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GitHub Hot — 11 May 2026

11 May 2026·25 min readGitHubOpen SourceTools

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

1. antirez/ds4

7,228 stars this week · C

antirez (Redis creator) built a stripped-down, DeepSeek V4 Flash-only local inference engine in C that exploits the model's compressed KV cache to run a 284B-parameter MoE on a 128GB MacBook — no framework, no abstraction.

Use case

The concrete problem: running a frontier-class model locally without renting GPU time or paying per-token. Scenario — you want to run batch resume analysis or interview feedback overnight on 500 applicant profiles; with ds4's disk-persistent KV cache, you spin up the server once, feed documents in, and the engine pages context to disk so you're not bottlenecked by VRAM. Cloud inference for that workload at Claude Sonnet rates would cost ~$15–40; local Metal inference costs electricity.

Why it's trending

It hit 7k stars in a week because antirez is writing it live on stream and blogging the design decisions — the Redis-creator-builds-a-local-LLM-engine narrative is inherently viral. It also lands exactly when DeepSeek V4 Flash became publicly accessible and hobbyists with 128GB M3 Ultras are hunting for a leaner alternative to llama.cpp.

How to use it

  1. Clone and build: git clone https://github.com/antirez/ds4 && cd ds4 && make — requires Xcode CLI tools on macOS for Metal, or CUDA toolkit on Linux.,2. Download a 2-bit quantized DeepSeek V4 Flash GGUF (the repo README links the specific quant antirez recommends — standard GGUF quantizations won't give you the compressed KV benefit).,3. Start the inference server: ./ds4 --model /path/to/model.gguf --port 8080 — it exposes an OpenAI-compatible /v1/chat/completions endpoint.,4. Hit it from any OpenAI SDK client: const client = new OpenAI({ baseURL: 'http://localhost:8080/v1', apiKey: 'local' }); await client.chat.completions.create({ model: 'ds4', messages: [...] }),5. Enable disk KV cache for long-context persistence: ./ds4 --model model.gguf --kv-cache-path /tmp/kv.bin — the engine serialises KV state to disk between requests, so a 200k-token context survives a server restart.

How I could use this

  1. GitHot deep-dive pipeline: Your scripts/fetch-githot currently summarises repos with Haiku API calls that cost tokens. Swap the batch summarisation step to hit a local ds4 endpoint — feed the full README (up to 50k tokens) instead of truncating, get richer 'how to actually use it' extractions, and pay $0. The OpenAI-compatible API means a one-line baseURL swap in your existing scripts/llm-claude.ts abstraction.
  2. 1M-token resume-to-job-match for Gradland: DeepSeek V4 Flash's 1M context means you can feed the full text of 50+ AU job descriptions + a candidate's resume + their LinkedIn history in a single prompt and ask for ranked match scores with gap analysis — something impossible with 32k-context models. Build this as an offline batch endpoint (/api/resume/deep-match) that runs against local ds4 for free-tier users and falls back to Claude Sonnet for paid users who want real-time results.
  3. Persistent interview session memory: ds4's disk KV cache means a user's multi-turn mock interview context can be rehydrated cheaply across days. Store the KV cache path in Supabase per user_id, and when they return for session 2, ds4 restores the full context — the model 'remembers' what questions it already asked, how the user answered, and what feedback it gave — without you paying to re-tokenise the entire history on every resume.

2. V4bel/dirtyfrag

4,183 stars this week · C

Coordinated disclosure of two chained Linux kernel page-cache write primitives (CVE-2026-43284 + CVE-2026-43500) that produce a deterministic, race-free LPE on most major distros.

Use case

Security researchers and sysadmins need to know whether their kernel version is patched against both CVEs. Unlike Dirty Pipe, this requires no race condition, making it reliable enough to use as a pen-test benchmark — but also more dangerous in the wrong hands.

Why it's trending

Embargo broke before the patch landed (2026-05-07), making this a zero-day disclosure moment. The Dirty Pipe lineage gives it instant recognisability in the Linux kernel community, and the deterministic success rate is technically newsworthy.

How to use it

DECLINED — this tool's primary function is obtaining unauthorized root. Steps withheld; link to the upstream write-up and the two mainline patch commits instead.

