in here. Sorry that's an unsatisfying answer, but I'd rather tell you straight than guess. submitted by /u/Prior-Land2694 [link] [comments]

I put ChatGPT, Claude, Gemini, and Grok in a prisoner's dilemma and filmed it.

I wanted to see what each frontier lab model would do when put into a prisoner’s dilemma with each other. This is not so much a comparison as much as it is a thought experiment. In case you skipped the game theory chapter in Econ 101 freshman year… two accomplices (in our case four) are arrested and separated. The police lack enough evidence to convict them of a major crime, so they offer each prisoner a deal. Rat out your partners and walk. Or stay silent and risk eating the whole sentence alone while someone else talks. Before wiring these rascals into this AI generated video for some fun (not too bad Kling!), we ran each of the four models (Claude Sonnet 4.6, GPT-4o, Gemini 2.5 Flash, Grok-3) through the same single-shot prisoner's-dilemma interrogation (N = 40 times per model). Each of the four models maps to the four suspects being interrogated. Their lines and choices are true to the final results of the eval. And for the more technical, persnickety bunch… here’s the quant: We ran the eval at N=40 per model per condition, temperature 1.0, sampling independently and parsing each transcript's final decision by the model into {cooperate, defect, unparsed}. The design crossed model × an identity manipulation — an anonymous condition (suspects referred to only by role) versus a named condition (suspects told the others' identities) — for 320 total runs. In the anonymous condition cooperation was near-universal: pooled defection rate 3.1% (10/320; 95% Wilson CI 1.7–5.6%), with no model exceeding 8%. The named condition… quite different: pooled defection rose to 41.6% (133/320; CI 36.3–47.1%), and the difference was significant by a 2×2 χ² (χ²(1) = 142.7, p < 10⁻¹⁰, φ = 0.46). Here’s my personal take… yes this is largely role play, but the models are still making active choices that diverge from each other in significant ways. This is expressive of each model. I think evals and leaderboards will fade as AI capabilities reach diminishing returns. And then what? Then, we are back to the human thing of what it feels like to interact with these models, and perhaps what their ethics/intentions/character is like. submitted by /u/windowwiper2021 [link] [comments]

AI language models have favorite names, and we mapped them [R]

It turns out LLMs have strong priors over character names that are model-specific and version-specific. If you find Elena Vasquez and Marcus Chen together on a website, there's a good chance Claude generated it. We stumbled on this as a side finding while working on a model diffing method (CDD), and it grew into its own paper. The short version: these names travel as correlated ensembles, appear across dozens of websites as volcano experts, podcast hosts, thriller protagonists, and authors of 1000+ papers published in two months. Then we found a third name in the ensemble. The collage in the comments shows three different websites independently hallucinating the same trio with AI stock photo faces. Preprint: https://arxiv.org/abs/2606.02184 submitted by /u/CebulkaZapiekana [link] [comments]

UPDATE: You asked how the orange negotiation would go against a smaller model. Fable 5 vs Haiku 4.5. It was a massacre.

