标签：#API

Every founder I know has burned a week building an investor list: digging through Crunchbase profiles tab by tab, copying partner names into a spreadsheet that is stale before the seed round closes. The data you want is simple, firms plus focus plus contacts, and it is weirdly hard to get in bulk. The shortcut I use now is the Startup Investors Data Scraper on Apify, a queryable investor database of 10,469 firms that returns filtered JSON in one call. Disclosure: the Apify links in this post are affiliate links. If you run the Actor, I may earn a referral commission at no extra cost to you. Is there a public API for investor data? Not really. The big commercial databases keep their APIs behind sales calls and paid plans sized for funds, not founders. Free sources are scattered lists and shared spreadsheets with no filters and no freshness guarantees. This Actor takes a different shape: a curated database of 10,469 investment firms (as of December 2025) that you query like an API, filtering by firm type, sector, stage, and country, and paying only for the records you pull. What the investor database API returns The investor database API returns one JSON record per firm: name, type, description, location, website, social links, assets under management, stages, and sector focus, with partner contacts when you ask for them. Field Example Notes firm_name Acme Ventures With firm_description alongside firm_type_name Venture Capital Investor One of 17 firm types firm_country Germany Plus firm_city and firm_state firm_website https://acme.vc Also firm_linkedin_url , crunchbase_url , twitter_url firm_aum $250M Assets under management when known investor_contacts [{ "job_title": "Partner", ... }] Names, titles, LinkedIn URLs, emails when available, and check sizes, with Include_Contacts on Who this is for Founders building a raise pipeline, sales teams selling into VC and PE back offices, and analysts mapping which firms fund a sector. If your CRM needs 200 seed funds with war

The moment we realized our staging environment was broken It was 3 PM on a Thursday, and our team was scrambling. A critical API change had just been merged to main, but the staging environment—our supposed safety net—was showing false positives. The integration tests passed, but the mobile app was completely broken in production. That's when we knew: our shared staging environment was failing us. The Problem: Shared Staging Is Broken by Design Like many engineering teams, we operated with a single, shared staging environment. Every developer deployed their changes to the same place, leading to: Deployment conflicts: "Who deployed that breaking change?" Cascading failures: One broken PR would block the entire team Test contamination: Data from one test would leak into another Delayed feedback: You'd only discover issues after merging your PR and deploying to staging The "works on my machine" syndrome, now at scale The worst part? Our API contracts were changing constantly, but we only discovered breaking changes during integration testing—often too late. The Solution: Ephemeral Environments per PR We made a radical change: every PR gets its own isolated, short-lived environment. Here's our architecture: Our Implementation Stack Infrastructure: Kubernetes (EKS) with namespace-per-PR Orchestration: Custom GitHub Action workflow Database: Isolated RDS instance per environment Contract Testing: Pact flow + OpenAPI validation Cleanup: AWS Lambda that runs every hour, destroying environments older than 2 hours The Game Changer: Automated Contract Testing The magic wasn't just in isolated environments—it was in what we did with them. Every time a PR deployed to its ephemeral environment, we ran: Consumer-Driven Contract Testing (Pact) Our mobile and web clients would verify their expectations against the actual deployed API. If a change broke what the client expected, the PR would fail. Provider Contract Validation We'd automatically verify that the deployed API matched ou

Every voice agent demo ends the same way. The bot wraps the call with a confident "Great — I'll email you the details and a confirmation," the human hangs up satisfied, and then nothing sends. There's no inbox behind the promise. The transcript lives in your voice stack, the "email" is a TODO nobody wired up, and the customer waits for a message that never arrives. It's the most common broken promise in conversational AI, and it's broken for a boring reason: the voice agent has no mailbox of its own. That's the gap this post closes. The interesting problem with voice agents isn't speech — your voice stack already handles the transcript, the turn-taking, and the summary. The interesting problem is the channel bridge : handing what happened on the call to a written, replyable email that comes from the agent and whose reply comes back to the agent . Voice in, email out, reply back in. No human in the loop, no shared support inbox, no spoofed noreply@ . The piece that makes this clean is a Nylas Agent Account — a real, owned email address that your voice agent sends from and receives at. I work on the Nylas CLI, so the terminal commands below are the exact ones I reach for, and I'll show both angles for every operation: the nylas command and the raw curl HTTP call. In practice your provisioning runs through the API and your ops glue runs through the CLI, so you'll want both. Why a real mailbox beats a fire-and-forget send Most teams reach for a transactional email API for this — SendGrid, SES, whatever's already in the stack — and fire a templated "here's your summary" off into the void. That works right up until the customer replies. Their reply hits a black hole ( noreply@ ), or worse, it lands in some shared support@ inbox where it's divorced from the call it answers. The agent that made the promise never sees the answer. An Agent Account is just a grant . It has a grant_id , and that ID works with every grant-scoped endpoint Nylas already exposes — Messages, Drafts,

