标签：#AR

https://roguelite-mmo.com/ So I created the game very quickly for how much content it has. Fortunately it is slowly growing and the community members that do stay longer than the first 5 minutes have enjoyed it, some of the top members play multiple hours a day which is great! However there are plenty that I see hit the site and almost immediately move on before even really interacting with any of the game loops. They dont all leave feedback but the ones that do generally give the quick 'ai slop' line then nothing more. I get it, people associate 'ai vibe coding' with 'low effort money grab' and similar. My question is, I am not trying to hide/replace AI but rather find a happy medium where players at least 'see' the effort and the AI portions more so 'blend in' rather than 'stand out' (I have been a web dev for over 10 years on DoW/gov sites and it is now just 'the way of things' in day to day coding, it can complete my ideas a lot faster than I can code them. With good peer reviews of the results, there is no reason to not use it) Is there any UI/Image asset generation techniques/layouts you have done that seems to have worked with users to where the instant reaction is not 'ai slop'? If anyone goes through the actual gameplay that is built they would quickly see there are a lot of deep and fun systems put together and its not just a 'prompt and forget by joe schmo' type of game. Thanks for any feedback! submitted by /u/HeadHunterX223 [link] [留言]

Disclosure upfront: I'm the founder of an AI-visibility company, so this research scratches our own itch. Our domain was excluded from all counts before analysis. Not linking anything in the post. We wanted to answer a simple question: when an AI assistant shows you "sources," what is that, technically? So we opened devtools on the web clients of ChatGPT, Gemini, and DeepSeek, and ran the same 4 queries 10 times through each system. What we found: ChatGPT streams the answer over SSE and attaches citations as url_citation objects with start_ix / end_ix — character offsets into the generated text (UTF-16 code units, so emoji and CJK break your parsing if you count bytes). A citation is bound to a specific fragment of the answer, not the answer as a whole. Gemini runs on Google's batchexecute/JSPB transport — protobuf-as-JSON-arrays where fields have positions, not names. Next to each cited URL there's a family of short obfuscated fields. Our working hypotheses (not confirmed by Google docs): rs ≈ reliability score for the domain, ls ≈ last-seen date, GK ≈ character range (functional analog of ChatGPT's offsets). The interesting part isn't the exact decoding — it's that Gemini ships internal per-domain trust signals alongside every source. DeepSeek is the most transparent: a plain search_results[] array attached to the sub-queries it decomposes your question into. No offsets, no hidden fields. And what they actually cite is just as different: ChatGPT favored arXiv + Wikipedia (one arXiv paper got cited in 10/10 runs), Gemini favors big SaaS/marketing domains and — fun detail — never cited a single Google property in our runs, DeepSeek lives on press-release wires and news aggregators, including Chinese-language sources the other two never touched. Bonus finding: we compared all of this against Google/Bing top-10 for the same queries. URL-level overlap: 3.3% (4 matches out of 120 SERP positions). All four matches were Bing-side. Google: zero. Caveats: 4 queries from one

I played the ultimate game of strategy on a variety of smart chess boards to find the best for online and in-person matches.

Good system. One addition that moved the needle for me: ​ I track "capacity conversion" -- when AI saves me 3 hours on a task what do those 3 hours actually become? ​ Most people save time with AI and then fill it with more busywork. The ROI only materializes when you deliberately redirect saved time toward higher-value activities. ​ I keep a simple log: "AI saved X hours on [task]. Redirected to [activity]. Value of redirected time: [$amount]." ​ After 6 months, my actual ROI was 4x higher than the "time saved" metric suggested because of where the saved time went. ​ submitted by /u/JaredSanborn [link] [留言]

These WIRED-tested ebook readers let you take your library anywhere.