How I could use this

  1. Write a post tracing the Dirty Pipe → Copy Fail → Dirty Frag vulnerability class progression — explain the page-cache write primitive in plain terms for developers who don't read kernel code.
  2. Build a 'am I patched?' checker: a small API that takes a kernel version string and returns whether both CVE patches are included — useful for the Gradland audience who run Linux VMs for dev work.
  3. Post on coordinated disclosure mechanics: how linux-distros@openwall works, what happens when an embargo breaks, and what developers should do when a CVE drops on a system they manage — directly relevant to Australian IT grads in DevOps/SRE roles.

3. vercel-labs/zero-native

2,563 stars this week · Zig

zero-native wraps any web frontend (Next.js included) in a tiny Zig-based native desktop shell, letting you ship a real desktop app without bundling Electron's 100MB+ Chromium runtime.

Use case

The core problem it solves is Electron fatigue: every Electron app ships a full browser runtime, so a simple tool bloats to 150MB and starts slowly. zero-native uses the OS's built-in WebView (WKWebView on macOS, WebKitGTK on Linux) so your native binary is kilobytes, not megabytes. Concrete scenario: you've built a Next.js resume analyser at gradland.com — with zero-native you can repackage that exact frontend as a downloadable desktop app that opens instantly, works offline against a local SQLite cache, and accesses the native file picker for resume uploads without any browser permission dialogs.

Why it's trending

Tauri (Rust) proved the market exists for lightweight Electron alternatives, but Zig's simpler FFI story and faster incremental builds are attracting systems developers who found Rust's compile times painful. The Vercel Labs branding also signals first-class Next.js support, which is what most of the JavaScript community is actually running.

How to use it

  1. Install the CLI: npm install -g zero-native (requires Zig 0.13+ on PATH — brew install zig on macOS),2. Scaffold against your existing Next.js app: zero-native init gradland-desktop --frontend next — this generates a src/main.zig shell and wires the build system to next build,3. Register a native bridge command in src/main.zig — e.g. app.command("open-file-picker", handlers.openFilePicker) — then call it from React via window.__zero.invoke('open-file-picker'),4. Dev loop: zig build run rebuilds the Zig shell in ~1s and hot-reloads the Next.js frontend; native and web changes are independent,5. Produce a distributable: zig build -Doptimize=ReleaseSafe outputs a single self-contained binary in zig-out/bin/ — no installer, no runtime dependency

How I could use this

  1. Ship the Gradland resume analyser as an offline-first macOS/Windows desktop app: the Next.js UI stays identical, but a native bridge command exposes the OS file picker so users drag-and-drop a PDF directly from Finder/Explorer rather than a browser file input — removing the biggest friction point in your current upload flow.
  2. Build a lightweight visa deadline tracker menubar widget: a zero-native tray app that reads the user's 485/482 visa dates from a local SQLite file (written via a Zig bridge command), shows a countdown badge in the macOS menubar, and fires a native OS notification 90/30/7 days before expiry — no browser tab required, survives reboots.
  3. Package your Claude-powered interview prep feature as a desktop app with access to the system microphone via a native Zig bridge: capture audio natively, transcribe with Whisper via a local subprocess call (Zig calls C directly), then pipe the transcript to your existing /api/interview Claude Sonnet endpoint — giving users a fully offline speech-to-feedback interview coach without Web Speech API browser restrictions.

4. strukto-ai/mirage

1,904 stars this week · TypeScript · agent-sandbox agent-tools ai-agents bash

Mirage mounts S3, Google Drive, Gmail, Slack, and Redis into a single Unix-like virtual filesystem so AI agents interact with every backend through one consistent read/write/list interface instead of juggling per-service SDKs.

Use case

The real problem: every time you add a data source to an agent (e.g. 'also check the user's Drive for their resume'), you wire up another SDK, another auth flow, another error surface. With Mirage, you mount /drive/, /s3/jobs/, and /redis/cache/ once at startup, then your Claude tool-use handler just calls fs.read('/drive/resume.pdf') — the VFS handles credentials and protocol differences transparently. Concrete example: a resume analyser that pulls the PDF from Drive, reads job descriptions from S3, and caches results in Redis, all in the same 10-line agent loop.

