标签：#art

The Google Nest Cam with Floodlight is marked down to $179.99 ($100 off) at multiple retailers, including Amazon, Best Buy, Home Depot, and directly from Google. This weather-resistant outdoor camera captures 1080p video across a 130-degree diagonal field of view, with snappy notifications and great customization options. Even without a subscription, the camera can store […]

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system.

It's actually a security feature.

submitted by /u/ExtensionEcho3 [link] [留言]

Decart is launching Oasis 3, a real-time world model that generates photorealistic driving environments for autonomous vehicle testing, now available via API for developers to build on.

There are many ways to create memorable experiences. Sometimes it's as simple as a form that completes smoothly. But here I'm interested in sharing techniques I reach for when I want a site to feel alive and be remembered. Creating Memorable Web Experiences: A Modern CSS Toolkit originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

and does that mean I won't need to work to survive? submitted by /u/Global-Primary7240 [link] [留言]

I was reading GitLab's recent statements around agentic software engineering, and one quote really stood out: "Git itself is being reengineered for machine scale." ( Business Insider ) According to GitLab, future software development will involve AI agents that: plan, code, review, deploy, and repair software, with humans providing oversight and architectural judgment. ( Business Insider ) That got me thinking. There has been projects for some time arguing that AI agents shouldn't simply be treated as better autocomplete systems . Instead, they argued that agents should become first-class participants in software development : with their own identities, their own branches, their own merge requests, their own audit trails, and infrastructure designed for machine-rate collaboration. One example is GitLawb , which has described itself as a kind of "Git for agents." At the time, a lot of people dismissed these ideas as unnecessary or overly ambitious. But now GitLab—a multi-billion-dollar DevSecOps company—is talking about: agent-specific APIs, machine-scale Git infrastructure, orchestration layers coordinating agents, and agents acting as first-class users of development platforms. ( Business Insider ) It does raise an interesting question: Was the underlying thesis correct all along? We've seen similar patterns before: Containers existed before Kubernetes became the standard. Electric vehicle startups pushed ideas that incumbents later adopted. Cloud-native companies advocated architectures that the rest of the industry eventually embraced. The original innovators don't always dominate the market. But when major incumbents begin rebuilding around similar assumptions, it often suggests that the problem itself is real . So I'm curious what this community thinks: Do AI agents require an entirely new layer of collaboration infrastructure? Or will existing platforms simply evolve enough to absorb these workflows? Because if GitLab is right, software development may be tran

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

Adi Polak discusses the architecture required to transition from stateless prompts to state-aware, context-rich AI agents. Drawing on 15 years in distributed systems, she shares how engineering leaders can leverage Apache Kafka and Flink for real-time stream processing, dynamic memory tiering, and tool orchestration via MCP to solve token limits, cost spikes, and latency bottlenecks. By Adi Polak

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [留言]

submitted by /u/fxboshop [link] [留言]

Text based, I don't want any cheesy porn AIs please 😅 submitted by /u/holupIgotthis [link] [留言]

AI is already deciding who gets loans, who gets job interviews, who gets flagged for benefits fraud. Not assisting humans in making those decisions. Making them. And in most countries there is no law requiring anyone to tell you AI was involved, explain why it decided what it did, or give you any way to challenge it. That needs to change. We need laws that say if an AI makes a decision about you, you have the right to know, the right to understand why, and the right to challenge it. A human must always be accountable for the outcome. That’s not anti-innovation. That’s just basic protection for people living in a world already being shaped by these systems. Most governments don’t understand it well enough to even write those laws yet. Most politicians making AI policy genuinely cannot explain how these systems work, who owns them, or what accountability looks like when they go wrong. Voluntary frameworks have failed every single time. Social media companies voluntarily committed to reducing harm. They didn’t. Financial firms voluntarily committed to responsible lending. They didn’t. Voluntary always means the least responsible actor sets the standard. Hard law is the only mechanism that has ever reliably produced accountability at scale. We need it for AI before the damage is done — not after. The window to get this right is still open. But it won’t stay open forever. submitted by /u/United-Actuator-3527 [link] [留言]

I have watched founders lose sales calls they should have won. Not because they lacked skill. Not because the offer was wrong. Because they walked in to prove they were smart — instead of finding out whether the pain was real. Sales Is Diagnosis Plus Decision The call is not there for you to pitch. The call is there to find out: Is the pain real? Does the buyer have urgency? Does the budget exist? Can a fixed-scope sprint create a clear win? That is it. Four questions. Everything else follows from those. Sales is not pressure. Sales is diagnosis plus decision. 1️⃣ The Call Structure That Works Frame the call in the first 60 seconds: "I'll understand the current state, ask what is costing you, then tell you whether a sprint makes sense. If it doesn't, I'll say so." That sentence does 3 things: Sets expectations — no pressure, no hard close Signals competence — you have done this before Removes the buyer's guard — they can be honest about what is broken Then run this flow: 1️⃣ Current state — what exists now? 2️⃣ Pain — what is broken or slow? 3️⃣ Cost — what does it cost in time, money, trust, or delay? 4️⃣ Urgency — why now? 5️⃣ Decision — who approves? 6️⃣ Success — what would make this worth paying for? 7️⃣ Close — recommend the sprint or walk away 2️⃣ The Questions That Reveal Money These are the 6 questions I use to find whether a sprint is worth recommending: "What happens if this stays broken for another 30 days?" — reveals urgency and cost "What have you already tried?" — reveals how serious they are "Where does the current process lose leads, users, time, or trust?" — reveals the money leak "Who feels this pain most inside the business?" — reveals whether the buyer is also the decision-maker "What would make this an obvious win?" — reveals success criteria before you price "If we fixed only one thing first, what would matter most?" — reveals scope Listen for the answer with the money in it. That is the thing you fix. That is what you price. That is the sprin

