GitHub Hot — 16 April 2026

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

1. yizhiyanhua-ai/fireworks-tech-graph

3,345 stars this week · Python

A Claude Code skill that converts plain-English descriptions into publication-ready SVG+PNG technical diagrams across 14 diagram types and 7 visual styles — no manual diagramming tools required.

Use case

Technical writers and engineers waste hours in Lucidchart or draw.io keeping architecture diagrams in sync with evolving systems. This repo lets you describe a system in a sentence ('RAG pipeline with reranking, dark terminal style') and get a 1920px retina-ready PNG in seconds — ideal for blog post headers, RFC docs, or portfolio visuals that need to look polished without a design team.

Why it's trending

Claude Code's skill/plugin ecosystem just hit critical mass and this is one of the first high-quality, domain-specific skills shipping with real AI/Agent pattern knowledge (RAG, Mem0, multi-agent flows) — exactly the diagrams developers need right now but nobody wants to draw by hand.

How to use it

1. Install Claude Code and confirm the skill runner is available in your environment.
2. Clone the repo: git clone https://github.com/yizhiyanhua-ai/fireworks-tech-graph && cd fireworks-tech-graph
3. Install the system dependency for PNG export: brew install librsvg (macOS) or apt-get install librsvg2-bin (Linux).
4. Register the skill with Claude Code per the README, then invoke it in a Claude Code session:

# Inside Claude Code chat
@fireworks-tech-graph Generate a RAG pipeline diagram showing query → retriever → reranker → LLM, blueprint style

5. Collect the output files (rag-pipeline.svg + rag-pipeline.png) from your working directory — drop them directly into your MDX blog post or README.

How I could use this

1. Auto-generate a fresh architecture diagram for every major blog post — wire up a Next.js API route that calls Claude Code with the post's frontmatter description field and stores the resulting PNG in Supabase Storage, then renders it as the post's hero image automatically.
2. Build a 'System Design Explainer' page on the blog where visitors type a system description, hit generate, and see a live SVG rendered inline — powered by a Supabase Edge Function invoking Claude with the fireworks-tech-graph skill, making the blog itself a shareable diagramming tool for other developers.
3. Generate visual changelogs: each time Henry merges a PR that touches his AI pipeline (e.g., adds a new agent or tool), a GitHub Action runs the skill against the updated architecture description in a markdown file and commits the new PNG to the repo — keeping docs permanently in sync with zero manual effort.

2. AgentSeal/codeburn

2,244 stars this week · TypeScript · ai-coding claude-code cli codex

CodeBurn is a zero-config TUI dashboard that reads AI coding tool logs directly from disk to show you exactly which tasks, projects, and models are burning your token budget.

Use case

When you're using Claude Code or Cursor daily, your AI spend becomes invisible until the billing hits. CodeBurn parses session files already written to disk by these tools and surfaces breakdowns like 'you spent $12 this week on test-fix retry loops in your auth module' — no proxy, no instrumentation, no changed workflow required. It's the difference between a surprise invoice and an actual feedback loop on how you're prompting.

Why it's trending

Claude Code crossed critical mass adoption this month and developers are genuinely shocked by their token bills — CodeBurn dropped at exactly the right moment to address the 'I have no idea where my $50 went' problem that's dominating dev Twitter right now. The zero-friction install (npx, reads existing files) removes every excuse not to run it.

How to use it

1. Run npx codeburn in your terminal — no install required, no config files, no API keys needed.
2. It auto-discovers session data from Claude Code (~/.claude/), Cursor, and Codex on disk — just use your tools normally first to generate data.
3. Navigate the TUI with arrow keys: drill into cost by project, task type (edit/test/fix), model, and MCP server.
4. Identify your worst one-shot failure categories (e.g., 'refactor' tasks burn 3x tokens vs. 'explain' tasks) and adjust your prompting strategy.
5. Export a CSV with codeburn --export csv > ai-spend.csv to pipe into a spreadsheet or Supabase table for historical tracking.

