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Hey everyone 👋 Is anyone here playing RF Online Next? I recently built a fan guide website for it: 👉 https://rf-online-next.net RF Online Next Guide — Starter Finder & Beginner Tips New to RF Online Next? Answer 3 questions to get your starter Biosuit, faction lean, and first-day checklist — personalized for your playstyle. rfonlinenextguide.com The idea is pretty simple. When a new MMO launches, information is usually all over the place — Discord messages, random posts, outdated guides, fake code pages, and long videos when you only need one quick answer. So I wanted to make a cleaner guide hub for players who just want to know: how to download and play which faction to pick what Biosuits/classes are good whether there are any real codes how to fix server full/login issues how Mining War / Chip War works what Sacred Weapons do The site focuses a lot on Mining War, the big 450-player faction war between Bellato, Cora, and Accretia. I also tried to keep the content honest. For example, the codes page doesn’t list fake “working codes” just for clicks. If there are no confirmed codes, it says that clearly. From the dev side, I structured the site around search intent instead of a normal blog feed. So the homepage points players directly to the guide they probably need. It also has multilingual sections for different regions, since RF Online Next has players from many countries. Would love to hear feedback from other devs, especially on: site structure SEO approach guide layout content clarity anything that feels confusing If you’re into MMOs, gaming websites, or niche SEO projects, feel free to check it out: 👉 https://rf-online-next.net RF Online Next Guide — Starter Finder & Beginner Tips New to RF Online Next? Answer 3 questions to get your starter Biosuit, faction lean, and first-day checklist — personalized for your playstyle. rfonlinenextguide.com

Check how on-premises AI solutions empower enterprises to safeguard sensitive code, ensure data residency, and maintain full compliance without compromising performance. Why privacy and security matter in AI-powered development? As enterprises increasingly adopt AI to automate code reviews, testing, and vulnerability scanning, ensuring data privacy becomes paramount. Cloud-based AI tools may expose sensitive source code, customer data, or intellectual property to external risks. By contrast, on-premise AI tools allow organizations to keep data within their controlled environments by aligning with data sovereignty and compliance requirements like GDPR and CCPA. According to Gartner, by 2026, 75% of organizations will demand AI solutions that guarantee strong data residency and compliance assurances. What are on-premise AI tools for software development On-premise AI tools are artificial intelligence solutions that are deployed and operated within an organization’s own infrastructure, rather than relying on external cloud services. In the context of software development, on-premise AI allows teams to leverage advanced AI capabilities such as code analysis, automated testing, and security scanning while keeping all data and processes within their own controlled environment. Core components of on-premise AI infrastructure include: Hardware: servers, GPUs, and storage devices physically located on-site or in a private data center. Software: AI models, orchestration tools, and management platforms installed and maintained by the organization. Security Measures: firewalls, access controls, and monitoring systems tailored to the organization’s specific needs. Examples of on-premise AI tools in software development: AI-powered code review platforms installed on internal servers automated vulnerability scanners running within the company’s network machine learning models for test automation, hosted locally. Primary connection to data privacy: on-premise AI ensures that sensit

Three bugs this month. All three looked fixed before they broke. The date was quoted in 51 out of 52 posts. The model was pinned to a specific version. The upload feature had been working in production for weeks. Each one passed the obvious checks and failed somewhere else. That's the theme for this Friday Fixes: the fix that wasn't. Not bugs that went unnoticed, but bugs where a defense existed and the failure found its way around it. 1. The Unquoted Date, Part Two If this one sounds familiar, it should. I wrote an entire Friday Fixes post about this exact bug class five weeks ago. An unquoted YAML date. gray-matter parsing it as a Date object instead of a string. A crash downstream. Last time it took down /admin/drafts . The fix hardened formatDate() to coerce Date objects before calling .includes() . I verified it. I shipped it. I wrote 2,000 words about it. I moved on. This time it took down the homepage. The symptom: vibescoder.dev loaded for a split second, then flashed to Chrome's "This page couldn't load" screen. Every browser, every profile, every device. The site was completely dead to visitors. The twist: curl returned HTTP 200 with ~900KB of fully rendered HTML. The server was fine. The crash was happening during React hydration in the browser, invisible to any server-side test. The cause: A new post had date: 2026-06-19 in its frontmatter. No quotes. gray-matter parsed it as a Date object. In posts.ts , the code does const meta = data as PostMeta and then spreads ...meta into the return value. The as PostMeta cast told TypeScript the date was a string . At runtime, it was a Date . That Date object flowed through the server component, through the RSC serialization boundary, and into PostListWithFilters , a "use client" component. React couldn't hydrate it. No global-error.tsx existed to catch the crash. Dead page. Why the May fix didn't prevent this: Because the May fix was in the wrong layer. It hardened formatDate() , the function that happened to cras