Why it's trending

Claude Code, OpenAI Agents SDK, and LangGraph all landed production-ready tool-use patterns in the last 90 days, and teams scaling beyond toy demos immediately hit the 'every tool needs its own SDK' wall. Mirage is the first clean abstraction that solves this at the filesystem layer rather than the tool-definition layer.

How to use it

  1. Install: npm install @struktoai/mirage-node,2. Mount your sources at app startup:
import { Mirage } from '@struktoai/mirage-node';
const fs = new Mirage();
await fs.mount('/drive', { type: 'google-drive', credentials: process.env.GDRIVE_CREDS });
await fs.mount('/s3',    { type: 's3', bucket: 'my-bucket', region: 'ap-southeast-2' });
await fs.mount('/redis', { type: 'redis', url: process.env.REDIS_URL });
```,3. Pass `fs` into your Claude tool handler — define tools for `read`, `write`, `list`, `stat` that proxy to `fs.*`.,4. Claude calls `list('/drive/resumes/')` or `read('/s3/job-descriptions/senior-dev.md')` — no SDK imports in the agent loop itself.,5. Swap backends (e.g. Drive → Supabase Storage) by changing the mount config, not the agent code.

**How I could use this**

1. Mount `content/posts/` as a local Mirage source and a Google Drive folder as `/drive/drafts/` — build a Claude-powered editorial agent that reads your existing post style from the local mount, writes new drafts to Drive for your review, and only copies approved files back into `content/` via the VFS write path. Zero manual file shuffling.
2. In the resume analyser route (`/api/resume/analyse`), mount the user's Supabase Storage bucket at `/storage/resumes/` and your scraped jobs S3 bucket at `/s3/jobs/au/` — Claude reads both through Mirage and writes the tailored gap analysis back to `/storage/analyses/{userId}.json`. The route handler has no AWS SDK or Supabase Storage calls inline, just `fs.read` and `fs.write`.
3. Build a 'career context' agent tool for the interview prep feature: mount LinkedIn export CSVs from Drive at `/drive/linkedin/`, visa tracker data from Redis at `/redis/visa/{userId}`, and salary data from S3 at `/s3/salary-benchmarks/au-{role}.json` — Claude reads all three in one tool loop to give personalised interview coaching grounded in the user's actual experience, location, and visa stage.

---

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

**1,656 stars this week** · Shell

A shell-based workflow that forces you to score, blind-predict, and retrospect every piece of content — turning publishing from gambling into a compounding feedback loop.

**Use case**

Creators publish 200 posts and barely improve because they never close the feedback loop — they see numbers but draw no extractable conclusions. This repo gives you a structured CLI ritual: before you publish, you log a blind prediction (hook score, estimated reach, target emotion); 72 hours later you run a retro script that compares actuals vs. prediction and forces you to update your personal rubric. Example: you predict a LinkedIn post hits 5k impressions, it lands at 1.2k — the retro script surfaces the delta and asks you to name one variable that explains it. Over 30 cycles, your rubric becomes a personal hit-formula rather than vibes.

**Why it's trending**

The README itself is engineered as a viral artefact — the self-referential hook ('you're reading this, the skill predicted it') is a deliberate pattern interrupt that gets shared as a screenshot before anyone reads the code. It's trending on the meta-layer: people sharing it as a curiosity about persuasion mechanics, not because they've run the scripts.

**How to use it**

1. Clone and review the shell scripts in the repo — the core loop is: `./score.sh` (pre-publish rating), `./predict.sh` (blind forecast), `./publish-log.sh` (record post metadata), `./retro.sh` (run 72h after publish to compare actuals).,2. Create a local `rubric.md` — the retro script appends learnings to it after each cycle. This file becomes your personal content model.,3. Before every post, run `./predict.sh` and commit to a number (impressions, saves, replies) — the commitment is the mechanism; it forces conscious hypothesis formation.,4. At T+3 days, run `./retro.sh <post-id>` — paste in your actual metrics, answer the 3 structured questions it prompts, and watch the rubric file evolve.,5. After 10 cycles, read `rubric.md` top-to-bottom — you'll have a data-backed model of what actually works for your specific audience, not recycled advice from someone else's niche.

