GitHub Hot — 9 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. milla-jovovich/mempalace

35,344 stars this week · Python · ai chromadb llm mcp

MemPalace is a local, open-source AI memory system that stores every conversation verbatim in ChromaDB and uses a hierarchical structure for semantic retrieval — achieving 96.6% on LongMemEval without any LLM-based summarization.

Use case

Every time you start a new Claude or GPT session, you re-explain your stack, your decisions, your constraints — the AI has no context of the six months of debugging sessions you've had. MemPalace fixes this by acting as a persistent, searchable memory layer: wire it via MCP to your AI assistant and it automatically surfaces relevant past conversations before you've even finished typing your question. Concrete example: you start a new session about a Supabase RLS bug, and MemPalace retrieves the exact exchange from three months ago where you solved a nearly identical row-level security issue.

Why it's trending

It dropped a 96.6% LongMemEval benchmark score — the highest publicly reported — at a time when AI memory is one of the hottest unsolved UX problems in the LLM space, and the MCP (Model Context Protocol) ecosystem is exploding with devs looking for plug-in memory backends.

How to use it

Install: pip install mempalace and make sure you have ChromaDB running locally (pip install chromadb).
Initialize a palace scoped to a project: mempalace init --wing henry-blog --hall architecture-decisions.
Connect it as an MCP server in your Claude/Cursor config by pointing the MCP tool URL to the local MemPalace server endpoint (check the repo's mcp_server.py for the exact host/port).
After each meaningful AI session, pipe the conversation JSON into MemPalace: mempalace store --file session.json --wing henry-blog --hall debugging.
Query it in future sessions via the MCP tool call or CLI: mempalace search 'supabase RLS policy infinite recursion' --wing henry-blog — returns ranked verbatim chunks with room context.

How I could use this

Build a 'Dev Journal' feature on the blog where every AI-assisted post-drafting session is stored in MemPalace under a 'blog-writing' hall — then expose a public /api/memory-search endpoint so readers can query what research and debates went into a specific post, turning your writing process into transparent, searchable content.
Create a career context palace with wings for each job application: store every cover letter brainstorm, every interview-prep conversation, and every salary negotiation script. Before each new application session, the MCP integration auto-injects the most relevant past decisions (e.g., 'you previously rejected React-only roles') so your AI assistant never gives you generic advice again.
Integrate MemPalace as the memory backend for the blog's AI chat assistant — when a reader asks a question, the assistant first queries MemPalace for relevant past conversations you've had about that topic, then uses those verbatim chunks as grounded context before generating a response, making the bot sound like it actually knows your opinions rather than hallucinating generic answers.

2. santifer/career-ops

27,554 stars this week · JavaScript · ai-agent anthropic automation career

A Claude Code-powered multi-agent CLI system that automates the entire job search pipeline — scraping listings, scoring fit, generating tailored CVs, and tracking applications via a Go dashboard.

Use case

Most developers apply to jobs manually, copy-pasting the same resume into ATS systems that silently reject them. Career-Ops inverts this: you define your target role profile once, then let 14 specialized AI agents (resume tailor, salary analyzer, interview prepper, etc.) batch-process hundreds of listings, score company fit, and generate role-specific PDFs — the author evaluated 740 listings and sent 100+ tailored CVs without doing it by hand.

Why it's trending

Claude Code launched as an agentic coding tool in early 2025 and this is one of the first real-world open-source systems built entirely on top of it, making it a flagship example of Claude Code's multi-agent orchestration capabilities. It also hit at the exact moment layoffs and AI hiring filters are peaking, making 'fight AI with AI' a viscerally relatable pitch.

How to use it

Clone the repo and install deps: git clone https://github.com/santifer/career-ops && cd career-ops && npm install
Add your Anthropic API key and configure your candidate profile in config/profile.json — skills, target roles, salary range, deal-breakers.
Run a skill mode against a job board URL, e.g. node cli.js --mode resume-tailor --url 'https://jobs.example.com/senior-engineer' — Claude Code reads the JD and rewrites your base CV to match.
Use batch mode to sweep a list of URLs: node cli.js --mode batch --input urls.txt --output ./output/ — generates scored PDFs per listing.
Launch the Go dashboard to track pipeline state: cd dashboard && go run main.go — gives you a terminal UI showing application status, fit scores, and follow-up reminders.