How I could use this

1. Add a 'AI Cost per Blog Post' widget to your blog's admin dashboard: export CodeBurn CSV data to a Supabase table keyed by date, then surface a small stat on each post's edit page showing the approximate token cost to write/refactor that post's codebase changes — makes a great 'building in public' transparency feature.
2. Build a weekly AI spend digest email for your career tools: run codeburn --export json in a cron job, push the JSON to a Supabase edge function, and send yourself (or subscribers) a Monday morning summary comparing token burn across your resume-matcher, cover letter generator, and blog projects — concrete ROI data for deciding which AI features to keep.
3. Write a blog post series benchmarking Claude Code vs. Codex on your actual Next.js/Supabase stack using CodeBurn's one-shot success rate metric: run both tools on identical tasks (write a Supabase RLS policy, generate a TypeScript API route), capture the retry token cost diff, and publish the numbers — this kind of empirical, reproducible comparison would stand out heavily in the AI tooling content space right now.

3. OpenMOSS/MOSS-TTS-Nano

1,224 stars this week · Python · audio-tokenizer chinese english multi-modality

A 0.1B parameter TTS model that runs on CPU in real-time, making on-device speech synthesis actually deployable without a GPU budget.

Use case

Most open-source TTS models require a GPU to hit acceptable latency, making them impractical for hobby servers or edge deployments. MOSS-TTS-Nano solves this by fitting in 0.1B parameters while still supporting multilingual output and voice cloning — so you can run a streaming TTS API on a $5/mo VPS or a Raspberry Pi without any CUDA dependency.

Why it's trending

The 'small model' wave is peaking right now as developers want local AI that doesn't require cloud spend, and CPU-runnable TTS is still a rare combination. The voice-cloning capability on top of tiny size is the specific trigger driving stars this week.

How to use it

1. Install dependencies: pip install moss-tts or clone the repo and pip install -e .
2. Download the model weights from HuggingFace: huggingface-cli download OpenMOSS-Team/MOSS-TTS-Nano --local-dir ./moss-tts-nano
3. Run inference in Python:

from moss_tts import MossTTSNano

model = MossTTSNano.from_pretrained('./moss-tts-nano')
audio = model.synthesize(
    text='Hello, this is Henry speaking.',
    language='en'
)
audio.save('output.wav')

4. For streaming output, use the stream=True flag and pipe chunks to a WebSocket or HTTP streaming response in your Next.js API route.
5. For voice cloning, pass a 3-10 second reference WAV via reference_audio='your_voice.wav' to bind synthesis to a specific speaker.

How I could use this

1. Add a 'Listen to this post' button on each blog article that calls a Next.js API route wrapping MOSS-TTS-Nano running on a cheap VPS — streams the blog post text as audio without any cloud TTS cost, and lets you clone your own voice as the narrator using a reference recording.
2. Build a portfolio narration feature: when a recruiter visits Henry's resume or case study page, a floating audio player auto-reads the 'about me' section in Henry's cloned voice — a memorable differentiator that's technically impressive and deployable at zero marginal cost.
3. Create an AI interview prep tool where users paste a job description, the app generates practice questions via an LLM, then reads each question aloud using MOSS-TTS-Nano with a configurable voice — giving a realistic spoken interview simulation loop entirely server-side without paying per character to a cloud TTS API.

4. Mouseww/anything-analyzer

1,016 stars this week · TypeScript

An Electron-based universal traffic interceptor that combines MITM proxy + CDP capture with AI-powered protocol reverse-engineering — think Fiddler + Charles + an AI analyst in one tool.

Use case

When you need to integrate with a third-party service that has no public API docs, you'd normally spend hours manually sifting through hundreds of network requests in DevTools or Fiddler. Anything Analyzer captures all traffic (browser, desktop apps, CLI tools, mobile apps via Wi-Fi proxy) into a single session and then uses an AI model to automatically generate a structured protocol analysis doc — auth flows, request signatures, encryption schemes — so you can build an unofficial SDK in minutes instead of days.

Why it's trending

Reverse-engineering undocumented APIs is a perennial developer pain point, and the LLM wave has finally made automated protocol analysis practical — this repo hits that intersection at exactly the right moment. It also fills a genuine tooling gap: no existing free tool combines cross-source capture (browser + system proxy + mobile) with AI summarization in a single desktop app.