While experimenting with GRPO training, I kept running this shit that when reward increases, it becomes difficult to tell whether the policy is genuinely improving or simply exploiting the reward function. So I built a small library called rewardspy that wraps an existing reward function and continuously monitors indicators that often precede reward hacking. It currently tracks things like rolling reward statistics, reward variance collapse, reward component imbalance, response length drift, reward slope changes, GRPO group collapse, anol. This is my first major RL project so I would absolutely love some technical advice Check it out here: https://github.com/AvAdiii/rewardspy (credits to u/Oranoleo12 , posting on their behalf) submitted by /u/BaniyanChor [link] [留言]

Prime Day is making it much easier to pick up a Fitbit without spending more than you want. Both the Fitbit Charge 6 and Fitbit Ace LTE are on steep discounts today. Right now, you can buy the Charge 6 for $85.45 ($74.5 off) at Amazon, while the kid-friendly Fitbit Ace LTE is $69.99 (a […]

This is just an opinion of what I experience and am witnessing, but looking at how LLMs scale feels like I've seen it before: with CPUs trying to outrun Moore's Law and break the rules of physics. Heat, power leakage, and diminishing returns made it increasingly expensive to squeeze out even small gains in clock speed. The GHz race shifted because it had to. For LLMs, more compute, more data, more parameters, and everything just keeps getting better? That curve seems to hit a ceiling and innovation needs to succeed the scaling race now. History does not repeat itself, but it rhymes. What learnings can we make from history to "predict" a potential future? History In the early 2000s, CPUs ran into a wall, a very physical one ^^ So makers adapted. Instead of crunching every single watt out of a single core, multi-cores became common. Athlon 64 x2, Pentium D, PS3 with its heavy Cell approach. From linear to parallel. From sequential to multi-threaded (and funny race conditions ;). Talks of distributed systems, SIMD/MIMD and new benchmarking spawned into what we have today. We still use CPUs, but differently. We still have Memory, but think about Cache, RAM, GPU or Unified. Same same, but different. Innovation because of limitation. Present I feel something similar is about to happen to gen AI. Yes, there are improvements in different areas, some in scaling, some optimisation, some performance, but the slope is becoming slippery. The last 12 months went from "Opus 4.5 is the pinnacle" to "What the hell is wrong with Claude?". The perfect (business) storm of scaling execution! But the low-hanging fruits have been eaten and the crops don't grow as fast anymore. Costs rise quickly, latency becomes a constraint, and even large context windows feel more like extensions than breakthroughs. What remains is more incremental, more expensive, and more complex. You could argue the whole venture of "agents" is the same multi-core experience repeating itself. A different kind of orch