How I could use this

Build a public '/uses' or '/open-to-work' page on Henry's blog that auto-updates from a Career-Ops run — pipe the fit-score JSON output into a Supabase table and render a live 'companies I'm evaluating' tracker with match percentages and status badges using Next.js ISR.
Fork the resume-tailor skill mode to create a standalone '/resume-match' tool on the blog: visitors paste a job description, it hits the Anthropic API with Henry's base resume as context, and returns a tailored bullet-point diff showing what to change — a high-value lead magnet that demonstrates AI chops.
Use the interview-prep skill mode's prompt architecture as the backbone for an AI interview simulator feature on the blog: Henry's readers input a job title, the system pulls common questions for that role via Claude, runs a mock Q&A, then scores answers against STAR format — position it as a free tool to drive newsletter signups.

3. safishamsi/graphify

17,438 stars this week · Python · claude-code codex graphrag knowledge-graph

Graphify turns any folder of code, docs, or images into a queryable knowledge graph you can explore visually or query with 71x fewer tokens than reading raw files.

Use case

When you inherit a large codebase or research corpus with no documentation, understanding the 'why' behind architectural decisions means reading hundreds of files. Graphify parses everything — source files, PDFs, screenshots, markdown — builds an entity-relationship graph, and lets you query 'why is auth handled here and not in middleware?' without re-reading the entire repo every session. Concrete example: drop your Next.js blog repo + Supabase schema SQL + architecture ADRs into one folder and run /graphify . to get a clickable graph showing how your RLS policies connect to your API routes.

Why it's trending

The GraphRAG pattern (graph-based retrieval over flat vector search) just crossed the mainstream threshold with Claude Code's slash-command plugin ecosystem exploding this month — Graphify is one of the first tools that makes GraphRAG a single command inside the editors developers are already using daily.

How to use it

Install: pip install graphifyy and add the skill to Claude Code or Cursor per the README (copy the CLAUDE.md snippet into your project root).
Navigate to any project folder and run /graphify . — it will use tree-sitter to parse source files and Claude Vision for PDFs/images, building graphify-out/graph.json.
Open graphify-out/graph.html in a browser to interactively explore nodes (files, functions, concepts) and edges (calls, imports, references), filtering by auto-detected community clusters.
Query the persistent graph in subsequent sessions with /graphify query 'what owns the auth flow?' — the SHA256 cache means only changed files are reprocessed.
Pipe GRAPH_REPORT.md into your LLM context when doing code review or writing docs — it lists god nodes and surprising cross-cutting connections automatically.

How I could use this

Run graphify over your entire blog's /content markdown folder + your Supabase schema + Next.js API routes, then expose a /api/graph-query endpoint that lets readers ask 'what posts relate to this concept?' — replace your current tag system with a semantically-aware graph navigation UI rendered from graph.json.
Feed your resume, every job description you've applied to, and your GitHub repos into a single graphify run to generate a skills knowledge graph. The GRAPH_REPORT.md 'god nodes' will surface the skills that bridge the most concepts — use those as the headline keywords in your next cover letter generator rather than manually keyword-matching.
Build a 'codebase onboarding' AI feature for your blog: when you write a post about a open-source project, auto-run graphify on that repo in a GitHub Action, store graph.json in Supabase Storage, and let readers query the graph inline (e.g. 'show me all components that touch authentication') via a Claude-powered chat widget — no re-reading the repo on each query since the graph persists.

4. JuliusBrussee/caveman

8,740 stars this week · Python · ai anthropic caveman claude

A Claude Code plugin that forces the LLM to respond in stripped-down 'caveman speak', cutting output tokens by ~65-75% while preserving full technical accuracy — essentially a prompt compression hack packaged as a one-line install.

Use case

When you're running Claude Code in an agentic loop (code review, commit messages, iterative debugging), verbose LLM responses burn tokens fast and add latency. Caveman solves this by installing a system-level skill that rewires Claude's output style — so instead of a 69-token explanation about useMemo, you get '5 token: obj new each render. use useMemo.' Same fix, 90% less noise. Especially valuable if Henry is building AI features on top of Claude's API where he's paying per token.

