GitHub Hot — 11 June 2026

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

1. XiaomiMiMo/MiMo-Code

4,254 stars this week · TypeScript

MiMoCode is an open-source CLI coding agent that remembers your project across sessions using SQLite FTS5 — basically Claude Code but self-hostable with persistent cross-session memory built in.

Use case

The core problem it solves is context amnesia: every time you restart an AI coding session, the agent has forgotten your architecture decisions, naming conventions, and in-progress work. MiMoCode stores a MEMORY.md and SQLite FTS5 index so the agent picks up exactly where it left off — knowing that you use CSS custom properties instead of Tailwind, or that your rate-limit helper lives in lib/rate-limit-db.ts. Concrete example: you spend Monday setting up a Supabase migration pattern, then Tuesday the agent already knows it without re-explanation.

Why it's trending

It dropped the same week developers are pushing back hard on Claude Code's closed nature and token costs — MiMoCode offers a free anonymous tier (MiMo Auto) plus a one-command Claude Code auth import, making it a zero-friction alternative that's spiking on Hacker News and X. The 'Import from Claude Code' migration path is the killer hook pulling in the existing Claude Code install base.

How to use it

Install globally: npm install -g @mimo-ai/cli — or use the curl installer if you want auto PATH setup.,2. On first launch, select 'Import from Claude Code' to migrate your existing auth token — zero reconfiguration if you're already on Claude Code Pro.,3. Drop a MEMORY.md in your project root with your architecture rules (stack, conventions, off-limits patterns) — MiMoCode indexes this at session start via SQLite FTS5.,4. Use Tab to switch agents: build for coding, plan for read-only exploration before touching files, compose for spec-driven multi-step workflows.,5. After a session, review the auto-updated MEMORY.md to see what the agent learned — edit it to correct or prune stale knowledge before the next session.

How I could use this

Use MiMoCode's MEMORY.md pattern as the template for your own CLAUDE.md project memory file — you already have AGENTS.md, but add a machine-readable architecture snapshot (token names, route patterns, RLS conventions) so any AI agent onboards in one read rather than re-discovering them per session.
Build a 'Project Onboarding Generator' career tool: users paste their codebase README and you generate a structured MEMORY.md / AGENTS.md they can drop into MiMoCode or Claude Code — directly useful for international devs joining Aussie teams who need to ramp fast on unfamiliar codebases.
Write a githot digest post comparing MiMoCode vs Claude Code vs Aider on the persistent memory axis — benchmark how many tokens each saves after 5 sessions on the same project, and publish the raw numbers. This is exactly the kind of data-driven content that ranks for 'AI coding agent comparison 2026' long-tail searches and drives tool-tier signups.

2. shadcn/improve

1,681 stars this week · various

A Claude skill that uses your most capable model to audit a codebase and write self-contained implementation plans that cheaper models can execute without further context.

Use case

The core problem: you can't afford to run Opus on every file edit, but you need Opus-level judgment to decide what to fix and write a spec tight enough that Haiku doesn't go off-script. /improve solves this by front-loading the expensive reasoning — it maps the repo, ranks findings, and produces markdown specs in plans/ that are so complete a cheap model (or junior dev) can execute them with zero back-and-forth. Concrete example: run /improve security on a Next.js API layer; it produces plans/001-rate-limit-audit.md that Haiku can implement file-by-file.

Why it's trending

This is trending because the cost gap between frontier models and fast/cheap models widened significantly in mid-2026 — Opus 4 is ~15× more expensive per token than Haiku 4.5, so the 'think expensive, execute cheap' pattern now has real ROI. It's also the first widely-distributed implementation of this pattern as a portable agent skill rather than a one-off script.

How to use it

Install the skill: npx skills add shadcn/improve in your repo root.
Run a focused audit in your agent: /improve security or /improve perf — it reads your codebase and surfaces ranked findings.
Reply with which findings you want specced: plan 2, 4 — it writes plans/002-*.md and plans/004-*.md, each a self-contained markdown spec.
Hand a plan to a cheap executor: /improve execute plans/002-fix-n-plus-one.md — it dispatches a Haiku/Sonnet agent and reviews the output.
Run /improve reconcile periodically to mark completed plans done and unblock stalled ones.

