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Honestly, deepSeek vs Qwen vs Kimi vs GLM: Which AI API Wins in 2025? I'll be honest — when I first started comparing these four Chinese AI model families, I thought it would be a quick exercise. Spoiler: it wasn't. I spent two weeks running prompts through every endpoint, tracking every dollar, and tallying tokens like a part-time accountant. The good news? I now have very strong opinions about which one deserves your money. Here's the thing: most "AI comparison" posts online are written by people who clearly haven't paid a single API bill. They throw around vague phrases like "good value" without ever showing you the math. That's not me. I'm the person who sees $0.01/M and immediately thinks "wait, that's a 99% discount compared to GPT-4o." I calculate things. I notice things. And when I noticed I could replace most of my OpenAI spending with these four providers, I lost my mind a little. So buckle up. This is going to be the most cost-obsessed AI comparison you'll read this year. I've tested DeepSeek, Qwen, Kimi, and GLM through Global API's unified endpoint, and I'm going to break down exactly what each one costs, what each one delivers, and where your dollars should actually go. The Price Reality Check Before we dive into individual models, let me set the stage. Look at these price ranges side by side: DeepSeek: $0.25–$2.50/M output Qwen: $0.01–$3.20/M output Kimi: $3.00–$3.50/M output GLM: $0.01–$1.92/M output Check this out — Qwen and GLM both start at $0.01/M for their smallest models. That's literally one cent per million tokens. If you've been paying OpenAI prices, that's a 99%+ reduction. On the other end, Kimi sits at $3.00–$3.50/M, which is the premium tier. That's not crazy compared to GPT-4o, but it's noticeably more expensive than the other three. The price spread across all four families combined is enormous. From $0.01/M to $3.50/M. That's a 350x range. Which means the model you pick matters more than any other decision in your AI stack. DeepSeek:

A GitHub repository called design.md has been trending recently, accumulating over 1,400 stars. The concept is straightforward: provide AI agents with a persistent design document they can reference throughout their work. This approach addresses a practical challenge in agent development that many teams encounter. The Context Challenge When working on complex tasks, AI agents need to understand the broader picture. What's the architecture? What constraints exist? What approaches have been tried before? Typically, agents get context from: Current conversation (limited window) Code comments (often outdated) Documentation (if it exists) The issue is that this context is fragmented and temporary. When conversation moves forward, earlier context disappears. When documentation is outdated, agents make incorrect assumptions. A design.md provides a single source of truth that persists across sessions. What Belongs in design.md An effective design.md answers these questions: What are we building? Beyond feature lists, document the core purpose. Why does this project exist? What problem does it solve? What are the key architectural decisions? Document major choices and their rationale: "PostgreSQL was chosen over MongoDB because ACID guarantees are required for financial transactions" "Microservices architecture was adopted because components have different scaling requirements" What constraints exist? Technical constraints (performance requirements, browser support), business constraints (budget, timeline), and regulatory constraints (GDPR, HIPAA). What has been tried before? Document failed approaches to prevent agents from suggesting rejected solutions. What are the current challenges? Known issues, technical debt, areas needing improvement help agents prioritize work. How Agents Use design.md When starting a task, agents can: Read design.md to understand context Make decisions aligned with documented architecture Avoid solutions violating constraints Reference design.md i

Almost every "generate a PDF" feature starts the same way. You already have HTML. You already have CSS. So you reach for the obvious move: render the page, screenshot it to PDF, ship it. Puppeteer, Playwright, wkhtmltopdf, a hosted "HTML to PDF API" — pick your flavor. In an afternoon you have an invoice coming out the other end and it looks fine. Then it goes to production. And "fine" slowly turns into a backlog of weird, hard-to-reproduce bugs. This is not an argument that HTML-to-PDF is useless. For a one-off export or an internal report, it's great. The argument is narrower: the moment PDF generation becomes a real, automated, customer-facing part of your product, "screenshot a web page" is the wrong abstraction — and the failure modes are predictable enough to list in advance. The core problem: a PDF is not a web page A browser renders for an infinite, scrollable, single-width viewport. A PDF is a stack of fixed, finite, printable pages. Those are different physics. HTML-to-PDF works by rendering your page in a headless browser and then slicing that continuous render into page-sized pieces. Everything that's hard about it comes from that one mismatch: you designed for a stream, and now you're forcing it into pages. Most of the bugs below are just that mismatch showing up in different costumes. Failure mode 1: pagination This is the big one. A browser has no concept of "page 2." So when your content is taller than one page, the engine has to guess where to cut — and it cuts wherever the pixel ruler lands. That means: a table row sliced in half across the page break a heading stranded alone at the bottom of a page, its content on the next a total row that floats away from the table it belongs to a signature block split from the line above it CSS has break-inside: avoid , break-before , and friends — and they help. But support is uneven across engines, they interact badly with flex/grid, and you end up hand-tuning rules per document until it looks right for the da

