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GitHub Hot — 10 May 2026

10 May 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. antirez/ds4

5,893 stars this week · C

A minimal, Metal-native inference engine purpose-built for DeepSeek V4 Flash on Apple Silicon — not a generic runner, just this one model, fast.

Use case

Running a 284B-parameter MoE model locally on a 128GB MacBook Pro without cloud API costs or latency. Concrete scenario: Henry wants to power his blog's AI features (resume analysis, interview prep) with a quasi-frontier model that costs $0/month in API fees — ds4 lets him run DeepSeek V4 Flash locally via a server API, swapping out Anthropic calls for localhost:port during dev or for self-hosted prod.

Why it's trending

DeepSeek V4 Flash dropped recently with a freakishly compressed KV cache (on-disk persistence, 2-bit quant that still performs) making a 284B model actually viable on consumer Apple Silicon for the first time. antirez (Redis creator) writing a from-scratch C inference engine signals this is a serious engineering artifact, not a hobby wrapper.

How to use it

  1. Clone and build: git clone https://github.com/antirez/ds4 && cd ds4 && make — requires Xcode CLT for Metal compilation on macOS.,2. Download the 2-bit quantized weights (look for the GGUF link in the README) — expect ~35-40GB for the quantized version that fits in 128GB RAM.,3. Start the local inference server: ./ds4 --model /path/to/deepseek-v4-flash.gguf --server — it exposes an OpenAI-compatible /v1/chat/completions endpoint.,4. Point your existing Anthropic SDK calls at it during dev: set baseURL: 'http://localhost:8080/v1' and swap the model ID. Most chat completion shapes are compatible.,5. Test thinking mode with a bounded budget: send 'thinking': { 'budget': 500 } in the request body — ds4's proportional thinking means a simple prompt won't burn 10k tokens of chain-of-thought.

How I could use this

  1. Blog 'AI diff' feature: run ds4 locally to generate a weekly 'what changed in Australian tech immigration law' diff by feeding the last 4 visa-news posts into the 1M-token context window in a single prompt — no chunking, no embeddings, just one giant context read that a smaller model couldn't handle.
  2. Resume analyser cold-start: use ds4 as a zero-cost local backend during development and load testing of Gradland's resume analysis endpoint — iterate on prompts against a frontier-class model without burning Anthropic credits, then swap back to claude-sonnet-4-6 for prod only when the prompt is dialled in.
  3. On-disk KV cache for interview prep sessions: ds4's persistent KV cache means a user's entire interview prep history (all Q&A turns) can be loaded back into context across sessions without re-encoding — prototype a 'resume from last session' interview feature that genuinely remembers the previous conversation rather than faking it with a summary.

2. V4bel/dirtyfrag

3,974 stars this week · C

Use case

Why it's trending

How to use it

How I could use this

3. vercel-labs/zero-native

2,200 stars this week · Zig

A Zig-based native shell that wraps any web frontend (including Next.js) into a desktop app — Tauri's spiritual successor but lighter, with a 1-command npm setup.

Use case

Electron ships a full Chromium runtime per app, which means 200MB binaries and 150MB RAM at idle. zero-native instead delegates rendering to the OS WebView (WKWebView on Mac, WebKitGTK on Linux), so the native layer is tiny. Concrete example: you have a Next.js resume analyser — instead of asking users to open a browser tab, you ship a 3MB native .app that opens instantly, can read local files via native bridge commands, and never needs a server.

Why it's trending

This is a Vercel Labs repo — meaning the company that maintains Next.js is actively experimenting with shipping Next.js apps as native desktop binaries, which signals a potential first-class deployment target in the Next.js roadmap. It's also riding the 'anti-Electron' wave that Tauri started, but with Zig's compile speed and C interop making native bridges dramatically less painful to write.

