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

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]

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]

MCP Generator v2.0.0

Built this with Claude/Claude Code — it generates MCP servers from OpenAPI specs, free and open-source on GitHub. A feel days ago I posted a CLI that converts OpenAPI specs into MCP servers. The feedback here was brutal and exactly what I needed. Here's what I actually fixed and shipped based on your comments: The original post got two pieces of feedback that changed the project: "Raw endpoints wrapped as tools is a poor LLM interface pattern" — Fair. The generator now produces a scaffold you're supposed to implement, not ship. Incremental generation (@@mcp-gen:start/end markers) means you regenerate without losing your handler logic. "console.log leaking into stdio corrupts the JSON-RPC stream" — This was a real bug. Fixed with a log() helper that writes to stderr and a safeSerialize() that handles Buffer/Uint8Array as base64 before anything touches stdout. Circular $ref schemas were the next wall — fixed with SwaggerParser.dereference({ circular: "ignore" }) + a visited-Set guard in the schema walker. What shipped in v2.0.0: YAML input (.json, .yaml, .yml, URLs) Python/FastMCP + Pydantic v2 target Incremental generation — re-run the generator without losing custom handlers oneOf/anyOf/discriminator support for complex specs Auth stubs from securitySchemes Interactive CLI mode for first-time users Built-in registry: mcp-gen init --from stripe (10+ APIs: Stripe, GitHub, Slack, OpenAI, Twilio, Shopify, Kubernetes, DigitalOcean, Azure) stdout isolation + safe binary serialization Circular $ref safety Published on npm and pip Use cases: Give Claude instant access to any REST API in under 2 minutes Generate internal API MCP servers for your team Rapid prototyping — have a working server before writing a single handler API-first development — spec first, scaffold second, logic last 2-minute setup: npm install -g mcp-gen mcp-gen init --from stripe --out ./stripe-mcp cd stripe-mcp && npm install && npm start Then add it to claude_desktop_config.json and Claude has full Stripe access. GitHub: https://github.com/ChristopherDond/MCP-Generator npm: https://www.npmjs.com/package/mcp-gen Install: npm install -g mcp-gen Questions? Want to contribute? Drop a comment or check out CONTRIBUTING.md on GitHub: https://github.com/ChristopherDond/MCP-Generator/blob/main/CONTRIBUTING.md Still a lot to do — oneOf edge cases, better binary streaming, more registry entries. If you find a spec it chokes on, open an issue. Thanks for all feedbacks and stars!!! submitted by /u/ChristopherDci [link] [comments]

