标签：#m

Stanford Just Published Rules for AI Coding Agents — What Devs Should Know Stanford dropped a document last week that every developer using AI coding tools should read. It's called CLAUDE.md , it's part of CS336 (Language Modeling from Scratch), and it's a brutally honest set of rules for how AI agents should — and shouldn't — help students write code. The document hit #1 on Hacker News for good reason. It doesn't just apply to students. If you use Claude Code, Cursor, Copilot, or any AI coding assistant, these rules expose the uncomfortable gap between what these tools can do and what they should do. GitHub just rolled out token-based billing for Copilot, and developers are furious. The tension is the same: when does AI assistance stop helping and start hurting? The Core Principle: Teaching Assistant, Not Solution Generator Stanford's position is unambiguous: "AI agents should function as teaching aids that help students learn through explanation, guidance, and feedback — not by completing assignments for them." This isn't academic hand-wringing. It's a design constraint that maps directly to professional development. The same agent that writes your PR in 30 seconds is also the one that leaves you unable to debug it when it breaks at 2 AM. The AI agent role framework from Stanford's CS336 guidelines: teaching assistant vs solution generator The document draws a hard line: What agents SHOULD do: Explain concepts by guiding toward understanding Review your code and point out areas for improvement Ask guiding questions instead of giving fixes Reference documentation, lectures, and debugging tools Suggest sanity checks, assertions, and profiler investigations What agents SHOULD NOT do: Write any Python or pseudocode Complete TODO sections in assignments Give solutions to problems Edit code in the student repo Convert requirements directly into working code Point to third-party implementations If you're a professional developer, the "SHOULD NOT" list probably looks extr

I learned this the hard way: when an LLM says “it matches the docs”, it can still be wrong for a boring reason—it didn’t read the part that matters. I’m building a small SaaS (checklists as a service). No users yet. Plenty of documentation already. And at some point my docs stopped being an asset and started turning into a liability. This is the story of how I rebuilt my documentation so that an LLM could actually read it end-to-end —and how that restructure helped me. The moment I got scared: “silent misses” The docset grew. I kept asking the LLM to verify tasks against it. And then I noticed a pattern that felt worse than hallucinations. Not “the model invented stuff”, but “the model confidently said it matches ”—while quietly missing exceptions, prohibitions, and thresholds. Keyword scanning instead of reading. I called it silent drift : code slowly moves away from conventions, while the invariants remain only in my head. In a project with roles, audit, and CI/CD security gates, that kind of drift isn’t “just messy docs”. It’s how you lose the ability to implement and review changes consistently. I couldn’t do it manually (and I couldn’t delegate it fully) I knew I had to redo the documentation. But I also knew I couldn’t realistically do it all by hand. At the same time, I couldn’t just tell an LLM: “Rewrite everything according to approach X.” Not enough context, too easy to lose control. So I went with a third option: build a reliable process out of unreliable components— me + an LLM . Step 1: I separated my docs into domains (and forced the model to actually read) First, I extracted domain areas from the old documentation—the vocabulary I was using to describe the project and its parts. I tried to keep domains mutually independent (so the overall framework stays holdable in my head). Then I ran the same loop for each domain: I asked the LLM to read all old docs carefully and extract requirements for that domain. I moved those requirements into a dedicated fil

Keeping up with trending technical topics and new tools on developer forums can be time-consuming. To save time, I wanted to automate the process of finding popular articles, reading the comments to understand community sentiment, and drafting a summary. While I could write a standard Python script to scrape the dev.to API, simple scripts tend to be brittle. If an article doesn't have comments yet, a basic script will likely crash unless you write extensive error-handling logic. Instead of a rigid script, I built an Agent —a program that can dynamically reason about errors and adjust its approach. If one task fails, it can figure out the next best step. In this tutorial, I'll show you how to build a Trend-Spotting Agent using Python, the Google Agent Development Kit (ADK) , and Gemini 2.5 Flash. What We're Building We are going to write a Python application that acts as an autonomous agent. We'll give it three abilities: Search the dev.to API for rising technical articles based on specific tags. Dynamically fetch the top comments of those articles to read real community sentiment. Automatically draft a newsletter-style article on your DEV.to account summarizing its findings. Prerequisites Python 3.9+ installed on your machine. Google ADK . (Check out the Google ADK Docs if you need help installing). A DEV API Key . Grab this from your DEV.to account settings under "Extensions" and throw it in a .env file. Step 1: Giving the Agent its "Hands" (API Tools) Large Language Models (LLMs) are incredibly smart, but out of the box, they can't actually do anything on your computer. The coolest part about Google ADK is that we can write standard Python functions, hand them to the LLM as "tools", and let the AI decide how and when to use them. Let's write our API functions. Tool 1: Finding Rising Articles Here is our function to fetch rising articles. Pay close attention to the docstring ( """Fetches the top...""" ). We aren't writing this for other developers; the ADK actually