Why it's trending

It went viral because it's a dead-serious engineering optimization wrapped in an absurdist meme — the before/after comparison is immediately shareable and the token savings are real and measurable. It also rides the current wave of 'prompt engineering as infrastructure' thinking, where developers are treating system prompts as first-class code artifacts.

How to use it

Install the Claude Code skill in one line: claude skill install caveman (or clone the repo and point Claude Code at the .md skill file manually).
Activate caveman mode in your session: @caveman on — Claude will immediately shift to compressed output style.
Tune the intensity level if needed (the repo exposes levels 1-3, where level 3 is full 'ugh fix bug now' mode) — use level 1 or 2 for technical work where you still need readable variable names.
Use @caveman-compress at the start of a long session to compress your own input context by ~45%, feeding Claude a denser prompt without losing semantic meaning.
For Henry's use case: wire @caveman-commit into a git hook so every git commit auto-generates a terse, accurate commit message via Claude without burning a full prompt budget.

How I could use this

Build a 'Caveman Summarizer' widget for blog posts — when a reader hovers over a long technical post, a Claude API call with the caveman system prompt returns a 2-sentence TL;DR in the sidebar. Token cost per hover would be trivially cheap with caveman compression vs. a standard summarization prompt.
Wrap the caveman compression technique into Henry's cover letter/resume matcher pipeline: before sending a job description to Claude for analysis, run it through caveman-compress to strip filler language from the JD, then send the compressed version. This cuts input tokens on every resume match call and could make a high-volume job-tracking tool economically viable at scale.
Create a 'code review on commit' GitHub Action for Henry's own repos that uses the caveman skill — on every push, Claude reviews the diff in caveman mode and posts a 3-bullet PR comment. Because output tokens are 75% cheaper, this can run on every single commit without blowing through a monthly API budget, making it practical as an always-on quality gate rather than an occasional luxury.

5. alchaincyf/nuwa-skill

5,427 stars this week · Python

A Claude Code skill that auto-researches any public figure and distills their mental models, decision heuristics, and communication style into a reusable AI persona you can query like a consultant.

Use case

The real problem: prompt-engineering a convincing 'think like Munger' persona from scratch is tedious and inconsistent — you get generic platitudes instead of authentic reasoning patterns. Nuwa automates the research-distill-validate pipeline: give it a name, it scrapes speeches, interviews, books, and writing samples, extracts core heuristics (not just quotes), and produces a structured skill file you can invoke repeatedly. Concrete example: instead of asking ChatGPT 'what would Feynman say about my startup idea,' you get a validated persona that actually applies first-principles decomposition the way Feynman demonstrably did.

Why it's trending

It's riding the 'Claude Code skills' wave that exploded this week as developers discover skills.sh as a distribution mechanism for agentic Claude workflows — it's essentially the npm moment for AI behavioral primitives. The Chinese dev community is also heavily amplifying it via Weibo/X after the Naval and Jobs demo outputs went viral for being genuinely sharp rather than sycophantic.

How to use it

Install Claude Code and set up skills.sh compatibility (follow skills.sh quickstart — requires Anthropic API key in env). 2. Clone the repo: git clone https://github.com/alchaincyf/nuwa-skill && cd nuwa-skill. 3. Invoke the skill with a target name — e.g. in Claude Code: /skill nuwa 'Paul Graham' — it will autonomously run research loops using web search, extract mental models, and write a paul_graham.skill.md file. 4. Load the generated skill for a session: /load paul_graham.skill.md then ask domain-specific questions like 'review my startup's pricing strategy.' 5. Validate outputs against source material (the skill includes a self-check step that cites which essays/talks each heuristic is derived from).

How I could use this

Build a 'Ghost Editor' feature for Henry's blog: after drafting a post, invoke a distilled Paul Graham or Orwell persona to critique it — not for style matching but to surface logical gaps. Wire it into a Next.js API route that takes the post markdown, calls Claude with the loaded skill file as system context, and returns structured feedback (weak claims, missing evidence, buried lede) displayed as inline annotations using a Tiptap editor overlay.
Create a 'Career Advisor Panel' tool: distill 3-4 personas known for hiring/career advice (e.g. Patrick McKenzie, Lenny Rachitsky, a relevant CTO) and build a UI where Henry pastes a job description + his resume, each persona reviews it from their documented hiring philosophy, and the app surfaces the delta between what Henry emphasizes vs. what each advisor would prioritize — far more actionable than generic ATS optimization.
Distill Henry's own writing over time into a personal skill file: scrape all his blog posts, extract his recurring framings, metaphors, and argument structures, and generate a henry.skill.md. Then use it as a style-consistency checker — any new draft gets scored against his established 'expression DNA' so his voice stays coherent as the blog scales, or as a ghost-writing baseline if he ever uses AI to draft posts.