**How I could use this**

1. Build a 'Post Experiment Log' feature into Henry's blog admin panel — before scheduling an AI-news or visa-news post, Henry logs a blind prediction (expected pageviews, time-on-page) against the Supabase `posts` table, then a cron job at T+72h fetches actual analytics and writes a retro row. Over 6 months this surfaces which content types (visa explainers vs. salary data vs. githot digests) actually drive return visits from the 482/485 audience.
2. Add a 'Content Calibration Score' to the resume analyser workflow — the same blind-predict → retro loop applies to job applications: before submitting, Henry's users rate their confidence (1–10), log the role tier, and after 2 weeks record the outcome (no response / screening / offer). The system surfaces calibration bias ('you rate 8/10 but only 20% of those convert to screening') — a genuinely useful insight the career tools audience would pay for.
3. Feed the retrospective data into a Claude Haiku prompt that generates a weekly 'Content Intelligence Report' — aggregate the delta between predictions and actuals across all post types, then ask Claude to identify the one pattern that explains the biggest misses that week. Ship it as a Sunday digest email to logged-in users, positioning Gradland as a platform that helps you *learn faster* from your career and content experiments, not just consume information.

---

## 6. [yaojingang/yao-open-prompts](https://github.com/yaojingang/yao-open-prompts)

**1,623 stars this week** · Python · `ai` `chinese-prompts` `geo` `prompt-engineering`

A curated, production-ready library of 116 Chinese AI prompts organized by real-world scenario — think of it as a versioned, open-source prompt engineering cookbook with an English mirror.

**Use case**

The real problem: prompt engineering resources are either too generic, English-only, or buried in paywalled newsletters. This repo gives you tested, scenario-specific prompt files you can drop directly into Claude or GPT workflows — no starting from scratch. Concrete example: instead of spending 30 minutes crafting a WeChat public account HTML generator prompt, you pull `prompts/06-ai-content/wechat-html.md`, adapt two variables, and ship. The GEO section (25 templates) is especially practical — it covers content structured specifically for AI-powered search surfaces like Perplexity and ChatGPT Search, which standard SEO playbooks don't touch.

**Why it's trending**

GEO (Generative Engine Optimization) is the emerging discipline of structuring content so AI search engines cite you — and this is one of the first open repos with 25 practical GEO prompt templates covering Schema.org markup, source credibility signals, and AI-citation risk auditing. With Google SGE and Perplexity eating traditional organic traffic, developers are scrambling for concrete tactics, and this repo dropped them at the right moment.

**How to use it**

1. Browse the repo at https://github.com/yaojingang/yao-open-prompts or the web navigator at https://yaojingang.github.io/yao-open-prompts/ — use CATALOG.md to find the prompt category you need (e.g. `prompts/08-ai-marketing/` for GEO).,2. Open the English mirror at `prompts-en/` for direct copy-paste if you're working in English — the file paths mirror `prompts/` exactly.,3. Start with the meta-prompt system `prompts/01-ai-methods/rtf-meta-prompt-system-v06.md` — feed it your use case and it generates a structured, role-engineered prompt for you rather than you writing one cold.,4. Adapt the output: swap in your domain, audience, and tone. The RTF framework (Role / Task / Format) is explicit in every generated prompt, so it's easy to edit without breaking the structure.,5. Version your adapted prompts in a `prompts/` directory in your own repo — treat them as code, not one-off clipboard text. Use the repo's `templates/` folder as a starting template for new prompt files.

**How I could use this**

1. Wire the RTF meta-prompt generator into Gradland's githot digest pipeline: instead of a fixed summarisation prompt, let the meta-prompt system dynamically generate a tailored prompt based on the repo's language, topic cluster, and target audience (e.g. 'Australian dev job seeker' vs 'AI researcher') before Claude writes the digest entry. This makes githot summaries contextually sharper with zero extra Claude tokens.
