标签：#API

Two audio players do not make a fair before-and-after test. If the second player restarts from zero or takes half a second to load, the user is no longer comparing two versions of the same moment. They are comparing two memories. That is a weak way to evaluate any audio effect. It is especially weak for AI processing. A denoiser can remove a fan while softening consonants. A de-reverb model can reduce the room tail while making the voice sound less natural. The output may be cleaner without being better. The preview therefore has one job: let the listener switch quickly enough to hear both the improvement and the damage. The rule I use is deliberately boring. Both versions should contain the same edit and play from the same position. Switching should not restart playback or create a pause. The interface should not hint that one version is supposed to win. Two independent <audio> elements fail surprisingly quickly. Each owns its playback state, buffering behavior, clock, and seek operation. The user ends up finding the same position twice and comparing one sound with a memory of another. A better interface has one transport and one version control: [ Play ] [ Original | Processed ] 00:18 ━━━━━━━ 00:42 The transport decides where playback happens. The segmented control decides which signal is audible. One transport, two signals For a short preview, I decode both files into AudioBuffer s, start them at the same AudioContext time and offset, and route each through its own GainNode . Both sources run; only one gain is open. decodeAudioData() decodes complete file data and resamples it to the context's sample rate. The decoded buffers can then share the same audio clock. See the MDN documentation for format and loading details. The core is small: const context = new AudioContext (); const originalGain = context . createGain (); const processedGain = context . createGain (); originalGain . connect ( context . destination ); processedGain . connect ( context . destination )

Building AI Agents with frameworks like CrewAI or LangChain often hits a bottleneck: heavy, slow web scraping that bloats context windows and increases LLM token costs. To solve this, I built Smart Scraper M2M — a lightweight, high-performance web scraper API designed specifically for machine-to-machine (M2M) communication. 🌟 Key Features ⚡ Ultra-fast: Returns clean structured JSON in ~30ms . 🧠 Context-optimized: Strips out useless HTML/CSS junk so your LLMs process only relevant data. 🤖 Agent-friendly: Built to integrate seamlessly into CrewAI, LangChain, or custom Node.js agents. 🚀 Quick Start You can test the API or check the full source code directly on GitHub: 🔗 GitHub Repository: https://github.com/MRIGL/smart-scraper-m2m 💬 Feedback & Community I’m actively improving the API and would love to hear your thoughts, feature requests, or contributions! Feel free to star the repo or leave a comment below.

If you invoice or onboard Polish companies, sooner or later you have to check two dull things that turn out to matter a lot: is this company actually a registered VAT payer, and is the bank account they gave you the one that's on the government's official white list ("Biała Lista")? Both of those affect whether you can deduct the cost and reclaim VAT, so it's not really optional. The annoying part is that the data lives in four different places: the Ministry of Finance, the KRS court register, GUS (the stats office), and the EU's VIES service. Each one has its own API and its own quirks. I got tired of gluing those together every time, so I wrapped them behind a few plain HTTP calls that return JSON. Full disclosure: skanfirmy.pl is mine. It's free, no key, no signup, and the web layer runs client-side with no tracking. Here's how you'd actually use it. REST: one GET, one JSON Cheapest thing you can do is check a NIP (the tax ID): curl https://skanfirmy.pl/nip/5260250995 You get back the VAT status (active, exempt, or not registered), the company details from the VAT register, and the accounts sitting on the white list. The paths: GET /nip/{nip} gives VAT status + white-list data for one NIP GET /nips/{list} takes several NIPs at once (comma-separated) GET /regon/{nip} returns data from the REGON register (GUS) GET /vies/{country}/{number} validates an EU VAT number, e.g. /vies/DE/811128135 It's a plain GET that returns JSON, so it drops into anything that can make an HTTP request: a cron job, a lambda, a CI step, whatever. Python requests and a few lines. This one raises if the company isn't an active VAT payer: import requests def check_vat ( nip : str ) -> dict : r = requests . get ( f " https://skanfirmy.pl/nip/ { nip } " , timeout = 10 ) r . raise_for_status () data = r . json () status = data . get ( " vatStatus " ) or data . get ( " status " ) if status != " Czynny " : # status comes back in Polish; compare against the raw value raise ValueError ( f " NIP { n

