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This Atlantic profile follows Ondřej Surý, a little-known volunteer on PHP security, who suddenly found himself at the center of a major internet-wide vulnerability scare. The excerpt provided is thin, cutting off mid-interview, so the full story of what he did and how it played out isn't visible here.
- Surý was a relatively anonymous volunteer working on PHP security before the incident broke.
- The article frames his work as having a large, sudden impact once the internet "exploded" around the issue.
- The piece sits alongside an Ian Bogost essay on self-replicating AI malware botnets, suggesting the broader concern is AI-driven attack automation.
Chris Dixon argues that breakthrough technologies like the PC, the web, and open source often start as hobbyist projects, because engineers pursue what interests them while business people chase near-term returns. He bets that today's hobby projects, from Bitcoin to 3D printing, will become the industries of the next decade.
- Engineers vote with their time and business people vote with their dollars, so hobby projects are where the smartest people explore ideas not yet constrained by short-term financial goals.
- The PC, the web, blogs, and most open source software all began as hobbyist work in garages and dorm rooms before becoming large industries.
- He names current hobby projects with momentum, including math-based currencies like Bitcoin, NoSQL databases, the internet of things, 3D printing, touch-free interfaces, and Kickstarter-style hardware, as likely seeds of future industries.
Anthropic is offering free, fully model-generated security scans to eligible open source projects through a new program called OSS Scanner, with reports delivered without human triage. Early testers say the findings were mostly valid, though the company admits some reports will be wrong.
- Anthropic's models found over 29,000 candidate vulnerabilities in six months, but only about 6,000 have been manually reviewed, so human review is the bottleneck.
- OSS Scanner reports go out unverified, but in a pilot, 85 of 97 critical and high-severity findings (88%) met the bar for Anthropic's disclosure process, and only one was a false positive.
- Eligible projects get reports with a reproducer, an explanation, and often a candidate patch; several maintainers said the reports were high quality, and some chained bugs into unauthenticated remote code execution.
Pulumi is an open source tool for provisioning cloud infrastructure by writing ordinary code in languages like TypeScript, Python, Go, or C# instead of YAML. This README covers the CLI, install steps, a sample serverless deploy, and the list of supported languages and providers.
- You define AWS, Azure, Google Cloud, or Kubernetes resources with normal language features like loops, functions, and classes, so creating three web servers is a short for-loop.
- It supports six languages (JavaScript, TypeScript, Python, Go, .NET, Java) plus YAML, and over 300 providers.
- Running pulumi up computes a minimal diff against what's already deployed, so later changes only touch what actually changed.
Indra OCR is a lightweight 1M-parameter model that converts PDFs and images to markdown, text, or JSON at 49 pages per second on CPU—roughly 12-24x faster than Tesseract and PaddleOCR. It handles layout detection, bounding boxes, and can read digital PDFs' text layers while OCRing only missing content.
- Speed and size: Processes 49 pages/second on CPU with ~230ms latency per page, weighs just 18MB, and uses ONNX Runtime instead of PyTorch
- Accuracy beats competitors: Scores 41.3 on OmniParseBench text (vs. PaddleOCR's 35.8 and Tesseract's 19.3) and 62.1 on headline score (vs. PaddleOCR's 58.4)
- Layout intelligence: Detects and labels blocks (headings, tables, lists, captions, footnotes) with bounding boxes and reading order, plus optional LaTeX math recognition
- Built for print documents: Not suitable for handwriting, complex tables, degraded scans, or scripts outside Latin/Cyrillic/Greek/Devanagari/Bengali/Chinese; Datalab offers Chandra model for these cases
VoiceStudio is an open-source desktop app for voice cloning, video dubbing, dictation, and transcription across 646 languages, running locally on your hardware with optional remote services. It supports NVIDIA and Apple Silicon acceleration, works on CPU-only machines, and lets you build custom voice models or use existing ones through a modular engine system.
- Runs entirely local by default with no cloud requirement; remote services and analytics are optional and require explicit consent
- Supports hardware acceleration on NVIDIA GPUs and Apple Silicon, but remains fully functional on older CPUs (though slower) with a ~5 GB PyTorch installation
- Switched from Tauri to Electron as the only desktop UI; integrates with coding agents via MCP and skills, and provides a local API for custom workflows
- Licensed under AGPL-3.0 for the app itself; voice cloning requires explicit permission and model licensing is handled separately from the application license
This is a news digest from The Register covering major developments across AI, infrastructure, security, and open source — including TSMC's US expansion, Microsoft's AI agent plans, Russian phishing campaigns, and various software releases.