How to use it

  1. Install the CLI: npm install -g zero-native
  2. Scaffold a Next.js app: zero-native init my_app --frontend next && cd my_app
  3. Build and run: zig build run — this compiles the Zig shell, builds your Next.js frontend, and opens a native window
  4. Add a native bridge command in Zig to expose OS-level features (file picker, local DB, system tray) that the WebView calls via a typed IPC layer
  5. Ship: zig build -Doptimize=ReleaseFast produces a single native binary with no runtime dependency

How I could use this

  1. Package your Gradland blog as a downloadable macOS/Linux app — users who 'install' a career tool feel more committed than browser-tab visitors, and you get a 'Download for Mac' CTA that no other Next.js career blog has yet.
  2. Build a local resume analyser desktop app: the native bridge lets users drag-and-drop a PDF directly from Finder into the Zig shell, parse it locally without an upload, then POST only the extracted text to your Anthropic endpoint — solves the 'I don't want to upload my CV to a random site' objection that kills conversion on resume tools.
  3. Ship a local-first AI interview coach: native bridge connects to a locally running Ollama instance for private practice sessions (no API cost, no data leaving the machine), with the polished Gradland UI running in the WebView — position it as the privacy-safe alternative to cloud-only tools for visa applicants worried about sensitive employment history.

4. strukto-ai/mirage

1,746 stars this week · TypeScript · agent-sandbox agent-tools ai-agents bash

Mirage gives AI agents a single Unix-like filesystem interface that transparently mounts S3, Google Drive, Gmail, Slack, and Redis side-by-side — so an agent never has to know which backend it's talking to.

Use case

The real problem: every multi-source AI pipeline ends up with a rats nest of bespoke SDK calls — one for S3, one for Drive, one for Slack — and the agent prompt has to explain each one separately. Mirage collapses those into read('/s3/resumes/henry.pdf'), read('/gdrive/cover-letters/accenture.docx'), and write('/redis/cache/job-match') with identical semantics. Concrete example: a resume-analysis agent that pulls the user's PDF from S3, the job description from a scraped Postgres row, and the output template from Google Drive — without a single SDK import beyond Mirage.

Why it's trending

The 'claude-code' topic is the tell — this is being built specifically for tool-use agents like Claude Code and OpenAI Agents SDK, both of which exploded in adoption Q1 2026. Filesystem metaphors map cleanly onto the read_file/write_file tool signatures these frameworks already expect, so Mirage is essentially a zero-friction adapter layer for the current agent tooling wave.

How to use it

  1. Install: npm install @struktoai/mirage-node
  2. Mount your sources in a config file:
import { createMirage } from '@struktoai/mirage-node';
const fs = await createMirage({
  mounts: [
    { path: '/resumes', adapter: 's3', bucket: process.env.S3_BUCKET },
    { path: '/jobs',    adapter: 'supabase', table: 'scraped_jobs', key: 'id' },
    { path: '/drafts',  adapter: 'gdrive',   folderId: process.env.GDRIVE_FOLDER }
  ]
});
  1. Pass fs.read and fs.write as tool functions directly to your Claude/OpenAI agent call — the signatures already match tool_use expectations.
  2. The agent navigates with ls('/resumes'), read('/jobs/42'), write('/drafts/cover-accenture.md', content) — no SDK knowledge needed inside the prompt.
  3. Add new mounts (Gmail, Redis, Slack) without touching agent code — just extend the config.

How I could use this

  1. Wire the githot/ai-news/visa-news content pipeline through Mirage: mount content/githot/, the GitHub trending API response cache, and an S3 bucket for generated images as one tree. The daily Claude agent reads /github/trending, writes the analysis to /content/githot/2026-05-10.md, and uploads the OG image to /s3/og/githot/ — all with the same three lines. No more bespoke file-write + S3 upload boilerplate in each content script.
  2. Build a holistic resume-match feature for Gradland: mount the user's uploaded resume from Supabase Storage at /resumes/{userId}, the scraped job listings table at /jobs/, and a prompt-template folder at /templates/. The Claude Sonnet agent runs ls('/jobs?filter=au&role=software'), reads the top 10, scores them against the resume, and writes a ranked shortlist to /cache/matches/{userId} — all in a single agent loop with no custom adapters per data source.
  3. Use Mirage to give the interview-prep feature persistent working memory across sessions: mount Redis at /memory/{userId}/ and Supabase at /history/{userId}/. The interview agent reads prior answers from /memory/, writes session summaries back, and queries the question bank from /history/ — turning what would be three separate Supabase/Redis clients into one stateful filesystem the agent navigates naturally, and making it trivial to swap Redis for a different cache layer without touching prompt code.

