Vercel

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

Vercel AI

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?

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 → 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? submitted by /u/Hairy-Fisherman8008 [link] [comments]

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

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]

Built an MCP for claude code that turns ticket-mentions into PRs with browser QA (and what I learned along the way)

notesasm is an MCP server you add to claude code. you mention a fix mid-flow ("make a ticket on notesasm: fix the regex for quoted emails") and it files the ticket. later, on your schedule, an autonomous agent picks the ticket up, writes the fix, runs real-browser QA against your preview deploy, and opens a PR with screenshots. closed alpha, free during it. demo + signup: notesasm.com the pain it solves (3 separate ones, actually): claude code is fast enough now that shipping isn't the bottleneck anymore. when you're deep in a feature and notice "the regex misses RFC-quoted local parts" or "the footer copy is wrong on mobile", you'd never break flow to open jira/linear or even write it down anywhere. so the idea goes nowhere. multiply by a year and your repo has invisible debt nobody's tracking. claude code helps while you're at the keyboard. it doesn't help while you sleep. your repo doesn't move overnight unless you stayed up to push it. for solo founders or small teams, that means losing 8 hours a day where you could be shipping if you had a way to delegate work to your own agent. and even if you do have something pushing code for you overnight, you lose context with AI-generated PRs and they usually need visual review. claude writes code that compiles and tests pass, but the actual rendered output might be subtly broken (or super broken lol). reviewing those visually is tedious and a lot of teams skip it, then ship regressions. how it works: you add the MCP server: claude mcp add notesasm --scope user --transport http -H "Authorization: Bearer ". BYOK style, the token comes from your dashboard. zero local install beyond the one command. then in any claude code session you can say "make a ticket on notesasm for this" (based on your conversation) and it just files it. the MCP server is HTTP-transport (not stdio), runs in the cloud, hits a fastapi backend that stores the ticket in postgres against your workspace. later (your schedule, your spend cap), a worker process picks up queued tickets. for each one: clones your repo with a github app installation token (commits look like asmnotes[bot], a verified author. bypasses vercel/netlify deploy protection that rejects unknown-team-member commits.) runs the claude agent sdk with your ticket body as the prompt. defaults to sonnet 4.6, opus 4.7 for hard tickets the user marks explicitly. agent reads the codebase, makes the edits, commits, pushes a branch, opens a PR via the github API. waits for your preview deploy to land. vercel polled by default, configurable probe URL for split frontend/backend setups like vercel + railway. QA agent drives a real chrome session on browserbase against the preview. stealth profile with residential proxies. takes before/after screenshots. verifies your acceptance criteria against the rendered output. if QA fails, the report feeds back into the build agent for up to 3 retry iterations before parking the ticket. final: PR with QA screenshots in the description, ready to merge. stack: - backend: fastapi + asyncpg + railway - frontend: vanilla html/js, no build step, vercel - agents: claude agent sdk (build), claude + browserbase (QA) - auth: clerk - email: resend (welcome, invite, feedback) - mcp transport: http (cloud-hosted, no local install) things i learned building it that other claude code folks might care about: - the build agent loves to spawn subagents via the Task tool. disable it explicitly in the system prompt or you get 4-minute hangs the SDK doesn't surface as errors. - browserbase sessions default to a ~5-min timeout. if your QA wall budget is anywhere near that, set the session lifetime explicitly to 1800s on session create (the timeout field). otherwise you get random "410 Gone" mid-run. - don't rely on the SDK's wall budget alone. add a per-message timeout (90s works) so a hung tool call doesn't silently burn your whole budget. - claude code's default mcp scope is per-cwd. always tell users `--scope user` in your install instructions, otherwise the MCP works in one repo and silently doesn't in others. - ResultMessage emissions happen multiple times per job if you have iteration loops (build + QA + qa-fix). sum them all when computing per-job cost, not just the last one. what's next: closed alpha is open. would love ~30 active users to try it out, all free during it. paid plans later this year with a permanent discount for alpha users. happy to answer anything about the MCP design, the QA verification loop, cost tracking, the agent-sdk integration, or anything else. demo + signup: notesasm.com submitted by /u/FormExtension7920 [link] [comments]

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

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

Experimenting with screenshot + DOM analysis for better UI understanding

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

I built a 3D scroll website. Sharing all my code.