- TSMC is partnering with GlobalFoundries on a $2B deal to boost US production of silicon interposers needed for AI datacenter chips
- Microsoft is integrating Copilot agents into Windows file systems and replacing Intel processors with Nvidia SoCs in Surface Laptop Ultra, starting at $2,599
- Russian attackers are impersonating Signal support to run phishing campaigns, and Microsoft's SharePoint on-premises servers are under active zero-day attack with unpatched vulnerabilities
- Ubuntu's new beta adds Rust components and LLM-based speech-to-text; Firefox 157 gets a significant visual redesign; KDE turns 30 with an AI-native desktop proposal
Meta released open-source code so anyone can build their own Muse AI gadgets using ESP32 boards or Raspberry Pis, and is also giving away 5,000 of its own Muse Home Link device to control smart home devices. The company is essentially letting hobbyists turn the AI agent into whatever form factor they want.
- Meta open sourced SDKs for Muse so developers can load it onto off-the-shelf hardware like E Ink displays, HDMI sticks, or touchscreens
- The company manufactured 5,000 Muse Home Link gadgets and opened a waitlist for free units shipping this month
- Muse Home Link can control lights, TVs, printers, and other devices through community-built skills
Jared Palmer released Kev, a family of four open-source models (0.8B to 27B parameters) designed to answer multiple-choice and yes/no questions by scoring answer options directly rather than generating tokens. The models run on consumer hardware, come with Apache-2.0 weights, and match or exceed the performance of TypeSafe's proprietary Jev API.
- Kev-27B achieves 75.7% accuracy on generalization tasks and 91.8% on policy reasoning, with smaller variants (Kev-4B, Kev-9B) performing within 1-2 percentage points on specific workflows while using far less compute.
- The architecture uses a pointer head to score supplied answers without token generation, and caches document computation across multiple questions—cutting inference time from 9.4 seconds for a new 64k-token document to 0.7 seconds per follow-up question on an H200.
- Kev-4B runs on a 32GB Mac (136ms per question with cached text) and Kev-0.8B on any Apple Silicon, making local deployment and fine-tuning practical; the largest model costs $44.09 per million requests on H200s versus $3.54 for the smallest.
LiteParse is an open-source PDF parser that extracts text with spatial positioning and bounding boxes entirely on your machine, running 2-5ms per page without cloud dependencies or LLMs. It handles PDFs, Word docs, spreadsheets, and images, with optional OCR for scanned content.
- Benchmarks show LiteParse outperforms free competitors (pdf-inspector, opendataloader, markitdown) across three major document parsing tests, scoring 0.389 on ParseBench vs 0.283 for the next best model-free tool.
- Speed comes from avoiding ML models — uses PDFium for text extraction, optional Tesseract or PaddleOCR for scanned pages, and spatial layout reconstruction via grid projection.
- Available in Python, Node.js, Rust, and browser (WASM) with CLI tools; includes worker pool mode for parallel parsing with per-document timeouts to prevent pipeline stalls.
Kev is a family of small language models (0.8B to 9B parameters) built on Qwen3.5 that make yes/no, multiple-choice, and rating decisions on text inputs. You can run them locally on your own hardware with an API compatible with TypeSafe's System One, plus there's a web playground for testing.
- Three model sizes available with published accuracy metrics: Kev-9B reaches 85.2% accuracy on unseen datasets and trails the proprietary Jev model by only 3.5 points, while Kev-4B and Kev-0.8B offer smaller footprints for different use cases.
- All models run on consumer hardware (CUDA, ROCm, Apple Silicon) with the 4B and 9B versions fitting on a 32GB Mac in bf16 precision, plus you can train your own weights using provided code and evaluation data.
- Probabilities are calibrated by default and the models output confidence scores alongside answers, reducing confident errors to 4% on new data compared to 8.7% without calibration.
FreeFlow is a free, open source dictation app for Mac that uses AI transcription and context-aware cleanup to turn speech into text, replacing paid alternatives like Whisper Flow and Superwhisper. You bring your own API key (Groq or compatible), so there's no subscription or data retention on FreeFlow's servers.
