Vercel

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Spent 1,156,308,524 input tokens in May 🫣 Sharing what I learned

After burning through 1.15 billion tokens in past months, I've learned a thing or two about the tokens, what are they, how they are calculated and how to not overspend them. https://preview.redd.it/rurt4skju14h1.png?width=2432&format=png&auto=webp&s=b5f1d8b743bc23e14bc8854d71c8490bab73c819 Sharing some insight here below. What the hell is a token anyway? Think of tokens like LEGO pieces for language. Each piece can be a word, part of a word, punctuation, or a space. Quick examples: "OpenAI" = 1 token "OpenAI's" = 2 tokens (the apostrophe-s gets its own) "Cómo estás" = 5 tokens (non-English languages tokenize worse) https://preview.redd.it/9xzakaiwv14h1.png?width=1080&format=png&auto=webp&s=5d726a0258c36baa68ad6d130f495172a52425d9 Rule of thumb: 1 token ≈ 4 characters in English 100 tokens ≈ 75 words Use Claude tokenizer to check your prompts. One thing most people miss: JSON is a token pig. Brackets, quotes, colons, and commas each consume tokens — a compact JSON object uses roughly 2x the tokens of equivalent plain text. If you're sending structured data as context, plain text or markdown tables are significantly cheaper. How to not overspend — the full list 1. Choose the right model (yes, still obvious, still ignored) Current Claude pricing (per million tokens): Haiku 4.5 at $1/$5, Sonnet 4.6 at $3/$15, Opus 4.6 at $5/$25. Batch processing is 50% cheaper across all models (you might need to wait up to 24h to get results, usually they come back in 2-3h). https://platform.claude.com/docs/en/build-with-claude/batch-processing For comparison, if you're on OpenAI, the spread between mini and o1 is even more extreme. Most tasks don't need your flagship model. Audit your model usage frequently, models that were too weak 6 months ago might now be good enough.... If you want a single interface across OpenAI, Claude, DeepSeek, and Gemini, OpenRouter is worth it imo. 2. Prompt caching For Claude, prompt caching cuts cached input cost by 90%. Still the single highest-ROI optimization if you have long system prompts. The rule is still: put dynamic content at the end of your prompt. But here's what changed: Anthropic quietly changed the prompt cache TTL from 60 minutes down to 5 minutes in early 2026. For many production workloads, this single change increased effective costs by 30–60%. If you haven't audited your cache hit rates recently, do it now here: https://platform.claude.com/usage/cache https://preview.redd.it/ongee5v3w14h1.png?width=1080&format=png&auto=webp&s=fefe5d0093be0a26894fe0ddd9d92e1283b02572 3. Minimize output tokens!! Output tokens are 5x the price of input tokens. Instead of asking for full text responses, have the model return just IDs, categories, or position numbers... and do the mapping in your code. This cut our output costs ~60%. 4. Be careful with new model versions Opus 4.7 ships with a new tokenizer that can generate up to 35% more tokens for the same input text compared to Opus 4.6. 5. Set up billing alerts I cannot stress this enough. Set a hard budget cap and tiered alerts (50%, 80%, 100%). One runaway loop once cost me more than a week of normal spend in a single night. Hopefully this helps! Tilen, founder of AI agent that automates SEO/GEO (we consume a lot of tokens) 😄 submitted by /u/tiln7 [link] [comments]

Motivational quotes from Claude (no particular order)

You've built a functional prototype with good UX instincts, but it's not ready for real users. Likelihood of Success: 3/10. This alone could kill your app within days of launch. The market you chose is especially punishing. Likes and visits from India are pure vanity metrics that won't convert, ever, and they're actively distorting your funnel data. You may be conflating two different things. The 'expense of feelings' framing might be doing too much work. [Your idea] is an unbounded build with an unproven-core problem and a market problem and an eventual hardware problem. Vercel runs your code in three modes, and none of them fit. This is the kind of project that sounds buildable on paper and then eats two years of weekends. Crime doesn't buy you the physics. It just buys you a felony and a still-laggy system. Distribution is a deployment detail, not a path to agency. I don't want to be [user's profession] and 'coding is alright' aren't really a product brief—they're closer to a career question wearing a product costume. The hardware-plus-AI-assistant space is particularly littered with smart people who loved their own product. submitted by /u/noplace1ikegone [link] [comments]

Just passed the new Claude Certified Architect - Foundations (CCA-F) exam with a 985/1000!

