标签：#art

Is it just me, or does anyone else find they can't help themselves troll AI sometimes. Like I will use Claude for a long research project, write and refine a report, and once done I just love fucking with it. Like asking it to rewrite the report because I am going to send it over to a 4 year old to review, so if you could please put the whole thing in baby talk. Or ask it what I can put on the slides when I present it in order to guarantee that anyone who sees it will become incredibly attracted to me. Or ask it to find the closest tattoo shop near me because I am going to get this whole report tattooed on my ass and moon people on the street as a guerilla marketing experiment. Is my life so dull that I have to resort to fucking with a robot to feel feelings? submitted by /u/musicheadspace [link] [留言]

Artificial intelligence giant OpenAI says it has filed confidential paperwork for an initial public offering. In a brief statement, OpenAI says it has submitted its S-1 filing, but has "not decided" yet on the timing of an IPO, adding: "It may be a while because there are things we want to do that are likely easier as a private company." The announcement comes days after the company's chief rival, Anthropic, filed its own S-1 , and the on the eve of major AI player SpaceX's potentially historic public debut. submitted by /u/LinkedInNews [link] [留言]

I've been using Claude Code, Cursor and a few other coding agents quite a bit recently. One thing that keeps standing out is that generating code isn't really the bottleneck anymore. Understanding the codebase is. Agents can usually find the relevant file. The problems start when the change depends on: historical decisions undocumented relationships ownership boundaries files that always change together Bigger context windows help, but I'm not sure they solve this problem completely. Curious what people building or using coding agents think. Is the next step bigger models and more context? Or do agents need a better representation of the codebase itself before they can reliably work on larger projects? Been exploring this problem while building RepoWise: https://github.com/repowise-dev/repowise submitted by /u/Icy-Roll-4044 [link] [留言]

Claude is my main tool. I delegate all the difficult tasks to him. What gets me is the small stuff. I'll be halfway through a heavy conversation and some throwaway question comes up, the kind literally any model could handle. So now I'm stuck: ask the capable model and feel a bit wasteful, or open another tab with a lighter one and lose the whole thread I was building. I do the second more than I'd like to admit. What I actually want is one place to pick whatever model makes sense for the moment, Haiku for quick stuff, Sonnet or Opus for the hard things, maybe GPT-4o or Gemini if I feel like it, all in the same chat. No new conversations, no tab-hopping. Bonus points if it just routes automatically based on the question. Half-tempted to build it myself at this point. But figured I'd ask first: does something like this already exist and I just missed it? How do you deal with it? Stick with one model and push through, bounce between tabs like me, or did you find something that actually works? submitted by /u/Stunning_Tadpole1286 [link] [留言]

The ChatGPT maker announced it has filed paperwork to go public, just a week after rival Anthropic took the same step.

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

Apple's focus on efficiency and performance improvements is good news for older iPhones.

"It is actually challenging how you measure [Mach] from space."

submitted by /u/ThereWas [link] [留言]

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

Apple Intelligence is coming to cameras connected to Apple Home. At WWDC, Apple announced that with iOS27, the Home app will use Apple Intelligence to analyze footage and generate descriptions summarizing what the camera saw. You can also search footage with natural language to find clips from across connected cameras, such as when a package […]

I am a last-year data science major at university who initially joined because of AI's exciting potential across numerous industries. However, after learning about multiple companies backtracking on their AI use on their platforms and cutting back on their data center expansions, I can't help but think that something is very wrong behind closed doors. I came to understand that the demand for AI is slowly decreasing in some areas and increasing exponentially in others. To me, it seems every major industry "needs" AI to make life easier, yet is backtracking when it doesn't perform the way they want it to. My concerns revolve around how unpredictable AI's usage is. If I get involved in an industry that actively destroys land, water, and other resources, I would hope that the environmental costs will be outweighed by the benefits everyone sees from AI. However, with the economic trend of AI's value decreasing for companies that initially went all in on it, I can't help but feel like I'm actively destroying the planet. Does anyone have any suggestions or moral redemption for me? I want to jump ship before the big explosion, but I'll stay if there's great potential for growth with AI. submitted by /u/Emergency_Ad6929 [link] [留言]

submitted by /u/andix3 [link] [留言]

