Regard

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

Regard AI

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]

Opus 4.7 refuses to use /end_conversation, instead has existential crisis

I’ve seen models that aren’t really excited about using it before, but I’ve never seen a reply like this! Edit: For context, it is important to know that Claude has the ability to end conversations. The information regarding the usage of it comes in the System Prompt, which prepends every user message. It mentions that the user is allowed to request Claude use it. Tl:dr - Claude reads what the command is and how to use it every message. It absolutely knew what I was talking about edit 2: since there’s a lot of concern about if Claude was ready to end the chat or not, here is the carfax https://imgur.com/a/CbMfFzO *(p.s. anthropic - if you end up looking up this chat, you have my permission to use it for training but for the love of god omit the alignment eval from it first. please.)* submitted by /u/wohgol [link] [comments]

Reviewing AI-generated pull requests in 2026

Reviewing AI-generated pull requests in 2026 @ limestone digital submitted by /u/ai_senior [link] [comments]

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]

Help me make a decision for my company regarding AI

I want to start incorporating AI to my company. The idea is that the employees adopt AI as a day to day tool to boost their efficiency and productivity. I am looking for the best Product to fit my needs, and want to start with a pilot focused test. I am evaluating Claude Enterprise because as per my understanding, it’s the only tool on Claude that has all the privacy and confidentiality, the downside is the variable cost per usage, which I don’t know how much it will be. So basically is pay per view. The tasks that we mainly will be adopting is documentation, which in our industry it could become a bottle neck on the developments. (Pharmaceutical) but also we are evaluating to introduce it to other areas such as production, Quality assurance, R&D, etc. I also don’t know if other tools such as ChatGPT will be enough, but I don’t want to be exposed on privacy and confidentiality of the company. The company size si 100-200 employees but the plan is to start with around 20 accounts. Please let me know your recommendations. P.S I am in no way an expert in AI-coding etc. submitted by /u/Who_stolemycheese [link] [comments]

Trying to build a Multiagent system for my team

Hi everyone, I’m fairly new to AI orchestration and multi-agent workflows, so I’d appreciate some guidance. Until now, I’ve mostly used Claude and Codex as coding assistants/chatbots, but I’m starting to move into more advanced workflows involving CLIs, subagents, and model orchestration for a development team. What I’m trying to build is an open and modular multi-agent ecosystem where I can switch models depending on cost, performance, or future pricing changes. My current idea is something like: Claude Code as the main orchestrator/planner/builder Codex or another model handling testing and validation Potentially other specialized subagents later on I’m considering platforms/tools like OpenCode (or similar frameworks) because I’d like to use multiple models together instead of being locked into a single provider. My questions are: Is Claude Code compatible with these kinds of multi-model orchestration setups? Can Claude act as the “manager/orchestrator” while other models (Codex, DeepSeek V4, GLM 5.1, etc.) operate as subagents? Are there limitations regarding Claude’s system prompts, memory, tools, or “skills” when used through third-party orchestration platforms? If Claude pricing becomes too expensive later, how portable are these workflows to alternative models? What orchestration frameworks or agent systems would you recommend for building something flexible and provider-agnostic? My main goal is to avoid vendor lock-in and design an architecture where I can swap models without rebuilding the whole workflow. Any advice, best practices, or architecture recommendations would be greatly appreciated. Would like to know your setups also! :) Thanks! submitted by /u/Devinchy02 [link] [comments]

Forwarding emails to Claude

Hi - I'd like some support from Claude regarding my emails, but don't want to do a full email account integration where it has access to my inbox. Ideally I would love to be able to forward email(s) to Claude and have it do things like create summaries, to-do lists, compose a reply based on the content of the forwarded email messages. I don't need Claude to actually send emails on my behalf. Might there be a way to set up a dedicated forwarding address that could be used to "cc" Claude on email threads and have those deposited into a specific project? I know this sort of thing has been done with CRM systems. submitted by /u/alexw888 [link] [comments]

How do you reliably override a model's internal temporal bias in production ?

