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

Maven, a personal AI agent that feels like JARVIS — what an open agent harness looks like in 2026

With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. What stood out in practice • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users. submitted by /u/qasimsoomro [link] [comments]

I built an open-source Desktop App that gives your AI persistent memory across all platforms (100% Local SQLite, Zero-Docker)

Hey everyone, A few weeks ago I shared the CLI version of my project, ArcRift, on Reddit. After listening to your feedback—specifically the requests to remove heavy Docker dependencies and make it easier to install—I have just released the v1.6.1 Desktop App. If you regularly use LLMs for coding or research, you know the frustration of "amnesia." Every time you open a new chat, you have to painstakingly copy and paste your project structure and previous context just to get the AI up to speed. ArcRift is a 100% offline, local-first RAG and memory layer. It bridges the gap between your AI web chats (like Claude and ChatGPT) and your local tools (like Cursor or Claude Code) using a unified local database. I wanted something lightweight that did not require pulling Docker containers or subscribing to third-party memory APIs. It now runs as a native Tauri desktop app in your system tray, powered completely by local Ollama instances and a local SQLite database. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://arcrift.vercel.app/ Codebase: https://github.com/Eshaan-Nair/ArcRift How it works & Core Features: Seamless Integration: The Chrome extension silently intercepts your prompts, surgically retrieves exactly the sentences relevant to your question from your database, and injects them before the prompt is sent to the LLM. Hybrid Search Retrieval: Uses sqlite-vec (with nomic-embed-text locally) + FTS5 keyword prefix matching to instantly find your past context. Knowledge Graph Extraction: An offline task queue uses a local LLM to extract entity relationships from your chats, mapping out a graph of your projects over time. Direct Codebase Indexing: The new Desktop App allows ArcRift to scan and index your actual project files into the graph, bridging the gap between your chat memory and your actual code architecture. Total Privacy (PII Redaction): The extension aggressively scrubs JWTs, API keys, emails, and IPs before data is even saved to your local disk. The extension works natively with Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. If you save a conversation in ChatGPT today, you can instantly recall that exact context in Claude tomorrow. ArcRift is completely open-source (MIT). You can download the new .exe installer directly from the GitHub releases page. If you find this useful for your daily workflow, PRs are very welcome, and a star on GitHub helps the project get discovered! submitted by /u/Better-Platypus-3420 [link] [comments]

How does AI help with Job productivity?

For Context: I work in a semiconductor manufacturing company as a modelling engineer, I use some modelling softwares etc but none of them use AI. I wanted to understand the whole AI craze nowadays, people say that AI will replace jobs/Increase productivity and I don't get it at all. All I see is a simple chatbot (ChatGPT) which is a super impressive version of google and can solve some basic math/science questions and Co-Pilot in my workplace which I found to be useless, for example the facilitator thing which is supposed to make meeting notes is so bad at summaring meeting minutes etc. I don't think AI is there yet to do very basic things. So yes in theory if AI gets better in few years/decades sure it take the non-technical part of my job like making meeting minutes/making ppt's etc but I think its still not there yet. For AI to take over my job it needs to get the basic shit correct first and then maybe it can do the technical stuff. One really good use-case of AI that i can see is to generate Code based on the project requirement, So I can see how entry level coder's jobs might be affected sure, but that's a very small portion of the economy, right? submitted by /u/the_axe_effect [link] [comments]

I Tried to Sell My House With a Chatbot

A NYT tech reporter out of all people just sold his house for $605,000 using nothing but AI. This is the second time I have heard of AI helping someone sell their house. I'm sure there are many more examples. The part that got me was during negotiations, the chatbot had to physically stop him from typing "I'm not playing games" — and then explained exactly why that phrase destroys your leverage. The author ends with a line that stuck with me — he says real estate agents are heading the way of travel agents. Still useful for people who want the hand-holding, but no longer essential for anyone willing to do the work. Are we watching an entire profession get quietly hollowed out in real time? submitted by /u/RaspberryOk1888 [link] [comments]

the take that 'ai doesn't do anything useful yet' held up for me until i ditched the chat window

Counted it last week: one monday review had me opening 6 apps and copy-pasting between all of them, while a chatbot sat in a 7th tab handing me summaries i still had to go act on. that's the part the 'ai is useless' crowd is actually right about. text out, the work is still on you. what moved me off that take wasn't a smarter model. it was dropping the chat window for a desktop agent that reads gmail, calendar and slack inside the same task and takes the next step itself, with a permission prompt before each action so it isn't running wild. the $500m-wasted-on-claude thread up top is the same thing from the money side. paying for tokens that spit out paragraphs nobody executes is just the expensive way to do nothing. If you're still in the 'it doesn't actually do anything' camp, fair, i was there too. the line for me was the day it finished a task instead of describing one. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

