It starts at just $599 for students or $699 for everyone else.

Alienware is also releasing an updated 34-inch ultra-wide alongside new affordable 32 and 34-inch displays.

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/wineandcode [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]

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

You've seen the developer. Maybe you are the developer. They discover DRY — ✨ Don't Repeat Yourself...

In May, I continued my "One Commit a Day" Challenge and spent more time contributing across different open-source projects. Compared to April, I was able to contribute a bit more and explore a wider variety of repositories. Repositories That Stood Out Some of the projects that left the biggest impression on me were: python-odpt Huggin Face Context Course Human Signal ML ScribeSVG A Stable Checkpoint One milestone I was happy about this month was reaching a stable checkpoint for my Tokyo MCP Server project. It is still a work in progress, but getting to a point where the project feels stable enough to build upon was a satisfying moment. Documentation Matters Another contribution that stood out was helping improve a python-odpt README documentation . It wasn't a large technical contribution, but it reminded me that making a project easier for others to understand can be just as valuable as writing code. Good documentation lowers the barrier for future contributors. Sometimes, a clearer README can help more people than a small code change. Learning Beyond Python One practical lesson I learned this month was that being a Python-focused contributor doesn't mean I can ignore the JavaScript ecosystem . While working with different repositories, I finally installed Node.js and started using npm . Many modern open-source projects rely on TypeScript-based tooling, build systems, or development workflows, and understanding those tools makes contributing much easier. The Biggest Challenge: Finding Information And Communication Matters The biggest challenge I faced wasn't coding. It was documentation. Every repository has its own way of organizing information. There are definitely common patterns, but every project also develops its own style over time. Sometimes the information I need is in the README. Sometimes it's in a wiki. Sometimes it's buried in a docs folder several levels deep. And sometimes it's spread across all three. Open Source Is Also About Navigation As a contri

Local Natural Language Search Across All Your Files. Discussion | Link

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

This online game helps you learn keyboard shortcuts for a bunch of different apps/software including VS Code and VIM! submitted by /u/ColterRobinson [link] [留言]

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