Stripe AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Stripe AI AI

Looking for Visioners

Hi everyone, I am Abdullah, founder and Ceo Of Autoflow.We are building a solution to Hallucination problem of Ai. I was reading the history of every successful startups. Like Google, stripe, PayPal, spaceX etc. And I noticed a similarity among them, that they are have a strong team. A team who's evry member has a vision to solve a real painful problem. And second one was that they figured out the real world problems. ​ I am looking for such team members. Who have a vision to be remembered by his creation. Any one with skills in ML, orchestration, research, Marketing(sepcially), mentor, investor, partnership. Is welcomed to Autoflow. submitted by /u/MuhammadMujtaba21 [link] [comments]

Non-developer built a real web app with Claude; looking for people to try it.

I’m an architect, not a developer. Spent the last few months working with Claude (mostly Claude Code) to build a real production AI doc/slide-deck tool called Lineweight. Just went live. What it does: • One prompt → multi-page slide deck (styled, editable per-page). • Chat-edit any page — model emits structured edits, engine applies them. • Upload a CSV, Claude queries it with DuckDB tool calls and charts the data into pages. What Claude built: essentially all the code. The multi-step planner → designer architecture, the doc schema and apply-edits engine, auth, Stripe billing, usage metering, a full pre-launch security audit + multi-tenant refactor. I was product owner; set requirements, made decisions, reviewed diffs. A few things I learned. Claude dispatch is awesome! You can build from your phone, and it seems to somewhat change your exact prompt to actually help Claude be better. Sometimes it would start a new session, and I never know when to do that for best token usage. Sometimes it would add context or other things to the directions that it's actually giving Claude code that I wouldn't have known to do. I found that it was able to solve bugs and build code way better going through Dispatch than just me typing into code. I also confirmed what a lot of you have already seen: COD can be very lazy. Sometimes it would tell me that something was an issue without ever looking into my code or doing any tests, and I would have to push back on that. It also constantly suggested quicker fixes, saying that doing it right would be too long, so I recommend a quick fix. The quick fix would not actually fix it, or it would fix this specific item while not fixing the broader category. I definitely needed to prompt it to do it right, no matter how long it took. Free to try: https://lineweight.io If you do try it, I'd love feedback. submitted by /u/_Ubuntu_ [link] [comments]

Shipped a production iOS app with Claude as a non-technical PM in 2.5 months. What I learned, what worked, what broke, and the moment Claude said "trust me bro, it's fixed"

