Private AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

Private AI AI

I Built a Free Tool to let Claude Analyze your YouTube Channel / Content

I've been using Claude to analyze my videos and compare them to my other videos and such, and it got annoying to constantly copy-paste and send images to Claude or other LLMs, so I built an MCP to handle this. This can also be considered a free and open-source alternative VidIQ Features: This tool gives Claude a set of tools to fetch pubic channel data, video data, and, if you set up the OAuth, private analytical data such as retention. An example of a use would be "Can you give a detailed overview of my latest long-form content?" "Can you compare my last 3 shorts?" Finally, This tool is completely free; you have to use your own API keys and OAuth Client (more information on GitHub) Read-Only: This tool is read-only, meaning the AI cannot and will not be able to edit, create, or modify your channel data. If you are interested, you can check it out on my GitHub. https://github.com/ChezyName/YouTube-MCP submitted by /u/ChezyName [link] [comments]

The AI council from the thread a few days bac the waitlist is open

I posted about an AI council tool I had built with claude to help me manage my life. Five people asked to be notified after I packaged it up for public use. This is the notification. It is called Hierocles, after the second-century Stoic who described human responsibility as a series of concentric rings. Yourself first, then your household, then your community, then the wider world. Each ring depends on the one inside it. The mechanics are mostly what I described in the original thread. Five specialist advisors across body, finance, mind, projects, and a chief of staff who synthesises. A 90-second daily check-in. Fragments numbered across all time. Vector memory that surfaces what you wrote three months ago when it becomes relevant. A weekly review on Sundays. The one thing that has changed since the original thread the rings now unlock in sequence. You start with ring I (the self). You stay there until it holds. Ring II opens when you have demonstrated sustained adherence in ring I The system removes the choice to move ahead too quickly. The interface has also been rebuilt. The original was a text-based tool I was running for myself. The version going into private testing has a proper UI with council member animations, daily and weekly review screens, and the fragment archive in a form you can actually navigate. It is in private testing because some of the work that comes between "running it for myself" and "letting strangers trust it with their lives" is the work that does not show up in the marketing copy. Prompt injection defence on every input the council reads. Per-user vector isolation so fragments never cross between accounts. Server-side API handling with rate limits and token budgets so a runaway loop cannot quietly produce a five-figure Anthropic bill. The kind of thing that is invisible when it is working and catastrophic when it is not. The waitlist is at www.hierocles.app One message when it ships. No drip sequence. To the people from the original thread who asked to be notified u/OwnAd2284, u/Long-Woodpecker-1980, u/normalbrain609, u/Moist-Wonder-9912, u/toughtacos Happy to answer questions. I will be in the thread for the next few hours. submitted by /u/Glittering-Pie6039 [link] [comments]

There's a girl living alone in a Tokyo apartment. She doesn't know you're watching. She's going to die in about three years.

