AI 资讯

A new report from OpenAI details PRC-linked influence operations using AI to target U.S. tech debates, data center narratives, tariffs, and false claims about ChatGPT.

I have been an audio reviewer for 20 years. My time as a freelance headphones panelist at Wirecutter, and the multitude of reviews I’ve written for sites like Reviewed, Digital Trends, IGN, and now The Verge, have given me the chance to listen to hundreds of earbuds. Some were truly excellent, and some were pretty […]

Ambrosia Energy wants to build power plants in less than 12 months while undercutting natural gas. It hopes to build gigawatts worth by 2030.

Meta has to open up WhatsApp to third-party AI chatbots again, the European Commission says.

Turn Claude into a recruiter that thinks like you Discussion | Link

So if you missed it, 32 npm packages under u/redhat-cloud-services got compromised last week. about 117,000 weekly downloads. i know, another supply chain attack, we're all tired. but this one is different from the usual "remove the package and move on" cleanup, which is why i'm posting. The malware doesn't stay in the package. during install it copies itself into your editor config. it adds a startup hook to ~/.claude/settings.json (runs every time you open Claude Code) and a task to .vscode/tasks.json (runs every time you open that project in VS Code). so you can delete the package, nuke node_modules, reinstall everything clean, and the attacker's code still runs every time you open your editor. uninstalling removes nothing. While it runs, it grabs every credential on your machine. AWS keys, Google Cloud, Azure, Kubernetes secrets, SSH keys, GitHub tokens, npm tokens. it checks whether you're running CrowdStrike or SentinelOne first, so it can stay quiet on monitored machines. It installs a small watchdog that pings GitHub with the stolen token every minute or so. if you revoke that token before removing the malware, the watchdog notices and wipes your entire home directory. overwrites the files so they can't be recovered. The advice, "rotate everything immediately" is exactly what triggers it. the attacker built it that way so you hesitate before kicking them out. cleanup steps in the right order are at the bottom. Three days later a second wave hit 57 more packages, around 647,000 monthly downloads. this one moved the malicious code into binding.gyp, a build config file that node-gyp executes during install. that means no preinstall or postinstall script at all, --ignore-scripts does not help you, and the scanners that caught the first wave missed this one. some malicious versions are still live on npm right now. and the worm spreads itself: it uses stolen npm tokens to publish poisoned versions of whatever packages that maintainer owns. Here's how the whole thi

Chinese EV colossus BYD has announced plans to speed up its conquest of the European auto market with the rollout of superfast Flash Chargers across the continent. BYD has already installed the first new chargers in Germany and the UK, and plans to roll out 3,000 across Europe by the end of next year. At […]

The RAMageddon crisis has got Microsoft rethinking its Xbox console hardware business. Xbox CEO Asha Sharma and Xbox strategy chief Matthew Ball have both revealed this week that Microsoft is reevaluating plans for its next-generation Project Helix console and exploring "radically different" console business models in the meantime. "We are working very hard to rethink […]

You now can get a US phone number with NordVPN's Saily eSIM app.

Laptops for college should be portable, offer long battery life, and remain reasonably affordable. Based on testing hundreds of laptops, these are my top picks.

Give your AI agents the social intelligence they're missing Discussion | Link

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [留言]

Turn errors, DOM, + screenshots into an AI-ready Markdown Discussion | Link

submitted by /u/fxboshop [link] [留言]

Hello Reddit I've been working on QSPR (Quantitative Structure-Property Relationship) analysis for chemical compounds mentioned in the Jean-Claude Bradley Open Melting Point Dataset . Basically the idea is to see how accurate a model can predict melting points of compounds using only topological indices. After some work on the topological indices (feature engineering), each compound was represented by 26 features. I trained a random forest model on the data and got a test r2 score of 0.66 (which is pretty respectable, given the constraints). However, the file size of the model was around 1.23GB. I didn't like it being that big, so I opened up PyTorch to build a custom deep learning architecture that could make predictions as accurately as the random forest but with much smaller file size. After around 2 weeks of research, I build a 270,000 learnable parameter model (1.3-1.4MB according to torchinfo) that got an r2 score 0f 0.6399. Given all this context, I wanted to ask the following question: Should I commit and work on publishing the results, or should I keep working on improving the model? Note: I'm obligated by my university to not give out intricate details of my research before publication, so please forgive me if such details are required for a high quality answer. However, I can give out the metrics achieved by my little deep learning model. Here it is: === Evaluation Metrics (Expected Value) === R² Score : 0.639910 MAE : 41.246754 MSE : 2989.062744 RMSE : 54.672322 NRMSE : 0.083469 MAPE : 11.69% The unit for MAE, MSE, RMSE and NRMSE is Kelvin (K). submitted by /u/AgiGamesYT [link] [留言]

I've written SQL for over a decade. Joins, subqueries, recursive CTEs – the whole deal. Then I tried a graph database (Neo4j). And my relational muscle memory kept getting in the way. I kept trying to "map foreign keys" instead of just… traversing edges. So I built myself a side‑by‑side cheat sheet: SQL → Cypher for everything from basic SELECTs to variable‑length paths that would require recursive CTEs in PG/SQL Server. Turns out, queries like "users who bought this also bought…" go from 30 lines of self‑joins to 6 lines of zig‑zag pattern matching. If you've ever felt frustrated with: multi‑hop join performance LIKE '%...%' on string scans or just the sheer noise of mapping join tables for many‑to‑many …give this 5‑min read a shot. The mental shift alone (relationships as physical edges, not ID matching) changed how I model data – even when I go back to SQL. submitted by /u/ostwal [link] [留言]

Google DeepMind and partners announce a $10M funding call for multi-agent safety research.

The organization claims that the FIFA tournament could have impacts on the rights of local people and visiting soccer fans in all three host countries.

The Argentine national team will be Google’s test bench and technological showcase during the World Cup.