Bluesky starter packs look like a single thing in the app — a shareable page that lets a new user follow a curated group in one tap. At the protocol level they are three separate records glued together by references, and if you create them from code (we do, as part of an automated outreach pipeline), the decomposition matters: it decides what you can update later, what you can only create once, and where a naive script will quietly make a mess. The three records Everything below is plain com.atproto.repo.createRecord / putRecord calls against your own PDS — no special API surface. 1. The list — app.bsky.graph.list . A starter pack is backed by an ordinary Bluesky list with purpose: app.bsky.graph.defs#referencelist . The list record itself holds metadata — name, purpose, createdAt, plus optional description and avatar. Members live elsewhere. 2. The memberships — app.bsky.graph.listitem . One record per member, each holding the member's DID and the list's AT-URI. There is no "add 20 members" batch call in the record layer: twenty members means twenty listitem creates. Plan for partial failure in the middle of that loop — more below. 3. The pack — app.bsky.graph.starterpack . The record that makes the share page exist. Per the lexicon, name , list , and createdAt are required; list is the AT-URI of the referencelist from step 1, the name is capped at 50 graphemes, and optional feeds can attach custom feeds. The official limits: up to 150 people, up to 3 feeds. The share URL is derivable, not returned: https://bsky.app/starter-pack/{your-handle}/{rkey} where {rkey} is the tail of the starterpack record's AT-URI. The trap: creation is not idempotent Every createRecord mints a fresh rkey. Run your create-starter-pack script twice and you have two packs with two URLs, both live, both indexed — and the one you already shared is not the one your script now reports. There is no natural key (like a title) that the protocol dedupes on. Our rules after learning this: Creation

WordPress is often treated as a traditional CMS, but its REST API makes it possible to use WordPress as the backend for applications, dashboards, mobile clients, automation systems, and external services. The difficult part isn't registering an endpoint. The difficult part is designing the endpoint so that authentication, authorization, validation, error handling, and data access are all handled correctly. A production API needs a contract. It needs to know: Who can access it What data they can access What input is accepted What output is returned What happens when something fails Here's a practical approach. Register a Custom Route A basic WordPress REST API route can be registered with register_rest_route() . add_action ( 'rest_api_init' , function () { register_rest_route ( 'myplugin/v1' , '/posts' , [ 'methods' => WP_REST_Server :: READABLE , 'callback' => 'myplugin_get_posts' , ]); }); This creates an endpoint similar to: /wp-json/myplugin/v1/posts The namespace matters. Using: myplugin/v1 gives the API a version boundary. If the response structure changes later, a new version can be introduced without immediately breaking existing clients. Don't Put Authorization Inside the Callback A common beginner implementation does everything inside the callback: function myplugin_get_posts () { if ( ! current_user_can ( 'manage_options' )) { return new WP_Error ( 'forbidden' , 'Access denied' , [ 'status' => 403 ] ); } // Query data... } This works, but WordPress provides a cleaner place for the permission decision. Use permission_callback . register_rest_route ( 'myplugin/v1' , '/posts' , [ 'methods' => WP_REST_Server :: READABLE , 'callback' => 'myplugin_get_posts' , 'permission_callback' => function () { return current_user_can ( 'manage_options' ); }, ]); Now the endpoint has a clearer separation: Request ↓ Permission check ↓ Callback ↓ Data That separation becomes increasingly valuable as an API grows. Authentication Is Not Authorization These concepts are easy to m

Most people's first experience with an LLM API is deceptively simple: send a prompt, get a reply. It feels like magic, and for a single question-answer exchange, it basically is. But the moment you try to build something that holds an actual conversation one where the model remembers what you said three messages ago you run into a problem that isn't obvious until you hit it: LLM APIs are stateless. Every request is a blank slate unless you explicitly hand the model its own memory. That was the core challenge behind a recent project I built during my internship a chatbot backed by a real LLM API ([OpenAI / Gemini]) with genuine multi-turn conversation support, not just a scripted request-response loop. * The problem nobody mentions upfront * You can't just "turn on" memory. Every conversation turn has to be manually tracked and resent with each new API call, which means the developer, not the model, is responsible for deciding what counts as context. And that decision has real consequences: send too little history and the bot forgets things it should remember; send too much, and you run into token limits and rising costs as the conversation grows. This is where most simple chatbot tutorials stop short. They show you how to get a reply from an API, but not what happens once a conversation runs long enough that you can't keep resending everything forever. * Where the actual engineering happens * Solving that meant implementing a context management strategy deciding what to keep, what to drop, and eventually exploring smarter approaches like summarising older parts of a conversation instead of just discarding them. It also meant thinking about the bot's identity through a system prompt, handling API failures gracefully instead of letting the UI break, and treating credentials properly by keeping API keys out of source code entirely. None of this is complicated in isolation. What's interesting is how much of it is invisible until you actually build the thing yourself. Us

