今日已更新 223 条资讯 | 累计 36701 条内容
关于我们

今日精选

HOT

最新资讯

共 36701 篇
第 1672/1836 页
AI 资讯 Reddit r/MachineLearning

LLM agents patch security bugs, pass all tests, but still leave the vulnerability open [R]

I built CVE-Bench: 20 real-world CVEs across 18 Python projects (Pillow, GitPython, yt-dlp, urllib3, others), 5 frontier models, 3 prompt conditions, 300 runs total. Each agent runs in a sandboxed container and is scored against a hidden test_security.py derived from the maintainer's own fix. Binary pass/fail (a 90%-patched vulnerability is still a vulnerability). To better understand failure modes, I've tested three prompt conditions : advisory (full GHSA report), diagnose (exploit description only, no file or function), and locate (exact file and function, no description of the flaw). The three conditions test meaningfully different things. A model that does well on advisory but drops on diagnose can’t translate a behavioral description into a location in the codebase. A model that holds up on locate is recognizing dangerous code on its own. The leaderboard isn't the finding. Best solve rate is 50% overall, 60% under advisory. Cross-family separation (OpenAI vs Laguna) is confirmed under McNemar's test with continuity correction (all four pairs cross α = 0.05). Within-family gaps are noise: a power analysis puts the task count needed to detect a meaningful within-family edge at ~700. That cuts both ways: if the expensive models had a large true advantage, 20 tasks would have been enough to surface it. gpt-5.5 at 12× the cost of gpt-5.4-mini is not the rational choice. All four cross-family pairwise comparisons reach statistical significance at α = 0.05 (McNemar test with continuity correction, n = 60 tasks per model pair): gpt-5.5 vs laguna-m.1 (p = 0.015), gpt-5.4-nano vs laguna-m.1 (p = 0.017), gpt-5.5 vs laguna-xs.2 (p = 0.028), gpt-5.4-nano vs laguna-xs.2 (p = 0.040). Within-family comparisons remain far from significance; those rankings should be read as approximate. The failure taxonomy is the most interesting finding. Wrong-search drift — model finds the right file early, makes one incorrect inference, spends the remaining turns chasing it. Budget expires,

/u/Fickle-Box1433 2026-06-02 16:38 5 原文
AI 资讯 Reddit r/MachineLearning

Browse CVPR 2026 papers on PapersWithCode [P]

https://preview.redd.it/se5nr2z7tt4h1.png?width=3046&format=png&auto=webp&s=7db15b73afb749da236e5bb50ff96372f6a3239b Hi, Niels here from the open-source team at Hugging Face. It's been 2 weeks since I launched paperswithcode.co , a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. I've just added conference support as a new feature. The idea is that you should be able to easily browse all papers of major AI conferences like NeurIPS, CVPR, and ICML. As CVPR 2026 takes place next week in Denver, USA, I've indexed all papers with corresponding arXiv IDs. They are categorized by task, and tagged with linked GitHub and project page URLs, Hugging Face artifacts, and evals. You can also browse the papers which were accepted for an Oral presentation as well as the Spotlight papers. You can try it at https://paperswithcode.co/conferences ! Feel free to leave feedback. submitted by /u/NielsRogge [link] [留言]

/u/NielsRogge 2026-06-02 16:32 6 原文
AI 资讯 Reddit r/artificial

What are the best 10 \ 20 buck coding ais left?

So basically Claude at 20 buck sub is not much better than free. Chatgpt. It is pretty much shit. Gemini. It seems to have some reductions to its abilities in the last couple of months as well. The 20 buck price range used to have lots of good ais. Now they are all limited, downgraded. What would be the king in this price range? I have found myself using gemini ai pro with other ais as free on the top of that. submitted by /u/aluode [link] [留言]

/u/aluode 2026-06-02 15:54 5 原文
AI 资讯 Reddit r/artificial

I wanna discuss medical ai researchs with major, and I improve my english skills. Please...discuss them helping my english skills

