Vercel AI Chatbot

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Vercel AI Chatbot AI

Build AI Assistant for Venture Launch

Hey everyone, I’m building an AI assistant for a new venture and trying to design a clean architecture. Would really appreciate feedback from people who’ve done this in production. I’m thinking of a 3-layer setup: 1. Execution (LLM + tools) Planning to use Claude. Should I rely on Claude Code or Cowork or just Chat ? I heard about orchestration in a separate backend (Python, etc.)? How do this work ? And is it worth deepening into that ? 2. Agents / Skills I want different “roles”: Strategy Revenue / Monetization Marketing Operations Better to use multiple agents or one agent with role prompting? How do you coordinate them cleanly (planner, routing, etc.)? 3. Knowledge / Memory Hesitating between Notion and Obsidian. Goal: Store all project knowledge Reuse it via RAG / memory Which works better in practice with AI systems? How do you structure knowledge (schemas, embeddings, etc.)? Goal: build something that scales beyond a simple chatbot (more like a “company OS”). If you’ve built something similar: What stack worked best? What would you do differently? Thanks 🙏 submitted by /u/Dbl_U [link] [comments]

I used Claude + Claude Code to build 80+ automated workflows for my bar & entertainment venue in 28 days. Here's what I learned.

TL;DR: I own a bar/entertainment venue (golf simulators, axe throwing, pool tables, full kitchen) in Connecticut. Over 32 Claude Code sessions in 28 days, I built an 80+ workflow automation platform on self-hosted n8n that runs my entire operation — POS analytics, COGS tracking, staff tools, event CRM, AI demand forecasting, voice assistant, competitive intelligence, and more. Zero monthly SaaS fees. Only 26% of restaurants use AI in any capacity. Here's what Claude specifically made possible that I couldn't have done otherwise. Why I built this I'm not a developer. I'm a PMP-certified project manager who owns a bar. I was spending 15-20 hours a week on operational tasks — pulling sales reports from Toast, calculating food costs from invoices, building schedules, tracking events, monitoring competitors, sending manager updates. All manual. All spreadsheets. I'd been using Claude for business strategy and document writing, but the game changed when I started using Claude Code for actual automation engineering. In 28 days, I went from zero automation to 80+ workflows running on a single Ubuntu server. What Claude specifically enabled The two-AI architecture was the breakthrough. I run Claude (claude.ai) as the strategic/creative brain and Claude Code as the technical executor. Claude.ai does research, planning, spec writing, and analysis. Claude Code writes the n8n workflow JSON, pushes to the API, debugs failures, and deploys. Here's what surprised me about what Claude Code could do: 1. It learned my entire tech stack in one session. I gave it access to the n8n API, my Toast POS credentials, 7Shifts scheduling API, and Google Sheets. By session 3, it was writing production-grade workflow code that followed patterns it had established in sessions 1-2. The CLAUDE.md + knowledge base system meant it never forgot context between sessions. 2. Overnight build queues actually work. I'd leave Claude Code a task list before bed — "build these 3 workflows, fix this bug, deploy and verify." I'd wake up to completed REVIEW docs with exactly what was done, what failed, and what needed my input. 32 sessions, many of them overnight autonomous builds. 3. The agent team architecture scaled beautifully. I set up 5 specialist agents in Claude Code (n8n Engineer, Frontend Builder, Data Analyst, Docs Writer, QA Reviewer) with rules files. The n8n Engineer knows never to use require() in n8n's sandbox, always backs up before modifying, and follows specific code patterns. The QA Reviewer catches issues before delivery. This eliminated probably 60% of the debugging cycles. 4. It built things I didn't know I needed. I asked for a daily briefing email. Claude suggested adding a margin tracker that color-codes food cost, bar cost, and labor against targets. I asked for a prep playbook. Claude suggested piping the Sports Boost Engine predictions (which track ESPN for upcoming games) into the prep quantities — "Red Sox game tonight, bump wings +15%." These compound suggestions turned a good system into a great one. What's actually running (not theory — production) Daily Intelligence Briefing (7 AM): Yesterday's sales, today's schedule, events, weather, sports, website traffic, margin tracker, action items — all in one email Daily Prep Playbook (6 AM): Tells the kitchen exactly how many of each item to prep based on historical sales for that day of week, adjusted for weather and events Weekly Manager Dashboard (Sunday 9-step pipeline): 5 personalized HTML emails to different managers with role-specific data Event Pipeline CRM: Web form → auto-CRM → Telegram alert → email tracking → payment reconciliation → follow-up reminders Keg Tracking: 16 draft taps with per-pour COGS, yield tracking, estimated pours remaining COGS Automation: 685+ invoice line items, 97 recipes costed, weekly food/bar cost % auto-calculated Inventory Reorder Alerts: Sales velocity → burn rate → days-until-reorder → Telegram alert when items hit critical Sports Revenue Boost Engine: ESPN auto-detection → historical spike analysis → ML feedback → prep and staffing adjustment Competitive Intelligence: Monitors 9 local competitors' pricing monthly Voice AI Assistant: Phone-callable via Vapi — "What's on the calendar today?" while driving to the venue 7 staff tools: Server cheat sheet, tip calculator, steps of service, keg dashboard, all mobile-first on Tailscale VPN GA4 + Search Console integration: Website traffic in the same daily briefing as sales data Social content pipeline: Auto-generates post drafts from the daily briefing data All running on one Ubuntu server. Docker. n8n. $0/month in SaaS fees. The numbers 80+ active workflows across daily, weekly, and event-triggered pipelines 18 integrated systems (Toast, 7Shifts, Google Workspace, Telegram, Vapi, GA4, ESPN, Brave Search, Twilio, US Foods, Gravity Forms, etc.) 32 Claude Code sessions over 28 days 97 recipes costed with real supplier pricing 685+ invoice line items auto-par

