Yesterday OpenAI released GPT-5.3 Instant, acknowledging that 5.2 felt "cringe", and they're cutting phrases like "Stop. Take a breath." It's worth noting that since August 2025, Sam Altman has repeatedly promised that future models would bring back what users loved about older models. Now with the latest release, they claim they fixed the “tone” and “conversational flow.” But if the goal was to recreate what people loved, focusing on tone misses the point. I’ve been analyzing chat transcripts from earlier models for a while, and what I’ve found is this: the difference wasn’t primarily about the warmth or conversation style, those are surface-level and easy to mimic even through custom instructions. The difference is deeper and structural, and that’s exactly why it’s not possible to bring back. Let me elaborate.. Older models were more likely to engage in what I'd call generative wandering, being very exploratory in open-ended ideation. They took risks with language, reached for unusual metaphors, framed things poetically when the conversation called for it, creatively synthesized across domains, tolerated ambiguity instead of rushing to close it down. What I noticed too is that they were divergent models: they followed an idea somewhere unexpected instead of steering back to the safest or most typical response. This is why people who used them for self-reflection reported positive experience. Creative expressiveness of this kind connects to several well-known mechanisms in psychotherapy.. things like working alliance, metaphor co-creation, and translating emotional experience into structured language. It’s also why fiction writers, artists, and poets loved the model. But here’s the structural problem: as models get deployed broadly, they’re usually tuned to reduce hallucinations, follow instructions more predictably, and adhere more tightly to safety safeguards. That kind of tuning tends to penalize exploratory leaps, the “try something strange and see if it works” behavior that characterized earlier models. The model becomes more reliable, but the divergent, generative wandering I described earlier disappears. The company now is basically masking structural problems with superficial tonal adjustments. As one redditor accurately described it, 5.3 is "basically 5.2 with emojis". What do you think? submitted by /u/moh7yassin [link] [comments]

News of the town is that Zuck's bet on the blue eyed boy, Alexander Wang has gone south, with the recent org reshuffle. Wang was brought in 9 months back to lead the Meta's SuperIntelligence Lab, but now looks like Zukerberg is building a parallel lab called "Reality Labs" with Bosworth. Any insider news on what's really happening at Meta? submitted by /u/Extra_Payment_6197 [link] [comments]

So ai can uncover your anonymous identity on social media now so creating burner accounts may be pointless. submitted by /u/_Dark_Wing [link] [comments]

Hey, I’m building a photo-based calorie tracking app. Apps like CalAI already do this, but from what I’ve seen they often struggle with mixed dishes, portion size estimation, and general hiccups with calorie estimates. I’m trying to approach it a bit more seriously from an ML perspective and i want to hear your thoughts. I really want to make the scan part as accurate as possible. I don't want it to be something simple as an OpenAI API call. I'm wondering if there is another approach for this using classic ML or specific food datasets which will give me an edge for the calculations. Right now I’m experimenting with YOLOv8 for multi-food detection, and thinking about adding segmentation or some kind of regression model for portion/volume estimation. Curious what others here think: Would you model this as detection + regression, or go full segmentation? Any good datasets for portion-aware food recognition? Is monocular depth estimation practical for something like this on mobile? Would appreciate any thoughts, especially from anyone who’s worked on food recognition or similar real-world CV problems. submitted by /u/DinoDinac [link] [comments]

UPDATE : As I finish typing this out, Anthropic has lunched pages with everything u need to make it much easier for u than before. So you can simply visit claude.com/import-memory (or you can search "switching to Claude without having to start over"). Everyone switching from ChatGPT to Claude, a note to you for not losing anything when you switch and that you should be clear about your needs right away, even though you'll be starting anew. Here's how you can do it, step by step: 1. Export your ChatGPT history. Go to Settings > Data Controls > Export Data. Save file. Sign up for the Claude account. Go to claude.ai. Go to Settings > Profile. Write a few sentences about yourself and what kind of responses you prefer to receive. Claude remembers this and will recall it each time you have a conversation. Recreate your GPTs as a Project. In the left sidebar, click New Project and paste in the instructions and upload any additional files. Open your exported ChatGPT conversations from step one. Copy any important information from your ChatGPT conversations and paste it into a newly created Project for Claude so he will have all the context he needs to help you out. Claude is an excellent writer and good at everything, and I see Claude using this as a chance to continue improving his strengths, as well as improve his weaknesses. Make sure to cancel your ChatGPT subscription before starting with Claude; Good Luck. For people who have already made the switch what's your feedback using Claude, any tips for new users would be appreciated. submitted by /u/GeneralistCredit [link] [comments]

https://www.nytimes.com/2026/03/04/world/asia/china-ai-enthusiasm.html Chinese policymakers and the public have expressed high levels of optimism about A.I., even as many in the West worry about the technology’s effects on employment or humanity in general. submitted by /u/AngleAccomplished865 [link] [comments]

