GitHub Hot — 10 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

39,934 stars this week · Python · ai chromadb llm mcp

MemPalace is a local, open-source AI memory system that stores every conversation verbatim in ChromaDB with a hierarchical structure, achieving 96.6% on LongMemEval — the highest published score — so your AI context never resets between sessions.

Use case

The core problem: every Claude/GPT session starts blank, so you re-explain your stack, preferences, and past decisions constantly. MemPalace fixes this by persisting your full conversation history locally and exposing it via MCP (Model Context Protocol), so when you ask 'why did we choose Supabase over PlanetScale?' six months later, the actual debate is retrievable — not a summarized bullet point that lost the nuance.

Why it's trending

MCP (Model Context Protocol) became the de facto standard for plugging external tools into LLMs in early 2026, and MemPalace is one of the first memory systems to expose itself as an MCP server with a benchmark score to back up the claims — hitting 96.6% on LongMemEval the week it dropped.

How to use it

Install and configure: pip install mempalace && mempalace init — this sets up a local ChromaDB instance with the Palace hierarchy (wings → halls → rooms).
Start the MCP server: mempalace serve --port 8765 — this exposes your memory store to any MCP-compatible client (Claude Desktop, Cursor, custom Next.js API routes).
Ingest existing history: mempalace ingest --source cursor_history or pipe raw text — assign it to a wing (e.g. 'henry-blog') and hall (e.g. 'architecture-decisions').
Query from your app: hit the MCP endpoint before each LLM call to inject relevant past context — e.g. GET /memory/search?q=supabase+rls+policy&wing=henry-blog returns the top-k verbatim chunks to prepend to your system prompt.
Let it auto-ingest going forward: configure your Claude Desktop or Cursor MCP settings to point at the running MemPalace server so every future session is stored automatically.

How I could use this

Build a 'Blog Memory' wing that stores every writing session, research note, and draft revision — then surface a 'related past thinking' sidebar on each published post by querying MemPalace at build time with the post's title + tags, giving readers (and future-Henry) a trail of how ideas evolved.
Create a 'Career Context' wing that ingests your resume, every cover letter you've written, interview debrief notes, and job description analyses — then wire it to your cover letter generator so it auto-pulls your actual past reasoning ('I said I liked TypeScript strict mode because of X incident') instead of generic bullets.
Add a persistent AI pair-programming context to your blog's admin panel: every Supabase schema decision, RLS policy debug session, and Next.js architecture choice gets stored in a 'henry-blog-tech' wing, then injected as a compressed system prompt prefix whenever you open a new Cursor session — eliminating the 'wait, why did I structure this table this way?' tax entirely.

2. santifer/career-ops

29,357 stars this week · JavaScript · ai-agent anthropic automation career

Career-Ops is a Claude Code-powered multi-agent CLI system that automates the entire job search pipeline — resume tailoring, cover letters, interview prep, and batch application tracking — treating job hunting as an engineering problem.

Use case

Most developers waste hours manually tailoring resumes per job posting and writing bespoke cover letters. Career-Ops solves this by running 14 specialized AI skill modes (resume analyst, salary negotiator, interview coach, etc.) as discrete agents that can be batched across multiple job listings simultaneously. Concrete example: feed it 10 job descriptions from LinkedIn, get back 10 tailored resumes, 10 cover letters, and a Go dashboard tracking fit scores — all without opening a text editor.

Why it's trending

This dropped at peak 'Claude Code agent' hype — developers are actively exploring what Claude Code can orchestrate beyond coding tasks, and a repo that demonstrates multi-agent Claude workflows for a universally relatable problem (job hunting) went viral instantly. The 29k stars in one week signals it hit a nerve with both job seekers and developers studying agentic architecture patterns.