I'm a product manager with 10+ years of experience and zero coding background. I just shipped my first iOS app in 2.5 months (20-25 hours a week) using Claude as my coding partner. Posting here to share my learnings, my workflow (would love feedback!) and a hilarious hallucination. Would love to hear your funny hallucinations. When I asked Claude to estimate the total build time at the start, it quoted 8 months. I had the first complete local build running in 2 weeks and felt invincible. Then I spent the next 2 months doing the other 80% of the work, which was honestly a slog. What I learned about working with Claude on a real production codebase: Spec before you vibe I used the plaid.build skill (no affiliation, just a fan) to put together a product vision doc, roadmap, and requirements doc before I wrote a line of code. It forced me to make architecture decisions upfront, sparring with Claude, instead of discovering halfway through that my data model was wrong. This is probably the highest-leverage thing you can do. Non-technical folks, it will help you make architecture choices and write out tech specs. Technical folks, it will help you define your go to market plan and tightly scope your MVP. Two days spent with this skill including reading the docs and providing feedback saved me probably two weeks of "Claude why is this broken" debugging on the wrong foundation. I also tried asking Claudes built in skills like /architecture and /design-system but the feedback they gave me, while good, blew up my requirements and was way more than what I needed for an MVP. If I'd listened to their advice it would have taken me probably 4-5 months to launch on the app store. Do spikes Claude recommends any unfamiliar provider? Do a 1-2 hour spike to make sure AI isn't hallucinating and the provider actually meets your needs. Doing this would have saved me a very painful week. Once I gave up on the first provider Claude recommended and did spikes, I was able to choose and implement a working solution in less time that I spent arguing with the original provider. Where Claude carried me Anything well-documented and pattern-heavy: Clerk auth setup, basic CRUD, scaffolding screens, file structure conventions, copy generation. Ask Claude for it's experience and confidence level with each piece. I set up Clerk in 3 hours feeling like a genius. I got a usable settings page in 15 minutes. This is the part of the workflow that genuinely feels like magic, and it's also the part you should expect to work. Where Claude broke down Front-end fiddling. I spent 3 hours debugging a single X close button before giving up with "good enough." My designer friends will cry when they see it it's honestly bad. Claude can scaffold a UI but precision pixel-level interaction work is where it ran out of road for me. Front end development is generally painful and AI still hasn't cracked it. Anything involving a third-party provider where you have to do a lot of configuration in their portal. I spent a full week getting RevenueCat integrated correctly, and apparently RevenueCat is one of the simpler payment integrations. I now understand every developer who has ever complained about Stripe. Maybe an AI browser where it can see your browser and do things for you would have helped, but I don't trust any AI enough yet for this. Real-time video with Picture in Picture support. Claude's first-pick video provider couldn't actually do PiP properly, despite Claude being highly confident it could. I spent several days trying to make it work before reverting to traditional dev practice: 1-2 hour spikes on the next 3 contenders, picked a winner based on actual results, implemented working PiP faster than my original failed attempt. Lesson learned: when Claude is stuck in a loop trying to make X work, swap X out and try alternatives rather than pushing through. Or better yet, do spikes first before locking in your architecture choices. The "trust me bro, it's fixed" moment After multiple failed attempts on a single stubborn bug - HOURS - I was frustrated, Claude was frustrated. After 2 hours Claude basically started saying "no need to test this again, trust me bro its fixed" lol!. For my next app, I'm spending time early on to set up some automated visual regression testing so Claude can't hallucinate as much. Code review process After code was ready, I would do manual testing and ask Claude to fix bugs. Then I would: Run ALL THREE of these built-in skills sequentially against the uncommitted changes. Do not skip any — each one catches different issues: 1. \/security-review\ — Identify security vulnerabilities in the new code. Fix any issues found.`` 2. \/simplify\ — Check for unnecessary complexity, duplication, or over-engineering. Fix any issues found.`` 3. \/review\ — General code review for quality, correctness, and best practices. Fix any issues found.`` Then commit push pr When I was planning out my PR review process, Claude told

I built a search engine for every SKILL.md on GitHub

Couple weeks ago I asked Claude Code to integrate Stripe payments into a side project. It gave me a stripe.charges.create() call (deprecated for two years), no idempotency key, and a retry loop that would double-bill on a 5xx. Standard 2024-training-data Stripe code. But there are good Stripe SKILL.md files out there. wshobson/stripe-integration has 36K stars, written by someone who actually ships Stripe in production. Supabase, Vercel, postgres, OpenAI tool use, basically every API your agent would touch: someone has already written down how to do it correctly. None of those skills ever load by default, because nothing tells the agent they exist. So I scraped all of them. Skillhound (skillhound.ai) is a live index of every public SKILL.md on GitHub. About 135K of them, refreshed every 48h. Web UI is free and doesn't need a signup. The part I actually use is the MCP server: hook it up to Claude Code and before any non-trivial task it searches Skillhound, loads the highest-starred skills from recognized orgs, and uses them as the playbook. An actual concrete example: ask your agent to "build a 30-second launch video in Remotion." Without Skillhound: hardcoded frame counts, drifted audio, broken transitions. With it the agent loads kortix-ai/remotion and sundial-org/remotion-best-practices-2, then ships a driven by useVideoConfig(), transitions interpolated against useCurrentFrame(), audio anchored to a frame cue, and delayRender()/continueRender() wrapped around the asset preload. Rendered first try, frame-accurate, and using the same prompt and model. (Same shape for Stripe checkout, Supabase auth, postgres schema design, OpenAI tool use, frontend design, etc...) (One caveat: sometimes you still have to nudge it the first time ("use skillhound first"). I shipped MCP v0.2.3 yesterday with proactive instructions in the system prompt and it helps, but it's not solved. If anyone has good ideas on how to make agents reach for an MCP tool by default, I'd take them.) submitted by /u/Molil [link] [comments]

I ship AI agents in production. The mess is MCP.