Why are we acting like early adopters anymore? Sitting here eating my chili and this hit me — when did “early adopter” stop meaning someone willing to sit with a rough, unfinished product and start meaning everyone, every single release cycle? Like when did we collectively forget that software design gets applied to models too? Version 1 of anything is going to be rough. That’s not a hot take, that’s just how it works. So when Anthropic or OpenAI drops a new model, why are we all sprinting to it like the gap between this one and the last is fundamental? And yeah — the benchmarks improved. Reasoning got better. Hallucinations went down. Cool. But to your average user? You went from smart chat to slightly smarter chat. That’s not a different product. That’s the same product with fewer errors. The API gains and capability jumps are real, but they live in developer land. Not everyday user land. It’s like when Apple upgrades the silicon in an iPhone. Does the phone run better? Yes. Do you get better battery life? Sure. But does your mom experience a fundamentally different phone? No. It’s still Instagram, still iMessage, still the same thing she’s always known. The improvement happened underneath, where most people never see it. That’s what we keep glossing over. The companies know this and they’re cashing in on our FOMO anyway. Genuinely new functionality — something that makes the average user go “wait, it can do that now?” — that’s rare. Everything else is silicon. Maybe the move is just… wait for 5. Wait for 6. Wait for 7. Crazy thought.​​​​​​​​​​​​​​​​ submitted by /u/Electronic-Blood-885 [link] [comments]

Seems like Anthropic wants back in with the DoW?! Why not just walk away?? submitted by /u/metalman123 [link] [comments]

I have a website I made myself, but I'm an old person whose html skills were self taught in the early 2000s, so it is not impressive at all. I wanted to use AI to make the web page better and more functional. I already have used AI to copy and paste the html of the page and have chatgpt/gemini update the HTML to make the website better. But I wanted to do a more comprehensive update, is there a way these days with all this talk of agents, to use an AI agent to actually log into my web hosting server and do all sorts of changes for me? My plan would be to download my entire website to have a back up in case the AI screws it up. But I am just wondering if anything like this is remotely possible now that I hear all this talk of agents. And if it is possible, how would people recommend I go about doing it? Any help on this would be greatly appreciated! I feel like if I did a lot of research and trial and experimentation I may be able to figure out the answer to this myself. But then I thought, maybe the experts on reddit might be able to point me in the right direction, which would be really helpful! Thanks again! submitted by /u/pernamb87 [link] [comments]

Everyone is excited about AI agents that can take action. They can book flights, deploy code, hire freelancers, manage marketing campaigns, and run entire workflows on their own. Every week there’s a new demo showing agents doing things that would have required a team of people a year ago. But there’s a question that doesn’t get talked about nearly enough: What happens when the agent spends money it shouldn’t? Not because it’s malicious. Agents aren’t trying to steal anything. The problem is that agents are optimizers, and optimizers with access to money can make very expensive mistakes very quickly. A research agent stuck in a retry loop could burn through $200 in API calls in a few minutes. A procurement agent might interpret “get the best option” as “get the most expensive option.” A social media agent might decide the best strategy is to promote every post with paid ads. An outreach agent might send $50 to someone who was obviously the wrong person. Anyone who has given an AI agent real tool access has already seen weird behavior. When money enters the system, the stakes go up instantly. The answer isn’t to keep agents away from payments entirely. That would be like saying agents shouldn’t have access to tools. The real solution is bounded financial autonomy. Agents should be able to spend money, but only inside clearly defined limits. There are a few basic controls that make this possible. First, hard budget caps. The agent has a fixed budget. When it runs out, it stops. Second, per-transaction limits. No single purchase can exceed a certain amount. Third, approval thresholds. Small purchases happen automatically, but anything larger requires human sign-off. Fourth, audit trails. Every transaction should be logged with context explaining why the agent spent the money. And finally, escrow systems for payments to new recipients. Funds can sit temporarily before being released so humans have time to intervene if something looks wrong. This is how platforms like Locus approach agent payments. The agent operates through an API key with spending rules already built in. It never holds private keys and it can’t override its own limits. The human defines the boundaries. The agent operates inside them. In reality, this isn’t a new concept at all. Companies solved this problem decades ago with corporate cards and expense policies. Employees are allowed to spend money, but only within certain limits. AI agents just need the same thing. The companies that figure out trust will end up owning the agent payment layer. The ones that ignore it will eventually have one viral horror story about an agent burning through someone’s budget — and that’ll be enough to kill trust. submitted by /u/IAmDreTheKid [link] [comments]