5. yaojingang/yao-open-prompts

1,542 stars this week · Python · ai chinese-prompts geo prompt-engineering

A curated library of 116 production-ready Chinese-language AI prompts across work, learning, marketing, and GEO — the Chinese-language equivalent of a systematic prompt engineering playbook.

Use case

Most prompt libraries are either English-only or a random dump of one-liners. This repo solves the 'blank page' problem for developers building AI features for Chinese-speaking users — instead of prompt-engineering from scratch, you get battle-tested templates organized by use case. Concrete example: if Henry is building a resume analyser for international graduates, the 'AI Work' category has structured prompts for contract generation, product prototyping, and productivity workflows that map directly onto career tool features.

Why it's trending

GEO (Generative Engine Optimization — getting your content cited by AI answers, not just ranked by Google) is the breakout SEO topic of mid-2026, and this is one of the first structured prompt libraries with 25 dedicated GEO templates covering opportunity analysis, citation building, and compliance. That's the specific thing drawing stars this week.

How to use it

  1. Browse the CATALOG.md to find the prompt category matching your feature (e.g. prompts/02-ai-work/ for career tools, prompts/08-ai-marketing/ for SEO/GEO). 2. Open the raw .md file for your chosen prompt — each file is a self-contained, copy-pasteable system prompt with no HTML residue. 3. Use the RTF Meta-Prompt Generator (prompts/01-ai-methods/rtf-meta-prompt-system-v06.md) as a wrapper to adapt any prompt to your specific context — feed it your product constraints and it outputs a tailored system prompt. 4. Drop the result into your Claude API call as the system message:
const response = await anthropic.messages.create({
  model: 'claude-haiku-4-5-20251001',
  system: adaptedPromptFromYaoLibrary,
  messages: [{ role: 'user', content: userInput }]
});
``` 5. Mirror the prompts-en/ directory pattern to maintain bilingual parity if you're building for both EN and ZH audiences.

**How I could use this**

1. Build a 'Prompt Kitchen' page on Gradland where international graduates can generate tailored cover letters and LinkedIn summaries using pre-configured prompts from the 02-ai-work category — surface the RTF meta-prompt system as the backbone so users can customize tone (formal AU corporate vs. startup casual) without prompt-engineering themselves.
2. Feed the 25 GEO marketing templates into a new Gradland content strategy: use the 'Schema.org GEO' and 'citation building' prompts to structure every blog post and visa-news article so Gradland content gets cited in AI-generated answers when graduates ask ChatGPT or Perplexity about 482/485 visa requirements in Australia.
3. Use the Feynman questioning prompts (prompts/05-learning/) as the system prompt backbone for Gradland's interview prep feature — instead of generic Q&A, the interviewer AI forces the user to explain concepts from first principles, then scores the explanation, directly mimicking the whiteboard interview format used by Australian IT employers.

---

## 6. [XBuilderLAB/cheat-on-content](https://github.com/XBuilderLAB/cheat-on-content)

**1,542 stars this week** · Shell

A Claude Code skill that enforces a structured content experiment loop — score before publishing, blind-predict performance, T+3 retro, evolve a personal hit-formula — replacing gut-feel publishing with compounding calibration.

**Use case**

The real problem: most creators publish 200 pieces and learn nothing because outcomes are never tied back to pre-publish predictions. This forces you to write down 'I think this will hit X views because Y hook + Z audience segment' before hitting publish, then revisit when the data lands. Example: Henry publishes a Next.js App Router deep-dive, scores it 7/10 on his rubric, predicts 800 views in 3 days, gets 2,100 — the retro surfaces that 'benchmark-heavy technical how-tos outperform opinion posts 2.6×' and that insight bakes into the next piece's scoring.

