标签：#AR

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]

From a stand-alone app to a Google Gemini partnership, here’s everything you need to know from WWDC 2026 about Apple’s upcoming overhaul of Siri.

Yes, I'm calling it out. It IS racism. As an active member of r/MachineLearning and a researcher who is ethnic Chinese, I am DISGUSTED by unfounded accusations against the group of researchers who constitute over half of the field. Such posts pop up every other week, grounded in conspiracy theories, and creating a sinophobia echo chamber. I understand the salty feeling when one's paper is rejected, no matter whether the paper actually deserves acceptance or not. Given the noise in conference organization and reviewing process, and a relatively junior body of participants, it is very likely that one finds a paper "worse than mine" slip into the conference, and there's a high chance that the paper has a Chinese author. That's simply because of the composition of the authors, and does not warrant accusations, aka witch hunts, towards certain ethnic groups. This sub is about an important scientific subject in the modern world. If anyone agrees with the logic "80% of the authors are Chinese, so my rejection is their fault.", they should seriously rethink their career plan since such thinking does not belong to serious scientists. We should be open to discussing the problems we have in the current conference organization and reviewing process, but racism should not have a foothold in our field. submitted by /u/AffectionateLife5693 [link] [留言]

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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.

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Amazon Web Services has recently announced the general availability of the next generation of Amazon OpenSearch Serverless, with a redesigned architecture that enables 20 times faster resource provisioning than the previous serverless architecture, true scale-to-zero capability, and up to 60% lower cost than a provisioned cluster for peak loads. By Gianmarco Nalin

The cars are too big to race well, but the competition for pole position is thrilling.

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]

I've been admitted into both UPS and TUD for Applied Mathematics, and I wanted to hear some advice on which one would be better. For context, I'd like to work in some form of AI research, most likely within industry. At the moment, I'm most interested in privacy preserving machine learning or mechanistic interpretability. Which one do you think would leave me with better career opportunities after completion, alongside the best chances of getting admitted into competitive PhD positions? Thanks! submitted by /u/Far_Investigator6900 [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]

Prerequisites (what you need before starting) Account and tokens : user:MODDER credentials and access to the proposal inbox. Local tools installed : qemu-system-x86_64 , libfuzzer or afl++ , boofuzz (optional), openssl , jq , base64 . Artifact store access : S3 or equivalent with write permissions. HSM access for owner : owner HSM is required only for final autonomy=1 apply; Modder does not sign. Test harness : test-harness CLI that runs vectors (provided by platform). If not present, use the included run-vectors.sh wrappers. Network : ability to reach staging Overcrest endpoint and Zclarity3D collector. Basic skills : copy/paste, editing JSON, running shell commands. submitted by /u/GabenHood [link] [留言]

Startup Battlefield applications are due tomorrow, so now's the time to put the finishing touches on your submission!

Hi r/MachineLearning , Wanted to share something I'm excited about. I’ve been fascinated by AlphaEvolve and its results for more than a year now, but using open source frameworks seems overwhelming because of the high costs. I can’t really afford hundreds of Claude Opus calls every time I want to run it. I want to be able to try it out many times and all sorts of unique domains. What if it was possible for AlphaEvolve to be much more affordable while getting a better performance? Over the last six months or so, I’ve been working on LEVI, an open source AlphaEvolve-like system that can outperform existing open source frameworks at a fraction of the cost (upto 35x cheaper!). It can also run on Claude Code or Codex, making it even more accessible (I've mostly been using it with a QWEN-30B). LEVI comes in two flavors where I felt it’ll make the most difference: Code Optimization, and Prompt Optimization (sorry math, you got a less direct path; workable through the code route). The core thesis behind LEVI is that with the right search architecture, smaller models can substitute for or outperform larger ones. This means it’s much more economical to rely on smaller models for most of the work. That’s the entire takeaway. Making this work in practice is a different problem, but if you forget everything else from this post this is the only message I think I’m really trying to convey here. LEVI does it in three ways: 1) Invest in solution diversity from the start and ensure its maintained. We don’t want to converge to the same solution, especially with smaller models in the mix, and rely on large models to pull us out of the basin. 2) Use smarter routing across larger and smaller models (i.e. most mutations don’t require a Claude Opus X) 3) For prompt optimization not every rollout is as important. Build a proxy subset to approximate. I’ve tried LEVI on systems problems (like MoE scheduling or database transaction scheduling) and found that LEVI outperforms existing framework

I use AI tools all day for work and the thing that drives me crazy is starting a new chat window.If I don't paste a massive block of text about my current project stack, my writing guidelines, and what I'm trying to do, the model just defaults to that generic, robotic corporate speak. But keeping a sloppy text file on my desktop and copying it in over and over feels incredibly stupid.Even worse is that ChatGPT custom instructions don't format right when you try to move them over to Claude or DeepSeek. They just drift or start ignoring instructions after a few prompts. How are you guys managing your background data across different browser tabs? Are you just dealing with the text dumps or is there a way to actually lock this context in permanently so it doesn't get messed up? submitted by /u/alazar_tesema [link] [留言]

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Apple's WWDC nears: Here's what you can look forward to.