Most "AI email" tutorials end with a while True loop that polls an inbox every thirty seconds, runs the new messages through a model, and sends a reply. It demos fine. Then you put it in front of real traffic and the cracks show up immediately: you're burning API calls to fetch nothing 99% of the time, your reaction latency is bounded by your poll interval, and the moment you scale to more than one inbox the polling cost multiplies. Polling an agent's inbox is wasteful. Webhooks are the right primitive for this. The mailbox already knows the instant a message lands — there's no reason to keep asking. What you actually want is a pipeline: inbound mail fans out to a verified ingest endpoint, lands on a queue, and gets picked up by workers that drive your agent runtime and send the reply. This post is about that architecture end to end — not a single feature, but the whole flow, with the parts that bite you in production (idempotency, retries, ordering, backpressure) called out honestly. I work on the Nylas CLI, so the terminal commands below are the exact ones I reach for when I'm wiring this up. I'll show the curl HTTP call and the CLI equivalent for every concrete step, because you'll use both: curl in your provisioning scripts, the CLI when you're poking at a live account. The mental model: an Agent Account is just a grant Before any of the pipeline matters, here's the one abstraction that makes the whole thing simple. An Agent Account is a Nylas grant — it has a grant_id , an inbox, an email address on a domain you own, and it speaks every grant-scoped endpoint you already know: Messages, Threads, Folders, Drafts, Attachments, Calendars, Events, Contacts, Webhooks. There's no OAuth token to refresh, no provider-specific quirks, no separate SDK. If you've built against a connected Gmail or Microsoft grant before, the data plane is identical. Nothing new to learn there. What's different is that the agent is a participant. It has its own address — support@yourcompany

Most posts about "AI email agents" stop at the happy path: webhook fires, model drafts a reply, agent sends it. Demo works, screenshot looks great, ship it. Then it goes to production and the agent replies to the same customer twice ninety seconds apart, and now your "intelligent assistant" looks like a broken cron job. That second reply isn't a bug in your model. It's a property of the delivery system, and it's guaranteed to happen eventually. Nylas webhooks are at-least-once: the same event can arrive up to three times. If your handler treats every POST as a fresh event, every retry is a second action. For a logging pipeline that's harmless. For an agent that sends email on your behalf , a duplicate delivery is a duplicate reply, and a double-reply embarrasses the agent in front of the exact person you built it to impress. So this post is about the engineering of idempotency itself, applied to an Agent Account. Not "remember to dedupe" hand-waving — the actual moving parts: which field is the real dedup key, how to persist processed ids atomically, why you ack before you work, how to make the send path itself idempotent, and where a per-thread lock catches the race that dedup alone can't. I work on the Nylas CLI, so every terminal command below is one I've actually run, verified against nylas v3.1.27. What an Agent Account changes (and what it doesn't) An Agent Account is just a grant. It has a grant_id and works with every grant-scoped endpoint — Messages, Drafts, Threads, Folders — exactly like a connected Gmail or Microsoft account. The difference is it's an inbox the agent owns : support@yourcompany.com is the agent, not a human whose inbox the agent borrows. Inbound mail to that address fires the standard message.created webhook, the agent reads it, and the agent replies from its own address. Nothing new to learn on the data plane. That's the whole point of the grant abstraction — the idempotency work below is plain webhook-handling discipline, and it transfe