**Why it's trending**

Platform algorithm saturation and 'AI slop' fatigue are pushing creators toward quality signals over posting volume — this repo frames content strategy as a compounding skill loop rather than a schedule, which resonates right now. The viral README copy (self-referential, fate-baiting) is doing heavy distribution work by making readers feel personally called out.

**How to use it**

1. Copy the skill definition file into your Claude Code skills folder: `cp cheat-on-content.md ~/.claude/commands/cheat-on-content.md`,2. Before drafting any post, invoke the pre-publish rubric: `/cheat-on-content score` — Claude walks you through hook strength, audience fit, and format match, outputting a 1-10 score with rationale,3. Fill in the blind-predict form (expected views/reach at T+3, primary reason you expect that, target emotional response) — this gets written to a local `content-ledger.json`,4. After T+3 days, run `/cheat-on-content retro <post-id> --actual-views=1200` — Claude compares prediction vs reality and extracts a pattern hypothesis,5. After 10+ retros, run `/cheat-on-content evolve` — it rewrites your personal rubric JSON based on what has actually performed, replacing generic advice with your specific signal-to-outcome map

**How I could use this**

1. Wire the scoring loop into the existing githot pipeline: before each `content/githot/` post is published, add a frontmatter field `predicted_clicks: N` and `score: N` — then after 3 days, a Supabase edge function cross-references actual page_views from your analytics table and writes the delta back as `retro_result`. Surface the running calibration accuracy on Henry's dashboard so he can see if his repo-selection instincts are improving week-over-week.
2. Build a 'Cover Letter Confidence Calibrator' into the career tools: before submitting, users score their cover letter on three axes (relevance 1-10, specificity 1-10, hook strength 1-10), predict their callback probability, and set a 14-day reminder. When the reminder fires, they log the actual outcome. Store in a `cover_letter_attempts` table and after 5+ entries, use Haiku to surface the pattern — e.g. 'Your high-specificity letters (9+) have a 40% callback rate vs 12% for generic ones' — directly actionable signal for international graduates who are iterating blind.
3. Add a pre-publish scoring API route (`/api/content/score`) that accepts a post title, excerpt, and target audience, calls Haiku with a structured rubric prompt, returns a JSON score object with per-dimension ratings and a blind-predict template pre-filled with reasonable baseline numbers drawn from Henry's historical `page_views` Supabase data — so the friction of starting the calibration loop is near zero.

---

## 7. [lightseekorg/tokenspeed](https://github.com/lightseekorg/tokenspeed)

**889 stars this week** · Python · `blackwell` `deepseek` `gpt-oss` `kimi`

TokenSpeed is a Blackwell-optimized LLM inference engine that combines TensorRT-LLM throughput with vLLM's developer-friendly API, specifically tuned for high-concurrency agentic workloads where you're running hundreds of parallel tool-calling loops.

**Use case**

The core problem: vLLM is easy to deploy but leaves ~30-40% GPU throughput on the table at high concurrency; TensorRT-LLM squeezes out that performance but requires hand-writing CUDA parallelism. TokenSpeed targets teams self-hosting models at scale for agentic pipelines — think 200 simultaneous AI agents each making 10-20 tool calls, where KV cache thrashing and scheduler overhead become the bottleneck. Concrete scenario: a job-matching platform running 500 concurrent resume-analysis agents where TTFT and tokens/sec directly affect cost per analysis.

**Why it's trending**

NVIDIA Blackwell (B200/GB200) hardware started reaching cloud providers in Q1 2026, and the benchmarks showing TokenSpeed outperforming TensorRT-LLM on Kimi K2.5 agentic tasks on B200 landed at exactly the moment teams are evaluating which inference stack to commit to for new GPU clusters. The MLA (Multi-head Latent Attention) kernel claim — fastest on Blackwell — is what's driving share velocity among ML infra engineers.