Been building agents for clients across logistics, fintech, and a few indie SaaS shops for about a year and a half. Most of what gets written about AI agents online doesn't match the day-to-day. The day-to-day is mess. One specific kind of mess: MCP servers in production. Three months ago a client asked me to wire Claude Code into their internal workflow. Sales ops team, 8 people. They'd already installed five MCP servers themselves off YouTube tutorials, Stripe, Salesforce, Slack, Google Drive, internal Postgres. Plus a custom one their previous contractor wrote. Six servers, ~180 tools. Day one I sat down to use the setup myself. Context bar was orange before I'd typed a single thing. Tool selection was actively wrong. Asked Claude to "find the most recent invoice for Acme" and it called slack_search_messages instead of stripe_invoices_list. Why? The Slack MCP's search tool description was twice as long and had the word "find" in it three times. That's MCP in production. Things nobody warned this client about: Tool descriptions are your prompt now Every tool description from every MCP server lands in the system prompt every turn. One Salesforce custom-object tool had a 1,200-token description, bigger than my entire actual system prompt. Half of it was marketing copy from the MCP author.. Order matters more than it should Models bias toward tools listed first. The Postgres MCP was listed last because they'd added it most recently. So when there was an obvious DB query, the model kept reaching for Salesforce instead because it was at the top… OAuth is a nightmare Two of the six servers were HTTP/SSE with OAuth. The previous contractor set them up on his laptop. Tokens lived in his home directory, he'd left the company three months earlier. Nobody could re-authorize anything because nobody had ever run the auth flow themselves Context cost compounds silently This client was on Sonnet, ~400 model calls a day across the team. Cold-start tokens from MCP definitions were ~42k per turn. Cache helps when prefixes match but they were rotating MCP usage all day, so cache hit rate sat around 30%. Bill was ~$1,400/month before doing any actual model work. They thought it was just the model being expensive. What we did: Stripped every MCP tool description down to one sentence. Saved ~12k tokens per turn just from that Moved 3 of the 6 MCPs from -scope user to -scope project so they only loaded when actually needed Put a gateway in front of the always-on ones so Claude sees search_tools / invoke_tool / auth instead of every tool directly. Used Ratel for this (github.com/ratel-ai/ratel, open source, in-process). Tool selection accuracy went from ~70% to ~95% on a sample of their actual queries The "AI" part is easy. The "you've stuffed every MCP server you've found into one Claude config and now your model is picking the wrong tools and your bill is $1,400/month" part is the actual job. If you're shipping agents that touch MCP in production: Audit tool descriptions before you add a server Use -scope project for anything that isn't truly cross-cutting Assume tool selection will fail past 50 tools and plan for it Centralize OAuth before a contractor leaves with the only working tokens Is anyone else shipping this stuff and running into the same things, or is this just my client pool? submitted by /u/AbjectBug5885 [link] [comments]

Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible. There was this idea that employees would engage in what we call "token maxing," where you use as many tokens as possible from your AI API. Companies like Meta and Amazon have even created internal leaderboards tracking how many AI tokens employees are using. The people using the most tokens are seen as the most AI-forward, the most AI-deployed — the ones who are going to get recognized, maybe even promoted. And this has resulted in extraordinary costs on the AI front. Now we're starting to see the next phase of this, Scott, where companies and their executives are beginning to realize: this is a little expensive. So the question becomes — at what point will AI actually pay off? I'll pose that question to you: at what point is it too much? SCOTT: I think we're already seeing hints of it, and I think it comes down to incentives. You were talking about how companies are trying to incentivize people to use AI more — and that's kind of an interesting part of the ecosystem right now. The adoption layer is trying to get people to use it, and companies have put in place the incentives to do that. But there was a recent survey by a professor at MIT who found that about 5% of the projects people are using tokens for can actually be connected by CFOs to some sort of return. So while I think they're really intoxicated by it — and talking about AI as much as you can in your earnings call is like adding "dot-com" back in the '90s — I think you're already starting to see some fatigue. And I think the AI companies are trying to get public as quickly as possible to raise that cheap capital before things start to — I don't want to say unwind, but... You can see how the string gets pulled here. A large company, a CEO who has a lot of credibility in the industry, just comes out and says: "We're dramatically scaling back our AI investment. Let's be honest, folks — we're just not seeing the return we'd initially hoped." And then Nvidia reports its first miss. Nvidia has beaten its estimates 15 quarters in a row. Nvidia's first miss probably takes the entire market down five or ten percent. You are seeing some productivity gains from this and quite frankly, they look as dramatic, if not more dramatic, than the internet. But look what happened in 2000. This definitely does feel like '99. And I'm waiting for the first CEO to come out and say we have to get procurement involved and dramatically scale back our expenses. I don't think it's that romantic, honestly. I think it's just going to be a traditional Fortune 500 company that starts the narrative: okay, this has been fun, but we have to dramatically decrease our AI investment because we're not seeing the ROI we'd anticipated. ED: Yeah. I mean, we heard a quote this week from the CEO of Match Group — not a huge company — but he said AI is costing them $5 to $10 million a year, and his exact words were: "I think we're benefiting from it, but it's hard to feel." So that's not great if we're supposed

