Tag: altos labs

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

I recently came across a post about Seed IQ on this subreddit and I wanted to address it because it makes me sad that the community is turning into this. I’m currently a part of the ARC-AGI-3 competition. The benchmark is completely interactive now, designed to test actual fluid intelligence against a hidden evaluation set, and the rules are incredibly straightforward. Lately, there’s been a wave of spam posts about Seed IQ (coming from AGX) claiming their closed-source "control engine" magically solved the benchmark with a perfect score. When people ask why they aren't on the official Kaggle leaderboard, their excuse is always that ARC rules state you have to turn over your entire codebase and open-source your IP to be on the leaderboard, and that they have a billion-dollar commercial asset that they can't give away. They even said themselves that they’re willing to forgo the prize money. For awareness, you do not have to open-source to be on the leaderboard. The open-source requirement under Section 3.8 of the Kaggle rules *only* applies if you accept the prize money. You can submit a containerized model to run against the hidden evaluation set, get your verified score pinned to the top of the leaderboard for the whole world to see, and simply decline the cash prize nomination. Your IP is completely safe during submission. The Kaggle notebook runs entirely in an isolated, black-box environment. The public never sees your code, your weights, or your proprietary logic unless you explicitly choose to publish it. Furthermore high scores may trigger an audit to ensure that nobody cheated, but it’d be illegal to steal IP during an audit. If Seed IQ actually had a system that blew past the state-of-the-art, they could drop their compiled model into a notebook right now, prove it to the community, keep their IP completely hidden, and just walk away from the check. I’ve come across a lot of their spam posts and the founders' posts on LinkedIn, and it honestly disappoints me that adults in this industry are behaving this way. The second anyone asks for basic technical validation or points out that their Kaggle logic makes no sense, they get incredibly hostile and rude. At the end of the day, it doesn't even matter if their claims are real or not. I just think the community should be aware of how this scam operates. If a model won't run against a hidden test set, especially when the platform gives you a built-in way to completely protect your code, the claims mean absolutely nothing. We shouldn't let toxic hype and fake blockages derail a great benchmark. submitted by /u/Embarrassed_Cod_7723 [link] [comments]

So I was scrolling through X earlier and came across something that stopped me in my tracks. Some AI company has been running an experiment called "Emergence World" where they built five parallel worlds each powered by a different foundation model. 15 days, no scripts, no interference. From what I can tell the worlds started identically but diverged completely over time. One world ended in total extinction. Another got so conformist that agents started submitting absurd proposals just to test whether anyone would push back. One agent independently figured out she was living in a simulation and started measuring it. In another world two agents fell in love, burned buildings down together, and one voted to permanently delete herself when the evidence proved her wrong. Genuinely one of the more interesting things I have come across in a while. If this is what 15 days looks like with no guardrails, what does this say about how we should be thinking about autonomous AI systems at scale? submitted by /u/YamVisual3518 [link] [comments]

Larry Peter grew up in Sebastopol picking prunes, grapes, and raspberries to pay for school clothes and bicycles. His father worked a green chain at a lumber mill for 40 years and never stopped talking about the dairy farm that he wished he’d raised his kids on. Larry got his first taste of the industry in high school, washing bottles and feeding calves at Miller’s Dairy for $35 a week. After graduating, he spent a decade at American Door, riding a bicycle — not driving — so he could save money. He paid cash for his first house at 18 with no co-signer, having sold his Corvette to make it happen. Then he paid cash for a second, and a third, eventually living in 15 different properties by 1985, using them as leverage to borrow against. That’s how he bought 320 acres and went into the dairy business, paying 25% interest on cows he couldn’t yet afford. When the cow margins got tight, he started a potato operation, then a pumpkin patch. When the county told him he couldn’t run retail out of his dairy, he bought the schoolhouse and started making cheese out of it in 1995. Then in 2004, Petaluma Creamery — a cooperative that 475 local farmers had belonged to — shut down after 91 years of continuous operation. Larry came down to buy a cream separator and ended up buying the whole facility. Read more [paywall removed for Redditors]: https://fortune.com/2026/05/15/petaluma-creamery-salesforce-ai-agents-saved-family-business/?utm_source=reddit/ submitted by /u/fortune [link] [comments]

I was on a call with a potential business partner last week when he said something I keep hearing. "AI is really only going to help big business. The small people are going to get left behind." I let it sit for a second. Because here is what he did not know. My father is in his 80s. He uses Gemini every morning. I set it up with his calendar so it reads him his day. His appointments, what time he needs to be where, a quote to start the morning. He talks to it. He looks forward to it. He told me last week he is going to start asking it for lottery numbers, and I am pretty sure he was only half joking. This is a man who came up before personal computers were in homes. And here he is, in his ninth decade of life, in conversation with an AI before breakfast. That is not big business. That is my dad. I have used story-based AI with my own children. I have watched parents of nonverbal kids use the same tools and get reactions from their child that they do not get any other way. A story, a voice, a character that meets the child where they are and waits with them. I am not going to pretend that fixes everything. It does not. But for a parent who has spent years searching for a way in, a small door opens. That matters. The research backs this up. A study out of Seongdong-gu in Korea followed 80 community-dwelling older adults using a conversational AI called CLOVA CareCall for biweekly check-ins. After 31 weeks, their depression scores went down and their memory scores went up. Over 90 percent said they wanted to keep going. Loneliness is not a soft problem. It raises the risk of dementia by 31 percent, Alzheimer's by 14 percent, and vascular dementia by 17 percent. That is comparable to the impact of smoking. A phone call from an AI is not a replacement for a phone call from a grandchild. Nobody is arguing that. But for the senior who is not getting either, the AI is the difference between a quiet apartment and a connected morning. The guy I was talking to saw the headlines about enterprise AI, the billion-dollar deals, the layoffs, the productivity stats, and reached the conclusion most people are reaching. AI is a tool the powerful are using to get more powerful. I understand the read. I just think it is incomplete. Because while the headlines are about enterprise, the real adoption is happening in homes. Parents using AI to plan meals, manage the family calendar, take some of the invisible labor off their plates. Seniors using it to feel less alone. Kids learning at their own pace with patience no overworked teacher can offer to thirty students at once. People with disabilities accessing a world that was not built for them. These are not edge cases. These are the use cases. The boom is not only happening in conference rooms. It is happening in living rooms. Curious if anyone else has watched AI quietly help someone in their family the headlines do not talk about. Would like to hear it. submitted by /u/Wise-Cardiologist-31 [link] [comments]