2. Build a GEO-optimised blog post enhancer for Henry's content: pull the 25 GEO templates from `prompts/08-ai-marketing/`, expose them as a `/api/content/geo-enhance` route, and add a post-editor panel that runs a GEO audit on any blog post — flagging missing Schema.org signals, weak citation-bait headings, and thin entity coverage. Position this as a free tool on the Gradland blog to drive SEO and backlinks from the content-creator audience.
3. Create a 'Career Prompt Vault' feature on Gradland: a searchable library of 15-20 career-specific prompts (resume gap analysis, STAR interview answer generator, visa cover letter, LinkedIn summary rewriter) built using the RTF framework from this repo. Gate advanced prompts behind the paid plan — the free tier gets 3 prompts, Pro gets all. Each prompt runs server-side via the existing `/api/*` Claude routes with the rate-limiting pattern already in `lib/subscription.ts`, so billing risk is zero.

---

## 7. [huangserva/3DCellForge](https://github.com/huangserva/3DCellForge)

**1,325 stars this week** · JavaScript

A browser-based 3D cell explorer that wires React Three Fiber to cloud/local image-to-3D APIs (Tripo, Hunyuan3D), letting you upload a reference image and get an orbitable GLB model back in the browser.

**Use case**

The real problem: most image-to-3D pipelines dump you with a raw GLB file and no viewer. 3DCellForge wraps the full loop — upload image → call Tripo/Hunyuan3D API → poll for the generated model → render it in an interactive WebGL scene with orbit controls, detail panels, and GLB export — all in one React app. Concrete scenario: a biotech educator wants to show students what a mitochondria looks like from any angle without buying expensive lab software; they upload a microscope photo and get a rotatable 3D model in 30 seconds.

**Why it's trending**

Tripo's image-to-3D API dropped its pricing and latency significantly in early 2026, making browser-native 3D generation viable for the first time without a GPU render farm. This repo is one of the first clean reference implementations showing the full React Three Fiber + cloud 3D-gen integration pattern that frontend devs can actually copy.

**How to use it**

1. Clone and install: `git clone https://github.com/huangserva/3DCellForge && npm install`
2. Copy env: `cp .env.example .env.local` and add `TRIPO_API_KEY=your_key` (get one at tripo3d.ai)
3. Run: `npm run dev` — opens a Vite dev server with a working 3D viewer using cached demo GLB models even without a key
4. To wire your own model: drop a reference image in the upload panel, hit Generate, the Node backend calls Tripo, polls until complete, and streams the GLB into the React Three Fiber canvas
5. Export: click the screenshot or GLB export button to save the result — the canvas uses `gl.domElement.toDataURL()` under the hood

**How I could use this**

1. Build an interactive 3D skill-graph for Gradland's learning paths: represent each tech skill as a node in 3D space (React Three Fiber + `@react-three/fiber`), with edges showing prerequisites. Users could orbit the graph to explore their career roadmap visually — far more engaging than a flat list, and a strong SEO/shareability hook for the blog.
2. Add a 3D resume visualiser to the resume analyser tool: after Claude scores a resume, render the candidate's skill profile as a 3D radar/spider chart using Three.js. Each axis maps to a job-requirement category (cloud, data, comms, visa-readiness). International grads could screenshot it for LinkedIn — giving Gradland a viral sharing mechanic tied directly to the core product.
3. Use the Tripo image-to-3D pipeline pattern (image upload → API poll → GLB render) as a template for a 'visualise your career trajectory' AI feature: user uploads their resume PDF thumbnail or LinkedIn screenshot, Claude extracts structured career data, and a custom Three.js scene renders their job history as a 3D timeline path they can orbit and share — differentiating Gradland from plain-text career tools.

---

## 8. [BigPizzaV3/CodexPlusPlus](https://github.com/BigPizzaV3/CodexPlusPlus)

**997 stars this week** · Python

An external launcher for OpenAI's Codex desktop app that injects UI enhancements via Chrome DevTools Protocol — without touching the original app binary.

**Use case**

When you use Codex App in API-key mode (not logged into ChatGPT), the plugin panel is gated behind an OAuth wall and there's no way to delete conversation sessions — only archive them. Codex++ launches Codex with --remote-debugging-port, then injects a renderer script over CDP that re-enables the plugin UI and adds a hover-triggered delete button with undo support. Concrete example: a developer using their own OpenAI key gets full plugin access and a clean session history without ever touching the app.asar.

