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GitHub Hot — 18 April 2026

18 April 2026·23 min readGitHubOpen SourceTools

Top 10 repos trending on GitHub this week — what they do, why they matter, and how to use them in your projects.

1. getagentseal/codeburn

2,704 stars this week · TypeScript · ai-coding claude-code cli codex

CodeBurn is a zero-config TUI dashboard that reads AI coding tool session files from disk and shows you exactly where your Claude Code, Cursor, or Codex tokens are being spent — by project, task type, and model.

Use case

When you're shipping a Next.js/Supabase blog with heavy AI assistance, you have no visibility into whether your $50 Claude Code bill this month came from schema migrations, component generation, or debugging TypeScript errors. CodeBurn parses session files locally (no proxy, no API key) and surfaces one-shot success rates per activity — so you can see that, say, your auth refactor burned 40k tokens across 12 retries while your component scaffolding nails it first try.

Why it's trending

Claude Code just crossed mainstream adoption and Cursor's API costs are genuinely shocking developers for the first time — people are getting surprise bills and have zero tooling to audit them. CodeBurn fills that gap with zero setup friction (npx codeburn and you're in).

How to use it

  1. Run npx codeburn in your terminal — no install required, no config, no API keys needed.
  2. It auto-discovers session data from Claude Code (~/.claude/), Cursor, and Codex on disk — just navigate the TUI with arrow keys.
  3. Filter by project directory to isolate costs for a specific repo (e.g., your blog vs. a side project).
  4. Use the task-type breakdown to identify which activity categories (test, fix, refactor) have low one-shot rates and are burning disproportionate tokens.
  5. Export a CSV snapshot with the export shortcut to pipe into a spreadsheet or log to Supabase for historical tracking.

How I could use this

  1. Build a 'AI Dev Cost Transparency' blog post series: run CodeBurn weekly while building your blog, export the CSV data, store it in a Supabase table, and render a live public dashboard page on your blog showing cumulative token spend by feature shipped — a genuinely unique form of build-in-public content that differentiates you from typical dev blogs.
  2. Create a personal ROI tracker for your career tools project: log CodeBurn CSV exports alongside git commit counts and features shipped per week into Supabase, then build a simple Next.js page that calculates your 'cost per shipped feature' over time — concrete evidence of AI-assisted productivity you can reference in interviews or your resume.
  3. Wire CodeBurn's CSV export into a weekly automated digest: write a Node.js cron script that runs npx codeburn --export json, parses the output, and posts a Slack/Discord message or email summary showing your top 3 token-burning activities — then use that signal to decide which parts of your AI coding workflow to prompt-engineer or batch differently to cut costs.

2. Robbyant/lingbot-map

1,815 stars this week · Python

LingBot-Map is a real-time 3D scene reconstruction model that processes streaming video at ~20 FPS without needing iterative optimization passes, making live 3D mapping actually practical.

Use case

Traditional 3D reconstruction (NeRF, SLAM, photogrammetry) requires either expensive offline processing or accumulated drift errors over long sequences. LingBot-Map solves this by running feed-forward inference on a video stream with a paged KV cache that maintains geometric consistency across 10,000+ frames — think scanning an entire building with a phone camera and getting a clean 3D map in real time, not hours later.

Why it's trending

Spatial computing and AR/VR tooling are heating up around Apple Vision Pro and Meta's push into mixed reality, and a model that does stable long-sequence 3D reconstruction at 20 FPS on commodity GPU hardware is a direct unlock for that stack. The HuggingFace model drop this week made it immediately accessible without reproducing training.

How to use it

  1. Set up the environment and install dependencies:
conda create -n lingbot-map python=3.10 -y && conda activate lingbot-map
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt
  1. Pull the model from HuggingFace:
from huggingface_hub import snapshot_download
snapshot_download('robbyant/lingbot-map', local_dir='./weights')
  1. Run inference on a video file or webcam stream using their provided demo script:
python demo.py --input your_video.mp4 --weights ./weights --output ./output_3d
  1. Inspect the output pointcloud or mesh in MeshLab or use Open3D in Python to load and visualize it programmatically.
  2. For streaming use, pipe frames from OpenCV into the model's streaming API rather than batching — the paged KV cache is designed to handle indefinite-length sequences without memory blowup.