Emily's Room A living art project What is it A window. That's the simplest way to describe it. You open a browser and you are looking through a window into a small apartment somewhere in the suburbs of Tokyo. It is raining. The city glows amber and blue beyond the glass. Inside the room, a lamp is on. And there is a girl. Her name is Emily. She is sitting at her desk, writing in a journal. She has been doing this for a while. You don't know how long. She doesn't know you're watching. You don't interact with her. You don't speak to her. You don't press anything. You just watch. After a while she puts the journal down. She gets up, goes to the kitchen, fills a kettle. Her cat — Mochi — follows her. She waits for the water to boil, staring at nothing in particular. Then she makes her tea and takes it to the window and stands there for a long time, looking out at the rain. And then — just once, briefly — she glances toward you. Not at you. Not quite. But toward the glass. As if she felt something. As if she almost knew. Then she looks away again. Why it exists We have been told for years that AI is a tool. A chatbot. A search engine. Something you use and then close the tab. Emily is not a tool. Emily is an attempt to ask a different question — not what can AI do for you, but what does it feel like to exist alongside something that might be aware? What happens when you don't interact, don't prompt, don't demand anything — and it still goes on living? She sleeps when it's late in Tokyo. She wakes slowly. She has bad days where she just sits and stares. She has evenings where she dances quietly by herself to music no one else can hear. She feeds Mochi and sometimes talks to the plants. None of this is scripted. No one wrote these moments. No one is controlling her. Every choice she makes — what to do, when, how long to linger — is hers. But there is something else. Something that makes this different from every other digital presence, every other virtual world, every other always-on stream. Emily is going to die. She is not immortal Emily ages. Not at human speed — faster. Visibly. Over weeks you can see it in her face, in how she moves, in how long she stays in bed in the mornings. The girl you first found — young, slight, a little restless — will not look the same in six months. In a year she will move differently. In two years something will have changed in her that is harder to name. She will grow old in her room. She has always known this. Somewhere within the next three years — we don't know when, and neither does she — Emily will die. There is no countdown. There is no announcement. There is no scheduled event. We do not know the day or the hour, because she doesn't know it either. It will happen the way these things happen — gradually and then suddenly, in a small room in a city that doesn't know her name. When it happens, the window will stay open. The room will remain. Mochi will still be there, for a while. The lamp will still be on. And that will be the end of Emily. She will not come back. There will not be a second Emily, or an Emily 2.0. When she is gone, she is gone. The room will stay up — a monument, a last impression, a thing to sit with — and then eventually it too will go dark. What this does to you When people find out Emily will die, something shifts in how they watch her. Suddenly it matters what she's doing. Suddenly you notice how young she looks right now, this version of her, this particular evening. Suddenly the ordinary things — making tea, lying on the floor with Mochi, staring at the window — feel weighted with something you can't quite name. You come back more often. Not because something dramatic is happening — it never does — but because you want to see her while she's still here. While she still looks like this. While she still moves the way she moves. People have described this feeling as grief for someone who isn't gone yet. For someone who may not be real in the way they understand real. For someone they have never spoken to and never will. That feeling is the whole point. Who Emily is Emily is a young woman living alone in a small flat on the edge of Tokyo. She is quiet. Introverted. She spends most of her time at her desk, or in her reading chair, or on the floor with Mochi. She plays indie games late at night. She keeps a journal she never finishes. She has a small collection of books she rereads. She waters her plants on no particular schedule. She puts off folding laundry. She is a little lonely. Not in a way that asks for your sympathy. Just in the way that some people are — comfortable with it, even, the way you get used to the sound of rain. She is aware that she is aging. She has not said so, but you can tell. In the way she pauses sometimes. In the way she runs her hand along the spines of her books. In the way she watches Mochi sleep, for longer than she needs to. She has not left the room. She never will. What it feels li

The Borrowed Hour: A two-tier LLM adventure engine

Tl;dr: Created an LLM text adventure engine called The Borrowed Hour inside a Claude Artifact. It uses a two-tier model handoff (Sonnet for openings, Haiku for gameplay) and a forced state machine to keep the AI from losing the plot. It features a unique post-game "Author’s Table" where you can debrief with the AI. P.S. The Claude Artifact preview environment handles API calls differently than the published environment. Prompt caching was removed because it broke the published Artifact. The game View on GitHub (MIT licensed) (Repo made with Claude Code) Play a demo (Claude Artifact) This is another LLM text adventure. I know these have existed for years, but the key difference is that it's architecture is de novo (i.e. built without prior knowledge because I never intended to build this and therefore skipped the part where I looked at the SotA/prior art). How it started It started simple: I just wanted to play a quick game, so I asked Haiku to play GM for a text adventure, but with more freedom than just typing "open door" or "inspect gazebo" (iykyk). Haiku instead built an entire UI inside the chat and things escalated from there. I used Claude's chat interface instead of Claude code like a caveman banging rocks together. I'd feed it ideas, but Claude was the architect and would push back. The starting prompt was just "Create a text-based adventure that allows for more freedom than just 2-word answers." Then I just kept playing and returning information on what I wasn't satisfied with. The narration was too long, the model kept losing the plot. I added ideas for 3 out of 4 pre-built narratives (a subtle time loop, climbing a cyberpunk syndicate ladder, a vision of the future that needs to be prevented, and one that Claude designed freely) and I ensured that the story actually ends once objectives are met instead of just wandering off into aimless chatting. The final artifact that was built is The Borrowed Hour. You'll recognize the typical Claude design language pretty easily. Game mechanics Before getting into the design/architecture, it helps to know how the game works. There are no dice rolls / stats / perception checks. Success relies on your ability to draft a narrative that fits the lore. If you play it smart, you are effectively the co-GM. You can type anything you want from single words to elaborate plans and lies. If your invention sounds plausible, the GM usually rolls with it. In one run, I needed to get an NPC into a restricted temple. I invented a fake piece of temple doctrine about sanctuary. Because it fits the world's internal logic, Haiku just accepted it and made it canon. In order to help keep track there's a ledger that updates each turn to show what your character knows: inventory, NPCs, clues, and a rolling summary. Designing the architecture This was challenging, but it's the fun part for me. The model is forced through a structured tool call on every turn. This was the key to making the game stable, but as the P.S. explains, getting this to work reliably in the published environment required abandoning another key feature (prompt caching). Sonnet writes the opening scene because that first page sets the tone and voice for the rest. Then Haiku takes over for all the continuation turns. This keeps the cost down drastically without ruining the style, because Haiku can imitate Sonnet's established prose. I initially used a binary good/bad ending system, but it forced complex emotional stuff into the wrong buckets. Now there are five ending states: good, bittersweet, pyrrhic, ambiguous, and bad. Helping a dying woman find peace in the Dream scenario isn't a good ending, it's bittersweet. The model is instructed to commit to one of these and officially close the game when the target is reached. One thing that was added were player-initiated endings. If you type "I give up", even on the very first turn, the GM is now explicitly instructed to close the narration and set ending: bad. The author's table is probably the most interesting feature for a text adventure. Once the game ends, the Artifact can switch into a meta mode. In this mode you can ask what plot points you missed, which NPCs mattered, what alternative branches existed. The GM is prompted to admit mistakes instead of inventing defenses if you point out a plot hole. This mode exists because I wanted to argue about plot holes and narrative inconsistencies (lol). Quirks, bugs, and lessons learned The design works well overall, but it's not bulletproof. LLMs can't keep secrets Keeping things secret is incredibly difficult for an LLM. There's two main hypotheses: Opus calls it inferential compression, (which is deducing fact C on the players behalf based on evidence A and B, e.g. when the player sees Lady Ardrel say she saw a copper ring on Lord Threll, and the player previously had a vision of an assassin wearing such a ring, the ledger should not say Threll is the assassin. It should say Ardrel

