GitHub Hot — 31 March 2026

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

1. instructkr/claw-code

54,260 stars this week · Rust

A viral repo claiming to be a clean-room rewrite of leaked Claude Code internals, now being ported to Rust — but treat this with significant skepticism given the murky legal and authenticity questions.

Use case

This repo is less about solving a real engineering problem and more about capitalizing on the viral moment around alleged Claude Code source exposure. The claimed value is a faster, memory-safe 'harness runtime' for orchestrating LLM coding agents — think a lower-level alternative to LangChain for wiring up Claude-style agentic loops. In practice, the actual utility is unverified and the legal risk of depending on anything derived from potentially leaked proprietary code is real.

Why it's trending

It hit 30K+ stars in hours purely due to viral FOMO around 'leaked Claude Code' — not because the technical content has been vetted. This is a social event, not a technical milestone, and the stars reflect curiosity and hype rather than adoption.

How to use it

1. DO NOT integrate this into any production or portfolio project until the legal status is clear — Anthropic has not authorized this and any derivative work could expose you to IP liability. 2. If you want to study the architectural patterns for agentic harnesses (which is the legitimate learning value here), read the code academically and implement your own clean-room version inspired by the concepts, not the code. 3. Instead, look at the oh-my-codex (OmX) project referenced in the README — that's a legitimate OpenAI Codex orchestration layer you can actually use. 4. If you want a Rust-based CLI tool for LLM agent orchestration, look at established alternatives like llm (by Simon Willison) or aichat which have clear provenance. 5. Watch the repo's legal status — if Anthropic issues a DMCA takedown, that confirms the IP risk and you'll want no commit history touching this.

How I could use this

1. Write a blog post titled 'What the Claude Code leak actually reveals about agentic harness design' — analyze the architectural patterns (tool-use loops, persistent execution, parallel review modes) that appear in the rewrite WITHOUT touching the original leaked code. This positions Henry as a thoughtful analyst rather than a hype chaser, which stands out.
2. Build your own transparent, legally-clean agent harness in TypeScript for your blog's AI features — use the concepts discussed (parallel review mode, architect-level verification loops) with the Anthropic SDK directly. Document the architecture publicly as a portfolio piece showing you understand how Claude Code works under the hood.
3. Add an 'AI writing assistant' mode to Henry's blog CMS that uses Claude's official API with a multi-step harness: draft generation → self-critique loop → final polish. This mimics the orchestration patterns discussed in the repo but built entirely on Anthropic's official SDK, giving Henry a legitimate demo of agentic patterns he can show in interviews.

2. openai/codex-plugin-cc

5,560 stars this week · JavaScript

An official OpenAI plugin that bridges Claude Code and Codex, letting you trigger AI code reviews and delegate background tasks to Codex without leaving your Claude Code workflow.

Use case

Developers who use Claude Code as their primary AI coding environment but want Codex's code review quality don't have to context-switch between tools. For example: you're mid-session in Claude Code refactoring your Supabase RLS policies, and instead of opening a separate Codex session, you run /codex:adversarial-review inline to get a security-focused challenge review of your uncommitted changes before pushing.

Why it's trending

This dropped directly from OpenAI the same week Codex CLI gained traction as a serious Claude Code competitor, so it's essentially OpenAI acknowledging Claude Code's dominance and meeting developers where they already are — that narrative alone is driving curiosity.

How to use it

1. Inside Claude Code, add the marketplace and install: /plugin marketplace add openai/codex-plugin-cc then /plugin install codex@openai-codex then /reload-plugins
2. Run /codex:setup — it will detect if Codex CLI is missing and offer to install it via npm automatically
3. Authenticate: !codex login (uses your ChatGPT subscription or OpenAI API key)
4. Kick off a background review of your current uncommitted changes: /codex:review --background
5. Poll and retrieve results without blocking your session: /codex:status then /codex:result

How I could use this

1. Wire /codex:adversarial-review into your blog's pre-commit hook via a Claude Code task so every time you ship a new AI feature (e.g., your post summarizer or embedding pipeline), Codex automatically stress-tests the TypeScript interfaces and Supabase query logic before it hits main — surface the review output as a GitHub PR comment using the result JSON.
2. Build a 'live code audit' page on your portfolio site: use the codex:rescue delegation pattern as inspiration to create a Supabase Edge Function that accepts a GitHub repo URL, shells out to the Codex CLI with a review prompt, and streams the structured review back — let recruiters paste any public repo and see an AI review in real time, positioning you as someone who ships AI tooling, not just uses it.
3. Use /codex:adversarial-review as a second-opinion layer in your blog's AI writing pipeline: after Claude drafts a code snippet for a technical post, programmatically pipe it through Codex review via the CLI (codex review --json) and display a 'code health' badge or inline diff on the published post, showing readers the snippet was adversarially reviewed — differentiates your technical content from generic AI-generated blogs.