How I could use this

  1. Build a 'scan my desk setup' interactive blog post where readers upload a short phone video of their workspace and your Next.js app calls a Python FastAPI wrapper around LingBot-Map to return an embedded 3D pointcloud viewer (Three.js/React Three Fiber) — a genuinely viral demo that showcases both the model and your full-stack skills.
  2. Create a portfolio piece that auto-generates a 3D walkthrough of a physical project space (e.g., a makerspace, home lab, or office) from recorded footage, then embeds it on your personal site as a living 'about me' environment — far more memorable than a headshot and bio.
  3. Prototype an AI blog post format where technical tutorials about physical setups (e.g., 'my mechanical keyboard build') include a navigable 3D reconstruction of the subject alongside the text, generated from a short video you record while writing — differentiated content that no static blog can replicate.

3. Nightmare-Eclipse/RedSun

1,492 stars this week · C++

RedSun is a Windows Defender privilege escalation PoC that abuses Defender's own file-restoration behavior to overwrite system files and gain SYSTEM-level access.

Use case

This exposes a logic vulnerability where Windows Defender, upon detecting a 'malicious' cloud-tagged file, reinstates it to its original path rather than quarantining or deleting it — an attacker can craft a payload, let Defender 'find' it, and have Defender itself write the file into a protected system location. The real-world implication: a low-privilege user process could achieve arbitrary file write to System32 or similar protected directories without triggering UAC, purely by weaponizing the antivirus engine. This is relevant to any Windows environment running Defender with cloud protection enabled, which is the default for most enterprise and consumer machines.

Why it's trending

This dropped recently and the irony of an antivirus actively enabling the attack vector is generating significant attention in the security community — it's a logic bug in a trust boundary that Microsoft has been slow to acknowledge, making it a hot topic on infosec Twitter and in CVE-watch circles this week.

How to use it

  1. DO NOT run this on any system you don't own — this is for security research in isolated VMs only. Set up a Windows 11 VM with snapshots and Defender cloud protection enabled.,2. Read the repo's README carefully — the author deliberately withholds the full PoC but describes the mechanism: craft a file with a cloud detection tag that Defender will flag, then position it such that Defender's restoration target path is a privileged system location.,3. Study the Windows Defender cloud protection flow using WinDbg or Process Monitor — watch for MsMpEng.exe file write operations after a detection event to understand the restoration path logic.,4. Cross-reference with prior 'antivirus as a weapon' research (e.g., Aikido's AVGater, or the 'bring your own vulnerability' class of bugs) to understand the attack family before experimenting.,5. If doing defensive research, use Sysmon + Event ID 4663 (file write auditing) to detect anomalous writes by MsMpEng.exe to system directories as a detection signature.

How I could use this

  1. Write a technical deep-dive blog post titled 'When Your Antivirus Is the Attack Vector' — walk through the logic flaw class (antivirus-as-primitive), compare it to AVGater and similar bugs, and include a Mermaid.js diagram showing the Defender restoration flow. This kind of explainer for a well-known-but-complex vuln performs extremely well on Hacker News and gets dev-adjacent security readers.
  2. Build a 'Security Digest' sidebar widget for the blog using the GitHub API + Supabase — auto-fetch trending security repos weekly, store metadata (stars delta, language, topics), and surface them with AI-generated one-line summaries via OpenAI. This showcases full-stack + AI integration and keeps the blog content self-updating without manual curation.
  3. Create an AI-assisted 'Vulnerability Explainer' tool as a portfolio project — user pastes a CVE ID or GitHub repo URL, and GPT-4 with a structured prompt generates a plain-English breakdown (attack vector, affected systems, mitigation) stored in Supabase. This directly demonstrates RAG-adjacent tooling, TypeScript API routes in Next.js, and is a genuinely useful security learning tool you can ship publicly.

4. vercel-labs/wterm

1,472 stars this week · TypeScript

A high-performance web terminal built on a Zig/WASM core that renders to the DOM, giving you native text selection, accessibility, and near-native VT100/VT220 emulation inside any React app.

Use case

Building interactive code demos or live shell environments in a browser has historically meant shipping heavy canvas-based terminals (xterm.js) that break native text selection and accessibility. wterm solves this by rendering to actual DOM nodes, so users can Cmd+F to search output, screen readers work out of the box, and the ~12KB WASM core handles escape sequences without a bloated JS parser. Concrete example: you want readers of your blog to run code snippets against a real bash shell in the browser without leaving the page.