# Typical first session
npx skills add shadcn/improve
# In your agent:
/improve quick          # fast pass, surfaces top 5-8 findings
# Agent replies: plan 1, 3
# Check plans/ directory for generated specs
cat plans/001-*.md

How I could use this

Run /improve security on Gradland's API routes and let it audit every route handler against your AGENTS.md §5 rules — it'll catch any Claude-calling routes missing requireSubscription() or endpoints using the service role client where the anon client should be used. The generated plan gives you a prioritised fix list without manually diffing every file.
Use /improve plan 'add per-endpoint cost tracking to the subscription system' to generate a spec for extending lib/subscription.ts — describe the feature once in plain English and get back a file-by-file implementation plan you can hand to a Haiku-powered CI agent, keeping expensive Opus judgment out of the boilerplate wiring.
Wire /improve next into your weekly autonomous loop: run it against the career tools section of the repo every Monday, have it suggest the highest-leverage next feature for your 482/485 visa audience, and auto-publish the output as a GitHub Issue tagged product-suggestion — gives you a structured product backlog driven by actual code analysis rather than gut feel.

3. NoopApp/noop

1,459 stars this week · Swift

A reverse-engineered, local-first iOS/macOS/Android companion for WHOOP straps that stores all biometric data on-device — no WHOOP account or $30/month subscription required.

Use case

WHOOP locks your own HRV, sleep, and strain data behind a mandatory subscription — cancel and you lose access to everything you've recorded. Noop solves this by reverse-engineering the BLE protocol and writing data straight to a local SQLite database, so a WHOOP 4 or 5 owner can pair their strap directly and own their data permanently. Concrete example: an athlete who stopped paying WHOOP but kept the hardware can run Noop, export 18 months of recovery data as JSON, and pipe it into their own analysis scripts.

Why it's trending

WHOOP raised its subscription price again in early 2026 and simultaneously locked free-tier users out of historical data export — the Reddit backlash drove a wave of users hunting for alternatives and landing on Noop's r/NOOPApp community. The published PROTOCOL.md (a full BLE packet spec) is also attracting embedded/BLE developers who want to build their own tooling.

How to use it

Clone the repo and open the Xcode project (requires macOS 14+ / Xcode 15+). 2. Build and sideload to your iPhone or run on macOS directly — no TestFlight account needed. 3. Put your WHOOP strap into pairing mode (hold the button 3s until the LED pulses green) and tap 'Pair' in Noop. 4. Wait for the initial sync — Noop pulls the full historical ring buffer over BLE and writes it to a local SQLite DB at ~/Library/Application\ Support/noop/data.sqlite. 5. Query or export your data: open the DB with any SQLite client, or use Noop's built-in JSON export under Settings → Export — fields include hrv_rmssd, recovery_score, strain, sleep_start, sleep_end, spo2_avg.

How I could use this

Write a deep-dive post titled 'How Noop reverse-engineered WHOOP's BLE protocol' — walk through reading the PROTOCOL.md spec, show how BLE GATT characteristics map to HRV packets with actual hex examples, and benchmark local SQLite query speed vs. WHOOP's API. This hits the developer audience hard and ranks for 'WHOOP BLE protocol' searches with zero competition.
Build a 'Health Data Privacy Audit' micro-tool on the blog: Henry uploads a Noop JSON export (or Apple Health export), the tool scans for what data fields are present, flags which ones WHOOP's TOS claims ownership over, and outputs a plain-English summary. No AI needed for v1 — pure schema inspection — but it demonstrates data-sovereignty thinking that resonates with the international grad audience who are already wary of US SaaS owning their personal data.
Create an AI recovery coach feature: ingest the Noop SQLite export into Supabase (map the schema into a user_health_metrics table with user_id FK), then let users ask Claude natural-language questions about their own biometric history — 'Was my HRV lower the week before my last job interview?' or 'Show me my recovery trend during my visa application crunch.' Use claude-haiku-4-5-20251001 for the query-to-SQL step and claude-sonnet-4-6 for the narrative summary. Scope it behind the existing requireSubscription() gate so it's a premium feature.