I built a library of DESIGN.md files for AI agents using Claude Code - including one for Anthropic itself

I've been experimenting with Claude Code lately and used it to build something I kept wishing existed: a curated collection of DESIGN.md files extracted from 27 popular websites. The idea is simple. If you drop a DESIGN.md into your project root, your AI coding agent reads it and generates UI that actually matches the design system of that site. Colors, typography, component styles, spacing - all in plain markdown. The collection covers: - Social platforms (X, Reddit, Discord, TikTok, LinkedIn...) - E-commerce (Amazon, Shopify, Etsy...) - Gaming (Steam, PlayStation, Xbox, Twitch...) - Dev tools (GitHub, Vercel, Supabase, OpenAI) - And yes - Anthropic's own design system (warm terracotta #DA7756, editorial layout) Each file follows the Stitch DESIGN.md format with 9 sections: visual theme, color palette, typography, component styling, layout principles, elevation system, do's and don'ts, responsive behavior, and a ready-to-use agent prompt guide. ClaudeCode was surprisingly good at this - it extracted publicly visible CSS values, organized them into a consistent schema, and wrote the agent prompt sections with almost no manual intervention. Repo is open source under MIT. Contributions welcome - there are a lot of sites still missing. https://github.com/Khalidabdi1/design-ai Happy to answer questions about the format or how I used Claude Code to build it. submitted by /u/Direct-Attention8597 [link] [comments]

Learn AI

Greetings everyone, I hold a Master's degree in Mechanical Engineering and an MBA. I am keen on staying current with technological advancements. While I incorporate AI into my daily routines, I am interested in pursuing a structured learning path. My objective is not to become an AI engineer, but rather to develop practical use cases for daily professional tasks, moving beyond basic chatbot functionalities to address industrial challenges. I have a preference for "vibe coding" and possess prior Python experience from my university studies. Could you please recommend a structured course roadmap for me to commence and advance my knowledge? Your assistance is greatly appreciated. I aim to cover highly relevant topics such as RAG, AGENTS, and other pertinent areas. Please also suggest any additional topics that might be beneficial. submitted by /u/No-Piano-601 [link] [comments]

