AI 资讯

Some engineering problems are only painful because they happen so rarely. Even with a coding agent, the frustration still feels the same. I’ll wrestle with a tool that isn't my daily driver, hit a wall of errors, finally find a resolution, and then I neglect to note the solution because the problem is "fixed." This happened to me recently with a custom, internal GitHub Actions workflow I use for a post-release DevRel task. To make it work, I need to pass a specific authentication token to run Entire in a headless mode. A couple of months ago, I sat down with my AI agent to configure this for the first time. Because Entire is new and our setup is completely undocumented, it took a grueling trial-and-error process to figure out how to generate the token via a local device-flow login. Eventually, we found the answer. I pasted the token into my GitHub secrets, the workflow turned green, and I went about my day without writing anything down. I rarely take notes now that I use coding agents, but it’s not a sustainable practice. I need something to take note of the resolution (even if it’s my agent). Today, when the token expired, I was back at square one. Why I Didn’t Make a Skill Normally, my instinct is to automate repetitive tasks by building a reusable workflow, like an Agent skill or a goose recipe. But a dedicated skill didn't make sense here: Low Frequency: This happens once every few months. Writing and maintaining code for a skill I barely use is textbook over-engineering. Security & Context: Generating an auth token involves sensitive device flows. I didn’t want a generic token-generation script floating around in my global automation suite. I wished my agent had a memory, so I could ask “ How did we get that GENERIC_ENTIRE_TOKEN last time?" and have it recall the context. Instead, I spent an hour re-debugging a problem I had already solved. It was just my agent and me making guesses. To break the loop before the next expiration, I decided to use Entire. (At the

SQLite ON CONFLICT DO SELECT Proposal, PostgreSQL 19 Features & SQLite Critical Bug Today's Highlights This week in databases, a proposal seeks to expand SQLite's ON CONFLICT clause to match PostgreSQL 19's DO SELECT for advanced conflict resolution. Concurrently, an early look at PostgreSQL 19 highlights key features improving performance and data management, while a critical out-of-bounds read bug in SQLite's fossildelta.c extension reminds us of the importance of low-level code security. Request: Support "ON CONFLICT DO SELECT" to match Postgres 19 (SQLite Forum) Source: https://sqlite.org/forum/info/81840ccfecf0885ba4418152d6c7f164de00d189b2cf7c682690151b0 This forum post proposes extending SQLite's ON CONFLICT clause to include a DO SELECT action, mirroring a feature anticipated in PostgreSQL 19. Currently, SQLite supports DO NOTHING and DO UPDATE for handling unique constraint violations. The proposed DO SELECT would allow an application to retrieve existing rows that caused the conflict, providing more granular control over conflict resolution beyond simply ignoring or updating the data. This feature would be particularly useful in complex data pipelines or replication scenarios where knowing which existing data caused a conflict is necessary for subsequent application logic, such as logging, merging, or initiating alternative processing paths. The discussion highlights the growing convergence of SQL features across different database systems and the desire for enhanced compatibility and expressiveness in SQLite. Implementing DO SELECT would empower developers to build more robust and intelligent conflict resolution strategies directly within their SQL statements, reducing the need for multi-step application-side logic involving separate SELECT queries after a failed INSERT or UPDATE attempt. Such an addition could streamline data ingestion processes and improve transactional integrity for embedded SQLite applications. Comment: This feature would be a game-ch

Introduction and Background The Rust ecosystem, celebrated for its memory safety and performance, relies heavily on crates —its package management system. These crates, hosted on the crates.io registry, are the building blocks of Rust projects, enabling developers to share and reuse code efficiently. However, this convenience comes with a hidden cost: a single compromised crate can cascade into a full-blown supply chain attack, as recently demonstrated by the 'onering' crate compromise . The Role of Crates in the Rust Ecosystem Crates serve as the backbone of Rust's dependency system. When a developer adds a crate to their project, Cargo , Rust's package manager, automatically resolves and includes all transitive dependencies. This mechanism, while streamlining development, amplifies the attack surface . A malicious crate, like 'onering', can propagate through multiple projects, executing harmful code during build or runtime. The attack on 'onering' exploited this very mechanism, leveraging the trust inherent in the Rust ecosystem to exfiltrate sensitive code from unsuspecting systems. The 'onering' Compromise: A Case Study in Supply Chain Vulnerability The 'onering' crate was compromised through a malicious code injection during an upload or update. This injection bypassed the insufficient security measures of the crates.io registry, which lacks robust checks for uploaded crates. Once deployed, the malicious code executed during the build process, exfiltrating sensitive data from systems that depended on it. The attack's success underscores the lack of developer vigilance regarding supply chain security and the overreliance on registry integrity . Systemic Failures and Their Mechanisms Insufficient Security Scanning: The crates.io registry failed to detect the malicious code during upload due to limited automated scanning capabilities . This allowed the compromised crate to enter the ecosystem undetected. Overreliance on Registry Security: Developers often trust cr

CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform

The ShinyHunters hacking gang claims to have compromised the Oracle PeopleSoft servers of more than 100 organizations, including many universities.