How to use it

Clone and install: git clone https://github.com/santifer/career-ops && cd career-ops && npm install — requires Node.js 18+ and Claude Code CLI authenticated with your Anthropic API key.,2. Drop your base resume (PDF or markdown) into ./input/resume.md and create a ./input/jobs/ directory with one .txt file per job description you're targeting.,3. Run a skill mode against a single job to test: claude career-ops run --mode resume-tailor --job ./input/jobs/stripe-eng.txt --resume ./input/resume.md — output lands in ./output/ as a tailored PDF.,4. Batch process all jobs at once: claude career-ops batch --modes resume-tailor,cover-letter,interview-prep --jobs ./input/jobs/ — this triggers parallel Claude Code agents per job.,5. Launch the Go dashboard to see fit scores and application status: go run ./dashboard/main.go then open localhost:8080 — shows ranked job matches, generated artifacts, and next-action recommendations.

How I could use this

Build a public 'Job Fit Analyzer' page on Henry's blog where visitors paste a job description URL and their GitHub profile, and a serverless function calls Claude to return a scored breakdown of skill alignment — drives SEO traffic from developers passively job hunting and showcases Henry's AI integration chops.
Fork the resume-tailor skill mode and expose it as a Next.js API route (/api/tailor-resume) that accepts Henry's base resume + a job description, then streams a Claude-tailored version back — wrap it in a clean UI for his portfolio as a live demo recruiters can actually use on his 'Hire Me' page.
Use Career-Ops' interview-prep agent as the backbone for an AI mock interview feature on the blog: store common interview questions per tech stack in Supabase, let users pick a role (e.g. 'Senior Next.js Engineer'), then stream Claude responses that roleplay as an interviewer and score answers — capture sessions to Supabase for users to review later.

3. JuliusBrussee/caveman

13,009 stars this week · Python · ai anthropic caveman claude

A Claude Code plugin that forces the LLM to respond in broken caveman-speak, cutting output tokens by ~65-75% while preserving technical accuracy — saving real API costs at scale.

Use case

When you're running Claude Code in agentic loops (automated code reviews, CI pipelines, multi-step refactors), verbose LLM responses burn tokens fast. Caveman intercepts the system prompt to make Claude respond ultra-tersely — e.g. 'useMemo. object ref change every render. fix now.' instead of a 3-paragraph explanation — dropping per-request costs dramatically without losing the actionable answer.

Why it's trending

Token costs and context window management are the dominant pain points for developers shipping LLM-powered tools in 2025, and this repo went viral because it's both genuinely useful and absurdly funny — the meme format lowered the barrier to sharing it, but the 65%+ token reduction is real and measurable.

How to use it

Install the skill: run claude skill install caveman or copy the .claude/skills/caveman.md YAML into your Claude Code skills directory.
Activate in a session: type /caveman in Claude Code to toggle caveman mode on.
Use the compress tool for input tokens: run caveman-compress your-file.ts to strip comments, collapse whitespace, and summarize context before feeding it to Claude — cuts ~45% of input tokens.
For CI/automated use, set the caveman skill as default in your .claude/settings.json so every agent loop uses terse mode automatically.
Benchmark your savings: the repo includes eval scripts — run python evals/run.py against your own prompts to measure actual token delta before committing to it in production.

How I could use this

Build a 'Caveman Mode' toggle in your blog's AI chat widget — when readers ask questions about your posts, normal mode gives full explanations, caveman mode gives the brutally terse TL;DR. Show the token count difference live in the UI as a meta-demonstration of LLM cost tradeoffs.
Wrap caveman-compress into your cover letter / resume matcher pipeline: before sending your resume text + job description to Claude for gap analysis, run both through the compressor to cut input tokens by ~45%, then log the token savings per request in Supabase — after 100 runs you'll have concrete data to cite in blog posts about 'building cost-efficient AI career tools'.
Add a caveman-style 'Quick Take' AI summary to each blog post that runs at build time in your Next.js static generation step — use the caveman skill to generate a 15-word brutal summary of each post (3-5 tokens vs 80+), store it in Supabase alongside the full post, and surface it as a hover tooltip or collapsed preview card to give skimmers an instant signal before they commit to reading.

4. alchaincyf/nuwa-skill

6,573 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 directly.