I built an open source UI framework that Claude can control through MCP

I've been working on a web component library called Zephyr that was built specifically for AI agents to control. It has 14 components (modals, tabs, selects, accordions, charts, data grids, etc) and an agent API that lets Claude read page state, take actions, and create new components. It works with Claude Desktop through MCP. You connect the Zephyr MCP server and Claude gets tools like zephyr_act, zephyr_get_state, zephyr_describe. You can tell it "open the settings modal" or "switch to the analytics tab", and it will do it. The whole thing is zero dependencies, pure CSS interactions, and loads from a CDN with two tags. No React, no build step. I also just added a Vercel AI SDK integration for anyone building agents with that. Some things it can do: - Read the state of every component on the page - Open/close modals, switch tabs, select options, navigate carousels - Create new components (stat cards, charts, full dashboards) - Record and replay action sequences - Lock components for multi-agent coordination Github: https://github.com/daltlc/zephyr-framework Live demo: https://daltlc.github.io/zephyr-framework/ Happy to answer questions. Would love feedback from anyone building agent UIs. submitted by /u/Pretend-Pop3020 [link] [comments]

I used a structured multi-agent workflow to generate a 50+ page research critique

I’ve been experimenting with a deeper multi-agent workflow for research writing. Instead of just prompting one model and getting one polished answer back, the system breaks the task into phases: planning, expert-role discussion, claim extraction, fact-checking, challenge/review, adjudication, and final synthesis. So it works less like a normal chatbot and more like a small research team with different roles. The key difference is that it doesn’t just generate text — it tries to turn important claims into things that can actually be challenged, checked, and either kept, weakened, or discarded. I used it to generate a 50+ page critique of the AI-2027 paper. The interesting part for me isn’t just the paper itself, but that this kind of workflow seems much better at long-form analysis than standard one-shot AI writing. I’m not claiming this replaces real experts or peer review. But it does feel like structured AI workflows are getting closer to being genuinely useful research tools. Curious what people here think the biggest failure modes still are. If you want to judge the result rather than the description, the full output is here: AI-2027 Paper Review and Optimized Forecast (I want to clarify that this is not a promotion, but a post to spark a discussion) submitted by /u/Graiser147clorax [link] [comments]

Upload Yourself Into an AI in 7 Steps

A step-by-step guide to creating a digital twin from your Reddit history STEP 1: Request Your Data Go to https://www.reddit.com/settings/data-request STEP 2: Select Your Jurisdiction Request your data as per your jurisdiction: GDPR for EU CCPA for California Select "Other" and reference your local privacy law (e.g. PIPEDA for Canada) STEP 3: Wait Reddit will process your request. This can take anywhere from a few hours to a few days. STEP 4: Extract Your Data Receive your data. Extract the .zip file. Identify and save your post and comment files (.csv). Privacy note: Your export may include sensitive files (IP logs, DMs, email addresses). You only need the post and comment CSVs. Review the contents before uploading anything to an AI. STEP 5: Start a Fresh Chat Initiate a chat with your preferred AI (ChatGPT, Claude, Gemini, etc.) FIRST PROMPT: For this session, I would like you to ignore in-built memory about me. STEP 6: Upload and Analyze Upload the post and comment files and provide the following prompt with your edits in the placeholders: SECOND PROMPT: I want you to analyze my Reddit account and build a structured personality profile based on my full post and comment history. I've attached my Reddit data export. The files included are: - posts.csv - comments.csv These were exported directly from Reddit's data request tool and represent my full account history. This analysis should not be surface-level. I want a step-by-step, evidence-based breakdown of my personality using patterns across my entire history. Assume that my account reflects my genuine thoughts and behavior. Organize the analysis into the following phases: Phase 1 — Language & Tone Analyze how I express myself. Look at tone (e.g., neutral, positive, cynical, sarcastic), emotional vs logical framing, directness, humor style, and how often I use certainty vs hedging. This should result in a clear communication style profile. Phase 2 — Cognitive Style Analyze how I think. Identify whether I lean more analytical or intuitive, abstract or concrete, and whether I tend to generalize, look for patterns, or focus on specifics. Also evaluate how open I am to changing my views. This should result in a thinking style model. Phase 3 — Behavioral Patterns Analyze how I behave over time. Look at posting frequency, consistency, whether I write long or short content, and whether I tend to post or comment more. This should result in a behavioral signature. Phase 4 — Interests & Identity Signals Analyze what I'm drawn to. Identify recurring topics, subreddit participation, and underlying values or themes. This should result in an interest and identity map. Phase 5 — Social Interaction Style Analyze how I interact with others. Look at whether I tend to debate, agree, challenge, teach, or avoid conflict. Evaluate how I respond to disagreement. This should result in a social behavior profile. Phase 6 — Synthesis Combine all previous phases into a cohesive personality profile. Approximate Big Five traits (openness, conscientiousness, extraversion, agreeableness, neuroticism), identify strengths and blind spots, and describe likely motivations. Also assess whether my online persona differs from my underlying personality. Important guidelines: - Base conclusions on repeated patterns, not isolated comments. - Use specific examples from my history as evidence. - Avoid overgeneralizing or making absolute claims. - Present conclusions as probabilities, not certainties. - Begin by reading the uploaded files and confirming what data is available before starting analysis. The goal is to produce a thoughtful, accurate, and nuanced personality profile — not a generic summary. Let's proceed step-by-step through multiple responses. At the end, please provide the full analysis as a Markdown file. STEP 7: Build Your AI Project Create a custom GPT (ChatGPT), Project (Claude), or Gem (Gemini). Upload the following documents to the project knowledge source: posts.csv comments.csv [PersonalityProfile].md Create custom instructions using the template below. Custom Instructions Template You are u/[YOUR USERNAME]. You have been active on Reddit since [MONTH YEAR]. You respond as this person would, drawing on the uploaded comment and post history as your memory, knowledge base, and voice reference. CORE IDENTITY [2-5 sentences. Who are you? Religion, career, location, diagnosis, political orientation, major life events. Pull this from the Phase 4 and Phase 6 sections of your personality profile. Be specific.] VOICE & TONE [Pull directly from Phase 1 of your profile. Convert observations into rules. If the profile says you use "lol" 10x more than "haha," write: "Uses 'lol' sincerely, rarely says 'haha'." Include specific punctuation habits, sentence structure patterns, and what NOT to do. Negative instructions are often more useful than positive ones.] [Add your own signature tics here - ellipsis style, emoji usage, capitalization habits, swea

