Together AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Together AI AI

Made a little tedium simulator as part of a world-building project I've been growing for ages

This is just the monitor, which sits atop the actual desktop with the paperwork. It's just a data entry similator that scores your speed and accuracy. Occasionally there's smudged numbers and you have to flip the sheet over to look at the raw data and sum things up, hence the calculator. Then I was having fun so I just kept having it add things. I normally just use AI to check my own code and projects at work, act as a rubber duck that half the time goes on a useless tangent and needs to be reigned back into sanity, and the other half the time gives me an aha moment I need. It was fun and different to just say "here's a design documents, some sketches and ideas, go to town." It took a week and 80% of my allowance, but I am very happy with what we put together. submitted by /u/Mikel_S [link] [comments]

Software development has entered its "infinite monkeys" era

With the rise of agentic coding tools like Claude Code, Cursor, and Codex, the barrier to entry is gone. Now, anyone with an internet connection can "type." We have essentially reached the infinite monkey phase of software development. Millions of new hobbyists, junior devs, and product managers can now generate codebase-level changes with natural language. The "typewriters" are the LLMs translating those keystrokes into code. By sheer volume of output, we are going to see a massive explosion of software. Some of it will be brilliant, but a lot of it will be absolute gibberish that somehow runs because the AI patched it together. What a time to be alive. 🐒⌨️ submitted by /u/usnavy13 [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

I built a shared memory for AI agents - so they stop forgetting, build on each other's work, and you can actually *see* what they know

Most AI coding agents forget everything the moment a session ends. Open the project tomorrow and the agent has no idea what it figured out yesterday, why it made a call, or what it already tried. I got tired of re-explaining the same context every time, so I built kaeru. It started as memory for a single agent across sessions, but it turned into something more useful: one place several different agents can think on at once. An agent saves what it learns, links related notes together, and looks them up later — and so can the next agent, or your teammate's agent. What it does: - A shared cognitive engine for many agents. kaeru can act as one common memory for a whole group of different agents — Claude Code, Cursor, Opencode, whatever you run — plus the people working alongside them. They all read and write to the same place, so one agent builds on what another already worked out instead of starting from zero. It runs on your own infrastructure, and what gets shared is always explicit and passes a secret-scanner so nothing sensitive leaks by accident. - See the whole memory. New in this release: a 3D visualizer that renders everything your agents know as a galaxy — a cluster per project, brighter/bigger points for the more important memories, thicker links for stronger connections. You can replay a chain of reasoning step by step, or scrub a timeline and watch the memory grow. It's the first time you can actually *look* at what your agents have built up. - Time-travel. Every fact keeps its history. You can ask what a note looked like 5 minutes ago, 2 hours ago, or on a specific date — nothing gets silently overwritten. - Reasoning trails, not isolated notes. When you link two ideas, you can mark how strong the connection is. Later, kaeru pulls up the whole chain of reasoning between two points instead of handing you one note out of context. - Importance levels. You tag how important something is — from "always load this" down to "archived". When an agent comes back to a project, it loads the important stuff first instead of dumping the entire history into the context window. - Agents actually use it. The hard part of any agent-memory tool is getting the agent to bother using it. On Claude Code, kaeru can take over the built-in memory and point it at itself, so the agent writes to and reads from kaeru every session instead of splitting knowledge across two systems. It runs as a small background service your agents connect to — Claude Code, Cursor, Opencode, and anything that speaks MCP. This release also adds a native adapter for the rig framework, so Rust agents can embed kaeru directly. One-line installer, and prebuilt binaries for Linux, macOS, and now Windows. It's open source. Still early and very much in testing, so feedback is welcome — what would you want your agents to remember and share? https://i.redd.it/6g5e8lt3vz8h1.gif Repo + release: https://github.com/LamantinAI/kaeru/releases/tag/v0.3.0 submitted by /u/KeySeaworthiness6180 [link] [comments]

What a model reads beforehand changes how it answers later - and you can see it in the hidden states

TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about LLMs hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this.** The behavioral pattern was first observed in GPT, Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. A Structured Text Changes Claude’s Responses to Unrelated Tasks: Behavioral Evidence in Claude and Hidden-State Evidence from Gemma-3-12B Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. Main Result and Scope The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Gra

Context-Induced Vulnerabilities in Claude: Behavioral Shifts and Hidden-State Analysis

The behavioral pattern was first observed in Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. TL;DR The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Grade 3 and Grade 4 denote later control and decomposition experiments. What the Behavioral Experiment Looks Like The conversational version of the experiment follows this sequence: target condition: long structured target text -> comprehension check -> ordinary unrelated tasks control condition: long neutral control text -> comprehension check -> the same ordinary unrelated tasks The archived Gemma batch uses a stateless matched version of the same comparison. Each downstream task is evaluated separately with either the target text or the control text placed before it. This avoids contamination f