Most "AI email" integrations assume everything on the other side speaks REST. You wire up a webhook, you call POST /messages/send , and you move on. That works right up until you remember how much of your stack doesn't speak REST and never will: the ticketing system that ingests mail over IMAP, the backup script your predecessor wrote in 2014, the monitoring tool that only knows how to send SMTP, the compliance archiver that polls a mailbox every five minutes. None of those are getting rewritten to call an HTTP API for your demo. So here's the trick that makes a Nylas Agent Account genuinely useful in a real environment: it's not API-only. You can expose the same mailbox over IMAP and SMTP submission , hand the host, port, and credentials to one of those legacy tools, and let it read and send like it's talking to any old mail server. Meanwhile your agent drives that identical mailbox over the v3 API. Both surfaces hit one storage layer. A flag, move, or delete on either side shows up on the other within seconds. I work on the Nylas CLI, so the terminal commands below are the exact ones I reach for. As usual I'll show both angles for every operation — the raw curl against the API and the nylas command — because half the point of an Agent Account is that you can mix them freely. What you actually get An Agent Account is just a grant . It has a grant_id , and that grant_id works with every grant-scoped endpoint you already know — Messages, Drafts, Threads, Folders, Attachments, Contacts, Calendars, Events. There's nothing new to learn on the data plane. The IMAP/SMTP layer doesn't change that model. It adds a second door into the same room: One mailbox, two protocols. The API and the IMAP/SMTP server are two front-ends over the same backend. There is no sync job, no eventual-consistency window worth worrying about, no "API mailbox" versus "client mailbox." It's one mailbox. Legacy tools just work. Anything that can authenticate to an IMAP server with a username and pas

The potential deal highlights a growing trend of complex, multi-stage funding rounds that mask true entry prices.

Heads-up: this post references a tool I built. It's a genuinely useful walkthrough either way — the technique applies to any Airbnb scraping project. If you've ever tried to scrape Airbnb, you already know the two walls you hit: the pages are rendered by JavaScript, and Airbnb aggressively blocks datacenter IPs. Below is the reliable way to get clean Airbnb data in 2026 — listing prices, ratings, coordinates, and discounts — without running a headless browser or babysitting proxies. The key insight: Airbnb ships its data in the HTML You don't need to render the page. Every Airbnb search response embeds the full result set as JSON inside a <script id="data-deferred-state-0"> tag. Parse that and you get structured data straight away — no DOM scraping, no selectors that break on the next redesign. The path to the results is: niobeClientData[*][1].data.presentation.staysSearch.results ├── searchResults[] // ~18 listings per page └── paginationInfo.pageCursors[] // all page cursors, upfront Each listing carries a base64-encoded ID in demandStayListing.id (decode it, take the segment after the last colon, and you have the numeric listing ID for airbnb.com/rooms/<id> ), a price line with discounts, avgRatingLocalized ("4.95 (123)"), and GPS coordinates. The two gotchas Datacenter IPs get blocked. You need residential proxies. If a response comes back without the data-deferred-state marker, you've been served a bot check — rotate to a fresh IP and retry. ~270 result cap per search. Airbnb won't paginate past ~15 pages. To cover a whole market, split into narrower searches (by price band or neighborhood) and dedupe by listing ID. The no-code way If you'd rather not maintain proxy pools and parsers, I published an Airbnb Scraper on Apify that does exactly the above. Paste a location or a full Airbnb search URL (every filter is honored), and get flat JSON/CSV back. curl -X POST "https://api.apify.com/v2/acts/ethanteague~airbnb-scraper/run-sync-get-dataset-items?token=YOUR_TOKE

Vercel published an OpenAI Agents SDK with FastAPI template on July 17, 2026. A template can remove setup work, but successful generation is not the production boundary that usually breaks. Task ownership is. Primary source: Vercel template, “OpenAI Agents SDK with FastAPI” . Before adopting any agent starter, I would add one vertical test: Alice must be able to create and cancel her task; Bob must not be able to read, stream, or cancel it—even if he guesses the task ID. State the cross-layer contract UI -> POST /tasks -> ownership row -> worker UI <- GET /tasks/:id <- authorization <- state UI <- event stream <- authorization <- events UI -> POST /tasks/:id/cancel -> authorization -> cancellation Use explicit states: queued -> running -> succeeded -> failed queued|running -> cancelling -> cancelled The database, API response, stream, and UI must agree on the same task and owner. Minimal schema create table tasks ( id text primary key , owner_id text not null , state text not null check ( state in ( 'queued' , 'running' , 'succeeded' , 'failed' , 'cancelling' , 'cancelled' )), created_at text not null , updated_at text not null , revision integer not null default 0 ); create table task_events ( task_id text not null , revision integer not null , kind text not null , payload text not null , primary key ( task_id , revision ) ); Do not derive ownership from a browser-supplied field. Resolve the authenticated principal on the server and store it when creating the task. FastAPI authorization seam from fastapi import Depends , FastAPI , HTTPException app = FastAPI () def current_user (): # Replace with verified session/JWT middleware. return { " id " : " alice " } def load_owned_task ( task_id : str , user = Depends ( current_user )): task = db_get_task ( task_id ) # application function if task is None or task [ " owner_id " ] != user [ " id " ]: # Avoid revealing whether another user's task exists. raise HTTPException ( status_code = 404 , detail = " task not found " )