ClaudeGauge - Tired of opening claude.ai to check my 5h limit? Here.. a real-time Claude.ai monitor on ESP32-S3 with a Star Trek LCARS interface

Hey r/ClaudeAI Got tired of refreshing claude.ai to check how close I was to my 5-hour limit or how much I'd spent on the API this month. Wanted ambient awareness -p glance at a small screen on my desk, get the answer. So I built ClaudeGauge - a physical dashboard that runs on a ~$25 ESP32 AMOLED and pulls live data from the Claude API + claude.ai. https://reddit.com/link/1tsb1eo/video/ut20yc7f9bng1/player https://preview.redd.it/hbjbhwag9bng1.png?width=320&format=png&auto=webp&s=a84f12293ef5ab3d0179c0d48ca9772feed848f1 https://preview.redd.it/zdjy46bp9bng1.png?width=320&format=png&auto=webp&s=53c2cd21370ef096e6357cc996d17b7a0282cb36 https://preview.redd.it/ei5amd7h9bng1.png?width=320&format=png&auto=webp&s=dfafd79d83e0afc887b4fb2f912b17dd6d92573a What it does: Tracks API spending (today + monthly) in USD Shows token usage broken down by model (input, output, cached) Claude Code analytics: sessions, commits, PRs, lines modified Rate limit monitoring with live countdown timers System health: WiFi, memory, uptime, firmware version 7 dashboard screens you cycle through with a button press Hardware supported: LILYGO T-Display-S3 — 1.9" parallel display, USB-C, dual buttons + touch Waveshare ESP32-S3-LCD-1.47 — 1.47" SPI display, USB-A, single button Both boards are cheap ($25-40) and easily available. Tech stack: PlatformIO + Arduino framework TFT_eSPI with full-screen PSRAM sprite for flicker-free rendering Captive portal for WiFi/API key setup (no hardcoded credentials) Vercel Edge Function proxy (ESP32 can't connect to claude.ai directly — Cloudflare blocks mbedTLS fingerprints) Chrome extension for session key auto-fill WYSIWYG layout editor for designing custom screens Some ESP32 gotchas I ran into: If you're using TFT_eSPI in SPI mode on ESP32-S3, you MUST add -DUSE_FSPI_PORT to your build flags or you'll get a crash in begin_tft_write(). Took me a while to figure that one out. Cloudflare Workers don't work as a proxy either — only Vercel (Fastly-based TLS) gets through to claude.ai. Looking for contributors! The project is MIT-licensed and there's plenty of room to help: Support for additional ESP32 display boards New dashboard screen layouts Improving the LCARS designer tool Adding support for other AI provider APIs (OpenAI, Gemini, etc.) General firmware improvements and bug fixes Links: GitHub: https://github.com/dorofino/ClaudeGauge Website: https://claudegauge.com If you've got one of these boards sitting around, give it a try and let me know what you think. PRs and issues welcome submitted by /u/Prudent-Purchase-558 [link] [comments]

The next AI problem might not be intelligence. It might be responsibility.

AI systems are moving from answering questions to taking actions. That changes the risk. A wrong chatbot answer is annoying. A wrong action inside email, CRM, payments, customer support, or internal data can create real damage. So maybe the next big AI challenge is not just better reasoning. It is knowing: what the AI can access what it can do alone what needs approval who is accountable when it fails As AI agents become more common, who do you think should be responsible when they make a bad decision? submitted by /u/Alpertayfur [link] [comments]

Training AI chatbots to be warm and empathetic makes them less factually accurate

submitted by /u/Doug24 [link] [comments]

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. ​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: ​Rule of thumb: 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 ​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, we get businesses customers from ChatGPT (and yes, we consume a lot of tokens). DM if interested (dont want to promote here) 😄 submitted by /u/tiln7 [link] [comments]

Help with AI tool design logic

Hey guys, doc working at an oncology ward here (barely any coding skills plus restrictive hospital IT policy requiring me to use Claude browser interface) We have an Excel sheet for patient charts that we use as a template to fill out and print at admission (our hospital system runs on an MSDOS emulator, don't even ask 😛), and I thought about designing a small AI chatbot tool that would generate these for us based on the (anonymous) admission report. I want it for everyday use by me and my colleagues to save some time for more important stuff. I created a Project in Claude that has the template uploaded among its files and has pretty complex, specific instructions about what to fill into each individual cell. It does a surprisingly good job, but it's designed so that each new conversation means a new patient (need to make it simple for my colleagues) - the consequence is that it always takes Claude sooo long to create it, presumably because it has to re-read the context window including the template file every time. Can you suggest a better design solution for me, please? submitted by /u/ScabbyCoyote [link] [comments]

Me, a small api user, got openai tech support to help me in a few hours.

