AI 资讯

Repository: public-apis/public-apis What public-apis actually is public-apis is a community-curated directory of free and public APIs, maintained by contributors together with staff at APILayer. It is not a library, SDK, or gateway: there is no package to import and nothing to run in production. The repository is essentially one very large, structured README that catalogs APIs across roughly fifty categories, from Animals and Anime to Finance, Machine Learning, Security, and Weather. Each entry is a row in a table with five columns: the API, a short description, the authentication model ( apiKey , OAuth , or none), whether it serves over HTTPS, and whether it sets permissive CORS headers. That last detail is the part most engineers undervalue. Why engineers keep coming back to it The star count, now past 438,000, is less interesting than the metadata discipline. When you are prototyping and need a currency-exchange or geocoding endpoint, the Auth/HTTPS/CORS columns let you filter candidates before you ever open a browser tab. "No auth, HTTPS yes, CORS yes" tells you that you can call the endpoint directly from a front-end spike without standing up a proxy or registering for a key. For throwaway demos, hackathons, internal tools, and teaching material, that triage saves real time. The category index doubles as a map of what kinds of public data are actually available, which helps when you are scoping whether an idea is even feasible. How it is maintained Curation is manual and community-driven: changes arrive as pull requests against the README, governed by a contributing guide, with issues and PRs as the moderation surface. The project's primary language is Python, reflecting validation tooling that checks entries rather than any runtime you would consume. There is also a separate companion project that exposes the list itself as an API. The model is simple and has clearly scaled, but "manually curated" is both the strength and the weakness. Limitations worth statin

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Street-Gate7322 [link] [留言]

Computex 2026 is underway in Taiwan, and we're expecting all manner of flashy computers with jaw-dropping pricetags (or no pricetags at all) as the entire industry navigates RAMageddon. But for desktop PC gamers, AMD has a different pitch. It's relaunching three old components alongside a big new promise: you won't need to buy a new […]

The Radeon RX 9070 GRE is finally available outside of China

If you're a fashion or DTC brand doing under €50M GMV, you'll get more out of a monolithic platform than a composable build, because the complexity tax for that flexibility runs higher than a year of performance marketing for most brands at that tier. Composable was sold as freedom, but for most brands at small-to-mid GMV it lands as engineering-org overhead you fund forever just to keep the lights on, while monolithic platforms ship 80% of what most brands need with one team and one contract. The exception is brands running multi-brand groups across countries or genuine enterprise peaks, where composable does pay off, but you want the platforms built by retailers rather than the ones built for analyst slides. Centra, NewStore, and SCAYLE land closer to that brief than commercetools or open-source MACH stacks. If a vendor opens their pitch with microservices and best-of-breed architecture, they're selling you an engineering project, and most brands just need a platform that ships things. submitted by /u/Majestic_Shoulder188 [link] [留言]

The AI agent that handles checkout. Discussion | Link

Alienware is taking to this year's Computex 2026 in Taipei to announce some cool gaming monitors, most notably two exciting OLED options that are coming at different points this year. First off, the company is debuting the successor to its very first QD-OLED gaming monitor from 2022 with a refreshed design and high-end specs that's […]

You can’t see AC comments yet, but you can see the Accept/Reject consoles. My paper (with scores of 8,6,3) got rejected. submitted by /u/GeeseChen [link] [留言]

submitted by /u/Mushroom-Severe [link] [留言]

seems that the power markets are not able to keep up with all these demand data centers coming online even with all of the new power plants and renewables coming online. will the grid be able to keep up with all these data centers and will ai developments be affected by it? submitted by /u/FF430 [link] [留言]

Real API data in your mockups made as easy as lorem ipsum. Discussion | Link

I do a fair amount of competitor backlink research, and the workflow always annoyed me: open a dashboard, run a query, export a CSV, eyeball it, copy domains into a doc, switch to email. Lots of tab-hopping for what is fundamentally a data-filtering problem an agent should handle. So I wrapped the backlink API I'd been using into an MCP server. Now I stay in Claude Code (or Cursor, Cline, Zed, Windsurf) and just describe the goal. This is the build: the architecture, the four tools, and the one design decision I'm still not sure about. The data source The server runs on the Common Crawl hyperlink webgraph — about 4.4 billion edges across 120 million domains, published quarterly as Parquet. That matters for an MCP tool specifically: the data is open, so there's no scraped-proprietary-index liability in handing it to an agent, and the same query is reproducible by anyone. The HTTP API in front of it ( CrawlGraph ) does the heavy DuckDB work; the MCP server is a thin TypeScript stdio client over it. Keeping the server thin was deliberate — all the query cost, caching, and quota logic lives server-side, so the MCP package stays a ~300-line wrapper that's easy to audit before you hand it your API key. The four tools backlinks → referring domains for a target, with authority scores gap_analysis → domains linking to your competitors but not to you gap_outreach_targets → the composite play (below) releases → list the Common Crawl snapshots backlinks and gap_analysis map 1:1 to API endpoints. gap_analysis is the interesting primitive: submit your domain plus 2-5 competitors, and it returns every domain that links to at least one competitor but not to you, each tagged with a found_on array listing which competitors it links to. The composite tool, and the decision I'm unsure about Most API-wrapper MCP servers are pure 1:1 mappings. I added one opinionated composite tool, gap_outreach_targets , because the raw gap output isn't the thing you actually want — it's the raw materia