I built an OLX scraper for 24 countries — the boring version that actually ships OLX runs classifieds in about two dozen countries. Same brand, different domains, different anti-bot setups. Everyone scraping it does one country at a time. I got tired of forking. So I put 24 countries behind one input. country: "id" or country: "pl" or country: "br" — same schema out. It's live on Apify as primesieve/olx-global-scraper . One file. No browser. Here is the boring part that matters. What it does Input: { "country" : "id" , "keywords" : [ "iphone 13" ], "maxResults" : 50 , "maxPages" : 3 , "proxyConfiguration" : { "useApifyProxy" : true , "apifyProxyGroups" : [ "RESIDENTIAL" ] } } country — two-letter code ( id , pl , in , br , ua , pt , ro , bg , kz , uz , pk , za , ng , ke , eg , lb , ph , co , ar , pe , ec , gt , az , ma ). Default id . keywords — one or more search terms. Each runs sequentially. maxResults / maxPages — caps. Defaults 50 / 3, max 1000 / 30. proxyConfiguration — optional for Indonesia, required for the other 23. Output — same shape every country: { "listingId" : "123456789" , "title" : "iPhone 13 128GB mulus" , "price" : 6500000 , "priceText" : "Rp 6.500.000" , "currency" : "IDR" , "city" : "Jakarta Selatan" , "location" : "Tebet, Jakarta Selatan, DKI Jakarta" , "images" : [ "https://...jpg" ], "thumbnailUrl" : "https://...jpg" , "listingUrl" : "https://www.olx.co.id/item/123456789" , "country" : "id" } Title, price (numeric plus display text), currency, location, images, URL. No seller PII beyond what the listing page shows. No tricks. Try: https://apify.com/primesieve/olx-global-scraper The boring stack // no playwright, no puppeteer // apify + fetch + cheerio. That's it. The scraper is one file. Apify SDK for input, dataset, and pay-per-event. Native fetch for HTTP. cheerio for the HTML path. Undici ProxyAgent when a proxy is configured. Node 20, 512 MB, 600s timeout. I check the endpoint before I write the scraper. Indonesia answered with clean JSO

Every "best text-to-speech API" list I have read is ranked. Number one, number two, number three, with a verdict at the bottom. That shape cannot express the actual decision, and I want to show you why with something you can run. The problem is that the three things that decide a TTS vendor are measured in units that do not convert into each other. Price is dollars per million characters. Transport is a shape — held-open socket, chunked body, finished file. Compliance is a document that either exists or does not. There is no exchange rate between them, so there is no ordering. A ranked list has to pick one axis and pretend the others are tiebreakers. They are not tiebreakers. They are filters, and filters compose by intersection. The three sets Price spans about 40x. Google Cloud's legacy voices and Amazon Polly's standard engine sit at $4 per million characters. The mid-market — OpenAI's tts-1 , Deepgram Aura-1, Inworld TTS-2 Flash — clusters at $15. Cartesia runs $37.38 to $50. ElevenLabs is $166.11 at its Scale tier. A million characters is roughly 22 hours of speech, so at prototype volume this axis is noise; at a hundred million characters a year it is the difference between a $400 bill and a $16,600 one. Transport comes in three shapes and the difference is architectural, not incremental. WebSocket streaming holds a connection open and pushes audio as it is synthesised. The first syllable can reach the caller while the model is still working on the sentence. This is what a live agent needs. Chunked REST streams the response body back progressively. OpenAI works this way, and its docs recommend wav or pcm output specifically because those start playing sooner than a compressed container. Meaningfully better than waiting for a whole file; meaningfully worse than a held-open socket. Batch returns a finished file. Correct for narration, e-learning, anything rendered ahead of time. Wrong for conversation. Two entries in that column are routinely stated wrong, so th