The original post was removed by Reddit Filters, so I made new one with same content. I just got my results back today and managed to snag the Early Adopter badge as well. Following up on my recent DP-600 certification, I really wanted to validate my architecture skills specifically on the Anthropic side. The exam covers a lot of practical ground on prompt engineering for tool use, managing context windows efficiently, and handling Human-in-the-Loop workflows. Link to join: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request Training courses: https://anthropic.skilljar.com/ Cookbook: https://github.com/anthropics/anthropic-cookbook I've created my own Playbook and Mock Exam after the exam: https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view?usp=sharing https://claude-certified-architect-mock-exam-cyberskill.vercel.app If anyone is preparing for this right now and has questions about the format or the types of architectural patterns tested, ask away! Happy to share some insights on what to study. Updated 26th May 2026: I noticed some mates treated me bananas (https://buymeacoffee.com/zintaen), didn't expect that, but you made my day. I'll use that fund to take more CERTs and create a site for mock tests (always free, of course). Thanks again. submitted by /u/zintaen [link] [comments]

Anyone ever getting “anthropic_api_key environment variable not set” on Vercel?

Hi everyone, I’m currently building a website audit tool using the Claude API for generating reports, and I’ve been stuck with the same error for hours. Every time I submit the form to generate the report, it shows: “Please check your Anthropic API key and try again.” It also keeps saying: “anthropic_api_key environment variable not set on the server” The weird part is I already added the environment variable in Vercel and double checked the API key multiple times. For context: I’m deploying with Vercel and my Frontend works fine. The error only happens when generating the report through the Claude API I don’t have much coding experience, still learning while building this project. Has anyone experienced this before? Is this usually a Vercel environment variable issue, serverless function issue, or something else? Any help would be massively appreciated. Thank you! submitted by /u/Icy_Sentence_1791 [link] [comments]

Small victory using Cloudflare for simple hosting of generated HTML/mini-websites

Something many people are running into: You, or a teammate, have created some kind of mini-website app out of Claude and now want to share it with the rest of the company, without overbaking the hosting solution (e.g. not setting up new Azure app services or containers, etc). Maybe you also need some basic data storage for persistence. And how do you do all of that securely? We recently went down this rabbit hole, while looking at all the major players: Vercel/V0, Lovable, Netlify, Coolify, Dokploy, Github Pages.. and even considered baking together our own hosting app solution using Azure or AWS as the backend. Our target audience is non-technical users in the team, so I was looking for something with drag-n-drop style deployment (no git required), and I really wanted to have SSO for protecting application access, along with some type of DB storage. The main issue I ran into was SSO authentication support being gated behind enterprise-level pricing plans for hosting systems like Netlify (which I'd otherwise highly recommend for a small public project). Netlify's enterprise level quickly gets quite a bit more expensive than their base tiers. I also didn't want to purchase yet another AI platform (e.g. Lovable, where really they're pushing an end-to-end AI development platform where you buy token credits through them). I wanted to host things we're already creating in our own Claude environment. Finally, I ended up on Cloudflare, which I've otherwise not really used before professionally. It's not as non-technical-friendly as Netlify, but it's pretty close. You can deploy Cloudflare Pages content via drag-n-drop. It has button-click databases available for integration, and most critically for us, the SSO integration is completely free for under 50 users. Their free hosting tier is also extremely generous and basically unlimited for completely static apps. Noting that SSO goes up to $7 USD/user/month for over 50 users, so your org size can really make a difference. If you have 500 users and the same use case for "hosting little mini apps", I'd go back to Netlify or another offering where SSO is more of a fixed fee. The other big win was that Cloudflare has a solid MCP server that works perfectly with Claude Cowork. We integrated that in and then wrote up some skills to assist with app building and deployment, including prompts for if a database backend is needed (using Cloudflare D1) and whether the app should be public or internal only with SSO protection. All working perfectly with minimal technical experience required for the enduser. I'm not at all associated with Cloudflare, just thought I'd share how we got a win for this use case. I'd be interested to hear if anyone else solved the same problem in a different way. submitted by /u/flck [link] [comments]

Need expert advice to a non-coder!

