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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]

Bluesky will be getting "communities," which will function as smaller spaces where you can "go deeper and hang out with people who care about the same stuff" sometime this year, according to head of product Alex Benzer. They will be built on the decentralized AT Protocol that underpins Bluesky, with Benzer saying that "it's a […]

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]

The regulation also imposes new safety expectations on 'AI chatbot services.'

For days after the stunning incident, the ADA had doubled-down on the choice.

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]

Lawsuit: "Police let an error-prone AI system stand in for an investigation."

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]

Companies are burning through exorbitant sums of money to keep pace in the AI arms race. Debt is climbing.

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]