security, #api, #cybersecurity, #discuss On July 30, 2026, my batch job finished 300,000 domain-to-company lookups. 39,847 of them (13.3%) resolved to defense contractors, military-adjacent parent companies, or headquarters within a few miles of named bases. I wasn't hunting for that. I was just trying to clean a CRM. The same day, lina published a post about hijacking e164.arpa zones and accidentally logging hundreds of thousands of phone calls to military bases. Different protocol, same smell: an infrastructure lookup that was supposed to be boring turned into a classified-adjacent data spill. That parallel is what made me sit down and write this. Here is the exact call I used, with the live response for github.com so you can see the shape of the data before I explain what went wrong. import requests , json , time # Full source notes: https://github.com/On13uka/company-info-api RAPIDAPI_KEY = " YOUR_RAPIDAPI_KEY " BASE = " https://company-info1.p.rapidapi.com " def lookup ( domain ): r = requests . get ( f " { BASE } /lookup?domain= { domain } " , headers = { " X-RapidAPI-Key " : RAPIDAPI_KEY , " X-RapidAPI-Host " : " company-info1.p.rapidapi.com " }, timeout = 20 ) return r . json () print ( json . dumps ( lookup ( " github.com " ), indent = 2 )) The response I got back looked like this. It is a cached sample from a real call — the endpoint was asleep when I drafted this, but the fields are exactly what the pipeline consumed. { "domain" : "github.com" , "company_name" : "GitHub Inc" , "wikipedia" : "GitHub is a developer platform..." , "ceo" : "Thomas Dohmke" , "founded" : "2008" , "headquarters" : "San Francisco, California" , "employees" : "3000+" , "parent_company" : "Microsoft" , "twitter" : "@github" , "github_org" : { "repos" : 200 , "stars" : 50000 , "followers" : 12000 }, "health_score" : 78 } The Finding I started the job because a sales team had 300,000 stale domain records and wanted company names, headcounts, and a rough health score for each. The pla

You'll build a repeatable service that lets plumbers, dentists, and other service-business owners answer calls with a natural-sounding, AI-driven voice that schedules appointments, qualifies leads, and captures payments. The result is a hands-free phone front-desk that you can sell as a monthly subscription and use to acquire new clients for your agency. What you'll get: a working n8n workflow that wires Anthropic's Claude, ElevenLabs text-to-speech, and Twilio Programmable Voice together, plus a go-to client-acquisition script that turns the service into a scalable AI automation agency. What you need Tool Plan / Price* Role n8n (self-hosted Docker) Free (self-hosted) - see Docker Hub for latest image Orchestrates API calls, stores conversation state Twilio Programmable Voice Pay-as-you-go - check Twilio pricing page Provides inbound phone numbers and SIP bridge Anthropic Claude API Usage-based - check Anthropic pricing page Generates conversational replies ElevenLabs TTS API Usage-based - check ElevenLabs pricing page Turns Claude's text into a lifelike voice Cloudflare DNS + SSL Free tier available - verify limits Publishes a secure webhook for Twilio Git (optional) Free Version-controls workflow JSON *We avoid stating exact free-tier caps; always verify the current provider pricing. Estimated time-to-build: 12-16 hours total (including testing and client-onboarding script). Defining the core pieces Voice AI is the combination of speech-to-text, natural-language generation, and text-to-speech that lets a computer hold a phone conversation. In this guide we skip the speech-to-text step by letting Twilio forward the caller's audio to our n8n webhook; the rest happens via APIs. Key insight: The biggest revenue lever for an AI automation agency is the repeatable client-acquisition funnel, not the underlying technology. Building voice ai agents for local businesses Below is a step-by-step walkthrough. Every step mentions the exact UI field, API endpoint, or n8n node na