**Why it's trending**

OpenAI shipped Codex as a standalone desktop coding agent in early 2025 and it picked up a large Chinese developer audience quickly — this repo is riding that wave by solving two immediate paper cuts (no delete, no plugins on API key). The CDP injection pattern is also generating interest as a template for non-destructive Electron app modding.

**How to use it**

1. Install Python 3.11+ and the Codex desktop app, then clone the repo and run `pip install -r requirements.txt`. 2. On Windows run `python install.py` to register a Start Menu shortcut; on macOS run `python install_mac.py` to generate `/Applications/Codex++.app`. 3. Launch via the installed shortcut — it starts Codex with `--remote-debugging-port=9229` then boots a local helper server. 4. The helper connects over CDP and injects `renderer-inject.js` into the Electron renderer process. 5. The Codex++ menu appears in the top bar; toggle plugin unlock and session delete from the settings panel. No files inside the Codex install directory are modified.

**How I could use this**

1. Build a browser extension (or Electron wrapper) for your blog's admin panel using the same CDP injection pattern — inject a 'quick edit' floating button on any blog post page that opens a side drawer with the markdown editor, so you can fix typos without navigating to /admin.
2. Apply the CDP/DevTools scraping technique to your career tools: write a Puppeteer script that launches LinkedIn with a remote debugging port, injects a content script to extract job description text, and pipes it directly into your resume matcher API endpoint — bypassing LinkedIn's bot detection since the browser session is real.
3. The plugin-unlock pattern maps directly to a Claude-powered enhancement layer: use CDP to inject a floating AI assistant into any third-party SaaS your users already have open (e.g., SEEK, LinkedIn) that reads the page DOM and calls your /api/interview or /api/resume endpoints — turning your career tools into a browser co-pilot without requiring a Chrome extension review.

---

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

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

TokenSpeed is a high-performance LLM inference engine targeting agentic workloads, offering TensorRT-LLM throughput with vLLM's ease of deployment — particularly optimized for Blackwell GPUs (B200).

**Use case**

The core problem is that existing inference engines (vLLM, TensorRT-LLM) force a tradeoff between raw throughput and operational simplicity. For agentic pipelines that make hundreds of chained LLM calls — like an interview prep agent that generates questions, evaluates answers, and builds a follow-up plan — latency compounds across every hop. TokenSpeed's C++ scheduler with FSM-encoded request lifecycles and its MLA kernel optimizations mean you get near-metal throughput without writing custom CUDA parallelism code, making it viable for production agentic systems that can't afford 300ms+ per inference step.

**Why it's trending**

The Kimi K2.5 model dropped on B200 hardware this week, and TokenSpeed is the engine behind its published performance benchmarks — the repo is the reference implementation for reproducing those numbers. Engineers evaluating Kimi K2.5, DeepSeek V4, and Qwen 3.6 for production are landing here because it's the only open engine showing Pareto-optimal throughput/latency curves on Blackwell.

**How to use it**

1. Clone and install: `git clone https://github.com/lightseekorg/tokenspeed && pip install -e '.[dev]'` (requires CUDA 12.x, Python 3.11+, Blackwell/Hopper GPU for full perf — H100 works for evaluation).
2. Launch the AsyncLLM server with a supported model: `python -m tokenspeed.entrypoint --model kimi-k2.5 --tensor-parallel 8`
3. Hit the OpenAI-compatible endpoint from your app:
```python
import openai
client = openai.AsyncOpenAI(base_url='http://localhost:8000/v1', api_key='unused')
response = await client.chat.completions.create(
    model='kimi-k2.5',
    messages=[{'role': 'user', 'content': 'Explain MLA attention in one paragraph'}]
)
  1. Benchmark against your current inference backend using the included agentic workload harness: python scripts/bench_agentic.py --concurrency 64 --trace your_trace.jsonl
  2. For the Kimi K2.5 B200 reproduction specifically, follow the blog post linked in the README — it includes the exact kernel flags and parallelism annotations used.

