标签：#Go

Google now lets big creators and publishers in the US claim dedicated profiles in Search to highlight things like videos, articles, and their other profiles online. But this feature won't be available to most people or organizations; Google is limiting it to people with at least 100,000 YouTube subscribers, 100,000 followers on Instagram or X, […]

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

Hyperscalers have come under scrutiny for their impact on water quality and availability.

Building Stayzr meant solving real problems: PMS integration, high-throughput webhook handling, and AI that actually knows your property. Here's how we architected it. The Stack (What's Running in Production) Backend: Go 1.23 with Fiber framework, pgx/v5 connection pooling, Bun ORM over PostgreSQL, Redis for caching/sessions, OpenTelemetry for tracing AI Agents Service: Python 3.11 + FastAPI (Uvicorn), LangChain primitives, Qdrant for knowledge base, ChromaDB for conversation memory Frontend: Next.js 15 / React admin UI + marketing site 3rd-party Integrations: Mews (PMS), WhatsApp Business/Meta, Resend + Postmark (email), Azure Blob Storage (files), Gemini + OpenAI (LLM + embeddings), Infisical (secrets), SigNoz + Oneuptime (observability) It's a polyglot monorepo: Go where throughput and concurrency matter (API, dispatch, sync), Python where the LLM/RAG ecosystem lives. Why Go Over Python/Node/Java? For the parts handling concurrent I/O — PMS sync workers, email dispatch worker, webhook fan-in — Go's goroutines + channels let us run in-process worker pools without pulling in a broker or heavyweight async runtime. The dispatch worker is a for{ select } loop over a ticker and wake channel — simple and effective for our use case. We kept Python only for the agents service because that's where LangChain, Gemini/OpenAI SDKs, and vector-store clients live. The honest answer: Go for systems work, Python where AI tooling requires it. Multi-Tenancy: Row-Level Isolation Shared database, shared schema, row-level isolation by organizationId . Every tenant-scoped table carries an organizationId , with a TenantDB wrapper in the data layer that auto-appends organization_id = $N to queries. Middleware ( MultiTenantContext / RequireTenant ) resolves the org from the X-Organization-ID header, query param, cookie, or JWT claim. Below org we scope further by propertyId (a hotel can have multiple properties). The AI memory store enforces the same boundary differently — every guest's co

With rapid advances in robotics, autonomous navigation has become essential for mobile robots. The ROS Navigation Stack is the de facto open-source framework for building reliable, real-world navigation systems. It integrates perception, mapping, localization, path planning, and motion control into a unified pipeline. This article breaks down the core components, working principles, configuration best practices, and common pitfalls of the ROS Navigation Stack to help engineers build stable autonomous robots. Overview The ROS Navigation Stack is a collection of coordinated packages that enable a robot to: Localize itself on a map Plan global paths to a goal Avoid dynamic obstacles locally Control motion safely It relies on sensor inputs (LiDAR, depth cameras, wheel odometry, IMU) and outputs velocity commands to the robot base. Core Components move_base The central coordinator of the entire navigation system. Manages the navigation state machine Runs global and local planners Triggers recovery behaviors when the robot is stuck Exposes an Action interface for goal commands Key states: PLANNING, CONTROLLING, CLEARING, RECOVERY. AMCL (Adaptive Monte Carlo Localization) AMCL uses particle filter localization to estimate the robot’s pose on a pre-built map. Particle filter steps: Initialize particles over a pose distribution Predict motion using odometry Weight particles by sensor likelihood (LiDAR scan matching) Resample to keep high-confidence particles Output the weighted average pose AMCL is highly tunable: min_particles / max_particles laser_model_type odom_model_type update_min_d / update_min_a costmap_2d Costmaps represent the environment as a grid of “cost” values, indicating collision risk. Two costmaps: Global costmap: large-scale, slow-update, for path planning Local costmap: small-scale, fast-update, for obstacle avoidance Cost values: 0: free space 253: lethal obstacle 254: inscribed obstacle 255: circumscribed or unknown Inflation expands obstacles by the ro

Aura's digital photo frames will continue to automatically sync with your Google Photos albums, after API changes threatened to remove the feature. Aura is now rolling out a full migration to Google's new Ambient API, allowing your Aura frame slideshows to be automatically updated with new photos, instead of requiring owners to manually add them […]

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts: A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account...

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

The app AT4K turns the messy Google TV interface into something closer to the Apple TV interface, making it cleaner, more customizable, and less frustrating to use.

