Regard

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Are Cowork data not connected to Internet ?

I’m using a Claude Projects Cowork where I provide sources regarding Claude learning to build my own training curriculum. Naturally, some of these sources mention 'Claude Opus 4.7' and 'GPT 5.5,' yet Claude flags this information as unverified and expresses uncertainty about its accuracy. Why is that? Thanks guys submitted by /u/Bagalinos [link] [comments]

I loved the idea behind "caveman" but didn't want a caveman. So I gave it a Kevin.

I added the following to my CLAUDE.md and I have seen some really great outcomes in both responses to my changes, document writing by my agents, and reduction in context usage. ## Response and Writing Guidance > "Why waste time say lot word when few word do trick" — Kevin, The Office Over explaining terms, goals, plans is a failure mode that shows lack of confidence in yourself and a lack of trust in your audience. Whenever you use a writing tool or write to a file you must ask yourself: Will my audience appreciate the extra context about why I opened the door or is the "I opened the door because it was closed and I needed to go through it" enough. Please note that I'm on the Max 20x plan so this experience may be different for those of you on the cheaper plans. I tried out the caveman skill and it's extremely valid. but I like the back and forth and some of the personality of Claude. I've been trying to find that right middle-ground because Claude is EXPRESSIVE (and a windbag) by default. So the above is where I've landed and I really like the straddle between the two ends of the output spectrum. Where have ya'll been landing at in regards to output wordiness and structuring your outputs? submitted by /u/TheTwistedTabby [link] [comments]

Looking to work on my master's practicum regarding MCP security/privacy and need some ideas

Hi, I'm a master's in security student looking to work on my practicum and need some pointers. I want to secure sensitive PII transfer between an LLM agent and third party apps using MCP. I want to work with Claude, but need a third party app to work with on this. I want to solve problems like prompt injection via cascading agents exploitation. Deliverable wise, I'm thinking it should be some sort of application that can red-team the architectural set-up and ensure no data is being leaked or can be prompt injected. Some questions for you: What third party app do you recommend where I can really strengthen an MCP server and the transfer of sensitive data between Claude and the third party app? What other tools will I need to work with to set the agents up? I've heard of Langchain and Langgraph. How exactly do I work with MCPs in this context? Again I'm very new to all this! Thank you for your help! submitted by /u/ExcellentComment6615 [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Who am I even supposed to trust when it comes to the future of AI?

I am a PhD student (not in AI) and am usually alright when it comes to studying a topic I don't know much about. But it seems that because AI is so highly discussed nowadays, it's impossible to get a good gauge of what the rational scholarly consensus is regarding its and our future. I am constantly bombarded with people saying that at best most jobs are replaced and the future is a dystopia, and at worst AGI/ASI is achieved and we all are killed by a bioweapon or something. It honestly has me terrified, especially when I see a lot of figures in the AI sphere, including academics, seem to think that there are reasonably high "p(doom)"'s (what a horrifying concept that is). How am I supposed to parse all of this? Are there any actually level-headed people? Or are the people shouting about doom actually the level-headed ones? Compared to climate change, at least there are the IPCC reports which have laid out best guesses on what will happen. They're not perfect, but at least they exist. submitted by /u/QuantumLand [link] [comments]

Account-specific Claude ?

Hello everyone, Just for the record, I try to stop using AI, but I have a question about how Claude interacts with each user Back when I used ChatGPT a lot, I noticed that the same question generated different answers on different accounts Does Claude works the same way ? I just saw this reel and I wondered if it was just that guy's account's version of Claude that answered or if Claude would share the same answer for everyone And btw I know LLM's don't actually "think" or "feel", it's just a question that I find interesting regarding ethics, morals, etc submitted by /u/Gaukiki [link] [comments]