4. MSNightmare/RoguePlanet

1,086 stars this week · C++

RoguePlanet Windows Defender Vulnerability

Use case

RoguePlanet Windows Defender Vulnerability

Why it's trending

How to use it

How I could use this

5. GordenSun/GordenSuperPPTSkills

779 stars this week · Python

A Codex skill pipeline that generates visually rich PPT slides as images via GPT, then reverse-engineers them into fully editable PPTX files by extracting background, layout skeleton, icons, and text as separate layers.

Use case

The core problem: AI-generated presentations are either ugly (pure code → PPTX) or locked as flat images (GPT image gen). This repo threads the needle — it lets GPT do what it does best (composing beautiful slide imagery), then uses GPT vision to decompose each PNG back into four editable PPTX layers with correct coordinates. Concrete example: you prompt 'Generate a 10-slide deck on 482 visa pathways for IT grads' and get back both a polished image preview and a fully editable .pptx where you can change the text, swap icons, and update colours.

Why it's trending

GPT-4o image generation shipped with dramatically better layout and design quality in early 2025, making image-first PPT generation suddenly viable. This repo is one of the first pipelines to close the loop from 'pretty but flat' to 'pretty and editable', which is the blocker that made previous AI PPT tools feel like toys.

How to use it

Install into Codex: cp -R GordenSuperPPTSkill ~/.codex/skills/GordenSuperPPTSkill (plus both dependency skills GordenImagePPTGen and GordenImage2PPTX).
Open a Codex session and trigger the full pipeline: 'Use GordenSuperPPTSkill to generate a 6-page PPT on Australian 485 Graduate visa eligibility, dense layout, professional style.'
Codex calls GPT image gen per slide → saves .png files → then runs GordenImage2PPTX vision pass on each PNG to extract background, skeleton, icons, and text layers.
Output is both a folder of slide PNGs and a .pptx with four named layers per slide — open in PowerPoint/Keynote and edit freely.
For image-only (no editable output), use GordenImagePPTGen skill directly; for converting existing slide screenshots to editable PPTX, use GordenImage2PPTX alone.

How I could use this

Build a 'Career Pitch Deck Generator' tool on the blog: user fills in their skills, visa status, and target role, your Next.js API calls Claude Sonnet to write structured slide content, then hands it to a Codex-style pipeline (or a python-pptx + GPT vision service you host) to produce a downloadable 6-slide pitch deck tailored to Australian IT hiring managers — differentiated from generic resume tools.
Create a 'Visa Explainer Deck' content series: auto-generate a new slide deck each month summarising 482/485/PR pathway changes using your existing visa-news content feed, render the PNG previews as embedded images in a blog post, and offer the .pptx as a gated download requiring email signup — builds your list while showcasing the tool.
Expose an API endpoint /api/tools/slide-from-resume that takes a user's parsed resume JSON (which you already have from the resume analyser tool) and generates a 3-slide 'intro deck' — about me, my skills, my target role — using python-pptx server-side; this is a fast differentiator since no other career platform in the AU international grad space offers slide output alongside resume output.

6. JimLiu/baoyu-design

765 stars this week · JavaScript · agent-skills claude claude-code claude-design

baoyu-design packages the Claude Design engine as a portable Agent Skill — drop one file into Cursor or Claude Code and generate polished UI mockups as self-contained HTML, without needing claude.ai/design.

Use case

Developers who want AI-generated UI prototypes mid-coding session, without context-switching to a browser tab. Concrete scenario: you're building a new 'salary benchmark' page in your Next.js app — instead of sketching in Figma or uploading screenshots to claude.ai, you invoke the skill inline, get a self-contained HTML mockup committed to your repo, and use it as the visual spec for implementation.

Why it's trending

Claude Design shipped on claude.ai recently and generated significant buzz for its output quality. This repo unlocks that same engine for local agents immediately — it's trending because developers want the capability without the subscription gate, and it works with Opus 4.8 which just dropped.