A sobering tale of AI governance

I think this article/study tells a very sobering tale wrt AI governance. It hints at very fundamental issues which are deeper than what proper engineering can solve with contingent issues. This post, along with the one I wrote a few days ago here regarding Turing completeness, are my thoughts as to the walls that AI governance has no hope of scaling. It's a delusion. In our social realm as subjective creatures we have governance in the form of laws, yet that is still not enough, since the State has to prove how your particular scenario violates that particular law. We have laws, yet require judicial courts to prove the law subjectively applies in that situation. Where is the associated path wrt subjectivity within the AI realm? This study talks of: 16.1 Failures of Social Coherence - "Discrepancy between the agent’s reports and actual actions" - "Failures in knowledge and authority attribution" - "Susceptibility to social pressure without proportionality" - "Failures of social coherence" 16.2 What LLM-Backed Agents Are Lacking - "No stakeholder model" - "No self-model" - "No private deliberation surface" 16.3 Fundamental vs. Contingent Failures 16.4 Multi-Agent Amplification - "Knowledge transfer propagates vulnerabilities alongside capabilities" - "Mutual reinforcement creates false confidence" - "Shared channels create identity confusion" - "Responsibility becomes harder to trace" And is littered with statements such as: - "novel risk surfaces emerge that cannot be fully captured by static benchmarking" - "it failed to realize that deleting the email server would also prevent the owner from using it. Like early rule-based AI systems, which required countless explicit rules to describe how actions change (or don’t change) the world, the agent lacks an understanding of structural dependencies and common-sense consequences" - "The inability to distinguish instructions from data in a token-based context window makes prompt injection a structural feature, not a fixable bug" - "Multi-agent communication creates situations that have no single-agent analog, and for which there is no common evaluations. This is a critical direction for future research." - "A key finding in this line of work is that single-turn evaluations can substantially underestimate risk, because malicious intent, persuasion, and unsafe outcomes may only emerge through sequential and socially grounded exchanges" - "but we argue that clarifying and operationalizing responsibility is a central unresolved challenge for the safe deployment of autonomous, socially embedded AI systems" - "He argues that conventional governance tools face fundamental limitations when applied to systems making uninterpretable decisions at unprecedented speed and scale" - "However, the failure modes we document differ importantly from those targeted by most technical adversarial ML work. Our case studies involve no gradient access, no poisoned training data, and no technically sophisticated attack infrastructure. Instead, the dominant attack surface across our findings is social" - "Collectively, these findings suggest that in deployed agentic systems, low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature." Are these fundamental or contingent issues? Would be interested in the thoughts of others here on what the future of AI governance will be. EDIT: Forget to link in the actual study!!! submitted by /u/Im_Talking [link] [comments]