Stop Shipping 20 Locale Files in React Native: On-Device Translation for Dynamic Language Packs Internationalization in mobile apps usually starts clean and then gets expensive. At first, you keep a couple of JSON files: en.json es.json fr.json That works when your product is small and the set of languages is stable. It breaks down when: you want to support many languages the product team keeps changing copy translated files drift out of sync some languages are only partially used you do not want to run every string through a server-side translation pipeline This is the problem @tcbs/react-native-language-translator is trying to solve. It lets a React Native app keep a source language, translate missing keys on device, and cache the generated language pack locally. Package: @tcbs/react-native-language-translator The problem Many React Native apps treat localization as a static asset problem: keep one JSON file per language ship all of them in the app update all of them whenever English changes That model has real costs. 1. Translation files become operational debt Every new feature adds more keys. Every copy change forces translators to update multiple locale files. Over time, the translation layer becomes a maintenance queue. The result is predictable: missing keys stale translations untranslated fallback strings inconsistent release quality across languages 2. Shipping many locales is wasteful Most users only need one target language. But many apps ship every locale anyway. That increases bundle size and creates a lot of dead weight for users who will never use most of those files. 3. Dynamic product copy is hard to localize well If your app changes quickly, static translation files lag behind. Teams either accept stale translations or build a backend workflow to keep everything synchronized. That is often more infrastructure than the app actually needs. 4. Server-side translation is not always the right tradeoff Calling a translation API at runtime introduces: la

presenting a poster there, and have registration covered. but they are placing me on waitlist for travel funds. As my travel depends on whether I get the travel grant, I need to get this off of my mind, either invite me or just say no. I'm waiting forever for this, more wait again? should i ask for a decision, or what to do. submitted by /u/Active-Tip3130 [link] [留言]

Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.

submitted by /u/Dear-Economics-315 [link] [留言]

Amazon has announced that its Prime Day event will take place this year from June 23 to June 26, a couple of weeks earlier than it happened last year.

Ameer Al-Khatahtbeh was just trying to find an outlet for Muslim news. Now he has more than 12 million followers.

In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution. By Aaditya Chauhan

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,

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]

Frontend automation has been getting pretty wild lately. 🫠 A few months ago, this comparison would...

Amazon Prime members will be able to buy July 29 tickets for Spider-Man: Brand New Day.

A hands-on knowledge-distillation project: Qwen2-VL-7B → 2B for UI-screenshot understanding, trained, evaluated and benchmarked end-to-end on an M4 Pro. 2.4× faster — and why the teacher lost on ROUGE-L.

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]

submitted by /u/r_retrohacking_mod2 [link] [留言]

If you write code for Adobe Commerce or Magento 2, you spend a lot of time waiting. Build times are slow, static content deployment takes forever, but the real time sink is documentation. The EAV architecture, nested XML layouts, and ever-changing GraphQL mutations mean you are constantly Alt-Tabbing to a browser to double check a syntax pattern. Every time you leave your IDE to search the Experience League portal, you lose your train of thought. You copy error codes, dig through unrelated search results, and try to find a working code snippet. It is exhausting. I wanted my coding assistant to just know this stuff without making me look it up. That is why I configured this MCP server. The adobe-commerce-docs-mcp package connects your IDE directly to the official Adobe documentation. It works with Cursor, Claude Desktop, VS Code, and Windsurf, pulling raw markdown docs right into your chat context. The architecture: bridging AI and docs Instead of relying on web search or stale training data, the server queries the live Adobe Experience League site. It indexes the content locally, caches pages, and handles queries via the MCP protocol. 1. BM25 search ranking The server parses the official Adobe sitemap and ranks pages using BM25 relevance scoring. This is the same search algorithm databases use to weigh search term frequency against document length. It means your assistant gets the most relevant setup guide first, not just the page that mentions a keyword the most. 2. Synonyms and fuzzy matching You do not have to query exact terminology. The search engine maps Magento specific synonyms: graphql searches also find pages with gql module searches also match extension cloud searches match ece It also corrects simple typos like chekout or catlog to checkout and catalog. 3. Local caching Network requests are slow, so the server uses two layers of caching: An in-memory cache for recent queries. A persistent file cache on your disk. Sitemap data lasts 24 hours, while downlo