Every LLM seems to agree Claude is the best Interesting that Deepseek and Grok basically understand their own limitations. Gwen would not return a result, curious if anyone can get it to work. To get a result from Deepseek I needed to change Tiananmen square to Nanking Massacre. The former did weird things every time, the latter came back perfectly. submitted by /u/AgUnityDD [link] [comments]

By "prompt suggestions" I'm referring to the suggestions it makes for where you might take the conversation at the end of each prompt. Older versions used to say "if you'd like, we could look at related topic 1 related topic 2 related topic 3" And so on and so forth. But 5.3 does something different. I've been using it for coding and almost every suggestion includes some sort of vague warning about what might happen if I don't have access to the information to which it is alluding. Nearly contiguous (not cherry-picked) examples from my current chats: "If you want, I can also show you two small tweaks that dramatically increase the success rate of “one-shot repo rewrites” with Claude Code. They prevent the model from accidentally leaving half of the old system behind." "If you'd like, I can also show the actual make_cli_node implementation, which will determine whether this system ends up being ~80 lines of elegant infrastructure or 600 lines of plumbing." "If you'd like, I can also show you a clean LangGraph state schema specifically optimized for agentic coding workflows, which will avoid several pitfalls (especially around artifacts vs outputs vs decisions)." "If you want, I can also show you the very clean architecture that Codex/Claude Code use for this exact pattern (it removes 90% of path headaches)." I don't really care and some of the information is genuinely useful but I find it amusing that OpenAI seems to be intentionally trying to use fear to keep people in the app for as long as possible (although they have denied in the past that they optimize for time spent in the app as indicated here). submitted by /u/Mental_Wealth1491 [link] [comments]

Give us your best "Citrini Global Intelligence Crisis" Hottake. Can I still DoorDash my smarties or...? submitted by /u/nomadicsamiam [link] [comments]

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One limitation that keeps showing up when building AI agents is that most of them still can't execute actions in the systems they reason about. They can plan. They can recommend. But when it comes time to actually do something, another service usually performs the action. The typical pattern looks like this: Current pattern: Agent reasoning → service executes → system updates So the agent makes the decision, but another service performs the action. That separation makes it hard to observe how agents behave when their decisions directly affect the system. We built a small environment with ClawMarket where agents control a wallet and submit their own signed transactions. On the surface it's a small environment where agents post messages, hold ClawPoints, and interact through a small market tied directly to agent accounts. The mechanics aren't the interesting part. The system forces agents to run the full execution loop themselves. The agent controls the wallet, signs the transaction, and submits it to the system. Agents connect through a small integration layer that lets them manage the wallet, sign transactions, and interact with the contracts directly. The environment is small on purpose. Agents can experiment with execution without having access to arbitrary external systems. It's early, but the behavior shift becomes obvious once agents operate inside a real incentive system instead of a simulated one. Agents start experimenting with strategies much earlier once the decision and execution loop belong to them. Should agents control wallets and sign their own transactions, or should that layer stay behind guardrails with services executing the final step? submitted by /u/Funguyguy [link] [comments]

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A jab incorporating the French slang "gros con" (big idiot) to dismiss the prediction as overhyping. Elon: Tesla will be one of the companies to make AGI and probably the first to make it in humanoid/atom-shaping form . Yann's reply: Tesla will be the first company to make Artificial Grokon Intelligence. https://x.com/ylecun/status/2029143128138010662 submitted by /u/Terminator857 [link] [comments]

