People in Europe will soon lose access to Studio Canal movies they paid for on the PlayStation Store.

submitted by /u/Cultural_Wheel_6936 [link] [留言]

Repo link and results - https://github.com/Abhinand20/MathFormer Task: Given a factorized expression like (7-3*z)*(-5*z-9), predict the expanded form -> 15*z\*2-8\*z-63 Key takeaway: A tiny (4M param) seq2seq model trained with no math knowledge reaches ~98.6% accuracy on symbolic math tasks, suggesting it learns structural token transformations rather than any notion of operators or variables. Scaling this up could help explain why LLMs appear to “reason” mathematically, when they may actually be performing large-scale structured pattern completion. How does RL change this paradigm given the inherent architecture is still based on attention? submitted by /u/AlphaCode1 [link] [留言]

In the landscape of modern programming, delimiters serve as the essential scaffolding that organizes logic and defines structure. Among these, curly braces—often referred to as braces or squiggly brackets—occupy a unique position. While they are ubiquitous, they are frequently the source of developer frustration and logic errors. A common pitfall for many programmers is the tendency to treat all delimiters as interchangeable, leading to a fundamental misunder身 of how a compiler or interpreter parses a script. Confusion often arises when developers conflate the purpose of curly braces with those of parentheses or square brackets. For instance, in many languages, curly braces denote a scope or a code block, whereas square brackets handle indexing. However, the nuances become even more complex when examining specific environments like R, where the semantic meaning of a symbol can shift depending on the context—moving from defining a function to facilitating list extraction. Understanding the specific curly braces semantics is not merely an academic exercise in syntax; it is a practical necessity for writing clean, maintainable code. When a developer understands why a brace is used, they can more easily debug nested structures and communicate intent to their teammates. Grasping these distinctions reduces the cognitive load required to read complex scripts and prevents the subtle bugs that emerge when syntax is used incorrectly. Curly Braces vs. Other Delimiters: Semantic Roles in R and Beyond To master programming syntax, one must move beyond recognizing symbols and begin understanding their semantic intent. While many developers treat curly braces as just another set of punctuation, their role is fundamentally distinct from parentheses and square brackets. Understanding the nuance of curly braces semantics is essential for writing logic that is both functional and readable. The Primary Role: Defining Code Blocks In most procedural and object-oriented languages (such as

Hello Devs 👋 AI coding assistants have changed the way many teams build software. Tasks like generating components, creating tests, writing boilerplate, or handling repetitive refactors can now happen in minutes instead of hours. The productivity gain is real and that part is easy to notice. What becomes interesting after using these tools for a while is that a different bottleneck starts appearing. Code generation becomes faster, but the review process often stays the same. Teams can generate hundreds of lines of code within minutes, but someone still has to answer important questions: Does this actually solve the requirement? Are edge cases covered? Will this introduce side effects? Does it align with existing patterns? The speed of writing code has changed. The need for confidence has not. That is where I think the conversation around AI-assisted development is starting to shift. The challenge is becoming less about generating code and more about making sure the generated code is actually safe to ship. The Problem With Reviewing AI-Generated Code Like Regular Code Imagine asking an AI coding assistant to implement coupon validation for premium users. Add coupon validation for premium users and create tests A few seconds later you get: if ( user . isPremium ){ applyCoupon (); } Nothing immediately looks wrong. The code is clean, there are no syntax issues, tests may pass, and the implementation appears complete. But pull request reviews usually go beyond reading diffs. Reviewers start asking questions such as: What happens if the coupon has expired? Does this affect payment calculations? Should audit logs be updated? Are there services depending on this behavior? This is where AI-generated code becomes interesting. It can often be functionally correct while still missing important implementation details. Research around larger AI-generated projects has also shown that functional correctness does not necessarily translate into maintainable system design. Teams stil

Maraget Atwood, the storied author of The Handmaid's Tale and The Blind Assassin, was interviewed as part of the Babell Literary and Cultural Festival in Porto, Portugal. As it usually does at these things, the issue of AI came up, and Atwood didn't mince words. According to Deadline's recap, Atwood said she'd used an AI […]

high-performance open-source Bonkfun Bundler for Solana A high-performance open-source Bonkfun Bundler for Solana. It allows users to create a token + bundle up to 12 purchases in a single atomic transaction. Features Jito-powered bundles, delay sniping, pure sniping mode, automatic wallet generation, SOL airdrops, and wallet cleanup/refund tools. Optimized for fast meme coin launches on letsbonk.fun. I open-sourced solana-bonkfun-bundler for developers in Solana Web3 development . This post walks through what it does, how the pieces fit together, and how to run it locally. Why I built this Explore solana bonkfun bundler patterns in solana web3 development Fork the repo as a starter template for your own project Contribute features, docs, or tests via pull requests Most tutorials stop at a smart contract or a UI mockup. I wanted a complete vertical slice — wallet flow, on-chain logic, backend state, and a responsive frontend — so you can study or fork a production-shaped codebase. What it does Wallet connect — players sign in with a Solana wallet A high-performance open-source Bonkfun Bundler for Solana It allows users to create a token + bundle up to 12 purchases in a single atomic transaction Features Jito-powered bundles, delay sniping, pure sniping mode, automatic wallet generation, SOL airdrops, and wallet cleanup/refund tools Optimized for fast meme coin launches on letsbonk.fun Jito-powered atomic bundles Non-Jito delayed-snipes Pure sniping mode Architecture at a glance Wallet layer — users connect a Web3 wallet to sign transactions Application layer — TypeScript backend/frontend tying on-chain and off-chain flows Feature — Wallet connect — players sign in with a Solana wallet Feature — A high-performance open-source Bonkfun Bundler for Solana Feature — It allows users to create a token + bundle up to 12 purchases in a single atomic transaction User Wallet → On-chain Program → VRF / Settlement ↓ Backend (API + WebSockets) → MongoDB / state ↓ Frontend UI (rea

