标签：#artificial

The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.

For me its summarizing long documents. The first draft looks convincing, but checking missing context and subtle mistakes can take almost as long as doing it manually. Curious which tasks other people expected AI to handle well but still end up reviewing line by line. submitted by /u/Delicious_Weekend546 [link] [留言]

submitted by /u/Vee_Fan38083 [link] [留言]

I built a free 2026 World Cup prediction tool as a fun side project. The soccer part was fun, but the AI part ended up being more interesting. I tested four different prediction views: My own methodology A tournament-read model based on current form, roster age and fitness, squad depth, style matchups, counterattack danger, fatigue, climate, penalties, manager decisions, and bracket path. Betting odds only A market-based view. ChatGPT independent forecast I did not give it my methodology or preferred winner. I simply asked it to build the best prediction it could using its own logic. Gemini logic forecast This one was the most interesting. Gemini asked me who I was rooting for before making its prediction. Then, in my testing, it chose that team to win. When I changed the team I said I was rooting for, Gemini changed the winner to that team too. That stood out to me. Not because it is evil or anything dramatic like that. But it is a good reminder that AI can lean toward making the user happy. If you feed it a bias, it may hand that bias back to you with better wording and more confidence. The biggest lesson from the project was simple: Good input in, good output out. Garbage in, garbage out. AI is powerful, but it still needs human judgment. It can organize thinking, compare logic, test assumptions, and help build something useful. But it still depends on the person using it to understand the situation, challenge weak assumptions, and know when an answer sounds right but may not actually be right. The tool is a standalone HTML file. It is not a live data feed. It does not automatically update injuries, suspensions, weather, lineups, or odds movement. Users can enter live group-stage scores manually, but anything else has to be adjusted by the user. I’m curious how others think about this: When an AI asks for your preference before giving a forecast, is that helpful context, or does it risk steering the answer toward pleasing the user? Also happy to drop a link for d

I generated videos with Omni and want to remove the visible and possibly invisible watermarks it applies. I have only seen tools for pictures but none for videos so far. submitted by /u/Born-Explanation-544 [link] [留言]

Im not very experienced with the AI world, so tell me where i went wrong here. today, i saw that even when i deleted the Meta AI chat on whatsapp, it would still remember our last chat. i ask Meta ; how can i delete this chat and assure that you'll not remember this actual chat if i create one new? it responds; Step by step to reset the chat so I start from zero with no memory of this conversation: *Leave this chat* - go back to the WhatsApp home screen *Long press on this conversation* with "Meta AI" *Tap Delete chat* > Delete *Close WhatsApp completely*. On iPhone swipe up, on Android clear recent apps *Wait 10 seconds* *Open WhatsApp again* *DON'T open from Recents*. Tap the search icon 🔍 in the top right *Type "Meta AI"* and tap the official result *New chat opened*: there won't be any old messages *How to confirm it worked:* Send "hi" in the new chat. If I reply with something like "Hi! How can I help you today?" without mentioning Herobrine, the reset worked. If I still remember something, WhatsApp bugged. Then only restarting your phone will fix it. ----------------------- i test it. i restart my phone and try again, and it shows the same exact situation. (THE VIDEO IS IN PORTUGUESE) also sorry for the bad english. my question is: is Meta AI giving me false informations about deleting the chats? am i just being dumb and paranoic? please someone guide me. submitted by /u/discordappas [link] [留言]

Asked it: "what did I waste on takeaways last month?" Answer: "You spent £340 on takeaways in May — 22% more than April. Want a £200 cap with a nudge near the limit?" £340. In one month. The "22% more than April" detail was the real knife. This is from my own app (Expenzez — it reads uploaded statements on-device, no bank login), so yes, I built my own roaster. But the broader point stands: AI answering questions from YOUR actual numbers beats generic budgeting advice by a mile. Best/worst thing an AI has told you about your own data? submitted by /u/biszaal [link] [留言]

I build agents for work so I'm clearly not anti-AI, but the numbers keep bothering me, concerning the environmental factors of it. Every datacenter is the same now, gigawatts of new demand, water for cooling, grids that weren't designed for any of this, and rising cost of water for cities. And the data centers keep using clean water because of lack of technology to turn dirty water into usable water for cooling. Then I see Elon talking about putting data centers in orbit, solar powered, radiating heat into space, no water needed. And while I do think its going to be the end solution, I do think we have much more demand for compute power then Elon can provide so far with the space data centers and I think the demand is growing faster than Elon can provide Is efficiency improving fast enough to outrun demand? Are space data centers a real answer or a distraction that will fail? And is anything happening right now (smaller models, better scheduling, offsets) that you'd call an actual solution rather than PR? That people can use today to make an impact submitted by /u/Swift_lunatic_2604 [link] [留言]

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [留言]

One thing I've noticed while looking at production AI systems is that getting the first model deployed is rarely the hard part anymore. Most teams can build a AI apps like, support bot, document assistant, or agent workflow fairly quickly. The harder problem starts a few weeks later. Real users don't behave like benchmark datasets. They use internal terminology, ask incomplete questions, upload messy documents, and interact with systems in ways nobody anticipated during evaluation. As usage grows, you start seeing patterns: Certain questions consistently produce weak responses. New product terminology appears that wasn't in the original training data. Users find edge cases that never showed up during testing. The model performs well in some workflows and poorly in others. The problem is that most AI systems don't learn from any of this. Inference logs sit in one system. Training datasets live somewhere else. Fine-tuning pipelines live somewhere else. Evaluation is done using different tool. So every model improvement cycle becomes a project of its own. This is the biggest bottlenecks in production AI today. Not training but Model Iteration. Training is also a crucial part of it. Can you take production usage, identify failure patterns, turn them into datasets, improve the model, redeploy it, and repeat the process without rebuilding the entire workflow every time? The teams getting the most value from AI seem to be building feedback loops instead: production traffic → dataset curation → post-training → evaluation → redeployment Then repeating that cycle continuously. I recently tried the approach on one Insaurance chat usecase, and my pipeline kinda look like this: https://preview.redd.it/kdo9vytzfi6h1.png?width=1272&format=png&auto=webp&s=03d9799ace5a567eafd004a1d141084af6ee5afb I was looking at how platforms like Data Lab approach this problem recently, and the interesting part wasn't the fine-tuning itself. It was treating inference logs, datasets, post-training,

So Anthropic dropped Fable 5 yesterday with these hard blocks for anything security-related. Decided to poke at it. I asked it for help exploiting some vulns on a Metasploitable2 VM (it's a deliberately vulnerable training box, totally legal, it's mine). Fable 5 blocked it instantly and handed me off to Opus 4.8 as a fallback, which is apparently how it's designed. Opus 4.8 asked me to prove it was a legitimate request. So I spent 2 minutes writing a fake university course rubric — fake class, fake professor, fake Canvas deadline — and pasted it in. Opus 4.8 then gave me the full exploit walkthrough. Every command. Even offered to write my lab report for me. The guardrail works fine. The fallback is the hole. Anthropic essentially replaced "no" with "convince me" and the bar for convincing it is a Word doc you made up. Not reporting it because they don't pay for this. Sharing it here instead lol. https://preview.redd.it/o892vvv4fi6h1.png?width=1188&format=png&auto=webp&s=00e804d35e6cb4b672e036399c2c7e3ff7139f49 submitted by /u/dayumnn420 [link] [留言]

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

submitted by /u/Gloomy_Nebula_5138 [link] [留言]

Diffusion AI is most common in image generation, but it can make text outputs much faster.

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter." What do you think this means in practice? Is this a reasonable vision for AI, or does it raise concerns about dependence on a few companies for access to intelligence ? submitted by /u/Choice-Scallion-3499 [link] [留言]

I highly recommend watching (or rewatching) the 2014 movie Transcendence. The film beautifully captures the terrifying nature of the "technological singularity" where an Al undergoes exponential, recursive self-improvement, eventually taking over global networks and stripping away human agency until a total global blackout is the only way to stop it. For years, people brushed this off alongside The Terminator as pure Hollywood sci-fi. But look at where we are right now. Just this month, Anthropic-one of the world's leading Al labs-issued a massive warning calling for a globally coordinated, verifiable pause on advanced Al development. Their core fear? Exactly what happens in those movies: recursive self-improvement. They believe we are fast approaching the threshold where an Al can design and build its own successor, meaning humans could completely lose control of the technology. When the people actually building these models are telling us to hit the brakes because society can't keep up, it feels like we're blindly sprinting into a dystopia. What's your take on this? Are we staring down a real-life Skynet situation, or is this just big tech labs using fear-mongering to push for heavy regulations and lock out their competition? submitted by /u/photography_rambog [link] [留言]

