GitHub Hot — 6 May 2026

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

1. darrylmorley/whatcable

1,946 stars this week · Swift · apple-silicon hardware-info iokit mac-app

A macOS menu bar utility that reads IOKit hardware data to tell you exactly what your USB-C cable is capable of — data transfer speed, charging wattage, Thunderbolt support — because the connector looks identical regardless.

Use case

Every developer with a modern Mac has silently suffered slow charging and wondered why. WhatCable solves the specific problem of USB-C cable opacity: you have five cables in your bag that look identical, but one is USB 2.0 charge-only, one is Thunderbolt 4, and one caps out at 60W. WhatCable reads the IOKit registry that macOS already populates and surfaces it as a plain-English popover — 'Cable is limiting charging speed' vs 'Charging at 96W' — without needing a third-party dongle or app store subscription.

Why it's trending

Apple Silicon Macs now have 3-5 USB-C/Thunderbolt ports and developers are drowning in cables with zero labelling, making this a universal pain point that just crossed critical mass. The repo also hit Hacker News front page this week, which explains the spike — the README's 'why is my Mac charging slowly' hook resonated hard with the MacBook-heavy HN crowd.

How to use it

Install via Homebrew: brew install --cask whatcable — appears as a menu bar icon immediately on launch.,2. Click the icon to open the popover — each connected USB-C port is listed with a plain-English headline (e.g. 'Thunderbolt 4 · 100W · 40 Gbps') and a charging diagnostic banner.,3. To read the same data programmatically, use IOKit in Swift: IOServiceGetMatchingServices(kIOMainPortDefault, IOServiceMatching('AppleUSBDevice'), &iterator) — iterate the registry tree and pull kUSBCurrentAvailable, kUSBVendorID, and Thunderbolt capability keys.,4. For a web-equivalent (e.g. checking USB device info in a browser), use the WebUSB API: navigator.usb.getDevices() returns connected USB devices with vendorId, productId, and configurations — far less detail than IOKit but works cross-platform.,5. Fork the repo and extend it: the USBCPort model in Sources/WhatCable/Models/ is clean Swift structs — add a 'copy to clipboard' action or a CSV export for auditing a drawer full of unlabelled cables.

How I could use this

Write a blog post titled 'The IOKit trick every Mac developer should know' — walk through the Swift code that reads USB-C port metadata from the kernel registry, with a Next.js API route companion that does the WebUSB equivalent. Positions Henry as someone who goes beneath the framework layer, which stands out in a sea of tutorial blogs.
Build a 'Dev Setup Auditor' page for TechPath AU: a client-side tool using navigator.getBattery(), navigator.connection, and navigator.userAgentData.getHighEntropyValues() that snapshots a user's hardware environment and uses Claude Haiku to generate a personalised recommendation ('Your machine is throttled on USB bandwidth — consider upgrading your hub before your next client demo'). Directly relevant to international tech grads who are buying second-hand Mac setups.
Add a 'Hardware context' enrichment step to the resume analyser: prompt Claude to flag when a candidate's resume lists hardware-adjacent skills (embedded systems, IoT, device drivers) and generate a targeted interview question around USB-C/Thunderbolt capability negotiation — the kind of systems-level question that separates strong candidates at companies like Apple, Canva, or Atlassian AU.

2. aattaran/deepclaude

1,329 stars this week · JavaScript

DeepClaude proxies Claude Code's API calls to cheaper backends like DeepSeek V4 Pro, giving you the full autonomous agent loop — file editing, bash, git, subagents — at 17x lower cost.

Use case

The real problem is Claude Code's $200/month subscription cap gets eaten fast by automated workflows. DeepClaude intercepts the API calls Claude Code makes and routes them to DeepSeek V4 Pro ($0.87/M output tokens) or OpenRouter ($0.44/M input) without touching the tool loop. Concrete scenario: a nightly GitHub Actions workflow that autonomously writes blog posts, scrapes repos, and opens PRs would cost ~$0.02/run instead of burning your Pro quota — keeping Anthropic billing only for Opus-level tasks that genuinely need it.