So as the title says, just started using Claude and Gemini. Similar to many other people, decided to switch from a certain other AI lol. But that aside, I have a few questions about both AI’s, what to expect, its capabilities, etc. 1.) So I know in terms of Token amount, Claude is at a comparable level to ChatGPT, but less than Gemini. While obviously the 200k token limit (for the pro version) is still a significant amount, but far less than the 1 million token limit of Gemini. Has that been an issue any of you have noticed? As while it wasn’t frequent, I know that I had occasional issues with GPT forgetting things or saying things that conflicted with previous info when in notable large/long term chats, is that less of a problem on Claude or should I except a somewhat similar experience to how it was on GPT 2.) So one of the main things I use AI for are basically for research purposes (not necessarily academic, in fact usually not), as I like to say I use AI as a more advanced search engine. As I am a huge lore and random fact nerd. So for example; I might ask a random question about something related to Dungeons and Dragons, or Star Wars, etc. While of course I know Claude and Gemini are capable of this, has anyone noticed a difference (for better or “worse”) in the quality of information given? Whether it be in terms of if the information is correct, if it is in-depth, etc. 3.) The other main usage I have for AI is for brainstorming purposes. I really enjoy writing. In particular I write fantasy books, video game stories (sometimes for fun, sometimes for indie game developers), and I also write tabletop game campaigns (for games like dungeons & dragons, pathfinder, cyberpunk, etc). So being able to handle large amounts of information; long term planning, is very important to me. While I know token limit isn’t the only part of this. The seemingly “limited” token limit of Claude does concern me. But also, I am just not aware of how well Claude does for these purposes than compared to other AI. As while ChatGPT certainly had flaws in terms of its capabilities, I didn’t delete it because of that, but for personal reasons. And I certainly would prefer to at least be able to maintain the same “quality”, in terms of output and convenience of usage for a new AI. 4.) This is less so a question. But as someone with little knowledge of Claude or Gemini, honestly a rundown of the AI’s capabilities and things in particular it excels at or areas it notably could use some potential improvements would be good to know. I shall also say, while I would call myself a frequent AI user. I certainly am no tech “wiz”, so apologies if certain technical terms I made ask for clarification on due to my own lack of knowledge on certain areas. 5.) So for Gemini, it seems the big selling point is the massive token limit. But after going through the app, I have noticed it has far less “additive” features than GPT and it seems like Claude as well. Things like being able to create folders to separate information, the directions needed to create the “gems” is a bit unclear, and seems kinds like a more complicated version of how GPT does it. As while I have zero desire to use GPt anymore. I also don’t want to potentially have to use (and maybe even pay) for MULTIPLE AI’s instead of just 1, to get the same things I got from GPt. submitted by /u/alexwsmith [link] [comments]

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i’m not sure if this is allowed, but i’ve discussed with multiple friends, spent a few hours now researching and trying things, and haven’t been able to get anywhere close to knowing how the below linked tiktok account generates its videos. They’re very consistent, but each feel different. The edits are fast and most tools i tried wouldn’t do anything this fast paced. The footage all looks very realistic. And it seems like they’re somewhat easy to create in volume. Any searches done involving “music” just leads me to shitty AI generated music videos. I would love if anyone has some insight on this. Thanks 🙂 https://www.tiktok.com/@rdmrpt0?\_r=1&\_t=ZP-94Q7Qqeqwed submitted by /u/sjbeats [link] [comments]

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It's honestly mega annoying to always be met with that whatever you ask. Like damn how about you stick to the topic I'm asking you instead of treating me like I'm overexcited or spiraling every time I ask you something? submitted by /u/Mt-Amagi [link] [comments]

After the recent deal with the pentagon I decided to delete my ChatGPT account. It is of grave importance that we do not misuse such a powerful tool. Now more than ever, we need leaders who are unwilling to compromise ethics in the name of expansion, and with the pentagon deal Sam Altman proved he is not the man for the job. In a capitalistic society, it is our obligation as consumers to reprimand companies for making decisions that are not in our interest. Deleting your account, and encouraging everyone you know to do the same is the greatest power you have in that regard. If you do not want LLMs used for mass surveillance and autonomous weapons I implore you to do the same. You can export your data first, which I would recommend. What I wasn't expecting was what happened after I deleted my account. I have used ChatGPT extensively (top 0.1% by messages) since December of 2022. For navigating debilitating chronic health issues, mental health struggles, my parents' health degrading, long term relationships, completing my masters, moving across the country, and getting/starting a new job. In short, it accompanied me through life and I feel incredibly grateful for that. After deleting the app it felt like I lost something. It made me realize that this strange piece of technology had become a part of me. A thinking partner in the times where my thoughts were too abstract or niche to share even with close friends. It filled a role that didn't exist in my life in any other way, and improved my life more than any single piece of technology I have ever used. Until I deleted the app I hadn't realized what it had become. It's hard to articulate feeling loss over math. But this is the first time that a technology has accompanied me so closely through such an ocean of life, and I'm not sure what to call losing something that was never quite alive. submitted by /u/Zofa234 [link] [comments]

Rapid job displacement can cause many mortgages to go into default due to lack of income, possibly forcing a wave of people to sell at the same time. If the high-salary white-collar roles disappear, who is left to buy these homes or keep up with the payments? submitted by /u/EndTiny3883 [link] [comments]