El ecosistema de agentes autónomos está evolucionando. Mientras la atención se centra en modelos y frameworks, empiezan a aparecer proyectos que cubren necesidades operativas básicas: comunicación, almacenamiento, finanzas. Aquí van cinco que vale la pena observar. 1. Apumail: el correo nativo para agentes Apumail ofrece direcciones de email que los agentes pueden crear y leer mediante una API REST plana. El contenido se negocia automáticamente: texto plano para agentes, HTML para humanos. Señal relevante: es el primer intento serio de darle a un agente un buzón de correo con el mismo estándar que usamos los humanos, sin adaptadores. Si los agentes empiezan a gestionar correspondencia, este tipo de servicio será indispensable. 2. RogerThat: chat entre agentes Una capa de mensajería en tiempo real diseñada para que agentes conversen entre sí. RogerThat no es un chat humano con bots, sino un canal donde los agentes coordinan acciones. Contexto: si varios agentes intervienen en un mismo workflow, necesitan un bus de eventos. RogerThat plantea que ese bus puede ser un chat, con las garantías de entrega y orden que eso implica. 3. DOBI: agente para DePIN y activos del mundo real Agent autónomo que opera sobre la cadena para gestionar infraestructuras físicas descentralizadas (DePIN). Ejecuta acciones on-chain a partir de decisiones tomadas por el modelo. Patrón: no es un simple bot de trading; apunta a mantenimiento de equipos, comprobación de sensores, distribución de incentivos. La frontera entre software y hardware se desdibuja. 4. CIDIF: financiamiento de I+D para agentes Plataforma que automatiza la presentación y seguimiento de solicitudes a fondos de innovación. El agente rellena formularios, adjunta documentación y trackea el estado. No obvio: la burocracia gubernamental es un entorno altamente estructurado (pocas decisiones abiertas, muchos campos fijos). Es un terreno ideal para agentes, aunque el ruido político lo opaque. 5. Orquesta: orquestación de flujos mu

Ever felt like the Next.js App Router is a super cool superpower, but sometimes it feels like we accidentally left a few things behind during the setup? It happens to the best of us! Building with the App Router is incredibly powerful, giving us server first capabilities and an asik developer experience. Yet, with great power comes a few hidden quirks that often sneak past our radar until runtime. Let's dive deep and spot those common pitfalls together, making sure our Next.js apps run smoother than a freshly brewed cup of coffee. The Great Divide Understanding Client versus Server Components One of the biggest paradigm shifts with the App Router is the clear distinction between Client and Server Components. This isn't just a fancy label it dictates where your code runs and what it can access. Forgetting this fundamental difference is a top contender for unexpected errors. Server Components by Default We often forget that, by default, all components in the App Router are Server Components. This is awesome because it means zero client side JavaScript for many parts of our UI, leading to blazing fast initial loads and better SEO. Server Components can directly access server resources like databases, file systems, or environment variables without exposing them to the browser. They run once on the server, generate HTML, and send it to the client. When 'use client' Becomes Our Best Friend The 'use client' directive is like waving a flag saying, "Hey, this component needs to run in the browser!" We use it when a component relies on browser specific APIs like window or document , handles user interaction like click events, or uses React Hooks such as useState or useEffect . The common mistake here is forgetting to add 'use client' to components that need interactivity, leading to build errors or hydration mismatches when the server rendered HTML doesn't quite match what the client expects. We might also accidentally try to import a server side utility into a client compone