Google has released a new CLI for Google Workspace, offering a unified interface for various services like Drive, Gmail, and Calendar. Built in Rust, the tool dynamically adjusts to API changes and features over 100 bundled skills. It requires Node.js and a Google Cloud project for setup. Initial community feedback is mixed, highlighting both its dynamic capabilities and setup challenges. By Daniel Curtis

Hey fellow productivity nerds, We’ve all been there: piling 50 hours of tasks into a 40-hour workweek, only to feel completely burnt out and defeated by Thursday. Remember The Milk is fantastic for capturing what needs to be done, but it doesn't inherently tell you if you actually have the time to do it. I got tired of constantly overflowing my schedule, so I spent some time leveraging MilkScript (RTM's automation engine) to build something I’m calling the RTM Agile Coach. It’s completely free and open-source. Basically, it transforms RTM from a passive checklist into an active, capacity-aware project manager. Here is what it actually does behind the scenes: ⏳ Precision Scheduling Engine: You tell it your working hours (e.g., 9 AM - 6 PM, Mon-Fri). It simulates your task list minute-by-minute. If a task hits 6 PM, it automatically carries the remaining hours over to the next working day. 📅 实时战略排期推演 (Schedule) • 预计完工: 2026-06-06 10:06:15 星期六 （注：排期表展示的预计完工是“最坏情况”（Worst Case）：如果你白天完全没时间做这个任务，晚上要搞到几点。） 🟢 [06-02(二) 10:29 - 10:39] 检查* 回复-0.33🍅 (10m) 🟢 [06-02(二) 10:39 - 11:39] 查询 材料？-1.00🍅 (30m) 🟢 [06-02(二) 11:39 - 13:40] 2.2.5-如何 -2.00🍅 (60m) 🟢 [06-02(二) 13:40 - 15:40] 3-2-1-在 更新 -2.00🍅 (60m) 🟢 [06-03(三) 09:00 - 09:05] 3. 验证-0.17🍅 (5m) 🟢 [06-03(三) 09:05 - 09:35] 弄清楚 是什么-1.00🍅 (30m) 🟢 [06-03(三) 09:35 - 09:40] 3. 验证-0.17🍅 (5m) 🟢 [06-03(三) 09:40 - 11:40] 准备 材料-2.00🍅 (60m) 🟢 [06-04(四) 09:00 - 09:05] 3. 验证-0.17🍅 (5m) 🟢 [06-05(五) 09:00 - 09:05] 3. 验证-0.17🍅 (5m) ➖➖➖➖➖➖ 🧨 标准容量耗尽 (转入加班推演) ➖➖➖➖➖➖ 🧨 [06-06(六) 10:00 - 10:06] 3. 验证-0.17🍅 (5m) (加班) ↳ 📉 * 阻塞瓶颈 : 高顺位任务占据加班通道，后续2任务被迫顺延。 🧨 [06-06(六) 10:06 - 10:06] 4.发放 ** (0m) (加班) 🧨 [06-06(六) 10:06 - 10:06] 4.发放**** (0m) (加班) • 目标死线: 2026-06-06 23:59:59 星期六 🌡️ Visual Workload Heatmaps: It generates a literal heatmap inside an RTM note. At a glance, you can see which days are 🟩 (idle/comfortable), 🟧 (saturated), or 🟥 (dangerously overloaded). 🌡️ 每日实时战略负载热力 (Load Heatmap) 🟨 06-02(二): 69% [ 5.2/ 7.5h] 🟢空闲2.3h 🟩 06-03(三): 35% [ 3.2/ 9.0h] 🔒含日