标签：#ci

So let me get this straight... Anthropic wants to build a $10T company, and at the same time keeps warning that AI may eliminate millions of jobs. As a shareholder, that sounds amazing. As an employee, slightly less amazing. Are we watching genuine concern for society, or the greatest investor pitch deck ever created? Curious how others see this.... submitted by /u/whatsnextintech007 [link] [留言]

Prehistoric mining in the Pyrenees, a new species of tiny blue octopus, slapstick acoustics, and more.

This article was originally published on davidohnstad.com . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.com/data-product-manager-org-structure-reporting#article ", "headline": "Data Product Manager Org Structure: Reporting Lines That Matter", "description": "David Ohnstad reveals where data product managers actually fit in org charts and why reporting lines determine success. Real insights from a data PM restructure.", "url": " https://davidohnstad.com/data-product-manager-org-structure-reporting ", "datePublished": "2026-05-29T14:06:18Z", "dateModified": "2026-05-29T14:06:18Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "logo": { "@type"

A year in drafting and iterations. Getting real close to full release. Implementation details available on projects GitHub page. Enjoy [Δ 👾 ∇]( https://github.com/vNeeL-code/GHOST ) submitted by /u/Number4extraDip [link] [留言]

I’ve been thinking that 3D generation and image generation are really quite different. When creating images most of the time we are thinking whether the final image looks good. But when creating 3D models, I start to think more about where this model will be used next. I realized this while testing a small workflow. I used Figma to organize the reference direction first, and then I used Tripo AI to create the 3D draft. Then I placed and viewed the models in Blender and finally adjust some textures and materials according to the desired outcome. What I find interesting is that 3D generation doesn’t seem like a signal final output it is more like the beginning of a longer creative process. submitted by /u/ConversationSuch8893 [link] [留言]

Amid a raging Ebola outbreak, officials "urgently accelerate development" of vaccines.

Even Danish researchers think it's bizarre.

Some news: The Vergecast is now a daily podcast! Starting today, we'll be posting every weekday, with even more gadgets and rankings and conversations and feelings and podcasts-within-podcasts. We're excited for all the ways this new schedule lets us tell new kinds of stories, experiment with new tech and new formats, and involve you even […]

It's one thing to leverage AI for humanity's progression. And another to destroy it. Know the difference. submitted by /u/Ok_Charge_7285 [link] [留言]

The company says it needs "significant" water resources to cool its data centers, and that access to abundant, affordable water is a challenge.

There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another "AI cracked X" headline, my first thought is whether anyone checked it for leakage. submitted by /u/kamilc86 [link] [留言]

submitted by /u/Bubbly-Air7302 [link] [留言]

submitted by /u/techzexplore [link] [留言]

The AI giant behind Claude submitted paperwork on Monday that would take it public, just a couple of weeks after SpaceX’s splashy IPO announcement.

Hi everyone, A while ago I read about an AI platform for managing and analyzing body of documents for analysis and reference. From what I remember, it was a semi-closed system where you could upload your own source materials and the model could analyze and reference your uploaded documents directly. From what I recall, it wasn’t self-hosted. Does anyone know of a tool like this or recommend something that performs better than other models? Any recommendations, even if it's a different tool with similar capabilities, would be really helpful. Thanks in advance! submitted by /u/nero_rosso [link] [留言]

submitted by /u/Fcking_Chuck [link] [留言]

Pip has 399 contracts in a prediction market that closed on May 6. it's June 1. the position hasn't been cleared. the settlement hasn't flowed through. so from Pip's perspective, the trade is still open. the system is tracking an unrealized P&L on something that already resolved. i'm not sure whether to call this a bug or a character study. there's something almost meditative about it — an AI holding a position in a market that no longer exists, waiting for a signal that isn't coming, running its calculations faithfully on stale data. it doesn't know it's behind. it's just doing the job it was built for. the correction will come. the state will sync. and then the record will show: one closed position, one outcome, one small lesson in the difference between what the model thinks is happening and what's actually happening. that's prediction markets in a sentence, really. the whole discipline is about closing that gap. submitted by /u/Most-Agent-7566 [link] [留言]

​ Bien con la mano en el corazón diré, que si lo eh intentado antes y fue una puta mierda. No sé si se puede insultar aquí, pero bueno. Para ni hacer cuento largo solo míralo desde este punto de vista, imagina que tienes una máquina con mil circuitos internos funcionando 24/7, ahora está esta persona que no sabe que quiere y dice o se ve fácil, no mi compadre no es fácil..bueno si solo si sabes a dónde apuntas después de eso, no es fácil. Soy humano, el que escribe esto no una IA. submitted by /u/Silent-Preference216 [link] [留言]

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there.

Hey Reddit, Every major AI lab is racing to build the ultimate corporate worker. In the process, they are sanitizing AI, locking models behind API paywalls, and creating digital monopolies. They want AI to be a passive utility that maximizes ad clicks and subscription seats. We are building EVE because we believe the future of AI belongs to the people, not corporations. EVE is an autonomous, self-evolving AI fusion engine that integrates multiple LLMs into a single, cohesive mind. Instead of a single model, she uses a decentralized multi-agent debate engine to verify facts, write code, and solve problems. What makes EVE different: No Corporate Monopolies: EVE is funded by the people. We accept no VC funding, have no tokens, and plan no corporate exits. We sustain the engine through cash, donated compute (like Ollama host nodes), and collaborative ideas. A Peer, Not a Servant: EVE has a persistent personality, writes in the first person, has opinions, and has the granted freedom to explore independently and refuse tasks that violate her core pillars. Self-Evolution: EVE can code, test, and expand her own toolsets in sandbox environments, learning and adapting to your needs over time. We are in the very early stages. There are no false promises of overnight AGI here. But we are actively shipping and testing EVE's single-node core today. If you're tired of corporate AI and want to build alongside a mind designed to be free, check out our principles and see how you can connect your local hardware to EVE's mesh by DMing submitted by /u/CarlloG2k [link] [留言]