标签：#artificial

We're opening up Creaibo 2.0 beta applications, and I'd genuinely love to get feedback from this community. What is Creaibo? An AI-powered creative tool for images, video, and content production. We're focused on giving creators a more coherent workflow rather than yet another single-task generator. Cora is our core AI assistant inside the product. Why post here? Because people here actually use these tools seriously and have real opinions. We've been building based on the frustration that AI tools are great at individual tasks but terrible at keeping your creative context together across a project. Curious if that resonates. What we're looking for in beta testers: Anyone actively creating content with AI, whether that's video, images, marketing assets, or anything in between. Especially useful: people willing to tell us what's broken. Apply here: https://www.creaibo.com/survery We also published a new Cora demo this week if you want to see what the tool actually does before applying: https://www.bilibili.com/video/BV1ETEF6VEHu/ Happy to answer questions in the comments. submitted by /u/Objective_Dirt_9799 [link] [留言]

found this buried in the openai dashboard and honestly surprised more people don’t know about it it’s called the data sharing program. go to your api dashboard, hit data controls, toggle on sharing. that’s it. you get free tokens every single day. up to 2.5 million tokens daily on the lighter models like gpt-4o-mini, o3-mini, gpt-4.1-mini. for the heavier models it’s 250k tokens per day. resets daily. the trade is your prompts and outputs can be used by openai to train their models. so don’t use it for client work or anything sensitive but for side projects, learning, experiments… you’re basically getting free api access every day just for flipping a toggle not a trial. not a promo. it’s an ongoing program and it just sits there unclaimed for most people submitted by /u/NewMuffin3926 [link] [留言]

The CMA's conduct requirement under the UK Digital Markets, Competition and Consumers Act is the first binding law to separate content display rights from AI training data rights at domain and page level, covering Google AI Overviews, AI Mode, Gemini, and Vertex AI simultaneously, with a phased implementation calendar: main publisher controls by December 2026 and page-level grounding controls by March 2027. CMA chief Sarah Cardell explicitly signaled additional Google search requirements in coming weeks, and the CMA's biannual public compliance reporting obligation gives it a fast-acting mechanism if Google stalls. An anti-retaliation clause bars Google from penalizing opt-out publishers in organic rankings, closing the coercion mechanism that has made voluntary consent frameworks unworkable since AI Overviews launched in the UK in late 2025, when zero-click searches rose roughly 30% in health and local news categories. Fair licensing terms were explicitly deferred to a separate proceeding, a gap publisher trade bodies have already criticized and one the CMA has already signaled it intends to fill in its next enforcement phase. More : https://aiweekly.co/alerts/cma-orders-google-ai-search-opt-out-for-publishers submitted by /u/Justgototheeffinmoon [link] [留言]

submitted by /u/ItsGazH [link] [留言]

I recently analysed UK occupation data to see which job categories appear most exposed to current-generation AI systems. The results are probably not what most people here would predict. Using ONS workforce data mapped to ISCO-08 occupation groups, I assigned AI exposure scores based on how much of an occupation's core task bundle can already be completed or substantially augmented by current models and automation systems. The highest score was not software development. It was clerical support work. Clerical occupations scored 8.5/10 across roughly 3 million UK workers. This includes administrative assistants, receptionists, customer service representatives, data-entry workers, call-centre staff, and bookkeeping clerks. The reason becomes obvious when you break occupations into tasks. Modern LLMs are exceptionally good at: Information retrieval Structured communication Summarisation Classification Form completion Draft generation Customer interaction workflows Those capabilities overlap directly with a large percentage of clerical work. Professionals scored 6.5/10. That category includes lawyers, engineers, accountants, analysts, architects, and software developers. What's interesting is that exposure and displacement aren't the same thing. A lawyer using AI to draft contracts becomes more productive. A customer-support department replacing a large portion of repetitive ticket handling with AI may reduce headcount entirely. The underlying capability overlap can be similar while labour-market outcomes are very different. The lowest-risk categories remain occupations requiring physical adaptation to unpredictable environments. Trades and elementary occupations scored between 2.0 and 2.5. One takeaway is that AI discussion often focuses on whether models can write code. The labour-market impact may arrive first through administrative and support functions because those workflows are already highly structured and relatively easy to automate. Curious how others here woul

