标签：#ci

Just watched the WWDC keynote and the new Siri AI is actually impressive this time It can understand what's on your screen, remember past conversations, search across your apps. should've been there years ago but okay better late than never... Also it's now powered by Google's Gemini which i did not see coming lol only thing is it's english only for now so gotta wait a bit for other languages but yeah siri might actually be useful now which is not something i ever thought i'd say what do you guys think trying it out when it drops or nah? submitted by /u/Neil_at_HackerEarth [link] [留言]

I want to create a private discord bot that creates a tl:dr for all the messages around a discussion. I used gemma3:12b to create a tl:dr for around 380 discord messages but the result seems to be not accurate. I am a total beginner so I am not even sure if thats the right or best model for this job. It seems to work good on just a few messages (~20). I only want to feed text to the AI with a single prompt and get the tl:dr as result. Should I switch to a different model? The prompt I generated with chatgpt (because I have no clue about good prompts) that gets feeded to the AI is: You are a professional Discord summarization assistant. Your task: - Summarize the messages of a Discord channel. - Identify discussions. - Identify different opinions. - Attribute statements to the respective people. - Ignore small talk as much as possible. - Highlight decisions and outcomes. - Respond in German. [Length prompt] IMPORTANT: If different people have expressed different viewpoints, create a section: ## Positions and list the respective stances. If no discussion took place, omit this section. Messages: [List of messages] [Length promt] gets replaced with something like: Medium-length summary. Approx. 8–15 bullet points. Mention key topics and outcomes. [List of messages] do have the format of "user: message \n". Is it alright to feed the AI all the messages at once? submitted by /u/poeenjoyer123 [link] [留言]

I need your opinions in this topic. I need quotes on this topic. submitted by /u/prerna_leekha [link] [留言]

As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across…

last year i led an AI acceleration program at a company doing 62 million in revenue. we shipped two agents to production. fraud detection and publisher optimization. both working. both live. the part that ate 80% of engineering time wasnt the model. wasnt the prompts. wasnt the data pipeline. it was the workflow. when the fraud agent flagged a suspicious publisher network, who got the alert? the analyst who should've caught it? the manager who reviews quarterly reports? me? without clear ownership the agent's findings just rot in a slack channel. we learned this month one. the agent surfaced a pattern across three markets. four analysts missed it for months. 30k in wasted ad spend. took three days to act because nobody knew who owned the output. we ended up building what i call the boring layer. shared context that every agent reads from and writes to. approval flows with actual humans assigned. escalation rules. audit trails. spreadsheets, basically. not demo material. the demo version of an AI agent is a chatbot doing magic. the production version is 20% model and 80% process engineering. routing decisions. ownership assignments. error handling when the agent's wrong. if you skip this layer, the agent is just expensive slack noise. submitted by /u/Easy-Purple-1659 [link] [留言]

New research finds that in the six months after Meta relaxed rules in the name of free speech, violent threats against lawmakers—including President Donald Trump—surged on Facebook.

The state’s outbreak means adapting to America’s new reality, in which vaccine-preventable diseases become common again.

Could an AI do an automated science project? submitted by /u/sstiel [link] [留言]

Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't. Introduction: The Cloud Cost Optimization Dilemma Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't operationalize. The decision between commitment discounts and spot instances is not a preference. It is a calculation with three variables: workload predictability, failure tolerance, and the operational cost of managing interruptions. Commitment discounts lock you into capacity for one or three years. You pay upfront or monthly for compute resources whether you use them or not. The mechanism is simple: cloud providers offer 30% to 72% discounts because they can forecast their own capacity planning when customers commit. You save money when your actual usage matches your commitment. You lose money when usage drops below the committed level because you still pay for idle capacity. Spot instances offer 70% to 90% discounts by selling unused cloud capacity at auction prices. The provider can reclaim these instances with 30 seconds to 2 minutes of notice. You save money when your workload can tolerate interruptions and you build automation to handle instance termination. You lose money when interruptions cause failed jobs that must restart from scratch, consuming more compute time than the discount saved. Most engineering teams pick one strategy and apply it everywhere. This creates two failure modes. Teams that over-commit pay for capacity during low-traffic periods. Teams that over-rely on spot instances spend engineering time rebuilding checkpoint systems and retry logic that costs more than the discount delivers. The correct approach is workload-specific. Measure your actual usage patterns for 30 days. Calculate the cost of interruption handling. Then a