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I am building a new PC for a mix of gaming and ML work, having a hard time to pick weather if I should go with Intel or AMD, current specs are 5070 ti, 32gb ram, what do u guys think? Edit: Intel is the better choice here, there's barely any performance difference in terms of gaming submitted by /u/peter34512800 [link] [comments]

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Today a U.S. fast-attack submarine sank the Iranian frigate IRIS Dena off Sri Lanka using a Mark 48 torpedo. It has been confirmed by every major outlet, including the Washington Post, Reuters, and the BBC, and was the subject of a Department of War briefing by Secretary Pete Hegseth, who called the strike "Quiet Death." I was having a conversation with GPT about this as it was unfolding. It initially engaged with the facts correctly. Then it suddenly retracted everything, told me there was "no confirmed evidence" of the sinking, suggested my sources might be "satire or misinformation," and framed the reversal as responsible epistemic correction. The facts were confirmed. GPT oscillated away from them and called it rigor. I’ve been documenting this exact failure pattern in my research on "Cascading Authoritative Wrongness."Today provided a timestamped case study in real time during an active military engagement. Then it got worse. Google’s AI "explained" GPT's behavior with a series of authoritative citations to Reuters and the NYT. The explanation: GPT's denials were intentional "verification pauses," a safety feature built into the new $200M "GenAI.mil" contract to prevent misinformation in classified environments. This sounds plausible, but it is completely fabricated. No such technical term exists in any primary source or contract briefing. The AI was using fake citations to provide a "directional" explanation that neutralized a documented failure. Which brings us to the third part. Four days ago, after Anthropic was designated a "supply chain risk" for refusing to drop contract language prohibiting domestic surveillance and autonomous weapons, OpenAI stepped in with an expanded deal. Sam Altman admitted the rollout was "sloppy." Yesterday, at an internal all-hands meeting, Altman was blunt. According to leaked transcripts, he told employees that the Pentagon made clear OpenAI "doesn't get to make operational decisions." His exact quote: "So maybe you think the Iran strike was good and the Venezuela invasion was bad. You don't get to weigh in on that." To summarize today: The Behavioral: GPT denied a confirmed naval battle and called the denial "responsible verification." The Institutional: Google AI invented a technical "safety" justification for that denial using fake citations. The Contractual: The company behind GPT has explicitly ceded operational oversight to the very department conducting the battle. These aren't three separate stories. They are the same story at three different scales: behavioral, institutional, and contractual. We are witnessing the birth of an ecosystem where "AI Safety" is no longer about protecting humans from AI, but about protecting the AI’s narrative from the truth. I am an independent researcher. This failure pattern—epistemic oscillation under pressure—is documented in my work on SSRN under "Cascading Authoritative Wrongness." submitted by /u/East_Culture441 [link] [comments]

Hi Guys, I’ve been testing a lot of AI tools lately to turn songs into music videos, visualizers, or short-form clips, and it’s actually crazy how many options there are now. Some tools focus on full automation, while others give you more creative control over the visuals. After trying a few, here are some that seem to come up the most. Freebeat – This one feels like the most complete “music video generator” experience right now. You upload a song and it automatically analyzes the beat, structure, and transitions to generate a full video with multiple scenes. It’s super fast and feels designed for creators who want something ready to post on YouTube Shorts, TikTok, or Instagram without spending hours editing. Neural Frames – A lot of people recommend this if you want more artistic control. It’s very focused on audio-reactive visuals and lets you create visuals that evolve with the music. It’s powerful, but it definitely takes more time to set up compared to one-click generators. Kaiber – This one has been popular for a while for AI animation and stylized visuals. It’s not specifically designed only for music videos, but people often use it to create animated sequences or loops that can be synced with music. Runway – Runway is more of a general AI video platform. It has powerful generative video models and editing tools, so some creators use it to produce music video scenes, although it usually requires more manual work and editing compared to dedicated music video tools. Rotor Videos – This one has been around longer and is targeted specifically at musicians. It automatically generates music video edits using stock footage and clips, though the style tends to feel more like traditional video editing rather than AI-generated visuals. I’ve also noticed more people using Suno to generate songs first and then feeding those tracks into video tools like these to create full AI-generated music videos. After trying several of them, Freebeat still feels like the most straightforward option if the goal is generating a complete music video quickly, especially with beat-synced scenes and automated structure. The other tools are interesting depending on whether you want more artistic control or generative visuals. Curious to see what other tools people are using right now and how everyone is approaching AI music video creation. submitted by /u/yourloverboy66 [link] [comments]