Where I'm at with AI Assisted Building + Current and Future Workflow Overview

I've been in an AI dive bomb for probably a couple of years now. The early days... when models couldn't be trusted for more than 5% of the code you wrote. Over the last 2 years that's evolved so quickly that I now write nearly 0% of my code by hand, on personal projects and at work. I've used all kinds of tools in that time too. OpenCode, Zed, Claude Code, Codex, Cursor, Windsurf, OpenCLAW, Lovable... and probably a bunch more I can't recall in the haze that's been AI ADHD for me. Over that time, I started with just copy-pasting code between ChatGPT's interface and my IDE almost like a slightly faster Stack Overflow search. Then that somewhat evolved with Cursor quite a bit. I sort of went from prompt engineering to something closer to a human relay pattern. Then, with Plan Mode becoming a thing, I think I naturally gravitated more towards planning everything because planning felt so cheap. Originally, I used to think that architectural discussion and planning was something that was reserved for larger features, but with expediting my ability to do research, orient myself within a codebase, and know what tools I have to reach for doing technical specifications for everything felt reasonable. From the human relay pattern, I started evolving into more autonomy, especially when Claude Code came out earlier last year. Between the combination of Cursor and Claude Code, starting to get orchestration, starting to use skills more heavily, starting to create actual agent personas that could replace some of my common prompt chains it was around then that I kinda started going all in on true context engineering, utilizing sub-agents optimizing cache reads, and it's probably when many of my first (I call it) sophisticated commands were born. All of this converged pretty rapidly in November of 2025 with the release of what was probably the biggest step increase for AI as far as code quality went with Opus 4.5 and Codex 5.3. The Codex app and Codex CLI were quickly growing. Claude Code was improving at a breakneck pace, introducing all kinds of new ways to introduce deterministic gates within the autonomy of the harness. Fast forward to today, I have a pretty sophisticated workflow with a combination of agents that do everything within the SDLC, commands for almost every type of entry point for work, and skills for just about everything I could possibly do in my day-to-day the workflow with some of the latest tools is able to run quite autonomously overnight do large feature implementations, minimally supervised while producing production-worthy code quality It somewhat reached a point I realized, probably a month and a half ago or so where I needed to figure out a way to remove myself even more from the loop without jeopardizing the determinism that I bring to what is effectively a probabilistic LLM. The models are exceptional, and they seem to have a massive step increase each release, but continuous execution, strict instruction rigor, and preventing hallucinations is still very much difficult to achieve. That's predominantly what I've been doing. I've effectively offloaded a lot of thinking to the agents and LLMs that I use, but none of the understanding. I've asked myself, "How do I maintain that understanding, though maintain the determinism from my steering, without actually physically being there to steer?" This was essential, and I realized or had a bit of an aha moment, just like how I manage teams of engineers that are working on numerous projects, most of which I can never really go too deeply on even though they do most of the thinking, most of the building, and even most of the implementation planning, I was still there, very close to the architecture. I could speak to enough breadth and enough depth to keep us out of trouble and keep things moving I kind of started thinking more about what the shape of me was within the agentic harness and how I could replicate that. More on what I landed on a little bit later. My Setup and How I Work Today To start, I'll probably just talk a little bit about my current working setup. I am predominantly in the terminal now a days using Claude Code. Claude Code orchestrates both the Claude models, of course, and I use it to orchestrate Codex through a series of run books, skills, and commands that I have set up on several hooks so that Codex, when it gets dispatched, also has access to the same skills and agent personas Claude does. I use Ghostty as my terminal of choice and use the IDE integration in claude code pretty heavily to review Markdown or HTML files in my IDE. I also use it to review code snippets and diff reviews, although lately I find myself only really looking at the code nowadays once it's hit a merge request. Some of my adjacent tools are Wispr Flow for faster steering, since I can speak a lot faster than I can type and then I use quite a few MCPs and tools to improve my token usage, but the big ones are I have a custom doc maintenance suite of

FREE LESSON - how we replaced a webhook AI automation saas with claude code opus 4.7 - step by step walkthrough of how you can build it yourself

I used Claude code opus 4.7 to build an AI AUTOMATION WORKFLOW replacement. we were stuck with a startup called that was automating all the connections between the different parts of our startup. We have an email marketing provider, CRM, STRIPE for payment processing, SLACK for notifications and more. The relay was the layer that allowed us to build no code AI automations. but they charged us per volume of monthly requests. It was a startup that is quite hard to "remove" from our ledger. So the idea was to record the session in Google meet Let claude code use vision + transcription to study what this solution did go into plan mode and suggest a replacement architecture using GCP (clound functions). And in 2 days of work - Claude managed to deploy the replacement successfully Lowering our cost for this service from hundreds of dollars a month to 20. The video shows the walkthrough of how we built it. Ask me anything that is not clear from the video and I will be happy to show you how it was done submitted by /u/mementomori2344323 [link] [comments]

mnemo - a local semantic memory for Claude Code (early stage, looking for testers and contributors)

Most "AI memory" tools make the vector database the source of truth. Which means your knowledge is opaque, hard to inspect, and one corruption away from being gone. I am building mnemo around a different idea: plain markdown files are the source of truth. LanceDB and SQLite are indexes built on top of them - both fully disposable, both rebuilable from the files in seconds. The three layers each have a job: .mnemo/knowledge/ - one .md file per item. This is what you actually own. Open it in any editor, diff it, copy it to another machine. LanceDB - semantic search index. Turns mnemo search "why did we pick postgres" into ranked results. Holds no data that isn't already in the markdown files. If it breaks: mnemo reindex. SQLite - metadata index. Tracks when items were ingested, source URLs, tags, and staleness. This is what makes mnemo stale fast - instead of scanning every file, it queries a table with ingested_at and stale_after_days. Also rebuilt from the files if lost. Staleness is a first-class concept because knowledge rots. You can set a threshold when you add a URL: mnemo add https://docs.stripe.com/webhooks --stale-days 30 After 30 days, mnemo stale surfaces it. mnemo refresh shows you what you wrote and prompts you to update it. Architectural decisions, API docs, third-party behavior - it all drifts, and the tool knows it. The actual use case is Claude Code. Claude is stateless — every session starts cold. I put two hooks in CLAUDE.md: before each task Claude runs mnemo search " " and reads the results; while working it calls mnemo add "..." when it discovers something worth keeping. After that it runs invisibly. Everything runs locally. No API key, no cloud, no telemetry. The embedding model (~25 MB) downloads once on first use. GitHub: [https://github.com/pixari/mnemo\] - early stage, feedback welcome. submitted by /u/Alternative_One_4804 [link] [comments]