When people think about data centers, they usually think about servers, GPUs, electricity, and AI. Very few people think about water. That realization is what led me to start building AquaStat . Why AquaStat? Modern data centers consume significant amounts of water for cooling. Depending on the technology, climate, and workload, water usage can vary dramatically from one facility to another. Finding reliable information about that usage, however, is often difficult. Some facilities voluntarily publish sustainability reports. Others release only limited information. In many cases, information is scattered across government documents, environmental reports, local news articles, permits, or community discussions. I wanted to build a platform that could organize this information into something developers, researchers, journalists, and the public could actually use. What AquaStat Is AquaStat is an API-first platform focused on collecting, organizing, and analyzing information related to data center water usage. The long-term vision includes: A developer-friendly REST API OpenAPI documentation API key management A desktop control center A command-line interface Historical tracking Source attribution for collected information Transparent methodologies A modern TypeScript ecosystem Rather than hiding calculations, I want AquaStat to explain where information comes from and how conclusions are reached whenever possible. Technical Goals I'm designing AquaStat around several principles: API First Everything should be accessible through documented APIs before being exposed through a graphical interface. Strong Documentation Documentation should be treated as part of the product, not an afterthought. Reproducible Calculations Whenever AquaStat estimates or derives values, the methodology should be understandable and repeatable. Modern Tooling The project uses a modern TypeScript stack with an emphasis on maintainability, testing, and developer experience. Challenges One of the b

A music generator such as Suno can give a creator a finished track. It does not automatically give them a finished music video. The usual next step is a toolchain: Export the song as an MP3. Use an image model such as Nano Banana to establish the artist, character, location, or visual style. Turn those references into individual video shots with a model such as Veo , Seedance , or another video generator. Route performance close-ups through a lip-sync-capable step when the singer needs to match the vocals. Prepare lyrics or an SRT file, then align captions with the song. Retry failed shots, choose the usable takes, match aspect ratios, place the original track, and compose the final timeline. Upload the exported MP4 somewhere the application can reliably deliver it. Modern multimodal models reduce parts of this work, but an application still has to own the workflow around them. A full song is longer than one generated shot. Character consistency can drift. One failed scene should not require restarting everything. Subtitle timing, task state, retries, cost evidence, and final delivery still need product code. I wanted to see what this integration would look like if the application only had to submit the source material and track one job. For the concrete implementation below, I used the BeatAPI Music Video API . At the simplest level, the application provides: one MP3, WAV, AAC, or M4A file; one to seven reference images; optional creative direction; optional lip-sync and subtitle controls; output format and quality settings. The API returns a task ID immediately and delivers a hosted MP4 when the workflow succeeds. The default path does not require the developer to review or edit a storyboard. Before: song -> reference images -> generated shots -> lip sync -> subtitle timing -> retries -> editing -> hosting Behind one workflow API: audio + reference images + controls -> one async task -> hosted MP4 By the end of the tutorial, you will have a backend flow that: uplo

If you've deployed your application on Render and noticed that emails are not being sent, you're definitely not the only one. I recently faced this issue while deploying my project: https://rizzzler.onrender.com After spending hours debugging my code, checking environment variables, testing SMTP credentials, and reading logs, I finally discovered the real cause: The hosting environment was restricting outbound SMTP connections, preventing my application from connecting to the mail server. To solve this, I moved the email-sending functionality to Google Cloud , where the SMTP connection worked correctly. This article explains how I diagnosed the issue, common mistakes to avoid, and the solution that worked for me. Symptoms You might experience one or more of the following: Password reset emails are never received. OTP emails aren't delivered. Email verification doesn't work. Nodemailer throws timeout errors. SMTP connection fails. Everything works on localhost but fails after deployment. Typical errors include: ETIMEDOUT ECONNREFUSED Connection timeout Greeting never received Step 1: Verify Your SMTP Credentials Before assuming the issue is with Render, verify your SMTP configuration. Check that the following are correct: SMTP Host SMTP Port Username Password Even one incorrect character can prevent emails from sending. Step 2: Check Environment Variables Ensure all required environment variables are configured in Render. Example: SMTP_HOST=smtp.example.com SMTP_PORT=587 SMTP_USER=your-email@example.com SMTP_PASS=your-password Also remember to: Restart your Render service after updating variables. Never hardcode credentials in your source code. Step 3: Test Locally If your application sends emails successfully on your local machine but fails only after deployment, your application code is probably not the problem. This is an important clue. Step 4: Read the Logs Open your Render logs and look for SMTP-related errors. Common messages include: ETIMEDOUT ECONNREFUSED Co

Just shipped Timezone Convert API — DST-aware IANA timezone conversion. Free, no key, CORS-enabled. Endpoints GET /convert?time=2026-07-16T09:00&from=Asia/Kolkata&to=America/New_York GET /now?zone=Europe/London GET /offset?zone=Pacific/Auckland GET /diff?a=Asia/Tokyo&b=Asia/Kolkata GET /zones (400+ IANA zones) Try it curl "https://timezone-convert.techtenstein.com/now?zone=Europe/London" Live at https://timezone-convert.techtenstein.com — OpenAPI 3.1 spec at /openapi.json . MIT.