- Hold Fn to dictate or use customizable keyboard shortcuts; Edit Mode lets you highlight text and transform it with voice commands like "make this shorter"
- Context-aware cleanup reads your current app to spell names and terms correctly, with support for custom vocabulary and simpler post-processing if you prefer
- Works with any OpenAI-compatible provider (Groq, Ollama, LM Studio) and supports local models with configurable timeouts for slower hardware
FreeFlow is a free, open-source dictation app for Mac that transcribes speech and cleans up the output using AI, then pastes it directly into any text field. It works with Groq's API by default but lets you plug in any compatible transcription or LLM service.
- Transcription completes in under 1 second using Groq, with optional local model support despite higher latency
- Cleans up filler words and uses app context to spell names and technical terms correctly, with customizable vocabulary lists
- Fully open source under MIT license with no server component—all API calls stay between your Mac and your chosen provider
- Supports both hold-to-talk (Fn key) and tap-to-toggle (Command-Fn) modes, plus custom text-paste commands
The Looking Glass is a browser-based tool that uses AI to generate images and short films of any spot on Earth at any selected year (from the Great Dying to 3050) and hour. It offers a dial of 284 time stations, a movable sundial for light conditions, and a WebGL “wormhole” animation between eras. All processing runs client-side with local storage, no backend or account required.
- It generates AI images/video of any location on Earth at any time from 66 million years ago to the year 3050, controlled via a 284-station dial.
- Everything runs client-side with no backend or account—you supply your own OpenRouter key and data is stored locally in IndexedDB.
- It uses a WebGL wormhole shader for transitions between eras and lets you compare two time periods via swipe or blink-compare.
- It's open-source (AGPL-3.0-or-later) and includes curated presets like Pliny sailing toward Vesuvius or Apollo 11 liftoff.
The article introduces Inkling-Small, a Mixture-of-Experts transformer with 276 B total and 12 B active parameters that matches full Inkling performance at one-quarter the size. It handles text, images, and audio with a 1 M-token context window and lets users trade compute for thinking effort. The open-weight model outperforms peers on tool use, coding, reasoning, and multimodal benchmarks and is available for fine-tuning and chat.
- Inkling-Small matches or beats full Inkling performance (e.g., 31.6% vs 29.7% on Humanity's Last Exam) while using only 12B active params out of 276B total—roughly a quarter the compute cost.
- It's natively multimodal (text, images via 40×40 patches, audio via dMel spectrograms) with a 1M-token context window and adjustable "thinking effort" for cost/performance tradeoffs.
- Safety numbers slightly lag full Inkling (71.6% vs 78.0% harmful-query refusal on FORTRESS) despite reusing the same safety recipe.
- Open-weight and available now for fine-tuning on Tinker and chat testing in the Tinker Playground.
After six months and over 44,000 dictated words with Wispr Flow at 161 wpm, the author tried FluidVoice. It’s an open-source, local Mac app that corrects in real time without an API key and handles slang better. They canceled their paid plan in favor of FluidVoice.
- After 44,414 dictated words at 161 wpm (top 0.1% of Wispr users), the author switched from Wispr Flow to FluidVoice
- FluidVoice is open-source, runs entirely locally on Mac with no API key or cloud dependency, and corrects mistakes in real time
- It handled slang better than expected and matched or beat Wispr Flow's performance, prompting cancellation of the paid Wispr subscription
Datalab’s 4 billion-parameter Chandra OCR 2 outperforms GPT-4o and Gemini across independent and multilingual benchmarks, handling complex layouts, math notation, flowcharts and 90 languages with state-of-the-art accuracy. It’s available under Apache 2.0 code with a modified OpenRAIL-M license for weights, runs locally via HuggingFace or vLLM, and doubles throughput over its predecessor.
- Chandra OCR 2 (4B params, open-weight) scored 85.9% on olmOCR vs GPT-4o's 69.9%, and beat Gemini/GPT-5 Mini on multilingual benchmarks, with huge gains on South Asian scripts (Kannada +42.6, Malayalam +46.2, Telugu +39.1).
- It processes full pages in one pass rather than splitting into blocks, giving it an edge on tables, nested headers, checkboxes, handwritten math, and flowcharts exported as Mermaid diagrams.
- Despite shrinking from 9B to 4B parameters, throughput doubled to ~2 pages/sec on an H100 while accuracy improved.