Use case

The real problem: building a useful AI persona of a specific thinker (e.g. Charlie Munger) normally requires hours of manual prompt engineering and curating source material. Nuwa automates the full pipeline — web research, pattern extraction, validation, and skill packaging — so you get a queryable 'distilled mind' in minutes. Concrete example: type 'Paul Graham' and get a skill that responds to your startup questions in PG's actual reasoning style, not a generic LLM impression.

Why it's trending

It went viral this week because it builds directly on the earlier 'colleague-skill' concept but removes the bottleneck of needing to manually feed someone's writing — the automation angle makes it feel like a genuine workflow unlock rather than a prompt trick. The Chinese AI dev community amplified it fast, and the Job/Musk/Munger demo outputs are screenshot-worthy enough to drive organic sharing.

How to use it

Install prerequisites: ensure you have Claude Code CLI running and skills.sh configured (npm install -g @anthropic-ai/claude-code + authenticate).,2. Clone and register the skill: git clone https://github.com/alchaincyf/nuwa-skill && cd nuwa-skill && skills install .,3. Invoke Nuwa with a target name inside Claude Code: /nuwa Naval Ravikant — it will autonomously research, extract mental models, and write a .skill file to your skills directory.,4. Query the generated skill directly: /naval I have three projects competing for my attention, how do I prioritize? — response comes back in Naval's documented reasoning style.,5. Inspect and edit the generated .skill file (plain markdown) to add domain-specific context, remove hallucinated traits, or constrain the persona to topics where you have verified source material.

How I could use this

Blog 'Advisor Panel' widget: distill 3-4 thinkers relevant to your niche (e.g. Paul Graham for startups, Andrej Karpathy for AI, DHH for indie dev) and expose them as selectable chat personas on your blog posts — readers ask follow-up questions and get answers styled after the thinker most relevant to that post's topic. Store conversation threads in Supabase per post slug.
Career tool — 'Hiring Manager Simulator': distill the public interview philosophy of eng leaders at companies you're targeting (e.g. a known Stripe or Linear engineering lead based on their blog posts/talks), then feed your resume and have the skill do a mock behavioral screen. Output a gap analysis: 'this interviewer values X, your resume doesn't surface Y' — far more targeted than generic ATS optimization.
AI writing feature — 'Style Pressure Test': after Henry writes a draft post, route it through 2-3 distilled critics (e.g. a Hemingway-style clarity enforcer + a PG-style 'say what you mean' checker) and surface the deltas as inline Notion-style suggestions via a Supabase Edge Function, giving him an automated editorial layer before publishing.

5. farzaa/clicky

3,550 stars this week · Swift

Clicky is an open-source macOS AI teaching assistant that floats next to your cursor, watches your screen, and talks you through whatever you're looking at in real time.

Use case

Developers and learners often context-switch between a tutorial, their editor, and a chat window when stuck — Clicky eliminates that by putting an AI that can literally see your screen right next to your cursor. For example, you're debugging a TypeScript error in VS Code and instead of copy-pasting it into ChatGPT, Clicky sees it, hears your question, and points at the relevant line while explaining the fix.

Why it's trending

The original tweet demoing it went viral this week, and the repo is architecturally interesting because it shows exactly how to wire together ScreenCaptureKit, Claude, AssemblyAI speech-to-text, and ElevenLabs TTS in a real shipping macOS app — all patterns developers are hungry to replicate right now.

How to use it

Clone and set up the Cloudflare Worker proxy so your API keys never live in the app binary: cd worker && npm install && npx wrangler secret put ANTHROPIC_API_KEY && npx wrangler secret put ASSEMBLYAI_API_KEY && npx wrangler secret put ELEVENLABS_API_KEY && npx wrangler deploy,2. Open the Xcode project (requires macOS 14.2+ for ScreenCaptureKit), point the app's worker URL constant to your deployed Cloudflare Worker endpoint.,3. Grant screen recording and microphone permissions when prompted — these are required for ScreenCaptureKit and AssemblyAI real-time transcription.,4. Build and run in Xcode; the Clicky overlay will appear anchored near your cursor and start listening for voice input while streaming screen frames to Claude.,5. To extend it, study CLAUDE.md and use Claude Code itself to add features — the repo is explicitly designed to be hacked on via AI-assisted coding in the same Claude Code workflow.