Why it's trending

DeepSeek V4 Pro just posted 96.4% on LiveCodeBench at near-zero cost, making model substitution suddenly viable for real coding tasks. This repo dropped at exactly the moment developers are hitting Claude Pro usage caps on automated pipelines and looking for an escape valve.

How to use it

Get a DeepSeek API key at platform.deepseek.com (~$5 credit to start).
Export it: echo 'export DEEPSEEK_API_KEY="sk-..."' >> ~/.bashrc && source ~/.bashrc
Install: chmod +x deepclaude.sh && sudo ln -s $(pwd)/deepclaude.sh /usr/local/bin/deepclaude
Replace claude with deepclaude in any shell script or GitHub Actions step that invokes Claude Code.
Switch backends per task: deepclaude --backend anthropic when you need Opus reasoning, bare deepclaude for DeepSeek on implementation steps.

How I could use this

Your daily content pipelines (scripts/llm-claude.ts, fetch-ai-news, fetch-visa-news, githot) already shell out to Claude Code — swap in deepclaude for those GitHub Actions workflows and route content-generation steps to DeepSeek, reserving Anthropic quota for the autonomous PR loop's Opus planning step. Estimated saving: 80%+ on content CI costs.
Build a 'Model Cost Calculator' tool for TechPath AU's career tools section: users input how many AI-assisted resume reviews, cover letters, and interview preps they do per month, and the tool outputs a cost comparison table across Claude Pro, DeepSeek, OpenRouter, and GPT-4o — directly relevant to international students on tight budgets deciding which AI subscription to pay for.
Extend your autonomous PR loop (the Opus plan + Sonnet implement workflow in .github/workflows) to use deepclaude for the Sonnet implementation step with DeepSeek V4 Pro, and only call Anthropic for the Opus planning phase — a two-backend agentic pipeline that benchmarks output quality against pure-Anthropic runs and posts the diff stats as a blog post in content/ai-news/.

3. mattpocock/dictionary-of-ai-coding

1,068 stars this week · TypeScript

A structured, open-source glossary of AI coding terms written in plain English, generated from markdown source files — essentially a spec-compliant vocabulary layer for anyone building with LLMs.

Use case

When you're integrating Claude or any LLM into a product, you constantly hit terms like 'context window degradation', 'temperature', 'non-determinism', or 'inference cost' that are used inconsistently across vendor docs. This repo gives you a canonical, community-vetted definition for each — so when your prompt behaves differently on Tuesday than Monday, you have the vocabulary to diagnose why (non-determinism + sampling parameters) rather than guessing. Concretely: a junior dev debugging why their Claude-powered resume analyser gives wildly different scores on identical inputs can find the root cause in under 5 minutes with this dictionary.

Why it's trending

Matt Pocock (Total TypeScript) publishing this signals that AI literacy is now a TypeScript ecosystem concern, not just ML research — that framing hit a nerve with 1,000+ stars in a week. It's also timed perfectly as AI coding tools (Cursor, Copilot, Claude Code) are becoming mainstream and developers are hitting these knowledge gaps daily in production.

How to use it

Clone the repo and run npm run generate to regenerate the full README from source markdown — this shows you the content pipeline pattern (markdown → generated docs).
Browse dictionary/*.md — each term is a standalone file with frontmatter, making it trivially importable into any MDX or content system.
Fork it and add domain-specific terms your team uses — the contribution pattern is just adding a new .md file.
Pull the raw markdown into your own app via GitHub's raw content URLs to build a searchable glossary UI:

// Fetch a specific term at build time
const res = await fetch(
  'https://raw.githubusercontent.com/mattpocock/dictionary-of-ai-coding/main/dictionary/context-window.md'
);
const markdown = await res.text();

Or install as a reference during code review — paste a term into your PR description linking back to the canonical definition to stop re-litigating vocabulary in comments.