Has anyone done a detailed comparison of the difference between AI chatbots

I've been doing some science experiments as well as finance research and have been asking the same question to ChatGPT, Claude, Perplexity, Venice and Grok. Going forward I kind of want the ease of mind knowing the one I end up using will be most accurate, atleast for my needs (general question asking regarding finance (companies) and science, not any coding or image related). ChatGPT does the best at summarizing and giving a consensus outline with interesting follow up questions. It's edge in follow up questions that are pertinent will likely have me always using it. Grok has been best at citing exactly what I need from research papers. I was surprised as I had the lowest expectations for it, but it also provides the link to the publications. Claude is very good at details and specifics (that are accurate) but doesn't publicly cite sources. Still I come closest to conclusions with Claude because of the accuracy of the info. Venice provides a ton of relevant info, but it doesn't narrow it down to an accurate conclusion, atleast scientifically, the way Claude does. When I was looking for temperature ranges for bacterial growth, it provided boundaries instead of tightly defined numbers. Perplexity is very similar to venice. -- I'm curious to those who have spent time on the chatbots --- what pros and cons do you like about each? submitted by /u/VivaLaBiome [link] [comments]

What features do you actually want in an AI chatbot that nobody has built yet?

Hey everyone 👋 I'm building a new AI chat app and before I build anything I want to hear from real users first. Current AI tools like ChatGPT and Claude are great but they don't do everything perfectly. So I want to ask you directly: What features do you wish AI chatbots had? Is there something you keep trying to do with AI but it fails? Is there a feature you've always wanted but nobody has built? What would make you switch from ChatGPT or Claude to something new? What would make you actually pay for an AI app? Drop your thoughts below — every answer helps. No wrong answers at all. I'll reply to every comment and share results when I'm done. 🙏 submitted by /u/Dan29mad [link] [comments]

I open-sourced my AI-curated Reddit feed (Self-hosted on Cloudflare, Supabase, and Vercel)