A million dollars is emotional as a dream. As math, it is boring. And that is exactly why most people never get close. Break It Down Here is the thing: $1M/year is not one big bet. It is a machine. And machines are built from boring, repeatable components. 20 clients at $50,000? That is $1M. 100 clients at $10,000? That is $1M. 12 retainers at $4,000/month? That is $576k — plus 4 sprints at $10,000 each gets you to $616k. The question is not whether the number is possible. The question is which machine can realistically produce it — from where you actually stand today. 1️⃣ The Practical Ladder Here is how the staged path actually works for an AI service business: Stage What You Are Doing Why It Matters Stage 1 Sell fixed-scope sprints Creates cash and proof Stage 2 Turn repeated sprint work into templates, SOPs, automations Reduces delivery time, increases margin Stage 3 Sell retainers around highest-demand system Predictable monthly cash Stage 4 Productize repeated workflow into software or toolkit Scalable without more hours Stage 5 Scale the thing the market already proved it wants Compound the machine Notice what is missing from Stage 1. There is no SaaS. No product. No cold paid traffic. No team. Just skill, packaged cleanly, sold to people with money and a painful problem. That is the fastest path — not the most glamorous one. 2️⃣ The Proof-of-Force Line The first mission is not $1M. The first mission is $10k/month — reliably, from sprint work. Here is what that actually looks like: 2 × $1,500 teardown/audit packages = $3,000 2 × $3,500 implementation sprints = $7,000 2 × $5,000 launch/GTM sprints = $10,000 3 × $2,000 retainers = $6,000/month That is not the finish line. It is the proof-of-force line. It proves the machine works. It funds the next iteration. It creates the case studies that make the next sprint easier to sell. Then you go from $10k/month to $25k. Then $50k. Then you make the productization decision from a position of demand — not hope. 3️⃣ The

The demo worked perfectly. ✅ Production? First real users. 50% failure rate. ❌ The Gap Nobody Warns You About I see this pattern every week — a founder launches, pushes traffic, and watches their app fall apart in real conditions. Not because the core idea was wrong. Because "it works on my machine" is not a launch-readiness standard. AI-built apps in 2026 ship fast. That is the superpower. But fast shipping without hardening means you are presenting a demo as a product — and real users will find every crack within 48 hours. 1️⃣ What "Launch-Ready" Actually Means Launch-ready is not "the feature works." Launch-ready is when auth, payments, logging, analytics, database permissions, and rollback are boring — because they have already been thought through and tested. Here is the difference: Demo State Launch-Ready State Auth works for happy path Auth handles edge cases, token expiry, role conflicts Payments go through in test mode Webhooks confirmed, retries handled, failures logged Console.log for debugging Structured logging with alerts on errors No analytics Core events tracked from day 1 Manual deploy Automated deploy + rollback path exists No onboarding flow User activation measured from first session If your app is in column one — you are not ready. 2️⃣ The Launch-Readiness Checklist Copy this. Run it before you push traffic. Authentication and authorization — roles, permissions, token handling, session expiry Environment variables — nothing sensitive exposed, prod secrets separate from dev Database permissions — row-level security, no open-read tables, no admin keys in frontend Payment webhooks — test confirmed, failure logged, retry logic exists Error logging — uncaught exceptions surfaced somewhere you will actually see them Analytics events — signup, activation, key action, churn signal — all firing Rate limits — LLM calls protected, API routes guarded Backups and rollback — you have a path back if something breaks Onboarding flow — first session gets the use

Something worth paying attention to in the Fable 5 launch that I think will get buried under benchmark comparisons. The most consequential line in the AWS announcement wasn’t about context windows or coding performance, it was tucked into the infrastructure section: “Once you opt into data retention, your data will leave AWS’s data and security boundary.” That’s not a model feature, that’s an enterprise architecture constraint. For a lot of companies that sentence alone disqualifies Fable 5 from touching certain workloads no matter how good the model is. The Fable vs Mythos split is also worth sitting with. Same underlying capability apparently, but Mythos is gated behind Project Glasswing and vetted partners only. Anthropic is essentially saying some capability is too sensitive for flat API access, which is a pretty different philosophy than “here’s our best model, go build.” Does the Fable/Mythos split read as responsible deployment to people here or more like managed scarcity? And anyone in enterprise AI already hitting the retention requirement as an actual blocker? submitted by /u/Old_Cap4710 [link] [留言]

It kinda creeps me out. Firstly it started from like on chinese word in my chatgpt, now it's fully in chinese? submitted by /u/Oldrus [link] [留言]

hey everyone, sharing something i think will be genuinely useful for anyone building with AI agents. most agent failures aren't caused by the model — they're caused by poor evaluation. agents that work in demos but fail in production, tool calling workflows that silently break, prompt updates that introduce regressions. teams discover these problems only after deployment when it's already too late. we're hosting the Agent Evals Bootcamp on June 27 with Ammar Mohanna, PhD, an AI engineer, researcher and expert in production AI and agent evaluation. 5 hours live, hands on throughout. you work through real evaluation scenarios across 4 layers — component evaluation, trajectory evaluation, outcome evaluation and adversarial evaluation. what every attendee gets: practical evaluation framework you can apply immediately 6 months access to an AI Evals assistant hands on exercises and implementation templates capstone project completed on the day Packt endorsed certification for your LinkedIn link in first comment submitted by /u/Plenty-Pie-9084 [link] [留言]