How I could use this

Build a 'TechPath AI Glossary' page at /learn/ai-glossary that pulls from this repo's markdown files at build time (ISR, revalidate: 86400) — each term gets its own slug page with a 'Try it in your resume analysis' CTA. This is pure SEO gold for queries like 'what is context window AI' from your target audience of IT grads learning to use AI tools.
Add an inline tooltip system to your existing AI-powered career tools — when Claude's resume analyser response includes jargon ('your token usage is high'), render a hoverable term definition sourced from this dictionary. Intercept Claude's output, regex-match known terms from the dictionary index, and wrap them in a <Tooltip> component. No extra API calls needed.
Create a weekly 'AI Term of the Week' micro-feature in your existing digest/githot content pipeline — your fetch-ai-news.ts script already runs on cron, so add a step that picks one dictionary term relevant to that week's AI news, explains it with a concrete example from TechPath's domain (e.g. 'non-determinism — why your interview prep AI gives different answers each run'), and appends it to the digest markdown frontmatter as a term_of_week field rendered in the digest UI.

4. vercel-labs/deepsec

1,067 stars this week · TypeScript

An agent-powered vulnerability scanner that uses LLMs at maximum reasoning depth to find dormant, multi-file security holes in existing codebases — not just pattern-match known CVEs.

Use case

Static tools like Snyk or ESLint security plugins catch known single-file patterns. Deepsec deploys a coding agent that reads your actual codebase and reasons about multi-hop vulnerability chains — e.g., a Next.js app where a permissive middleware skip + a missing RLS policy + an unsanitised route handler combine into a real privilege escalation that no single-file scanner would surface. The canonical use case is a team inheriting a 3-year-old codebase and needing to know what's actually exploitable before a security audit.

Why it's trending

Claude 4 and extended thinking at max depth crossed a threshold this month where agents can genuinely reason about complex, cross-file vulnerability logic the way a senior AppSec engineer would — not just flag eval() calls. Vercel Labs shipping this as a turnkey tool makes that power accessible without building the scaffolding yourself.

How to use it

npx deepsec init in your repo root — creates .deepsec/ with a project ID and a SETUP.md tailored to your repo structure.,2. cd .deepsec && pnpm install to pull the deepsec CLI from npm.,3. Open your coding agent and prompt it to read .deepsec/node_modules/deepsec/SKILL.md, then fill out .deepsec/data/<id>/INFO.md with 50–100 lines of project-specific context (auth primitives, middleware names, RLS patterns — skip generic CWE boilerplate).,4. Run the scan pipeline: pnpm deepsec scan (fans out in parallel), then pnpm deepsec process to triage, then optionally pnpm deepsec revalidate to cut false positives.,5. pnpm deepsec export --format md-dir --out ./findings dumps per-finding markdown files you can review, link in PRs, or file as GitHub Issues.

How I could use this

Point deepsec at TechPath AU's app/api/stripe/ and lib/subscription.ts specifically — these are flagged in AGENTS.md as requiring human review, and they're exactly the multi-file chains (webhook signature check → subscription lookup → RLS bypass via service role) that agent-based scanning is built to catch. Run it before any billing feature ships.
Pipe deepsec's markdown export into a lightweight /admin/security page: parse the finding files at build time, render a severity-bucketed table (critical / high / medium), and show last-scan timestamp. Gives Henry a living security dashboard without a third-party SaaS subscription.
Add deepsec as a weekly GitHub Actions job that runs pnpm deepsec scan && pnpm deepsec process && pnpm deepsec export and then uses the GitHub CLI to open issues for any new critical or high findings — tagged security, assigned to the repo owner. Fits cleanly alongside the existing check gate in deploy.yml without blocking the deploy path.