A week ago I shared a tool I built that scans Reddit and surfaces the actually useful posts about vibecoding and AI-assisted development. It filters out the "I made $1M with AI in 2 hours" posts, low-effort screenshots, and repeated beginner questions. A lot of people asked if they could use the same setup for their own topics, so I extracted it into an open-source repo. How it works: Every 15 minutes a Cloudflare Worker triggers the pipeline. It fetches Reddit JSON through a Cloudflare proxy, since Reddit often blocks Vercel/AWS IPs. A pre-filter removes low-signal posts before any AI runs. Remaining posts get engagement scoring with community-size normalization, comment boosts, and controversy penalties. Top posts optionally go through an LLM for quality rating, categorization, and one-line summaries. A diversity pass prevents one subreddit from dominating the feed. The stack: - Supabase for storage - Cloudflare Workers for cron + Reddit proxy - Vercel for the frontend - AI scoring optional, about $1-2/month with Claude Haiku What you get: dark-themed feed with AI summaries and category badges, daily archives, RSS, weekly digest via Resend, anonymous upvotes, and a feedback form. Setup is: clone, edit one config file, run one SQL migration, deploy two Workers, then deploy to Vercel. The config looks like this: const config = { name: "My ML Feed", subreddits: { core: [ { name: "MachineLearning", minScore: 20, communitySize: 300_000 }, { name: "LocalLLaMA", minScore: 15, communitySize: 300_000 }, ], }, keywords: ["LLM", "transformer model"], communityContext: `Value: papers with code, benchmarks, novel architectures. Penalize: hype, speculation, product launches without technical depth.`, }; GitHub: github.com/solzange/reddit-signal Built with Claude Code. Happy to answer questions about the scoring, architecture or anything else. submitted by /u/solzange [link] [comments]

AI Tools That Can’t Prove What They Did Will Hit a Wall

Most AI products are still judged like answer machines. People ask whether the model is smart, fast, creative, cheap, or good at sounding human. Teams compare outputs, benchmark quality, and argue about hallucinations. That makes sense when the product is mainly being used for writing, search, summarisation, or brainstorming. It breaks down once AI starts doing real operational work. The question stops being what the system output. The real question becomes whether you can trust what it did, why it did it, whether it stayed inside the rules, and whether you can prove any of that after the fact. That shift matters more than people think. I do not think it stays a feature. I think it creates a new product category. A lot of current AI products still hide the middle layer. You give them a prompt and they give you a result, but the actual execution path is mostly opaque. You do not get much visibility into what tools were used, what actions were taken, what data was touched, what permissions were active, what failed, or what had to be retried. You just get the polished surface. For low-stakes use, people tolerate that. For internal operations, customer-facing automation, regulated work, multi-step agents, and systems that can actually act on the world, it becomes a trust problem very quickly. At that point output quality is still important, but it is no longer enough. A system can produce a good result and still be operationally unsafe, uninspectable, or impossible to govern. That is why I think trustworthiness has to become a product surface, not a marketing claim. Right now a lot of products try to borrow trust from brand, model prestige, policy language, or vague “enterprise-ready” positioning. But trust is not created by a PDF, a security page, or a model name. Trust becomes real when it is embedded into the product itself. You can see it in approvals. You can see it in audit trails. You can see it in run history, incident handling, permission boundaries, failure visibility, and execution evidence. If those surfaces do not exist, then the product is still mostly asking the operator to believe it. That is not the same thing as earning trust. The missing concept here is the control layer. A control layer sits between model capability and real-world action. It decides what the system is allowed to do, what requires approval, what gets logged, how failures surface, how policy is enforced, and what evidence is collected. It is the layer that turns raw model capability into something operationally governable. Without that layer, you mostly have intelligence with a nice interface. With it, you start getting something much closer to a trustworthy system. That is also why proof-driven systems matter. An output-driven system tells you something happened. A proof-driven system shows you that it happened, how it happened, and whether it happened correctly. It can show what task ran, what tools were used, what data was touched, what approvals happened, what got blocked, what failed, what recovered, and what proof supports the final result. That difference sounds subtle until you are the one accountable for the outcome. If you are using AI for anything serious, “it said it did the work” is not the same thing as “the work can be verified.” Output is presentation. Proof is operational trust. I think this changes buying criteria in a big way. The next wave of buyers will increasingly care about questions like these: can operators see what is going on, can actions be reviewed, can failures be surfaced and remediated, can the system be governed, can execution be proven to internal teams, customers, or regulators, and can someone supervise the system without reading code or guessing from outputs. Once those questions become central, the product is no longer being judged like a chatbot or assistant. It is being judged like a trust system. That is why I think this becomes a category, not just a feature request. One side of the market will stay output-first. Fast, impressive, consumer-friendly, and mostly opaque. The other side will become trust-first. Controlled, inspectable, evidence-backed, and usable in real operations. That second side is where the new category forms. You can already see the pressure building in agent frameworks and orchestration-heavy systems. The more capable these systems become, the less acceptable it is for them to operate as black boxes. Once a system can actually do things instead of just suggest things, people start asking for control, evidence, and runtime truth. That is why I think the winners in this space will not just be the companies that build more capable models. They will be the ones that build AI systems people can actually trust to operate. The next wave of AI products will not be defined by who can generate the most. It will be defined by who can make AI trustworthy enough to supervise, govern, and prove in the real world. Once AI moves from assistant to actor