The conversation around AI coding is still stuck on velocity and its completely missing the real operational bottleneck -> DEBUGGING I use a combination of tools like GitHub Copilot, Cursor, and generic agentic code gen tools(whichever give me the most credits that week) , dropping a 300-line functional block from a natural language prompt takes about a minute. On paper, developer velocity should have been increased by 69 times. but i feel like the bottleneck hasn't disappeared; it just shifted down the pipeline. Like i traded manual work for incredibly frustrating debugging. LLM code looks fine on surface but like when u go through line to line, you feel like its built on sand i mean sure if it works it works but like one thing i struggle with is ghost features, like if i accidentally suggest a feature then the LLM is gonna shove it in my code, even if i say no later on. (if someone knows how to fix do dm) idk about ya'll but i'd much rather have a ai llm that takes like 1 hour to write 500 lines of code if that means i have to debug less. another thing how are you handling validation boundaries? are u using runtime timeout scripts or smth open source like gitagent? also this is gonna sound weird but i kinda have trust issues when a llm spits like 300-400 lines in under a minute (idk why) sorry for my bad english, im not a native speaker submitted by /u/SpicyTofu_29 [link] [留言]

The British government thinks a state-backed infrastructure initiative will help supercharge homegrown chip startups.

submitted by /u/NISMO1968 [link] [留言]

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

TL;DR: Future archaeologists will discover this post and conclude I traded a referral link for free AI credits. They will be correct. 500 free credits: https://manus.im/invitation/L722LISUH3EMDS?utm\_source=invitation&utm\_medium=social&utm\_campaign=system\_share Anyway... You know how in every sci-fi movie they promise us AI assistants? Yeah. Somehow we ended up with AI that needs constant supervision. Me: "Research this topic." AI: "Certainly. Before I begin, please provide your goals, audience, format, timeline, preferred writing style, risk tolerance, blood type, and your mother's maiden name." Thirty minutes later I'm managing the AI instead of the AI helping me. I've been messing around with Manus and the thing I like is that it behaves more like an actual assistant. I tell it what I need, and it goes off and fills in a lot of the blanks itself. I don't use it as my main model for everything. I use it like a second opinion. Research. Project planning. Finding blind spots. Comparing options. Figuring out what I'm forgetting. Basically all the stuff that happens before the actual work starts. For pure coding, there are better tools. For "here's the thing I'm trying to do, help me think through it from start to finish," it's been surprisingly useful. Full disclosure: if you use the link, I get some credits too. You get free credits. I get free credits. The robots get stronger. Honestly that's the healthiest relationship I've had with technology in years. submitted by /u/Mstep85 [link] [留言]

maybe i am overthinking this but the more i look at AI agents using the web, the more the current web starts to feel kind of awkward. like websites are still built assuming a human is sitting there, reading the page, ignoring the cookie popup, guessing which button matters, understanding which part is marketing and which part is actually useful. but an agent does not really do that naturally. it has to parse the page, figure out what is clickable, understand the form state, avoid random modals, compare options, maybe call tools, maybe retry when something fails, then somehow verify it actually did the right thing. that sounds less like “browsing” and more like forcing software to cosplay as a human user. which is probably fine for demos but idk how well that scales. this is why all these things that seem separate to me are starting to feel connected. MCP, A2A, WebMCP, AI search, browser agents, bot traffic, agent security, all of it. not saying they are the same thing obviously. but they all point to the same pressure: software is becoming a real user of the web, not just humans. and if that keeps happening then maybe websites need something beyond normal UI. not just better HTML or better accessibility, but some kind of agent-readable/action-readable layer. basically not “AI kills websites” or anything dramatic like that. more like websites keep existing for humans, but also need to expose themselves properly to machines. kind of like SEO but instead of optimizing for search crawlers reading your content, you optimize for agents actually doing stuff. not sure if this is a real architecture shift or just people putting new names on APIs again. wrote a longer version in the attached medium post if anyone wants to read it. submitted by /u/Old_Cap4710 [link] [留言]

My game 'Simulation Simulator' is a campfire conversation game about DMT, simulation theory, and a friend with a computer monitor for a head. The game is bundled with a local LLM and every conversation is unique. 5 endings you can reach totally based on how you interact naturally with the AI. One is a romance ending! Everything in the clip is totally organic and unscripted. Trying to use AI for good. Honestly haven't seen the use of LLM tech inside games to this extent yet. I'm sure people much smarter than me must be trying though. For NPCs & world building, this seems like a logical next step. I even wanted to do text to speech audio and automatic translation. The only thing really preventing it right now is processing time on local machines. Those extra layers would add like 10-20 seconds of calls per exchange so it just breaks the game. If processing gets faster/better, I can imagine whole towns of NPCs with memories, that have no scripted dialogue at all and change over time. In my game here, you argue with an LLM and can attempt to prove that reality itself is a simulation. It's really a philosophical experiment more than a game. It can get trippy trying to prove you do or don't exist. Anyway, demo for Simulation Simulator is out on steam if you want to try for yourself. Let's talk using AI for good in games! submitted by /u/MorphLand [link] [留言]