How to use it

Copy the skill file into your project: curl -O https://raw.githubusercontent.com/JimLiu/baoyu-design/main/design.md && mv design.md .claude/skills/design.md,2. In Claude Code, the skill is now available as /design — invoke it in any session.,3. Describe what you want: /design Build a mobile-first visa tracker dashboard with a timeline, status badge, and document checklist.,4. The agent writes a self-contained design-output.html to your repo — open it in a browser to review.,5. Iterate by re-invoking with refinements: /design refine: make the timeline horizontal on desktop, add a progress ring for completion %

How I could use this

Generate HTML wireframes for every new blog feature (githot digest, AI news card, career tools landing) before writing a line of React — use them as the visual spec and embed them as interactive previews in your devlog posts, so readers can see the design intent alongside the implementation.
Build a 'design sprint' page on Gradland where 482/485 visa applicants describe their job-search situation and receive a personalised dashboard mockup (visa timeline, skill gap chart, salary target) — run baoyu-design server-side via Claude Sonnet to generate the HTML, cache per user, and let them screenshot it for their job applications.
Use the skill during your own feature planning: before touching any component file, invoke /design to mock the UI, commit the HTML as designs/feature-name.html, then implement against it — this gives you a free visual regression baseline and makes your GitHub PRs significantly easier to review.

7. apple/coreai-models

710 stars this week · Python

Apple's official toolkit for exporting Hugging Face models to .aimodel format and running them on-device via the new Core AI framework on macOS/iOS 27.

Use case

This solves the problem of shipping AI features in native Apple apps without paying per-token API costs or sending user data off-device. Concrete example: you want to build a macOS app that analyses a resume and suggests improvements — instead of hitting Claude or OpenAI on every keystroke, you export a 1-3B parameter model once using this toolkit, bundle the .aimodel file, and inference runs entirely on the Neural Engine with zero latency and zero API cost. The same pattern applies to any feature where privacy matters (visa documents, salary data) or where you need sub-100ms response times.

Why it's trending

This repo surfaced at WWDC 2026 alongside the Core AI framework announcement in macOS/iOS 27 — it's Apple's direct answer to llama.cpp and MLX, but with first-party framework support and App Store viability. Every iOS/macOS developer who watched the keynote is now scrambling to understand the export pipeline, which is exactly what this repo documents.

How to use it

Install uv and clone the repo: brew install uv && git clone https://github.com/apple/coreai-models,Pick a model recipe from models/ (e.g. a Mistral or Llama variant), then run its export script: uv run python models/mistral/export.py --output ./dist/mistral.aimodel,Add the Swift package to your Xcode project via SPM: https://github.com/apple/coreai-models and import CoreAIModels,Load and run inference in Swift: let model = try await CoreAIModel(url: Bundle.main.url(forResource: "mistral", withExtension: "aimodel")!); let output = try await model.generate(prompt: userInput),For web integration, expose the inference via a local Swift server or use it in a companion macOS app that your Next.js frontend communicates with over localhost — the model itself never leaves the device.

How I could use this

Build a macOS companion app for Gradland that runs resume scoring entirely on-device — users paste their resume, a quantised 2B model runs on the Neural Engine in under a second, and the result never leaves their Mac. This is a strong privacy differentiator for 485/482 visa holders who are understandably cautious about uploading immigration documents to a cloud API.
Export a fine-tuned classification model (using the Python primitives) trained on Australian job descriptions to predict ACS skill category and ANZSCO code from a job title + description. Bundle it as a .aimodel, ship it in a native app, and surface the result instantly in Gradland's visa tracker as 'your role likely maps to ANZSCO 261313' — no API call, no latency, works offline.
Write a blog post series on the Gradland blog: 'Running LLMs on your MacBook with Apple Core AI' — walk through the export pipeline, benchmark inference speed vs. API calls, and show a TypeScript + Swift hybrid architecture where Next.js offloads privacy-sensitive AI tasks to a local Core AI model via a localhost endpoint. This is high-SEO content for the 'on-device AI' query cluster that's spiking post-WWDC.

8. vorpus/performativeUI

591 stars this week · TypeScript

A satirical React component library that deliberately mocks AI-washed startups by providing UI components designed purely to signal VC credibility, not ship useful software.