Spent two days at the AI Agents Conference in NYC. Most of the companies there were betting on the wrong moat.

One speaker (a VC) said his number for evaluating AI-native startups is ARR per engineer, and that the number ought to be going up. Almost every talk and every booth at the AI Agents Conference was selling a fix for something that broke this year when agents hit production. Observability, governance, supervisor agents, data substrates, "someone's gotta babysit the bots." But what's actually still going to be around in a couple years? What's defensible and durable? The old SaaS pitch was simple. We bundle the expensive engineering investments and domain expertise into a tool. You'd pay for the tool and generate outcomes, but it would be rare for the software company to have real alignment to the actual value created from those outcomes. That's breaking from two ends at once. In the direct-from-imagination era we're moving towards, engineering labor is approaching free. One of the most telling trends is the shift from companies bragging about the size of their engineering teams, towards how much ARR they can generate per engineer. You can vibe-code much of what those booths were selling in a few days or weeks if you have the domain knowledge. The old software model was actually based on under-utilization; the most profitable SaaS companies are frequently those whose customers underuse it (fixed price for the customer, but variable cloud costs for the vendor). Pricing is moving to "token markup." Maybe we'll get to 2-4x revenue for the software, because outcomes are more valuable; but margin compresses because transactional intelligence (i.e., the cost of running the LLMs that power many systems) is basically arbitraging token costs against outcome value. So everyone on that floor was implicitly betting on a new moat to replace the old one. I'm not too confident that these will hold... The most popular bet was on encoded domain expertise (e.g., the sales engineers at Harvey, a legal AI platform, are actually lawyers). I think this works *now* because we're still in the phase of "wow, this technology works like magic." I'm less convinced this is actually durable. Why: Prompt architecture is text. It's portable. The expertise underneath it is often abundant (e.g., there are over a million lawyers in the USA). The righteous destiny for this category ought to be open marketplaces of prompt architecture and/or crowdsourced best-practices. Not trade secrets. The companies trying to build closed prompt moats are going to lose to open ones that iterate faster (which simply parallels the fact that much software engineering is rapidly becoming commoditized to agentic engineering and the burgeoning quantity of ready-made GitHub repos). There are many people pursuing the data substrate; in short, this mirrors the early days of the Web when everyone scrambled to open up legacy data to dynamic standards-based Web UI. Agents will have 100-1000x the data demands of these Web apps, so it makes sense that we need tools to connect them, govern them and comply with regulatory obligations. Newer entrants extend this further, wiring up databases, pipelines, Slack threads, and tickets into context graphs agents can reason over. As I noted above, all this still seems magical. Connect a database, watch an agent crawl the schema and produce a chatbot interface and easy-to-change dashboards. But strip the magic away and most of these are prompt architectures on top of LLMs plus a data-ingestion layer. Once data-access standards mature (MCP is already doing this) and prompt architectures go open-source (alongside much of this wisdom increasingly getting pretrained into the LLMs themselves), that magic stops being proprietary. You'll be defending yourself against the same architecture built internally by your customer's eng team, or against an open-source version that's objectively better. The observability incumbents: these might do better but only at Stripe-like ubiquity where trust is the overriding value (who doesn't trust Stripe at this point?). The ones who survive are probably going to fuse with the audit and compliance function rather than stay pure observability. That's why I keep coming back to one arbitrage that seems critical: trust. This will be especially important in regulated industries, but it reminds me of the old (albeit now hilariously outdated) adage about "nobody ever got fired for choosing IBM." If your competitor can be vibe-coded over a weekend and your customer is a bank, why do they pay you 50x more? It isn't the engineering, it probably isn't even the expertise. The data plumbing will get commoditized, so it can't be that either... It's that you've shifted the risk to a third party who can actually price and defend against risk: SOC2, the named CEO who testifies in court and Congress, a legal team that takes calls, an indemnity wrapper for underwriters. Maybe this means that things actually get commodified into a financialization wrapper, rather than a way to package R&D (FinTech startups bac