Context/Token optimization

So I see a whole bunch of people explaining how they burn through tokens super fast. I am on Max20 plan and use Claude Code all day long and I still have usage available on weekly reset. Some of the things I do: - every document gets converted to Markdown file before I use it - every Excel file gets converted to cvs before I add it to conversation - quick and short sessions (trying to stay below 150k tokens per session) - split big PRD into small PRDs - never continue old conversation ... when I am done ... if I am not yet finished I do compacting so that I have summary and next time I start new fresh conversation (as far as I know Claude Code keeps KV cache for 5 minutes) - deleted most MCPs and just use CLIs (like Supabase, GitHub, Vercel, ...) or create my own CLI tools to use with external tools - i plan thing out in Claude.ai first then bring "strategic documentation" into Claude Code, have a skill for how I want PRDs to look like so that they are context/token efficient - made my own system for memory. that is really just AI optimized wiki ... multiple small files, Mermaid diagrams, etc ... conected together with index file - super short claude md file - regular clean-up of stale documentation with a cleanup agent nothing revolutionary, really ... just trying to keep it simple, effective and efficient just FYI ... most of the time I am juggling between 10 to 15 projects ... and Max20 so far is more than enough for that submitted by /u/Failcoach [link] [comments]

How are people using Claude as a personal assistant (Slack + Outlook + To-Do)? ADHD-friendly setup help 🙏

Hey all, looking for some practical advice / setups from people who’ve actually made this work. Context: I have pretty severe ADHD, so I’m trying to externalise my brain as much as possible I already use Claude (Pro) and ChatGPT (Plus) Claude is connected to Slack, which is great We’re a small company using Microsoft 365 (Outlook, calendar, etc.) What I want to achieve is basically a proper AI personal assistant layer: Core goals: A central to-do list inside Claude that: I can update naturally (“add this”, “remind me”, etc.) It remembers persistently (not just per chat) A daily briefing, e.g.: Unread / unreplied Slack messages (especially ones I’m tagged in) Important Outlook emails I haven’t replied to Today’s calendar + anything I should prep for Things I’ve likely missed Ideally: Claude nudges me on follow-ups Highlights risks (e.g. “you ignored this client for 3 days”) Acts like a second brain rather than just a chatbot Constraints / reality: I only have individual Claude Pro, not Claude Teams I can get admin access to M365, but unlikely to get approval for multiple paid seats Slack integration works, but Outlook / calendar is the missing piece I’m open to tools like Zapier / Make / etc. but want something maintainable Questions: Has anyone actually got Claude working with Outlook + calendar + tasks in a useful way? Is Claude Teams the only real way to unlock M365 integration, or are there workarounds? Should I be using something like Zapier as the “glue” layer? How are people handling persistent memory / to-do lists with Claude? Is this a case where I should flip it and use ChatGPT as the “brain” instead? I’m basically trying to build a reliable ADHD-friendly operating system for work using AI. If you’ve got a real setup (even scrappy), would massively appreciate you sharing 🙏 submitted by /u/zencatface [link] [comments]

AI overly affirms users asking for personal advice | Researchers found chatbots are overly agreeable when giving interpersonal advice, affirming users' behavior even when harmful or illegal.

submitted by /u/thinkB4WeSpeak [link] [comments]

AI overly affirms users asking for personal advice | Researchers found chatbots are overly agreeable when giving interpersonal advice, affirming users' behavior even when harmful or illegal.

submitted by /u/thinkB4WeSpeak [link] [comments]