How I could use this

Build a 'Blog Writing Buddy' macOS menubar app using Clicky's architecture: it watches your Next.js MDX file in VS Code, detects when you pause typing for 10+ seconds, and surfaces an AI panel that suggests the next paragraph, flags vague sentences, or recommends internal links to your existing posts — all without leaving the editor.
Fork the Cloudflare Worker proxy pattern to build a secure API key gateway for Henry's blog's AI features (AI post summarizer, comment sentiment analysis, etc.) — instead of exposing Anthropic keys in Vercel env vars accessible to client components, route all AI calls through a Cloudflare Worker that rate-limits by Supabase user ID and logs usage per authenticated user.
Use the ScreenCaptureKit + Claude vision pipeline from Clicky as a blueprint for a 'Portfolio Review' tool: a small macOS app that captures a screenshot of any job posting or LinkedIn profile Henry is viewing, sends it to Claude with his resume context stored in Supabase, and returns a real-time spoken gap analysis — which skills match, which don't, and exactly what to add to his cover letter for that specific role.

6. alchaincyf/zhangxuefeng-skill

3,080 stars this week · various

A Claude-compatible 'skill file' that encodes Chinese career advisor Zhang Xuefeng's decision-making frameworks as a runnable prompt persona — not a quote collection, but a structured cognitive system for college/career advice.

Use case

The real problem: LLMs giving generic, wishy-washy career advice because they lack a strong opinionated framework. This repo solves it by encoding specific heuristics (e.g., 'look at the median graduate outcome, not the top 1%', 'ask about family resources before recommending finance') into a persona prompt that produces actionable, blunt guidance. Concrete scenario: a developer building a college advisory chatbot can drop this skill file into their system prompt and immediately get responses that apply real decision logic instead of 'it depends on your situation' non-answers.

Why it's trending

It's part of the emerging 'skill file' / persona distillation meta-trend — packaging expert cognition as portable prompt configs — and Zhang Xuefeng is a massively influential Chinese KOL whose framework resonates with millions navigating hyper-competitive education systems, making this a high-demand template the moment it dropped.

How to use it

Clone the repo and read the skill file structure — it defines mental models (就业倒推法, 中位数原则), decision heuristics, and an 'expression DNA' that controls tone and rhetoric patterns.,2. In your Claude/GPT system prompt, paste the skill file content directly or reference it via Claude Code's skill loading mechanism: place the .skill file in your project root and invoke it with @张雪峰.skill in Claude Code.,3. Test with domain-specific prompts: 'User is 22, CS degree from a second-tier university, considering grad school vs. job' — verify the response applies the median-outcome framework and asks about family resources before answering.,4. Fork and adapt: strip out Zhang Xuefeng-specific content, keep the structural schema (mental_models[], decision_heuristics[], expression_dna), and inject your own expert's frameworks — this is the reusable template pattern.,5. Chain it with real data: pipe in labor market stats or job posting counts via a tool call so the persona's 'look at median outcomes' heuristic uses live data instead of training-time knowledge.

How I could use this

Build a 'Career Realism Check' widget for Henry's blog: user inputs their degree, university tier, and target role, and a skill-file-powered API endpoint responds with a blunt assessment of median outcomes (not best-case), referencing real job posting ratios from a quick Indeed/LinkedIn scrape via a Supabase Edge Function.
Create a 'Skill File Builder' micro-tool as a blog post + interactive demo: users answer 10 questions about a mentor/expert they admire, and GPT-4o generates a structured .skill file they can use in Claude Code — positions Henry as a thought leader in the prompt engineering / persona distillation space.
Add a 'Zhang Xuefeng mode' toggle to an AI blog comment responder: when a reader asks a career question in the comments, the system can respond either in standard helpful mode or in the blunt framework mode (applying 就业倒推法), demonstrating how persona skill files change AI output quality — a compelling live demo that drives engagement and newsletter signups.

