惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Security Archives - TechRepublic
Security Archives - TechRepublic
T
The Exploit Database - CXSecurity.com
P
Proofpoint News Feed
Scott Helme
Scott Helme
NISL@THU
NISL@THU
Cisco Talos Blog
Cisco Talos Blog
C
Cybersecurity and Infrastructure Security Agency CISA
AWS News Blog
AWS News Blog
V
Vulnerabilities – Threatpost
J
Java Code Geeks
U
Unit 42
The GitHub Blog
The GitHub Blog
H
Help Net Security
T
Tenable Blog
aimingoo的专栏
aimingoo的专栏
Jina AI
Jina AI
Spread Privacy
Spread Privacy
Apple Machine Learning Research
Apple Machine Learning Research
人人都是产品经理
人人都是产品经理
L
Lohrmann on Cybersecurity
T
Threatpost
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Engineering at Meta
Engineering at Meta
A
About on SuperTechFans
I
InfoQ
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog
L
LINUX DO - 最新话题
K
Kaspersky official blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
T
Threat Research - Cisco Blogs
C
Check Point Blog
T
The Blog of Author Tim Ferriss
有赞技术团队
有赞技术团队
宝玉的分享
宝玉的分享
Help Net Security
Help Net Security
Google DeepMind News
Google DeepMind News
A
Arctic Wolf
Y
Y Combinator Blog
N
News | PayPal Newsroom
M
MIT News - Artificial intelligence
Latest news
Latest news
H
Hacker News: Front Page
Blog — PlanetScale
Blog — PlanetScale
腾讯CDC
I
Intezer
爱范儿
爱范儿
F
Fortinet All Blogs
P
Palo Alto Networks Blog
C
CERT Recently Published Vulnerability Notes

Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor GitHub - GenAI-Gurus/awesome-eu-ai-act: Curated tools, official sources, OSS, templates, and guides for EU AI Act compliance. Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders How to Switch AI Chatbots and Why You Might Want To GitHub - MattMessinger1/agentic_refund_guardrail: Safe refund policy layer for AI agents — Python + TypeScript. Same behavior, shared tests. Adam/papers/emergent_values_whitepaper.md at master · strangeadvancedmarketing/Adam Ask HN: How do you stop playing 20 questions with your AI coding tools How far can automation and AI support psychotherapy? - @theU GitHub - stagas/rtdiff: realtime git diff gui and AI-assisted commits A Mac Studio for Local AI — 6 Months Later A History of the Early Years of AI at the University of Edinburgh Why AI Coding Tools Still Feel Stuck on Localhost MSN AI Datacenters Are Becoming Strategic Targets twitter.com Penn Researchers Use AI to Surface Unreported GLP-1 Side Effects in Reddit Posts Show HN: MoodSense AI (ML and FastAPI and Gradio, Deployed on Hugging Face) Moodsense Ai - a Hugging Face Space by aman179102 AI models are terrible at betting on soccer—especially xAI Grok GitHub - xialeistudio/echoic GitHub - HimashaHerath/github-dev-wrapped: AI-powered weekly GitHub activity reports deployed to GitHub Pages GitHub - alejandrobalderas/claude-code-from-source: Architecture, patterns & internals of Anthropic's AI coding agent — reverse-engineered from source maps AI and Tech brief: Ireland ascendant GitHub - Titovilal/context0: Context0 - Never Surrender Training for a Marathon with an AI Coach: What Worked and What Didn't Cyber Pulse: Agentic Intel - Apps on Google Play I Built an AI PR Reviewer That Catches Bugs by Not Looking for Bugs Gen Z workers are so fearful AI will take their job they’re intentionally sabotaging their company’s AI rollout | Fortune How AI Is Reimagining the Game of Golf–For Both Players and Courses GitHub - nattergabriel/reseed: A CLI tool for managing and distributing agent skills across projects Is SVG the final frontier? My AI workflow evolved from prompts to a near-autonomous workflow MLSharp Help - 3DGS Viewer & Generator I put my cognitive field based AI's runtime on GitHub Is Numble the first AI-proof game? A3: Kubernetes for autonomous AI agent fleets | Emergent Principles Deepali Vyas ("The Elite Recruiter") GitHub - msmarkgu/RelayFreeLLM: A restful API designed to route user prompts to various AI model providers. Unionized ProPublica staff are on strike over AI, layoffs, and wages Unleashing the Advantage of Quantum AI We're heading for an AI-fueled 'dementia crisis,' brain scientist warns The AI-Assisted Breach of Mexico's Government Infrastructure [pdf] GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. MSN GitHub - visionscaper/collabmem: Enabling long-term collaboration with Agentic AI - building up episodic and world model memory over time with in-context awareness We gave an AI a 3 year retail lease in SF and asked it to make a profit | Andon Labs AI Code is Hollowing Out Open Source, and Maintainers are Looking the Other Way What leaked "SteamGPT" files could mean for the PC gaming platform's use of AI AI is the boss at this retail store. What could go wrong? GitHub - Wuzu11517/agentic-proxy: Local proxy meant to help reduce With Drones, Geophysics and ArtificiaI Intelligence, Researchers Prepare to Do Battle Against Land Mines A Single Operator, Two AI Platforms, Nine Government Agencies: The Full Technical Report 在 Steam 上购买 FriedrichAI: Offline AI 立省 10% GitHub - inevolin/resume-cli: Hit Claude usage limits? Resume any AI coding session elsewhere. Switch tools at zero friction. GitHub - atripati/ark: AI Runtime Kernel — a context operating system for AI agents. Eliminates tool bloat, loads only what’s needed, and gives LLMs their reasoning space back. How to Build a Secure AI PR Reviewer with Claude, GitHub Actions, and JavaScript This Startup Wants You to Pay Up to Talk With AI Versions of Human Experts Intel Arc Pro B70 Brings 32GB VRAM to Local AI for $949 WordPress 7.0: The Good, the AI, and the Still Missing AI on the couch: Anthropic gives Claude 20 hours of psychiatry IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures AI Agents Know About Supabase. They Don't Always Use It Right. The history and future of AI at Google, with Sundar Pichai Inside an AI‑enabled device code phishing campaign How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines AI for Systems: Using LLMs to Optimize Database Query Execution Forecasting the Economic Effects of AI Introducing Tinker: Play with AI, bring your ideas to life AI sheds light on an ancient gaming mystery People really hate AI but not as much as Iran—or Democrats | Fortune What is an AI Product Engineer? Phoebe Gates wants her $185 million AI startup to succeed with 'no ties to my privilege or my last name': 'I have a chip on my shoulder' | Fortune
Design prompting: describe the world, not the widget
pancomplex · 2026-05-22 · via Hacker News - Newest: "AI"