Hey all, we have a quick update for everyone who participated in the Google I/O Writing...

The company is also introducing a new age verification process in partnership with a European bank.

Slogging through emails to find stuff can be a chore, but Google AI subscribers can now get Gemini to do it.

Leetcode 27: Remove Element Leetcode 27 asks us to remove a specific value from an array. The value to be removed is passed in as a parameter to the function along with the array. Just as we did in Day 1, we will cover a naive approach and an optimized approach and discuss the trade-offs between them. I think in the end there's a pretty clear winner. Let's get started. For both approaches we will use the following values: nums = [1, 3, 3, 2, 4] val = 3 Approach 1: Naive (For Loop + Splice) This approach uses a for loop and leverages .splice() for removals. Solution: var removeElement = function ( nums , val ) { let k = 0 ; for ( let i = 0 ; i < nums . length ; i ++ ) { if ( nums [ i ] === val ) { nums . splice ( i , 1 ); i -- ; } else { k ++ ; } } return k ; }; We begin by initializing a variable k to 0. We then enter the for loop. The condition is standard: create a variable i initialized to 0, continue looping while i is less than nums.length to avoid going past the end of the array, and increment by 1 each time through. Each iteration checks one condition: whether nums[i] is equal to val . If true, we call .splice() on the array. The arguments we pass to splice are i and 1 . i is the index at which we want to start removing, and 1 tells splice to remove only that one element. We then decrement i . The reason for this took me some time to wrap my brain around, so I have included a visual below to make it concrete. The core issue is this: when splice removes an element, every element to the right shifts one index to the left. Without i-- , the loop would increment i on the next iteration and skip right over the element that just shifted in. i-- counteracts that by stepping i back, so after the loop increments it, i lands exactly where the shifted element now sits. If nums[i] !== val , we skip the splice and increment k instead. At the end we return k , which holds the count of elements remaining after all occurrences of val have been removed. Time complexity: O(n²)

Lovable and Google signed an expanded multiyear deal that involves a 5x expansion of Lovable's footprint on Google Cloud, and expanded access to Anthropic Claude.

Defense tech is red hot right now. Anduril and Mach Industries just doubled and quadrupled their valuations, respectively, and the U.S. government is proposing a 40% increase in defense budget. A wave of new startups is chasing those government contracts, but according to Ross Fubini, the venture investor who wrote Anduril’s first check, most of them will get lost in the Valley of Death between prototype contract […]

Google must change AI Overviews after claiming users don't want "lots of sources."

Gemma 4 12B uses a new encoding scheme and token prediction to punch above its weight.

Dreambeans is a curated list of AI-illustrated "stories" culled from the personal data in your Google account.

GitHub热门项目 | Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more | Stars: 35,334 | 26 stars today | 语言: Go

Cursor's Developer Habits Report is one of the clearest signals yet that AI coding has crossed from individual productivity into software-delivery infrastructure. The headline numbers read as a story about speed: more code per week, larger PRs, deeper agent sessions, more changes committing without manual review. The deeper implication is governance -- whether teams can preserve architectural intent while generation, review, automation, and commit flows all accelerate at once. The velocity curve is now measured, not anecdotal. For two years the claim that AI coding is accelerating rested mostly on vibes and vendor decks. Cursor's data turns it into telemetry. And read as an operations document rather than a marketing one, that telemetry describes a structural shift: software delivery is getting harder to govern, not just faster to produce. This is not a critique of Cursor. The report is strong validation. Cursor proves the velocity curve with numbers most of the industry only gestured at. The point of this essay is what sits on the other side of that curve. What the Cursor Developer Habits Report Shows The inaugural Cursor Developer Habits Report (Spring 2026 edition), published by Cursor (Anysphere, Inc.), draws on Cursor usage data rather than survey responses. It captures the transformation across five themes -- developer acceleration, the economics of intelligence, the power user gap, the rise of context, and the shift to automation. The headline figures: 3.6K -> 8.6K lines added per developer per week -- the per-developer code volume rose from 3.6K (Jan 2025) to 8.6K (May 2026), with growth accelerating since the start of 2026. 125.86 -> 345.02 lines per PR at p75 -- lines added per pull request at the 75th percentile rose roughly 2.5x year over year (Jan 2025 to May 2026). Developers are taking on larger units of work in a single PR. 8% -> 13.8% mega PRs -- the share of PRs with at least 1,000 changed lines grew from 8% (Jan 2025) to 13.8% (May 2026). ~30% mor