Stripe

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Stripe AI

Four backend concepts for Product Managers using Claude Code

You don't need to write backend code. But if you understand how backend systems behave, your prompts get dramatically better because you're speaking the same language as the system. Async vs Sync: user clicks "generate," you call OpenAI, it takes 3-5 seconds. If that's synchronous, the entire UI freezes, Nothing responds. The fix is to make the call async. Show a loading state immediately, let the user keep interacting, update the screen when the response arrives. Tell Claude Code "handle this asynchronously" and watch the output quality jump. Race conditions: two users click "claim this spot" on the last available slot at the same second. Backend reads the database, sees one spot, confirms both. Now you have a double booking. You don't need to write the fix, but you need to spot this pattern in your specs. Anytime a user action reads a value then updates it, ask one question: what happens if two users do this at the same time? The fix is an atomic transaction read and write happen as one indivisible operation. Idempotency user submits a form, internet cuts out for half a second. Did it go through? They don't know, so they click again. Without idempotency, you now have two records. With it, the second request returns the same result without creating a duplicate. The fix is an idempotency key is unique ID generated on the frontend, sent with every request. Backend checks if it already processed that key. Stripe uses this for every payment call. Graceful degradation: your app calls OpenAI and the API is down. If you haven't planned for this, users see a blank screen or a raw error code. Every feature needs three states: happy path (everything works), loading state (we're waiting), error state (something failed). Retry up to three times. If it still fails, show a friendly message and keep the rest of the page working. Never let one dependency take down the whole experience. TLDR: Next time you're in Claude Code, try using these terms in your prompt — "handle this asynchronously," "make this endpoint idempotent," "add graceful degradation." The output gets significantly better when you speak the system's language. Post inspired from this video, you can checkout SkillAgents AI on Youtube for similar content. submitted by /u/InfamousInvestigator [link] [comments]

I built this SaaS boilerplate using Claude Code

The project includes auth, organizations, roles and permissions, Stripe billing, dashboard pages, emails, i18n, tests, logging, monitoring, CI/CD and docs. But the most interesting part was not the feature list. The interesting part was seeing how much Claude Code improved once the repo had strong conventions. Clear folders, typed APIs, tests, shared UI patterns and Agents md made a much bigger difference than trying to write the perfect prompt. Stack: Next.js 16, Better Auth, Drizzle, PostgreSQL, Stripe, Shadcn UI, TypeScript, oRPC, Vitest and Playwright. Live demo at Next.js Boilerplate demo submitted by /u/ixartz [link] [comments]

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. The math nobody talks about At ~$15/M output and ~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: 38k input × 200 turns = 7.6M tokens/day = ~$23/day = ~$700/month JUST in MCP tool definitions This is before any actual work Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates What actually breaks The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using linear_search_issues when I asked it to read a file). The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. Errors compound silently. One badly-described tool taints the ranking for every related query. What the "MCP optimizer" startups won't tell you Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. What I do now Switched to a gateway pattern. Claude sees three tools: search_tools, invoke_tool, auth. Everything else gets ranked on-demand. Cold start dropped from 38k to ~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running Ratel (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production. submitted by /u/AbjectBug5885 [link] [comments]