**How to use it**

1. **Check prerequisites** — you need an NVIDIA Blackwell GPU (B200/GB200); this is not runnable on A100/H100 today. Rent a B200 instance on CoreWeave or Lambda Labs.
2. **Install** — `pip install tokenspeed` (Python 3.11+, CUDA 12.6+), or clone and build kernels from source for the MLA optimizations.
3. **Spin up the async server** — `tokenspeed serve kimi-k2.5 --tensor-parallel 8` exposes an OpenAI-compatible `/v1/chat/completions` endpoint via the SMG-integrated AsyncLLM entrypoint.
4. **Point your existing client at it** — swap your base URL: `client = openai.OpenAI(base_url='http://localhost:8000/v1', api_key='local')` — no other code changes needed.
5. **Benchmark your workload** — run `tokenspeed bench --model kimi-k2.5 --concurrency 128 --scenario agentic` to get ISL/OSL Pareto curves for your specific request mix before committing to the stack.

**How I could use this**

1. Write a deep-dive post titled 'Inference engines explained for app developers' — demystify TPS, TTFT, KV cache, MLA vs MHA, and prefill/decode overlap using TokenSpeed's published Pareto curves as real data. This is high-value SEO for your Gradland audience of IT graduates who will interview for ML infra roles at companies running self-hosted models — knowing this vocabulary cold is a genuine edge in technical interviews at Canva, Atlassian, and Seek.
2. Add an 'AI infra cost calculator' to Gradland's salary/career tools section: given a user's target role (ML Engineer, AI Platform Engineer), show them the going rate for inference-stack experience (vLLM vs TensorRT-LLM vs TokenSpeed), cross-referenced with AU job listings. The differentiation angle: these roles pay $30-50K more than generic backend roles, and your visa-tracker audience needs to know which skills to prioritise on a 485 visa timeline.
3. Build a live latency comparison widget for your blog using Claude's streaming API — show readers the real TTFT and TPS they're experiencing against Haiku vs Sonnet for a fixed prompt, with an explanation of why the numbers differ. This teaches inference concepts interactively without needing a GPU: `const stream = await anthropic.messages.stream({...})` with `stream.on('text', ...)` timestamped on the client side. Position it as 'what TokenSpeed is optimizing, but in your browser with the Claude API.'

---

## 8. [WenyuChiou/awesome-agentic-ai-zh](https://github.com/WenyuChiou/awesome-agentic-ai-zh)

**718 stars this week** · Python · `agentic-ai` `ai-agents` `awesome-list` `bilingual`

A structured 7-stage trilingual (Traditional Chinese / Simplified Chinese / English) curriculum that takes a developer from 'what is a token' to writing multi-agent systems and custom MCP servers — with mandatory exercises and checkpoints at every stage.

**Use case**

The real problem is that AI agent learning resources are scattered across English-only docs, random Medium posts, and framework READMEs with no coherent progression — so a developer who can call the Claude API has no clear path to building a real multi-agent system. This repo curates high-quality projects and exercises into a sequential Track A (use existing CLI agents) or Track B (build your own agents) path with explicit stage-exit criteria. Concrete example: a developer who knows Python and has used Claude but doesn't know whether to reach for LangGraph, raw tool calls, or AutoGen for their next project — this roadmap tells them exactly what to learn first and in what order.

**Why it's trending**

MCP (Model Context Protocol) became a de facto standard in early 2025, and Claude Code shipped as a production CLI agent — this repo is one of the only structured curricula that covers both claude-code and MCP server authoring together, filling a gap Anthropic's own docs don't fill. The trilingual format also gives it outsized reach into the large East Asian developer community building on top of Claude.