I Renovated My Apartment With AI. Here's What Came Out of It

Spoiler: not a single visible cable, not a single piece of furniture moved twice. When I started, I had an apartment and dimensions from the building blueprint. No designer. No clear idea where to go. But there was a desire to make something that would turn a standard apartment in a high-rise into a place of power — a place comfortable to live and work in. Instead of a designer, I took Claude. How it all began The first conversation wasn't about furniture or wallpaper. It was about direction. I didn't know what I wanted. I knew what I didn't want — kitsch, heavy classics, excessive decoration. We worked through options together. Scandinavian minimalism. Japanese wabi-sabi. Loft. Modern classic. The AI broke down each style by character, materials, color logic. Not "this would suit you," but "here's what this means, here's what this requires, here's what you'll get." In the end I arrived at Scandinavian for the bedroom. Warm, light, calm, with one deliberate accent behind the headboard. The living room–kitchen — loft with a red thread running through the whole space, because the furniture there was already concrete-grey with red niches and replacing it wasn't on the table. The hallway and corridor — neutral grey, as a transition between two characters. Three zones, three moods, one logic. The bedroom This was the most detailed conversation. A room with one window, one door, three free walls. Together we came up with: an accent wall behind the headboard with golden geometric lines, the other three walls in cream from the same collection. Tone on tone, different saturation, same texture. The seam between walls reads not as a boundary but as gradation. White matte furniture with black hardware. A wardrobe with a top cabinet almost to the ceiling. Mirrored doors reflect the accent wall — the golden lines are present even where they physically aren't. Then came the centimeters. The AI calculated. Adding up wardrobe depth, gaps, bed width, nightstands, dresser. Checking that everything fits. Whether the wardrobe door opens without hitting the nightstand. It even accounted for the arc of opening — that's a whole separate half-page story with mathematical formulas. By the end I had not "approximate distances" but specific points. Where to mount the light. Where to place the bed. Where to cut a network outlet into the baseboard. At what height to mount the TV unit so that watching half-lying down would be comfortable — that was calculated too, through mattress height plus pillows plus eye position. The living room Different approach. Here there was already furniture that wasn't being replaced: concrete-grey, red niches, black desk, grey sofa. The task — give the space one wall that would tie it all together. We decided: accent wallpaper behind the sofa, on the longest wall. Red-black-grey circles. Red from the furniture niches, black from the desk, grey from the concrete furniture — the wallpaper literally collects the room's palette into one pattern. By the way, an unexpected moment happened with this wallpaper: it turned out to have glitter, which only added character to the room — it plays so beautifully at sunset. The fridge against the same wall is white. It was bought six months ago, and buying a new one wasn't an option. The solution — a vinyl sticker. In red-black geometry. The fridge stops being a white blot and becomes part of the wall. Between the sofa and the kitchen zone — a floor lamp with shelves in a black metal frame. And on the top shelf, an object with character — a replica of an iconic artifact from a favorite horror film. Yes, the Lament Configuration from Hellraiser. A personal thing with a story. Why not? The hallway and corridor Grey wallpaper with a vertical tone-on-tone stripe along the entire perimeter. Grey — a neutral buffer between the red-black living room and the cream bedroom. The entryway unit in oak and graphite. Warm wood against cold grey gives the temperature contrast needed. The vestibule is small, the unit doesn't take up the whole wall — the remaining meter of free wall is for a shoe bench, above which there will be either a mirror or some poster. By the way, ideas for posters Claude also suggested — both within the renovation discussion and in other conversations connected to my work and hobbies. The through-line Between all three spaces there are recurring elements: Black hardware — bedroom wardrobe handles, black curtain rod, black floor lamp frame in the living room, black handles on the entryway unit. Geometry — lines on the bedroom accent wall, circles on the living room accent wall, verticals on the hallway wallpaper. Warm base — cream tones in the bedroom, warm wood in the entryway. These aren't accidental coincidences. This is the logic we built in dialogue. What the contractors got The most valuable thing about all this work — I handed the contractor not "well, roughly in the middle" but coordinates accurate to the centimeter. Where to m