You can have a Vapi agent answer every inbound call, ask qualifying questions, and hand the prospect off to Calendly to lock in a meeting - all without writing a single line of custom telephony code. The result is a self-contained voice AI agent that routes calls, captures lead data, and books calendar slots automatically. voice is the audible sound produced by a human speaker that can be captured, transmitted, and synthesized by software. voice AI agent is a software component that receives spoken input over a phone line, runs speech-to-text, applies a language model, and returns synthesized speech to the caller. Below you'll find everything you need to reproduce the exact workflow, from the required services to the n8n JSON that creates the Vapi agent, plus the pitfalls that usually bite new builders. What you need Tool Plan / Price Role Vapi Free tier or paid plan - check the Vapi pricing page Voice AI platform that hosts the conversational model and performs voice synthesis Twilio Pay-as-you-go voice minutes - check Twilio pricing Provides the inbound phone number and SIP termination for Vapi Calendly Free tier or paid plan - check Calendly pricing Calendar link generator and meeting scheduler n8n (self-hosted) Community edition - free (Docker) Orchestrates the webhook chain between Vapi, Twilio, and your CRM HubSpot CRM (optional) Free tier - check HubSpot pricing Stores qualified lead details for follow-up Estimated build time: 1-2 days for a minimal production-ready flow, assuming you already have accounts for the services above. how to build voice ai for inbound calls The core of the solution is a Vapi "agent" that runs a scripted dialogue, a Twilio phone number that forwards calls to Vapi, and an n8n workflow that receives the webhook payload, enriches the lead, and creates a Calendly event. Follow each numbered step precisely; the configuration values are written exactly as they appear in the UI. 1. Provision a Twilio phone number Log into the Twilio Conso

The best free AI models 2026 are the ones that give you production-grade quality without a bill at the end of the month. In practice that means using Groq's ultra-low-latency mix, Google Gemini's 1 M-token free quota, Meta's LLaMA 2 (self-hosted), DeepSeek's open-source v2.5, and Mistral-7B-Base on a free cloud tier. Hook them up to an automation platform like n8n and you can run a full SaaS pipeline - lead scoring, email drafting, image captioning, or ticket routing - without paying for inference. Below you'll find the exact stack, a step-by-step build, the gotchas that usually bite newcomers, and a short FAQ so you can get the best free AI models 2026 live in under two hours. What you need Tool / Model Plan / Price (as of 2026) Role in the pipeline Groq (Mixtral-8x7B-instruct) Free tier: 200 k tokens / month, no credit-card required (see Groq pricing) Low-latency text generation for chat & summarisation Google Gemini 1.5 Flash Free tier: 1 M input tokens / month, 0.5 M output tokens / month (check Google Cloud AI) Multi-modal (text + image) support, best for classification and translation Meta LLaMA 2 13B Self-hosted Docker (CPU) - $0, or hosted on Runpod free credits (up to $5) Deep-knowledge base Q&A, fine-tuning on proprietary data DeepSeek-V2.5 Free tier on DeepSeek API: 150 k tokens / month (no card) Creative writing, code suggestions Mistral-7B-Base Free tier on Mistral Cloud: 100 k tokens / month (requires OAuth) Structured data extraction, function calling n8n (automation) Community Edition (self-hosted Docker) - free Orchestrates API calls, branching, retries Docker Desktop Free for personal use Container runtime for LLaMA 2 Node.js 18+ Free (runtime) Needed for custom JS functions inside n8n Estimated build time: 90 minutes for a fresh machine (install Docker, pull LLaMA, configure n8n) plus 30 minutes of testing. Total ~2 hours. Building a production-grade automation pipeline with the best free AI models 2026 Below is a concrete example: an inbound-lead

A sequence I cut to a music bed was three frames out at the first transition, nine at the second, and by the sixth segment nothing lined up with anything. I had asked every generation for ten seconds. Every generation had returned a file that was not ten seconds. Nothing in the API said so. The request took duration: 10 , returned 200 , and produced an MP4 whose container duration was 8.708 . No warning field, no note in the response body, and — the part that actually cost me the afternoon — no mention of it on the docs page I had read three times. This is a general property of latent video models rather than a bug in one provider, and once you know the shape of it you can handle it in about twenty lines. Here is the shape. Seconds are the wrong unit A video diffusion model does not work on frames. It works on a compressed latent tensor, and the compression is temporal as well as spatial: a causal 3D autoencoder folds a run of input frames into a single latent frame. Because the encoder is causal, the first frame is kept whole and everything after it is compressed in groups. With a temporal stride of s , a clip of F frames becomes latent_frames = ( F - 1 ) / s + 1 which only divides evenly when F ≡ 1 (mod s) . Frame counts that miss that condition get padded or truncated, so implementations pick the nearest legal count and render that instead. Stack a second constraint on top — many of these models generate in fixed blocks of latent frames rather than one at a time — and the set of renderable lengths collapses into a short arithmetic progression: F = head + block · n n ∈ ℕ Every legal duration is one of those F values divided by the frame rate. Nothing between them is reachable. duration: 10 is not a request. It is a hint that gets snapped to a grid you were never shown. What the snapping does to users Three separate problems, and only the first is obvious. The output is not the length you promised. Your UI said 10s, the file is 8.708s, so your UI lied. Not by much,