3. larksuite/cli

5,434 stars this week · Go

Official Lark/Feishu CLI with 200+ commands and 19 pre-built AI Agent Skills that lets both humans and LLM agents programmatically control the entire Lark workspace from the terminal or inside an agentic workflow.

Use case

If you're building an AI agent that needs to take real-world actions — send a message, create a doc, schedule a meeting, update a spreadsheet — you normally have to hand-roll OAuth flows, parse Lark's REST API docs, and handle pagination yourself. This CLI abstracts all of that into structured, agent-callable commands. Concrete example: an AI writing assistant that drafts a blog post in your Next.js app, then automatically creates a Lark Doc, shares it to your team channel, and adds a calendar reminder — all triggered by a single agent skill call.

Why it's trending

The repo dropped 19 structured 'Skills' explicitly designed for LLM tool-use (think OpenAI function calling or Claude tool_use), landing at exactly the moment developers are moving from chatbot demos to agents that actually manipulate business software. It's also npm-installable despite being a Go binary, which dramatically lowers the barrier for JS/TS developers.

How to use it

1. Install via npm: npm install -g @larksuite/cli — no Go toolchain needed.
2. Authenticate interactively: lark login — follow the OAuth prompts; credentials are stored in your OS keychain, not a dotfile.
3. Smoke-test with a message: lark message send --chat_id <YOUR_CHAT_ID> --text 'Hello from CLI'
4. Wire it into an AI agent by pointing your tool-use config at the Skills directory (./skills/*.json); each JSON file is a ready-made function schema. Example with LangChain: load the skill JSON as a StructuredTool, set func to execSync('lark <skill_command> --json').
5. For server-side use in Next.js API routes, use Node's child_process.execSync or execa to call lark commands and parse the --json structured output directly into your Supabase inserts.

How I could use this

1. Auto-publish blog post summaries to a Lark group channel whenever Henry merges a new post to main — wire a GitHub Action that runs lark message send with the post title, excerpt, and URL, so his team/followers get a push notification without any manual sharing step.
2. Build a 'weekly career log' micro-feature: every Friday, a Vercel cron hits a Next.js API route that calls lark sheet append to log that week's GitHub commits, job applications, or interview notes into a Lark Base spreadsheet — giving Henry a searchable, structured career timeline without maintaining a separate CRM.
3. Create an AI content pipeline where the blog's draft editor (in Next.js) has a 'Ship to Review' button that: (1) sends the markdown to an LLM for editing suggestions, (2) calls lark doc create to post the revised draft as a Lark Doc, and (3) calls lark task create to assign a review task to a collaborator — all in one server action, turning Lark into a free headless CMS/editorial workflow.

4. ChinaSiro/claude-code-sourcemap

4,238 stars this week · TypeScript

Reconstructed TypeScript source code of Anthropic's official Claude Code CLI (v2.1.88), extracted from the public npm package's embedded source maps — giving developers a rare look inside a production AI coding agent.

Use case

Developers building their own AI coding tools have no reference implementation to study — how does a real agent handle multi-step bash execution, file editing, git integration, and multi-agent coordination safely? This repo exposes exactly that: 4,756 files including tools like FileEdit, Grep, and a multi-agent coordinator, so you can reverse-engineer patterns like how Claude Code structures its tool-calling loop, context management, and plugin system rather than guessing from blog posts.

Why it's trending

Claude Code went viral this month as developers integrated it into their workflows, and this sourcemap extraction surfaced just as demand to understand its internals peaked — the timing of someone publishing the extraction method on linux.do triggered a wave of AI-agent-curious developers who want to clone its architecture.