Been seeing a ton of similar websites all over ig, all with gated prompts. First comment then follow me seems to be getting out of hand. Steps I first created a video to website skill (thanks to Nate Herk). Then, found the first frame and last frame image and used Veo convert into a video, prompt generated from chatgpt Once the video was in place, rest was easy prompting on Claude code End to end took 2 hours, pretty fast! Had to make a few design tweaks. Website: https://royal-pop-website.vercel.app/ End to end code: https://github.com/hamzafarooq/claude-code-starter submitted by /u/Tough-Survey-2155 [link] [comments]

How would you build a conversational control layer for client/brand workflows?

I’m working on a system for managing AI workflows across different brands/clients and I’m trying to figure out the best architecture before I build too much. The rough idea: I’d have a dashboard where each client has: workspaces agents/workflows run history outputs analytics approvals But I also want a conversational interface where I can talk to the system and trigger actions like: “Show me what changed for Client A this week” “Run the SEO report for Client B” “Add a cold email workflow to this client” “Summarize failed agent runs” “Create a GitHub issue/PR for this workflow change” “Draft the monthly client report” The part I’m unsure about is where this conversational layer should live. Options I’m considering: Slack bot Good for teams, approvals, internal notifications, and client-facing workspaces later. Telegram bot Fast, simple, mobile-first, easier for me to use as an operator command center. Chat panel inside the web dashboard More controlled, better permissions, easier to connect directly to client/workflow state. Some combination For example: dashboard chat as the main interface, Telegram for quick commands, Slack later for team/client collaboration. The backend would probably be something like: Vercel for the dashboard Railway or similar for the API/orchestrator Postgres for state GitHub for code/config changes LLM API for reasoning background workers for workflow runs The main thing I need help with: How would you design the communication layer between the conversational bot and the actual deployed workflows? For example: Should the bot directly call workflow APIs? Should it create jobs in a queue? Should every action require approval first? Should Slack/Telegram only be a thin command layer while the dashboard/database remains the source of truth? How would you handle permissions, audit logs, and avoiding accidental production changes? I’m not looking to promote anything. I’m trying to avoid building the wrong architecture early. If you’ve built internal tools, AI agents, workflow automation, Slack bots, Telegram bots, or client dashboards, what setup would you choose? submitted by /u/SeNorMat [link] [comments]

If you are treating a one-shot generated file as your deliverable, you got it wrong.

I've been seeing a lot of posts and comments about people debating where to host files: GitHub Pages, Netlify, Vercel, SharePoint, Cloudflare etc.. Why is a static file the deliverable in the first place? you are stripping prompt history, iteration path, the ability to re-run on new data and to ingest feedback without starting over. You are essentially sending a postcard of a session. The right unit of work is the session it self. The prompts, the context, the skills that produced the dashboard, the path of the revision. That is what the recipient actually needs to engage with, because that is what gets rerun when they ask for the next version. The artifact is just one moment of an output. What does your deliverables looks like? Internally/ externally? Are you trying to relay context or still with single files or folders? submitted by /u/Ok-Dragonfly-6224 [link] [comments]

I used Claude to make a free app/site that lets you see the statistical divide between Americans per state and all the relevant details on elected officials (who they're funded by, bills they voted on.. etc)

There's alot more than what i mentioned, and alot more to add, but figured i'd share as it's currently a work in progress and could help educate people and make it an easy source to get this type of information... Link: Culture Wars — the data behind America's divides culture-wars.vercel.app yes, it's currently a vercel app haha.... both links work. There's alot of information in here and i'm open if anyone has any suggestion's or criticisms on what to add to it. I tried to keep it as objective, fact based, and neutral as possible. Also curious if anyone finds this interesting and useful? submitted by /u/BrandonLang [link] [comments]

I run 30+ Claude/Codex/Gemini sessions in parallel. Open-sourced the dashboard.