We've all been there: a client needs a quick QR code for a print campaign, or a short link for social media. You search Google, click the first result, and realize you need to create an account, verify your email, and potentially pay after 14 days. To solve this friction, we built klick.tools . It's a collection of simple web tools that just work - no signup, no expiration dates, and full GDPR compliance. What's inside? QR code generator: 7 content types (URL, text, WiFi, vCard, email, phone, geo), custom colors and module shapes, automatic WCAG contrast check, and clean export as PNG, SVG or PDF. Rendering happens in the browser, so the payload never leaves the device. URL shortener: shorten a link in seconds, see click stats, and change the target later without reprinting anything. The developer API We didn't want to build just another consumer site, so everything is backed by a REST API. Base URL: https://klick.tools/api/v1 . Responses are always JSON - lists as { data, count } , writes as { message, data } , errors as { error } with a stable machine-readable code . Creating a short link is a single POST, and it works without an account at all (rate-limited per IP): curl -X POST https://klick.tools/api/v1/links \ -H "Content-Type: application/json" \ -d '{"targetUrl": "https://example.com/a-very-long-campaign-url"}' With an API key ( kt_live_... , generated in your account) the link is bound to you, so you get click counts, editing and higher quotas. Pass it as a Bearer token or via x-api-key : const res = await fetch ( " https://klick.tools/api/v1/links " , { method : " POST " , headers : { " Content-Type " : " application/json " , Authorization : `Bearer ${ process . env . KLICK_TOOLS_API_KEY } ` , }, body : JSON . stringify ({ targetUrl : " https://example.com/landing " , title : " Summer campaign " , }), }); const { data } = await res . json (); console . log ( data . shortUrl , data . clickCount ); The same pattern covers QR codes via /api/v1/qr - create, li

Iran Doesn't Need to Mine Hormuz — Your requirements.txt Is Already Rigged Every headline you've read this week is a diversion. The Strait of Hormuz is not the target. You are. And you have been for months, possibly years, while you retweeted tanker tracking maps and debated whether Brent crude would touch $150. Iranian state-sponsored groups — OilRig, APT33, MuddyWater, Agrius — did not spend the last decade pivoting to cloud infrastructure so they could watch you panic about a waterway. They did it so they could own your build pipeline while you were distracted. And they have. This is not speculation. CISA Advisory AA24-038A explicitly maps Iranian APT campaigns against U.S. and allied critical infrastructure to cloud identity, Kubernetes targets, and software supply chains. Not SCADA. Not PLCs. Your kubectl binary. Your Helm charts. That FastAPI microservice running payment webhooks that you deployed on a Friday and haven't touched since March. The Revolutionary Guard does not need a mine. They need a maintainer who hasn't updated python-jose in fourteen months. The Theater and the Operation You watched the Strait. They watched your CI/CD. Geopolitical analysis is a spectator sport for infrastructure engineers, and Iranian cyber command is the bookie. While your LinkedIn feed filled with satellite imagery and retired admirals explained chokepoint logistics, the actual operation ran silently against: Public Helm charts with hardcoded cluster-admin ServiceAccounts FastAPI services with python-multipart handling unbounded file uploads on single-threaded Uvicorn workers .kube/config files exfiltrated from developer laptops in a dev-legacy namespace that predates your current CTO Terraform state stored in a single S3 bucket with versioning disabled and a policy written by someone who left in 2021 The Hormuz closure narrative is Information Operations . The closure of your API gateway due to an unpatched ASGI memory exhaustion vulnerability is the kinetic effect. You a