This article is part of the Comprehensive Guide to Microservices Architecture in .NET Core, Cloud and Azure series. API gateways serve as the entry point for client applications in distributed architectures, handling request routing, composition, and protocol translation. As systems grow more complex with multiple client types and backend services, choosing the right gateway pattern becomes crucial for maintaining performance, scalability, and developer productivity. This article explores two powerful API gateway patterns in .NET: the Backend for Frontend (BFF) pattern, which creates client-specific gateways, and GraphQL, which offers flexible, query-driven data fetching. Both patterns address the challenge of efficiently serving diverse clients while maintaining clean architecture and optimal performance. Backend for Frontend (BFF) Pattern Web BFF Implementation The web BFF returns detailed, enriched data suitable for desktop browsers with higher bandwidth and processing power: [ ApiController ] [ Route ( "api/web/[controller]" )] public class OrdersController : ControllerBase { private readonly IOrderServiceClient _orderClient ; private readonly ICustomerServiceClient _customerClient ; private readonly IInventoryServiceClient _inventoryClient ; [ HttpGet ( "{id}" )] public async Task < WebOrderDto > GetOrder ( Guid id ) { // Execute parallel calls to improve response time var orderTask = _orderClient . GetOrderAsync ( id ); var customerTask = _customerClient . GetCustomerAsync ( id ); var inventoryTask = _inventoryClient . GetInventoryStatusAsync ( id ); await Task . WhenAll ( orderTask , customerTask , inventoryTask ); return new WebOrderDto { Order = orderTask . Result , Customer = customerTask . Result , InventoryStatus = inventoryTask . Result , // Include additional rich data for enhanced web UI experience RecommendedProducts = await GetRecommendationsAsync ( id ) }; } } Mobile BFF Implementation The mobile BFF provides optimized, lightweight responses to min

Laravel API Development in Morocco: Architecture Guide 2026 Laravel remains the #1 PHP framework for API development in 2026 Laravel remains the #1 PHP framework for API development in 2026, and Morocco has become a hub for quality Laravel freelancers and teams. Here is the complete guide to building production-grade APIs with Laravel, based on 40+ projects shipped. Why Laravel for APIs in 2026 Eloquent ORM — most expressive DB layer in any framework Sanctum for SPA/mobile auth (simpler than Passport for most cases) Scout for Meilisearch / Algolia / Elastic full-text search Queues with Horizon for background jobs Octane for performance (Swoole / RoadRunner) Deep ecosystem : Telescope, Pulse, Forge, Vapor REST vs GraphQL — What to Choose Criteria REST GraphQL Learning curve Low Medium-high Caching Easy (HTTP) Complex Over-fetching Common Solved Mobile bandwidth Higher Optimized Best for Public APIs, simple CRUD Complex dashboards, mobile apps My default : REST with Laravel API Resources unless the client has clear GraphQL-specific needs (mobile app with variable fields, highly nested data). Standard Laravel API Architecture app/ ├── Http/ │ ├── Controllers/Api/V1/ │ ├── Requests/ (FormRequest for validation) │ └── Resources/ (API Resources for shaping output) ├── Models/ ├── Services/ (business logic) ├── Repositories/ (optional, if complex queries) ├── Jobs/ └── Events/ Key architectural decisions Versioning via URL (/api/v1/users) not headers — simpler FormRequest for validation (never validate in controller) API Resources for every response (shape, transforms, conditionals) Services layer when controllers exceed 100 lines Dedicated DTOs for complex payloads (spatie/laravel-data) Authentication — Sanctum Setup SPA on same domain : cookie-based, CSRF protected Mobile app / 3rd party : personal access tokens Revocation endpoint for logout Token abilities for granular permissions Rate Limiting & Security RateLimiter facade — per user, per IP, per endpoint CORS : use c

