Enlace a post en Español Click If you are developing games in Godot and using AI to help you code, you are probably tired of constantly switching tabs between your editor and the browser. Copying code, pasting it, explaining your scene context over and over again... it is a massive workflow killer. To solve this, I built Golem-AI (named after the Godot Engine logo because let's face it, it looks like a tiny, friendly mechanical golem). It is a "Cursor-style" AI assistant extension integrated directly into a dock right inside your Godot 4.2+ editor. Today, I am opening the repository to the community as a completely open-source project. It is currently in Beta and has some bugs, but it is fully functional, and I want to share it so we can improve it together. / ____/___ / /__ ____ ___ / | / _/ / / __/ __ \/ / _ \/ __ `__ \______/ /| | / / / /_/ / /_/ / / __/ / / / / /_____/ ___ |_/ / \____/\____/_/\___/_/ /_/ /_/ /_/ |_/___/ 🎮 How it Looks Inside the Editor Here is a glimpse of the integrated dock interface, its session history, and the context autocomplete system in action: 🔥 Key Features 🦙 Local & Cloud Providers: Connect it to Ollama or LM Studio for a 100% free, offline local workflow, or hook it up to OpenAI, Anthropic, Gemini, or Cursor proxies. 🧠 Cursor-Style UX & Context (@ Mentions): Type @ in the chat composer to automatically attach open scenes, specific project files, or custom skills directly into the prompt. 🛠️ Editor Tool Calling: It features an optional multi-step verification loop. The AI can actually interact with native Godot editor tools to help you iterate and fix things faster. 📚 Markdown Skills System: Feed the assistant specific workflows, style guides, or documentation using standard markdown files (/skill or @skill :id). 💬 Advanced Chat UI: Built-in "thinking blocks", agent step progress tracking, searchable history sessions, and a native bilingual UI (English / Spanish). 🛠️ The Current State: "It works, but..." (Looking for Beta Testers!) L

If you've ever worked on an e-commerce project in Chile, sooner or later you bump into Transbank. There's no avoiding it. I built a reference template that use NestJS on the backend and Next.js 16 on the frontend, and in this post I want to walk you through the whole thing: what Webpay Plus actually is, why the integration looks the way it does, and how each piece fits together. Github Repository Reference: Link A bit of context Webpay Plus is one of the most important online payment methods in Chile. The user experience is straightforward: your customer enters an amount on your site, gets redirected to Transbank's branded payment page, fills in their card details there, and comes back to your site with a result. From a UX perspective it's not as slick as Stripe Elements or a fully embedded checkout — the user always sees Transbank's domain in the address bar during the payment — but from a developer's perspective that's actually a feature. You never touch card numbers. PCI scope stays minimal. Transbank handles 3D Secure, fraud rules, and bank routing. The trade-off is that the integration model is what you might politely call classical . It's built around full-page redirects and form POSTs, not modern APIs with JSON responses and webhooks. That's important to understand up front, because it shapes every decision you'll make in the code. The integration flow, step by step Before looking at any code, it helps to have a clear mental model of what's happening . There are three distinct moments where your system talks to Transbank's system, and each one has a specific shape. First Step: When the customer clicks "Pay", the backend asks Transbank to create a transaction (amount, order ID, return URL) . Transbank returns a transaction token and a redirect URL where you must send the user. Second Step: Transbank requires the redirect to be an HTTP POST with the token in a token_ws form field, so you must generate an HTML form with a hidden token_ws input and submit it prog