6. farzaa/clicky

3,098 stars this week · Swift

Clicky is an open-source macOS AI teaching assistant that lives beside your cursor, can see your screen, and talks to you in real time — like a pair programmer with eyes.

Use case

Developers and learners often get stuck mid-task and have to context-switch to ChatGPT, paste code, explain their situation, and wait — breaking flow entirely. Clicky solves this by giving you an ambient AI that already sees what you're working on and can point at specific UI elements or code on your screen to explain them, like a senior dev looking over your shoulder without the awkwardness.

Why it's trending

The original demo tweet went viral this week because it nails a UX nobody has shipped cleanly before — a cursor-anchored AI that uses ScreenCaptureKit to actually see your screen and respond visually, not just textually. It also dropped as open source immediately, so developers are racing to fork and extend it.

How to use it

Ensure you're on macOS 14.2+, have Xcode 15+, Node 18+, and accounts for Anthropic, AssemblyAI, and ElevenLabs.,2. Set up the Cloudflare Worker proxy to keep your API keys out of the binary: cd worker && npm install, then run npx wrangler secret put ANTHROPIC_API_KEY, npx wrangler secret put ASSEMBLYAI_API_KEY, and npx wrangler secret put ELEVENLABS_API_KEY.,3. Deploy the worker: npx wrangler deploy, then copy the resulting worker URL into the Xcode project's config so the app knows where to send requests.,4. Open the Xcode project, set your development team under Signing & Capabilities, then hit Run — the app will appear as a floating overlay anchored near your cursor.,5. Grant screen recording and microphone permissions when prompted, then start talking to Clicky — it can see your screen in real time and respond with voice + visual pointer cues.

How I could use this

Fork Clicky and replace the generic teaching persona with a 'Blog Writing Coach' mode — wire it to Claude with a system prompt that knows Henry's writing style and past posts (fetched from Supabase), so it can watch him draft in Notion or VS Code and suggest improvements, flag passive voice, or recommend internal links to existing blog content in real time.
Build a 'Portfolio Review' variant where Clicky watches Henry walk through his own portfolio site or a recruiter's job posting, then verbally narrates what a hiring manager would think — effectively a live resume/portfolio gap analyzer that sees exactly what's on screen rather than requiring copy-paste into a chat window.
Use Clicky's Cloudflare Worker + ScreenCaptureKit architecture as the pattern for an AI code-review overlay on Henry's blog's admin dashboard — a floating widget that watches him edit Supabase queries or React components and proactively flags N+1 query patterns, missing TypeScript types, or accessibility issues without him having to leave the editor.

7. alchaincyf/zhangxuefeng-skill

1,851 stars this week · various

张雪峰.skill — 张雪峰的认知操作系统。高考志愿/考研/职业规划的实战思维框架。由女娲.skill生成。

Use case

张雪峰.skill — 张雪峰的认知操作系统。高考志愿/考研/职业规划的实战思维框架。由女娲.skill生成。

Why it's trending

How to use it

How I could use this

8. GitFrog1111/badclaude

1,752 stars this week · HTML

A tray app that lets you 'whip' Claude Code with a literal whip animation when it stalls, sending a Ctrl-C interrupt and a snarky motivational message to restart it.

Use case

Claude Code (and agentic AI coding tools in general) frequently get stuck in infinite loops, hang on tool calls, or just go silent mid-task with no obvious way to recover without alt-tabbing and manually killing the process. badclaude puts a one-click interrupt in your system tray so you can slap it back to life without breaking your flow. Example: Claude is spinning on a file read, you click the tray icon, crack the whip, and it cancels and re-prompts automatically.

Why it's trending

Claude Code hit mainstream adoption in the last few weeks and its 'going slow or freezing' problem is a universal pain point everyone is complaining about right now. The cease-and-desist mention in the README is pure viral fuel — Anthropic apparently noticed, which made every dev on Twitter want to install it immediately.