Why it's trending

Vercel Labs dropped this right as the AI coding tool wave is peaking — every AI product (v0, Cursor, Replit) needs an embedded terminal, and xterm.js's canvas approach is showing its age. The React package and WebSocket PTY transport make it a drop-in for Next.js apps, which is exactly the stack most builders are on right now.

How to use it

  1. Install the React package: npm install @wterm/react @wterm/core
  2. Spin up a PTY WebSocket backend (e.g. node-pty + ws): connect it on a route like /api/pty that forks a shell and pipes I/O over binary WebSocket frames.
  3. Drop the component into your Next.js page:
import { Terminal } from '@wterm/react';

export default function DemoPage() {
  return (
    <Terminal
      wsUrl="wss://yoursite.com/api/pty"
      theme="monokai"
      style={{ height: '400px', width: '100%' }}
    />
  );
}
  1. For a zero-backend demo, use @wterm/just-bash to run an in-browser bash shell with no server needed.
  2. Style with CSS custom properties (--wterm-bg, --wterm-fg, etc.) to match your blog's design system.

How I could use this

  1. Embed a live, in-browser bash shell on each code-focused blog post using @wterm/just-bash so readers can immediately run the exact commands you describe — no copy-paste into a separate terminal, no 'try it on CodeSandbox' redirect.
  2. Build an interactive 'CLI resume' feature: wire wterm to a fake PTY backend (a Next.js API route returning scripted responses) so recruiters can type cat experience.txt, ls projects/, or curl henry.dev/skills and get your resume data back as terminal output — memorable and very shareable.
  3. Create an AI pair-programming widget: connect wterm to a WebSocket backend that proxies commands through GPT-4o before executing them in a sandboxed shell — the AI can intercept git commit -m '' with an empty message and auto-generate one, or suggest a safer rm flag before the command runs.

5. Mouseww/anything-analyzer

1,346 stars this week · TypeScript · 2api ai-tools analysis-cli api-analysis

A unified traffic capture + MITM proxy + AI analysis tool that lets you intercept and reverse-engineer API calls from any source (browser, desktop app, CLI, mobile) in one Electron GUI.

Use case

Traditional tools like Charles or Wireshark force you to switch contexts and manually sift through hundreds of requests. Anything Analyzer solves this by funneling all traffic — regardless of origin — into one session and letting an AI agent automatically generate protocol reverse-engineering reports. Concrete example: you want to replicate a third-party site's undocumented API for your blog's data pipeline — run this tool, browse the site in the embedded browser, and get an AI-generated breakdown of auth headers, request signatures, and payload schemas without manual digging.

Why it's trending

The MCP (Model Context Protocol) server integration is the hook — it makes this tool a first-class citizen in AI agent workflows and IDE copilot pipelines, which is a hot topic post-Claude MCP adoption. Developers building AI agents that need to interact with opaque external APIs are finding this immediately useful.

How to use it

  1. Clone and install: git clone https://github.com/Mouseww/anything-analyzer && cd anything-analyzer && npm install && npm run dev to launch the Electron app.
  2. Start a session: open the embedded browser or configure your system proxy to point to 127.0.0.1:8888 (the built-in MITM proxy).
  3. Browse the target site or run your script (e.g., HTTP_PROXY=http://127.0.0.1:8888 python your_script.py) — all requests land in the unified session panel.
  4. Select the captured requests you care about, click 'AI Analysis', and configure your LLM API key — it generates a structured report covering auth patterns, encrypted fields, and reproducible curl equivalents.
  5. Optionally expose the built-in MCP server endpoint so a Claude/Cursor agent can query captured traffic programmatically: the MCP server lets your AI agent ask 'what headers does this API require?' and get structured answers from live traffic.

How I could use this

  1. Use it to reverse-engineer Substack's or Medium's internal content API — capture the exact requests their frontend makes, replicate the auth flow, and build a serverless Next.js route in your blog that pulls cross-platform read stats or follower counts into a live /stats page without waiting for an official API.
  2. Point it at LinkedIn or job board sites while browsing job listings to capture their search API parameters and pagination logic — then build a lightweight personal job tracker that calls their API directly from a Supabase Edge Function, populating a private dashboard with role/salary/location data filtered to your criteria without scraping HTML.
  3. Wire the MCP server into your Cursor or Claude Dev setup so your AI coding assistant can autonomously inspect what API shape a third-party service actually uses at runtime — useful when building AI feature integrations where the SDK docs are wrong or incomplete, letting the agent self-correct its API call generation based on real captured traffic.