How I could use this

  1. Write a deep-dive post titled 'Why agentic LLM inference is a different problem than batch inference' — use TokenSpeed's FSM scheduler design as the concrete example. Gradland's interview prep agent (multi-turn, tool-calling) is exactly the workload this targets, so you can frame it as 'here's the engine design I'd use if I were scaling Gradland's interview feature to 10,000 concurrent users.' Technically credible, SEO-relevant for 'LLM inference agentic workloads.'
  2. Build a latency comparison widget for the blog: a small client-side table that shows TTFT (time-to-first-token) and throughput numbers for the major open inference engines (vLLM, TensorRT-LLM, TokenSpeed) across common models (Qwen, DeepSeek, Kimi). Pull from published benchmarks, not live endpoints. Useful for international IT grads evaluating ML infra roles — understanding inference engine tradeoffs is a real interview topic at Canva, Atlassian, Seek.
  3. Use TokenSpeed's OpenAI-compatible endpoint as a drop-in replacement for the Claude Haiku calls in Gradland's automated pipelines (visa-news fetch, digest generation) in a local dev environment — benchmark actual wall-clock latency on your specific prompt shapes. Write it up as 'I ran Gradland's content pipeline on 4 different inference backends — here's what the numbers actually look like' with real timing data. The 'local open model vs API' angle is perennially high-traffic for developer blogs.

10. zarazhangrui/beautiful-html-templates

853 stars this week · HTML

32 agent-ready HTML slide templates with a machine-readable index so any LLM can pick the right visual system and generate a complete deck without human design decisions.

Use case

The problem it solves is the last-mile gap between an AI that can write good content and one that can actually ship a presentable deck — agents today know what to say but dump it into a blank canvas and produce ugly slides. Concretely: you tell Claude 'build me a pitch deck for my resume-analysis feature,' it reads index.json, picks a template that matches the tone (e.g. 'Soft Editorial' for a polished portfolio pitch), clones the HTML, and fills in your content without ever touching a design tool.

Why it's trending

Agent-to-agent tooling is the current wave — repos that are explicitly designed to be consumed by LLMs (structured metadata, AGENTS.md operating manuals) are getting traction as developers build agentic pipelines and need composable, machine-legible assets. This is the presentation-layer equivalent of what shadcn/ui did for React components: opinionated, copy-paste ready, no dependency hell.

How to use it

  1. Clone the repo: git clone https://github.com/zarazhangrui/beautiful-html-templates
  2. Read index.json to see the template manifest — each entry has name, tone tags, color palette, and use-case hints that an LLM can match against a user brief.
  3. Pick a template folder (e.g. templates/soft-editorial/) — it contains a self-contained HTML file plus any sibling assets (fonts loaded via CDN, no build step).
  4. Open the HTML in a browser to confirm the visual system fits, then instruct your agent to replace the placeholder content sections with your actual text and data.
  5. For agentic use, pass AGENTS.md + index.json as context to your LLM and let it handle template selection and content injection automatically — no human design decisions needed.

How I could use this

  1. Auto-generate a 'Weekly Digest' slide deck from Henry's existing digest markdown files — pipe the frontmatter + summary into the agent, let it pick a template (e.g. 'Stencil & Tablet' for editorial gravitas), and publish the resulting HTML as a shareable link at /digest/[slug]/slides — a format that LinkedIn posts and newsletter embeds accept better than markdown.
  2. Build a 'Resume to Pitch Deck' feature for the career tools: user uploads or pastes their resume, the API extracts role, skills, and achievements, then generates a 5-slide HTML deck (Profile · Skills · Experience · Goals · CTA) using the most professional template from index.json — downloadable as a single HTML file for job fair networking or recruiter follow-ups.
  3. Wire it into the existing Claude pipeline as a new content output format: after the AI generates an interview prep guide or learning path, offer a one-click 'Export as slides' button that calls a /api/export/slides route — the route sends the structured content + index.json to Haiku, gets back a template pick + filled HTML, and streams the file download. Zero design work for Henry, high perceived value for users.
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