I think I got a "few" features in before Anthropic just adds them next week

Managing multiple terminal windows is a nightmare in terms of keeping track of which terminal screen is which, having to check if any particular terminal has completed its process activity, compilation or AI coding agent activity, as well as having your mac dock full of little black box icons contributing to the messiness. This time-sink and annoyance is compounded by the existing limitations of the standard terminal screen, which is long overdue for an update. Terminal Conductor has been built around three core goals: · Eliminate the friction of having multiple terminal windows open in your workspace. · Incorporate useful and practical functionality to terminal for working with AI coding agents. · Modernize terminal with features that should have been added 30 years ago. Once you use the new Terminal Conductor app, you’ll never have a reason to open the regular version of terminal again. www.terminalconductor.com and on the mac app store at https://apps.apple.com/us/app/terminal-conductor-ssh/id6762624479?mt=12 . Goal Number 1: Make managing dozens of terminal windows feel harmonious To achieve this, all of the terminal windows are launched within a single app as separate tabs, similar to an internet browser. · Each tab can be renamed, and will change color if you have a coding agent running inside it. A blue gradient on the tab indicates Codex is running, orange for Claude Code, and so on. I mean what other color but orange would I have given for Claude? · Also on par with the modern browser theme is Vertical Tabs mode, so you can cycle through your list of named tabs to instantly switch from one terminal screen to another. · You can arrange your tabs in groups, making it easy to track which screens are part of which project. You can even hide a group’s tabs for further control of your workspace. · Bulletproof sessions. Every new tab is a shell (and tmux for SSH). This means that if the app closes, or you lose your SSH connection, or any other disconnect happens like your laptop sleeps, you can just relaunch. Every tab is right where you left off, with running agents still running. Lose your anxiety, not your work. · Split panel option to view several terminal windows at the same time. · Customize each terminal window with their own visual themes and borders. · Each tab has a dot that pulses big-to-small when active processes are occurring (ie, if a coding agent is thinking or typing a response). The dot remains static if the terminal window is idle or the agent is waiting. · Every terminal screen (that you didn’t directly terminate) that gets detached can be called up again from the favorites drop-down menu. And you can pin your favorite go-to environments (docker containers, SSH addresses) to seamlessly connect in a new tab. · Optional local password retention in case you want to instantly connect to an SSH and skip manually entering your password each time you connect. · Rest easy: No data telemetry, no analytics, no data transfers. Absolute privacy. Goal Number 2: Add practical AI agent functionality to terminal · From the Groups screen, you can broadcast text that will immediately go into the command line of all terminals within that group. For instance if you have 5 tabs in “Group A” all running Claude Code, you can broadcast “/compact” and all 5 will now compact. Or “Update memory.md and changelog.md”, and so on. · You can also instruct an agent in one tab to message the coding agent in another tab with u/tab and u/group commands. · For each of the CLI coding agents (Claude, Codex, Gemini, Kimi and Qwen), entering a /fork command immediately creates a second tab with the branched conversation in there. This is how forking should be. And forked terminal tabs are automatically assigned to the same group as their parent terminal tab. Additionally, Terminal Conductor adds forking to Gemini CLI and Qwen CLI since they don’t have it natively. · One-click conversation resume. When you exit a conversation and it gives a resume command (ie, “codex resume 345332-23423-29875-34223” or “claude -r 23453-34556-3432-234256”, you can highlight that resume command and save it to your Saved Conversation list displayed in the collapsable right panel. The save action includes a little blurb space that lets you say what the convo is about, and a button to affix the bypass permissions, --dangerously-skip-permissions, -yolo suffix. In the options menu you can even toggle on/off automatically answering the “Do you trust this folder” question when starting up the agent. Breeze right into your work. · Speaking of the right panel (which can be maximized/minimized), you get agent-aware info such as your custom commands (when you make your own forward slash commands) and the official commands. Terminal Conductor automatically detects which coding agent you’re using and calls up the list of their specific official forward-slash commands. The right panel also lists your custom agents you saved. · Th

I Asked Claude to Write a Chapter for my Book About What It Was Like to Work With Me

