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

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]

Claude chat memory synthesis generation has stopped....

Fistly, please understand that I'm a not english-native so this post is translated with google translate. FIY: I'm a non-expert, general user who uses only the chat function of Claude chat through web and does not use Claude Code at all. Issue: Despite having started multiple new sessions over the past four days—both within and outside the scope of each project—neither project memory nor global memory has generated updates reflecting these activities for at least the past 100 hours; Fortunately, existing memory has not been lost, so I can still view the synthesized memory contents. (a) Regarding project memory, the most recently updated memory among the projects I have worked on shows the last update as being two months ago. For newly started projects, the project memory section in the upper right corner of the user interface screen remains stuck with the initial message ("Project memory will show here after a few chats.") for about five days since the project started; in other words, not even the first Project Memory has been generated. (b) The last update for global memory was about four days ago, during which I started multiple new sessions with Claude. --- Since the time I discovered the issue, the memory feature has never turned off by itself. Of course, it is possible to manually edit memories or request updates, but what I want is for the "automatic memory generation" feature to return, and I am currently at a loss. I have already googled this issue and received support from the Fin AI chatbot (which responded to my situation by stating, "Since there are currently no system outages, it appears to be an account-level data synchronization issue"). I have also tried every method except for "Settings > Features > Reset memory" (because I don't want lose existing memory peremanatly) —clearing browser cache and logging in, deleting browser extensions, turning off memory but selecting "Pause," logging out and refreshing the browser, reconnecting, and then turning memory off again, etc.). I have also checked numerous posts on Reddit (including this subreddit) within the last 2–3 months that reported similar problems to mine, but the problem is that I have no way of knowing how their situations were resolved afterward. Aside from cases where the problem resolved itself after waiting, or cases where the memory update issue was fixed after sending an email directly to Antropic (although there was no reply), I am posting this here because I cannot determine whether the numerous users who reported "I am experiencing the same problem!" subsequently resolved the issue, how they did so if they did, or if they are still experiencing the same problem. How can I resolve this issue? Has anyone else experienced or is currently experiencing the same issue? For those who have recently encountered it, how did you resolve it? submitted by /u/Existential_Donut237 [link] [comments]

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. What I have today (context Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.) Stack: Python Telegram bot as the frontend Claude (Sonnet) as the brain via API — single conversational agent with full tool access Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) Conversation history kept in memory (last 20 messages per session) What actually works: Natural conversation with full tool access — ask anything, agent decides which tools to use Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief Morning briefing on demand: tasks, calendar, open support tickets, suggested focus Drafting messages for any channel with the right tone Creating and updating tasks with natural language 7 problems I haven't solved: 1. No memory between sessions History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a hot_context.md that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. 2. Purely reactive Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. 3. Can't tell if I'm avoiding something or actually blocked I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? 4. No closure ritual I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. 5. Context loading blind spots Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. 6. Hosting kills the file sync Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? 7. Context files go stale Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these? submitted by /u/GOA05 [link] [comments]

Clankers

“Clankers” has become one of the internet’s favorite new slang terms for robots and AI systems. The word actually comes from Star Wars, where clone troopers used “clanker” as a derogatory nickname for battle droids because of their loud metallic movements. It appeared in games like Republic Commando (2005) and later became iconic in The Clone Wars series. In 2025–2026, the term exploded across TikTok, Reddit, Instagram, and X as AI systems became impossible to ignore. People now use “clanker” to describe: • AI chatbots generating low-quality content • Delivery robots roaming city sidewalks • Automated customer support systems • The broader feeling that AI is suddenly everywhere The term works because it captures a real cultural shift: AI has moved from something abstract to something visible, interactive, and increasingly disruptive in daily life. Like most internet slang, it’s usually used humorously or sarcastically rather than maliciously: “The clankers found this thread.” “Another AI clanker post.” “Filthy clanker” at a sidewalk robot. What makes it interesting is that language evolves alongside technology. Every major technological shift creates new vocabulary, memes, and social dynamics. “Clanker” is essentially the internet creating a sci-fi flavored shorthand for frustration, skepticism, and anxiety around automation. The meme may be silly, but the underlying sentiment is real. submitted by /u/Annual_Judge_7272 [link] [comments]

Testing Realtime 2 Voice API OpenAI.

We’ve been messing around with the new OpenAI realtime voice + translation APIs over the last little while and I keep coming back to the same thought… I don’t think people fully get where this is going yet. We wired it into our own website as a test. Nothing fancy. Just wanted to see what actually breaks when you let people talk to a site instead of click through it. At first I thought it would just feel like a slightly better chatbot. It doesn’t. Once I hooked it into tools and gave it the ability to actually do things (we’re using the Agents SDK + Playwright for web browsing and control by a sub-agent), the whole interaction changed. I can literally just talk to the site like I would talk to a person and it can move around, pull info, trigger actions, and respond in context. I wanted a layer that that could navigate and respond by just talking. I know that sounds obvious, but it’s not how websites are designed at all. Ours certainly was not. A few things that have been interesting (and honestly a bit brutal) is how quickly this exposed weak structure. Our content was vague... so if your metadata sucks, if your pages are bloated or unclear… voice didn't let us hide behind a pretty UI design. The model just struggles or gives bad answers immediately. There’s no masking it with a nice UI. Latency has improved way more than I expected with the new voice model API. Before, when someone was talking, even small delays felt awkward. The new Realtime 2API tolerates those pauses wonderfully. We also started playing with the realtime translation side and that also feels like a bigger deal than it’s getting credit for. Not in a “multi-language support” way, more like… you just speak however you want and the system handles it. No toggles, no switching context. It’s subtle but it completely changes the feel. Our website is language agnostic. (13 supported languages using the Realtime 2 API) The bigger shift for me seems to be changing the way I want to think about websites and interactions. People don’t think in menus. They don’t think in pages. They don’t think in navigation. They think by intent and the second I added voice, i was forced to deal with that reality whether our website system was not ready. Great learning lesson. My Takeaway so far: Right now most of what I’m hearing and reading, people/businesses treats voice like a feature. Like and Add-on. Cool. Nice to have. Unsure if its practical. I don’t think that’s where this ends. I think this starts pushing toward systems you can just interact with directly. Personal assistants that actually execute. Internal tools you can talk to. Intake flows that don’t feel like forms. Stuff like that. Minimal website visuals. More dynamically displayed content based on interpretation of user intent. [Basically a cool wave form that animates differently depending on interaction stage] No direct site content visually. We’re still early and there’s definitely some friction [writing a second voice prompt on top of the text prompt so there is parity between our text chat and voice chat, but I’m pretty bullish on this direction - Guardrails, Rate-limits, Prompt Injection...]. Curious if anyone else here is actually building with it yet and what you’re running into. Feels like we’re right on the edge between “cool demo” and “this changes how software works,” and I’m not sure which way most people are approaching it yet. submitted by /u/Early-Matter-8123 [link] [comments]