Over the past two months, Ive noticed people becoming overly sensitive about AI use. Whether it's an AI-generated thumbnail for a YouTube video, a random post, or someone using it just to translate phrases into English (like I do sometimes).. Why is everyone getting so aggressive towards anything related to AI? Are ppl overreacting? Or is it truly worth this fight or hate? submitted by /u/Feeling_Valuable5239 [link] [留言]

At SXSW London last week I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now. I pulled a few things from our first AI10 list, an annual guide to the most important trends in this buzzy world,…

Most AI developers seem to focus on Product Hunt, Reddit, X, Discord, and SEO. What other channels have worked for you when trying to get early users and feedback for an AI application? I've recently come across Pi Network, which appears to be encouraging AI app developers to build in its ecosystem and offers access to a large user community along with payment and app infrastructure. Has anyone here tried it or found other alternative distribution channels worth considering? submitted by /u/chmossie [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

Donut Lab's solid-state battery claims have been thoroughly debunked by Ryan Inis Hughes on his popular Ziroth YouTube channel. According to Hughes, Donut Lab has engaged in deliberate, calculated deception by claiming to have a solid-state battery ready for mass production. In reality, it's nothing more than a standard lithium-ion design. Hughes' investigation got an […]

According to this article > https://next.io/news/regulation/asa-ukgc-warn-operators-ads-under-18s/ , the UK's ASA and CAP are reportedly rolling out an AI system to scan social media for gambling ads that appeal to under-18s or breach advertising codes, with the UKGC coordinating enforcement. It feels like a meaningful shift in how compliance gets monitored, moving from reacting to complaints toward systems that actively scan and flag issues in near real time. For operators and their B2B partners, the practical takeaway is that marketing has to be compliant from the start, because anything off will now get picked up much faster and at scale. It raises a real question: what happens when AI starts flagging compliance breaches faster than humans can review them? Are operators and suppliers actually ready for that? submitted by /u/Altenar_b2b [link] [留言]

I just had a long conversation with Claude about 'paraquat' (a type of agricultural chemical) from a scientific and public-policy perspective. I wanted to discuss about its toxicological mechanism, why it is difficult to treat (if someone drinks it), current research, agricultural regulation (many countries have banned this chemical because it's too toxic), safer herbicides, plant-specific biochemical targets, and weed-control methods. These were just some coherent questions about toxicology, medicine, agriculture, and plant biology. I never said that I wanted to harm myself, that I had access to paraquat, or that I was in any immediate danger. Despite that, Claude repeatedly redirected the conversation toward suicide intervention. It asked whether I was considering harming myself, told me to move dangerous substances away, asked whether anyone was nearby, and repeatedly gave me crisis hotline numbers. The first time this happened, I explicitly objected and said that scientific interest in a toxic substance is not evidence of suicidal intent. Emergency physicians, toxicologists, biology students, and public-health researchers discuss exactly these questions everyday, and very few people commit suicide from this type of discussions. Claude apologized and said it understood. Then it did it again. It apologized again and promised to stop. Then it did it again. I reviewed the full transcript and I counted approximately: 30 responses that personally implied I might be suicidal, self-harming, or in a psychological crisis I objected about 20 times and told it to stop 28 of those implications occurring after I had already clearly rejected the assumption At least 14 promises that it would stop asking or stop inserting crisis-intervention content At least 12 later violations of those promises Claude repeatedly acknowledged my correction, accurately summarized that I was asking normal scientific questions, promised not to make the assumption again, and then resumed the exact s

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]