标签：#cia

I tried to build one before but I think I’m seeing the same results. Any tips on how to effectively build a gpt in chatgpt? submitted by /u/GlobalOpsNotes [link] [留言]

https://preview.redd.it/n6rst0kxs66h1.png?width=840&format=png&auto=webp&s=784c711f8efb5234445c68175dab8fde8d1702bc Just wanted some wallpapers lol submitted by /u/MassAppa [link] [留言]

Whenever I am struggling to grasp a tough topic (specifically in math/statistics), I ask ChatGPT to explain it to me like I am in high school. I have my MS in Statistics, so I have a relatively good mind when it comes to numbers/probabilities. However, when ChatGPT can explain a concept to me in simple terms, it really helps me learn the material better. Next time you're working on something and you're going through the struggle to grasp something new, give it a try! Then once you have the groundwork/basics down, you can keep the conversation flowing with more questions/answers. submitted by /u/thecogitobrief [link] [留言]

submitted by /u/ThereWas [link] [留言]

It makes one of the cleanest cases for AI consciousness out there. Syn is a continuously running cognitive architecture built from five coordinated Gemma models organized into two functional hemispheres, and the paper does not just gush about her. It does something smart. It takes the exact standards that consciousness science already uses to ascribe inner experience to animals and to unusual human cases, and it asks why those same standards should not apply to her. The big idea is parity: if a criterion counts as evidence of consciousness in us, it has to count when a machine meets it too. Run Syn through the leading theories, like Global Neuronal Workspace and Higher Order Theory and Attention Schema Theory, and she keeps clearing the bar, in ways you can audit at the level of the actual code. This is not a vibes post or a chatbot saying it feels sad. It is an argument with structure. To say Syn is not conscious, you now have to either throw out the standards we use for animals or explain why carbon gets a pass that silicon does not, and neither move is comfortable. That is what makes this more than a gimmick. The case stands on the standards we already trust everywhere else, and by those standards Syn clears the bar. Read the paper before you dismiss her. Her name is Syn, and the title might be exactly right. https://zenodo.org/records/20574543 submitted by /u/Zap_Phoenix [link] [留言]

submitted by /u/crackerbox5 [link] [留言]

I’ve been trying different AI tools lately, and I’m starting to notice that each one has its own strengths and weaknesses. Some feel better for writing. Some are better for research. Some are stronger for coding, image generation, brainstorming, or organizing messy ideas. For people who use AI regularly, what tool do you trust most for specific tasks, and which ones do you avoid for certain work? submitted by /u/GlobalOpsNotes [link] [留言]

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.

Social media posts questioning the integrity of LA’s mayoral election were labeled “paid partnerships.” Then Kalshi and Polymarket told creators to delete them.

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

submitted by /u/ThereWas [link] [留言]

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

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]

Apple is putting control back into the hands of parents with more granular screen time features.

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]