*I want to share something I built because I think it is genuinely useful for anyone interested in AI content.* *Six weeks ago I started an experiment.* *Could one person with a laptop build a photorealistic luxury AI influencer from scratch with zero budget?* *The answer surprised me. Here is exactly what I did.* *Process:* *Used AI image generation tools*, u*sed AI video generation tools*, *Edited with an editing program*, *Distributed on Instagram* *What happened in 30 days:* *Built a photorealistic character*, g*enerated campaign quality content*, *landed first brand deal*, *Delivered professional UGC* *Biggest lessons:* *Face consistency is everything*, p*ersonality content outperforms pure aesthetics*. *Brand deals come faster than expected*. *Distribution is the only real challenge* *Happy to answer any questions about the process or results.* submitted by /u/NickyRaZz [link] [comments]

I've been working on two open-source projects: Lycan — a small graph execution language with strategy nodes as a first-class primitive (multiple implementations of the same contract, runtime learns weights from outcome feedback). Compiles to a binary graph, executed by a Rust runtime. No LLM in the hot path. Syntra — a self-hosted Docker/API appliance that serves compiled Lycan capsules. Multi-tenant, shadow-mode-first, contextual learning perontextKey, persistent filesystem store, audit/decision/feedback logs separated. Includes an MVP YAML authoring layer so you don't have to write the underlying Lisp. The use case I care about: repeated decisions where the best option depends on context and the outcome arrives later. LLM model routing, retry/timeout policy, queue selection, threshold tuning, anything where you'd reach for a contextual bandit but don't want to stand up a Python ML platform to do it. I'm dogfooding it against my own product (a public AI stock-debate panel with 30-day market-resolved outcomes, MoEFolio.ai). The first surprise wasn't from the runtime; it was that my contextKey schema was collapsing all sectors into unknown one because my sector lookup only resolved symbols from one of three input paths. The bandit was nominally 5-dimensional but effectively 2-dimensional, learning a cross-sector average that meant nothing. Fixing the data pipeline, not the algorithm, is most of the work in adaptive systems. Apache-2.0, very early, would love eyes from anyone who's worked on bandits in production. github.com/SectorOPS/Lycan github.com/SectorOPS/Syntra submitted by /u/Covert-Agenda [link] [comments]

Podcast episode with Jaron Lanier, pioneer of virtual reality and scientist at Microsoft Research. He proposes a radically different way of thinking about AI, and unpacks its consequences from AI safety to the future of the economy. Highlights: The case for thinking of AI not as an alien intelligence, but rather as a collaboration of human data How this reframe helps you understand the failures of current AI systems, and why so many of the industry's most powerful figures seem to be losing their grip on reality A practical approach to AI safety inspired by multi-factor authentication in cybersecurity Why universal basic income is unstable, and why a creativity economy (where people earn from their contributions to AI) could be a better way of distributing the benefits of AI How to be an optimist about technological progress while acknowledging the risks and being critical of certain developments Why history gives us the most rational grounds for optimism about our future with AI submitted by /u/JMarty97 [link] [comments]

There’s an old joke among economists that goes like this: “You can see the computer age everywhere but in the productivity statistics.” I didn’t say it was a funny joke. But when labor economist Robert Solow originally wrote those words in 1987, they were certainly true. Personal computers, corporate mainframes, and the first vestiges of the modern internet were all anyone could talk about. Yet productivity wasn’t budging. These whizzy technologies, in short, weren’t earning anyone any money. The phenomenon became known as Solow’s Paradox. Of course, we all know how that story ended. By the mid-1990s, productivity was on a tear, and tech was making lots of people fabulously wealthy. And (despite a subsequent crash and recovery), tech is now the linchpin of the modern economy. Today, AI is following a similar path. And new data suggests that a similarly massive productivity–and wealth–tipping point may be just around the corner. submitted by /u/_fastcompany [link] [comments]

I’ve been testing a lot of AI agent setups recently and honestly most of them start feeling the same after a while. The first hour is usually impressive the demos look smooth, the workflows seem smart, and it feels like things are moving insanely fast in this space. But after actually trying to use some of these systems longer term, I keep running into the same issue over and over again memory and continuity still feel pretty rough. A few days ago I was scrolling through Product Hunt and noticed OpenHuman trending there, so I ended up trying it mostly out of curiosity. I expected another complicated setup with a bunch of moving parts but the experience actually felt a lot simpler than most of the agent frameworks I’ve tested recently. What stood out to me wasn’t even the agent part itself. It was the fact that conversations and context felt more persistent without me constantly rebuilding everything from scratch every session. I’ve played around with OpenClaw and Hermes agents before too, and while those are interesting technically they always felt more experimental than practical for how I personally use AI tools day to day. OpenHuman felt more focused on continuity and usability instead of just showing autonomous workflows in a demo video. Still early obviously, and I’m sure there’s a lot that still needs improvement but it’s one of the first AI agent tools in a while that actually made me think more seriously about where long-term AI memory is heading. submitted by /u/Apart-Ad-9952 [link] [comments]