Zoom AI Companion

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Zoom AI Companion AI

Three Memory Architectures for AI Companions: pgvector, Scratchpad, and Filesystem

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

Stanford CS 25 Transformers Course (OPEN TO ALL | Starts Tomorrow)

Tl;dr: One of Stanford's hottest AI seminar courses. We open the course to the public. Lectures start tomorrow (Thursdays), 4:30-5:50pm PDT, at Skilling Auditorium and Zoom. Talks will be recorded. Course website: https://web.stanford.edu/class/cs25/. Interested in Transformers, the deep learning model that has taken the world by storm? Want to have intimate discussions with researchers? If so, this course is for you! Each week, we invite folks at the forefront of Transformers research to discuss the latest breakthroughs, from LLM architectures like GPT and Gemini to creative use cases in generating art (e.g. DALL-E and Sora), biology and neuroscience applications, robotics, and more! CS25 has become one of Stanford's hottest AI courses. We invite the coolest speakers such as Andrej Karpathy, Geoffrey Hinton, Jim Fan, Ashish Vaswani, and folks from OpenAI, Anthropic, Google, NVIDIA, etc. Our class has a global audience, and millions of total views on YouTube. Our class with Andrej Karpathy was the second most popular YouTube video uploaded by Stanford in 2023! Livestreaming and auditing (in-person or Zoom) are available to all! And join our 6000+ member Discord server (link on website). Thanks to Modal, AGI House, and MongoDB for sponsoring this iteration of the course. submitted by /u/MLPhDStudent [link] [comments]

Is the Mirage Effect a bug, or is it Geometric Reconstruction in action? A framework for why VLMs perform better "hallucinating" than guessing, and what that may tell us about what's really inside these models

Last week, a team from Stanford and UCSF (Asadi, O'Sullivan, Fei-Fei Li, Euan Ashley et al.) dropped two companion papers. The first, MARCUS, is an agentic multimodal system for cardiac diagnosis - ECG, echocardiogram, and cardiac MRI, interpreted together by domain-specific expert models coordinated by an orchestrator. It outperforms GPT-5 and Gemini 2.5 Pro by 34-45 percentage points on cardiac imaging tasks. Pretty Impressive! But - the second paper is more intriguing. MIRAGE: The Illusion of Visual Understanding reports what happened when a student forgot to uncomment the line of code that gave their model access to the images. The model answered anyway - confidently, and with detailed clinical reasoning traces. And it scored well. That accident naturally led to an investigation, and what they found challenges some embedded assumptions about how these models work. Three findings in particular: 1. Models describe images they were never shown. When given questions about cardiac images without any actual image input, frontier VLMs generated detailed descriptions - including specific pathological findings - as if the images were right in front of them. The authors call this "mirage reasoning." 2. Models score surprisingly well on visual benchmarks without seeing anything. Across medical and general benchmarks, mirage-mode performance was way above chance. In the most extreme case, a text-only model trained on question-answer pairs alone - never seeing a single chest X-ray - topped the leaderboard on a standard chest X-ray benchmark, outperforming all the actual vision models. 3. And even more intriguing: telling the model it can't see makes it perform worse. The same model, with the same absent image, performs measurably better in mirage mode (where it believes it has visual input) than in guessing mode (where it's explicitly told the image is missing and asked to guess). The authors note this engages "a different epistemological framework" but this doesn't really explain the mechanism. The Mirage authors frame these findings primarily as a vulnerability - a safety concern for medical AI deployment, an indictment of benchmarking practices. They're right about that. But I think they've also uncovered evidence of something more interesting, and here I'll try to articulate what. The mirage effect is geometric reconstruction Here's the claim: what the Mirage paper has captured isn't a failure mode. It's what happens when a model's internal knowledge structure becomes geometrically rich enough to reconstruct answers from partial input. Let's ponder what the model is doing in mirage mode. It receives a question: "What rhythm is observed on this ECG?" with answer options including atrial fibrillation, sinus rhythm, junctional rhythm. No image is provided, but the model doesn't know that. So it does what it always does - it navigates its internal landscape of learned associations. "ECG" activates connections to cardiac electrophysiology. The specific clinical framing of the question activates particular diagnostic pathways. The answer options constrain the space. And the model reconstructs what the image most likely contains by traversing its internal geometry (landscape) of medical knowledge. It's not guessing - it's not random. It's reconstructing - building a coherent internal representation from partial input and then reasoning from that representation as if it were real. Now consider the mode shift. Why does the same model perform better in mirage mode than in guessing mode? Under the "stochastic parrot" view of language models - this shouldn't, couldn't happen. Both modes have the same absent image and the same question. The only difference is that the model believes it has visual input. But under a 'geometric reconstruction' view, the difference becomes obvious. In mirage mode, the model commits to full reconstruction. It activates deep pathways through its internal connectivity, propagating activation across multiple steps, building a rich internal representation. It goes deep. In guessing mode, it does the opposite - it stays shallow, using only surface-level statistical associations. Same knowledge structure, but radically different depth of traversal. The mode shift could be evidence that these models have real internal geometric structure, and the depth at which you engage the structure matters. When more information makes things worse The second puzzle the Mirage findings pose is even more interesting: why does external signal sometimes degrade performance? In the MARCUS paper, the authors show that frontier models achieve 22-58% accuracy on cardiac imaging tasks with the images, while MARCUS achieves 67-91%. But the mirage-mode scores for frontier models were often not dramatically lower than their with-image scores. The images weren't helping as much as they should. And in the chest X-ray case, the text-only model outperformed everything - the images were net negative. After months