How to use it

1. Clone and install: git clone https://github.com/Mouseww/anything-analyzer && cd anything-analyzer && npm install
2. Start the Electron app: npm run dev — this launches the embedded browser and starts the MITM proxy on port 8888.
3. For browser targets, navigate to any site inside the embedded browser and interact normally; for desktop/CLI targets, point your system proxy or set HTTP_PROXY=http://127.0.0.1:8888 in your terminal session.
4. For mobile/IoT, connect your device to the same Wi-Fi and set the proxy to your machine's IP:8888, then install the generated MITM cert on the device.
5. Once you've captured the relevant session, click 'AI Analyze' — paste in your OpenAI/Claude API key in settings, and the tool sends the captured requests to the model and returns a markdown protocol doc covering endpoints, auth headers, payload structure, and any detected encryption patterns.

How I could use this

1. Use it to reverse-engineer Supabase's internal dashboard API calls — capture what the Supabase Studio frontend sends when you run a query or manage RLS policies, then build a custom admin panel widget for Henry's blog that exposes only the specific DB operations he needs, bypassing the full Studio UI.
2. Capture the exact network protocol of a job board site (LinkedIn, Greenhouse, Lever) to auto-fetch and normalize job postings into Henry's Supabase DB, powering a 'jobs I'm tracking' career dashboard on his portfolio without waiting for an official API key approval.
3. Intercept and document the AI provider API calls (e.g., Vercel AI SDK internals, or a competitor's streaming endpoint) while dogfooding Henry's own blog's AI features, then publish a detailed 'how streaming AI responses actually work over the wire' technical post — a high-signal SEO magnet that also showcases reverse-engineering chops to potential employers.

5. vercel-labs/wterm

1,001 stars this week · TypeScript

wterm is a React-compatible, DOM-rendered terminal emulator backed by a Zig/WASM VT100 parser — it gives you a real shell in the browser with native text selection, accessibility, and near-native performance at ~12 KB core.

Use case

The real problem is that existing web terminals (xterm.js) render to Canvas, which breaks native browser text selection, Ctrl+F find, and screen readers. wterm solves this by rendering actual DOM nodes, so a blog post about a CLI tool could embed a live, interactive terminal demo where readers can copy output normally and search with the browser's built-in find — no custom clipboard hacks required.

Why it's trending

Vercel Labs dropped this publicly this week and it immediately signals a shift away from Canvas-based terminals; the Zig+WASM architecture story is also compelling to the performance-focused TypeScript community right now.

How to use it

1. Install the React package: npm install @wterm/react.
2. Import and drop in the component:

import { Terminal } from '@wterm/react';

export default function LiveDemo() {
  return (
    <Terminal
      websocketUrl="wss://your-pty-backend/ws"
      theme="monokai"
      rows={24}
      cols={80}
    />
  );
}

3. Spin up a PTY backend (e.g. a small Node.js server using node-pty + ws) that wterm connects to over WebSocket — wterm handles binary framing and reconnection automatically.
4. For a zero-backend demo, swap the WebSocket for @wterm/just-bash to get an in-browser Bash shell with no server at all.
5. Style with CSS custom properties (--wterm-bg, --wterm-fg, etc.) to match your site theme.

How I could use this

1. Embed an interactive code-snippet runner directly inside blog posts: when Henry writes a tutorial on a Supabase CLI command or a curl API call, render a wterm instance pre-loaded with @wterm/just-bash so readers can actually run the commands in-browser without leaving the post — far more engaging than static code blocks.
2. Build a portfolio 'terminal mode' easter egg: hitting a keyboard shortcut (e.g. Ctrl+) on Henry's portfolio/blog swaps the UI to a wterm shell with a custom command set (whoami, projects, resume, contact`) — a memorable differentiator that shows off both terminal knowledge and React skill to hiring managers.
3. Create an AI prompt playground inside the blog: wire wterm's WebSocket transport to a lightweight serverless backend (Vercel Edge Function) that streams responses from OpenAI or a Supabase Edge Function — readers type prompts in the terminal, see streamed token output in real-time with ANSI color coding for role labels (user/assistant), and can copy the full conversation with native browser selection.

6. alchaincyf/darwin-skill

955 stars this week · HTML

Darwin-skill applies Karpathy's autoresearch ratchet loop to Claude Code SKILL.md files — autonomously evaluating, mutating, testing, and only keeping improvements to your AI agent skills.