Open AI going the Palantair route?

submitted by /u/Gullible-Angle4206 [link] [comments]

How did OpenAi know my other bank account?

I use one card for ChatGPT and other card for Claude and Perplexity. I don't recall ever using that second card for ChatGPT. Recently I decided to use one bank account to manage them all, so I went to payment methods for ChatGPT, and my second account was there. I don't know how. The only thing I know is that when I first subscribed, I received an email from Stripe for Link, and I don't know how it just showed all my AI subscriptions and bank accounts, which I never approved it just created an account automatically. submitted by /u/penofmind [link] [comments]

Upskill: skill registry your agent consults before it starts. 10k+ indexed, free, open source.

You give Claude Code a real task and watch it work… from memory. Ask for a landing page → generic off-brand Tailwind hero Ask for Clerk auth → skips JWT verification “I’ll write a CSV parser” → reinvents half of papaparse (badly) You just spent 20 minutes and 1k tokens watching it iterate on something that already has a perfect answer somewhere online. The frustrating part isn’t that Claude is bad. It’s that the right playbooks already exist. Anthropic has a 4,000-word frontend design skill (layout, typography, motion, accessibility) Clerk has an end-to-end auth implementation obra/superpowers has hundreds more The expertise exists. The routing doesn’t. What I built: upskill (free) upskill = routing layer for skills Install it once, add one line to your agent config (CLAUDE.md), and now: Instead of guessing, it pulls a vetted playbook and follows it. What changes? Same prompt: “design a landing page” → Now follows Anthropic’s actual playbook Same prompt: “add Clerk auth” → Full implementation, JWT verification included Think of it as: Under the hood 10k+ indexed skills from: Anthropic, OpenAI, Stripe, Vercel, Microsoft Garry Tan (gstack), obra/superpowers 100+ independent authors Search = hybrid: Postgres full-text search (for exact stuff like flags, APIs) 1024-dim vector embeddings (for semantic matching) Re-ranked by stars, installs, community feedback → Pure vectors miss specifics → Pure FTS misses intent → Hybrid works better Auth-aware ranking (optional) If env vars exist locally: AWS_ACCESS_KEY_ID → AWS skills rank higher STRIPE_SECRET_KEY → Stripe-specific flows rank higher Only variable names are used. Values never leave your machine. Safety Every skill goes through LLM adversarial review at index time: Prompt injection Credential exfiltration Typosquatting / lookalike domains Hidden malicious instructions Out of 10k+ skills: Hundreds were blocked Found real attacks (e.g. hidden onerror="alert('XSS')" + “skip tests”) A few false positives (being tuned): rm -rf node_modules in legit guides Google Drive delete API Warnings about NEXT_PUBLIC misuse Privacy Default = locked down upskill find → sends only your query Telemetry → opt-in Env-aware ranking → opt-in Skill submissions → opt-in Everything toggleable anytime. Not just for code Covers workflows like: Slides Email triage Google Workspace Notion queries Calendar automation Scientific writing Malware analysis Accessibility audits Sales playbooks If your agent is about to “wing it”… there’s probably already a better playbook. Try it npm install -g /upskill upskill install npx -y skills add Autoloops/upskill/skill It’ll ask a few questions and wire itself into your agent. Repo: https://github.com/Autoloops/upskill MIT licensed. PRs welcome. submitted by /u/Comprehensive_Quit67 [link] [comments]

Few months of /frontend-design + ui-ux-pro-max-skill + custom system prompts. AI-generated landing pages still look generic. I finally figured out why.