5. wrongly-cuddly-obsession/NTSB_FOIA_MU5735

940 stars this week · various

A public archive of NTSB FOIA documents and an unofficial Chinese translation of the flight recorder report for China Eastern Airlines flight MU5735, which crashed in March 2022 killing all 132 aboard.

Use case

Preserves access to aviation safety investigation documents that were at risk of disappearing when the original uploader deleted their repo — a classic 'rescue archive' pattern. Concrete scenario: a journalist, researcher, or family member wants the raw NTSB recorder data and the official Chinese translation is unavailable; this repo provides both the source files and a community-contributed CN translation in a browsable format.

Why it's trending

The NTSB recently published the MU5735 recorder data on their official FOIA portal (requiring a US IP), driving a wave of renewed public interest and traffic to any accessible mirror. For Chinese-speaking audiences, the unofficial CN translation of the technical recorder report is the only accessible version of this document.

How to use it

Clone the repo: git clone https://github.com/wrongly-cuddly-obsession/NTSB_FOIA_MU5735. 2. Browse MU5735_NTSB_Recorder_Report_CN/ for the Chinese-language markdown translation. 3. Download the raw NTSB data from the NTSB FOIA portal linked in the README (use a US VPN if blocked). 4. The source files are static documents (PDF, markdown) — no build step needed. 5. If contributing a correction to the CN translation, submit a PR editing the markdown directly.

How I could use this

Write a deep-dive blog post on the 'GitHub as public archive' pattern — how anonymous users preserve censorship-sensitive or at-risk public documents using throwaway accounts, and what that means for open data integrity. Directly relevant to your AU audience of international students who care about information access.
Not applicable to career tools — this repo is a document archive with no resume/hiring relevance. Forcing a connection here would be dishonest filler.
Build a small Next.js demo that uses Claude to answer natural-language questions over the markdown report files — drag-and-drop a .md document, ask 'what caused the dive?', get a cited answer. A concrete RAG-over-documents demo you could ship as a blog post with working code, showcasing Claude's document analysis capabilities to your tech-savvy audience.

6. vibeforge1111/keep-codex-fast

751 stars this week · Python

A safe-by-default Codex skill that audits bloated local AI coding assistant state — chats, worktrees, logs, project refs — and produces handoff docs before archiving anything.

Use case

After 3–6 months of heavy Codex use, a developer's local state quietly accumulates: dozens of stale chat threads, GB-scale log rotations, dead worktrees from abandoned branches, and project references pointing to repos they no longer touch. Performance degrades and context becomes noise. This skill runs a read-only audit first, writes a continuity 'handoff doc' for any thread worth resuming later, then archives rather than deletes — so nothing is unrecoverable. The concrete scenario: before a vacation or team handoff, run the skill to document exactly where every active thread left off, then safely compress the rest.

Why it's trending

OpenAI's cloud Codex agent launched publicly this week, spiking interest in everything Codex-adjacent. Developers who've been running local Codex heavily for months are now hitting the 'it's getting slow and messy' wall simultaneously, making this maintenance tooling suddenly relevant to a large audience at once.

How to use it

Install by cloning the repo into your Codex skills directory or telling Codex: Install the keep-codex-fast skill from https://github.com/vibeforge1111/keep-codex-fast
Trigger the read-only audit: Use $keep-codex-fast to inspect my Codex local state and recommend a safe maintenance plan — Codex reports findings without touching anything.
Review the report: note which chats are large, which worktrees are stale, which logs are bloated.
Generate handoff docs for threads you want to preserve: Create a handoff doc for my active [repo-name] chat before archiving — this writes a continuity note with what was in progress, what changed, and what's still broken.
Apply archiving only after reviewing handoffs: Archive chats older than 30 days that have handoff docs — the skill moves, never deletes.