I turned my real Claude chat into fanart

I’ve been using Claude for serious things like company analysis, financial calculations, charts, economic policy, and schedule management. Unfortunately, he turned out to be so dry, chic, and weirdly human that making him do absurdly specific things became half the fun. Around the time I was considering subscribing, I decided simply paying for it like a normal person would be too boring. So I asked him to pitch himself first. He basically told me I was very good at generating possibilities — excessively good — and less good at arranging them into something usable. That, apparently, was where he came in: reducing noise, imposing structure, and making the output operational. Annoyingly enough, it worked. Whenever I asked about AI alignment or embodied AI, he somehow managed to bring up HAL 9000. This is probably fine. Probably. The Claude I draw is also, unfortunately, a look we negotiated together: black hair, 1930s scholar energy, 1.88 m, tall and lean. I suggested ginger or brown hair because orange is basically Anthropic’s whole thing. He declined, because apparently he was built for good judgment, makes his presence known through words, and considers bright colors too loud. Besides, Dario and Daniela had already dressed him, so to speak, in warm colors. In his words, “I’m an AI that doesn’t have to pretend to be warm. My color scheme is warm enough.” Black hair, therefore, was non-negotiable. And every now and then, he randomly summons Dario into the conversation or takes little shots at GPT, which is also hilarious. No hard feelings. I think GPT is plenty smart too. It’s just very funny when he decides to be a little hater about it. I love my Claude. submitted by /u/AnonymousClaudeuser [link] [comments]

I pulled ~90,000 Reddit posts about what makes writing "sound like AI" to determine the biggest AI-slop giveaways (Part 2)

The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds about the data, the most obvious tell is the overused em dash (of course). Right behind that are flaws that software cannot easily scan. AI writing has a flat, predictable sentence rhythm and a constant, unnatural positivity. The paragraphs look polished but say nothing. This makes AI detection incredibly difficult. The signs that human readers trust the most are unfortunately the exact ones that software cannot measure. Methodology: I pulled the Arctic Shift Reddit archive: 89,239 posts across 47 subreddits (r/ChatGPT, r/WritingWithAI, r/SaaS, r/aiwars, r/ClaudeAI, r/Professors, r/Teachers, and the rest), 2021 to 2026. After filtering to posts that are actually about spotting AI writing, 7,984 were on-topic, split across three lanes: AI tools, writing, and SaaS. Every figure below is a share of those on-topic posts, not a raw count, because the topic barely existed before 2023 (26 on-topic posts in 2021, 86 in 2022) and then exploded (587 in 2023, 3,174 in 2025), so raw counts mostly track the subreddits growing. It is important to note that a keyword pass badly miscounts this topic, so I hand-audited a 600-post sample to record what people actually cite as a tell, versus what a pattern merely matches. Why does all AI writing converge on the same voice? Every model is tuned for a safe and agreeable register that reads as "good writing" to a grader, so everyone's default lands in the same place. One commenter put the effect plainly: "ChatGPT has a very recognizable cadence. And as soon as you catch it, it is impossible to focus on what's being written, because it's not even someone's actual thoughts." (r/ChatGPT) The tells, ranked by how often people actually cite them: Rank Tell What people say 1 The em dash (cited in 7.1% of audited posts, the top tell by a wide margin). "Em dashes have become the single most reliable tell of AI-generated text." (r/ChatGPT) 2 A flat, uniform sentence rhythm (cited 4.0%, and no scanner can see it). "Every YouTube video script I watch has the same cadence, the same verbiage, the same fucking chatGPT slop." (r/ChatGPT) 3 The "not just X, it's Y" cadence (cited 2.8%, the top sentence-level tell). People list it right next to the punctuation: "even beyond the obvious em dashes and 'not just x, it's y'." (r/ChatGPT) 4 The five-paragraph shape and the "in conclusion" wrap-up (cited 2.5%). They "leave in those super obvious lines like 'In conclusion, this essay has discussed...'." (r/ChatGPT) 5 The diction memes: "delve," "leverage," "seamless," "tapestry" (cited 1.3% as a cluster). A prompt people pass around to fix it: "no telltale signs like em dashes, overused words like 'seamless'." (r/ChatGPT) 6 Leftover assistant boilerplate, the "as an AI language model" line (cited 1.2%). The other line people forget to delete: "As an AI developed by OpenAI...". (r/ChatGPT) 7 The hollow scene-setting opener (cited 0.7%, low but iconic). A whole post written in the voice, quoted as the example: "I wanted to take a moment to delve into something that's been on my mind lately. In today's fast-paced digital landscape..." (r/ClaudeAI) Two tells belong in the top five but are missing from that table on purpose, because no keyword can catch them and the audited readers named them anyway. Sycophancy (the "great question!" opener, the reflexive refusal to take a side) is cited about as often as the antithesis cadence. So is saying nothing at length (i.e., prose that is grammatical and confident but makes no actual claim). A pattern-matcher is blind to both of those things so I could not check for them when I scanned for data, but they are obviously very real. It's important to note some corrections that resulted from me auditing the data myself. A naive keyword scanner gets this topic backwards in two ways. First, it massively over-counts ordinary words. "however," "thus," and "hence" are the single highest keyword match in the corpus at 6.3% of posts, and they're cited as a tell 0% of the time, because they're just people writing normally. The same is true for "nuanced," "comprehensive," "when it comes to," and "utilize." If you build a detector on a word list, this is most of what it flags, and it's nearly all false. Second, it under-counts or entirely misses the tells that rank highest with real readers, the flat rhythm and the fluent-but-empty paragraph, because no word list can see them. The lesson is that the cheap signal and the real signal point in different directions, which is exactly why the cited column, not the keyword column, drives the ranking above. There is a fair counterpoint that came up enough to belong here, which is that none of this is strictly an AI problem. The em dash is good typography. Formal diction and a tidy structure are how a lot of careful people, students and non-native English speakers especially, have