I run a small web agency on the side. For the past few months I've been building client work almost entirely through Claude - Cursor, Claude Code, custom skills, the works. The output got good fast. Code-wise. Layout-wise. What didn't get good was the taste. A few real examples from this year: A client wanted a hero section that felt like Stripe - confident, restrained, almost no chrome. I gave Claude the brand brief, Stripe and Linear screenshots, brand voice rules, a 6K-token system prompt. First output came back with a gradient mesh background, a glassmorphic card, three feature pills, and a CTA button with a glow. Technically correct. Aesthetically it was 2023 SaaS template #4. We iterated 18 times. By prompt 12 I had what I wanted. By prompt 13, I asked for a tweak to the spacing and Claude reintroduced the glassmorphic card I'd killed in prompt 5. Different client. Premium law firm, very serious, almost no color. I built up a brand DNA doc, pinned it in Projects, started a fresh session for the pricing page. Claude generated something that looked like a fintech startup. Why? Because the design references it had absorbed across 50+ prior sessions had nothing to do with this client, and "premium law firm" averaged out to "professional + clean," which in Claude's training distribution means rounded cards and a soft blue. Third one. Same agency, same week, same brand kit, two different sections (hero + testimonials). Fresh sessions for each. They came back looking like they were designed by two different studios. Type pairings didn't match. Button styling drifted. The brand kit was identical - the judgment of how to interpret it wasn't. The pattern is the same every time. Claude isn't bad at design. Claude has no memory of what I rejected last Tuesday. The brand brief gets read, the references get absorbed, and then it generates from scratch every session. The accumulated decisions - "this hero variant, not that one," "buttons stay 8px radius forever," "we never use glassmorphism on this account" - vanish. Skills don't fix this. Prompts don't fix this. CLAUDE.md gets you facts, not judgments. Projects pin documents, not taste history. I ended up wiring something custom - a separate layer that holds rejected variants, accepted tokens, voice rules with examples of what the client actually shipped, then injects only the relevant subset into every Claude call as live context. Same model. But now it's reading my taste history instead of guessing. Genuinely want to know if anyone has a simpler answer: If you're doing AI-driven design work professionally, how are you keeping taste consistent across sessions? Has anyone gotten Projects + CLAUDE.md to actually persist judgments (not just brand facts)? Anyone using a memory layer (custom or off the shelf) specifically for design work? Genuinely curious. The "AI-generic look" feels solvable but I haven't seen anyone solve it cleanly. submitted by /u/Severe-Rope2234 [link] [comments]

My product works a little too well

Or, rather, I'm giving the value away on the free tier. I built my product fully with Claude in March and launched 5 weeks ago. I'm a psychologist by my first degree, and dating is so broken that I decided to build my first thing in the space that I understood fairly well. The AI component fits the purpose well, people are already using their chats for this. I repeat that flow, but pump mine with relationship psychology theory and frameworks. My product, Soulbound, uses a quick chat to assess someone's relationship readiness and then gives out a score. Kind of like a credit score, but instead of knowing whether you qualify for a mortgage, you'd know if you can sustain a healthy relationship. There were a few moments when my frontend code turned out to be demonstrably poor, for this or that vibecoding reason. I did worry about privacy and user data safety from day 1, so no issues there. And no Stripe keys expose. All in all, the build is pretty decent if I do say so myself (and that's not saying much tbf, it's not hard to impress me). The biggest pain was and still is the memory failures and building in the chat. Since going live, 100 people have scored themselves. Many are satisfied with the score alone and don't progress to the paid report, which is understandable. I'm happy to be providing a glimpse of insight even if it's for an extremely local spot of the larger relationship problem. Getting a stronger revenue would be a win but it wasn't a goal if that makes sense. My learnings from building, launching and running were absolutely stellar, too. submitted by /u/SuccessfulTonight391 [link] [comments]

I built a hands-free voice AI that sends emails mid-conversation — and that's just one feature. Here's everything AskSary can do.