We are excited to announce the first alpha release of Arc, a lightweight, C-based programming language and interpreter designed for simplicity, performance, and educational clarity. Version Overview Version: v0.0.1-alpha Status: Alpha (Experimental) License: GPL-3.0 This initial release establishes the foundational pipeline of the Arc language, from lexical analysis to AST-based interpretation, featuring a robust set of core language constructs and a custom memory management system. Key Features Language Core Variable System: Declaration and updates using the VAR keyword. Functions: Support for custom functions (FN) with parameters and RETURN values. Control Flow: Conditional branching with IF, THEN, ELIF, and ELSE. Iterative loops with WHILE, FOR, and THEN. Loop control with BREAK and CONTINUE. Exception Handling: Graceful error recovery using TRY...CATCH blocks. Data Types: Integrated support for Numbers (Integers/Floats), Strings, Booleans, and Lists. Import System: Modularize projects by importing other .arc files using IMPORT. Syntax Highlights Case Sensitivity: Keywords (e.g., VAR, WHILE, IF) are case-insensitive. Identifiers (variable and function names) are case-sensitive. Operators: Comprehensive set of arithmetic (+, -, *, /, ^), comparison (==, !=, <, >, <=, >=), and logical (AND, OR, NOT) operators. Comments: Single-line comments starting with #. Built-in Standard Library I/O Operations: print, get_input, open_file, read_file, write_file, close_file. Data Manipulation: len_of, typeof, to_int, split_string, append_list, range. Math Library: A comprehensive math.arc providing constants (PI, E) and functions (sin, cos, tan, sqrt, log, etc.). Tooling & CLI Arc comes with a powerful CLI and an interactive REPL: Interactive REPL: Run code line-by-line with syntax highlighting. CLI Options --debug (-d): View tokens and AST tree during execution. --code (-c): Execute a string of code directly. --float-precision (-p): Control decimal output. --mempool-size (-m):

The indie artist has played a string of surprise, small-venue shows with no phones allowed, prompting fans to piece together clues about a potential upcoming album.

Excited to share SkillMap AI, a platform designed to help organizations make faster and more accurate staffing decisions. The idea is simple: project requirements and candidate profiles often live in separate documents, making team allocation slow and inconsistent. SkillMap AI bridges that gap by converting project requirements into structured skill demand and matching them against candidate capabilities. ✨ Key Features • Requirement Intelligence – Transform project briefs into normalized skill requirements • Candidate Matching – Compare resumes against actual project needs, not just keywords • Skill Gap Analysis – Identify missing capabilities before project execution • Staffing Decision Support – Recommend validation, interviews, and upskilling paths 📊 Outcomes ✓ Faster staffing shortlists ✓ Reduced manual resume screening ✓ Better project-team alignment ✓ Evidence-based skill gap identification ✓ Improved workforce planning 🌐 Live Demo: https://skill-map-ai-delta.vercel.app Would love to hear your thoughts and feedback!

When using the Postman extension in vsCode I was wondering where all the Collections credentials and tokens are stored. Are they secure and what is best practice for its use? Should I switch to curl? And can I use environment variables like curl? The postman documentation just talks about using the postman vault but doesn't say where they are stored and how securely if you aren't using it. submitted by /u/ElectricYFronts [link] [留言]

TLDR: I’ve built a single player browser game which is static assets and 0 cost, I plan to release multiplayer which will cost me - would you split into 2 different URLs eg “multi.myurl.gg” vs “myurl.gg” etc (placeholder URLs) Hey reddit, I’ve built a browser game in my spare time and have a question about potentially hosting on 2 different URLs. My specific issue is - right now the game is single player only + bots, and cost wise most of the game is free for me to host at any scale due to the way I’ve architected single player. This includes sharing features and things like watching replays, I could effectively have a billion users and still pay the same as when I have 1 user. This is great! But, once I introduce multiplayer, I have to pay for Cloudflare workers, storage, egress fees potentially etc for those same sharing and replay features. I’ve estimated these costs to be quite low even at scale, but I’m extremely frugal and want to always keep the single player experience alive. The multiplayer experience is more dependant on how well it performs with the public. So I was thinking of hosting single player at something like “myurl.gg” and multiplayer at something like “multi.myurl.gg” for a clean separation of concerns. Am I over-engineering here?? @mods this is not self promotion, I’ve used placeholder URLs etc submitted by /u/ComfortablePeace8859 [link] [留言]

Hello. I am a 9th grade student from India. AI was used only for grammar correction. These are my own questions. I am confused about two concepts: 1. Second Law of Thermodynamics: It states that the entropy of an isolated system increases. Decreasing entropy requires work. Does this imply that while time dilation from General Relativity can cause time to pass slower, time can never run backwards, as that would require a spontaneous decrease in entropy? 2. General Relativity: Time passes slower i