How to use it

Clone the repo and navigate to restored-src/src/ — start with main.tsx to understand the CLI entry point and how the agent loop is bootstrapped.,Study tools/ directory — each tool (Bash, FileEdit, Grep) shows exactly how Anthropic structures tool definitions, input validation, and execution sandboxing that you can replicate with the Vercel AI SDK or LangChain.,Read coordinator/ for the multi-agent orchestration pattern: understand how sub-agents are spawned, how results are aggregated, and how context is passed — this is the architecture behind parallel agent tasks.,Cross-reference services/ with the public Anthropic API docs to see how they handle streaming, retry logic, and MCP (Model Context Protocol) integration in production code.,Use the commands/ directory (commit, review, config — 40+ commands) as a blueprint for structuring your own CLI or Next.js API routes that wrap Claude tool-use calls.

How I could use this

1. Clone the tools/FileEdit and tools/Bash patterns to build a blog post auto-updater: a Next.js API route that accepts a topic, uses Claude to generate MDX content, then uses the FileEdit pattern to write directly to your /posts directory and open a Supabase insert — fully automated post creation with a single API call.
2. Adapt the commands/review implementation to build a portfolio code reviewer: paste a GitHub repo URL into your personal site, and the tool fetches files, runs Claude through the same structured review prompts Anthropic uses internally, then renders an interactive diff-style critique — a standout career tool that shows hiring managers you understand agent architecture.
3. Use the coordinator/ multi-agent pattern to build a research-to-blog-post pipeline: one sub-agent scrapes and summarizes sources, a second writes the draft, a third fact-checks against the originals, and a fourth generates the SEO metadata — all coordinated in a single Supabase Edge Function with streaming status updates to your blog's admin dashboard.

5. TheTom/turboquant_plus

3,773 stars this week · Python

TurboQuant+ compresses the KV cache of local LLMs 3.8–6.4x with near-zero quality loss, enabling much longer context windows on consumer hardware like M-series Macs and RTX cards.

Use case

Running long-context inference locally is memory-bound — a 32k token context on a 70B model can exhaust 24GB VRAM entirely in KV cache alone. TurboQuant+ solves this by compressing K at q8_0 and V down to 2-bit with no measurable attention quality degradation, meaning you can fit 4–6x longer documents into the same VRAM budget. Concrete example: summarizing an entire codebase or long blog archive in a single prompt on an RTX 4090 instead of chunking it.

Why it's trending

The paper dropped at ICLR 2026 and this implementation includes community-validated findings (V compression is essentially free) that go beyond the paper — the asymmetric K/V precision insight is genuinely novel and researchers are actively replicating it this week. It also ships a llama.cpp fork, making it immediately usable without custom training infrastructure.

How to use it

1. Clone the repo and its llama.cpp fork: git clone https://github.com/TheTom/turboquant_plus && git clone https://github.com/TheTom/llama-cpp-turboquant
2. Build the llama.cpp fork with your backend (Metal or CUDA): cd llama-cpp-turboquant && cmake -B build -DLLAMA_METAL=ON && cmake --build build -j
3. Follow docs/getting-started.md to convert your GGUF model to a turbo3 format (recommended sweet spot): python turboquant_plus/convert.py --model ./your-model.gguf --format turbo3 --output ./your-model-turbo3.gguf
4. Run inference with the turbo llama.cpp binary, specifying the asymmetric config for best quality: ./build/bin/llama-cli -m your-model-turbo3.gguf --kv-cache-type q8_0-K+turbo-V -c 32768
5. Benchmark against baseline q8_0 KV cache on your hardware using the stress test scripts in docs/papers/m5-max-stress-test.md to confirm your compression ratio and throughput.

How I could use this

1. Run a local Llama 3 or Qwen model with turbo3 KV cache to power a 'chat with my entire blog archive' feature — feed all your markdown posts as a single long context prompt without chunking or RAG, since the compressed KV cache makes 32k+ token contexts feasible on a single consumer GPU.
2. Build a local resume-to-job-description semantic matcher that loads the full text of 50+ job postings in one context window — TurboQuant+ makes this feasible locally, meaning no API costs and no data leaving your machine, which is a strong privacy selling point to feature in a portfolio writeup.
3. Write a technical blog post benchmarking TurboQuant+ turbo2 vs turbo3 vs baseline q8_0 KV cache on your specific hardware (M-series Mac or RTX card), comparing perplexity and tokens/sec at 8k, 16k, and 32k context — this is high-signal content that will rank well and demonstrates you track cutting-edge inference research, not just application-layer AI.

6. sanbuphy/claude-code-source-code

3,386 stars this week · TypeScript

Decompiled and annotated TypeScript source of Claude Code v2.1.88, exposing the internal tool architecture, permission system, and agent loop that Anthropic ships as a minified CLI.