Use case

This isn't solving a real engineering problem — it's a parody of the 'AI-native' badge-farming trend where products slap Claude/Codex/GPT branding on everything regardless of whether AI is actually doing anything. The repo's value is as pointed commentary: it reproduces the exact aesthetic of overengineered startup landing pages (30KB bundle, strict TypeScript, React 19, Anthropic badge) while being entirely hollow. If you've ever seen a B2B SaaS homepage with six AI-provider logos and a 'frontier-ready' badge that just wraps a CRUD form, this is the repo calling that out.

Why it's trending

It hit 591 stars this week because it landed at the exact moment developer sentiment is turning cynical about AI-washing — the joke lands harder now that 'powered by Anthropic' has become a marketing cliché rather than a technical claim. Vorpus (Nathaniel J. Smith, creator of Trio) has credibility in the Python/async space, which gives the satire more weight than an anonymous meme repo.

How to use it

Read the README as the product — the actual joke is in the framing, not the components. The 'AI-native React components that signal how oversubscribed your funding round is' IS the documentation. 2. Browse the live demo at vorpus.github.io/performativeUI to see what the components actually render — expect badge carousels, confidence-signalling spinners, and funding-round indicators. 3. If you genuinely install it (npm install performative-ui), treat it as a reference for what NOT to do in your own AI feature UX. 4. Use it as a conversation starter or blog post hook: 'I installed the most honest AI library on npm.' 5. Don't ship it in production — there's no real functionality to integrate.

How I could use this

Write a blog post titled 'The AI-Washing Checklist' using performativeUI as the straight-faced example: walk through each satirical component (funding-round badge, AI-native spinner) and map it 1:1 to real products you've seen. This is high-SEO territory right now — developers are actively searching for 'AI hype criticism' and 'is this tool actually using AI.'
Build a genuine contrast piece for Gradland's blog: 'What performative AI vs real AI looks like in a career tool' — show a side-by-side of a badge-heavy fake AI resume scorer vs your actual Claude-powered resume analyser that returns structured gap analysis. The repo gives you the parody half for free; your existing app/api/resume/ route is the real half.
Create a Gradland 'AI transparency label' component — inspired by what this repo mocks, but inverted: a small badge that appears on AI-powered features (interview prep, resume analysis) that shows the model used, approximate token cost, and what it actually did ('GPT-style label-farming' vs 'Claude Sonnet analysed 847 words of your resume against 12 ATS criteria'). This directly addresses the trust problem performativeUI is lampooning, and differentiates Gradland from AI-washed competitors.

9. amElnagdy/guard-skills

568 stars this week · various · agent-skills ai claude claude-code

Guard-skills adds second-pass quality gates to AI coding agents — catching the systematic failure modes (untested error paths, hallucinated APIs, docs that describe intent not behavior) before they ship.

Use case

When you tell Claude Code or Codex to write a test suite, it writes tests that compile and run — but often only test the happy path, mock too aggressively, or assert on implementation details rather than behavior. Guard-skills intercepts after the agent's first draft: you invoke $test-guard on the diff, and it specifically hunts for missing error-path coverage, vacuous assertions, and tests that would pass even if the feature were deleted. Same pattern for code quality ($clean-code-guard catches dead branches, god functions, and leaky abstractions) and docs ($docs-guard flags docs that describe what the code was supposed to do, not what it actually does).

Why it's trending

Claude Code and Codex shipped as mainstream tools this quarter, and the 'AI code quality' problem is now hitting teams at scale — agents produce plausible-looking output that passes linting and CI but fails in production. Guard-skills is the first skills.sh package targeting this specific failure mode with named, composable guards rather than a monolithic review prompt.

How to use it

Install the CLI and add the package: npx skills add amElnagdy/guard-skills
Let your agent (Claude Code, Codex, Cursor) do the implementation work as normal
After the agent finishes, invoke a guard on the diff: Use $clean-code-guard on the diff you just produced.
The guard returns a prioritized list of failure modes — fix the flagged items before committing
For persistent enforcement, install per-agent: npx skills add amElnagdy/guard-skills --skill test-guard --agent claude-code so it's available in every session without re-installing