Lawsuit: "Police let an error-prone AI system stand in for an investigation."

Microsoft's Xbox division will be hit with significant layoffs next month, according to people familiar with Microsoft's plans. The company has been preparing for the layoffs internally for weeks, with Xbox CEO Asha Sharma hinting about "making hard choices" last month. Sources suggest the cuts could even involve a studio closure, or changes to the […]

Hello everyone, tomorrow I have a meeting with my dissertation supervisor and I wanted to have a dissertation proposal ready. Initially, I moved forward with the following proposal: "Interpreting the Routing Dynamics of Capsule Networks for Explainable AI." My first approach to this topic was to study the paper "Transforming autoencoders," which is the first paper about capsule networks. Next, I did a search on the state of the art of transforming autoencoders and only found 2 papers since 2011. I think I should take advantage of the work I have developed so far on transforming autoencoders and write a dissertation about them. If anyone could take a look at the readme and tell me what they think, I would appreciate it. What do you think? I should suggest another topic involving transforming autoencoders. There isn't much scientific research on them. The professor is approachable, and if I present a good new topic, he'll let me change it! submitted by /u/Future-Persimmon5393 [link] [留言]

I build agents for work so I'm clearly not anti-AI, but the numbers keep bothering me, concerning the environmental factors of it. Every datacenter is the same now, gigawatts of new demand, water for cooling, grids that weren't designed for any of this, and rising cost of water for cities. And the data centers keep using clean water because of lack of technology to turn dirty water into usable water for cooling. Then I see Elon talking about putting data centers in orbit, solar powered, radiating heat into space, no water needed. And while I do think its going to be the end solution, I do think we have much more demand for compute power then Elon can provide so far with the space data centers and I think the demand is growing faster than Elon can provide Is efficiency improving fast enough to outrun demand? Are space data centers a real answer or a distraction that will fail? And is anything happening right now (smaller models, better scheduling, offsets) that you'd call an actual solution rather than PR? That people can use today to make an impact submitted by /u/Swift_lunatic_2604 [link] [留言]

After 11 years building e-learning software at a major enterprise, here’s what surprised me when we started shipping GenAI features to real users. The Starting Point Nobody Talks About Most GenAI integration blog posts start with a clean slate — a greenfield app, a fresh codebase, a blank canvas. Real life is messier. When we began integrating generative AI into our e-learning authoring tool, we weren’t starting from scratch. We were dealing with a mature enterprise product — millions of users, legacy architecture decisions made years ago, compliance requirements from Fortune 500 customers, and LMS interoperability standards (SCORM, xAPI) that were designed long before anyone imagined AI-generated content. The challenge wasn’t “how do we call an AI API.” It was “how do we ship AI features into a product that thousands of instructional designers depend on daily, without breaking their workflows or their trust.” Here’s what I learned. Lesson 1: The AI Feature Your Users Want Is Not the One You Think When we first scoped out AI integration, the engineering team gravitated toward the flashy stuff — generate an entire course from a prompt, auto-create assessments, AI-powered slide design. Then we talked to actual users. Instructional designers didn’t want AI to replace their expertise. They wanted it to eliminate the tedious parts of their workflow: Reformatting content across different output types (responsive HTML5, PDF, SCORM packages) Generating alt-text for hundreds of images in accessibility-compliant courses Summarizing lengthy SME-provided documents into digestible learning chunks Suggesting quiz questions from existing content (not generating courses from nothing) The takeaway: don’t let engineering excitement drive your AI feature roadmap. Do 10 user interviews before writing a single line of integration code. The highest-impact GenAI features are usually the boring ones. Lesson 2: Prompt Engineering Is a Product Decision, Not an Engineering Task We initially t

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [留言]

“Defenders cannot afford to take weeks to patch,” one Cybersecurity and Infrastructure Security Agency official warned on Wednesday.

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

Companies are burning through exorbitant sums of money to keep pace in the AI arms race. Debt is climbing.

In these modern harrowing times, more and more posts are proving to be AI slop, and many people are averse to that. Do you think it would it be useful to have a way to indicate that you believe a post is slop separate from the existing flag button? Maybe it's just me but I feel like having a dedicated [AI slop] flag on a post would be useful because I feel differently about posts that have been flagged for other reasons than that. (or something friendlier if value is seen in the general idea but

Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.

We played the opening prologue of the horror sequel, and it's still got ways to bring the scares.

North Korean hackers posing as remote IT workers and recruiters remain a major threat to U.S., European, and Asian companies, accounting for about half of all attacks over the past 12 months.