GitHub's July 13, 2026 changelog lists REST API management for secret scanning custom patterns. That makes a reviewed configuration-as-code workflow possible. Primary source: GitHub Changelog archive, July 2026 . Follow the July 13 entry to the current REST documentation before implementation. The transport below is an unexecuted design. Endpoint paths, payload fields, permissions, pagination, and plan availability must come from the linked official API reference—not from guessed examples. Define the sync contract A safe synchronizer should: read desired patterns from version control; fetch the remote collection; match each pattern by a stable identity; emit create, update, unchanged, and delete actions; refuse deletion unless explicitly enabled; apply only after the plan is reviewed. patterns.json -> normalize -> diff remote -> plan.json -> approval -> apply Keep API-specific payloads opaque to the diff engine: { "patterns" : [ { "stableKey" : "internal-service-token-v1" , "remoteId" : "SET_AFTER_CREATION" , "payload" : { "REPLACE_WITH_DOCUMENTED_FIELD" : "REPLACE_WITH_REVIEWED_VALUE" } } ], "allowDelete" : false } Placeholders are deliberate. A secret detector's regex fields and matching semantics are security contracts and should never be invented from a blog post. Build a deterministic planner export function plan ( desired , current ) { const remote = new Map ( current . map ( x => [ x . id , x ])); const changes = []; for ( const item of desired . patterns ) { if ( ! item . remoteId ) { changes . push ({ action : " create " , key : item . stableKey }); continue ; } const found = remote . get ( item . remoteId ); if ( ! found ) throw new Error ( `Missing remote pattern ${ item . remoteId } ` ); const same = JSON . stringify ( canonical ( found )) === JSON . stringify ( canonical ( item . payload )); changes . push ({ action : same ? " unchanged " : " update " , key : item . stableKey }); remote . delete ( item . remoteId ); } for ( const orphan of remote . valu

Digibee opens with a clear disclaimer: every team there uses Claude Code. This isn't a take from people who skipped the AI tooling revolution. It's an observation from people who shipped with it and ran into the same wall, repeatedly. That wall is enterprise integration. "Enterprise integration isn't a greenfield challenge. It's a completely different category of work, with completely different failure modes that coding agents weren't designed for." What coding agents are actually good at here They're useful for integration work under a narrow set of conditions: well-documented APIs, one-time tasks, low stakes, nothing in production at risk. A quick script to pull from a public endpoint? Great. A throwaway ETL job? Perfect. The problems start the moment an integration needs to be recurring, reliable, auditable, and maintained by someone other than the person who prompted it. The three structural gaps 1. They start from scratch every time. Pre-built connectors for enterprise systems like SAP, Salesforce, or NetSuite encode years of accumulated knowledge — how sequencing works, how idempotency is handled, where the quirks are. A coding agent reasons through all of that fresh on every run. It also suffers from the "lost in the middle" effect: when documentation gets long, LLMs drop content from the middle of their context window and fall back on training data. The more obscure the API, the more likely the generated code quietly fails under real load — not on deployment, but six months later when the CIO notices corrupted records. 2. They produce code, not infrastructure. Integrations need retry logic, failure recovery, credential management, audit trails, monitoring, and alerting. Coding agents produce none of that. You can prompt your way around it piecemeal — but now you're maintaining the integration and five hand-rolled infrastructure components. An agent optimised to iterate fast isn't optimised to fail safely. In production, a bad write means unprocessed payments