LangGraph Production, RAG Memory Challenges, and AI Agent Patterns Today's Highlights Today's highlights dive into practical LangGraph pipeline construction for agentic AI workflows, reveal critical insights from real-world RAG retrieval failures, and unveil 29 open-source design patterns for building robust AI agents. Building Your First LangGraph Pipeline: A Decision-Maker's Guide (Dev.to Top) Source: https://dev.to/labyrinthanalytics/building-your-first-langgraph-pipeline-a-decision-makers-guide-4e25 This article serves as a comprehensive guide for developers looking to implement their first LangGraph pipeline for agentic AI workflows. LangGraph is highlighted as a leading framework for building complex, stateful multi-actor applications, particularly valued for its production readiness and active maintenance. The guide aims to demystify the initial setup and design choices, providing a structured approach for integrating LangGraph into real-world applications. It addresses the common challenges and decision points faced by teams adopting new AI orchestration frameworks, ensuring a smoother development process. The piece emphasizes the practical considerations for building robust and scalable AI agents. It likely delves into architectural patterns, state management within agentic systems, and how to effectively sequence different AI models or tools into a cohesive workflow. For those focused on production deployment, the guide would cover best practices for reliability, testing, and potential optimizations when scaling AI agents. By offering a "decision-maker's guide," it goes beyond mere syntax, encouraging readers to think critically about the implications of their design choices for long-term maintainability and performance in applied AI contexts. Comment: LangGraph is a critical tool for serious agentic AI development; this guide to building pipelines and making early design decisions is exactly what many developers need to get started right. I Published an A

Apple isn't just looking to take on Meta in the smart glasses market; it's looking to upend eyewear as a whole, according to Bloomberg's Mark Gurman. When the Apple Watch launched, it wasn't simply competing against the Pebbles and the Motorolas of the world. The company also had Swatch, Fossil, and Seiko in its crosshairs. […]

1. Introduction As the golden standard of secure remote access , the Secure Shell (SSH) protocol has several layers of protection. One of them involves recording and keeping track of the known servers on the client side. known_hosts By default, the known_hosts file for a given user is located at: cat /home/user_name/.ssh/known_hosts github.com ssh-rsa *** github.com ecdsa-sha2-nistp256 *** github.com ssh-ed25519 *** Basically, the file contains a list with several columns, separated by whitespace: Identifying host data Host key type Host key value Optional comment The first column can be hashed or cleartext, depending on the setting of HashKnownHosts in /etc/ssh/ssh_config . When hashed, the first field of each line starts with |1| , a HASH_MAGIC marker. After the latter, the field continues with a random 160-bit string, otherwise known as a salt, followed by a 160-bit SHA1 hash. Each of these is encoded in base64 . The main idea is to hide the IP address or hostname data, which would otherwise be directly visible Either way, known_hosts contains a mapping between a server as identified by its characteristics and its key . ## Known Hosts Checking When connecting to a remote host, SSH checks the known_hosts file of the client to confirm the address or hostname for the server match the key we get from it . If there is a match, the session setup can continue. Otherwise, we get an error. The entry for 192.168.6.66 in the known_hosts file doesn’t match the (Elliptic Curve Digital Signature Algorithm, ECDSA ) key we got back from the server at that address . Critically, if we don’t know what caused the error, we should heed the text in capital letters: something nasty can indeed be happening . On the other hand, the reasons for such an issue can be valid and trivial: dynamic IP address changed hostname reinstalled system reinstalled SSH Docker container misconfigured DHCP relocated client In fact, there can be many more. ## Bypass Known Hosts The error text when connectin

So I have a super busy job and I am by far the fastest out of the 3 others who have the same job as me. Problem is I have enough work where i could literally work 70-80 hours a week and still not catch up. Ive been using Chatgpt and Claude to help with my work load and ive found Claude to be much better for my actualy job duties. But Claudes usage caps kill me. I really need the best AI for basically being a work assitant. I need something that can create spreadsheets, analyze data, read emails, sort thru photos and catalog them. Grok was not really any help, Chatgpt is just meh, but ive found Claude to be the best out of what im looking for but again its usage limits kill me and i cannot afford to pay for the overages. Im already a pro user for chatgpt and claude. What AI can do the things im asking the best for the best price and usage? Most important to my work in order of most important to least: Photo cataloging, analyzing data, spreadsheet creation, and summarizing emails. submitted by /u/JumpyChemistry [link] [留言]

Looking for a local AI solution for film dubbing / audio sync correction (offline if possible). I have a foreign movie with an English audio version, but the video is low resolution and the audio timing slowly drifts out of sync over time. If I manually align it at the start, it gradually becomes offset, so I suspect there are missing/extra segments or timing inconsistencies. What I need is a tool or workflow that can: Listen to the video/audio track Detect dialogue timing Automatically realign or stretch/squeeze audio to match speech in the video Correct drift issues over long duration files (full movies) Online tools often fail due to file size/length limits, so I’m specifically looking for local software or AI models that can run on a PC . Any suggestions for tools, pipelines, or approaches appreciated. submitted by /u/dotmerlin [link] [留言]

Stepping up to give my first-ever presentation at UbuCon 2026 was a massive milestone, and honestly, it was pretty intimidating. The stakes felt high, especially with the live demo. It was a race against the clock to get everything running, and it only finally came together exactly ten minutes before I went on stage. Talk about a close call. While I am proud of what I delivered, I originally wanted to pack even more into the session. I had planned to showcase a simulated mission, Gazebo visualizations and RViz path simulations. While time caught up with me for the presentation, these features are still actively in the works over at the aeronix project. My goal is to have the entire end-to-end setup completed and ready by the end of the year. I connected with some incredible engineers and industry peers and I am looking forward to building on those conversations for future professional collaborations. This experience proved that the best way to grow is to just put yourself out there. Moving forward, I plan to keep speaking on topics that challenge me. It is the ultimate way to deepen my own technical understanding share what I have learned with the community and grow professionally.

submitted by /u/AdvertisingFancy7011 [link] [留言]