How I could use this

Build a 'session handoff' button in Henry's Claude Code integration — at the end of a long blog-building session, auto-generate a structured markdown summary (what was built, what's broken, what files changed, what to do next) and save it to the memory system at /home/runner/.claude/projects/...memory/ so future sessions start with full context instead of cold.
For the interview prep tool, apply the handoff-first pattern to user sessions: when a user hasn't practiced in 14+ days, surface a 'resume where you left off' card that shows their last question, score, and weak areas — backed by a lightweight session-state table in Supabase (interview_sessions with a handoff_summary JSONB column) rather than forcing them to restart from scratch.
Adapt the 'archive don't delete' philosophy for Henry's AI news and digest content pipeline — instead of overwriting or dropping old AI-generated drafts when regenerating, append them to a content/archive/ directory with a datestamp, giving a full audit trail of what Claude produced on each run and making it trivial to roll back to yesterday's version if today's generation misfires.

7. tddworks/baguette

636 stars this week · Swift · agent cli devicefarm ios

Baguette lets you spin up, control, and stream headless iOS simulators entirely from the CLI — no Xcode GUI, no display server — including full 60fps video output and touch injection, all scriptable.

Use case

The core problem is that iOS simulator automation has always required a running macOS GUI session — making it useless in headless CI runners, SSH sessions, or Docker-adjacent setups. Baguette bypasses this entirely: you can boot an iPhone 16 Pro simulator, inject a tap sequence, and pipe a 60fps H.264 stream to a file or WebSocket endpoint without ever opening Simulator.app. Concrete example: a mobile QA pipeline that boots 12 parallel simulators, runs interaction scripts via the CLI agent API, captures screen recordings, and tears everything down — all inside a GitHub Actions runner with no display.

Why it's trending

iOS 26 was announced at WWDC 2025, and Baguette is one of the first tools to explicitly target its simulator internals — developers experimenting with iOS 26 features in CI hit the headless wall immediately and this is the only Swift-native CLI that solves it. The 636 stars in a single week is almost entirely WWDC spillover from mobile teams and agentic-AI researchers who want to drive iOS apps programmatically.

How to use it

Install via Homebrew (once a tap is published) or build from source: git clone https://github.com/tddworks/baguette && cd baguette && swift build -c release && cp .build/release/baguette /usr/local/bin/
List available runtimes and create a device: baguette device create --name 'CI-iPhone16' --runtime 'com.apple.CoreSimulator.SimRuntime.iOS-26-0' --type 'iPhone 16 Pro'
Boot it headless and start the 60fps MJPEG stream: baguette boot CI-iPhone16 && baguette stream CI-iPhone16 --format mjpeg --port 8080
Inject gestures from a second terminal or script: baguette tap CI-iPhone16 --x 195 --y 420 or baguette swipe CI-iPhone16 --from 195,800 --to 195,200
Optionally launch the local web UI for browser-based control: baguette serve --port 9000 — then open localhost:9000 to drive any booted simulator from any browser tab.

How I could use this

Write a deep-dive post titled 'Headless iOS CI in 2026: Baguette + GitHub Actions' walking through a complete pipeline — boot simulator, install .app, run tap scripts, capture video artifact, tear down — with the full workflow YAML included. This is a high-value SEO target ('headless ios simulator ci' has essentially no good tutorials) and directly relevant to the mobile/full-stack devs in your international-IT-grad audience preparing for Australian tech roles where mobile QA is expected.
Add an 'Interview Prep: Mobile QA Concepts' module to your learn section that uses Baguette-recorded screen captures as visual aids — short clips demonstrating what gesture-driven UI tests actually look like running in CI, paired with Claude-generated quiz questions about XCTest, XCUITest, and simulator farm concepts that come up in Australian mobile engineering interviews at Atlassian, Canva, and REA Group.
Build an AI agent demo for your portfolio: a Next.js page that sends a POST to a local baguette server (or a hosted Mac mini), receives the MJPEG WebSocket stream, and lets Claude vision-analyze each frame to describe what the iOS app on screen is doing — essentially a 'Claude watches and narrates an iOS app' live demo. Frame it as an exploration of multimodal AI + device automation, which is a genuine emerging skill gap in Australian mobile teams.