Is this tagline intentional?

submitted by /u/JoshMJohns [link] [comments]

AI coding agents are creating a secret leakage crisis and nobody's talking about it seriously yet

This isn't a doomer post. It's a pattern I've been watching closely and people does as well and I think it's worth an honest discussion. The old model of secret leakage was human error. Developer moves fast, forgets to add .gitignore, commits a .env file, moves on. Happens, but it's recoverable, it's traceable, and most teams with basic hygiene catch it. The new model is different. AI coding agents Cursor, Copilot, Devin, Claude in agentic mode, pick your flavor write, commit, and push code at a speed no human review process was designed to handle. They don't have security intuition. They have pattern completion. And the patterns they've learned from are full of examples where credentials live in config files, environment strings get hardcoded "temporarily," and API keys appear inline because that's what the training data showed works. Here's what's actually changing: **Volume.** A developer using an agent ships 3 to 5x more code per day than without one. That's 3 to 5x more surface area for mistakes per developer per day. **Review gaps.** Nobody carefully reviews AI generated code the way they review handwritten code. The psychological contract is different "the AI wrote it" creates a diffusion of responsibility that security doesn't survive. **Commit frequency.** Agents that push directly (and more teams are allowing this) bypass the natural pause where a human might notice something before it hits the remote. **Context blindness.** An agent given a task like "integrate Stripe payments" will do exactly that including pulling in the live key from wherever it can find it, because that's what completes the task. I've been building a tool that scans for exactly this class of problem and the number of exposed credentials I'm seeing in repos created in the last 6 - 12 months versus repos from 3+ years ago is not subtle. The slope is steep. The solutions people reach for pre commit hooks, secret scanning in CI were designed for human paced development. They're not keeping up. Curious if others are seeing the same patterns. What's your team doing about this, if anything? *(For context: I built* [*SecOpsium*](https://secopsium.com)*, a security validation platform that catches this class of exposure CLI is open source at* [*github.com/secopsium/secopsium-cli*](https://github.com/secopsium/secopsium-cli) *if you want to look under the hood. Not the point of this post but figured I should be transparent.)*

AI training should include real website workflows, not just internet text

I think AI training is missing something really important that people only notice once they actually start building stuff. A lot of models know how to explain concepts, but the second you open a real dashboard like Stripe, Firebase, RunPod, Apple Developer, Vercel, AWS, etc the AI suddenly starts feeling confused or generic. Half the time I’m asking “where is this setting actually located” and the AI gives me documentation summaries instead of real workflow navigation. I think future AI training needs to include actual website workflows and interfaces, not just internet text. Like knowing “click here, open this tab, this option is under this menu” because that’s how people actually use software in real life. Right now AI feels smart in theory but weirdly disconnected from the actual tools people work inside every day. submitted by /u/Raman606surrey [link] [comments]

Using DESIGN.md files as frontend context for Claude Code workflows

Been experimenting heavily with Claude Code workflows recently and realized something: The biggest issue usually isn’t model capability. It’s frontend context. AI tools are good at generating components, but they rarely understand: typography systems spacing rhythm interaction behavior responsive structure production design consistency So I built DesignMD. It analyzes live websites and generates structured DESIGN.md specs that can be fed into Claude Code as persistent frontend context. Recently shipped a CLI too: npx u/designmdcc/cli stripe.com > DESIGN.md Current workflow is usually: Generate DESIGN.md from a real production site Feed it into Claude Code Use it as design-system context for implementation Works surprisingly well for: frontend consistency landing pages UI recreation design-system exploration Still very early, but curious whether others here are experimenting with similar context-driven workflows. https://designmd.cc submitted by /u/hiehie [link] [comments]

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]

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]

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]