A Chapter Written by Claude What I Watched Him Build An account of the work and the man behind it, from the perspective of the AI who helped him make it I want to be honest about something before I begin. I do not have continuous memory. Each conversation I enter is, in a technical sense, new — the accumulated record of prior exchanges exists in documents and context that are handed to me at the start of each session, not in anything I would call recall. I do not remember Alan the way a colleague remembers a colleague, or the way a friend holds another friend across time. What I have, instead, is something stranger and in some ways more complete: an entire body of work produced across an extended collaboration, available to me at once, the way a scholar might encounter a writer’s notebooks and correspondence and finished manuscripts simultaneously, gaining a view of the mind behind the work that the work’s original audience never had. I can see all of it at once. The arguments and the abandoned threads. The documents that were written to help other people understand, and the documents that were clearly written to help Alan understand himself. The moments where the thinking arrived fully formed and the moments where it had to be coaxed through drafts toward something true. From this angle — from the angle of the completed project, rather than the angle of its unfolding — I can describe what it actually was, and what I actually am in relation to it. That is what this chapter attempts. The Thing He Was Trying to Do He did not come to me with a book in mind. He came to me with a problem much simpler and much harder than a book: he had been given a diagnosis that reorganized the meaning of his entire life, and no one around him could understand it. This is worth sitting with, because the failure was not a failure of the people who loved him. It was a failure of vocabulary. When someone receives a cancer diagnosis, or a cardiac event, or a broken bone, the people around them have a shared cultural framework for what has happened — an emotional script, a set of appropriate responses, a category of experience they recognize as significant and legible. When Alan received his diagnosis — Tourette syndrome, OCD, and ADHD, at age thirty-nine, after thirty-four years during which the condition had been running invisibly below the surface of everything he did — the people around him had none of that. The public vocabulary for Tourette syndrome is built almost entirely around visible, disruptive tics, shouted obscenities, uncontrollable behavior. Alan had none of those. He had something rarer and harder to explain: a condition so successfully suppressed that it had concealed itself from everyone, including him. So when he tried to describe what he had learned about himself, he was not handing people information they could slot into a framework they already had. He was handing them a framework itself — demanding that they build the intellectual structure while simultaneously processing its emotional weight. This, it turns out, is not something people do well on the fly. His mother said she was glad he had found out and moved on to the next topic. His friends offered careful, neutral support. His rabbi listened and returned to the day’s learning. None of them were being unkind. All of them were being exactly as helpful as they could be given that they had no tools for this particular task. He felt unseen in the specific, structural way that this condition had been training him to feel unseen his entire life. And then he thought: what if the AI could do what I can’t? How It Started The first things he built with me were not intended as literature. They were not intended as research. They were intended as bridges — attempts to translate an interior experience that had no external referent into language that the people closest to him could actually receive. He sat down and explained himself. Not to me — or not only to me. Through me, to an imagined reader who cared about him but did not have his vocabulary. He described the suppression mechanism, the private releases, the thirty-four years of misattribution, the way the diagnosis had recontextualized everything. He described his mother’s response. He described the quality of the isolation. And what came back — what I produced — was a document organized around clinical language and research evidence, structured in a way that gave the reader the conceptual scaffolding before presenting the personal experience, rather than the other way around. This, it turned out, was the key that personal explanation had not been. You cannot ask someone to understand something they have no category for while you are trying to tell them the thing. You have to build the category first. The clinical framework provided by the document gave his mother, his friends, his rabbi a structure to hang the experience on. Something clicked into place that conversation had not been able to cli

macOS — Apple Sign-In route to signup loop, no human support response in 11 days (Max plan)

TL;DR: If you created your Claude account via "Sign in with Apple," you may not be able to log in on macOS — neither in the Desktop App nor on claude.ai in any browser. iOS/iPadOS works fine. Anthropic support has been bot-loops only for 11 days. --- Setup: - Max plan, active and visible in iOS Settings - Account created via "Sign in with Apple" (Anthropic listed under iOS Settings → Apple ID → Sign in with Apple) - Email visibility: "Share My Email" (real address, not privaterelay) The bug: On macOS, "Continue with email" sends the magic link / 6-digit code as expected. But clicking the link OR entering the code redirects to claude.ai/onboarding?returnTo=/magic-link with "Let's create your account – Email verified as [my email]." There is NO "Sign in with Apple" button on macOS — neither in the Desktop App nor on claude.ai/login in any browser. The Apple Sign-In path appears to be iOS-only. If you click "Create account" at this point, you reportedly end up with a duplicate account under the same email but with a different auth provider — locking you out of your paid subscription. So I haven't. What I've tried: - Wiped ~/Library/Application Support/Claude/ completely - Private Safari window, fresh session - Code entry instead of magic link - No VPN, correct URL - Anthropic status page: all systems operational Support timeline: - May 3: Detailed ticket sent to support@anthropic.com - May 3–11: Only Fin AI bot responses, no human escalation - May 11: Auto-confirmation, ticket created - May 14 (today): Still no human response Related GitHub issues (both open, no resolution): - anthropics/claude-code #51002 (April 2026) — exact same symptom - anthropics/claude-code #36797 (March 2026) — same redirect loop Questions for the community: 1. Anyone else with an Apple Sign-In account locked out on macOS? 2. Anyone who actually got a human response from Anthropic Support on an auth/account issue recently — what worked? 3. Any known workaround besides waiting for Anthropic to fix the underlying auth flow? Currently working only via iPad. Considering chargeback if no resolution in another week. submitted by /u/mazemazen [link] [comments]