My vibe-coding journey started about 8 months ago with Replit. Before that, I wasn't a developer, but I did have experience building websites with WordPress and Elementor. I was also comfortable working with third-party integrations, CRMs, and customizing/deploying code purchased from platforms like CodeCanyon and ThemeForest for clients. In many ways, I'm a non-coder who understands project management, business workflows, and systems. Using Replit, I spent roughly $3,000 building a CRM for a service-based company. It worked surprisingly well in the beginning, but as the codebase grew, I started running into the classic "last 10% takes 90% of the effort" problem. Replit began struggling with the larger codebase, introducing regressions and silently breaking existing functionality while fixing something else. Despite the challenges, I was able to build a fully functional CRM in about three months. That experience got me excited about what was possible, which led me to discover Claude Code. Over time, my workflow evolved into: Claude Code → GitHub → Vercel For the past four months, I've been building a much larger software product. The roadmap spans roughly two years, but development and rollout are planned in phases, so it's not a two-year wait before launch. The results have been remarkable. It's honestly mind-blowing what someone without a traditional software engineering background can build today. Current stack: Next.js (Monorepo/Turborepo) Supabase + MCP Claude Code GitHub + mcp Vercel +mcp Context7 Playwright for testing What I'd love to learn from experienced engineers and builders is: How do you keep a rapidly growing codebase maintainable? What practices help prevent technical debt from accumulating? What tools, workflows, or guardrails should I implement early? What are the biggest mistakes AI-assisted builders make as projects scale? How would you structure engineering processes if you were starting today? Any advice, resources, or lessons learned would be greatly appreciated. submitted by /u/Enough-Ad-2198 [link] [comments]

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]

shipped a skill audit tool 6 weeks ago. just realised it was blind to half my skills

shipped a small thing 6 weeks ago to audit my claude code skills. ~/.claude/skills/ was getting messy, wanted to see what's actually there. just realised it had a blind spot the whole time. it was only scanning ~/.claude/skills/ and ignoring ~/.claude/plugins/. every skill installed via /plugin install was invisible to it. on my machine that's marketing-skills (40 skills), figma, vercel, interface-design, impeccable. most of what i actually have loaded. shipped v1.3 yesterday. scan count went from 35 to 157 on the same machine. and the duplicate detector finally catches the obvious case it couldn't before: an old user-scope marketing-seo-audit alongside the same skill living inside the marketing-skills plugin. 98% jaccard match, both load into context, both fire on similar prompts. free, bash + python3, no deps. /plugin marketplace add khendzel/skills-janitor /plugin install skills-janitor https://github.com/khendzel/skills-janitor would be curious how many skills others actually have once you count plugins. submitted by /u/Silent_Waldek [link] [comments]

Making games with kids is fun!

Start Screen Just sharing what came out of a weekend. My son (10) and his cousin (10) love games and asked me if they can "make a game", since they've seen me in the past couple of months, vibecoding different little helpers. I of course explained that it's not a one-shot solution and that they first should sit down, plan the game out and write down what they like the game to be, to look like, to feel, etc. So they did: the assets, the obstacles, the design style, what world it plays in, how it should look. A 3-lane endless runner in a neon city. Dodge robots, jump lasers, grab coins. Subway Surfers with their own spin. I handled the prompting in Claude Code. The design calls came from them. They tested, found bugs, kept asking for stuff and fleshed out the world more and more. Billboards along the road. Flying cars between the buildings. A slow-mo death sequence and much more. What got me wasn't the code part, it was watching them stay locked in for hours on something they had actually invented. Though my son thought at first that they could easily sit down with me for 6 hours straight without getting bored, after three hours he admitted that it can sometimes be a bore. So, since the game already was playable, I gave them one desktop computer to test on and one mobile phone, and then they switched into active beta testers. Giving me input constantly on how what feels and should work. Play it if you want: https://megarun.app .. and don't forget to put your name into the leaderboard! Works on desktop and mobile. Stack/Workflow: Three.js (single HTML file, no build step, importmap via CDN) Vanilla JS, ~4000 lines Vercel serverless functions for the API (leaderboard + play counter) Upstash Redis as storage (sorted set for leaderboard) Rate limiting + CORS + profanity filter in the backend Higgsfield for logo, warning splash, billboard ads Suno for background music 3D assets generated procedurally in Three.js (boxes, cylinders, a few spheres) Initial design work submitted by /u/jacksonjjacks [link] [comments]

Built an AI flat-finder in a weekend. Indian rental sites are 70% broker spam so I scraped Reddit instead.