6. browser-use/video-use

1,227 stars this week · Python

video-use lets Claude Code autonomously edit raw video footage into a polished final cut using ffmpeg, Manim, and ElevenLabs — no video editor UI required.

Use case

Developers and content creators who record tutorials or demos face tedious manual editing: cutting filler words, color grading, adding subtitles, syncing audio. video-use solves this by letting you drop raw .mp4 files in a folder, describe what you want in plain English to Claude Code, and get back a finished final.mp4 — for example, recording 10 raw takes of a coding tutorial and getting a tightly edited, subtitled, color-graded video in one session.

Why it's trending

It's riding the Claude Code agent wave — people are discovering that Claude Code's skills/tools system can orchestrate entire multi-step workflows, not just write snippets. Launching from browser-use (an established AI automation org) gives it instant credibility and distribution.

How to use it

  1. Clone and symlink into Claude Code's skills directory:
git clone https://github.com/browser-use/video-use
cd video-use
ln -s "$(pwd)" ~/.claude/skills/video-use
pip install -e .
brew install ffmpeg
  1. Add your ElevenLabs API key to .env (for TTS/subtitle generation).
  2. Navigate to a folder of raw video takes:
cd ~/recordings/blog-post-demo
claude
  1. In the Claude Code session, describe your edit goal: 'cut filler words, add 2-word uppercase subtitles, warm cinematic grade, export as launch video'.
  2. Review the proposed strategy Claude outputs before it renders — it waits for your OK before producing edit/final.mp4.

How I could use this

  1. Auto-generate polished 'blog post companion videos' — record yourself explaining a new post in one take, drop it into video-use, and have Claude cut filler words, burn subtitles, and produce a 90-second video embed to attach to each blog post for YouTube/Twitter distribution.
  2. Build a 'portfolio demo reel' pipeline — record raw screen captures of your Supabase+Next.js projects, feed them all into one video-use session with the prompt 'edit into a 2-minute developer portfolio reel with code overlays', and ship it to your LinkedIn and personal site instead of spending hours in Premiere.
  3. Create an AI project explainer factory — whenever you ship a new AI feature on your blog (e.g. a new RAG pipeline or embeddings search), record a raw walkthrough, use video-use to auto-generate a Manim animation overlay visualizing the vector search flow, and embed the final video alongside the technical write-up to dramatically increase time-on-page.

7. alchaincyf/darwin-skill

1,190 stars this week · HTML

Darwin-skill applies Karpathy's autoresearch loop (evaluate → improve → test → keep/revert) to Claude Code SKILL.md files, so your AI agent skills self-optimize instead of rotting over time.

Use case

When you accumulate dozens of Claude Code / agent skills, manually reviewing SKILL.md files for quality becomes unscalable — and format-correct skills can still perform terribly at runtime. Darwin-skill scores each skill across 8 weighted dimensions (structure + live test output), proposes improvements via a sub-agent, runs your test prompts, and only commits changes that measurably raise the score — automatically rolling back regressions.

Why it's trending

Andrej Karpathy's autoresearch repo dropped recently and sparked a wave of 'apply self-improving loops to X' projects; this is the first credible adaptation targeting the fast-growing Claude Code / skills.sh ecosystem specifically, hitting at exactly the moment developers are accumulating unwieldy skill libraries.

How to use it

  1. Install the skill into your Claude Code project: npx skills add alchaincyf/darwin-skill
  2. Drop your existing SKILL.md files into the skills directory Claude Code already reads.
  3. Create a test-prompts.json alongside each skill — a small array of realistic prompts that skill should handle well (e.g. [{"prompt": "summarize this blog post in 3 bullets", "expectedBehavior": "returns exactly 3 bullet points"}]).
  4. Invoke the darwin-skill from within Claude Code: tell Claude to 'run darwin optimization on skills/blog-summarizer.skill.md' — the system scores it, generates a candidate improvement, runs the test prompts with a separate evaluator sub-agent, and either keeps or reverts.
  5. Confirm or reject each optimized skill at the human-in-the-loop checkpoint before it moves to the next one.