Creating Short Links with PHP: A Practical Guide URL shorteners are everywhere. They're used in marketing campaigns, email newsletters, QR codes, social media posts, affiliate links, and analytics platforms. While most developers are familiar with services like Bitly, integrating a URL shortener directly into your application is often much more useful. In this article, we'll build short links from PHP using an API. Why Create Short Links Programmatically? Creating links through a dashboard works for occasional usage. But applications often need to generate links automatically. Common examples include: Email campaigns User invitations Affiliate systems QR code generation Marketing automation Analytics tracking Customer portals An API allows applications to create and manage links without human interaction. The Traditional HTTP Approach Most URL shortener APIs work through simple HTTP requests. For example: $client = new GuzzleHttp\Client (); $response = $client -> post ( 'https://example.com/api/links' , [ 'headers' => [ 'X-Api-Key' => $apiKey , 'Content-Type' => 'application/json' , ], 'json' => [ 'url' => 'https://example.com/article' ] ] ); $data = json_decode ( $response -> getBody (), true ); echo $data [ 'short_url' ]; This works. But once your application creates dozens or hundreds of links, the amount of boilerplate code starts growing. Using a PHP SDK A PHP SDK removes most of the repetitive work. Installation is usually straightforward: composer require lix-url/php-sdk Creating a link becomes much simpler: $link = $client -> links () -> create ([ 'url' => 'https://example.com/article' ]); echo $link -> shortUrl ; The SDK handles: Authentication HTTP requests Response parsing Error handling DTO mapping This allows your application code to remain clean. Creating Your First Short Link Let's imagine an application that sends invitation emails. $inviteLink = $client -> links () -> create ([ 'url' => 'https://myapp.com/invite/abc123' ]); echo $inviteLink -> short

Disclosur: Ini dari tim Nexotao — saya bahas gateway kami sendiri di bawah. Saya jaga sebatas fakta yang bisa kamu cek sendiri: semua nama model, context window, dan harga ada di halaman pricing kami, dan saya kasih linknya. Kalau kamu developer di Indonesia, kemungkinan besar pernah kejedot ini: API OpenAI dan Anthropic minta kartu kredit luar negeri . Nggak punya kartu, nggak bisa pakai API. Banyak dari kita mentok di situ. Solusi yang jalan sekarang: gateway lokal yang nerima QRIS / Rupiah . Ini versi jujurnya — gimana cara kerjanya, berapa biayanya, dan apa yang belum bisa. Dua model live, satu API yang kompatibel Lewat Nexotao kamu pakai dua model teks: Claude Opus 4.8 ( claude-opus-4-8 ) — context window 350.000 token DeepSeek-V4-Pro — context window 131.072 token Itu angka context window yang dipublikasikan apa adanya — tanpa pemotongan diam-diam. Endpoint-nya kompatibel dengan OpenAI dan Anthropic , jadi biasanya cukup ganti base URL sama key-nya. Format OpenAI: from openai import OpenAI client = OpenAI ( base_url = " https://api.nexotao.com/v1 " , api_key = " sk-nexo-... " ) resp = client . chat . completions . create ( model = " claude-opus-4-8 " , messages = [{ " role " : " user " , " content " : " Halo " }], ) print ( resp . choices [ 0 ]. message . content ) Format Anthropic: curl https://api.nexotao.com/v1/messages \ -H "x-api-key: sk-nexo-..." \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{"model":"claude-opus-4-8","max_tokens":256, "messages":[{"role":"user","content":"Halo"}]}' Cara bayarnya Top up saldo Rupiah via QRIS , mulai Rp10.000 . Tanpa kartu luar negeri. Bayar sesuai pakai — dipotong per token. Tanpa langganan , dan saldo nggak hangus. Tiap response ada header X-Cost-Rp , jadi kamu lihat biaya rupiah persis tiap request. Berapa biayanya Saat tulisan ini dibuat, Claude Opus 4.8 lewat gateway sekitar 70% lebih murah dari harga retail resmi (input) — tapi jangan percaya saya gitu aja. Halaman perbandingan har

Disclosure: This is the Nexotao team — I'm describing our own gateway below. I've kept it to facts you can verify yourself: every model name, context window, and price here is on our live pricing page, and I link it. If you're an Indonesian developer, you've probably hit this wall: the OpenAI and Anthropic APIs want a foreign credit card . No card, no API. A lot of us get stuck right there. The fix that works today: a local gateway that takes QRIS / Rupiah . Here's the honest version of how it works, what it costs, and what it doesn't do. Two live models, one compatible API Through Nexotao you call two text models: Claude Opus 4.8 ( claude-opus-4-8 ) — context window 350,000 tokens DeepSeek-V4-Pro — context window 131,072 tokens Those are the real, published context windows — no silent truncation. The endpoint is OpenAI- and Anthropic-compatible , so you usually just change the base URL and key. OpenAI format: from openai import OpenAI client = OpenAI ( base_url = " https://api.nexotao.com/v1 " , api_key = " sk-nexo-... " ) resp = client . chat . completions . create ( model = " claude-opus-4-8 " , messages = [{ " role " : " user " , " content " : " Hello " }], ) print ( resp . choices [ 0 ]. message . content ) Anthropic format: curl https://api.nexotao.com/v1/messages \ -H "x-api-key: sk-nexo-..." \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{"model":"claude-opus-4-8","max_tokens":256, "messages":[{"role":"user","content":"Hello"}]}' How you pay Top up your Rupiah balance via QRIS , from Rp10,000 . No foreign card. Pay-as-you-go — deducted per token. No subscription , and the balance never expires. Every response carries an X-Cost-Rp header, so you see the exact rupiah cost of each request. What it costs At the time of writing, Claude Opus 4.8 runs roughly 70% below official retail input pricing through the gateway — but don't take my word for it. The comparison page shows live per-token rates and computes "vs official" automati

Here's a thing that surprises people the first time: an email sent through the API does not carry the signature the user set up in Gmail or Outlook. Provider signatures live in the provider's compose UI, and a programmatic send bypasses that entirely, so a message your app sends goes out with no signature at all unless you add one. The Nylas Signatures API is how you add it: store an HTML signature once, then attach it to a send by ID, and the signature gets appended to the message for you. This post covers signatures from two angles: the HTTP API your backend calls, and the nylas CLI for creating and testing one from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm setting a signature up. Nylas signatures are separate from provider signatures The first thing to get straight is that these are not the user's existing signature. Nylas doesn't sync the signature configured in Gmail, Outlook, or any other provider, and that provider signature is never applied to mail sent through the API. If a message your app sends needs a sign-off, you create that signature with this API and attach it explicitly; there's no inheriting it from the connected account. That separation is deliberate, because a programmatic send is a different context from a person typing in their webmail. It does mean the responsibility is yours: a user who connects their mailbox expecting their familiar signature to appear on app-sent mail won't get it automatically. Stored signatures are grant-scoped, living at /v3/grants/{grant_id}/signatures , so each connected account has its own set, and they're HTML, so a branded sign-off with a logo and links works the same as a plain one. Create a signature Creating a signature is a POST /v3/grants/{grant_id}/signatures with a name and an HTML body . The name is for you, a label to find it by later; the body is the markup that gets appended to outbound mail. The response returns the signature with its ID, which is w

Building a unified market intelligence platform for traders, analysts, researchers, and developers. After months of development, Jungletrade is now publicly available. The idea behind Jungletrade is simple: modern market analysis has become fragmented. Market data, indicators, analytical models, and trading signals are often distributed across multiple platforms, forcing users to maintain several subscriptions, workflows, and dashboards just to build a complete market view. We wanted to explore a different approach. 📊 The Problem Most market platforms focus on a specific layer of the analytical stack: Raw data Technical indicators Quantitative models Trading signals Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces. Our goal was to create a modular ecosystem where these layers can coexist within a single platform. 🧭 The Jungletrade Ecosystem Today, JungleTrade provides four product categories: 📦 Data Structured datasets for market research and discovery. 🧠 Models Analytical frameworks designed to identify patterns and relationships within market data. 📈 Indicators Tools that transform raw information into actionable insights. ⚡ Triggers Event-driven signals designed to highlight potential market opportunities. 🔍 Built for Transparency One design decision was particularly important to us: every product should explain itself. Each product includes: Product description Key features Use cases Interpretation guidelines Methodology overview The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works. 🔌 API First All products available through the platform are also accessible through API endpoints. Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform. 🏗️ Architecture JungleTrade is built using a modular, service-oriented architecture des

Cover: a real qa-probe run against our own stack, cropped to the summary - internal product detail withheld. We are building LightShield, a SIEM that is in active demo right now. We built most of it pair-programming with an AI coding assistant wired in over MCP - it ran our stack, read the errors, and patched its own code. For a small team that is a superpower. Until an endpoint failed. Here is the loop we kept hitting. A route returns a 500, or a 404, or an empty [] . The assistant looks at the status code and announces the cause with total confidence. Then it rewrites a handler that was never broken - because a status code is not a cause, and it had nothing else to go on. So it guessed, and it guessed wrong, and the diff made things worse. The thing is, that empty [] had at least six possible causes: the database was empty (nothing seeded) a feature flag was off a contract mismatch between the frontend and the backend an auth token that never got attached a 428 precondition a schema drift Same symptom, six different fixes. We could bisect to the real one. The AI could not - it had no ground truth, so it manufactured one. So we built qa-probe It analyzes the app, probes the live endpoints, and classifies each failure with a root cause and a fix hint. Three decoupled, cached phases: qa-probe analyze # parse source + OpenAPI -> route graph qa-probe probe # hit live endpoints (HTTP/SSE/WS), record evidence qa-probe report # classify root cause -> HTML / Markdown / JSON / AI-context # or just: qa-probe run It has adapters for FastAPI, Express, Next.js, tRPC, GraphQL, and a generic fallback, so it discovers your routes instead of you hand-listing them. The part that actually fixed our problem: every result is falsifiable Each result carries the evidence (the real request, a bounded response sample, the timing), a root cause from ~25 categories, and a calibrated confidence - high , medium , or none . When it cannot tell, it returns none instead of bluffing. No neural net

Today I launched Beach Day API , a developer API for real-time beach, ocean, water quality, advisory, amenity, access, and condition data. The goal is simple: make it easier for developers to build apps and tools around beach conditions without having to manually gather data from scattered sources. Beach Day API currently supports beaches across the United States and Australia , and returns structured JSON that can be used in travel apps, weather apps, surf tools, tourism websites, hotel and resort platforms, map-based search experiences, local discovery apps, and coastal safety dashboards. What the API includes Beach Day API can provide data such as: Beach profiles GPS and location data Ocean and weather conditions Water quality grades Advisories and closures Amenities Access details Beach-specific safety and visitor information A proprietary Beach Day Score The Beach Day Score is designed to give developers a fast way to surface whether a beach looks like a good choice for visitors on a given day. Why I built it Most weather APIs are broad. They can tell you temperature, wind, rain, or general conditions, but they usually do not answer the real user question: “Is this a good beach day?” That question depends on more than weather. It can involve water quality, advisories, closures, ocean conditions, amenities, beach access, and the actual visitor experience. Beach Day API is built around that more specific use case. Example use cases Some things developers could build with it: A beach finder app A surf or coastal conditions app A hotel or resort beach conditions widget A local tourism guide A travel planning tool A map-based beach discovery experience A safety dashboard for advisories and closures A recommendation engine for nearby beaches Built for simple integration The API uses API-key authentication and returns clean JSON responses. I wanted it to be straightforward enough that a developer could start testing quickly and then build it into a real product withou

Hey Dev Community, If you are running enterprise-scale web scrapers, pricing monitors, or data ingestion pipelines for LLMs, you’ve probably spent sleepless nights dealing with network latency and sudden 403 blocks. When choosing an infrastructure partner, every provider pitches the same script: "99.9% uptime guarantees, millions of residential IPs, and lightning-fast response times." But in the trenches of real-world data collection, we all know that marketing numbers rarely match production reality. Last quarter, my team ran an exhaustive infrastructure audit to compare proxy providers pricing performance and infrastructure stability. If you want to dive straight into our live dataset, telemetry scripts, and interactive monitoring utilities, you can check out the full workbench at ProxyVero . Here is a technical breakdown of how we built our benchmarking matrix, and the architectural gaps we discovered across mainstream enterprise proxy services. 📊 1. The Core Metrics: Uptime vs. Success Rates The biggest lie in the networking industry is confusing Server Uptime with Request Success Rate . A proxy gateway server can maintain a 99.9% uptime while the underlying residential peer network is failing 20% of your data collection requests due to strict target WAFs or high peer churn. When conducting our proxy providers uptime guarantees performance benchmarks , we evaluated three core parameters: TCP Handshake Latency : The time it takes to establish a connection with the proxy endpoint. TTFB (Time to First Byte) : Critical for parsing dynamic JavaScript targets. HTTP Status Code Reliability : Tracking the exact ratio of 200 OK vs. 403 Forbidden / 429 Too Many Requests . ⚖️ 2. The Big Three: Oxylabs vs Bright Data vs SmartProxy Comparison To provide an objective proxy network performance benchmarks comparison , we deployed standard headless browser worker instances (Playwright/Puppeteer) routed through different enterprise gateways. Below is a high-level summary of our a

Open Graph images are the single biggest factor in whether your shared links look credible or broken. Yet most sites ship one generic image on every page because making a unique one by hand is tedious. Here is a more sustainable approach: treat preview images as generated data, not hand-made design. The problem, concretely When you share a link, the receiving platform reads your page's og:image meta tag and renders a card. If that tag is missing, points to a low-res logo, or is the same image on all 200 pages, your links look generic in every feed, Slack channel, and group chat. Studies of social sharing consistently show that posts with a clear, relevant preview image get meaningfully more engagement than those without. The reason teams skip it is not ignorance. It is friction. Opening a design tool, duplicating a template, swapping the title text, exporting at the right dimensions, and uploading the file takes 10 to 20 minutes per page. Nobody keeps that up across a real publishing schedule. So the back catalog stays bare and new posts get whatever the default is. The insight: it is template work Look at a typical preview card and ask what actually changes between pages. Usually just the title, maybe the author and a category tag. The layout, background, logo, and fonts are constant. That is the textbook definition of a job you should template once and generate programmatically, not redo by hand each time. How to solve it The cleanest pattern is to generate the image at build time or on first request, then cache it. Conceptually: // During your build or in an API route async function getOgImage ( post ) { const params = new URLSearchParams ({ title : post . title , author : post . author , tag : post . category , }); // Returns a ready Open Graph image URL return `https://getcardforge.dev/api/card? ${ params } ` ; } // In your page head // <meta property="og:image" content={getOgImage(post)} /> You can build this yourself with a headless browser plus an HTML templ

Why API Breaking Changes Still Reach Production Even With CI/CD A few years ago I watched a "tiny" API change take down checkout for about forty minutes. The change was a one-liner. The pull request had two approvals. CI was green across the board. And it still broke production, because the thing that actually mattered was never tested. If you run microservices at any real scale, you have lived some version of this. Let's talk about why it keeps happening even with a mature pipeline, and what the teams who don't keep getting paged do differently. The Problem Here's the change that caused the outage. A payments service had a response that looked like this: { "status" : "ok" , "transaction_id" : "txn_8842" , "amount_cents" : 4200 } Someone renamed amount_cents to amount and switched it to a decimal, because "cents is confusing." Cleaner field, better docs. The producing service's tests were updated to match, everything passed, it shipped. The problem: three downstream services still read amount_cents . One of them was the order service, which now received undefined , multiplied it by a quantity, and wrote NaN into the database. The failures didn't even surface in the payments service. They surfaced two hops away, in a service the original author had never opened. This is the core issue. A breaking change is not defined by the service that makes it. It's defined by the consumers who depend on it. And the producer's CI pipeline has no idea those consumers exist. Why Existing Approaches Fail The natural reaction is "we need more tests." But look at what each layer actually checks. Unit tests verify the code does what the author intended. The author intended to rename the field. The unit tests were updated to expect amount . They passed because they were testing the new, broken behavior. Green unit tests told us nothing. Integration tests verify the service works with its own dependencies — its database, its cache, the APIs it calls. They almost never spin up the services

If you've ever tried to pull live options data into a Python script, you've probably hit the same wall I did: the cheapest real-time providers start at $99/mo. Here's how to do it for $20/mo — or free if you stay within 1,000 credits/day. What You'll Need Python 3.8+ requests library ( pip install requests ) An API key from market-option.com (free tier available, no card required) Fetching a Full Options Chain import os import requests API_KEY = os . environ [ " MARKET_OPTIONS_KEY " ] BASE_URL = " https://market-option.com/api/v1 " def get_chain ( ticker : str ) -> list [ dict ]: res = requests . get ( f " { BASE_URL } /options/chain/ { ticker } " , params = { " apiKey " : API_KEY }, ) res . raise_for_status () return res . json ()[ " results " ] contracts = get_chain ( " SPY " ) print ( f " { len ( contracts ) } contracts returned " ) print ( contracts [ 0 ]) Each contract in results looks like this: { "details" : { "contract_type" : "call" , "strike_price" : 530 , "expiration_date" : "2026-01-17" , "ticker" : "O:SPY260117C00530000" }, "last_quote" : { "bid" : 3.45 , "ask" : 3.50 , "midpoint" : 3.475 }, "greeks" : { "delta" : 0.42 , "gamma" : 0.031 , "theta" : -0.18 , "vega" : 0.29 }, "implied_volatility" : 0.182 , "open_interest" : 12418 } Filtering by Expiration and Strike def get_near_the_money ( ticker : str , expiration : str , spot : float , width : float = 0.05 ): """ Return contracts within ±width% of spot price. """ contracts = get_chain ( ticker ) low = spot * ( 1 - width ) high = spot * ( 1 + width ) return [ c for c in contracts if c [ " details " ][ " expiration_date " ] == expiration and low <= c [ " details " ][ " strike_price " ] <= high ] atm = get_near_the_money ( " SPY " , " 2026-01-17 " , spot = 530 ) for c in atm : print ( c [ " details " ][ " strike_price " ], c [ " details " ][ " contract_type " ], c [ " last_quote " ][ " bid " ], c [ " greeks " ][ " delta " ], ) Scanning for High IV Contracts def high_iv_scan ( ticker : str , iv_threshold :

Synchronous billing is easy, and that's the problem — it makes you think all billing is easy. When a request does its work inline, the billable number is in the response by the time you send it. The gateway meters from there — the meter write, retries and all, is its problem, not yours. From your side, synchronous billing is one number in the response. Asynchronous work breaks that. The request submits a job; the work happens later, in a worker; the result comes back through a poll or a callback. And the thing you bill for — characters processed, pages converted — isn't known when the request arrives. It's known when the job finishes . So you can't meter at the edge. The meter has to fire from the completion path. And the real difficulty is firing it exactly once per unit of completed work — because requests, polls, and retries all conspire to make that zero times or many times. This is platform-agnostic. Every submit-process-poll API has it. I'll use the system I run as the example, but the shape is the same anywhere. Three ways metering goes wrong On arrival. Carry the synchronous habit over and you meter when the job is submitted. But you don't know the size yet, so you're forced into a crude flat fee — or you bill for work that hasn't happened and might fail. Wrong unit, wrong time. On retrieval. The subtle one. You wire the meter to fire when the client fetches the result. Now a client who submits a job, lets it run — costing you real money downstream — and never bothers to poll is never billed. You did the work for free. "Completion" is not "the client picked up the result." It's the worker finishing. Without a fixed quantity. Input characters or output characters? Pages before OCR or after? If you haven't decided exactly what you measure and where, invoices drift and customers argue. Decide once; measure there. All three point the same way: meter on measured work-completion, with a fixed definition of the unit. Not on arrival. Not on retrieval. The mechanism:

Every quarterly earnings number for every US public company going back to 2009 is sitting in a free, well-documented JSON API run by the US government. No API key. No rate limit for normal use. No paywall. Almost nobody in the dev community seems to know it exists. It's at data.sec.gov , and it's the same data Bloomberg charges $24k/year for. What's in it The SEC requires all US-listed companies to file financial reports in XBRL — a structured XML format where every number is tagged with a standardised concept name. The EDGAR system has been collecting these since around 2009. The companyfacts endpoint exposes all of it as clean JSON: GET https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json Where CIK is the company's SEC identifier (10 digits, zero-padded). For Apple, that's 0000320193 . The response is a large JSON object with every concept the company has ever reported, broken down by period. The other endpoint you need is the ticker-to-CIK map: GET https://www.sec.gov/files/company_tickers.json This gives you a flat list of all US-listed companies with their CIK, ticker, and name. Load it once and cache it. One gotcha: concept names vary by company Companies don't all use the same GAAP concept names to report the same thing. Apple reports revenue as RevenueFromContractWithCustomerExcludingAssessedTax . Older companies use Revenues . Some use SalesRevenueNet . If you just look up one concept name, you'll get blanks for most companies. The fix is a concept alias map: try each name in order, use the first one that has data. const CONCEPT_MAP : Record < string , string [] > = { revenue : [ ' Revenues ' , ' RevenueFromContractWithCustomerExcludingAssessedTax ' , ' RevenueFromContractWithCustomerIncludingAssessedTax ' , ' SalesRevenueNet ' , ' SalesRevenueGoodsNet ' , ], netIncome : [ ' NetIncomeLoss ' , ' NetIncomeLossAvailableToCommonStockholdersBasic ' , ' ProfitLoss ' , ], operatingCashFlow : [ ' NetCashProvidedByUsedInOperatingActivities ' , ' NetCashProvidedB