Use case

When you're maintaining 20+ SKILL.md files for Claude Code (or similar agents), manually reviewing them for both formatting AND actual output quality doesn't scale. Darwin-skill runs an 8-dimension scored evaluation (structure + live test output), proposes mutations to a single skill file, re-tests, and only commits the change if the score improves — like a git ratchet for agent behavior. Concrete example: your 'write-blog-post' skill keeps producing mediocre intros — darwin-skill iterates on the prompt instructions automatically until test outputs score higher.

Why it's trending

Karpathy dropped autoresearch this week and the AI agent/skills ecosystem (Claude Code, Codex, Trae) is exploding with SKILL.md-based tooling — this is the first serious attempt to apply self-improving ML training loops to agent skill files rather than model weights, hitting the exact moment developers are drowning in unmaintained skill configs.

How to use it

1. Install the skill into your Claude Code environment: npx skills add alchaincyf/darwin-skill
2. Create a test-prompts.json file alongside your target SKILL.md with representative input prompts and expected output criteria (e.g., [{"prompt": "Write a blog intro about TypeScript", "criteria": "engaging hook, under 100 words"}])
3. Invoke the darwin-skill in Claude Code: point it at a specific SKILL.md you want to optimize and tell it to run one evaluation cycle
4. Review the 8-dimension scorecard it produces (structure 60pts + live test output 40pts) and confirm or reject the proposed mutation
5. Repeat — the ratchet ensures your skill score only ever goes up; use git log to audit every accepted change

How I could use this

1. Build a 'blog-post-generator' SKILL.md for Claude Code that handles Henry's specific writing style, then run darwin-skill weekly against a test-prompts.json of 10 real post topics — automatically surfacing which instruction tweaks measurably improve intro quality, SEO structure, and code snippet formatting without Henry manually A/B testing prompt wording.
2. Create a 'resume-tailoring' SKILL.md that rewrites resume bullets for a given job description, then use darwin-skill to iterate it against a test set of 5 real JD + resume pairs scored by keyword match rate and ATS-friendliness — giving Henry a provably optimized skill rather than vibes-based prompt tuning for his career tools.
3. Wire darwin-skill into a Supabase Edge Function cron job that runs nightly on Henry's 'supabase-query-generator' SKILL.md — logging scores to a skill_evolution table so Henry can chart skill quality over time in his blog's admin dashboard, and auto-opening a GitHub PR when a mutation improves the score by more than 5 points.

7. vyfor/rattles

879 stars this week · Rust · animation cli no-std ratatui

Rattles is a dependency-free Rust library for terminal spinners that works in both std and no_std environments, giving you fine-grained control over animation rendering without any runtime assumptions.

Use case

When building Rust CLI tools, you often need loading indicators but don't want to pull in a heavy crate that hijacks stdout or assumes a threaded runtime. Rattles solves this by exposing raw frame strings you control — so you can integrate spinners into a Ratatui TUI, a bare-metal embedded display, or a simple script without fighting the library's threading model.

Why it's trending

The Rust TUI ecosystem (Ratatui, Crossterm) is surging right now as developers migrate Python/Node CLI tools to Rust for performance. Rattles fills a specific gap: a spinner primitive that composes cleanly with Ratatui's retained-mode rendering rather than fighting it with its own draw loop.

How to use it

1. Add the dependency: cargo add rattles
2. Pick a preset and drive it manually in your render loop:

use rattles::presets::prelude as presets;
let rattle = presets::dots();
// In your loop:
print!("\r{} Processing...", rattle.current_frame());
std::io::stdout().flush().unwrap();
std::thread::sleep(std::time::Duration::from_millis(80));

3. For Ratatui, use frame_at(elapsed) with the widget's last render timestamp to stay in sync with Ratatui's own event loop — no extra thread needed.
4. For custom branding, define your own keyframes with the rattle! macro, e.g. ASCII art frames that spell out your app name.
5. In no_std contexts (WASM, embedded), switch to TickedRattler and call .tick() from whatever scheduler you control.