Use case

If you're building an AI coding agent or agentic workflow on top of Claude's API, you'd normally reverse-engineer how tool-calling, sub-agents, and permission gating should work. This repo hands you the actual reference implementation — for example, you can study exactly how Claude Code structures its 40+ tool definitions, how it chains sub-agents, and how it manages state across a multi-step agentic loop, then replicate that pattern in your own Next.js/Supabase app without guessing.

Why it's trending

Claude Code just hit mainstream adoption as a serious Cursor/Copilot competitor, and developers building their own AI agents are hungry to understand how Anthropic actually wires tool-use, permissions, and the agent loop under the hood — this is the first readable source dump of that implementation.

How to use it

1. Clone the repo and navigate to src/ — focus on the tool definitions directory first. Each tool is a typed object with name, description, input_schema (JSON Schema), and an execute function. This is the exact shape Claude's API expects for tool-use.,2. Read docs/en/01-telemetry-and-privacy.md and 02-hidden-features-and-codenames.md before anything else — these give you the architectural map so you're not reading code blind.,3. Locate the agent loop entry point (the Query Engine section in the README points you there). Study how it decides when to call a tool vs. return a final response — this is the core decision pattern you'd replicate in your own agent.,4. Copy the permission-flow pattern into your own project: Claude Code gates destructive tools (file writes, shell exec) behind an explicit approval step. Implement the same in your AI features using a pendingActions state in Supabase with a user-confirmation UI before execution.,5. Use the JSON Schema definitions from the tool input_schema fields as a reference for structuring your own tool definitions when calling anthropic.messages.create with tools: [...] — they're production-validated schemas you can adapt directly.

How I could use this

1. Blog post series: 'Reverse-engineering Claude Code's agent loop' — walk through the tool-chaining architecture with annotated code snippets from this repo, then show readers how to build a minimal 5-tool agent in Next.js using the same pattern. This would be a high-SEO technical post that positions Henry as someone who reads primary sources, not just docs.
2. Career tool idea: Build a 'resume action agent' that uses Claude with tool-use modeled after this architecture — tools like fetch_job_description, diff_resume_sections, rewrite_bullet, and score_match chained in sequence. Use the permission-flow pattern from Claude Code so the user approves each rewrite before it's committed to their Supabase-stored resume.
3. AI blog feature: Implement an 'AI editor agent' for Henry's blog posts that mirrors Claude Code's sub-agent pattern — a supervisor agent breaks a draft into sections, spins up sub-agents to fact-check, improve clarity, and suggest internal links, then aggregates results. Wire approval gates (inspired by Claude Code's permission system) so Henry reviews each suggestion before it updates the post in Supabase.

7. titanwings/colleague-skill

3,337 stars this week · Python

A Claude Code skill that ingests a departing colleague's chat logs, docs, and emails to generate an AI agent that codes in their style, answers questions with their domain knowledge, and replicates their working patterns.

Use case

When a key engineer leaves, critical institutional knowledge — their undocumented conventions, code review opinions, architectural preferences — walks out the door with them. This tool lets you feed their Slack/Feishu messages, markdown docs, and emails into a structured Claude Code skill so you can query 'how would Zhang Wei approach this auth refactor?' and get a contextually grounded answer instead of digging through 3 years of chat history yourself.

Why it's trending

Viral in Chinese dev communities this week due to its sardonic framing ('turn cold goodbyes into warm Skills, welcome to Cyber Immortality') hitting a nerve amid widespread layoffs and AI displacement anxiety — the quote about LLM developers being 'traitors killing all engineers including themselves' is both the joke and the product pitch.

How to use it

1. Install globally so it's available across all projects: git clone https://github.com/titanwings/colleague-skill ~/.claude/skills/create-colleague && pip3 install -r ~/.claude/skills/create-colleague/requirements.txt
2. Gather raw materials: export Slack DMs via API, grab .eml email archives, and collect any markdown docs or README files the colleague wrote.
3. In Claude Code, invoke the skill with your materials: /skill create-colleague --name 'Alex' --slack-export ./alex_slack.json --docs ./alex_docs/ --description 'Senior backend engineer, Go/Postgres, strong opinions on error handling, always deflects on deadlines'
4. The skill generates a .claude/colleagues/alex.md context file — a structured persona with their coding conventions, known domain knowledge, and communication style.
5. Reference it in future Claude Code sessions: 'Using alex.skill context, review this PR the way Alex would' and get feedback that mirrors their actual patterns.