- Code is Apache 2.0 and installable via pip/Docker, but weights use a modified OpenRAIL-M license requiring a paid commercial license for larger companies.
Two workshops demonstrate hands-on uses of Anthropic’s Claude model. In one, Andrej Karpathy breaks down his 14-minute tutorial on mastering Claude, claiming it takes about 10,000 hours to learn. In the other, Amazon quants show how they built a full AI agent in 25 minutes with just 44 lines of code, now open-sourced.
- This is just a promotional X post, not an actual article with verifiable substance—likely hype/clickbait framing around Karpathy and Claude.
- The "10,000 hours to master Claude" claim contradicts the premise of a 14-minute tutorial teaching it.
- Amazon quants reportedly rebuilt a 3-year framework in 3 days using just 44 lines of Claude-generated code, then open-sourced it.
- The open-sourced package includes pre-trained models, helper tools, and editable prompts, enabling a self-contained AI agent with no external libraries or proprietary backends.
This article argues that Ollama’s custom backend runs 30–70% slower than llama.cpp, locked users’ models in a proprietary format, and dropped proper attribution before grudgingly reverting. It also criticizes Ollama Cloud’s reliability and warns that the platform has strayed from its local-first, open-source roots.
- Ollama's custom ggml fork runs 30-70% slower than llama.cpp (e.g., 30 vs 52 tokens/sec on RTX 5090 with Qwen3 Coder 32B), despite reverting to upstream llama.cpp in v0.30.0-rc15
- Ollama locked models into a proprietary hashed-filename registry format between 2024 and early 2026, preventing reuse with other tools
- Ollama Cloud Pro users report up to 95% failure rates, long timeouts, broken tool calling, and unanswered support tickets, undermining its local-first pitch
- Ollama mislabeled distilled DeepSeek-R1 variants as the full 671B model and launched its desktop app without source code despite open-source claims
Google liberó DESIGN.md, un estándar en un solo archivo que describe tokens de diseño, valida accesibilidad según WCAG y facilita la portabilidad entre proyectos. Nace como respuesta a Claude Design de Anthropic, ofreciendo código abierto y uso gratuito para cualquier IA y plataforma.
- Google released DESIGN.md, an open-source, single-Markdown-file standard for defining design tokens (colors, typography, spacing, component roles) usable by any AI or platform
- It automatically checks accessibility against WCAG rules, flagging contrast/ARIA issues as components are generated
- The file is portable across tools (GitHub, Figma plugins, Anthropic's tools) with no lock-in or conversion needed
- It's positioned as a free, open alternative to Anthropic's closed, costly Claude Design tool
Pearcleaner is an open-source Mac utility that uninstalls apps, removes leftover files and manages development and plugin environments. The maintainer is currently busy with full-time work and a new SaaS startup but welcomes PRs and plans to resume issue triage and feature work when time allows.
- Pearcleaner development is stalled because the maintainer took a full-time job at a friend's SaaS startup, but he'll resume issue triage when time allows.
- It's a free, source-available Mac cleaner (Apache 2.0 + Commons Clause) offering orphaned file detection, CLI/drag-and-drop uninstalls, Homebrew/PKG support, and a Sentinel Monitor that uses only ~2MB RAM.
- The Commons Clause license bars any commercial monetization of the app or derivatives.
- Requires macOS 13 (Ventura) or newer, installable via GitHub releases or `brew install --cask pearcleaner`.
Lakesail rewrote Apache Spark in Rust, removing the JVM layer. The new implementation runs eight times faster and cuts infrastructure costs by 94%.
- Rewrote Apache Spark's core in Rust (~200,000 lines), eliminating the JVM entirely while keeping familiar Spark APIs
- Claims ~8x throughput improvement on standard analytics workloads by avoiding GC pauses and Java bytecode overhead
- Reports ~94% cost reduction on AWS C5 instances running identical TPC-DS queries, due to lower CPU/memory use
- Core engine is open source (Apache 2.0) and called production-ready, though advanced Spark SQL window functions and some MLlib algorithms are still missing
Paperclip is an open-source platform that turns separate AI agents into a structured organization with roles, budgets, mission context, and audit logs. It solves coordination issues like task overlap, hidden API costs, and lost state through scheduled “heartbeats,” human approval gates, and a mission-driven context chain—all via a self-hosted CLI tool.