Hey all. Been vibe coding for almost 2 years now (I think?). Previously was more focused on traditional micro-saas but recently decided to go in a different direction and see how far I could push lovable and try and make a commercial grade browser based game. Built it with Lovable + Supabase + Stripe -- full commercial browser game, gyroscope controls on mobile, no app store needed. Generated all my assets (I know, I know, there aren't a ton) with a combination of Gemini to prototype and the GPT 2 to finalize. I've made a few small games here and there that generally only get used by my kiddos, but with this one I wanted to try and create a full gaming experience (login rewards, leaderboard, store, powerup mechanics, simulated ads, etc.) Put a $100 bounty on it for the first player to reach level 100 on mobile. Nobody has claimed it since launch. So genuinely asking -- is it too hard, or is that the point? tiltra.io P.S. It is currently playable on both desktop and mobile but with the gyro mechanic it is definitely more fun and challenging on mobile. submitted by /u/BeltwayBro [link] [留言]

Scientists in Finland found bees could solve an insect version of the classic "box-and-banana" problem.

Bytedance released Bernini for video editing. The architecture splits the pipeline into a semantic planner that understands intent, then a renderer that executes. The planner draws a semantic sketch before any pixels are committed. This is the exact structure I want from coding agents. A planning stage that understands the codebase, the constraints, the dependencies. Then an execution stage that respects that plan. Right now most coding agents skip the planning layer or treat it as advice the agent can ignore mid flight. Bernini gets around the ignore problem by making the plan structural. The semantic sketch is a contract. The renderer's job is to match it. Coding agents need the same contract: a structured task graph where each step has inputs, outputs, and exit conditions. The hardware angle is interesting too. Gemma 4 at 2GB vram means the planner can run locally while the renderer stays in the cloud. Local intent understanding plus remote execution. Latency drops. Privacy improves. I have been using verdent partly for this reason: the plan comes before execution. Coding agents should pay attention to how video generation solved the same problem. submitted by /u/SherbertDazzling3661 [link] [留言]

Hi guys. I have been working on Hitoku Draft, an open-source, voice-first AI assistant that runs entirely locally. I posted about it already, and now it has also transcription with voice editing. Looking for feedback, as I found that outside tech circles other people still do not use this tech much. It's context-aware, in the sense that it reads your screen, documents, and active app to understand what you're working on. You can ask about PDFs, reply to emails, create calendar events, use web se

Opus 4.6 or sonnet 4.6 for rping Currently running on pro right now Im unsure what to choose between the two in terms of rping cause i prefer creative writing, stay in character, deep emotional prose, good character development, good memory, good character emotionals and stuff like that So far im using opus 4.6 but it drains the limits relatively quick For the sonnet i can use for hours and still be fine So like im wondering which is better for rping? I havent tested both deeply Also if they're an even better option, pls tell me. submitted by /u/Turbulent_Arrival_55 [link] [留言]

Gartner updated their 2026 forecast to $2.5 trillion in global AI spending. Same week, MIT's NANDA Initiative dropped a follow-up: 95% of enterprise gen AI projects deliver zero measurable return. Not low return. Zero. I've been on the delivery side of 14 of these projects since January. The MIT number doesn't surprise me. If anything it's generous. 1. 73% of the engineering work that gets AI into production has nothing to do with the model. Data pipelines, integration layers, legacy system remediation, human-in-the-loop tooling. That's where the hours go. The model is 27% of the work but gets 70%+ of the budget. Every time. 2. The budget ratio between projects that ship and projects that stall is almost exactly inverted. We tracked this through ticket history and commit logs across 14 engagements. Projects that made it to production: roughly 30% model, 70% infrastructure. Projects that stalled: 70% model, 30% infrastructure. Most companies think they're at 50/50. They're not even close. 3. One client went from 71% Copilot adoption to 34% in six months. Two other AI platform licenses dropped under 12%. Combined licensing: $340K/year. The tools worked fine. Nobody redesigned workflows to actually use them. 4. The median data error rate across our engagements is 14%. Teams always guess 5-10%. One client found 23% in month four of a $310K build. That's two months of an ML engineer building training pipelines against garbage data. $36K in salary discovering a problem a data audit would have caught in a week. 5. Medtech company. Four concurrent AI pilots. No kill criteria. $920K in engineer salary. Eleven months. Shipped: nothing. I've now seen this at six companies now. Nobody defines when to stop spending. So nobody stops. 6. Individual gains are real. Company-level ROI stays flat. HCLTech and Writer both found this from different angles. Only 29% of companies see significant ROI from gen AI, despite people at their desks reporting productivity jumps as high as 5x. I m