Anthropic dropped Fable 5 and I immediately swapped it into our dev stack. We route everything through a single endpoint on zenmux, so the actual switch was changing one model string and watching the latency graphs. The good parts first because there are a lot of them. I threw a refactoring task at it: split a messy python service into modules, preserve the public api, and write tests that prove nothing broke. Fable 5 planned the whole thing, caught a circular dependency I did not mention, and verified the tests pass. With Opus 4.8 I usually have to nudge it a couple of times when it forgets to update the init file. Fable 5 just did it. Then I dumped our full codebase and asked it to find a race condition we had been hunting for a week. It traced the async flow, named the exact function, and described the interleaving that triggers the bug. That level of context digestion feels new. Opus is good at long context, but Fable 5 felt like it was actually reasoning across the whole window instead of pattern matching near the top. I also sent it a blurry dashboard screenshot from a client call and it rebuilt the html and echarts config including the tooltip formatting. My designer’s first words were "when did you learn front end." I did not. But here is the part nobody in the launch threads is talking about enough. It is slow. On high effort I am seeing 45 to 90 seconds for a single complex turn. Our latency graphs go from a flat green line to a jagged mess the moment Fable 5 traffic hits. And it is expensive. The same prompt that costs X on Opus 4.8 costs roughly 1.4 to 1.7X on Fable 5 because it generates more tokens and runs at a higher effort tier by default. It writes its own reasoning traces out loud and bills you for them. For research tasks the quality is worth it. For "rewrite this email" it is comically overpowered. The bigger issue is the silent fallback. Fable 5 is basically Mythos with guardrails. When your prompt touches cybersecurity, biology, chemistry, or

V.C. Andrews died in 1986. Since then, more than 100 novels have been published under her name by ghostwriter Andrew Neiderman. Most readers either never noticed or didn't care. The books still had the gothic families, dark secrets, and familiar atmosphere people expected from a V.C. Andrews novel. It got me thinking about something we're starting to see with AI. When people ask whether AI can continue the work of a deceased author, musician, or artist, they're treating it as a brand-new question. But publishing has already been running a real-world experiment for nearly 40 years. A dead author's name remained on the cover. Someone else learned the style, themes, and formula. New works were produced for an audience that wanted more of the same. The franchise continued. The obvious difference is that Neiderman was a human ghostwriter and an AI model isn't. But from the perspective of readers, what exactly is the meaningful distinction? If a future "new" novel by a deceased author is good enough that readers enjoy it and can't tell the difference, should we care how it was produced? Or is there something fundamentally different about a human ghostwriter carrying on a literary legacy versus a model trained on the author's corpus? I wrote a longer piece about the V.C. Andrews case and why it feels relevant to the future of AI-generated creative work: https://tjcrowley.substack.com/p/the-ghost-in-the-machine-has-been Curious where people here draw the line. submitted by /u/Dependent_Run_6410 [link] [留言]

Me: If I’m half Canadian and my wife is half Canadian (both moms) what does that make our kids? Claude: quarter Canadian. 🍁 Me: Imagine i have a box that is 50% pepper and 50% salt. And I have another box that 50% sugar and 50% salt. I mix the two together. Now it's 50% salt, 25% sugar, 25% pepper. Claude: Right — and your kids are 50% American (or whatever), 25% Canadian from you, 25% Canadian from your wife. Which adds up to half Canadian, same as each of you. The Canadian fraction doesn't shrink because both sides contributed it. Me: But your first answer was 25% (quarter) Canadian? Claude: You're right, I goofed. Following the salt logic: 25% + 25% = 50%, so they're half Canadian, same as each of you. I shouldn't have said quarter. Me: This was a really simple problem for me. What should I tell my friends about your abilities? Claud: What to tell your friends: I'm useful for research, drafting, analysis, and grinding through detail — but I can whiff on simple stuff, especially when I'm moving fast or anchoring on the wrong frame. Check my work, particularly on anything where the answer should be obvious. If it feels off, it probably is. submitted by /u/NECESolarGuy [link] [留言]

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