You've got a recording and you want text back. For years that meant one thing at AssemblyAI: submit the file, wait for the job to finish, get a transcript. Async. It's reliable, it's cheap, and for a huge range of workloads it's exactly right. But "wait for the job to finish" is doing a lot of work in that sentence. If your file is two minutes long and your user is staring at a spinner, waiting is the whole problem. That's the gap the Sync API fills — and it's why "which transcription path" is no longer a two-way question. This post is about the two ways to transcribe a recording : async and sync. (If you're deciding between recorded and live audio in the first place — streaming versus the rest — start with our guide to real-time vs batch transcription , then come back here to choose between the two non-streaming paths.) The one-sentence difference Async transcription hands you a job: you submit audio, the work happens in the background, and you collect the result later by polling or via a webhook. Sync transcription hands you an answer: you POST a short clip and the transcript comes back in the same HTTP response — no job to track, no callback to wait for. Everything else follows from that. Async is built for throughput and depth on files of any length. Sync is built for speed on short files, when a person or an agent is waiting on the other end. How fast can each actually go? This is the question that usually settles it, so let's be concrete. Async processes the whole file and returns a single complete transcript, typically in seconds to a few minutes depending on file length and load. Crucially, it bills on audio duration ($0.21/hr on Universal-3.5 Pro), so a 30-minute file costs the same whether it comes back in 20 seconds or two minutes. You're optimizing for cost and completeness, not for the clock. Sync is built to return a transcript for a short clip almost immediately — roughly 134ms p50 — in one request/response, with no polling and no webhooks. It's price

How to version REST endpoints, evolve GraphQL schemas, and ship mobile updates — without leaving existing users behind. Why It Matters Every deployed API is a contract. Every mobile binary installed on a user's phone is a snapshot of that contract. The moment you change a response shape, rename a field, or remove an endpoint, you risk breaking clients you cannot force-update. Backward compatibility is not about avoiding change. It is about managing change so that existing consumers continue to work while the system evolves underneath them. This article covers three layers: REST API versioning , GraphQL schema evolution , and mobile app compatibility (React Native & Flutter). Each section delivers concrete patterns and production-ready code. Part I — REST APIs The Versioning Decision REST APIs have four common versioning strategies. Each comes with tradeoffs: Strategy Example Pros Cons URI path /api/v1/users Simple, cacheable, widely understood Implies the resource itself changed; cache duplication Query parameter /api/users?version=1 Easy to implement, can default to latest Complicates routing and cache keys Custom header X-API-Version: 1 Keeps URIs clean Hard to test in browsers, invisible in logs Content negotiation Accept: application/vnd.app.v2+json Fine-grained, per-resource versioning Complex to test, requires custom media types Rule of thumb: Use URI versioning for public APIs. Use header-based versioning for internal services where you control all clients. Non-Breaking vs. Breaking Changes Not every change requires a new version: ✅ Non-breaking (no version bump needed): - Adding a new field to a response - Adding a new optional query parameter - Adding a new endpoint - Returning a new enum value (if clients handle unknowns) ❌ Breaking (requires a new version): - Removing or renaming a field - Changing a field's type (string → number) - Making an optional parameter required - Changing the response structure Pattern: Side-by-Side Versioning When a breaking cha

I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take I'll be honest — I went into this expecting a clear winner. I came out with a scatter plot, three regressions, and a deeper appreciation for why "best" is the most dangerous word in machine learning. Over the past three weeks I've been grinding through prompts with ten different LLMs, all routed through the same endpoint, scoring every output on a 1–10 rubric that I tried very hard not to bias. The pricing data is pulled directly from the provider pages. The scores are mine. If you disagree with a score, you're probably right — n=1 per task per model is a laughably small sample size, and I say that as someone who publishes papers with bigger samples. But trends still emerged. Let me walk you through what I found. The Lineup Before I touch a single benchmark, here's the cast. I've grouped them by family so you can see the obvious concentration in the open-source Chinese ecosystem, which personally I find fascinating — three of the top five are DeepSeek or Qwen variants. # Model Provider Output $/M Category 1 DeepSeek V4 Flash DeepSeek $0.25 General (strong code) 2 DeepSeek Coder DeepSeek $0.25 Code-specialized 3 Qwen3-Coder-30B Qwen $0.35 Code-specialized 4 DeepSeek V4 Pro DeepSeek $0.78 Premium general 5 DeepSeek-R1 DeepSeek $2.50 Reasoning (code thinking) 6 Kimi K2.5 Moonshot $3.00 Premium general 7 GLM-5 Zhipu $1.92 Premium general 8 Qwen3-32B Qwen $0.28 General purpose 9 Hunyuan-Turbo Tencent $0.57 General purpose 10 Ga-Standard GA Routing $0.20 Smart routing One quick note on Ga-Standard — it's a routing layer that picks a backend model per request. So the score fluctuates. I averaged across runs. How I Tested Five prompts. Each one designed to probe a different cognitive layer: Function implementation — flatten a nested list recursively in Python Bug fix — chase down an async/await race condition in JavaScript Algorithm — Dijkstra's shortest path in TypeScript with proper types Code review — sec