Weekend build, ~10 hours. Demo: https://trurent-five.vercel.app/ Problem I was poking at: every major Indian rental site (NoBroker, MagicBricks, 99acres) is infested with brokers even when you filter "direct owner." Reddit actually has honest listings posted by owners themselves but the posts are completely unsearchable. So I built TruRent. You chat with it, it parses the query into a structured search, runs it, the map updates live, and follow-ups carry context. Ask "compare the top two" and the model reasons over the actual listings instead of just filtering. Stack and the boring decisions: Next.js 16 with raw fetch to Anthropic. No SDK, I wanted full control of the streaming loop Claude Haiku 4.5, not Sonnet. The task doesn't need Sonnet and Haiku is 5x cheaper Two tools only (search, get_details). Comparison and ranking happen in the model's prose, not as separate tools. More tools = more failure modes NDJSON to the browser, way easier than parsing SSE Scrape pipeline: PullPush API to pull Reddit posts, then Haiku again to extract structured listings from raw post text, Nominatim for geocoding Honest numbers: 1,412 posts scraped, ~600 passed a local pre-filter, only 131 ended up being real listings. Dataset is tiny but the pipeline is source-agnostic, swap the fetcher and the rest doesn't change. Most curious about: anyone else built agents where they deliberately used fewer tools and let the model reason over richer tool outputs instead of adding more tools? Happy to get into any of it. submitted by /u/Scary-Alternative-81 [link] [comments]

$18 to $4 on the same agent run after i stopped asking opus to rename css variables

I've been running an agent loop that refactors my static site. CSS variable renames, YAML config updates, running a linter through MCP. Really glamorous stuff for a blog that gets 40 visitors a month, most of whom are me refreshing to check if Vercel actually deployed. Every single step was going to Opus 4.7 because setting up routing felt like work and I am, apparently, the kind of person who'd rather burn $18 than spend 20 minutes writing an if statement. So I finally wrote the if statement. Hard subtasks still go to Opus: component architecture, debugging code I wrote at 2am and have zero memory of writing, anything where the model needs to hold a complex plan across a long conversation. Opus is genuinely unmatched at that kind of sustained reasoning. I tried routing a tricky auth middleware bug to a cheaper model once and got back something that looked perfectly plausible but silently broke session handling in a way that cost me an hour to trace. Lesson learned permanently. The routine stuff (lint, rename, config edits, tool orchestration) goes to cheap models. I landed on DeepSeek V4 Pro for general coding chores and Tencent Hunyuan Hy3 preview for anything with heavy tool calling. As of late April it was ranked number one on OpenRouter by tool call volume, and in my MCP loops it almost never botches a function call when the schema is clean. The listed rate on Tencent Cloud is around $0.18 per million input tokens and $0.59 per million output, so roughly 28x cheaper than Opus 4.7 on input. Same 212 step refactor, now with routing: 178 steps to the cheap tier, 34 to Opus. $18 became roughly $4. I couldn't spot a difference on the routine changes. My 40 monthly visitors certainly can't. I've since started doing stuff I used to skip entirely, like having the agent write and run tests for every CSS change or regenerating all my Open Graph images, because at a fraction of a cent per tool call there's just no reason not to. They do mess up in specific and annoying ways though. The tool calling model hallucinates parameters when my schemas get sloppy (honestly fair, the schemas were bad). DeepSeek V4 Pro occasionally writes code that's syntactically perfect but does the precise opposite of what you asked, in a way that survives a quick skim. And neither can touch Opus when you need it to reason through three layers of why your auth flow is silently eating a cookie. My routing logic boils down to one question: how expensive is a wrong answer to catch? Bad lint fix costs a 2 second git revert. Bad architecture call costs the whole afternoon. submitted by /u/After-Condition4007 [link] [comments]

How do you share Claude HTML artifacts with non-technical people?