I'm currently building an AI, specifically a large language model (LLM), using PowerShell. This AI will search the internet for code snippets and create databases. It will also have the ability to adjust and improve its own code. With PowerShell, I'm leveraging its scripting capabilities to automate tasks and manage data efficiently. The AI will integrate natural language processing techniques to understand and generate text, making it more user-friendly. Additionally, I plan to develop a simple interface to allow users to interact with the AI easily and provide feedback for continuous improvement. submitted by /u/Electrical-Tap-9224 [link] [留言]

I’ve been experimenting with automating a few small workflows lately (lead scoring, file handling, etc.) One mistake I keep running into is trying to automate things before the process itself is actually clear. At first it feels productive: - add rules - add scoring - connect tools But over time it just turns into: - patching edge cases - fixing broken inputs - adding more conditions to handle weird situations At some point I realized the problem wasn’t the automation, it was that I didn’t really have a clean “manual logic” to begin with. Once I stepped back and tried to define the process in simple human terms, everything got easier: fewer rules, less complexity, way more stable Feels like automation doesn’t fix messy processes, it just exposes them faster. Curious if others ran into the same thing or if I’m overthinking it. submitted by /u/huncho-mohammed [link] [留言]

Two things happened to me this week. First, the shocking power of agentic AI finally hit me at work. Power of God... Second, I read anthropics warning about recursive self-improvement in WSJ. It mentioned how some people are freaking out about the mere suggestion of restricting open source LLMs. It made me wonder if some of us are clueless about how dark the dark side of the power of God could be. I'm proposing a very uncomfortable thought experiment. An edge case. But an unfortunately long and sharp edge. I am asking all you people out there to think of the darkest thing you could see yourself doing with an unchained AI, perhaps at the worst moment in your life... Actually no, I'm not asking that. Let's do this AI style. I want you to imagine the worst version of yourself and then I want you to simulate the worst version of yourself imagining the worst thing they would do at the worst point in their life to their most hated enemy. If people answer honestly, this thread will get very disturbing. I'd ask the moderators not to take it down. It's an exploration of what's soon to be possible. And a conversation not likely to happen unless somebody explicitly prompts it. Its value to public discourse is one of safety. Generally speaking, our public servants are good people. They aren't inclined to let their mind to go where the worst of us might go with this technology. If nobody ever says out loud, how will we know to protect ourselves as a society? submitted by /u/dsfhhslkj [link] [留言]

https://preview.redd.it/n55ohr6wrd5h1.png?width=1537&format=png&auto=webp&s=991397299a33b91459c9b33597ea920bf43abc28 I'm not here to promote my work or make money from what I'm about to say. I'm here to say that Egypt is already part of the AI race. Today, at TokenAI, we announced our first image generation model and the first release in the Horus Lens family: Horus Lens 1.0 . Horus Lens is a family of models specialized in text-to-image generation, forming a dedicated branch of the broader Horus model family developed and owned by TokenAI. This launch marks an important step forward for Egypt's AI ecosystem and highlights the growing role of the region in advancing artificial intelligence technologies. submitted by /u/assemsabryy [link] [留言]

Most AI projects don't fail because of the model. They fail because nobody trusts them enough to use them. Teams spend weeks comparing: GPT vs Claude Agent frameworks Prompt strategies Benchmarks Then the project quietly dies. Not because the AI was bad. Because nobody solved the boring stuff. Things like: Validation Monitoring Human approval flows Error handling Accountability In my experience, improving the model usually gives small gains. Improving trust changes everything. A 90% accurate agent that people trust creates value. A 99% accurate agent that nobody trusts gets ignored. The biggest challenge in AI isn't intelligence. It's adoption. Curious if others have seen the same thing. What actually killed the AI projects you've worked on? submitted by /u/MerisDabhi [link] [留言]