Animations are hard to describe. Most of us don't have the vocab (keyframes, easing curves, durations) to spell out what we want, so we end up typing things like "make it pulse on hover, kind of a blue glow, soft, not too aggressive" and the model gives back something fine. Not what we wanted. We try again, hedge a few words, get something else fine. The loop is real.

The approach we've been getting good results from doesn't require learning that vocabulary. You describe a world for the animation to live in and let the model derive the timing and feel from there.

The story behind this post

A few weeks ago I was working on the highlight inside our assistant. The product is built around a small blue ping that travels across the screen and lands on the element it's pointing the user toward. Customers told us the landing didn't register clearly enough. The ping reached the target, but the moment of arrival was too subtle to feel.

The next version added an animated treatment on the border of the target element, triggered on landing. My first few drafts described the new treatment as a list of properties: an outline that grows, a glow that fades, a color, a duration. What came back looked fine and felt flat. I switched to describing the motion as a physical thing, something with weight and travel time and a place it starts and a place it ends. The version that came back from that clicked on the first try.

The move that mattered was switching from enumerating properties to describing a physical thing. The loop ended on the next try.

What metaphors actually do for an LLM

There's a mechanical reason this works. A metaphor ships with a coherent world attached, and a property list doesn't. When you tell a model "make it pulse softly, blue glow," you've given it a list of properties to satisfy and nothing to connect them. The model picks defaults for everything else and the result feels stitched together.

Try the other way. Imagine a stone trough that runs along the top of a castle wall in a siege, the kind defenders pour glowing liquid into. The liquid travels along the trough until it spills out the far end. The glow is the only thing alive on the wall. Everything else holds still.

A castle channel is slow because the liquid is heavy. A castle channel glows because the liquid is hot. A castle channel ends when the trough does. None of those are details the model had to invent.

There's a second thing the metaphor buys you: density. Describe an animation literally and you are stuck with a bad trade. Either you enumerate every property, which means knowing it exists and what it is called (the exact filter, the easing curve, the color stops), or you keep it short and let the model guess the rest. A metaphor refuses the trade. "Molten metal" is two words carrying temperature, color, weight, flow, and the way the glow dies when the source cuts off. That is the densest way to hand the model a world, and it never makes you name the parts.

Lakoff and Johnson made this argument forty years ago about humans, in Metaphors We Live By. We organize abstract reasoning in the shape of physical experience. The argument works on language models for the same reason it works on us: most of what we know is stored as worlds, not as specifications.

Try it: a button

Here's a plain button. No animation, no hover state.

We asked two separate Claude instances to animate it on hover. No shared context, same starting button.

Prompt A is how most of us actually describe animations:

On hover, do a little ring of gradient around the outside of the button. The ring should start from where the cursor enters and flow both directions until it meets on the opposite side.

Prompt B swaps the description for a world:

On hover, do a little ring of gradient around the outside of the button. Imagine the button border is a channel, and molten liquid is flowing through the channel and filling the perimeter. The liquid first starts from where the mouse enters the button, and flows both directions until it meets.

What came back from A (hover over the button):

What came back from B (hover over the button):

A spins. The gradient is a rotating loop of purple, pink, and amber, with a small gap that travels around the perimeter forever. The cursor entry sets where the gap starts. It looks fine, but it's not what I asked for. The prompt said "flow both directions until it meets on the opposite side." A didn't do that. It defaulted to a fancy-border pattern and never built a stopping point.

B did what I asked. The molten gradient starts at the cursor, flows both ways around the rim, meets on the far side, and holds. When the cursor leaves, it drains in reverse. It's also orange and red, with a soft heat glow around the rim. None of that color or glow was in the prompt. The metaphor said "molten liquid," and the model knew what molten looks like.

The literal prompt gave the model a list of behaviors to satisfy. It satisfied "ring of gradient" and "cursor entry," then reached for the nearest familiar pattern for everything else. The metaphor gave it a world that already had rules: liquid fills a channel and stops, liquid drains when the source goes away, molten metal glows orange. The model rendered the world, and the behaviors fell out of it.

The principle

The fastest way to get a coherent artifact out of a language model is to hand it a coherent world to draw it from. A spec gives the model a list of constraints. A metaphor gives it a generator. The list will produce something that satisfies each constraint locally and lands dead on the page. The generator will produce something that holds together because every detail came from the same source.

This shows up most clearly in animation, because animation is the part of the interface where every micro-decision (timing, easing, color temperature, rest) compounds into either coherence or noise. But it generalizes. Anywhere the work depends on a hundred small choices fitting together, telling the model the world the work lives in is cheaper than telling it the choices.

Next time you fire up your favorite LLM to animate something, write a sentence about a castle wall instead of a list of keyframe instructions. Hover the result. You'll see what I mean.