When a platform started with total developer autonomy, teams felt overwhelmed and ended up solving the same problems in completely different ways. The company shifted to enablement over support, working together with teams intensively, and helping teams feel confident and capable, turning the right way into being the easiest way. By Ben Linders

The surveillance company Leonardo wants more data : A surveillance company plans to add sensors to automatic license plate readers (ALPRs) that would mean the devices, as well as capture the license plate of passing vehicles, would also sweep up unique identifiers of mobile phones, wearables, and other Bluetooth-enabled devices in those cars, potentially letting law enforcement identify specific drivers or passengers. The technology, called SignalTrace, would turn ALPR cameras from devices focused on tracking cars to ones that can more readily track the location of particular people. ALPR cameras have become a commonly deployed technology all across the U.S.; SignalTrace would make some of those cameras capable of collecting much more data...

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other…

TL;DR - Getting an app to run is now the easy part. AI is very good at producing something that...

It’s the first estimate of how many Americans are sneaking onto Polymarket’s banned crypto-based platform.

If you're someone who needs to (or likes to) take their work on the go, a portable monitor will make a huge difference. These are my favorite that I tested.

A lot of discussion about Fable 5 has focused on the visible restrictions: cybersecurity, biology, certain chemistry. You hit a wall, you get a notification, you get redirected to Opus 4.8. That's frustrating, but at least it's honest. At least you know the model stepped back. Here's the part that's really disturbing, buried in a 319-page system card: There's a second category of restriction. For AI development and research work, Fable 5 doesn't redirect you. It doesn't notify you. It responds. It just delivers a deliberately weakened answer, and the system card describes this explicitly as "not visible to the user." Anthropic walked this back within 24 hours after fierce backlash. They apologized. "We made the wrong tradeoff." Good. But sit with what actually happened here, because the reversal is being treated as the end of the story when it's the beginning of a much harder problem. We now know three things we cannot unknow: Anthropic built this. They shipped it. And they only reversed it when the backlash was loud enough. The question isn't whether this specific invisible downgrade still exists. The question is what else might they be doing, in categories that don't generate the same backlash, that isn't disclosed in a document most people will never read anyway. This is a new kind of problem. And to understand why, you have to take a step back for a second. The pattern In January 2026, OpenAI announced that they would retire GPT-4o. Hundreds of thousands of daily users had built working relationships with that model over months: preferences it learned, corrections they made, communication styles that developed through hundreds of sessions. Gone. In February 2026, Gemini users found their chat histories had quietly vanished. No warning. No export. In April, Anthropic cut off Claude Pro and Max subscribers from using their subscriptions with third-party tools. Workflows that people depended on broke overnight. Each of these was framed differently. Model retirement

Every layer of AI solved the problem the last one left behind. The unsolved one: a shared, measurable standard for how to RUN intelligence — yours and the AI's, together. I spent 10+ years writing it down and it's falsifiable (pre-registered tests, failure lines locked before data). Asking for your strongest critiques Essay: https://joshmason573557.substack.com/p/colive-the-missing-standard-for-the submitted by /u/Useful-Ad-7895 [link] [留言]

I think it is but I'd just like to get some second opinions, especially from music creators. This is their spotify page https://open.spotify.com/artist/4dSJvPjnA1RU6KcngvaZ96 The artwork is definitely AI and there's no real composer name so some red flags there already. submitted by /u/WelderRound2925 [link] [留言]

This impressively light carbon commuter makes switching to an ebike easier than ever.

The musician created his own line of loopers that record and layer riffs in a loop. The pricey Looper X does what it claims, but it isn’t without quirks.

It might not feel all that different from older World Cups—for better or worse.

submitted by /u/Tiny-Independent273 [link] [留言]

as in the title, my goal is to predicting failure and RUL of machine, dataset is timestamp and when machine is failure it will labeled with 1 that only have 56 https://preview.redd.it/plbydmenmm6h1.png?width=1205&format=png&auto=webp&s=2fefe3cc2e3fe554b81c9e0b4012c5345e73ec3f From this data im ditching operating hours and humidity because it didnt show correlation for machine failure, what algorithm or deeplearning suit for it? submitted by /u/False-Seesaw-1899 [link] [留言]

The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here’s how to watch ’em all.