How I could use this

  1. Build a blog-writing.skill.md that encodes your personal writing style rules (tone, heading structure, call-to-action placement), then run darwin-skill weekly against a test-prompts.json of real post outlines — so the skill continuously tightens toward your actual output quality rather than drifting.
  2. Create a resume-tailor.skill.md that rewrites resume bullet points for a given job description, with test prompts sampled from real JD/resume pairs you've collected — darwin-skill will surface which prompt phrasings and constraint rules actually improve ATS-friendly output versus ones that just look correct structurally.
  3. Wire darwin-skill into a nightly Supabase Edge Function cron: store skill scores in a skill_versions table, trigger an optimization run on any skill whose rolling 7-day test-pass rate drops below a threshold, and surface a Slack/email diff of what changed — giving you a self-healing AI layer for your blog's content generation pipeline.

8. lewislulu/html-ppt-skill

1,135 stars this week · HTML

A zero-build-step AgentSkill that generates polished HTML presentations with 36 themes, 31 layouts, and a full presenter mode — entirely from static HTML/CSS/JS.

Use case

When you need to programmatically produce professional slide decks from AI-generated content without spinning up PowerPoint, Google Slides APIs, or a headless browser pipeline. Concrete example: an AI writing assistant outputs a blog post summary, and instead of copy-pasting into Keynote, you pipe the structured content into this skill to instantly generate a shareable, self-contained HTML deck with speaker notes and a timer.

Why it's trending

The 'AgentSkill' framing is hitting at exactly the right moment — developers are actively wiring tools like this into LLM agents (Claude, GPT-4o) as callable functions, and a zero-dependency static HTML output is uniquely portable compared to PPTX or PDF generation libraries. It's trending because it solves the 'last mile' output problem for AI content pipelines.

How to use it

  1. Clone the repo: git clone https://github.com/lewislulu/html-ppt-skill && cd html-ppt-skill
  2. Open any template directly in a browser — no build step: open templates/tech-dark/deck.html
  3. Press S in the deck to launch presenter mode with speaker notes, next-slide preview, and timer in a synced second window.
  4. To generate a custom deck programmatically, call the AgentSkill with a JSON slides payload — the skill injects your content into the chosen theme/layout template and returns a self-contained HTML string.
  5. In your Next.js app, serve the returned HTML as a static download or render it inside a sandboxed <iframe> using a srcdoc attribute: <iframe sandbox='allow-scripts' srcdoc={generatedHtml} />

How I could use this

  1. Blog post → slide deck converter: add a 'Generate Slides' button on each post page that sends the post's headings and paragraphs to GPT-4o with a structured prompt, maps the output to this skill's JSON schema, and serves the resulting HTML deck as a downloadable file — instant conference-talk-ready slides from any article Henry writes.
  2. AI portfolio case study presenter: build a '/case-studies' route where each project auto-generates a live HTML deck (embedded in an iframe) summarizing the problem, solution, stack, and results — recruiters get an interactive presentation instead of a static PDF, and Henry can press S to demo it live in interviews.
  3. Supabase-backed slide generator API: create a Next.js API route that accepts a topic string, calls OpenAI to produce structured slide content, persists the raw JSON to Supabase, renders it through this skill into HTML, and stores the output URL — giving Henry a personal 'presentation history' dashboard where he can track, re-render, or fork any previously generated deck.

9. Manavarya09/design-extract

995 stars this week · JavaScript · accessibility agent-skill ai chrome-extension

One CLI command scrapes any live website's complete design system into DTCG tokens, Tailwind config, shadcn/ui theme, Figma variables, and an LLM-ready markdown file — no Figma access required.

Use case

Designers and developers constantly waste hours reverse-engineering a client's or competitor's design system by hand — copying hex values, measuring spacing, guessing font stacks. designlang solves this by running a headless Playwright browser against any live URL, extracting every computed style from the real DOM (including hover states and responsive breakpoints), and outputting production-ready token files. Concrete example: you're rebuilding a client's site in Next.js and need their exact design system — run npx designlang https://theircurrentsite.com --full and in seconds you have a Tailwind config, shadcn/ui theme, and WCAG audit report.