Five states, one query language, and an "active licenses only" checkbox that only actually filters two of them. That's the trap in scraping US childcare-license open-data registries: Socrata SODA makes every state's API look identical, but "active" is defined — or not defined at all — differently in every dataset. Quick answer New York, Connecticut, Colorado, Delaware, and Texas all publish their childcare-facility registries through Socrata, and all five accept the same $where query syntax. But only NY and CT ship a server-side status filter this Actor can apply. Colorado and Delaware have no status column in the dataset at all — there's nothing to filter on. Texas does have a status column ( operation_status ), it's just not wired into the active-only filter, so toggling activeOnly doesn't touch Texas rows either way. Treating "active only" as a global switch that behaves the same everywhere will silently hand you closed and revoked facilities in three of the five states while you believe you filtered them out. STATE_CONFIGS : dict [ str , StateConfig ] = { " NY " : StateConfig (..., col_status = " facility_status " , active_where = " facility_status= ' Active '" ), " CT " : StateConfig (..., col_status = " status " , active_where = " status= ' ACTIVE '" ), " CO " : StateConfig (..., col_status = None ), # no status column to filter on " DE " : StateConfig (..., col_status = None ), # no status column to filter on " TX " : StateConfig (..., col_status = " operation_status " ), # status exists, filter isn't wired } Why does "active only" do nothing in three states? Because the filter is applied per-state, not globally, and only two states have both a status column and a configured $where fragment for it: async def _fetch_page (...): params = { " $limit " : str ( page_limit ), " $offset " : str ( offset ), " $order " : config . order_key } if active_only and config . active_where : params [ " $where " ] = config . active_where return await _get_with_retry ( session

"Just query the Socrata API" is true and also useless advice. Socrata SODA is a real open standard — New York, Connecticut, Colorado, and Texas all expose their professional-license registries through the same $limit / $offset / $where query language. The standard ends there. What each state puts inside that standard is four unrelated data models wearing the same protocol. Quick answer Every state's cosmetology/barber/salon registry is one giant multi-profession table with its own column names, its own beauty-credential filter, and its own idea of what "active" means — and one state (Texas) doesn't expose a status column at all, so an activeOnly toggle is a silent no-op there. A generic Socrata client that assumes one schema will either miss most of the data or crash on the first state whose columns don't match. The fix is a per-state config object that maps each state's real column names to one canonical output row, with the active-license filter applied only where the underlying data supports it. @dataclass ( frozen = True ) class StateConfig : state : str endpoint : str order_key : str col_business_name : str | None col_licensee_name : str | None col_status : str | None base_where : str | None = None active_where : str | None = None Why does the same query return different professions per state? Cosmetology licenses don't get their own dataset — they're rows buried inside each state's entire professional-licensing table, next to electricians, dentists, and notaries. Filtering has to happen server-side, in SoQL, before pagination even starts, or you're downloading (and paying to store) irrelevant rows. Texas needs a starts_with() match across three license-type prefixes plus an Establishment wildcard; Connecticut needs an exact in() list of six credential names; Colorado needs a four-code in() list: TX_BEAUTY_WHERE = ( " starts_with(license_type, ' Cosmetology ' ) " " OR starts_with(license_type, ' Class A Barber ' ) " " OR starts_with(license_type, ' Barber ' ) "

