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

I built a personal OS around Claude. The best part isn't asking it questions — it's being able to verify the answer.

Been building a small personal app on Cloudflare for a few months. It holds the operational mess of my life. Bank transactions from Chase, Apple Card, BoA business account. Receipts from Gmail going back to 2019. Green card paperwork. C-corp and LLC docs. Contractor agreements. Calendar events tied to people and locations. Notes, reminders. Health stuff: exercise, sleep, nutrition, wearable metrics. Before this, all of these lived in about fifteen different places. Finding anything meant remembering which place. The obviously fun part: asking Claude ""what did I spend on coffee in 2022?"" and getting $847 across 213 transactions, mostly Blue Bottle and Verve. Or ""what's my green card status and next deadline?"" Used to require digging through a folder of PDFs. Or ""which LLC signed the office lease?"" Previously: open three documents, compare dates, hope I got the right ones. That part is genuinely great. Killed a few SaaS subscriptions. Asking beats digging through folders every time. The time savings are real. The mental load savings are bigger. I don't have to remember where I put things. Setup isn't magic. Backend, REST API, that's it. Claude connects through a long-lived auth token. Simple instruction to query my system before answering anything personal. I upload stuff through Claude, tell it what it is: ""this is my 2024 tax return"" or ""this is the signed office lease."" Financial connections I refresh manually once a week. Handle 2FA myself. It's not fully automated and never will be. The 2FA on bank accounts is there for a reason. Not bypassing it for convenience. The manual step is intentional. I want to know when my financial data is being accessed. Even by my own system. After using this for a while, I realized the useful part isn't just more context. Plenty of people are building personal knowledge bases, second brains, AI-connected databases. The ""connect everything to AI"" pitch is everywhere. Not a differentiator anymore. Everyone's doing it. What's rarer is knowing whether the AI used the right data and reached the right conclusion. What I actually care about: can I tell what Claude did with that context? When it says I spent $847 on coffee, I want to know which accounts it checked. All three? Or just the first one it found? Which transactions counted? Coffee shop visits categorized as ""dining."" Did it catch those? Did it include reimbursements? Client meeting coffee, that $34 shouldn't count. Is the data current? Last sync miss two weeks? These distinctions matter. $847 vs $913 vs $712. All plausible. One is right. Legal deadline. Which filing did it pull from? Original notice or latest extension? Most recent version of the document or an old draft that got superseded? Immigration paperwork doesn't forgive ""the AI told me the deadline was different."" Financial summary. I don't want convincing. I want to see the run. What steps, what assumptions, what did it leave out? This got real when I started using it for things that matter: taxes, legal paperwork, contracts, monthly cash flow. AI produces something that looks right so easily. That's what it's built to do. But ""looks right"" and ""is right"" are not the same. The gap between them is where bad decisions live. Concrete example. I asked Claude for my average monthly burn rate for tax planning. It gave me $11,200. Looked reasonable. In the ballpark. But I dug in. It had included a one-time $8,000 equipment purchase, skewing every month. Double-counted a transfer between business and personal accounts as an expense. Real number: closer to $9,400. The answer was formatted beautifully, explained clearly, and wrong by almost 20%. If I'd used that number for quarterly taxes, I'd have underpaid. The IRS doesn't care that your AI was confident. An AI that says $12K runway when I have $8K because it double-counted a transfer. Worse than no answer. No answer, I go check. No answer, I know I don't know. The danger of AI isn't being wrong sometimes. It's being wrong in ways that look exactly like it's right. I've caught context drift in my own setup too. Stuff I built. Stuff I supposedly understand. One rule in the app. Another in a Claude project. Another buried in a three-month-old prompt I forgot about. Important exception like ""this contractor agreement has a different notice period than the template."" Still only in my head. Try a different AI tool or add an integration. I'm explaining my own system to myself again. If I can't keep my context straight, a team of five with twenty tools has no shot. The more I use this, the more I realize: what I built for myself is a prototype of something bigger. I can do this because I'm technical and I had the time to iterate. Most people can't. But the need is universal. Knowing whether an AI answer is trustworthy enough to act on. Everyone who uses AI for anything important has that question in the back of their head. ""Should I actually trust this?"" That got me exploring Claps. Not an

I built a local-first safety layer for AI agents (Runewall)