Persistent memory changes how people interact with AI — here's what I'm observing

I run a small AI companion platform and wanted to share some interesting behavioral data from users who've been using persistent cross-session memory for 2-3 months now. Some patterns I didn't expect: "Deep single-thread" users dominate. 56% of our most active users put 70%+ of their messages into a single conversation thread. They're not creating multiple characters or scenarios — they're deepening one relationship. This totally contradicts the assumption that users are "scenario hoppers." Memory recall triggers emotional responses. When the AI naturally brings up something from weeks ago — "how did that job interview go?" or referencing a pet's name without being prompted — users consistently react with surprise and increased engagement. It's a retention mechanic that doesn't feel like a retention mechanic. The "uncanny valley" of memory exists. If the AI remembers too precisely (exact dates, verbatim quotes), it feels surveillance-like. If it remembers too loosely, it feels like it didn't really listen. The sweet spot is what I'd call "emotionally accurate but detail-fuzzy" — like how a real friend remembers. Day-7 retention correlates with memory depth. Users who trigger 5+ memory retrievals in their first week retain at nearly 4x the rate of those who don't. The memory system IS the product, not a feature. Sample size is small (~800 users) so take this with appropriate skepticism. But it's consistent enough that I think persistent memory is going to be table stakes for AI companions within a year. What's your experience with memory in AI conversations? Anyone else building in this space? submitted by /u/DistributionMean257 [link] [comments]

AI companion with the best memory

For some people memory might not be important but for me I really hate talking to a stranger every night and going on and on about our me or story. This is not a scientific test or anything but my test on each one for a few days Replika memory is okay for surface level stuff, it'll remember your name and some basics but I kept having to re explain situations I already talked about. Felt like it stores keywords but doesn't really understand the full picture. Character ai I honestly couldn't test properly for memory because the conversations are so character driven that continuity isn't really the point. You're basically doing improv with different bots. Fun if that's your thing but if you want something that tracks your life this isn't it. Nomi probably the strongest for pure text memory. Remembered a trip I mentioned and brought it up days later on its own, kept track of people in my life by name, actually built on previous conversations instead of starting fresh. Only sometimes would nail something from week one then blank on what I said yesterday, but overall it was the most consistent for remembering details. Tavus is different because it does video calls so the memory includes stuff like your tone and expressions not just text. It referenced things from over a week back and sometimes texts you like hey how is this going, about something I mentioned in a call, memory works differently but works really well for context. Kindroid was decent, the customization is cool and you can shape how it responds. Memory wise it was mid though, sometimes it nails it and other times blank slate energy. About a tier below nomi for retention. If I had to pick, nomi and tavus were the best for memory. Nomi tracks details really well in text and builds on past conversations better than the others. Tavus also remembered things from over a week back and followed up on its own. Both stood out way above the rest, depends what you prefer but those two are the ones I'd recommend if memory matters to you, any I might be missing that their memory is worth a shout out? submitted by /u/xCosmos69 [link] [comments]

Everyone is looking for friend here, just curious do you guys talk you chatgpt or claude like they are your friend or it's just me ?

Im 24 m,and I really can't carry the conversation in real, so I find myself talking to chatgpt or claude I even tried to make myself ai companion but it's not that great ,just curious do you guys do like what I did ? submitted by /u/Short_Locksmith_9866 [link] [comments]

I don't quite understand how useful AI is if conversations get long and have to be ended. Can someone help me figure out how to make this sustainable for myself? Using Claude Sonnet 4.6.

First, please tell me if there's a better forum to go to for newbies. I don't want to drag anyone down with basics. I'm starting to use AI more in my personal life, but the first problem I'm encountering is the conversations gets long and have to be compacted all the time, and eventually it isn't useful because compacting takes so damn long. I also don't want to start a new conversation because, I assume, that means I lose everything learned in the last one. (Or maybe this is where I'm wrong?) For a relatively simple example like below, how would I get around this? Let's suppose I want to feed in my regular bloodwork and any other low level complexity medical results and lay out some basic things to address, like getting my cholesterol a little lower and improving my gut health. I want the AI to be a companion helping me with my weekly meal planning and grocery shopping list. Maybe I tell it how much time I have to cook each day, what meals I'm thinking about/craving, or even suggest a menu that I like. AI would help me refine it around my nutritional goals and build my weekly grocery list. Every 24 hours I will feed it basic information, like how well my guts are performing, how well I sleep, how often I feel low energy, etc. Every few months I might add new test results. How do I do this, but not lose information every time the conversation gets long? submitted by /u/MooseGoose82 [link] [comments]