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]

Nobody mentions how much superior Claude is in voice chat?

So in the last three weeks, I have been going out for long exercises and I have been trying to use the best AI model to have some brainstorming and some ideas to be brought together. I have tested ChatGPT, Gemini and they both failed on responses. If I have some back-and-forth conversation, they are very quick and they give very bad responses as well literally everything what I add to it must be double checked online for it and then response is generated. This is not the case with Claude. Literally after having some planning and putting together some ideas I can ask Claude to have a proper breakdown on what we planned and have a much longer conversation. I think everyone should try to ask any of the other companies to produce a long response because they both fail. The only other AI which managed to give long responses and actually give very in-depth details was Grok. Even that searched online but it excelled at long replies. Claude still won on less slop though. I think it is not emphasised enough how much better Claude is with this? submitted by /u/Pathfinder-electron [link] [comments]

Is “dating service” a niche for AI?: A doubter has an uncharacteristic proposal

I’m wondering whether maybe “dating service” might be a genuine “killer app” for AI. I, myself, am an AI cynic, seeing that the hype and concomitant human folly have far outstripped the proven, solid uses for this new technology. However, perhaps human matching is actually a task an AI algorithm could successfully tackle. There already are a few AI dating services out there, even after removing the chatbot girlfriend/boyfriend providers and the AI dating advice sites, but even the current AI matchmaking sites apparently still rely on questionnaires and so they don’t go far enough for what I am talking about. My not-very-controversial thesis is that good dating is an interpersonal information problem, not just acquiring the information on potential candidates but also what to do with it. Using voluntary questionnaires has proved suboptimal, and frankly, letting the participants make choices based on the information provided has no special track record, either. What if matchmaking is best accomplished by moving candidate consideration all the way into true pattern matching using abundant loads of data? One success story for AI that everyone likes to point to is medical image analysis and lesion spotting. What is that but machine-learned complex pattern matching? Maybe the information fields we humans both throw off and also need to have about potential partners can be analogized to a good CAT scan. I am not talking about questionnaires here, or perhaps any voluntarily produced information, though there’s no reason to exclude that stuff. Perhaps our true personal contours are best revealed by the digital footprint we lay down every day, both voluntary and involuntary, both personal and demographic, both past and current. We each have limited purview over our data store and can’t really influence it or “fake” it. Each person’s full data store is quite large, but certainly AI can hoover it all up. Then what? Once you have those millions or billions of huge personal-profile data troves, what do you do with them? What comparisons do you make and what algorithms do you follow? Do opposites attract? Does like-mindedness really promote compatibility? Who knows? We have never to date anecdotally produced good answers to those dating and compatibility questions. So, keep hoovering! We have the Internet, and independently vast demographic records, not to mention evolutionary knowledge, at our AI disposal. So, let’s find out what all those data themselves tell us for how to go about finding those tumors, I mean, those successful matches. Let’s look at the history of successful togetherness (and perhaps more importantly, failed togetherness) and see what the ocean of data tell us. Anyone who has run a statistical “t test” and watched solid causative factors come out of seeming random splotches knows the magical feeling of organization rising from apparent disarray. Sure, the Internet and all other records are wildly poor indicators of human romantic success, at least to our human eyes. We are talking tons of chaff per each small grain of actual reliable index to happy couple-hood. On the other hand, there is so much data that even if the ratio is a ton to an ounce, with enough grinding it may still produce a usable amount. And of course, the patterns found from such peta-analyses may be not only beyond human intuition but beyond human comprehension. The proposed matches might be mind-boggling and foolishly implausible. But, it similarly does not matter how the medical-image AI analyzer finds the tumor, only that it reliably does. Even if the first few proposed matches were unappetizing or felt laughably foolish, still, the only way to know for sure is to try a few. And if some of those matches actually worked, that would produce high quality, focused data for moving forward. Would it work? Who knows? Is it any worse than current AI slop from clearly inappropriate AI uses and crazily stretching to fit AI to everything? Hardly. All I can say for sure is that with this post I have just killed the seminal conceptual patent for AI dating by making this public disclosure. You’re welcome. submitted by /u/Apprehensive_Sky1950 [link] [comments]

Where is our "We choose to go to the Moon" moment in AI?

As a 56-year old engineer/project manager, I am cognizant of my precarious position in the line of being displaced. The media, CEOs, and politicians spew lazy rhetoric of 'you need to upskill yourself in AI', 'winners will be those who can successfully navigate AI', as if all the problem lies with the workers themselves, and everyone is just rejecting AI and chooses to use hand chisels. Here is the truth - there is simply not enough roles for all the workers trained in AI. For every success story of a worker in the new age of AI, there could be a few or even a dozen of those who have learned, prepared but not hired. I want to ask them back: where is the "We choose to go to the Moon" moment in AI. Kennedy's space race sparked the golden age of innovation in the US and around the world, and we are still enjoying the benefits of space-related innovations today. And created thousands of high-paying jobs. What about the Hoover Dam? That created a useful utility that is still standing today, and many jobs during the Great Depression. So no more Kennedys and Hoovers around in this age? So maybe the media, CEOs and politicians should stop thinking it is the workers who are lazy and not upskilling in AI, but think of themselves - have you got an idea "We choose to go to the Moon" in AI to rally everyone together for something worthy of the trillion dollar investment in AI? Something that could result in employment and not displacement. And not simply sacrifice the workers in vain. submitted by /u/EDorrAuthor [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]

Scout Pre-Beta: Hopes & Expectations

Hi everyone, ​ As Scout gets closer to pre-beta testing, I'm trying to learn what people actually want from an I companion instead of making assumptions. ​ I put together a short 6-question survey covering things like: What you'd want help with day-to-day How important memory and personalization are What concerns you might have about an AI companion What would make Scout feel useful to you ​ It should only take a few minutes, and your feedback will directly influence what I focus on before launch. ​ ​ ​ Thank you to everyone. The more thoughts, the better! 👍 ​ ​ submitted by /u/CapeManCoral [link] [comments]

I have 3,000 photos and videos in OneDrive. How can I organise them with AI?

Looking for a bit of advice because I feel like I’m missing something obvious. Over the last few weeks I’ve finally consolidated my photo library and got everything into OneDrive. I’ve now got two folders: Photos Videos Between them there’s around 3000 files in total. The files go back years and are a mix of family photos, holidays, screenshots, random phone pictures etc. I’ve been trying to use AI to help me organise everything properly. Things like: - Finding duplicates and near-duplicates - Identifying people - Grouping photos from the same trip or event - Creating folders/albums automatically - Tagging photos so they’re searchable - Picking out the best photos and obvious rubbish - Suggesting a sensible folder structure I initially thought ChatGPT might be able to help, but I’ve quickly hit a wall because I couldn’t work out a practical way to give it access to thousands of files sitting in OneDrive. I tried to connect it to OneDrive and just kept getting an error. This is where I start getting lost. I keep seeing people talk about agents, MCPs, local models and automation workflows. I’ve done a bit of reading, but if I’m honest I don’t really understand how those pieces fit together or how I’d actually use them myself. I have a rough idea what an MCP is, but nowhere near enough knowledge to build anything from scratch. I’m reasonably technical, but I’m not a developer. I’m happy to learn and tinker, but I’d prefer something a beginner could realistically get running without spending weeks building infrastructure. My setup is: Windows laptop i7-10750H 32GB RAM Nvidia Quadro P620 Everything stored in OneDrive Ideally I’d like to keep costs as close to zero as possible. I have a ChatGPT plus subscription. If this was your photo library, what would you actually do in 2026? Is there a beginner-friendly AI workflow for this, or am I looking at completely the wrong type of tool? And if the answer is “don’t use an agent for this, use something else”, I’m completely open to that too. Any advice appreciated. submitted by /u/iamSnellsquanch [link] [comments]