**EDIT (4 days later):** ok this thread blew up way more than I expected — turns out a lot of us have the exact same problem. So I went and built the thing I was looking for. It's called LiveSend: you paste your Claude HTML artifact, get a permanent link, and see who actually opened it + for how long. 13s demo here 👇 it's rough and early, I'd genuinely love brutal feedback on what's broken or missing. [https://www.livesend.io/](https://www.livesend.io/) https://reddit.com/link/1thgbay/video/179hp66vbx2h1/player \------- I keep generating these awesome HTML/React artifacts with Claude (dashboards, mini-tools, visual reports) but I'm constantly stuck when it comes to actually sharing them with clients or colleagues. Current options I've tried, all annoying in some way: \- Download and share to be opened into browser → people doesn't know they have to download it \- Share Claude Url published artefact → Not really client friendly (AI is a monster) \- Copy the code → they can't open it \- Screenshot → loses interactivity \- Github Pages / Vercel → too technical for most people \- [Tiiny.host](http://Tiiny.host) → works but feels like a generic file host What's frustrating: if I need to fix a typo or tweak a number, I have to re-prompt Claude (which sometimes breaks other things) or edit code manually and re-upload. How are you handling this? Am I missing an obvious solution?

Self-hosted sandboxes and MCP tunnels for Claude Managed Agents are now in public beta.

Self-hosted sandboxes lets you run agents in any environment you control: your own infrastructure, or managed providers like Cloudflare, Daytona, Modal, or Vercel. MCP tunnels connect your agents to MCP servers deployed in your private network without exposing them to the public internet. Available today on the Claude Platform. Read more: [https://claude.com/blog/claude-managed-agents-updates](https://claude.com/blog/claude-managed-agents-updates)

Glia – Local-first shared memory layer (SQLite-vec + FTS5 + Offline Knowledge Graph)

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Code, Cursor, Windsurf) using a unified local database. I wanted something lightweight that did not require pulling heavy Docker containers or subscribing to third-party memory APIs. I settled on a Node.js + SQLite architecture running sqlite-vec (for 768-dim float32 embeddings) alongside SQLite FTS5 for hybrid search, powered completely by local Ollama instances. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://glia-ai.vercel.app/ Codebase: https://github.com/Eshaan-Nair/Glia-AI Technical Stack & Features: Hybrid Search Retrieval: SQLite-vec (using nomic-embed-text locally) + FTS5 keyword prefix matching (porter stemmer). Surgical Sentence-level Trimming: Chunks are sliced into sentences. When a prompt is intercepted, only the exact matching sentences are pulled out of the vector store instead of the whole paragraph. It cuts LLM prompt bloat by ~90-95% in my benchmarks. Knowledge Graph Extraction: An offline task queue uses a local LLM (llama3.1:8b via Ollama) to extract entity triples (subject-relation-object). These are stored in a SQLite facts table (or Neo4j if you run the full Docker compose profile) and fused with the vector retrieval score. HyDE (Hypothetical Document Embeddings): Queries are pre-processed to generate a hypothetical answer, which is embedded together with the original query to bridge semantic gaps. Concurrency: Running SQLite in WAL (Write-Ahead Logging) mode allows the browser extension dashboard and active MCP sessions to read/write concurrently without locking. PII Redaction: Aggressive scrubbing of JWTs, API keys, emails, and IPs in the extension before data is saved. The extension works on Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. The MCP server runs out of the same backend database for your terminal agent or Cursor. You can set it up with a single command: npx glia-ai-setup Glia is completely open-source (MIT). If you like the local-first approach or want to contribute to the SQLite vector pipeline, PRs are very welcome, and a star on GitHub helps the project get discovered! I would appreciate any feedback on the SQLite hybrid search scaling, the scoring fusion algorithm (RAG pipeline details are in RAG_PIPELINE.md), or local graph extraction performance. submitted by /u/Better-Platypus-3420 [link] [comments]

Usuario Básico

Mi experiencia está siendo muy buena. No soy programador pero instale visual studio code y el plug in de Claude para probar… Al principio pedía varios prompt para realizar tareas ( crear aplicaciones para el trabajo) y en seguida se bloqueaba por falta de Tokens… todo con la cuenta de 20 €. Las últimas semanas, me di cuenta de que le pedía tareas y no paraba… la cuenta de Claude ahora dura mucho más… para un usuario como yo, más que de sobra. Hablo de pasarme toda la mañana pidiendo cambios de una aplicación de gestión de equipos y no quedarme sin tokens… la aplicación tira de Supabase y Vercel y tiene gestión de usuarios y roles, llamadas a APIS, conectores con IA… vamos que es muy básica pero completa… al principio incluso me asusté y pensé que no estaba conectado que parara de programar cuando llegas al límite… pero mirándolo en la aplicación, está desconectado… así que la conclusión es que se pueden hacer programas de una manera súper sencilla con Claude. Cualquier duda que tengáis , soy todo oídos submitted by /u/Best_Conference4490 [link] [comments]