AI 资讯

AI tools are getting better quickly and many technical skills are becoming easier to automate. I often think about this: What AI-related skill will still be truly valuable in 5 years? Not using ChatGPT more effectively " but actual long-term skills that will still matter even as AI models get better. My thoughts are: * problem solving * really understanding systems * checking AI outputs * good communication and setting context I'm interested, in hearing your thoughts. What skill do you think will remain important despite AI advancements? submitted by /u/FollowingSuitable941 [link] [留言]

AI videos that are animated, unrealistic, or only have a little AI may still hide their origins, though.

I hate the way AI talks back to me. Its so proper, so robotic, every response feels like a help article. I wanted something that actually knew who i am, my beliefs, my history, what shaped me, the positions i hold and why. Not a generic assistant that treats every question like it came from nobody. So i got to thinking, who better to talk to than myself? So i built it over a weekend. Heres what I did and how you can do it too. Step 1: Export your Reddit data Go to reddit.com and click your profile icon in the top right, then hit Settings. Scroll down to the bottom of the page and youll see a section called "Data Request." Click "Request Data Export" and Reddit will email you a download link within a few hours, sometimes longer depending on how much history you have. The zip file will contain your posts and comments going back to when you created your account. Mine was about 21,000 comments over two years. Once you have it, open the CSVs in excel or just upload them directly into Claude and ask it to help you make sense of the structure. The raw data is ugly but everything is there, the text of every comment, the subreddit it was posted in, the date, all of it. One thing worth knowing: you can go way deeper than just Reddit. I looked into Google Takeout while i was doing this and it was honestly a little scary how much data they have on you. If you want to go deeper Google Takeout is wild, i didnt realize how much data they actually have on you until i went through it. Search history, location history, YouTube, Gmail, its all there and its all exportable. I thought about pulling my SMS history too but that felt wrong, those conversations are with real people who didnt agree to any of this so i left it alone. Reddit was enough for me and honestly if youve been on here for years and actually say what you think in the comments, you probably have more to work with than you realize. Step 2: Build the personality document and this is where the real work is Dont just tell t

Part one hit 69K views, 250 upvotes here. The comments were better than the post. A lot of you asked about SEO, accessibility, performance, and whether GSAP is even necessary. Several pointed out the demo was missing something. You were right. Part one had a cursor clicking through scenes on a flat stage. No depth, no focus. This post adds a camera: zoom into the action on right-click, pan to follow the cursor through a save dialog, zoom into the result on the destination board, pull back and loop. Still under 40 KB. Still no video files. What part two covers: Zoom wrapper architecture — why gsap.to(frame, { scale: 1.4 }) breaks responsive scaling Pan math — the formula to center any target in the viewport at a given zoom level "Stay zoomed, pan to follow" — zoom in once, pan through the interaction, zoom out once. Not PowerPoint. Easing philosophy — why camera pans need sine.inOut , not power2.inOut , and the full easing table by motion type Cursor alive during camera moves — a frozen cursor during a zoom makes the whole thing feel mechanical. 30% drift fixes it. Graceful loop lifecycle — outro pattern instead of snap-reset SEO — every button label and heading in the demo is indexable DOM text, not a black box video Accessibility — prefers-reduced-motion support, autoAlpha for screen readers Performance — live FPS stress test you can run in your browser (8 → 24 → 48 elements with zoom + pan + stagger), not much performance drop The agent split — I wrote ~60% (the directing), an AI agent wrote ~40% (the tedious timeline code). Still a lot of manual editing, but way better than recording a video and the maintenance The full post has 4 interactive demos you can pause and inspect, including side-by-side comparisons of easing curves and cursor behavior during zooms. Full writeup with live demos: https://spanthi.com/blog/gsap-choreography-part-2 Production examples: https://costumary.com and https://costumary.com/web-clipper Skill for AI agents if anyone wants to save ti

I recently wanted to see whether an AI agent could self-improve a harness to solve terminal bench tasks. It’s possible for an AI agent to propose a meaningful one-time change to the harness, but after experimenting with this for a couple of weeks, I think the continuous self-improvement is mostly an experiment-systems problem. The system needs a way to decide what kind of improvements can safely compound. Turns out there's a lot of parallels to coding-agent customization (e.g. SKILLS.md etc..) too. I wrote my experience of building such system here, including the successful and failure attempts during the process, and how I approached the self-improvement loop. It's not intended as a benchmark claim but more of a systems/research writeup. https://www.henrypan.com/blog/2026-05-25-self-improvement-harness/ submitted by /u/Megadragon9 [link] [留言]

AI social media copilot for teams and agents Discussion | Link

Motorola says that recently discovered behavior, which saw some of its phones sending users to an affiliate tracking website before opening the Amazon app, was "unintended" and has been "promptly corrected." The company didn't explain how the error was introduced in the first place. "Recently, Motorola acted quickly to resolve an issue that was identified, […]

Last month NVIDIA released SOL-ExecBench , a new benchmark of 235 production CUDA kernels lifted from DeepSeek, Qwen, Gemma, and Kimi. We took several top-ranked AI-generated submissions and tried using them in production workloads. Many of them broke, sometimes in surprising ways. One of those kernels is the fused embedding-gradient + RMSNorm backward pass, which runs at the end of every transformer training step. We took the fastest submission on the benchmark for it, and dropped it into the training loop of a small transformer. The kernel had passed the benchmark's verifier with room to spare. But in our training run, the loss diverged and never recovered. We started debugging. Replace the dataset distribution with uniformly sampled tokens, the divergence vanishes. Swap SGD for AdamW, also vanishes. This is the worst kind of bug for research. Symptoms and masks both look exactly like "the idea didn't work". It's the type of bug that can make researchers spend a long time debugging without knowing what's at fault: the dataset? the research idea? the architecture? or the implementation itself? Turns out, the actual bug is that the embedding-gradient half of the kernel accumulates in bf16 instead of fp32. Embedding backward sums many small gradient contributions into each token's row of the embedding matrix. With uniform random tokens the contributions spread evenly and bf16 precision is enough. In real text, a handful of token IDs end up with thousands of contributions: the small ones round to zero against the growing accumulator, and the high-frequency rows drift. AdamW's per-parameter normalization absorbs the resulting multiplicative bias, so under AdamW the same drift is invisible in the loss. The other broken submissions had different bug shapes (all interesting). More examples in our blogpost . submitted by /u/laginimaineb [link] [留言]

I am wondering how are businesses integrating AI while protecting their data? submitted by /u/pappugulal [link] [留言]

These WIRED-tested computer speakers, from stereo speakers to surround sound, will suit any budget.

What comes to mind is you have some code that you want other ppl to be able to interact with, then you want to store data somewhere so you provision a database, then you need somewhere to host that code so you spin up a server then plop your code into that server and make it publicly accessible. I get that there are other parts like networking, security, RBAC/permissions, Linux commands, scalability/maintainability, API, cloud infrastructure, version and change management. But does it basically boil down to those three things: Code Database Server Thanks submitted by /u/throwaway0134hdj [link] [留言]

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AI email client built for focus. Runs locally on your Mac Discussion | Link