Google Trends has no public API. What it has is the same internal JSON endpoints the trends.google.com single-page app calls — and those endpoints do something most REST clients aren't built to survive: they answer with HTTP 200 and an empty body when Google decides you look like a bot. Quick answer A 200 OK from Google Trends' widgetdata endpoints does not mean you got data. If the response body is empty, Google soft-blocked the request without bothering to send a 429. The fix is to stop trusting the status code alone: check resp.text.strip() on every call that's supposed to return a payload, and if it's empty, rotate the proxy session and retry exactly like you would on a 429 — because that's what it functionally is. if status == 200 : if require_body and not resp . text . strip (): # Soft block: Google returns 200 with empty body when it detects bots. # Treat the same as 429 — rotate session and retry. logger . warning ( " %s: HTTP 200 but empty body (soft block, attempt %d/%d) " , ...) if proxy_cfg is not None : new_sid = _fresh_session_id () current_proxy_url = await proxy_cfg . new_url ( session_id = new_sid ) await asyncio . sleep ( delay ) continue return resp Why does a working response start with )]}' ? Every Trends JSON endpoint prepends an XSSI-protection prefix before the actual JSON body — a defence against cross-site script inclusion attacks that predates fetch() . Naive json.loads(resp.text) throws a JSONDecodeError on a perfectly healthy response. Worse, the prefix isn't even consistent: /trends/api/explore sends )]}'\n (no comma), some widgetdata endpoints send )]}',\n (with a comma). We check the longer variant first so a response using the short prefix doesn't get mis-stripped: XSSI_PREFIX = " )]} ' , \n " XSSI_PREFIX_NO_COMMA = " )]} ' \n " def _strip_xssi_prefix ( body : str ) -> str : if body . startswith ( XSSI_PREFIX ): return body [ len ( XSSI_PREFIX ):] if body . startswith ( XSSI_PREFIX_NO_COMMA ): return body [ len ( XSSI_PREFIX_NO_COMMA

Job postings are one of the most underrated public data sources on the internet. Recruiters use them to spot placement opportunities, B2B teams read them as buying signals (a new Head of Data means data-tooling budget), and job seekers want to apply on day one — not when a posting finally reaches the aggregators. The usual instinct is to scrape career pages. Don't. Most tech companies host their careers page on one of a handful of Applicant Tracking Systems (ATS), and the four biggest ones — Greenhouse, Lever, Ashby and SmartRecruiters — all expose public, documented JSON APIs . No auth. No proxies. No brittle HTML selectors. The career page itself loads the same JSON you're about to fetch. In this tutorial we'll build a single-file Python tool that: fetches every open job for a company from any of the four ATS, auto-detects which ATS a company uses, normalizes everything into one clean schema, monitors changes — run it on a schedule and get only new / removed / changed postings. The four endpoints ATS Endpoint Greenhouse GET https://boards-api.greenhouse.io/v1/boards/{slug}/jobs?content=true Lever GET https://api.lever.co/v0/postings/{slug}?mode=json Ashby GET https://api.ashbyhq.com/posting-api/job-board/{slug} SmartRecruiters GET https://api.smartrecruiters.com/v1/companies/{slug}/postings (paginated) The {slug} is the company identifier you see in career-page URLs: boards.greenhouse.io/stripe → stripe , jobs.lever.co/spotify → spotify , jobs.ashbyhq.com/linear → linear , careers.smartrecruiters.com/Visa → Visa . Try one right now — no API key needed: curl -s "https://api.ashbyhq.com/posting-api/job-board/linear" | head -c 400 Step 1 — fetchers, one per ATS Each API returns a different shape, so we normalize as we fetch. Here are all four (Python 3, only requests ): import requests UA = { " User-Agent " : " ats-jobs-tutorial/1.0 " } def get_json ( url , params = None ): r = requests . get ( url , params = params , headers = UA , timeout = 30 ) r . raise_for_statu

The wall Every MCP server that controls DaVinci Resolve connects to it the same way: a script running outside the app calls into Resolve's scripting API over the network. That works fine on Resolve Studio. On the free edition it doesn't work at all — Lite is sandboxed and blocks any script that isn't launched from inside Resolve itself. The one door left open Free Resolve still runs Python scripts launched from its own Workspace > Scripts menu. A menu script gets the resolve object injected for free, can run a long-lived loop, and — because the sandboxed app ships the com.apple.security.network.server entitlement — can open a localhost listening socket. That's the whole trick: the MCP server is the menu script. Claude Code ──HTTP JSON-RPC (MCP)──▶ 127.0.0.1:8765/mcp │ server runs INSIDE Resolve │ (Workspace > Scripts > Utility) ▼ command queue → main script thread ▼ global `resolve` object → Resolve API What it gets you 157 tools across editing, color, render, media pool, and Fusion title styling — driven from plain-language requests in Claude Code. Zero dependencies: pure Python standard library, so there's nothing to pip install into Resolve's bundled interpreter. Try it git clone https://github.com/2sem/davinci-resolve-lite-mcp.git cd davinci-resolve-lite-mcp ./install.sh macOS only for now. Full tools reference and demo video in the repo.