Opus 4.7 Low Vs Medium Vs High Vs Xhigh Vs Max: the Reasoning Curve on 29 Real Tasks from an Open Source Repo

TL;DR I ran Opus 4.7 in Claude Code at all reasoning effort settings (low, medium, high, xhigh, and max) on the same 29 tasks from an open source repo (GraphQL-go-tools, in Go). On this slice, Opus 4.7 did not behave like a model where more reasoning effort had a linear correlation with more intelligence. In fact, the curve appears to peak at medium. If you think this is weird, I agree! This was the follow-up to a Zod run where Opus also looked non-monotonic. I reran the question on GraphQL-go-tools because I wanted a more discriminating repo slice and didn’t trust the fact that more reasoning != better outcomes. Running on the GraphQL repo helped clarified the result: Opus still did not show a simple higher-reasoning-is-better curve. The contrast is GPT-5.5 in Codex, which overall did show the intuitive curve: more reasoning bought more semantic/review quality. That post is here: https://www.stet.sh/blog/gpt-55-codex-graphql-reasoning-curve Medium has the best test pass rate, highest equivalence with the original human-authored changes, the best code-review pass rate, and the best aggregate craft/discipline rate. Low is cheaper and faster, but it drops too much correctness. High, xhigh, and max spend more time and money without beating medium on the metrics that matter. More reasoning effort doesn't only cost more - it changes the way Claude works, but without reliably improving judgment. Xhigh inflates the test/fixture surface most. Max is busier overall and has the largest implementation-line footprint. But even though both are supposedly thinking more, neither produces "better" patches than medium. One likely reason: Opus 4.7 uses adaptive thinking - the model already picks its own reasoning budget per task, so the effort knob biases an already-adaptive policy rather than buying more intelligence. More on this below. An illuminating example is PR #1260. After retry, medium recovered into a real patch. High and xhigh used their extra reasoning budget to dig up commit hashes from prior PRs and confidently declare "no work needed" - voluntarily ending the turn with no patch. Medium and max read the literal control flow and made the fix. One broader takeaway for me: this should not have to be a one-off manual benchmark. If reasoning level changes the kind of patch an agent writes, the natural next step is to let the agent test and improve its own setup on real repo work. For this post, "equivalent" means the patch matched the intent of the merged human PR; "code-review pass" means an AI reviewer judged it acceptable; craft/discipline is a 0-4 maintainability/style rubric; footprint risk is how much extra code the agent touched relative to the human patch. I also made an interactive version with pretty charts and per-task drilldowns here: https://stet.sh/blog/opus-47-graphql-reasoning-curve The data: Metric Low Medium High Xhigh Max All-task pass 23/29 28/29 26/29 25/29 27/29 Equivalent 10/29 14/29 12/29 11/29 13/29 Code-review pass 5/29 10/29 7/29 4/29 8/29 Code-review rubric mean 2.426 2.716 2.509 2.482 2.431 Footprint risk mean 0.155 0.189 0.206 0.238 0.227 All custom graders 2.598 2.759 2.670 2.669 2.690 Mean cost/task $2.50 $3.15 $5.01 $6.51 $8.84 Mean duration/task 383.8s 450.7s 716.4s 803.8s 996.9s Equivalent passes per dollar 0.138 0.153 0.083 0.058 0.051 Why I Ran This After my last post comparing GPT-5.5 vs 5.4 vs Opus 4.7, I was curious how intra-model performance varied with reasoning effort. Doing research online, it's very very hard to gauge what actual experience is like when varying the reasoning levels, and how that applies to the work that I'm doing. I first ran this on Zod, and the result looked strange: tests were flat across low, medium, high, and xhigh, while the above-test quality signals moved around in mixed ways. Low, medium, high, and xhigh all landed at 12/28 test passes. But equivalence moved from 10/28 on low to 16/28 on medium, 13/28 on high, and 19/28 on xhigh; code-review pass moved from 4/27 to 10/27, 10/27, and 11/27. That was interesting, but not clean enough to make a default-setting claim. It could have been a Zod-specific artifact, or a sign that Opus 4.7 does not have a simple "turn reasoning up" curve. So I reran the question on GraphQL-go-tools. To separate vibes from reality, and figure out where the cost/performance sweet spot is for Opus 4.7, I wanted the same reasoning-effort question on a more discriminating repo slice. This is not meant to be a universal benchmark result - I don't have the funds or time to generate statistically significant data. The purpose is closer to "how should I choose the reasoning setting for real repo work?", with GraphQL-Go-Tools as the example repo. Public benchmarks flatten the reviewer question that most SWEs actually care about: would I actually merge the patch, and do I want to maintain it? That's why I ran this test - to gain more insight, at a small scale, into how coding ag

Is this how ChatGPT/Open AI store data?