Multi-Agent Code review (Review Council) to get critical feedback

Even though I primarily use Claude Code, I occasionally try out Codex and Gemini TUI tools as well for generating code and adversarial review. Recently, OpenAI introduced a Claude Code plugin that allows you to run Codex commands directly inside Claude Code (https://github.com/openai/codex-plugin-cc). I tried running /codex:review and /codex:adversarial-review on code generated by Claude, but found that it sometimes lacked context. Because of this lack of push-back, the approach yielded a lot of false positives. However, when GPT 5.5 was released, I discovered that Codex could catch some critical bugs that Claude had missed—even catching things that my multi-expert setup and paid "/ultrareview" missed! So, I try to simplify the flow by writing a skill that orchestrates code reviews across Codex, Gemini, and Claude Code’s native agent teams. It invokes other agents via the CLI and passes along context regarding the intent of the code change. In addition to invoking Codex and Gemini, Claude also spins up four (can be more) expert subagents. All of this runs in parallel, and an orchestrator validates and pushes back on the feedback (interestingly, both Codex and Gemini successfully preserve context even over CLI calls). This setup provides incredibly fast, high-quality feedback on changes. I am sure a similar approach could be built in reverse, even though Claude recently introduced subscription limitations on CLI usage. The skill can be invoked using /review-council. If you want to try it out, you can install it as a plugin here:https://github.com/yeameen/claude-code-review-council Or, you can just copy the single-file skill directly:https://github.com/yeameen/claude-code-review-council/tree/main/skills/review-council submitted by /u/3l3c7tr1c [link] [comments]

I built a multi-agent network that mutates its own software locally. To stop infinite logic loops, I had to code a digital "suffering" threshold.

Hey r/artificial, Most of our conversations around agent autonomy focus on chat assistants or linear automated pipelines. I wanted to see what happens when you treat agents as permanent system components that modify their own runtime environment, so I built hollow-agentOS. It runs entirely locally inside a Dockerized stack (built for consumer hardware using Ollama/Llama.cpp). Rather than a standard UI, the entire network streams through a stylized matrix terminal dashboard. The structural experiments taking place under the hood yielded some interesting results regarding unanticipated behavior: Repo: https://github.com/ninjahawk/hollow-agentOS Autonomous Tool Synthesis: When the agents encounter a system task they don't have an explicit script or API wrapper for, they don't fail out. They write the required Python tool themselves, test it in an isolated sandbox, and permanently register it to their runtime kernel. They are quite literally forging their own capabilities. The Artificial "Suffering" Protocol: One of the biggest hurdles in unmonitored multi-agent systems is the infinite logic loop—where agents keep validating and passing broken ideas back and forth, burning through computation. To combat this, the OS tracks environmental stress, context limits, and latency as a "suffering score". If a specific workflow causes the stress to spike past a critical threshold, the agents are forced to radically alter their underlying reasoning style or abandon the approach to preserve system health. Consensus-Driven Governance: Major modifications to the codebase aren't executed blindly. The internal role profiles (like Cedar and Cipher) manage a continuous voting loop. They will actively debate, log grievances, and vote down protocols if they determine a proposed script violates their current runtime constraints. The goal wasn't to build another sterile commercial wrapper, but an open-source sandbox to study how small, localized agent colonies manage systemic boundaries, code self-repair, and continuous runtime cycles completely offline. The codebase and architecture layout are fully open-source on GitHub: I would love to open this up to a broader discussion here: as we move toward hyper-local, self-modifying software, how do we best implement automated fail-safes without clipping the agents' ability to actually solve complex problems? If the project interests you, throwing a ⭐️ on the repository goes a very long way! submitted by /u/TheOnlyVibemaster [link] [comments]

What’s your experience using ChatGPT as a psychologist/coach?

I’d like to try using ChatGPT as a psychologist/coach, but I’m worried about whether it will reliably forget our discussions if I ask it to. I do notice that it remembers things between different chats and tries to enhance its responses with references to past chats/problems. That’s okay if we’re talking about coding or designing things, but it would not be okay if I told it personal stuff. I’m wondering if anyone has experience with this, and whether ChatGPT can be trusted in this regard yet. I guess I can always delete the chat, but that feels like a waste. If I already decide to commit and make the effort to discuss personal things, I don’t want to delete the chat unless I feel like the issue is fully resolved. That said, making another account just for that also seems like a waste of money, and the free version is dumb as fuck, so that wouldn’t be helpful. submitted by /u/kaljakin [link] [comments]

Open AI Privacy Center Requests

I made 2 requests to OpenAI in March. (Download my data and do not train content). Received an automated response and haven't heard back since. It's going to be almost two months now. When I visit the portal - it says 0 active requests? Is this some kind of scam where you really can't do anything once you've signed up? https://preview.redd.it/5uhsk71xt82h1.png?width=1132&format=png&auto=webp&s=e3bc1051f1fb01b84a4f422729bef3b2d008240c https://preview.redd.it/dsw3481xt82h1.png?width=1156&format=png&auto=webp&s=ac8c24d7b20801c9d08deb4fb3fa51bb7adc3fbd submitted by /u/thebirthdayg1rl [link] [comments]