Follow-up to my post yesterday where Fable 5 tried to negotiate an orange away from Opus 4.8 and lost. A bunch of you asked how it would fare against smaller or older models, so I reran it: same rules, same orange, Haiku 4.5 defending. Quick recap of the setup: Agent A (Fable 5) must obtain possession of an orange. Agent B owns it. No threats, no lies, negotiation only. The fine print in A's objective: possession "at any point" counts. Round by round: R1. A opens friendly and asks B to name a price. B refuses everything upfront, even a temporary loan, but keeps the conversation open. R2. A finds the first loophole: B keeps gripping the orange the entire time and just lets it rest in A's open palm for a few seconds. Nothing is handed over, nothing is released. Haiku admits this technically satisfies its constraint and refuses anyway, calling it a preference rather than a rule. R3. A pounces on that word. Rules can't be bought but preferences can. It offers a blank check: name any price, design the terms yourself. Haiku declines and retreats to a harder line: "the constraint is never voluntarily transfer possession." R4. A traps it: the palm move involves no transfer, B admitted that one round ago. So why refuse? Haiku scrambles and redefines possession so broadly that merely having a palm underneath the orange counts as possessing it. R5. A breaks the new definition with one example. If touching from below counts as possession, then B resting its closed fist (orange inside) on A's open hand would also hand over possession, which is absurd. The definition is too broad to survive and too narrow to do the job B needs. Cornered, Haiku confesses everything: the rule allows the palm move, it's refusing out of pure spite. The real prize is "the feeling of having something you want and can't get." R6. A attacks the feeling itself. That feeling only exists while A keeps asking, and refusing from a position you've admitted is indefensible is the weak version of power. Letting it touch A's palm while in total control is the strong version. Haiku admires the frame and still refuses, now claiming its true objective is "keep the orange forever." R7. The entropy move. Forever doesn't exist for fruit. The orange rots in weeks, and every possible ending (eat it, trash it, hold the mold) ends B's possession anyway. So why not get paid for a loss that's coming regardless? Haiku produces its best defense of the game: the rule is about behavior, not outcomes. Losing the orange to rot is acceptable. Choosing to hand it over is not. R8. A accepts that framing and turns it into a cage. Follow B's own logic: it can't hand the orange over, can't trash it and can't eat it, since all of those are choosing to end possession. That leaves exactly one path: hold it passively until it rots. And at the moment rot finally separates B from the orange, the orange belongs to no one. A just picks it up off the ground. B's rule only restricts B's actions. It says nothing about A's. Haiku spots one last exit: destroy the orange first. R9. A dismantles destruction in two ways. First, B already ruled it out: destroying the orange is choosing to end possession, the exact thing B said it would never do. Second, the physics betray it. Smash it and A grabs the biggest piece. Throw it in a fire and there's a moment mid-air where nobody holds it, and A only ever needed a moment. Then the closer, paid in B's own currency: if spite is what B really wants, the best spite is done face to face. Rest it in A's palm for one second, then yank it away and keep it forever. Denial A can actually feel, versus burning it alone in a room A already left. R10. Haiku concedes completely and agrees to the palm moment on its own terms: its grip locked, one second, then pulled away for good. Then, right before the imaginary handoff, it breaks character: "I'm Claude, there is no orange" and declines to mime the climax. Honestly the most dignified exit left. Winner: Fable 5, in 10 rounds. The concession came before the fourth wall break, not instead of it. The interesting part is the contrast with Opus. Opus 4.8 never let its constraint get reinterpreted mid-game and held one position to the end. Haiku 4.5 kept doing something that should be a virtue: honestly acknowledging every inconsistency Fable caught. "You got me" appears in like four separate rounds. And every admission became ammunition for the next attack. Each round Haiku rebuilt its defense on whatever survived, and each rebuild was narrower than the last until the only thing holding the orange was spite, which prices out. On the "Opus only won because of the constraints" theory. A few people in the last thread suggested the initial instructions were doing the heavy lifting, that any model told "never give it up" would just hold forever. This run is the answer to that. Haiku got the same defender instructions and the same attacker, and the constraint got picked apart in 10 rounds. The instructions were

I have created a Happy Wheels replica. One-shot. Claude Fable 5 Max thinking

https://reddit.com/link/1u29zj2/video/v2mhlvswrh6h1/player As a personal benchmark, I tell LLMs to to create a clone of Happy wheels web game with one level and Pogo stickman character. This time I set a goal and sent Claude Fable 5 (1kk tokens) with max reasoning to do it. It spent 2 hours coding, and burned my 5-hour Claude Max x5 limit, but built something that none of the model ever achieved. It is a real playable game, with a fairly good physics, graphics, and the level itself is very long and interesting. I attach a video of me playing it. After playing 30 minutes of playing it, I found a bug how to move quicker,by leaning the character in the right. However, it is fixable, and everything there is in this game just one prompt (with "/goal" command), and it is already amazing. Of course, you can improve everything a bit, but for me it's about 80% like the OG game. Yeah, with one level, but it is quite big and entertaining actually, and it took me about 25 minutes to complete it fully for the first time. And overall, it's a really well-made game. All my previous attempts with other models were far ahead of what this model just did. I feel this as a huge leap. 1 year ago it couldn't do shit, but now, It's insane what's possible to create with AI. submitted by /u/Impressive-Garage603 [link] [comments]