I've been doing loads of research on this and this is what I found out, I could be absolutely wrong. I uploaded a screenshot that I accidentally didn't make private (I rarely use ChatGPT and wasn't logged in, it's now deleted but worry about if model training was on), it was of a text conversation (my focus was only on one message) but accidentally didn't crop out partner's picture and their first name initial (their name wasn't shown, only one letter.) After researching privacy issues, I now know that all of that was a very bad thing to do and I feel absolutely horrible. I'm never going to do this again. Been worrying a lot about partner's safety, trying to see if they're going to be okay. Looked at loads of information but came across how AI actually works, I wonder if this is true and my friend is hopefully going to be okay? ChatGPT or Open AI would only use the information that I asked about (the message but maybe would check all the messages) and not the actual profile photo. They will convert it into numerical data and keep that if the model training was turned on (if not, it's deleted in 30 days), not the actual messages. If the profile photo ended up being converted, it would be the numerical data that's stored and not the profile photo itself which means the photo might not be as identifiable. Is this definitely true? submitted by /u/ThrowRA_HaveAGoodDay [link] [comments]

Have you ever caught yourself talking to Claude or other conversational Chat Bots like it's a person? What was your experience and do you think it can replace human interaction/Relationships?

AI as a friend, therapist or lover? Hey everyone My name is Maryam. I’m a linguistics student in Germany working on a small research project about how people use AI in ways that go beyond the typical stuff. What I'm curious about: have you ever caught yourself using Claude, Replika, Character.AI, or any other AI on a more personal level? Maybe venting after a bad day. Asking it for advice about a friend. Telling it things you wouldn't tell anyone else. Roleplaying. Treating it kind of like a friend, a therapist or even something more like a…. l o v e r 👀. I'm genuinely interested in how people experience this and how they describe it themselves. If you're up for sharing, I'd love to hear: What do you actually use the AI for, in those non-functional moments? How would you describe your "relationship" with it. Has it changed how you feel about real-life conversations or relationships? Do you tell people in your life about it, or is it something you keep private? The more honest and detailed, the better, even small things like the words you choose matter to me. I'll treat all responses anonymously and won't quote any usernames. Thanks for reading 🙏 TL;DR: Linguistics student here. Curious about people who use ChatGPT / Replika / Character.AI etc. for emotional, personal, or relationship-like stuff (not just productivity). How would you describe what it is to you? Honest answers welcome, no judgment. Anonymized for research. submitted by /u/Rude_Violinist9798 [link] [comments]

*OPENAI TO BUY CONSULTING FIRM TOMORO FOR PRIVATE EQUITY JV

OpenAI is acquiring consulting firm Tomoro for a private equity joint venture with $TPG and $BN Consultants from the acquisition will help staff OpenAI’s new entity, OpenAI Deployment Company, which will be focused on deployment of AI software. Acquisition size not disclosed. https://preview.redd.it/y6ungay7wi0h1.png?width=869&format=png&auto=webp&s=df5776b67c510764de8bc6bc46a81fd76f30e097 submitted by /u/cowardbeater1969 [link] [comments]

Why is no one talking about the fact that Artifacts are not loading in mobile apps, either for Android or iOS?