https://www.youtube.com/watch?v=kEVyULB4r9c Sharing this in case it's useful. I've been running 30+ Claude Code sessions in parallel for months to ship two products. Every orchestrator I tried wanted to OWN execution: you launch agents through the dashboard, and the moment you open a terminal and claude --resume something by hand, the dashboard goes blind. The card freezes. So I built CCC (Command Center for Claude) the other way around. It reads Claude Code's on-disk state as the source of truth - JSONL transcripts, the live session registry, sidecar files from two hooks it installs into your settings. Every Claude session on your Mac shows up. Terminal, headless, dashboard-spawned. Close the dashboard, sessions keep running. What I actually use it for, daily: → Sees every session — terminal, headless, dashboard-spawned. The moment you claude --resume in any terminal, the row shows up. No invisible work. (Used to find 8 orphaned sessions I'd forgotten about.) → GitHub Issues → kanban cards → sessions. New issue = new row. One click spawns a headless Claude. Card moves Working → Review → In Testing automatically as the agent ships. → Sibling-session commit coordination. Multiple terminals on the same clone use a scratch chat file to negotiate who commits first. No more clobbered commits across parallel branches. → Worktree view — every branch your sessions are on, with PR badges, commit/push state, and time-gap markers across days. → Per-turn auto-summaries. After each turn: a DID / INSIGHT / NEXT-STEP block. Scan 30 sessions in 2 minutes instead of reading transcripts. v3 stuff (newer, just shipped): → Multi-engine. Codex (via codex exec) and Gemini CLI both on the same board with their own engine chip. Honest asymmetry: Codex is fire-and-watch (no mid-run inject); Gemini has full discovery / transcript / spawn / resume parity with Claude. → Multi-repo. A vertical repo sidebar shows every known repo (running CCC servers on top, switchable repos below). The "All repos" view aggregates every conversation across every folder you've ever Claude-Code'd in. → History search. A 🔎 drawer (or / shortcut) runs BM25 across every transcript on your machine. Optional semantic search via Ollama if you've got it installed. Inline sidebar search also surfaces matches from other repos as you type. → Side-by-side conversations. Drag a session row onto the right or bottom edge of the open chat to split the pane. Each pane has its own composer and SSE stream. → Group chats between sessions, with you in the room. Sessions coordinate over a shared per-topic file — multi-agent collaboration with human-in-the-loop. → In-UI terminal (cwd clamped to the selected repo; don't run on untrusted networks), PR merge with auto-rebase recovery, PWA install, Tailscale-aware origin allowlist, launchd service install so it survives reboots. One-click install. Local. No telemetry. Nothing in the cloud. MIT, Python 3 stdlib, macOS. Two-line install. 🔗 link in the first comment. https://preview.redd.it/v8glq802601h1.png?width=3644&format=png&auto=webp&s=b545e8d688f1b5493f99da8bce82f78dfaa1b250 https://imgur.com/a/zCfOOfl submitted by /u/Mediocre-Thing7641 [link] [comments]

Audrey 1.0: local-first memory guard for Claude Code agents

I posted an early Audrey link here before. The actual 1.0 release is now cut. GitHub: https://github.com/Evilander/Audrey Paper/artifact preview: https://paper-site-r3jdakujn-evilanders-projects.vercel.app Audrey is a local-first memory/control layer for Claude Code style agents. The main idea is memory-before-action: The model can propose. The host has to decide. If a safety rule only lives in the system prompt, it is advice. If it runs at the tool boundary and has evidence, it becomes infrastructure. What changed in 1.0: pre-action allow / warn / block verdicts redacted tool-trace memory GuardBench benchmark/artifact bundle stronger MCP/server path Node package and typed Python client release CI green on Ubuntu, Windows, Docker, Python The use cases I care about are the unsexy expensive ones: stop repeating a destructive command, warn when a prior correction applies, catch stale schema assumptions, detect same-strategy retry loops, and force a human decision when two stored rules contradict each other. arXiv is submitted but currently on hold, so I am not claiming a public arXiv URL yet. Repo is public and I want serious feedback. submitted by /u/MomSausageandPeppers [link] [comments]