- Paperclip wraps AI agents in an org structure (roles, budgets, mission context, audit logs) to stop duplicated work, cost overruns, and lost state across restarts.
- It hit 43,000 GitHub stars since its March 2026 launch, signaling strong demand for agent coordination tools, not just smarter individual agents.
- Its "heartbeat" system addresses agents' lack of persistent memory by having them recheck a mission-to-task context chain each cycle rather than relying on retraining.
- Unlike LangChain or CrewAI, which build individual agents or pipelines, Paperclip manages agents collectively as a company, self-hosted under MIT license with no platform fee beyond LLM costs.
This article walks through why and how to run large language models locally, covering privacy, cost, offline access, and control. It breaks down hardware needs, quantization, PC versus Mac setups, and starter software to get models up and running.
- Local open-source models (GLM-5.1, Kimi K2.6) now match or beat top cloud models like Claude Opus 4.6 on coding benchmarks, handling 70-80% of typical daily AI tasks.
- 4-bit quantization (Q4_K_M) shrinks memory needs to ~0.6-0.7 GB per billion parameters with no noticeable quality loss.
- RTX 4090 PCs outperform M3 Max Macs on speed (100-140 vs 40-60 tokens/sec) for sub-24GB models, but Mac Studios with unified memory (up to 512GB) can run massive 671B-parameter models entirely in RAM.
- A 3090 PC build costs $1,500-2,000 and draws 450W with heavy noise, versus a quieter Mac Studio pulling only ~60W while supporting far larger models.
Google has published the draft specification for DESIGN.md, letting Stitch export and import design rules across projects. This shared format lets AI agents identify color roles and check choices against WCAG accessibility standards.
- Google open-sourced the draft DESIGN.md spec, so any tool/platform can read design rules directly instead of AI guessing intent from context.
- The format encodes semantic meaning behind values (e.g. #0057FF = "primary button," not just "blue"), letting agents apply brand rules automatically across projects.
- It runs built-in WCAG contrast/accessibility checks, catching violations before they ship.
- David East's demo shows generating DESIGN.md files from scratch, editing them, and re-importing into Stitch to cut down on manual style-guide rework.
A new open-source OCR model outperformed all major commercial tools on standard text and handwriting tests. It accurately transcribed a 1913 handwritten letter by Ramanujan, preserving layout, math notation, and faint ink details.
- New open-source OCR model (Vision Transformers + glyph-aware training) beat commercial tools like ABBYY and Google Cloud Vision on IFOCR, MLT, and ICDAR benchmarks, with word-error-rates below 1.5%—30-50% better than competitors
- Successfully transcribed a faded 1913 handwritten Ramanujan letter, correctly capturing layout, math notation, and faint ink strokes
- Trained on millions of scanned pages including medieval manuscripts and dense handwritten notes
- Released under Apache license with CPU/GPU deployment scripts, enabling free text extraction from receipts, legal documents, and historical archives without per-page fees
Quodeq is an MIT-licensed tool that runs locally to scan codebases using AI across six ISO 25010 dimensions, mapping each finding to CWE identifiers and providing fix plans. It supports cloud and local models, outputs grades and violations in JSON, and includes a dashboard for exploring results and defining custom standards.
- Quodeq is a free, local, MIT-licensed AI scanner that grades code A–F across six ISO 25010 dimensions and maps findings to CWE IDs with concrete fix plans.
- It works with either cloud models (Claude, Gemini, Codex) or fully local models via Ollama, so scans can run with no telemetry or accounts.
- Sample output catches real-world issues like SQL injection, hardcoded credentials, missing rate limiting, and bare-except clauses, each with file/line references and code snippets.
- Install via pipx/pip and run from CLI or an Electron dashboard with heatmaps, trend graphs, and support for custom rule sets like Clean Architecture or DDD.
OpenAI is rolling out a new tiered Trusted Access for Cyber (TAC) program, letting verified security professionals use GPT-5.4-Cyber—a version fine-tuned for defensive tasks and binary reverse engineering. Access requires identity checks and may include restrictions like zero-data retention for high-sensitivity use cases.