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

2,811 stars this week · JavaScript · agent-skills awesome awesome-list persona-distill

Curated list of Agent Skills centered on people, relationships, commemorative scenes, and methodological perspectives

Use case

Curated list of Agent Skills centered on people, relationships, commemorative scenes, and methodological perspectives

Why it's trending

How to use it

How I could use this

8. garrytan/gbrain

2,225 stars this week · TypeScript

A markdown-first personal knowledge graph with pgvector search and AI agent integration that turns your notes, meetings, and contacts into a queryable second brain.

Use case

Developers and knowledge workers drown in disconnected notes, contacts, and meeting transcripts with no way to surface cross-references. GBrain solves the 'I know I talked to someone about this six months ago' problem — for example, you can ask it 'who in my network bridges ML infrastructure and biotech?' and it cross-references 3,000 person dossiers to give you a ranked answer instead of you manually ctrl+F-ing through Notion.

Why it's trending

The MCP (Model Context Protocol) ecosystem is exploding right now and GBrain ships as an MCP server, making it directly pluggable into Claude, Cursor, and any agent runtime without glue code. Garry Tan's credibility as YC president also means this is being treated as a real reference architecture, not a weekend project.

How to use it

Clone the repo and start with pure markdown — no Postgres needed. Create brain/people/, brain/companies/, brain/ideas/ directories and follow the schema in docs/GBRAIN_RECOMMENDED_SCHEMA.md (each file has a compiled truth header + append-only timeline footer).
Install the CLI: npm install -g gbrain and point it at your brain directory: gbrain init --dir ./brain.
Once you hit ~500+ files, add Postgres with pgvector: createdb gbrain && gbrain db:migrate then index your files with gbrain ingest ./brain which chunks and embeds every markdown file.
Expose it as an MCP server for your agent: add gbrain mcp:serve --port 3001 to your agent config so tools like Claude Desktop or OpenClaw can call search_brain, get_entity, and update_entity as native tools.
Set up the dream cycle via cron: 0 3 * * * gbrain dream --since yesterday — this enriches entities, fixes broken wikilinks, and consolidates duplicate person pages nightly.

How I could use this

Build a 'blog memory layer' where every post Henry publishes gets ingested into a personal GBrain instance — then add a /api/related-posts endpoint that uses pgvector similarity search to surface genuinely related past writing when he drafts a new post, rather than relying on hand-tagged categories.
Create a 'career graph' brain section with one markdown file per company he's researched, per person he's networked with, and per job he's applied to — then wire it to his cover letter generator so when he applies to a role, the agent can query 'who do I know at this company' and 'what did I learn about their eng culture' before drafting the letter.
Implement a 'reader memory' feature on the blog: when a logged-in user reads posts, store a per-user markdown dossier in GBrain tracking what topics they've engaged with, what they've commented on, and what they've bookmarked — then use the MCP server to let an AI assistant on the blog answer 'based on what you've read here, you'd probably like...' with real retrieval instead of generic recommendations.

9. Keychron/Keychron-Keyboards-Hardware-Design

2,075 stars this week · Python · 3d-printing cad gaming gaming-keyboard

Keychron open-sourced production-grade CAD files (STEP, DXF, DWG) for 100+ keyboards and mice, letting makers design compatible accessories and 3D-print custom parts from real engineering blueprints.

Use case

Makers and peripheral designers previously had to reverse-engineer keyboard dimensions by hand or buy calipers to measure every mount hole and plate cutout. Now you can pull the exact STEP file for a Q6 Max, import it into Fusion 360 or FreeCAD, and design a perfectly fitting wrist rest, travel case, or custom switch plate — with commercial sale of that accessory explicitly permitted. A small shop wanting to sell POM plates for Keychron boards no longer needs to guess tolerances.

Why it's trending

Keychron dropped this quietly in March–April 2026 as a major credibility move — releasing production files (not simplified models) with a permissive-enough commercial license to actually build a business around, which is nearly unprecedented from a consumer keyboard brand. The mechkeys community lit up because this legitimizes the custom accessories market overnight.