Anthropic just bought the company that generates most production MCP servers

Anthropic acquired Stainless on Monday for a reported $300M+. Most coverage is framing this as a developer tools acquisition. Stainless is best known for generating the official Python and Node SDKs that ship with OpenAI, Google, Meta, Cloudflare, and Anthropic. The SDK story is real. The MCP side is the part that matters here. Stainless was one of the first vendors to extend their compiler to produce MCP servers from the same OpenAPI specs that produce their SDKs. MCP hit ~97M monthly SDK downloads by December 2025 and around 10,000 production servers by early 2026. A lot of that production code was Stainless-generated. Anthropic now owns the dominant MCP server generator. What actually changed hands on Monday: The engineering team. Roughly 40-50 people including founder Alex Rattray, who previously built Stripe's patented SDK generation system. Now reporting to Katelyn Lesse in Anthropic's Platform Engineering org. The technology. The generator, the templates, the language-specific runtimes, the OpenAPI extensions Stainless invented for SDK-specific edge cases. The hosted product is winding down. New signups stopped Monday. New SDK and MCP server generations stopped Monday. Existing customers keep what they've already generated but the pipeline is closed. My read: this is closer to what Google did with Kubernetes than to a normal acquisition. Anthropic created MCP. Anthropic donated MCP to the Linux Foundation last December. Anthropic now owns the dominant implementation toolchain. The protocol is vendor-neutral on paper. The implementation toolchain isn't. Six months of Anthropic M&A starts looking less coincidental: December 2025: Bun, the JS runtime, pulled into Claude Code February 2026: Vercept, computer-use AI April 2026: Coefficient Bio, ~$400M healthcare AI May 2026: Stainless, SDK and MCP plumbing They're not buying training infrastructure or GPU clusters. They're buying the integration layers around the model. The bet seems to be that frontier models are converging faster than anyone expected, so the moat is everywhere except the model. If you're building on MCP today, tooling quality probably improves. Stainless's generator was already the cleanest in the space and the team that built it is now at Anthropic. Patterns will standardize faster as Stainless-derived templates become the de facto reference. The flip side is concentration risk. Cloudflare's MCP server framework, Pulse MCP, and the open-source generators Stainless released during the transition all become strategically important if you want any diversity in your stack. Sources: Anthropic announcement Why Anthropic actually did this, and migration math Curious whether Stainless ending up inside Anthropic reads as good news (better tooling) or concentration risk (one company owns the standard and the reference implementation) from your seat. submitted by /u/Ok-Constant6488 [link] [comments]

Built a tool that turns websites into structured design docs for AI workflows

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. Happy to Share Link -- submitted by /u/hiehie [link] [comments]

Experimenting with screenshot + DOM analysis for better UI understanding

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. submitted by /u/hiehie [link] [comments]

If you've built a frontend with Claude Code, here's how to connect it to a backend

So people build using Claude Code but hit the same wall, you build a frontend that looks great, but it's running on hardcoded data. No database, no auth, no real API calls. You can use one of these to connect to other systems: API are raw HTTP calls the most granular option. Think of it like buying individual pages from a bookstore. You make one specific request, you get one specific response. Maximum control, maximum setup work. Every integration starts here under the hood. SDK (Software Development Kit) is a pre-packaged wrapper around APIs. Instead of assembling raw HTTP calls yourself, someone gives you a library with clean functions like supabase.auth.signUp(). Way less boilerplate, way fewer mistakes. Supabase, Stripe, Firebase all ship SDKs that Claude Code can use directly. CLI: for deployment and infrastructure tasks. You're not calling these from your app at runtime you use them to push code live, create database tables, set up environments. Claude Code runs these for you. MCP is the newest option. Lets Claude Code connect directly to external services as tools. Instead of writing integration code, Claude just calls the service natively. You can checkout this video for tutorial. submitted by /u/InfamousInvestigator [link] [comments]

I built SeeFlow – architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/l14h40ly3m1h1.png?width=2508&format=png&auto=webp&s=df60b2ba6da04fadf7e1039b9472a106ed163314 SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [link] [comments]

I built SeeFlow - architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/9svmg8ih3m1h1.png?width=2508&format=png&auto=webp&s=0d06df1f82fd417ee9a45e504efd26628eaf33fd SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [link] [comments]

OpenAI API payment keeps getting declined before even reaching the bank

Hey guys, A client of mine is trying to add a payment method to the OpenAI API platform, but every card keeps getting declined. At first we thought it was her bank/card issue, but then I tested on my own OpenAI account with my own card and got the exact same result. What we tested: Her C6 Bank credit card My Nubank virtual credit card Different OpenAI accounts Different devices (PC and mobile) Different networks (Wi-Fi and mobile data) ZeroTier/VPN completely disabled Incognito mode Different browsers The weirdest part is: neither Nubank nor C6 shows any authorization request no push notification appears no 3DS/authentication popup appears it looks like the payment is being blocked before even reaching the bank At this point I’m wondering if: Stripe/OpenAI antifraud is blocking the transactions there’s some billing address validation issue there’s a temporary issue affecting Brazilian cards/accounts Has anyone else experienced this recently with OpenAI API billing? submitted by /u/Easy_Blackberry506 [link] [comments]

Adding html files to website for client proposals?