Hi, I'm prepare for a phd in the US in medical AI, so I want to improve listening and speaking skills. I warry about admission interview, and I like discuss research. I find male friends because I have a girlfriend. I don't want to make her worry. I find the friends online firstly. Please feel free to contact me. submitted by /u/CrazyIndependent7436 [link] [留言]

/u/CrazyIndependent7436 2026-06-02 15:52 5 原文
AI 资讯 Reddit r/MachineLearning

I scraped over 2 million job postings across 100,000+ company career sites into a unified, daily-updated dataset. [P]

Over the past few months, I've been working on a high-scale scraping pipeline to aggregate listings directly from company job boards and applicant tracking systems. Mapping over 100,000 distinct companies to their career pages turned out to be a massive engineering headache, but it's finally stable. The result is a unified database of more than 2 million active job postings, which I'm opening up to everyone for free. I am running daily delta refreshes to keep it current. Dataset Overview Scale: 2M+ active job listings across 100,000+ unique companies. Format: Parquet. (To keep storage costs to minimum) Core Fields: job_title, company_name, company_website, job_description, location, post_date, and the original tracking URL. For more detailed info check here . Update Cadence: Refreshed daily straight from the source. View the stats here . (Currently it contains only minimal stats, but I plan on improving it based on the comments) Why I Built This Finding a clean, scaled, and up-to-date job dataset is surprisingly difficult. Most available options are either heavily gatekept by expensive subscription APIs or restricted to a single job board like LinkedIn. By scraping the actual employer sites directly, this collection sidesteps the noise and captures a much cleaner cross-section of the live market. How to Access It I set up a dedicated project space where you can grab the data directly: Open Job data Let me know what kind of analysis or projects you end up running with it. If you have questions about the engineering architecture behind handling this scale, or ideas for specific fields you'd like to see enriched next, let's discuss in the comments. submitted by /u/Invicto_50 [link] [留言]

/u/Invicto_50 2026-06-02 15:14 5 原文
AI 资讯 Reddit r/webdev

my Supabase and Snowflake has a spike every time I add a new drill-down feature tomy small analytics dashboard... any tips?

I've been building out embedded reporting for b2b clients (i use next.js), and as our user base grows, the P95 latency is becoming a nightmare. Every time a user changes a date filter, it triggers a fresh compute spin-up on the warehouse. I’m currently refactoring the stack to put a universal semantic layer (in my case, cube core) between the frontend and the database. and the goal is to move all the logic, like multi-tenant row-level security (RLS) and complex joins, out of the React code itself and into a declarative modeling layer since I started, the biggest win (so far) is using pre-aggregations. now, instead of hitting raw tables, the API hits a warmed caching tier which is in the 'cubestore'. it feels more like querying a structured API than a database for those of you here who do high-concurrency analytics in SaaS, a question! are you just throwing more money at warehouse compute, are you moving toward this kind of decoupled architecture, what other best practices do you have? now trying to figure out if I should stick with this or just move everything to a huge ClickHouse instance and hope for the best submitted by /u/sivyh [link] [留言]

/u/sivyh 2026-06-02 15:04 5 原文
开发者 Reddit r/webdev

DOCUMENTATION · BLOG · SEARCH (No Nodejs/ NPM Dependencies)

Inspiration Build a simple and modular technical documentation and blog. Build the website without using node/npm or any external frameworks(CSS, JS, icon, font). Only GoHugo normal binary + pageFind binary needed. Features [x] Responsive and adaptive layouts. [x] Support for light and dark modes. [x] Support for multiple documentation sets. [x] Support for a blog. [x] Implement a menu via Hugo configs. [x] Customizable sidebars using Hugo data templates. [x] Plug-and-play/ repeatable home page blocks using separate partials/ css/ data templates. [x] Integrate a search via Pagefind. [x] Inline SVG icons based on Google's Material Symbols GitHub Repo: https://github.com/dumindu/E25DX Example Docs: https://github.com/dumindu/E25DX/tree/gh-pages/example Example Live: https://dumindu.github.io/E25DX/ submitted by /u/dumindunuwan [link] [留言]

/u/dumindunuwan 2026-06-02 15:02 7 原文