**How to use it**

1. Clone and orient: `git clone https://github.com/WenyuChiou/awesome-agentic-ai-zh.git && open stages/00-foundations.md` — decide Track A (CLI power user) or Track B (agent builder) based on whether you want to use or build agents.,2. Work Stages 0–2 regardless of track: LLM fundamentals, prompt engineering, API tool-use — these are shared prerequisites and take ~1 week of evenings.,3. At Stage 3–4 (Track B): implement the required MCP server exercise — write a server that exposes one tool (e.g. fetch a URL and return structured data), connect it to Claude Desktop, verify tool calls show up in the conversation.,4. Stage 5–6 covers multi-agent orchestration patterns — study the mandatory project, then adapt the pattern to your own domain (e.g. a two-agent pipeline: one drafts, one critiques).,5. Use the stage-exit checklists as a forcing function — don't move forward until you can answer the checkpoint questions without looking at notes.

**How I could use this**

1. Replace Gradland's current `scripts/fetch-ai-news.ts` shell-out approach with a proper MCP server (Stage 4 pattern): write a `gradland-content-tools` MCP server that exposes `fetch_trending_repos`, `summarise_for_githot`, and `publish_draft` as tools — then Claude Code running in the daily GitHub Actions workflow can call them via tool use instead of raw subprocess calls, making the pipeline inspectable and testable.
2. Build Gradland's interview prep feature as a two-agent pipeline following the Stage 6 multi-agent pattern: Agent A receives the job description + user resume and generates calibrated behavioural and technical questions; Agent B receives the user's answer transcripts and scores them against STAR criteria, then writes a weakness report back to Supabase — structured exactly like the multi-agent orchestration exercises in this roadmap.
3. Generate dynamic 7-stage learning roadmaps for Gradland users: when the resume analyser identifies a skill gap (e.g. 'weak on AWS, missing CI/CD'), call Claude with the same stage-based curriculum structure from this repo to produce a personalised roadmap stored in Supabase and rendered in the existing `PathTracker` component — giving the learning paths feature real pedagogical structure instead of a flat list of links.

---

## 9. [zarazhangrui/beautiful-html-templates](https://github.com/zarazhangrui/beautiful-html-templates)

**715 stars this week** · HTML

A library of 32 HTML slide templates with a machine-readable index and an AGENTS.md operating manual so an AI agent can automatically pick the right visual style, clone it, and populate a full slide deck without human template-hunting.

**Use case**

The real problem is that agents producing slide decks default to either plain HTML or generic Bootstrap layouts because they have no structured way to discover and evaluate visual options. This repo solves it by pairing every template with structured metadata (in index.json) that an agent can query — so instead of hallucinating CSS, Claude can read the index, match tone/industry/formality to the user's brief, clone the matching template, and slot in the actual content. Concrete example: a user says 'make me a 10-slide deck pitching my portfolio to a fintech recruiter' and the agent reads the index, picks the clean-editorial template, and fills it in — no design decisions required.

**Why it's trending**

Agent-native tooling is the hot design pattern of mid-2025 — repos that ship an AGENTS.md operating manual alongside structured metadata (index.json) are getting traction because they're plug-and-play for Claude, GPT, and Gemini coding agents without any prompt engineering. 715 stars in one week suggests it hit the right communities (AI builders + devs tired of ugly agent output) at the same time agentic workflows went mainstream.

**How to use it**

1. Have your agent clone the repo and read AGENTS.md first — it explains the index.json schema and the template-selection algorithm.
2. Read index.json to get all 32 templates with their metadata (tone, palette, best-for fields).
3. Match the user's brief to a template — e.g. filter by `tone: professional` and `bestFor: technical-pitch`.
4. Copy the chosen template folder; swap the placeholder text nodes with your content (templates use semantic HTML, so a simple DOM walk or string replace works).
5. Open in browser — it's pure HTML/CSS, no build step. Export to PDF via `window.print()` or Puppeteer.