I've cancelled my CRM and want to create custom proposals for client projects. I have a Squarespace website and have deployed other HTML work via netlify, and one with valtown as well. If I build a proposal with claude, and want clients to select an option with a next step to go to a stripe invoice, for example, how can I do this? Would I have to connect each HTML proposal to netlify + valtown? Ideally its a url like https://clientname.mydomain.com or simliar. Curious what others are doing to make this happen? If I add the code to my SQSP website, the embed frame looks weird. Thanks! submitted by /u/Beginning_Plant_7931 [link] [comments]

Claude for Small Business launched this week with 8 integrations. Most SMBs use 20+. What does that mean for the rest of the stack?

Anthropic launched Claude for Small Business on Tuesday. The package includes 15 prebuilt agentic workflows and 8 named integrations: Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. The workflows handle things like invoice chasing, payroll planning, month-end close, sales campaigns, contract routing, and cash-flow forecasting. Owners approve before anything sends or pays. The basic facts are not in dispute. What's interesting is the math. Most small businesses use more than 8 tools. The common ones not on that list: Shopify, Stripe, Square, Klaviyo, Mailchimp, ActiveCampaign, ConvertKit, Pipedrive, GoHighLevel, Calendly, Notion, Airtable, ClickUp, Webflow, Zapier. Then vertical-specific tools: ServiceTitan, Jobber, Housecall Pro for trades. Kajabi, Teachable, Circle for creators. Toast, Resy, OpenTable for restaurants. Etsy, Faire, Printify for makers. Real question worth asking: how much of a typical small business stack does the 8-tool package actually cover, and which kinds of businesses are well-served versus left out? A rough walk through some common archetypes: Office-based service business (consultants, accountants, agencies, B2B services). Coverage is decent. Most are on Google Workspace or Microsoft 365, run finance through QuickBooks, communicate via Slack, and many use HubSpot. The 8 tools probably hit most of the core stack for this group. E-commerce or DTC brand. Coverage is thin. Shopify isn't there. Stripe isn't there. Klaviyo isn't there. The actual revenue stack of an online store is mostly outside the covered set. Local trades (HVAC, plumbing, insulation, electrical, landscaping). Coverage is essentially absent. The operating systems for these businesses are ServiceTitan, Jobber, Housecall Pro, Square for payments, sometimes QuickBooks for accounting on the back end. The customer-facing and operational tools are not on the list. Creators, coaches, course sellers. Coverage is absent. Kajabi, ConvertKit, Teachable, Circle, Substack. None of it is in the package. Restaurants and hospitality. Coverage is absent. Toast, Square POS, Resy, OpenTable, Toast Payroll. The actual operating systems are not on the list. A few patterns emerge from that walk. First, the package targets a specific kind of small business. Office-based, white-collar, finance running through QuickBooks, meetings on Google or Microsoft, sales through HubSpot. That is a real segment. Anthropic chose it deliberately and the workflows make sense for that profile. Second, for everyone else, the prebuilt workflows mostly don't touch the tools they actually use day to day. The choice isn't "use Claude for Small Business or not." It's "AI in my operations, yes, but via custom work outside this package." That's not a complaint about the launch. Building 8 polished integrations is hard and Anthropic had to pick. It's more an observation that "Claude for Small Business" as a category name covers a wider universe than what the package actually addresses on day one. Curious how this lines up with what people are actually running. If you operate a small business, how many of the 8 covered tools are in your stack? And what's NOT on that list that you'd most want connected to an AI agent? submitted by /u/KolioMandrata [link] [comments]

Claude skills are replacing SaaS one workflow at a time

I’ve been lurking in this sub for a while and the value dropped in random threads is massive, like the YouTube-to-SEO workflows or Stripe automations that save people 10+ hours a week but often disappear into the void. Seeing builders successfully package these as "Skill Stacks" inspired me to use Claude and Claude Code to build a dedicated home for them. I developed zelpful.com, a platform specifically for sharing and discovery of Claude skills, agents, and workflows. How Claude helped: I used Claude Code to architect the vendor logic and prototype the interface. Claude was instrumental in helping me strip out "SaaS bloat" to focus on a clean directory for structured knowledge. It helped me write the backend handling for how these "Skill Stacks" are indexed and displayed. Is it free? Yes, the platform is free to join and free to browse. To support the community here, I’ve made it free to list your agents and skills so you can try out the platform. My goal was to bridge the gap between "cool comment thread idea" and a searchable resource. If you’ve built something that solves a real problem, it might be worth putting it where people can actually find it. What is the most high-value or "over-engineered" workflow you’ve managed to package with Claude so far? submitted by /u/covidion [link] [comments]