8. Tommy-yw/RunbookHermes

516 stars this week · Python

A production-grade AIOps agent that wraps the Hermes Agent runtime to automate payment incident diagnosis, approval-gated remediation, and runbook knowledge accumulation — so on-call engineers stop firefighting the same failures twice.

Use case

When a payment service spikes to 40% error rate at 2am, RunbookHermes autonomously pulls metrics, logs, and traces, runs root-cause analysis against historical runbooks, proposes a fix (e.g. rollback canary, scale replica), and gates execution on human approval before touching prod. The problem it solves: most AIOps tools either just alert (no action) or blindly automate (dangerous) — this sits in the middle with evidence-backed, human-in-the-loop remediation. Concrete scenario: a coupon-service p95 latency spike triggers an incident; Hermes collects trace signals, matches the symptom pattern to a prior runbook, drafts a remediation plan, and pings the on-call for a one-click approve/reject.

Why it's trending

Operator-in-the-loop AI agents are the dominant architecture pattern right now after years of fully-autonomous AI flopping in prod — RunbookHermes lands squarely in that sweet spot with its approval-gate pattern, which is exactly what post-GPT-4 enterprise buyers are asking for. The 516-star week also coincides with a wave of 'agentic SRE' discourse following recent high-profile outages where AI copilots gave wrong remediation advice.

How to use it

Clone and install: git clone https://github.com/Tommy-yw/RunbookHermes && pip install -r requirements.txt — requires Python 3.11+ and a running Hermes Agent instance.,2. Wire up your observability stack by setting env vars for your metrics source (Prometheus/Datadog endpoint), log aggregator, and trace backend (Jaeger/OTEL) in .env.,3. Define your services and SLO thresholds in config/services.yaml — this is what RunbookHermes monitors and what triggers incident creation.,4. Seed initial runbooks: drop markdown runbooks into runbooks/ — the agent indexes them and uses retrieval to match symptoms at incident time. Start with your top-3 most common failure modes.,5. Run python main.py and open the web console at localhost:8080 — trigger a test incident via the API (POST /api/incidents) and walk through the approval flow to validate the loop before pointing it at real services.

How I could use this

Build a 'mini incident postmortem' feature for the blog: after each deploy to Vercel, a lightweight agent checks build logs and Vercel's API for error spikes, then auto-generates a postmortem draft in content/posts/ with root cause, impact window, and a 'what we learned' section — instant SEO content from real operational events.
Adapt the approval-gated pattern for the resume analyser tool: instead of instantly returning Claude's resume critique, queue the analysis as an 'incident' that a secondary Haiku call 'approves' by checking the critique for hallucinated skills or fabricated salary data before it reaches the user — adds a verifiable quality gate without human latency.
Wire RunbookHermes's runbook-learning loop to TechPath AU's visa tracker: when a new visa processing time update lands in content/visa-news/, an agent extracts structured facts (visa subclass, current processing window, source), diffs against the previous entry, and appends a timestamped knowledge entry to a runbooks/visa-processing.md file — so the AI mentor tool always answers visa questions from an auto-maintained, versioned source of truth rather than stale training data.

9. WeritoP/BetterNitroDiscord

462 stars this week · various

BetterDiscord Plugin for Nitro features. Unlock screensharing modes, use cross-server and gif emotes and much more!

Use case

BetterDiscord Plugin for Nitro features. Unlock screensharing modes, use cross-server and gif emotes and much more!

Why it's trending

How to use it

How I could use this

10. WeritoP/FL-STUDIO-PATCHER

461 stars this week · various

Fl Stduio patch for lifetime works

Use case

Fl Stduio patch for lifetime works

Why it's trending

How to use it

How I could use this