```bash
# Minimal agent workflow
git clone https://github.com/zarazhangrui/beautiful-html-templates
cat AGENTS.md          # read the operating manual
cat index.json         # query available templates
cp -r templates/soft-editorial my-deck
# agent populates my-deck/index.html with actual content

How I could use this

  1. Auto-generate a 'Week in GitHub' slide deck from each githot digest post: after the daily githot content script runs, pipe the 5 trending repos into Claude with a prompt that picks a template from this library matching the tech/developer tone and outputs a ready-to-share HTML deck — link it at the bottom of each githot post as 'Download as slides'. Zero extra UI work, big shareability uplift.
  2. Add an 'Export interview prep as slides' button to the Interview Prep tool: after a user completes a session, have Claude summarise the key Q&A pairs, pick the 'clean-minimal' or 'stencil-tablet' template from this library, and generate a 482/485 visa interview cheat-sheet deck the user can print or share — directly addresses the core Gradland audience of visa applicants who need portable prep material.
  3. Build a 'Pitch yourself' micro-tool: user fills a 5-field form (target role, top 3 skills, one career highlight, visa status, location), Claude picks the highest-signal template from index.json using structured metadata matching, generates a 6-slide personal-pitch deck, and serves it as a downloadable HTML file via a /api/generate-deck route — gated behind subscription as a premium feature since it hits Claude Sonnet.

10. BigPizzaV3/CodexPlusPlus

652 stars this week · Python

A non-invasive external launcher for OpenAI's Codex desktop app that uses Chrome DevTools Protocol to inject UX fixes (plugin unlock, session deletion) without touching the app.asar.

Use case

If you run Codex App with an API key instead of a ChatGPT subscription, the native plugin UI is locked behind a 'you must log in to ChatGPT' gate — completely unusable. Codex++ starts Codex with a remote debugging port, injects a script via CDP, and patches the renderer at runtime: plugins become accessible, a real delete button appears on sessions. No app files modified, no DLL injection, no asar unpacking — just a Python launcher and a JS patch delivered over CDP.

Why it's trending

OpenAI's Codex desktop app had a major uptick in adoption in early 2025 as developers shifted to API-key-based workflows to avoid subscription lock-in. The plugin restriction for non-ChatGPT users hit a large segment of power users simultaneously, making this fix immediately useful to a well-defined frustrated audience.

How to use it

  1. Install Python 3.11+ and clone the repo: git clone https://github.com/BigPizzaV3/CodexPlusPlus && cd CodexPlusPlus && pip install -e .
  2. On Windows, double-click setup.bat and choose [1] Install — this registers a shortcut that launches Codex++ instead of Codex.
  3. On macOS, run the install script to generate /Applications/Codex++.app.
  4. Launch Codex via the new shortcut — it starts Codex with --remote-debugging-port=9229, spins up a local helper, and injects renderer-inject.js via CDP.
  5. Plugin toggle and session delete are now live; the Codex++ menu appears in the top bar for settings.

How I could use this

  1. Write a deep-dive post: 'How to patch any Electron app at runtime using Chrome DevTools Protocol' — walk through the exact CDP flow this repo uses (attach to target, Runtime.evaluate, bridge pattern to bypass CSP). This is a high-intent developer search with almost no good English-language tutorials. Cross-post to dev.to and link from Gradland's blog for SEO.
  2. Build a lightweight CDP-based browser extension companion for Gradland's job search tool: inject a floating overlay onto Seek/LinkedIn pages that scores job listings for 482/485 visa sponsorship likelihood using a local model call. Same architecture — a local helper + injected script — no need to scrape, just augment what the user already sees.
  3. Use the CDP injection pattern to prototype a 'session replay for AI conversations' feature on Gradland: attach to a Chromium-based Claude or GPT window, capture conversation DOM snapshots via CDP, and POST them to a Supabase-backed endpoint. Gives users a searchable archive of their AI interview-prep or resume-feedback sessions without any browser extension store approval process.
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