How I could use this

1. Build a 'blog-author.skill' from your own post history: feed all your existing markdown blog posts into the skill generator to create a writing-style AI that drafts new posts in your voice, preserves your technical depth preferences, and flags when AI-generated drafts sound too generic — essentially cloning your editorial instincts for faster publishing.
2. Create a 'hiring-rubric.skill' for your portfolio site: scrape job descriptions you've saved, your own resume iterations, and cover letters you've written, then generate a skill that acts as your personal 'would I pass this interview?' evaluator — giving you a private AI that knows your actual skill gaps vs. job requirements, not generic advice.
3. Use the email/.eml ingestion pipeline to build a 'reader-persona.skill' from newsletter reply threads or GitHub issue comments on your open source work — if readers keep asking the same architecture questions, instantiate their collective confusion as a testable AI persona that you run new blog drafts past before publishing, catching gaps in your explanations proactively.

8. Kuberwastaken/claude-code

2,852 stars this week · Rust

A clean-room Rust reimplementation of Claude Code built from behavioral specs derived from Anthropic's accidentally leaked source map, exposing exactly how a production AI CLI agent is architected.

Use case

If you want to understand how a real AI coding agent orchestrates tool calls, manages context windows, handles file system operations, and structures system prompts — this gives you the internals without reverse engineering anything yourself. Concrete example: you're building an AI agent that can read/write files and run shell commands in your blog's admin panel, and you need a reference architecture for safely scoping tool permissions and handling streaming responses.

Why it's trending

The underlying story — Anthropic's entire Claude Code source leaking via an npm sourcemap on March 31st — is one of the biggest AI infosec moments of 2025, making this the most-watched technical teardown of a production AI agent right now. Developers are racing to understand what 'agentic AI CLI' actually looks like under the hood before Anthropic patches the gap.

How to use it

1. Read the spec/ directory first, not the code — the behavioral specs in spec/ describe tool contracts, context management, and agent loop design in plain language. This is the real gold.,2. Clone and build the Rust implementation to see a working agent loop: git clone https://github.com/Kuberwastaken/claude-code && cd claude-code/src-rust && cargo build --release,3. Study the tool contract definitions to understand how Claude Code gates file read/write/exec permissions — specifically look for the permission scoping pattern before each tool invocation.,4. Cross-reference the spec's 'system prompt architecture' section against your own Anthropic API calls to see what scaffolding Anthropic wraps around every Claude Code session.,5. Use the agent loop structure (spec → plan → tool call → observe → repeat) as a blueprint for any agentic feature you're building — it's the cleanest public reference for this pattern.

How I could use this

1. Write a technical blog post titled 'What the Claude Code Leak Teaches Us About Building AI Agents' — walk through the tool permission model and agent loop from the spec, then show Henry's readers how to implement a minimal version using Anthropic's API directly. This is SEO gold right now while the story is hot.
2. Build a lightweight 'AI blog assistant' CLI in TypeScript modeled on the clean-room spec's agent loop — it reads Henry's draft markdown posts, calls Claude to suggest edits, and writes diffs back to disk. Use the tool contract pattern from spec/ to safely scope it to only the /posts directory.
3. Extract the context window management strategy from the spec (how Claude Code decides what to keep vs. truncate in long sessions) and apply it to Henry's AI chat feature on the blog — implement the same summarization-on-overflow pattern so long reader conversations with his AI don't silently degrade in quality.

9. magnum6actual/flipoff

2,550 stars this week · JavaScript

A zero-dependency browser app that renders a pixel-perfect animated split-flap airport board, turning any screen into a retro display for free.

Use case

Hardware split-flap displays cost $3,500+ and require proprietary software — this solves the problem for anyone who wants the aesthetic without the price tag. Concrete example: a conference organizer wants a lobby screen showing session schedules with that classic Arrivals/Departures board feel, or a developer wants a retro hero section on their portfolio that actually animates letter-by-letter like a real board.

Why it's trending

The nostalgia-tech aesthetic is peaking right now alongside brutalist and retro UI design trends, and people are actively looking for ways to make portfolio sites and digital signage stand out visually without paying for Vestaboard or similar hardware. The zero-dependency, open-index.html approach also resonates with the current backlash against over-engineered JavaScript toolchains.