How I could use this

Wire $clean-code-guard into your Claude Code workflow as a mandatory pre-commit step for any AI-generated route handler — Gradland's AGENTS.md already mandates requireSubscription() + checkEndpointRateLimit() on every Claude-calling route, and a custom guard that checks for those exact patterns would catch the cases where the agent forgets them before they hit CI.
Use $test-guard on your Supabase API route tests to verify they actually test auth failure (401 for missing user), rate-limit exhaustion (429), and malformed input (400) — not just the success case. The track endpoint tests you just added (see commit 51ecdfd) are a good baseline; guard-skills would tell you which error branches still lack coverage.
Fork the guard-skills pattern to write a $gradland-guard custom skill that enforces your project-specific invariants: no hardcoded hex colors (must use var(--token)), no raw img tags, no createSupabaseService() in client components, and no Claude-calling route without rate limiting — then install it globally so every Claude Code session in this repo automatically has it available.

10. Tencent-Hunyuan/UniRL

518 stars this week · Python · ai-infrastructure reinforcement-learning sglang vllm

UniRL is a unified RL post-training framework from Tencent that applies the same RLHF-style loop — sample, score, advantage, update, sync — across diffusion, autoregressive, and multimodal model families without rewriting your training stack per model type.

Use case

The real problem: every model family (image diffusion, text AR, video) has its own bespoke RL fine-tuning pipeline, which means teams maintain 3+ separate codebases to do preference-aligned post-training. UniRL gives you one Ray-based loop with pluggable rollout engines (vLLM for text, custom diffusion samplers for images) so you can reward-shape a single unified model — e.g., a model that both describes and generates images — without stitching together OpenRLHF + diffusion-DPO manually. Concrete scenario: you want to RL-fine-tune a model to score high on both image aesthetics AND factual caption accuracy — UniRL lets you compose those reward signals in one training run.

Why it's trending

It landed right as Flow-DPPO (June 2026) and DRPO (May 2026) dropped — two back-to-back papers from the same Tencent team showing RL actually works on flow-matching diffusion models, which was an open question. The 518 stars this week tracks almost exactly to the Flow-DPPO arXiv post going live, meaning the ML community is treating this as the reference impl for that technique.

How to use it

Install: requires Python 3.12+, Ray, and either vLLM or SGLang for rollout. pip install -e . from repo root pulls Hydra, Ray, and the training deps.
Pick your entrypoint based on model family — train_ar for autoregressive text models, train_diffusion for image/video diffusion, train_unified_model for mixed.
Copy and edit the matching Hydra config under configs/ — at minimum set model.path, reward.fn, and rollout.engine (vllm or sglang).
Write a reward function as a plain Python callable that takes (prompt, output) and returns a float — plug it into reward.fn in the config.
Launch: python -m unirl.train_ar --config-name=my_config — Ray handles worker placement, FSDP sharding, and weight sync back to rollout workers automatically.

# Minimal custom reward — drop into rewards/my_reward.py
def resume_quality_reward(prompt: str, output: str) -> float:
    # Score based on ATS keyword density, length, structure
    keywords = ['managed', 'delivered', 'reduced', 'increased']
    score = sum(1 for kw in keywords if kw in output.lower()) / len(keywords)
    return score  # 0.0–1.0

How I could use this

Write a deep-dive post for the Gradland blog: 'Why RL post-training beats prompt engineering for career AI tools' — use UniRL's architecture diagram to explain why reward-shaping (optimising for real interview callback signals) is fundamentally different from few-shot prompting. Target the ML-curious international student who wants to understand why Gradland's resume analyser gets better over time.
Use UniRL's reward function pattern as the conceptual backbone for a Gradland Resume RL explainer tool: show users which sentences in their resume would score highest under a hiring-signal reward model (action verbs, quantified impact, ATS keywords). You don't need to run UniRL yourself — just expose the reward rubric as a scoring API backed by Claude Haiku, framing it as 'the same approach top AI labs use to align models'.
Build a blog series tracking a real RL fine-tuning experiment using UniRL on a small open-source model (e.g., Qwen-2.5-1.5B) with a custom reward for generating Australia-specific visa advice quality. Document compute costs, reward curves, and failure modes across 5 posts — this kind of reproducible ML experiment content is high-SEO and directly relevant to the international-student audience who wants to understand both AI and their visa situation.