Recently saw this comment on another HN thread about the US government gating access to GPT-5.6 and how it harkens back to the 1990s encryption-as-export-controllable-tech situation and how people tattoo'd the algo to their bodies: > I can't wait for the first person to tattoo model weights on their body! (https://news.ycombinator.com/item?id=48693721) And I can't help but wonder, what would be the smallest functional amount of weights from any sort of ML model you could realistically tattoo ont

Google's NotebookLM is great. But handing your research notes, PDFs, and meeting transcripts to Google's cloud is a hard sell for a lot of people — especially when those documents contain client data, unpublished research, or internal strategy. So I spent a weekend testing five open-source alternatives. Three things mattered: can I docker compose up in under 10 minutes, does the podcast feature actually work offline, and what breaks first? Here's what I found. The Contenders Project Deploy Time Min VRAM True Offline License Open Notebook (lfnovo) ~8 min 8 GB Yes MIT Notex (smallnest) ~3 min 4 GB Yes Open source KnowNote (MrSibe) ~2 min 4 GB Yes Open source NotebookLM-Local (nagaforcloud) ~15 min 8 GB Qwen-3 4B bundled Open source InsightsLM (phsphd) ~30 min 8 GB Yes N8N SUS license 1. Open Notebook — The One to Beat git clone https://github.com/lfnovo/open-notebook cd open-notebook docker compose up -d Eight minutes from git clone to the web UI on localhost:3000 . It ships with 18+ model providers pre-configured — Ollama, OpenAI, Claude, DeepSeek, Gemini, all selectable per notebook. The podcast generator supports 1-4 speakers with different voices, and it runs entirely offline when you point it at an Ollama backend. What works: Document ingestion is fast — SurrealDB's vector + full-text index handles 200-page PDFs without choking Model switching is genuinely useful — Claude for deep analysis on one notebook, local Qwen for quick summaries on another Podcast quality with 2 speakers is close to NotebookLM's. 4 speakers is still rough. What breaks: Citation highlighting is still being rebuilt (work in progress as of June 2026) Single-user only — no team/workspace isolation built in Docker required. No native binary. 2. Notex — Single Binary, Zero Dependencies Notex is written in Go. You download a single binary (~25MB) and run ./notex . That's it. No Docker, no Python venv, no database setup. It supports PDF, TXT, MD, DOCX, HTML, audio, and YouTube/Bilibili URLs as so

QR codes look like magic — a grid of black and white squares that encodes anything from a URL to a business card. But how do they actually work? I decided to find out the hard way: implement the full QR Code Model 2 algorithm in vanilla JavaScript, zero external dependencies. The result: QR Code Generator — a free, client-side tool that generates QR codes from any text or URL. 👉 https://qr-code-generator-e83.pages.dev Why No Libraries? I maintain a collection of browser-only developer tools at devnestio . Every tool has the same rule: zero external dependencies. No npm installs, no CDN scripts, no servers. For most tools (JSON diff, Base64 encoder, UUID generator) that's easy. QR codes are different. The spec is a 126-page ISO document. Most developers just npm install qrcode and call it a day. But writing it from scratch taught me more about error-correcting codes, Galois field arithmetic, and matrix encoding than I ever expected. Worth every hour. What the Tool Does Real-time generation as you type (debounced at 80ms) Size selector — 128 × 128, 256 × 256, or 512 × 512 pixels Error correction level — L (7%), M (15%), Q (25%), H (30%) Color picker — any foreground and background color PNG download via canvas SVG download with crisp vector output at any scale How QR Codes Actually Work QR Code Model 2 (the standard you see everywhere) has six major steps. Here's the short version: 1. Data Encoding Text gets encoded into one of three modes based on content: Numeric ( 0-9 ): packs 3 digits into 10 bits — most compact Alphanumeric ( 0-9 A-Z $%*+-./:space ): 2 chars into 11 bits Byte (everything else): UTF-8, one byte per 8 bits The encoder picks the mode automatically and finds the minimum QR version (1–40) that fits the data. function detectMode ( text ) { if ( /^ \d +$/ . test ( text )) return NUMERIC_MODE ; if ( text . split ( '' ). every ( c => ALPHANUMS . includes ( c ))) return ALPHANUM_MODE ; return BYTE_MODE ; } 2. Reed-Solomon Error Correction This is the hard