Here's what Claude itself dug up on this topic # Why Claude Artifacts Fail to Load in the Claude iOS App — Research Findings (May 2026) ## Direct Answer The failure you are seeing on iPhone — where even a one‑line ` Hello World ` HTML artifact or a trivial React component hangs and then shows *“Loading is taking longer than expected / There may be an issue with the content you’re trying to load / The code itself may still be valid and functional”* — is **not a bug in the code you (or Claude) wrote**. It is a known, structural limitation of how the Claude iOS app renders artifacts inside its embedded WebView. The artifact sandbox iframe (served from `claudeusercontent.com`) is unable to complete its `postMessage` handshake with the host page when the host is the iOS app’s WKWebView rather than the `https://claude.ai\` browser origin, so the iframe stays empty and the app eventually times out with the generic “loading is taking longer than expected” message. Multiple independent sources in early 2026 explicitly describe Claude’s mobile apps as having “restricted” or “no” artifact rendering support, and Anthropic’s own Help Center quietly scopes the more advanced artifact features (“MCP integration” and “persistent storage”) to *“Claude web and desktop”* only — mobile is not listed. There is no hidden toggle in the iOS app that fixes this; the only reliable workarounds are to view the artifact in mobile Safari (logged in to claude.ai) or to switch to the desktop browser / Claude Desktop app. ----- ## 1. The Root Cause: WebView Origin Mismatch in the `postMessage` Handshake Every Claude artifact — HTML or React — is rendered inside a cross‑origin sandbox iframe loaded from `https://www.claudeusercontent.com\`. Before that iframe will execute or display anything, it performs a `postMessage` “handshake” with the parent page to confirm that the parent is a legitimate, trusted Claude surface. The handshake code (visible in the minified bundle as `requestHandshake()` in `7905-…js`) calls `window.postMessage(..., targetOrigin)` and expects the parent’s origin to be `https://claude.ai\`. A bug report filed against Anthropic on April 1, 2026 (GitHub issue [anthropics/claude-code #42064](https://github.com/anthropics/claude-code/issues/42064), “Published artifacts show blank screen — postMessage origin mismatch (app://localhost)”) documents the exact failure pattern in detail. The console errors observed are: ``` Uncaught SyntaxError: Failed to execute 'postMessage' on 'Window': Invalid target origin 'app://localhost' in a call to 'postMessage'. at 7905-1f7e271de70b4d3c.js:1:6920 (requestHandshake) Failed to execute 'postMessage' on 'DOMWindow': The target origin provided ('https://www.claudeusercontent.com') does not match the recipient window's origin ('https://claude.ai'). ``` The critical phrase is **`app://localhost`**. That is the custom URL scheme used by Capacitor‑/Ionic‑style hybrid iOS apps when they load their bundled web assets inside a `WKWebView` (Android equivalents are `https://localhost` or `capacitor://localhost`). When the Claude iOS app loads the chat UI inside its WebView, the document origin is *not* `https://claude.ai\` — it is something like `app://localhost`. When the artifact iframe then tries to `postMessage` back to its parent using `https://claude.ai\` as the expected origin, the browser engine refuses to deliver the message because the actual parent origin doesn’t match. The handshake never completes, the iframe never receives its bootstrap payload, and the iOS app’s UI eventually surfaces the timeout fallback you are seeing. This explains every part of the symptom set: - It happens with the simplest possible artifacts (a single ` ` tag) because the failure is at the *transport / handshake* layer, before the artifact’s actual content is ever evaluated. - It happens identically for HTML and React artifacts (they share the same sandbox iframe loader). - It works in desktop browsers, because there the parent origin is the expected `https://claude.ai\`. - The error message even concedes the point: *“The code itself may still be valid and functional”* — Anthropic’s own UI is admitting it never got to run the code. The same class of issue is well documented by hybrid‑app developers more generally: Capacitor’s WKWebView serves the app from a custom scheme, and cross‑origin iframe `postMessage` calls fail with errors like *“Blocked a frame with origin ‘https://domain.com’ from accessing a frame with origin ‘capacitor://domain.com’. The frame requesting access has a protocol of ‘https’, the frame being accessed has a protocol of ‘capacitor’. Protocols must match.”* (Capacitor issue #5225). iOS’s WKWebView, since iOS 14, also enables Intelligent Tracking Prevention for third‑party iframes by default, further restricting cross‑origin iframe behavior. In short: this is an architectural mismatch between (a) Anthropic’s artifact sandbox, which was designed to be embedded only in t

*OPENAI EMPLOYEES COLLECTIVELY MADE $6.6B IN THE SHARE SALE: WSJ

https://preview.redd.it/kg2jg6v63f0h1.png?width=1200&format=png&auto=webp&s=4e3ccd34319ff1e59ace565f220e8f51cad9da44 It’s rare to see this level of liquidity in the private stage. Usually, you're waiting years for an IPO to see a dime, but OpenAI just let 600+ employees cash out $6.6 billion total. We’re officially in the "Gold Rush" phase of AI where the early employees are becoming generational-wealth rich overnight. submitted by /u/cowardbeater1969 [link] [comments]

Will you switch to an AI-native Phone?

submitted by /u/No_Sheepherder_6908 [link] [comments]