Hell has truly frozen over. OpenAI's own documentation to this day says that if you "pay $50" you get tier 2. The openAI platform console says you have to have "spent the $50" but they haven't changed what it says in the documentation. They had reneged on both of these. Today I argued with the AI Help chatbot for about an hour saying that I had absolutely met the stated requirements. It blinked first and said it submitted support ticket for me. I got contacted by support via email about an hour later and after another exchange my account said Tier 2!!! Perhaps there is hope for OpenAI after all. submitted by /u/Guilty-History-9249 [link] [comments]

Anyone else feel like AI assistants have amnesia?

I've been trying to use AI to help me stay on top of client relationships, tracking what we discussed, what I promised, what's coming up next. The problem is every conversation basically starts from zero. I get maybe 20 messages of history and then it's gone. So I end up re-explaining context every single time. "This client is waiting on the proposal [link] which is [xyz] ..." It defeats the entire purpose. I've tried dumping everything into markdown files and feeding them back in, but that's just more admin on top of admin. At some point I'm spending more time managing my AI system than it's saving me. What I actually want is something that remembers like a colleague who's been cc'd on everything and can just pick up where we left off. Not a chatbot, but something with actual continuity. How are you all handling this? Has anyone found a setup where long-term context actually works without you manually maintaining it? submitted by /u/Gorgottz [link] [comments]

I tried putting Claude on a tiny €20 device

I’ve been experimenting with Claude outside the usual browser/app interface, this time on a tiny StickS3 / Cardputer-style device. The experience is obviously limited by the small screen and input, but that constraint is also what makes it interesting. It feels less like “another chatbot window” and more like a small physical AI companion for quick prompts, reminders, or simple device interactions. Curious what Claude users here would actually want from a tiny dedicated Claude device. Quick notes? Voice? IoT control? Ambient reminders? submitted by /u/Pegeen-ice [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]

Claude makes documents into apps

Any document can become an app I’ve been working on an open-source document format and viewer called Adaptive Markdown. The basic idea is simple: A document should not have to stay static. It should be something a coding agent can extend, reshape, and turn into an interactive workspace. This is not just a canvas you edit with a chatbot. The bigger idea is that the document becomes both: the source of truth the programmable interface In other words, the document becomes a living app. You write notes, collect data, draft text, or import files. Then a coding agent can directly modify the document surface: add charts, create calculators, build filters, restyle sections, generate summaries, export views, or turn rough notes into an interactive tool. So instead of having: a document a spreadsheet a dashboard an app a changelog a separate AI chat about all of it You can have one living .md file that contains those layers together. Example A fitness log might start as a plain Markdown journal. Then the agent adds charts. Then it pulls in device data. Then it adds weekly summaries, rolling averages, goal tracking, export options, and a dashboard view. The document did not move into an app. The document became the app. Other use cases A billable time log that computes subtotals and rewrites rough notes into polished narratives A research notebook with experiment parameters, runnable code, outputs, and methodology notes A recipe book that scales servings and generates shopping lists A math textbook that can explain a theorem at different levels A project README that explains the system, demonstrates the system, and lets the agent modify it from inside the document A small data report with embedded CSV data, live charts, filters, and exportable views The thing I’m most interested in is not "Can Markdown support more widgets?" It is: What happens when the document itself becomes the programmable, agent-editable interface? Demos I made a few short video demos: Turn your document into a snake game: https://youtu.be/l-I2UiZd-Jw Basic Adaptive Markdown features: https://youtu.be/cLdzvZAL96I Import CSV, create tables, edit and format them: https://youtu.be/XKh9D3BlTCg Import MusicXML and transpose sheet music: https://youtu.be/8YV3zjMLvA8 Why I’m excited about this The biggest use case I’m excited about is academic and technical reading. In a few years, I don’t think people will just read papers passively. I think they’ll translate passages, ask questions, generate examples, explore alternate proofs, run code, attach notes, convert math to Lean where possible, and keep all of that inside the document instead of scattered across chats and notebooks. This is already pretty natural inside a browser when a coding agent has access to JS, CSS, and the document structure. It’s very early, but the workflow already feels useful to me. I’m using it for my own notes and documents. Right now it is configured for the Anthropic coding-agent SDK and experimentally for Codex. The longer-term goal is to make it run entirely locally. GitHub: https://github.com/SemiSimpleMath/Adaptive-Markdown I recently added per-document skills, so agents can automatically know how to style or transform the text or data inside a specific document. Curious whether this seems useful to anyone else, or whether I’m just overexcited because I built it. Feature requests welcome. submitted by /u/IDefendWaffles [link] [comments]