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Introducing DevPub - Open Source Dev.to CLI Tool

Recently I went looking for a CLI tool to manage my Dev.to articles from the terminal. I write 4-5 articles per month, track analytics obsessively, and wanted a git-backed workflow. I found 9 existing tools. Tried them all. Here's what happened: devto-cli (Node): Last commit 2 years ago. Broke on install. dev-to-git (Node): Only syncs TO local. Can't push back. slinkity : Abandoned. forem-cli : 3 endpoints implemented out of 40+. Every single tool does the same thing: publish an article. That's it. Maybe pull. Maybe validate tags. Meanwhile the Dev.to API has 40+ endpoints including analytics, semantic search, ML-powered content concepts, follower engagement, trend tracking, and reading list management. Nobody uses them. So I built devpub . Table of Contents What devpub does What I discovered in the API The build story Architecture Try it Contributing What devpub does (that nothing else does) # The basics (every tool does this) devpub push -f articles/my-post.md devpub pull # Analytics in your terminal devpub stats # Views: 246.5K | Reactions: 4.4K | Comments: 402 | Followers: 18.9K # Full dashboard with top articles devpub dashboard # AI-powered search (semantic, not keyword) devpub search "building serverless apps" --semantic # What's trending RIGHT NOW devpub trends # Catch problems before publishing devpub validate The difference isn't one feature. It's coverage. Here's the comparison: Capability devpub Everyone else Publish/update articles Yes Yes Pull articles to local Yes Some Analytics (7 endpoints) Yes No Semantic search Yes No Trend discovery Yes No Article validation Yes No Rate limiting (30 req/30s) Yes No Retry logic for failures Yes No Concepts API (ML topics) Yes No What I discovered in the Dev.to API While building devpub, I found several API endpoints that aren't documented anywhere obvious: 1. Semantic Search -- Dev.to has a full embedding-based search system using Gemini embeddings (768-dimensional vectors) with pgvector. You can search articles b

2026-08-01 原文 →
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Introducing Fitz LiveViews: real-time UI in one language, zero JS build

TL;DR — Fitz LiveViews is a real-time UI framework for Fitz , a compiled, gradually-typed language where HTTP, WebSockets, auth, and an ORM are part of the syntax. You write single-file components ( .fitzv ) with state / event / <template> , and the server renders HTML, diffs it, and patches the browser over a WebSocket — no JavaScript build step, no client framework . The same .fitzv can also compile to WebAssembly for offline, zero-round-trip widgets. There's a live component gallery, a course, and a full flagship app (an admin panel with auth + Postgres + Docker) already built with it. Repo : github.com/Thegreekman76/fitz-liveviews · Docs : thegreekman76.github.io/fitz-liveviews This is the first post in the FitzLiveViews series. I'll start with the pitch and the setup; the following posts build things. The problem Building a modern web UI usually means two languages, two type systems, and a build pipeline: a backend (Python / Node / Go) plus a frontend framework (React / Vue / Svelte) plus its toolchain (Vite / Webpack / Babel). You duplicate your types across the wire, you keep two mental models in sync, and node_modules grows a personality of its own. Phoenix LiveView (Elixir) showed there's another way: render on the server, push diffs over a WebSocket, and let the browser stay dumb. No client framework, no API to hand-write, no JSON serialization dance. Fitz LiveViews brings that model to Fitz — and adds a twist: the same component can also compile to WebAssembly when you want purely client-side, offline interactivity. What Fitz LiveViews looks like A component is a single .fitzv file — state, event handlers, and a template, like Vue or Svelte: component Counter { state { count : Int = 0 } event increment () { count = count + 1 } event decrement () { count = count - 1 } event reset () { count = 0 } < template > < div id = " counter-app " > < p > Count : { count } < /p > < button @ click = " increment " >+ 1 < /button > < button @ click = " decrement " >- 1 <

2026-08-01 原文 →
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Presentando Fitz LiveViews: UI en tiempo real en un solo lenguaje, sin build de JS

TL;DR — Fitz LiveViews es un framework de UI en tiempo real para Fitz , un lenguaje compilado y de tipado gradual donde HTTP, WebSockets, auth y un ORM son parte de la sintaxis. Escribís componentes de un solo archivo ( .fitzv ) con state / event / <template> , y el servidor renderiza HTML, lo diffea y parchea el browser por WebSocket — sin paso de build de JavaScript, sin framework de cliente . El mismo .fitzv puede además compilar a WebAssembly para widgets offline sin round-trip. Ya hay una galería de componentes en vivo, un curso, y una app flagship completa (un panel de administración con auth + Postgres + Docker) construida con esto. Repo : github.com/Thegreekman76/fitz-liveviews · Docs : thegreekman76.github.io/fitz-liveviews Este es el primer post de la serie FitzLiveViews . Arranco con el pitch y el setup; los siguientes construyen cosas. El problema Armar una UI web moderna normalmente implica dos lenguajes, dos sistemas de tipos, y un pipeline de build: un backend (Python / Node / Go) más un framework de frontend (React / Vue / Svelte) más su toolchain (Vite / Webpack / Babel). Duplicás tus tipos de un lado al otro del cable, mantenés dos modelos mentales en sync, y node_modules desarrolla personalidad propia. Phoenix LiveView (Elixir) mostró que hay otra forma: renderizar en el servidor, empujar diffs por WebSocket, y dejar que el browser quede tonto. Sin framework de cliente, sin API que escribir a mano, sin la danza de serializar JSON. Fitz LiveViews trae ese modelo a Fitz — y suma una vuelta de tuerca: el mismo componente puede además compilar a WebAssembly cuando querés interactividad puramente client-side y offline. Cómo se ve Fitz LiveViews Un componente es un solo archivo .fitzv — state, event handlers y template, como Vue o Svelte: component Counter { state { count : Int = 0 } event increment () { count = count + 1 } event decrement () { count = count - 1 } event reset () { count = 0 } < template > < div id = " counter-app " > < p > Count : { cou