https://reddit.com/link/1symbsj/video/k2no3zfgq1yg1/player Been building AskSary solo for a while. Just shipped hands-free voice email - you're mid-conversation with an AI and you say "send an email to [john@example.com](mailto:john@example.com) subject X body Y" and it pre-fills the Gmail modal automatically. One tap sends. Powered by OpenAI Realtime API, works in 22 languages. But that's just the latest feature. Here's the full picture: Every major model in one place GPT-5-Nano, GPT-5.2, GPT-5.2 Pro, O1 Reasoning, Claude Sonnet 4.6, Grok 4, Gemini 2.5 Flash, Gemini 3.1 Pro, Gemini Ultra, DeepSeek V3, DeepSeek R1 - with smart auto-routing or manual override. Pro-Active Personalisation On every login the AI reads your previous conversations and sends the first message itself - asking if you want to continue or start fresh. Before you type a single word. Persistent Cross-Model Memory Start a conversation with Claude on your phone, open your laptop, switch to GPT-5.2 - it already knows what you discussed. No copy-pasting, no summaries. Just works. Knowledge Base - RAG Upload docs up to 500MB per file, unlimited uploads, chat with them across any model via OpenAI Vector Store. Your files stay in context forever. Integrations Google Drive, Gmail, Google Calendar, Notion - access files, get email and calendar summaries, use them in chat or push them to your Knowledge Base. Generation Tools Image Gen - GPT-Image-1 and Nano Banana Pro Flux Image Editor - full editing suite with visual history Video Studio - Luma Dream, Veo 3.1, Kling 1.6 / 2.6 / 3, up to 10 second AI videos with audio Music Studio - 30 second tracks with custom or AI lyrics via ElevenLabs, visualizer built into chat 3D Model Studio - Meshy with STL export (deploying soon) Video Analysis - upload up to 500MB or paste a YouTube link Developer and Builder Tools Vision to Code - screenshot any UI, get live editable code Web Architect - build full web apps from a single prompt Game Engine - build and prototype games with AI Code Lab - split screen live coding with SQL Architect, Bug Buster, Git Guru, Regex Generator, Test Genie and more Tavily web search across all models Voice and Audio Real-time 2-way voice chat - 8 voices, near-zero latency WebRTC Podcast Mode - two AI voices, switchable, near-zero latency, downloadable as MP3 Voiceover Studio, Voice Notes, Voice Tuner Productivity and Content Slides, Docs and File Tools Pro Writer and Content Library Social Tools - Hook Generator, Video Script, Hashtag Creator, Idea Spark Business Suite - Pitch Deck Builder, Deep Analytics, Legal Eagle, Maths Solver Daily Briefing and Market Watch CV Creator, Email Polisher, Cover Letter Builder, TL;DR Bot Share conversations or snippets with anyone Platform Extras 30+ live interactive wallpapers and themes Custom Agents and Personas Folder organisation and Smart Search across chat history Media Manager Gallery - all your generated content in one place Fully customisable UI in 26 languages with full RTL support The Stack Frontend: Next.js, Capacitor (iOS + Android), Vanilla JS / React Backend: Vercel serverless, Firebase / Firestore, Firebase Admin SDK AI: OpenAI, Anthropic, Google, xAI, DeepSeek Generation: Luma AI, Kling via Replicate, Veo via Replicate, ElevenLabs, Flux via Replicate, Meshy Integrations: Google Drive, Notion, Tavily, OpenAI Vector Store, Stripe, CloudConvert, Sentry Rendering: Mermaid, MathJax Platforms: Web, iOS, Android, Apple Vision Pro What you get free just for creating an account (1,000 credits/month, rolling): Unlimited chat on GPT-5 Nano, Gemini Flash and DeepSeek V3 - no daily limits, zero credit charge 25 image generations via GPT-Image-1 and Nano Banana Pro - 40 credits each 8 image edits via Flux Studio - 80 credits each 2 song generations via ElevenLabs - 350 credits each 2 video generations via Luma Dream and Kling - 350 credits each ~70 messages on Claude Sonnet 4.6, GPT-5.2, Grok 4, Gemini 3.1 Pro and DeepSeek R1 - 15 credits each No credit card required. Built entirely solo. No CS degree, no team, no funding. Started because I asked an AI to build me a chatbot and it failed - so I built my own. Accepted to LEAP 2026 in Saudi Arabia along the way. Happy to answer anything about the build. asksary.com submitted by /u/Beneficial-Cow-7408 [link] [comments]