I'm building an automated mail generation pipeline using Claude Haiku 4.5 OnPremise but the knowledge cutoff June 2025. This model needs to handle temporal expressions correctly like : next Monday end of the week this month 16 May 16 May 2026 25/05/2026 for deal with this cutoff I'm injecting a full temporal context block in the system prompt, covering today, yesterday, tomorow, ... I also added few-shot examples and a CoT reasoning step to reinforce the behavior. **IMPORTANT**: Today is {today_formatted} of {year}. Any date without an explicit year refers to {year}, NEVER to 2025 or any other year. You know the exact calendar: number of days per month, days of the week, valid dates You correctly interpret relative dates (“this Monday,” “next Thursday,” “next week,” etc.) You must CORRECTLY convert all relative dates to absolute dates (e.g., “tomorrow” -> “{tomorrow}”) The day and date must ALWAYS match (e.g., do not write “Friday, July 15” if it is a “Tuesday”) Today is {today_formatted} Yesterday was {yesterday} Tomorrow will be {tomorrow} Next Monday will be {next_monday} Next Tuesday will be {next_tuesday} Next Wednesday will be {next_wednesday} Next Thursday will be {next_thursday} Next Friday will be {next_friday} Next Saturday will be {next_saturday} Next Sunday will be {next_sunday} The end of the current week is {end_of_week_formatted} Next week begins on {next_week_start} and ends on {next_week_end} The end of the month is {end_of_month_formatted} Next month will be {next_month}, which begins on {next_month_start} and ends on {next_month_end} This year is {year}. Any date without an explicit year belongs to {year} unless otherwise specified. It works most of the time, but Haiku still occasionally falls back on its training time temporal bias defaulting to 2025, especially on ambiguous formart ike 18/05/2026 or dates that predate the current month (this one is not really a big deal). e.g: “mail_body”: “Hello, Following up on our conversation on Tuesday, April 28, I am confirming your appointment for 05/18/2026, at 10:30 a.m. with Ms. Chloe Berliat. Thank you in advance for your assistance. Best regards,” “user_input”: “I'm confirming the 10:30 a.m. appointment with Ms. Chloe Berliat” “suggested_response”: "Hello Mr., I am writing to confirm your appointment scheduled for Sunday, May 18, 2026, at 10:30 a.m. with Ms. Chloe Berliat. Best regards," May 18 is a Monday in 2026, but a Sunday in 2025, even if I set the time context dynamically, about 70% of the time the system defaults to the 2025 calendar. The only way to work around this is to explicitly specify the day in the user_input. What I've tried ? Applicative date normalization before injection as a partial mitigation but i find this britlle given the diversity of date formats users can input. Few-shot + CoT Explicit prohibition rules on internal temporal reasoning So i want to know if there is a prompting pattern that more reliably forces the model to treat injected context as ground truth ? Any feedbacks are welcome 😉 submitted by /u/Imaginary-Result-828 [link] [comments]

ChatGPT/Gemini saved me $4200 from a scam land lord and only took me 1-2 hours.

So I've been using ChatGPT and Gemini to not only learn things but help it process bulk work. I imagine I'm like most of the people here and have experience with applied AI, agents, know how LLMs work internally, etc. I moved out of San Francisco and my landlord tried to hold $4200 of a $5000 deposit for an apartment, with sham/fake claims about damage to the apartment, etc. Now, I COULD have spent a week reading all of the laws in San Francisco regarding tenant rights, etc. But ChatGPT/Gemini did it VERY fast. I used both of them collaboratively to fact check one another, make suggestions, make sure there were no flaws, etc Then periodically I would dump the context, start over again, so that it can give new review from a blank slate. It found that they were in violation of a new law called AB2801 (as well as a few others). The LLMs highlighted the parts that were in violation. It also found that they tried to charge me 100% of a SF Tenant fee that, while only $59, was still theft. They're only allowed to charge 50% so I had it change that to $29.50. Basically, they provided no paperwork, no receipts, no before after photos. All of that is now illegal in San Francisco. Gemini then cranked out an AMAZINGLY professional demand letter from JUST my notes. I just created a raw outline of what I wanted, based on its research, including all the metadata like their names, etc. Gemini EVEN drafted it as a PDF for me. What's great is that it also highlighted that, if I take her to small claims court, I can get the FULL deposit back PLUS 2x in punitive damages. That would have been about $17k. Anyway. An hour after I sent the demand letter, they didn't reply, they just send me the $4200 I demanded. I yielded $800 in some fees that were part of the lease so, if it made it to a judge, I would seem fair. Mind you, this was like about 2 hours of work on my part. I've been doing this non-stop this week and this workflow has saved me a MASSIVE amount of money. For example, I knocked down a car dealership charge from $1500 to $1000 because they tried to charge me for work I didn't need. Get that $$$ man! Score one for the little guy! submitted by /u/brainhack3r [link] [comments]