2026-08-01 原文 →
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The 4-part brief that keeps coding agents from drifting

Coding agents usually do not drift because they are incapable. They drift because the task leaves too much room for interpretation. A request like “clean up authentication” sounds clear to a human who already knows the codebase. To an agent, it can mean anything from renaming one helper to replacing the entire authentication stack. The fix is not a longer prompt. It is a brief with four explicit parts : Outcome Context Guardrails Definition of Done Below is the exact structure I use. 1. State the outcome as an observable change Describe what should be different for the user or system when the work is complete. Weak: Fix the login bug. Better: When a user submits an expired magic link, show the existing “Link expired” message and offer a button that requests a new link without leaving the page. The better version gives the agent a destination. It does not prescribe the implementation, but it makes success testable. 2. Give only the context that changes the decision Context is useful when it removes ambiguity. It becomes noise when it is a tour of the whole repository. Useful context often includes: The relevant entry point or route The existing component or service that should be reused A similar implementation elsewhere in the codebase The command used to run the relevant tests A known constraint, such as backwards compatibility Example: The page is implemented in app/auth/verify/page.tsx . Reuse requestMagicLink() from lib/auth/client.ts . The existing error-message styles live in components/auth/AuthNotice.tsx . That is enough to start investigating without pretending we already know the final patch. 3. Add guardrails that define the change boundary Guardrails prevent a small task from becoming an accidental rewrite. A useful set might be: Do not change the public API. Do not add dependencies. Keep the current visual design. Do not edit generated files. Limit changes to the authentication flow and its tests. If a database migration appears necessary, stop and expl

2026-08-01 原文 →
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"Most Of Your Vectors Are Steerage. Why Are They In First Class?"

I was on a call last month with a startup CTO who had just gotten their AWS bill. They had built a beautiful RAG application: semantic search, conversational AI, the works. Their vector index was humming along with about 50 million embeddings. Then they hit product-market fit. Within six weeks, they scaled to 500 million vectors. Their monthly infrastructure costs went from $2,000 to $20,000. The real kicker? When we looked at the access patterns, over 80% of those vectors were queried less than once a week. They were paying hot-storage prices for data that was, by any honest measure, cold. The standard advice here is "just use a cheaper vector database." The more interesting question is: why are you storing all your vectors at the same temperature in the first place? The Cost-Recall-Latency Triangle Vector search forces a three-way tradeoff. You can optimize for cost, recall, and latency, but you only get to pick two. Want high recall and low latency? That costs money (in-memory HNSW graphs with full-precision vectors eating RAM). Want high recall at low cost? Latency goes up. Want cheap and fast? Recall suffers. Most teams pick a single point on this triangle and apply it uniformly to every vector in their index. That decision made sense when vector databases offered a single storage tier. It makes the same amount of sense as storing your entire filesystem on NVMe SSDs because some files need fast access. The conventional wisdom says you pick your point on the triangle and live with it. But the conventional wisdom was written before vector storage got interesting. The better approach: tier your vectors the same way you already tier your storage. Different access patterns deserve different economics. The same embedding that costs $0.12/month in RAM might cost $0.004/month on disk and $0.0002/month in object storage. When you have 500 million of them, those decimals matter. The Hot Tier: In-Memory HNSW and Exact k-NN For vectors that get hit constantly (your user-fa

2026-08-01 原文 →
AI 资讯

The Ultimate Quantified Self: Building a Private Health Knowledge Base with RAG (PKM for Health)

We've all been there: staring at a blood test report from three years ago, trying to remember if that "slightly elevated" glucose level was a one-time thing or a trend. Our health data is scattered across messy PDFs, fitness tracker exports, and physical medical folders. In the era of AI, why are we still manually digging through folders? 📂 Today, we are building the Ultimate Personal Health Knowledge Base . By leveraging Retrieval-Augmented Generation (RAG) , we will transform fragmented medical reports and logs into a searchable, private, and intelligent second brain. We’ll be using LlamaIndex for orchestration, Unstructured.io for parsing those pesky PDFs, and ChromaDB for local vector storage. If you're looking for advanced architectural patterns or production-grade data engineering strategies beyond this tutorial, I highly recommend checking out the deep dives over at WellAlly Tech Blog , which served as a major inspiration for this build. 🚀 The Architecture 🏗️ The goal is to create a pipeline that ingests raw data, vectorizes it, and allows for Hybrid Search —combining semantic meaning with keyword precision (crucial for medical terms!). graph TD A[Raw Health Data: PDFs, CSVs, MD] --> B(Unstructured.io Parser) B --> C{Chunking & Cleaning} C --> D[Sentence-Transformers] D --> E[(ChromaDB Vector Store)] F[User Query: Is my cholesterol improving?] --> G[LlamaIndex Query Engine] E <--> G G --> H[LLM: Local or OpenAI] H --> I[Actionable Health Insight] Prerequisites 🛠️ To follow along, you’ll need a Python environment with the following stack: Unstructured.io : To handle "dirty" PDF and image-based reports. ChromaDB : Our lightweight, open-source vector database. Sentence-Transformers : To generate local embeddings without sending data to the cloud. LlamaIndex : The glue that connects our data to the LLM. pip install llama-index chromadb unstructured sentence-transformers llama-index-vector-stores-chroma Step 1: Ingesting Messy Medical Reports 📄 Medical reports are

2026-08-01 原文 →
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