Chop Wood, Carry Water 3/6

[](https://substackcdn.com/image/fetch/$s_!wWe9!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd1bf0828-28c4-4779-86a7-62cb794aef7b_5673x4000.jpeg) The We The People weekly protest, Eau Claire, WI, Photograph, Liz Nash Hi, all, and happy Friday. We made it through another week! And what a week it was. It wasn’t great for us, of course, but boy was it worse for Trump. Not only did he have to fire the incorrigibly corrupt and sadistic Kristi Noem and continue to defend a horrifically unpopular and mismanaged war, but his newest economic numbers are absolutely disastrous. [According to CNN](https://www.cnn.com/2026/03/06/economy/us-jobs-report-february) the US economy lost 92,000 jobs in February and the unemployment rate rose to 4.4%. Economists were expecting a net gain of 60,000 jobs last month while December’s job gains of 48,000 were revised down to a loss of 17,000 jobs. This is bad, folks. More significant job declines were found in health care (down 28,000 jobs); leisure and hospitality (down 27,000 jobs); and construction (down 11,000 jobs). Should we be surprised? Of course not. Trump’s economic agenda, such as it is, is custom-made to destroy an economy. Mass deportation is known to [kill jobs](https://www.epi.org/305445/pre/789ab2a96c1c16fa04f30610bd97417d70ca8ac6179577810ba6fce978111df5/), [raise prices](https://sites.utexas.edu/macro/2025/09/09/the-economic-ripple-effects-of-mass-deportations/), and [shrink the economy](https://www.americanimmigrationcouncil.org/report/mass-deportation/); it is doing just that. Tariffs are skyrocketing prices. Tourism is down ([11 million fewer visitors in 2025](https://www.nytimes.com/2026/02/20/travel/us-tourism-declines-eu-canada.html)!), federal workers have been laid off in record numbers, and healthcare jobs are being gutted as hospitals and clinics close or cut jobs due to Trump’s Medicaid cuts. It’s all so predictable. But now we’ve got the price of gas to contend with as well. According to the [Gas Buddy](https://x.com/GasBuddyGuy/status/2029610494131089685) the last few days have seen the 6th, 8th and 9th largest single day increases in average diesel prices going back to 2000. Crude oil prices are up 25% [since the start](https://defendamericaaction.us16.list-manage.com/track/click?u=3eb4d08a510c32b2f2ff20fb3&id=719848d3c3&e=adb61354d3) of the conflict, [costing American consumers billions](https://defendamericaaction.us16.list-manage.com/track/click?u=3eb4d08a510c32b2f2ff20fb3&id=12cc0f04a8&e=adb61354d3) at the gas pump. Diesel prices are now [over $4 a gallon](https://defendamericaaction.us16.list-manage.com/track/click?u=3eb4d08a510c32b2f2ff20fb3&id=055e6b13e7&e=adb61354d3) – threatening consumers with sticker shock on anything that travels by truck – from food to furniture. Rising oil and gas prices will also cause utility bills to spike, since [44 percent of American electricity](https://defendamericaaction.us16.list-manage.com/track/click?u=3eb4d08a510c32b2f2ff20fb3&id=d9492bb2f1&e=adb61354d3) is generated from natural gas and oil products. Have I mentioned that the daily cost of Trump’s war in Iran is [an estimated $1 billion a day](https://democrats.us2.list-manage.com/track/click?u=90379082c3d9e6a03baf3f677&id=ef6b6844d9&e=aa53a71c78), enough to cover a full year of health care for 110,000 Medicaid enrollees. Anyway. You get the point. Trump’s presidency is a disaster in every conceivable way. Our job is to amplify that fact, hold our Congressional representatives’ feet to the fire about it, and get ready to throw a WHOLE lot of Republicans out of office over it in November. We also get to hold every Congressmember to account for their votes on the War Powers Resolution yesterday. This includes castigating the Republicans and [four Democrats](https://www.msn.com/en-us/news/politics/the-democrats-who-voted-against-the-war-powers-resolution/ar-AA1XFCfg)—Henry Cuellar, Greg Landsman, Juan Vargas, and Jared Golden—who voted against it, and thanking every lawmaker who supported it, which includes all Democrats other than the four above, plus Massie and Davidson. OK, all. I’m going to end it here and get on to our actions. Because that, after all, is how we rewrite the story. Let’s goooo! ## Call Your Senators (find yours [here](https://www.senate.gov/senators/senators-contact.htm)) 📲 Hi, I’m a constituent calling from [zip]. My name is \_\_\_\_\_\_. I’m calling to demand that Congress put an end to Trump’s unconstitutional and unwanted war with Iran. I urge the Senator to introduce and vote on another war powers resolution to exert Congress’s constitutional auth