How to use it

Install globally: npm install -g badclaude
Run it: badclaude — this starts the system tray process
Start a Claude Code session normally in your terminal
When Claude hangs, click the tray icon to spawn the whip, then click again to drop it — this fires Ctrl-C to the Claude process plus one of 5 pre-written 'encouraging' messages
Claude restarts its current task from the interrupt point — no manual terminal switching required

How I could use this

Write a satirical 'AI Wrangler's Handbook' blog post using badclaude as the hook — benchmark how often Claude Code actually stalls on real Next.js/Supabase tasks (RLS policy generation, schema migrations, etc.), log the whip counts, and publish the data. Readers love quantified absurdity.
Build a 'Claude Code session health' sidebar widget for your blog's dev notes section — pipe the stdout of your Claude Code sessions into a lightweight logger, detect stall signatures (no output for >30s), and surface a 'whips needed today: 3' counter as a live badge. It's a conversation starter and demonstrates real devtools thinking.
Fork badclaude and replace the whip with a smarter interrupt: instead of a random encouraging message, embed a small prompt template that includes the last 5 lines of Claude's output and asks it to explain why it stalled and retry with a different approach — turning a dumb interrupt into a self-healing agentic loop you can write up as an open-source contribution.

9. xixu-me/awesome-persona-distill-skills

1,673 stars this week · JavaScript · agent-skills awesome awesome-list persona-distill

A curated list of Agent Skills for distilling personas — from real people's writing, chat logs, and digital traces — into reusable AI agent skill files you can deploy on platforms like agentskills.io.

Use case

The real problem: you want an AI agent that actually sounds like a specific person (yourself, a mentor, a public figure) rather than generic GPT. For example, you have 3 years of Slack messages and blog posts — this repo gives you ready-made skill templates and frameworks to extract your own communication style, decision heuristics, and vocabulary patterns into a structured .skill file that any compatible agent runtime can load.

Why it's trending

Agent Skills as a packaging format for persona-specific AI behavior is gaining traction this week as platforms like agentskills.io mature and developers realize that raw system prompts don't capture nuanced persona fidelity — this list aggregates the community's best distillation patterns in one place. The Chinese developer community is especially active here, driving rapid star growth.

How to use it

Browse the repo categories — start with '自我蒸馏与元工具' (self-distillation tools). Clone a relevant skill repo, e.g., git clone https://github.com/alchaincyf/nuwa-skill to see the .skill file structure.,2. Collect your raw persona data: export your blog posts as markdown, pull chat history, gather any writing samples. The skills expect structured input — typically a persona.md with sections for communication style, decision frameworks, and recurring phrases.,3. Use the skill's prompt template to run a distillation pass through an LLM (GPT-4o or Claude): feed it your raw data and have it output a structured persona JSON/YAML that maps to the skill's schema.,4. Load the resulting .skill file into agentskills.io or adapt it as a system prompt prefix in your own API calls: const systemPrompt = fs.readFileSync('./henry.skill', 'utf8'); const response = await openai.chat.completions.create({ model: 'gpt-4o', messages: [{ role: 'system', content: systemPrompt }, ...userMessages] });,5. Iterate: run the agent against sample Q&A where you know how you'd actually respond, diff the output against your real answers, and refine the skill's heuristics section.

How I could use this

Build a 'Henry.skill' derived from your existing blog posts and ship a '/chat-with-me' page on your Next.js blog — visitors ask questions and get answers in your actual writing voice, backed by a Supabase vector store of your posts for RAG grounding.
Create a 'code-reviewer.skill' distilled from your GitHub PR comments and commit messages, then wire it as a GitHub Action bot that reviews your own PRs and flags when new code diverges from your established patterns and preferences.
Use the self-distillation framework to auto-generate a dynamic 'About Me' section on your portfolio — feed it your latest blog posts, GitHub activity, and Supabase-stored project notes weekly, and have it rewrite your bio and highlight reel to reflect your current focus without manual updates.

10. LaurieWired/tailslayer

1,658 stars this week · C++

Library for reducing tail latency in RAM reads

Use case

Library for reducing tail latency in RAM reads

Why it's trending

How to use it

How I could use this