I have a confession. Somewhere around day nine of this experiment, I almost quit and went back to my old setup. Not because ChatGPT was bad. Because I was bad at using it. I kept typing half-questions the way I'd type into Google, hitting enter, and getting answers that were technically correct and completely useless. It took me about a week to realize the problem wasn't the tool. It was twelve years of muscle memory. This post is the long version of what happened when I tried to go a full month without my usual stack of developer crutches — Google, Stack Overflow, Regex101, JSONLint, a SQL formatter site, a commit message generator, a pile of bookmarked Docker cheat sheets, and a few other tabs I didn't even realize I kept open until they were gone — and replaced all of it with a single ChatGPT window. I work as a backend-leaning full stack engineer at a small e-commerce company. Python and Django on the server, a chunk of Node for a couple of internal services, Postgres, Docker, and an AWS setup that I inherited rather than designed. Nothing exotic. Which is actually why I think this experiment is useful — most of you reading this aren't working on some bleeding-edge ML pipeline either. You're maintaining stuff, fixing stuff, shipping features under deadlines that someone in another department picked without asking you. So here's what happened. All of it. The good parts, the embarrassing parts, and the parts where I quietly reopened Stack Overflow in an incognito tab because I didn't want my browser history to judge me. TL;DR I tried to replace 12 daily developer tools with ChatGPT for 30 days straight, tracking what worked and what didn't. Google search volume dropped by roughly 70%, but it never hit zero — and I don't think it should. Stack Overflow was the hardest habit to break, and also the one I missed least once I'd broken it. The small utility sites (Regex101, JSONLint, SQL formatters) were the easiest wins. ChatGPT replaced almost all of them outright. Do

Making of Aantraa aantraa.site — AI audio & video translation, caption generator, and viral shorts cutter. Under the Hood I run a small YouTube channel. I'm not a full-time content creator, but YouTube is a solid platform to gain traffic for your online work, business, project, or idea. Aantraa is what I built in a week. The main concept is simple: Video translation into multiple languages Audio translation — including text-to-audio, with MP3 output for Premiere Pro Long-form to shorts — convert YouTube long-form video into short clips At that time, only three features were needed, so website development wasn't the heavy lift. The real work was building APIs, backend infrastructure to integrate AI into video, and dealing with heavy storage. Breaking the execution into steps: How I made Aantraa AI LLM layering and provider Aantraa is heavily dependent on AI APIs — we need reliable infrastructure for LLM providers. OpenRouter, Portkey, Vercel AI SDK labs, and individual APIs for Anthropic, Deepseek, and OpenAI are solid options. I prefer OpenRouter for Aantraa for one reason: multiple model support — it's easy to pick the cheapest capable model for each job. Easy to integrate, strong community support, free model access, and more. AI LLM APIs are needed at almost every stage in the backend: Understanding video context and creating a script Translating the script into target languages Recording the script into MP3 or WAV format Summarising the video Generating captions Cutting videos into shorts Building APIs and servers Each layer needs heavy AI context and prompt engineering. Loop engineering is the trend here — and it's required for aantraa. For example, video translation works in multiple connected steps: Video translation API breakdown AI understands the video, fed into the LLM via the ffmpeg module AI generates a script/caption from the video AI translates the script into the desired language AI generates audio (MP3 or WAV) of the new translation AI glues audio a

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This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. About three years ago, a doctor told me I had to lose abdominal fat. She didn't care about my lower […]

Rock weathering may release or draw down carbon dioxide—it depends on the rock.

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A scuffle between stan account Club Chalamet and another Heated Rivalry die-hard shines a light on how parasocial fans are a publicist’s greatest asset—and liability.

Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability. By Michael Webster

Meta is, by and large, a company built on other companies' ideas. It has almost perfected the strategy: wait for a new platform or social mechanic to take off, then either buy or clone it, put it next to Meta's unmatched user base and advertising engine, and watch the money pile up. Well, the next […]

My question on live continual learning use cases was removed by moderators here because they think i asked basic level question about live continual learning which i thought is a frontier level research. But anyways. Is anyone interested in talking about continual learning (live) and catastrophic forgetting? submitted by /u/fourwheels2512 [link] [留言]

Move would test whether group can turn ambition into a mass-market phone business.

One of the best deals this ongoing Prime Day has been on Apple’s latest flagship headphones. The AirPods Max 2 are still available for a heavily discounted price of $399 ($150 off) at Walmart, even though they sold out at Amazon. Since the Amazon deal kicked and Walmart is out of stock on one of […]

GitHub热门项目 | A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev | Stars: 123,517 | 48 stars today | 语言: HTML

Aseon Labs, which came out of Y Combinator's 2026 spring cohort, has raised $10 million from Crane Venture Partners and others.