标签：#c

Tesla, accused of failing to fix design flaws, blames driver pressing accelerator.

The safety board, known for its thorough investigations, is probing the crash alongside the National Highway Traffic Safety Administration.

As someone who wants to work in tech in the future, say 5-10 years from now, to what extent do you think coding will be a valuable skill? How much should I learn?

I started this project back in 2015, to translate the TeX original specification into an easily parsed format (s-doc), and to create an HTML rendering of that format as a proof of concept. The project is homed here: https://codeberg.org/dlowe/metaspectre/ Differences from the Hyperspec (from the README): - Most importantly, it is free to modify and distribute. - The original TeX is very hard to parse and use for things other than generating a printed copy. The Hyperspec is an HTML rendering whic

Hi, I've created an overview of the most important OCR benchmarks, along with the top open models, and links to their paper and code: https://paperswithcode.co/tasks/ocr . This week, new OCR models were released by Baidu and Mistral. Baidu released Unlimited OCR , a 3B-parameter model that introduces a key innovation called Reference Sliding Window Attention (R-SWA) and builds on top of DeepSeek OCR . Mistral released OCR 4 , which is available via an API. OCR, or Optical-Character Recognition, is the task of digitizing PDFs or scanned documents. There's, of course, a huge interest in this task, as it enables ingestion of all company data for agentic use cases. AI agents love Markdown; it can be valuable to turn all those messy PDF documents into a standardized, machine-readable format. This enables use cases like agentic RAG (retrieval-augmented generation), which powers chatbots, both internally and for external customer support. With a large number of OCR releases on Hugging Face over the last few months, it may be hard to know which one to use. Hence, I've built this page, which lists the major OCR benchmarks, along with the top-performing models and links to their code. This is obviously made available on Papers with Code , the website I'm maintaining (it's a revival of the old website, which was taken down). The top recommended benchmarks are OlmOCRBench, created by Ai2, and OmniDocBench, created by Shanghai AI Laboratory. Current top recommendations are Chandra OCR 2 by Datalab and Mistral OCR v4. The former is openly available, hence you can either self-host it or use their serverless API. Let me know which other tasks you want to see major benchmarks for now! Cheers, Niels open-source @ HF submitted by /u/NielsRogge [link] [留言]

Hi everyone, I trained a self-play RL agent for Generals.io that reached superhuman-level and ranked #1 on the human 1v1 leaderboard. It began as my master's thesis where the goal was to beat a prior algorithm based agent. We succeeded using behavior cloning, RL fine-tuning and reward shaping, but the agent was still consistently beaten by the top players. So I gave it a round two and fixed the largest bottlenecks: Reimplemented the whole pipeline in JAX (from NumPy/Torch) Used Vision Transformer instead of the CNN Both are a result of the same idea: to invest in scaling rather than human priors and ad-hoc patches. The blog is written as a guide for anyone building something similar — the dead ends, the decisions, and the intuitions and tricks I picked up along the way. It's all open source, including the fast JAX simulator — handy on its own if you want an imperfect-information RTS env to play with. Links - Guide: https://kam.mff.cuni.cz/~straka/blog/generals.html - Simulator (JAX): https://github.com/strakam/generals-bots - Agent: https://github.com/strakam/AverageJoe I hope you find the blogpost entertaining! Feedback and questions welcome 🤗. submitted by /u/shrekofspeed [link] [留言]

Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]

https://xcancel.com/ID_AA_Carmack/status/2069799283369345247

As web developers, engineering workflows are heavily dependent on the NPM registry. However, the traditional process of searching, auditing, and integrating packages remains highly fragmented. Developers are routinely forced to hop between npmjs.com, GitHub source repositories, and external documentation tabs simply to verify bundle sizes, check dependency trees, or generate setup boilerplate. Following a strong reception on LinkedIn, X, and Facebook, the Motion Mind Team has introduced LibPilot to the dev.to community. LibPilot is not a traditional registry interface; it is an AI-powered search engine and discovery hub engineered to index, track, and analyze over 4,000,000 NPM packages in real time. Here is an architectural breakdown of how LibPilot restructures package exploration for modern developers and autonomous AI code agents. Intent-Based Discovery and Global Search Architecture Traditional search engines require users to input the exact name or strict keyword of a library. LibPilot introduces a dual-input architecture on its home page to eliminate this constraint: Direct Registry Querying: Users can input full or partial package names into the global search bar to instantly surface clean, structured, and typed suggestions directly from the live NPM ecosystem database. Contextual AI Recommendations: For scenarios where the ideal package is unknown, developers can type out a complete description of their project architecture or system constraints (for example: "a lightweight, typed state management engine that handles server-side rendering natively"). LibPilot's internal AI agent processes the functional requirements and suggests production-ready libraries suited for that stack. Continuous Context AI and Interactive Onboarding A core goal of the platform is reducing developer friction and maintaining deployment momentum. LibPilot transitions static package documentation into an interactive environment: Unlimited AI Chat Architecture: Once a library is select

When designing systems like Facebook, WhatsApp, Netflix, Amazon, or Instagram, one of the first questions a system designer asks is: Can a single server handle the traffic? How much storage will be needed? Do we need caching? How much RAM should our cache have? How many servers should we deploy? Before discussing databases, load balancers, microservices, or caching layers, we need a rough understanding of the scale. This is where Back-of-the-Envelope Calculations come into the picture. Why Do We Need Back-of-the-Envelope Calculations? Imagine you're asked to design Facebook. If you immediately start drawing: Load Balancer ↓ Application Servers ↓ Redis Cache ↓ Database without knowing the expected traffic, you're designing blindly. System design is fundamentally about making trade-offs. To make those trade-offs, we first need estimates. Back-of-the-envelope calculations help us answer: How much traffic will the system receive? How much data will be generated? How much cache memory is required? How many servers are needed? The numbers don't need to be perfect. They only need to be close enough to make architectural decisions. What Exactly Is a Back-of-the-Envelope Calculation? A quick estimation technique used to approximate: Traffic Storage Memory Server Capacity using rough assumptions. Think of it as: "Getting the order of magnitude correct rather than getting the exact number correct." A system designer rarely needs perfect accuracy during interviews. They need reasonable estimates. The Standard Estimation Flow Whenever you get a System Design question: Users ↓ Traffic ↓ Storage ↓ RAM / Cache ↓ Number of Servers ↓ Architecture Design Always estimate first. Design later. The Ultimate Estimation Cheat Sheet Storage Units Unit Value 1 KB 10³ Bytes 1 MB 10⁶ Bytes 1 GB 10⁹ Bytes 1 TB 10¹² Bytes 1 PB 10¹⁵ Bytes Time Units Unit Value 1 Minute 60 Seconds 1 Hour 3600 Seconds 1 Day 86,400 Seconds Common Assumptions Metric Approximation Peak Traffic 3× Average Traffic Active

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh