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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

TL;DR An AI gateway is a middleware layer between your application code and your LLM providers - it centralises routing, auth, rate limiting, cost tracking, and guardrails in one place You probably don't think you need one until something specific breaks: a runaway cost spike, a failed model causing silent errors, a security audit you can't pass We went from scattered SDKs and shared API keys to a gateway-first setup over about three months - this post covers what changed and what we'd do differently Six months ago we had what I'd describe as a functional mess. We were running three LLM providers - OpenAI for our customer-facing chat, Anthropic for internal document summarisation, and a self-hosted Llama model for batch classification jobs. Each had its own SDK. Each had its own API key, living in .env files on whoever's machine had last run that service. Each had its own rate limiting logic, copy-pasted between services with slight variations. It worked, in the way that things work when nobody has had a bad enough incident yet. The incident arrived on a Tuesday. A background job that was supposed to run once a week got accidentally scheduled to run every minute. It was calling GPT-4o. We noticed when the Slack alert fired at 2am about an unusual credit card charge. By the time someone killed the job, we'd burned through $340 in about four hours. The API key had no spending limit. There was no alerting on token usage. The job had no rate limiting. All three of those gaps were things we knew about and hadn't prioritised. That week, we started properly looking at AI gateways. What an AI gateway actually is? The simplest definition: an AI gateway is a middleware layer that sits between your application code and your LLM providers. All your LLM requests go through it, and it handles the cross-cutting concerns that you'd otherwise have to re-implement in every service: routing, authentication, rate limiting, cost tracking, caching, fallbacks, guardrails. The analogy that

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

A deep dive into how Spark transforms your SQL into a physical execution plan — and how Databricks layers Adaptive Query Execution and the Photon vectorized engine on top to squeeze out maximum performance. Table of Contents Why Query Optimization Matters The Catalyst Optimizer Pipeline Stage 1: Parsing — From SQL to Unresolved Logical Plan Stage 2: Analysis — Binding to the Catalog Stage 3: Logical Optimization — Rule-Based Rewrites Stage 4: Physical Planning — Strategies and Cost Models Adaptive Query Execution (AQE) The Photon Engine Reading Explain Plans Tuning Reference Table References Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization — is what separates a job that runs in 3 minutes from one that runs in 3 hours on identical hardware. Databricks compounds Spark's native Catalyst optimizer with two additional layers: Adaptive Query Execution (AQE) — re-optimizes the query at runtime using actual statistics collected mid-job Photon — a C++ vectorized execution engine that replaces the JVM-based Spark executor for eligible operators Understanding all three lets you write queries that cooperate with the engine rather than fight it. The Catalyst Optimizer Pipeline Catalyst is Spark's rule-based and cost-based query optimizer. Every query — whether written in SQL, DataFrame API, or Dataset API — passes through the same four-stage pipeline before a single byte of data is read. Stage 1: Parsing — From SQL to Unresolved Logical Plan # ── Catalyst Stage 1: Parsing ───────────────────────────────────────────────── # Spark uses ANTLR4 to parse SQL into an Abstract Syntax Tree (AST). # At this point column names are NOT validated — the plan is "unresolved". from pyspark.sql import SparkSession spark = SparkSession . builder . appName ( " catalyst-demo " ). getOrCreate () # Both of these produce identical internal representations df_api = ( spark .

How to Access DeepSeek API from Outside China (2026 Guide) DeepSeek has quietly become one of the best open-weight LLM families available. Their V4-Pro model matches GPT-4o within 3-5% on coding benchmarks (HumanEval, MBPP) while costing roughly 90% less per token. The problem? Actually getting access as an overseas developer. The Registration Wall If you try to sign up for DeepSeek's official API directly, you'll hit this: ✕ +86 phone number required for SMS verification ✕ Alipay or WeChat Pay only — no Stripe, no PayPal ✕ Documentation is primarily in Chinese ✕ VPN required and it drops mid-request ✕ Different auth system than OpenAI This isn't a minor inconvenience — it's a hard blocker for most overseas developers. I spent a full weekend trying to work around it before finding a solution that actually worked for production use. Option 1: DIY Proxy (Not Recommended) You could technically set up a Chinese VPS as a relay, register through a Chinese friend's number, and proxy requests. I tried this approach. Problems: Your Chinese VPS adds 100-300ms latency You're responsible for keeping the integration working If your Chinese friend's number gets flagged, you're locked out No SLA, no support, no monitoring Payment still requires Alipay — you need a Chinese bank account or a friend After a weekend of futzing with this, I abandoned it. Not production-ready. Option 2: Third-Party Gateway (What I Use) There are now services that handle the China-side complexity and expose DeepSeek through a standard OpenAI-compatible API. They handle: Chinese phone number verification Alipay/WeChat payment (you pay via Stripe instead) API routing with global edge caching Load balancing across multiple Chinese providers Setup is literally two lines: # Before: Direct OpenAI client = OpenAI ( base_url = " https://api.openai.com/v1 " , api_key = OPENAI_KEY ) # After: Via gateway client = OpenAI ( base_url = " https://api.tokenmaster.com/v1 " , api_key = TM_KEY ) That's it. Same SDK, same i

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An AI agent that's flawless in the demo can still fall apart the first time a tool fails in production: a timeout, a network error, a response that comes back corrupted. The fix isn't a smarter model. It's testing the agent against those failures on purpose, before your users do, and hardening it one failure type at a time.

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With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns. By Mervyn Anthony

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