The Complete Guide to OpenAI-Compatible APIs for Chinese LLMs One of the smartest decisions OpenAI made was making their API the de facto standard for LLM interaction. The openai Python package, the ChatCompletion interface, and the message format have become the HTTP of AI — nearly every major model provider now supports some form of OpenAI compatibility. This means you can swap models without changing your code. Here's how to use that to access China's best LLMs. The OpenAI SDK Pattern If you've used OpenAI's API, you already know the pattern: from openai import OpenAI client = OpenAI ( api_key = " sk-... " ) response = client . chat . completions . create ( model = " gpt-4o " , messages = [{ " role " : " user " , " content " : " Hello! " }] ) To access Chinese models through an OpenAI-compatible gateway, you change exactly two things : client = OpenAI ( base_url = " https://api.tokenmaster.com/v1 " , # ← Changed api_key = " tm-... " # ← Changed ) Everything else stays the same. The same SDK, the same method calls, the same message format. What This Unlocks By switching to an OpenAI-compatible gateway for Chinese models, you gain access to: Model Family Top Models Competitive Advantage OpenAI-Compatible DeepSeek V4-Pro, V4 Flash, Coder Coding, math, reasoning ✅ Qwen (Alibaba) 3.7-Max, 3.5-Flash Long context (256K), multilingual ✅ GLM (ZhipuAI) 4.5, 4-Flash Reasoning, structured output ✅ Baichuan Baichuan 4 Chinese content generation ✅ All accessible through the same SDK, the same API key, the same base URL. Migration Guide Step 1: Get Your Gateway Key Sign up at an OpenAI-compatible gateway for Chinese models. Most offer free trial credits: # I use TokenMaster # Sign up at https://api.tokenmaster.com # Get your API key from the dashboard Step 2: Update Your Client Instantiation Python: # Before: OpenAI only import os from openai import OpenAI client = OpenAI ( api_key = os . getenv ( " OPENAI_API_KEY " )) # After: Multi-model access TM_KEY = os . getenv ( " TOKENM

How to Use Chinese LLMs Without a Chinese Phone Number If you've tried signing up for any Chinese AI service, you've seen the same message: Please enter your phone number (+86) to receive a verification code. This single requirement blocks most overseas developers from accessing some of the best-performing and most cost-effective LLMs on the market. This guide covers every workaround I've found — from least to most practical. The Problem China's major AI labs produce world-class models: DeepSeek — DeepSeek V4-Pro matches GPT-4o within 3-5% on coding benchmarks Qwen (Alibaba) — Qwen 3.7-Max beats GPT-4o on long-context tasks (256K tokens) GLM (ZhipuAI) — GLM-4.5 is competitive with Claude for reasoning tasks Baichuan — Strong for Chinese-language generation But every single one requires: A +86 Chinese phone number for registration Alipay or WeChat Pay for billing Chinese-language documentation Method 1: Virtual Chinese Phone Numbers (Fragile) Services like SMS-activate and 5sim offer temporary Chinese phone numbers for ~$1-2. The problem: Chinese providers have gotten aggressive about flagging virtual numbers. Your account gets banned within days. You lose any balance you've added. ❌ Not recommended — too unreliable for production use. Method 2: Third-Party Gateway Services (Recommended) The most practical solution is a gateway that handles the China-side complexity for you. These services: Maintain their own Chinese accounts and infrastructure Register with real Chinese business entities Handle Alipay/WeChat billing on their end Expose everything through a standard OpenAI-compatible API What this means for you: Sign up with email (no phone number needed) Pay via Stripe or PayPal Get a standard API key Use the OpenAI Python/Node.js SDK as-is Migration example (Python): # Before — can't access Chinese models at all # client = OpenAI(api_key="...") # Only works for OpenAI # After — full access to Chinese models client = OpenAI ( base_url = " https://api.tokenmaster.com