How I could use this

1. Build a Rust-based CLI companion for the blog (e.g. henry-cli post new) that uses Rattles spinners while waiting on Supabase API calls to create draft posts — gives the tool a polished feel without adding a heavy dep.
2. Create a resume/job-match CLI tool in Rust that streams OpenAI completions to score a resume against a job description, using a Rattles braille spinner during the API call so the user sees real progress instead of a frozen terminal.
3. Write a WASM-compiled Rust module (no_std + TickedRattler) that renders a spinner directly in a <canvas> element on the blog's AI chat interface, replacing the typical CSS spinner with a terminal-aesthetic braille animation driven by requestAnimationFrame ticks.

8. sterlingcrispin/nothing-ever-happens

797 stars this week · Python · meme not-financial-advice nothing-ever-happens polymarket

An async Python bot that systematically bets 'No' on non-sports Polymarket prediction markets, embodying the meme that dramatic world events rarely materialize as predicted.

Use case

Prediction markets like Polymarket often overprice the probability of dramatic outcomes (war, collapse, scandal) because fear and attention bias drive 'Yes' prices up. This bot systematically fades those overpriced tail risks by buying 'No' below a configured price cap — essentially automating a mean-reversion strategy on human anxiety. Example: a market asking 'Will X country default by Q3?' might trade at 18% Yes when fundamentals suggest 4%, and the bot quietly accumulates No positions across hundreds of such markets.

Why it's trending

It's trending because it's a dead-simple, funny, and surprisingly defensible trading thesis expressed as working code — and Polymarket volumes have surged post-2024 US election cycle, making prediction market tooling suddenly very relevant to a broad developer audience.

How to use it

1. Clone the repo and install deps: pip install -r requirements.txt, then cp config.example.json config.json && cp .env.example .env.
2. Leave BOT_MODE, LIVE_TRADING_ENABLED, and DRY_RUN unset in .env to run in paper trading mode — no real money at risk.
3. Configure strategies.nothing_happens in config.json: set price_cap (e.g. 0.15 to only buy No when Yes is above 85¢) and max_position_size.
4. Run python -m bot.main and open the dashboard on $DASHBOARD_PORT to watch it scan markets and simulate orders in real time.
5. Inspect open positions and P&L with the scripts in scripts/ before ever touching BOT_MODE=live.

How I could use this

1. Build a 'Prediction Market Pulse' widget for Henry's blog that fetches live Polymarket API data and displays the top 5 most-overpriced 'Yes' markets (highest implied probability vs. historical base rate) — purely informational, no trading, but a great data visualization post that would attract the rationalist/forecasting crowd.
2. Create a career tool that frames job-search anxiety as a prediction market: users input fears like 'I won't get a senior role in 6 months' and the tool pulls in real labor market data (BLS, Levels.fyi) to calibrate whether they're mentally overpricing a bad outcome — essentially a 'nothing ever happens' reality check for career decisions.
3. Use the same async scanning architecture as this bot to build an AI-powered 'Contrarian Alert' feature: a background worker that polls Polymarket for markets where the crowd consensus is high (>80% Yes), then feeds the market question into an LLM with relevant news context to generate a structured bear case — surfaced as a daily digest post Henry auto-publishes to his blog via Supabase + Next.js ISR.

9. sogonov/anubis

774 stars this week · Kotlin

Anubis uses Android's pm disable-user via Shizuku to completely freeze apps at the OS level based on VPN state, preventing tracking apps from ever detecting your real network — something sandbox solutions like Island can't guarantee.

Use case

The core problem: apps in sandboxed work profiles (Island, Shelter) still share the network stack and can detect whether a VPN is active, leaking behavioral signals. Anubis solves this by fully disabling apps at the system level so they execute zero code when they shouldn't be running. Concrete example: you want TikTok to only ever run through a specific VPN exit node — Anubis freezes it when the VPN is down, auto-unfreezes it when the VPN connects, and kills it again if the VPN drops mid-session.

Why it's trending

Privacy-focused tooling is surging as users grow more aware of app-level network fingerprinting, and Shizuku-based root-free system-level control is having a moment among Android power users who want deep OS access without full root. The technical differentiation from existing sandbox tools (Island/Insular) is sharp and well-articulated, which drives shares in developer and privacy communities.