Why it's trending

MCP (Model Context Protocol) server support for Claude Code, Cursor, and Windsurf just landed, making it directly usable as an AI agent skill inside the dev tools that thousands of developers switched to this month — that native integration is the spike.

How to use it

  1. Run npx designlang https://target-site.com --full (no install needed, Node 20+ required). 2. Grab *-design-tokens.json and drop it into your Tailwind v4 config or shadcn/ui theme setup. 3. Feed *-design-language.md directly to Claude/GPT with a prompt like 'Recreate this design system in my Next.js project using these tokens'. 4. Use the *-preview.html WCAG audit report to catch accessibility issues before your own site launches. 5. For ongoing sync, wire it into your CI pipeline: npx designlang https://yourlivesite.com --output ./tokens to keep local tokens in sync with the deployed site.
# Extract Stripe's design system and pipe the AI markdown to Claude
npx designlang https://stripe.com --full
cat stripe-design-language.md | pbcopy  # paste into Claude

How I could use this

  1. Run npx designlang against 3-4 top dev blogs (Josh Comeau, Lee Robinson, Overreacted) and feed all their *-design-language.md files into Claude with the prompt 'Synthesize a cohesive personal blog design system from these'. Use the output Tailwind config directly in Henry's blog — grounded in proven aesthetic choices, not guesswork.
  2. Build a 'Design System Diff' career tool: run designlang against a target company's public site before an interview, compare their token output to your portfolio's token file, and auto-generate a talking point — 'I noticed your design system uses an 8px base grid and APCA contrast; here's how I implemented similar patterns in my work' — as interview prep context.
  3. Wire designlang as an MCP tool in Cursor so that when Henry's AI blog generates content pages, it can auto-extract design tokens from any URL he pastes as a reference, then automatically apply matching typography and color tokens to the new page — making 'design like site X' a one-shot prompt instead of a manual process.

10. patterniha/SNI-Spoofing

972 stars this week · Python

A Python tool that manipulates IP/TCP headers to spoof SNI fields and bypass Deep Packet Inspection (DPI) firewalls used by ISPs and governments.

Use case

ISPs and government firewalls use DPI to inspect the SNI (Server Name Indication) field in TLS handshakes to block specific domains — even over HTTPS. This tool fragments or manipulates TCP packets so the SNI field is split across multiple packets, making it unreadable to DPI middleboxes while remaining valid to the destination server. Concrete example: a developer in Iran or Russia trying to reach GitHub, Supabase, or OpenAI APIs that are blocked at the ISP level.

Why it's trending

Spiking due to renewed internet censorship crackdowns in Iran and other regions, where developers are actively seeking low-level circumvention tools that don't require a full VPN. The Telegram community links suggest an active Persian-speaking dev community rallying around it this week.

How to use it

  1. Clone the repo: git clone https://github.com/patterniha/SNI-Spoofing && cd SNI-Spoofing
  2. Install dependencies (requires raw socket access, so run as root or with sudo): pip install scapy
  3. Identify your network interface (e.g., eth0, wlan0) and the target domain being blocked.
  4. Run the tool with elevated privileges, specifying your interface and target: sudo python3 sni_spoof.py --iface eth0 --host blocked-domain.com
  5. Route your browser or app traffic through the local proxy the tool sets up and verify connectivity. Note: this requires a Linux environment with raw socket permissions — WSL2 or a VPS works well.

How I could use this

  1. Write a deep-dive technical blog post titled 'How DPI Firewalls Read Your HTTPS Traffic (And How SNI Spoofing Breaks It)' — visualize the TLS handshake packet flow using diagrams, explain the SNI field in the ClientHello, and benchmark this tool vs. alternatives like ESNI/ECH. This is highly searchable content for developers in censored regions.
  2. Build a 'Developer Censorship Toolkit' page on your portfolio that aggregates open-source circumvention tools (SNI spoofing, Shadowsocks, WARP) with a comparison table — latency impact, OS support, detection risk. Position it as a curated resource for developers working in restricted environments, which is a genuine career differentiator if you're targeting remote-friendly global companies.
  3. Integrate a Supabase Edge Function health-check system into your blog that pings your own API endpoints from multiple geographies and logs which ones fail — then surface a banner like 'This API may be blocked in your region' with a link to circumvention options. Pair it with an AI assistant that recommends the right tool based on the user's detected country code.
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