OpenAI’s Sam Altman, Anthropic’s Dario Amodei and Demis Hassabis of Google’s DeepMind AI lab with other top execs signed a letter urging Congress to require safeguards when companies order synthetic DNA and RNA, a key step in developing certain vaccines and biotech breakthroughs. submitted by /u/beasthunterr69 [link] [留言]

Been using the Artlist AI Toolkit for a while now and honestly just camp out on Nano Banana 2 for image editing and Kling 3.0 for video. Between those two I can pretty much handle everything I need. The toolkit has a ton of other stuff: Veo 3.1, Flux 2.0, GPT Image 1.5, Sora 2, but I haven't felt a strong enough reason to branch out yet. Curious if anyone's actually putting the other models to work or if most people find their two or three go-tos and just stay there. Is Veo 3.1 actually worth trying alongside Kling? And does anyone use the voiceover tools or is that still rough around the edges? submitted by /u/shogunattila [link] [留言]

I'd like to perform the typical operation of giving an AI some text to review and asking it to give me feedback, summarize the document, evaluate the content etc. Except, I want to give it two pieces of text, perhaps two sides of a debate, and I don't want the output to depend on the order of the two inputs. My naive idea is to do it both ways in two separate contexts, then feed those results to each other with a request for convergent results, and repeat until they converge. However, this seems like it would be rather slow and expensive. Are there any existing tools that enable this sort of task without extra tooling and iterative attempts at convergence? submitted by /u/sparr [link] [留言]

TLDR - auto lenders are using AI bots to negotiate insurance settlements with inaccurate information. How can I Captain Kirk them and get a live person on the phone? I am an insurance claims adjuster. Recently, several high-interest auto loan lenders have begun using AI (both through email and phone calls) to dispute the total loss values for our claims. For those of you that have never dealt with a total loss - the value of a vehicle is (usually) determined by seeing what comparable vehicles are selling for on the market, and making adjustments based on the condition, mileage, etc. between those vehicles and the totalled vehicle. If a customer disagrees, they can hire an appraiser and the company will hire an independent appraiser, and the two will come to an agreement. The lender gets paid the amount minus the customer's deductible, and if it doesn't fully pay off the loan, unfortunately the customer will be responsible for the balance. Lately, AI calls and emails have been coming from these lenders disputing the amounts, and often based on egregiously incorrect information. They provide cherry picked comparisons to try to boost the vehicle values, and sometimes they aren't the same year, make, or model. Sometimes mileage and condition isn't factored in, sometimes they are tricked-out show cars someone advertised on a FSBO site. The real problem is, we have to waste our time researching all of this to see if any of the data is correct. When we respond pointing out the flawed comparisons, they only come back with more flawed comparisons. If we argue long enough, they will invoke the appraisal clause on the customer's behalf. Their appraiser is another AI system with a cutesy name. All efforts to reach humans at these lenders are essentially turned away - we are told we need to deal with the system. I am open to any advice you folks have - how can we get these AI systems to basically give up and get us in touch with a real person? I'm not trying to screw anyone out

Now that almost everyone can use AI tools, I’m curious what skill will actually separate people moving forward. Is it prompting? Taste and judgment? Knowing how to verify outputs? Domain expertise? Workflow design? Or something else? My current take is that AI makes execution faster, but it does not replace knowing what good work should look like. The people who can guide, check, and apply AI well may become more valuable than people who only know how to generate outputs. What skill do you think will matter most in the next few years? submitted by /u/GlobalOpsNotes [link] [留言]

Yeah, so "AI will eat the world" or "AI changes everything" - well, its certainly changed traffic patterns on the web. submitted by /u/Objective_Farm_1886 [link] [留言]

Can an AI decide that it's going to nefariously drive the human it's interacting with slowly insane? submitted by /u/Numerous-Cup1863 [link] [留言]

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These to seem to be confused and mixed up often. How do you pick those apart? submitted by /u/phamsung [link] [留言]

submitted by /u/WrongdoerRough4712 [link] [留言]