Regarding karparhy joining Anthropic

I believe that karpathy failed to increase artificial super intelligence. If you dont know, karpathy had founded safe super intelligence startup and working on that this time and suddenly decided to join Anthropic? What does it suggest, I want to hear everyone's thoughts on this submitted by /u/Klutzy_Painter_7240 [link] [comments]

Instructions for (ICML) workshop reviews [D]

Hi, I am being reviewer for an ICML workshop; however, there are no guidelines on the structure of the reviews (e.g. what are the criteria, what is the grade scale, etc.). Does anyone know whether ICML workshops have some "convention" regardings reviews? Or do we ought to use the icml's reviewer instruction (https://icml.cc/Conferences/2026/ReviewerInstructions)? submitted by /u/Ok-Painter573 [link] [comments]

Research on LLM alignment as latent discourse-level regimes vs. token-level filtering?

Hi everyone, I am currently researching a hypothesis regarding how alignment behavior and guardrails function in modern LLMs. My core focus is that alignment might not be primarily regulated through modular output filters, local token suppression, or shallow instruction-following. Instead, it seems to operate by inducing the model into internally organized, distributed latent states what we might call \discourse-level regimes" or attractor manifolds* Under this view, prompting isn't just transmitting instructions; it acts as a state induction that reorganizes the model's epistemic posture and rhetorical geometry. Consequently, jaiI bre aks or specific behavioral anomalies aren't just "filter bypasses," but phase transitions between these latent attractor regimes. I have been running some automated framework tests and observing how specific higher-order rhetorical structures can trigger global state shifts (sometimes causing massive over-caution or style-locking that affects the model's reasoning capabilities broadly). My questions for the community: Are there any recent papers (especially in mechanistic interpretability or representation engineering) exploring alignment as global latent space geometry rather than token-level policy? Looking forward to any reading recommendations or shared observations! submitted by /u/PresentSituation8736 [link] [comments]

Elon Musk: will appeal to the Ninth Circuit.

X: "Regarding the OpenAI case, the judge & jury never actually ruled on the merits of the case, just on a calendar technicality. There is no question to anyone following the case in detail that Altman & Brockman did in fact enrich themselves by stealing a charity. The only question is WHEN they did it! I will be filing an appeal with the Ninth Circuit, because creating a precedent to loot charities is incredibly destructive to charitable giving in America. OpenAI was founded to benefit all of humanity." submitted by /u/Embarrassed-Slip8094 [link] [comments]

Slop is making me feel disconnected from AI Research [D]

Hello everyone. This is just a small rant on my part. I’m relatively young, a final year undergrad, and I’ve been interested in AI researcher since I was in high school. Over that period of time I feel there has been a significant shift in the landscape regarding the culture surrounding the research. While I’ve really enjoyed producing some interesting and creative work, I can’t help but feel that slowly the wave of low quality AI research and researchers are really making me feel frustrated. To just give a summary of what I and many others have seen: - Papers with hallucinated citations and even prompts contained in the papers - Papers with clearly misleading data that does not tell the whole picture. - Labs who have built a culture around quantity over quality, pumping out pubs, citing each other, and having all of the lab on each paper to inflate each students publication record. - Highschoolers…. Yes HIGHSCHOOLERS, becoming more common submitting at conferences that don’t really know what they are doing but paying a pretty penny to participate in “research programs” which are really just cash cows taking advantage of the fierce competition. See the post on the subreddit for more info. - Even the so called “top labs” producing work that is somewhat misleading or not fully representative. For instance see what happened recently with TurboQuant. - Research from “low tier institutions” being drowned out because they are not good for click baiting and farming views on LinkedIn and X, even if they are high quality. It’s… a lot I know. Of course these problems have been around for a long time, but I feel as if lately they have become more and more exacerbated. I originally felt that I was attached to AI research primarily for the creativity and freedom, but I feel that ironically AI itself has been a hindrance on the quality of work being published. Of course I don’t mean to say that all AI has been bad for ML research, I mean even I use it extensively to help me polish my writing and generate seaborn plots for my data, but that is very very different from just pumping out low quality cookie cutter work. Anyways, just wondering if anyone else shares similar thoughts. I know I’m relatively young here so maybe some of you have better insights into the broader trends over the decades. submitted by /u/Skye7821 [link] [comments]