Built a morning brief agent on Apify that pulls from Slack, Gmail, Calendar, and Notion

I kept switching between apps every morning trying to figure out what actually needed my attention. Slack, Gmail, Google Calendar, Notion, two or three accounts each. I just wanted something that would tell me what matters. So I built an actor on Apify to see if it could work. It fetches everything, sends the raw data to Claude via the Anthropic API, and posts the brief to Slack. Took a few iterations to get the prompt right but it genuinely gets the job done now. What I liked: it fits within Apify's $5 free plan limit, so zero ongoing cost to run it once a day. Two ways to use it: The simpler one: the actor fetches all data, calls the Anthropic API directly to generate the brief, and posts it to your Slack channel. Everything in one run. But you need to have Anthropic API key. The more flexible one: connect Claude to Apify via MCP or the API, schedule the actor to run every morning to prefetch and store the data, then have Claude read it and generate the brief on demand. Useful if you want to ask follow-up questions or regenerate without waiting for another full crawl. Live on the Apify Store if anyone wants to try it. Glad for any feedback. submitted by /u/AmbiguousSun [link] [comments]

Most agent-memory tools are markdown you keep grooming. I wanted something that travels between models and machines, so I built a protocol.

If you use Claude across more than one editor or machine, you've probably hit this: your context never comes with you. The CLAUDE.md doesn't follow me to Cursor, the Cursor rules don't follow me to Codex, none of it follows me from my Mac to my Linux box, and none of it survives switching models. The other "agent memory" tools out there are mostly markdown you keep grooming, or a vendor-locked store tied to one client. That never worked for me. So I built ltm. It's not a file format, it's a small JSON protocol (the Core Memory Packet) plus a CLI and a server to move packets around. End of a session the agent calls ltm save, start of the next one it calls ltm resume, and the dossier on the current obstacle comes along, whether the next session is on a different model, a different harness, or a different machine. A packet is five required fields, typically 2 to 5 KB. Goal, decisions you've locked in, what you've already tried, what the next step is. The 90% of work that went fine doesn't need a packet, the commit log already carries that. The part agents can't reconstruct from a repo is the dead ends and the constraints that shaped the current code without ever appearing in it. That's the part ltm carries. A few things I cared about: Model, harness and machine agnostic. A packet written by Claude on your Mac reads fine for Codex on your Linux box, or for a teammate picking it up on theirs. The protocol is the product, the CLI and server are reference implementations. Saves tokens by not wasting them. A 2 to 5 KB packet at the start of a session is much cheaper than letting the agent re-explore the codebase to rediscover what was already tried and rejected. Self-host or use the managed hub, same protocol either way. One Go binary, SQLite on disk, runs on a low-end VPS if you go that route. Redaction is load-bearing. Every packet gets scanned before it leaves your machine. AWS keys, GitHub tokens, JWTs, private keys, absolute paths, Slack and Stripe tokens, all blocked by default. Packets travel, secrets don't. MCP support out of the box, so Claude Code, Cursor, Zed, Codex etc, can call save and resume as tools without you ever typing an ID. Intent is portable, configuration isn't. Packets never carry your CLAUDE.md, skills, prompts or tool setup. Those are yours and they stay local. You can see what a resume looks like without signing up or running a server: ltm example --resume runs the whole flow against a sample packet and drops the resume block on your clipboard. It's at the point where I'm using it on my own setup daily. Apache 2.0. Built with LLM assistance and it says so out loud, every agent-touched commit carries an Assisted-by: trailer in Linux kernel conventions. Repo: https://github.com/dennisdevulder/ltm Curious how others are solving this. The markdown-you-groom approach was where I started and I never managed to make it travel. submitted by /u/devulders [link] [comments]