How to use it

1. Clone the repo: git clone https://github.com/magnum6actual/flipoff && cd flipoff
2. Open index.html directly in a browser or serve it: python3 -m http.server 8080
3. To feed your own messages, locate the messages array in index.html and replace the default quotes with your own strings — each string maps to one board 'frame'
4. To embed in a Next.js page, drop the folder into /public/flipoff/ and render it inside an <iframe src='/flipoff/index.html' /> with allowFullScreen — this sidesteps the vanilla JS / React boundary entirely
5. Press F for fullscreen TV mode; press M to toggle the mechanical clack sound

How I could use this

1. Use it as an animated hero section on Henry's blog home page: embed the iframe and drive it with a small API route that fetches his latest blog post titles from Supabase, so the board cycles through real post headlines — giving returning visitors a live 'what's new' display with genuine retro character.
2. Build a 'job search dashboard' page where the flip board displays the current status of job applications stored in Supabase (e.g., 'GOOGLE — PHONE SCREEN', 'STRIPE — OFFER RECEIVED') — write a lightweight script that rewrites the messages array from a Supabase query on page load, turning the board into a live career tracker you can put on a second monitor.
3. Pipe AI-generated content through the display: set up a Supabase Edge Function that calls OpenAI to generate a 'thought of the day' or a one-line summary of Henry's most recent blog post, then expose it as a JSON endpoint that a small wrapper around the flipoff embed polls every 60 seconds — auto-refreshing the board with fresh AI copy without any manual input.

10. elder-plinius/G0DM0D3

2,432 stars this week · TypeScript

A single-file, open-source multi-model AI chat interface with red-teaming tools and parallel model racing — essentially a power-user's ChatGPT with no guardrails and 55+ models via OpenRouter.

Use case

The real problem: you're building AI-powered features and need to stress-test prompts across multiple models simultaneously to find which one performs best for your specific use case — without paying for five separate API subscriptions or clicking between tabs. Concrete example: Henry wants to generate blog post summaries and needs to know whether Claude 3.5, GPT-4o, or Gemini 1.5 Pro produces the most consistent, on-brand output. GODMODE CLASSIC runs all five battle-tested combos in parallel and scores them, cutting evaluation time from hours to minutes.

Why it's trending

It's spiking this week because GPT-5 just dropped and developers are racing to compare it against Claude Opus 4 and Gemini 2.5 Pro in real conditions — not benchmarks. The 'Parseltongue' red-teaming engine (33 perturbation techniques) is also getting attention from the AI safety and jailbreak research communities who are actively probing post-training alignment in the new model generation.

How to use it

1. Clone and open locally — it's literally one file: git clone https://github.com/elder-plinius/G0DM0D3 && open index.html — no npm install, no build step.,2. Get an OpenRouter API key at openrouter.ai (free tier available), paste it into the UI — it never leaves your browser.,3. Use GODMODE CLASSIC to run your target prompt (e.g., 'Summarize this blog post in 3 bullet points: [paste]') across 5 model combos in parallel and compare raw output quality side-by-side.,4. Use Parseltongue at 'Low' intensity to generate prompt variants (synonym swaps, reordering, rephrasing) and identify which of your prompts are brittle — i.e., produce wildly different results with minor wording changes.,5. Use ULTRAPLINIAN for final model selection: run your production prompt through all 55 models with composite scoring to get a data-driven answer on which model to wire into your Supabase Edge Function.

How I could use this

1. Blog prompt stress-tester: Before shipping any AI writing feature (auto-summaries, tag generation, SEO meta descriptions), run the prompt through ULTRAPLINIAN across 10+ models. Document the composite scores in a public blog post — 'I tested my blog's AI prompts against 55 models, here's what I found' is a high-signal piece that attracts developer readers and demonstrates rigor.
2. Cover letter A/B engine for career tools: Wire Parseltongue's perturbation output into your resume/cover letter generator — generate 5 structurally varied versions of the same cover letter prompt, then use GODMODE CLASSIC to score each against a rubric prompt like 'Does this cover letter match the job description? Score 1-10 with reasoning.' Surface the top scorer to the user automatically via a Supabase Edge Function.
3. Red-team your own blog's AI moderation: If Henry's blog has a comments section or user-submitted content with AI moderation, use Parseltongue's 'High' intensity mode to generate adversarial inputs and verify the moderation layer doesn't break under obfuscated toxic content — log failures to a Supabase table and build a regression test suite from real red-team results.