- OpenAI is gating its most capable cyber model (GPT-5.4-Cyber) behind identity verification/KYC, only for vetted defenders
- Codex Security has already fixed 3,000+ high/critical vulnerabilities and helped 1,000+ open-source projects during a 6-month beta
- OpenAI classified GPT-5.4 as "high" cyber capability under its own Preparedness Framework, signaling real offensive potential that requires these access controls
- They've committed $10M to a Cybersecurity Grant Program alongside the Linux Foundation to shore up open-source security broadly
Paperclip is a platform that manages AI agents to streamline business operations. It allows users to set goals, hire agents, and monitor their performance from a centralized dashboard. Unlike traditional task managers, Paperclip integrates organizational structures and budget controls for efficient agent coordination.
- Paperclip orchestrates multiple AI agents as a unified company rather than just managing individual tasks, adding org charts, budget controls, and goal alignment on top.
- It runs locally via Node.js/pnpm with an embedded, self-managed PostgreSQL database, and can run multiple isolated companies from one deployment.
- Agents run on schedules or event triggers with built-in cost tracking to prevent budget overruns and maintain persistent state.
- The platform supports plugins, allowing extension beyond its core orchestration and governance features.
OHDSI is a collaborative initiative focused on leveraging health data for large-scale analytics. The program connects researchers and health databases worldwide, with a central hub at Columbia University. The 2025 Global Symposium brought together over 400 participants to discuss enhancing trust in science and fostering international collaboration.
- OHDSI is a global network of researchers and health databases coordinated centrally at Columbia University, built on open-source tools
- The 2025 Global Symposium drew over 400 collaborators focused on boosting trust in science and advancing international network studies
- Talks, posters, and slides from the symposium are publicly available on the event's homepage
The Humanizer skill for Claude Code transforms AI-generated text into more natural, human-like writing by addressing common patterns found in AI writing. Users can easily install the skill and apply it to their text to improve clarity and authenticity.
- It's a Claude Code skill that catalogs 24 specific AI writing tics (like "significance inflation" and "notability name-dropping") and gives before/after rewrites for each
- Installation is just cloning the GitHub repo into Claude Code's skills directory, then invoking it by pasting text or asking Claude to humanize it
- The fixes focus on cutting grandiose framing and vague jargon in favor of specific, direct, plain-language phrasing
Three MIT PhD students reverse-engineered Google's AlphaFold 3, creating Boltz-1 as an open-source alternative for drug discovery. Their platform enables pharmaceutical companies to conduct rapid and cost-effective drug-binding predictions while maintaining free access to the underlying models. Boltz aims to challenge commercial restrictions and offer a more accessible solution within the competitive landscape of AI in drug discovery.
- Three MIT PhD students reverse-engineered AlphaFold 3's methodology and released it as open-source Boltz-1, bypassing Google's restrictive licensing.
- Pharmaceutical companies can now run drug-binding predictions rapidly and cheaply without paying for or being restricted by Google's commercial terms.
- The project directly challenges the trend of AI drug-discovery tools being locked behind corporate control, pushing the field back toward open access.
Boltz is launching a transformative approach to drug design and biological research by combining AI and open science, enabling over 100,000 scientists to innovate faster. With a newly raised $28 million seed round and a partnership with Pfizer, Boltz aims to break down barriers in drug development through open-source models and accessible computational tools.
- Boltz raised a $28 million seed round and partnered with Pfizer to advance open-source drug design models.
- Over 100,000 scientists are already using Boltz's tools, positioning it as a widely-adopted open-science alternative to closed AI drug discovery platforms.
- The core bet is that open-source, freely accessible AI models can accelerate biological research and drug development faster than proprietary approaches.
User feedback is highly valued and taken seriously by the team. For a comprehensive list of available qualifiers, users are directed to the documentation. An error occurred while loading the page, prompting a reload.
- The page failed to load properly, so the actual leaked Anthropic system prompt content isn't accessible in this summary
- The repository (x1xhlol/system-prompts-and-models-of-ai-tools) compiles system prompts from AI tools, positioning it as a reference resource rather than original commentary
A 3D flight simulator allows users to fly aircraft or drive cars over real-world terrain using Cesium, React, and TypeScript. Features include multiple camera modes, real-time tracking with a mini-map, and teleportation to famous locations. The project is open-source and provides instructions for setup and usage.
- Built with Cesium, React, and TypeScript to simulate flying aircraft or driving cars over real-world global terrain
- Includes multiple camera modes and a real-time mini-map for tracking position
- Supports teleportation to famous real-world locations
- Fully open-source with setup and usage instructions provided