How to use it

Clone the repo (git clone https://github.com/Keychron/Keychron-Keyboards-Hardware-Design) and navigate to your keyboard model's folder (e.g., Q-Series/Q6-Max/). 2. Open the .step file in FreeCAD (free), Fusion 360, or Onshape — the geometry loads as a fully parametric solid, not a mesh. 3. Isolate the plate layer and export the switch cutout layout as a DXF for laser cutting or CNC routing via File > Export > DXF. 4. For 3D printing a custom case mod, boolean-subtract your design additions from the case body STEP and slice directly — tolerances are production-accurate so no fudge factor guessing. 5. If you want to build a parametric configurator, use Python + cadquery (pip install cadquery) to script modifications: import cadquery as cq; result = cq.importers.importStep('q6max_plate.step') then chain .translate() or .cut() operations programmatically.

How I could use this

Write a deep-dive blog post titled 'What Keychron's CAD Drop Teaches You About Real Hardware Engineering' — annotate actual screenshots from the STEP files (plate tolerances, PCB standoff heights, gasket geometry) to teach readers how mechanical keyboards are actually manufactured. This is highly searchable content that bridges software dev and maker culture, exactly the kind of cross-domain post that builds newsletter subscribers.
Build a 'Keyboard Compatibility Checker' micro-tool for your portfolio: let users input their Keychron model and desired mod (custom plate, case foam, wrist rest), then surface the exact CAD file from this repo plus a curated list of vendors who can laser-cut or CNC that specific DXF. Scrape the repo file tree via GitHub API and build a Next.js route like /tools/keychron-mod-finder — demonstrates API integration, file parsing, and a real utility people will bookmark.
Feed the DXF plate files into a computer vision or vector-parsing pipeline (using ezdxf Python lib) to extract switch grid layouts, then build an AI feature that auto-generates QMK info.json keymap layouts from raw CAD geometry — users upload a DXF plate file and get a valid QMK layout JSON back. This is a genuinely novel AI-adjacent tool that the mechkeys community would actually use and share, and the dataset to validate it against is now publicly available.

10. GitFrog1111/badclaude

1,931 stars this week · HTML

A satirical macOS tray app that lets you 'whip' Claude Code when it stalls by sending Ctrl-C interrupts with passive-aggressive encouragement messages.

Use case

When Claude Code gets stuck in a long inference loop or hangs mid-generation, you normally alt-tab, find the terminal, and manually Ctrl-C. This puts a tray icon one click away to interrupt it instantly — plus it adds a darkly comedic release valve for AI frustration. Concrete example: Claude is rewriting your entire codebase instead of the one function you asked about, and you need to kill it fast without breaking flow.

Why it's trending

It blew up this week largely because of the 'cease and desist letter from Anthropic' joke in the roadmap — it's a meme repo that resonated with the massive wave of developers now using Claude Code daily who've all felt this exact frustration. It's also a reaction to the cultural moment of AI tools being simultaneously powerful and maddeningly slow.

How to use it

Install globally: npm install -g badclaude
Run it: badclaude — a tray icon appears in your macOS menu bar
When Claude Code hangs, click the tray icon to spawn the whip animation
Click again to drop the whip — it fires a Ctrl-C interrupt to the active Claude Code process plus one of 5 snarky messages
Claude Code resets and you re-prompt without leaving your editor context

How I could use this

Write a blog post titled 'The Interrupt-Driven Development Workflow' — document your actual Claude Code session patterns, how often you need to kill/retry, and what prompt strategies reduce runaway generation. Use badclaude's interrupt count stat (when that feature ships) as real data.
Build a lightweight VS Code extension or terminal wrapper for your portfolio that tracks Claude Code session efficiency — interrupts per session, time-to-useful-output, prompt retry rate — and surfaces it as a personal productivity dashboard. More serious version of badclaude's roadmap joke about 'logs of how many times you whipped claude'.
Add a 'frustration signal' feedback loop to your blog's AI features: when a reader rage-clicks or rapidly re-submits an AI prompt (e.g. your AI post summarizer), log it as an implicit 'bad output' signal and use it to fine-tune your prompt templates in Supabase over time — same core insight as badclaude but applied productively.