How to use it

1. Install Shizuku on your Android device and activate it via ADB: adb shell sh /sdcard/Android/data/moe.shizuku.privileged.api/start.sh — this grants Anubis permission to run pm disable-user without full root.,2. Install Anubis APK from the releases page, open it, and grant Shizuku permission when prompted.,3. Create an app group (e.g., 'VPN Only'), add apps like tracking-heavy social apps, and set the policy to 'VPN Only' — these apps will be frozen whenever your chosen VPN client is disconnected.,4. Set your VPN client in Anubis settings (WireGuard, Mullvad, etc.) so Anubis can orchestrate auto start/stop via the VPN client's own intent or force-stop mechanism.,5. Pin home screen shortcuts via long-press on an app icon — one tap will check VPN state, unfreeze the app, launch the VPN if needed, then open the app in the correct network context.

How I could use this

1. Write a deep-dive blog post titled 'Why Android Work Profiles Don't Actually Hide Your VPN' — benchmark Island vs. Anubis using a test app that logs detected network interfaces on launch, embed the Shizuku ADB commands, and publish the packet capture diff. This is SEO-rich technical content that targets a niche with high developer intent.
2. Irrelevant for resume/cover letter tooling — this is a native Android/Kotlin project with no overlap with Henry's Next.js/Supabase stack. Better career angle: document the architectural pattern (policy-driven app lifecycle orchestration via system commands) and reference it when writing about background job management in your own projects.
3. Build a blog feature that logs your own writing sessions: track when you opened your AI writing tools, how long the session lasted, and whether you were on a 'focus VPN' profile — then surface weekly analytics like 'you wrote 3 posts this week, avg session 47 min, all during VPN-on focus mode.' Store session events in Supabase and visualize with a Recharts dashboard on your blog's /stats page.

10. yaojingang/GEOFlow

755 stars this week · PHP · ai cms content-automation geo

GEOFlow is an open-source PHP/PostgreSQL CMS that automates the full pipeline from AI content generation to SEO-optimized publishing, with a built-in draft/review/publish workflow.

Use case

Running a content-heavy blog or niche site where you need to consistently publish SEO-optimized articles at scale without manually prompting an LLM each time. For example: you have a keyword list of 500 developer tool comparisons — GEOFlow lets you batch-schedule AI generation tasks, review the drafts in a queue, and auto-publish with structured data and Open Graph tags already injected, all without writing a custom pipeline.

Why it's trending

GEO (Generative Engine Optimization — optimizing content for AI search surfaces like ChatGPT, Perplexity, and SGE) is a hot topic right now as publishers scramble to stay visible beyond traditional Google rankings. This is one of the first open-source tools explicitly built around that workflow rather than just SEO.

How to use it

1. Clone and spin up with Docker Compose: git clone https://github.com/yaojingang/GEOFlow && cd GEOFlow && cp .env.example .env && docker-compose up -d. 2. Edit .env to point at your OpenAI-compatible endpoint (works with OpenRouter, Ollama, etc.) and configure your PostgreSQL credentials. 3. In the admin UI, add your AI model config under Settings → Models, then seed your keyword library and prompt templates under Materials. 4. Create a batch task: assign a prompt template + keyword set + target article count, set a schedule (cron or manual trigger), and let the Worker process the queue. 5. Review generated drafts in the Article Management panel — approve individually or flip on auto-publish — then verify the front-end outputs structured data and OG tags at /article/{slug}.

How I could use this

1. Use GEOFlow as a headless content backend: run it on a cheap VPS, hit its /api/v1 endpoints from your Next.js blog to pull published articles into Supabase, and use it to auto-generate deep-dive posts on specific TypeScript patterns or Next.js gotchas that you can then manually refine — letting you maintain a consistent publishing cadence without writing every post from scratch.
2. Build a 'portfolio content engine': create a GEOFlow task that takes your GitHub repo list as a keyword source and generates structured case-study drafts (problem, stack, outcome) for each project. Review and edit in the GEOFlow admin, then use the API to push finalized content into your portfolio site's Supabase projects table automatically.
3. Prototype a GEO-optimized developer glossary site: seed GEOFlow with 200 AI/ML/DevOps terms, generate definition articles with structured data (FAQPage schema), and publish them as a standalone subdomain. Use this as a real-world experiment to measure citation rates in Perplexity and ChatGPT responses — concrete evidence of GEO techniques you can write about and reference in job applications.