Hey all. I just shipped my first real project to PyPI and I'd genuinely value some honest feedback before I keep building. What it is: Runewall is a local CLI + Python SDK + MCP server that sits between an AI agent and a real-world action (file write, API call, deploy, etc). It previews what the agent is about to do, runs a dry-run by default, logs everything to a local SQLite file, and blocks execution unless you explicitly enable it. The pitch: agents are moving from answering to acting, and most agent frameworks treat safety as "a system prompt and a prayer." I wanted something local, inspectable, and CLI-first that doesn't require signing up for anything or sending data anywhere. What works today: Dry-run for 5 services with real test coverage: GitHub, Vercel, Netlify, Supabase, Cloudflare 3 more partially covered and under review: Slack, Discord, Linear Additional experimental map ideas in the repo (Stripe, Notion, Jira, AWS, etc.) — not yet claimed as supported until they have realistic dry-run coverage Local MCP stdio server (drops into Claude Desktop / Cursor / anything MCP-compatible) Policy explain / test / audit commands SQLite action log + snapshots 650+ tests, including a security suite that proves dry-run makes zero network calls and tokens never hit the log Adding a new integration is intentionally cheap (a map file + tests), but I'd rather have 5 I trust than 20 I don't. What doesn't (yet): Signature verification for community map packages Anything cloud / team-mode (and honestly I want to resist that as long as possible) Install: pip install runewall Repo: https://github.com/harims95/runewall What I'd love feedback on: Does the "local-first agent safety runtime" framing actually map to a problem you have, or does it sound like a solution looking for one? If you're using MCP, is the way I expose dry_run / policy_test as MCP tools the right shape? What would make you actually try this on a real agent project. submitted by /u/Hariharanms [link] [comments]

Bezos wants AI that designs jet engines, and admits it has no demo yet

So I came across the latest on Prometheus, Jeff Bezos's new AI company, and it is a noticeably different from everyone chasing the next chatbot. instead of text or code, Prometheus is aimed at the physical world. the idea is ai that understands real physics and manufacturing well enough to help engineers design and test actual hardware. Bezos calls the goal an "artificial general engineer" and describes it as a very modern version of cad software. He has also been clear it is not a robotics company, which surprised me. So although the vision is huge, the demo is the thing nobody can point to yet. And I think the reason is that the entire pitch rests on simulating the physical world accurately, which is far harder than generating text merely. A language model that is slightly wrong writes an awkward sentence but an engineering model that is slightly wrong could lead to unimaginable disaster, so the accuracy bar is very unforgiving. Also there is the data problem like text models had the entire internet to train on but high quality engineering data sits inside private companies and cost quite a lotand often comes from physical testing you cannot scrape. That is probably why Prometheus is reportedly trying to buy industrial firms outright, just to own the data pipeline. so the missing demo makes sense. shortening the design loop does not shorten the parts of the process that are slow on purpose, because being wrong there is dangerous. I am not predicting it fails. A team this funded, aimed at a real bottleneck, is worth watching. The honest read is that the demo is missing because the hard part has not been solved yet, not because they are hiding it. submitted by /u/Gullible-Tale9114 [link] [comments]

I built agentcn to help ship production-ready agents faster

A few days ago I came across eve on X, a filesystem-first framework for building AI agents that feels a lot like Next.js and deploys directly to Vercel Functions. While exploring it, I also found Flue, an open agent framework powered by Pi, the open agent harness. After playing around with both for a bit, I realized they'd fit really nicely into the shadcn/ui ecosystem, so I built agentcn. Some of the features: Built on Eve and Flue Zero-config, one-command setup shadcn/ui-compatible components (copy, paste, customize) Production-oriented recipes for orchestrators, subagents, tools, and skills instead of only hello-world examples 100% free and open source Claude helped with some of the boilerplate generation and iteration while putting together the initial recipes and documentation. It's still early days and only has a handful of recipes right now, but I'm planning to add more. I'd love to hear any feedback, especially from people already building with Eve or Flue. GitHub: https://github.com/shadcn-labs/agentcn Docs: https://agentcn.run submitted by /u/dank_clover [link] [comments]

OpenAI Unveils Custom Chip It Designed With Broadcom to Boost Its AI Infrastructure

OpenAI's engineers designed the chip, called Jalapeño, together with Broadcom to perform a specific AI task known as inference, during which data is crunched in order to answer a user's query to a chatbot like ChatGPT. submitted by /u/Fred9146825 [link] [comments]

South Korean AI app went viral for AI characters that can talk, react, and respond to camera context

https://reddit.com/link/1ue5o4i/video/ozv6sohrc69h1/player Instead of only texting AI characters, the app shows characters that can talk through voice, lip sync, react with facial expressions, and respond to camera context during the conversation. The demo suggests a shift from text-based character AI toward video-native AI characters, where the interaction feels closer to a live call than a chatbot. For ML developers, the interesting part is the underlying stack: vision, speech, memory, avatar animation, lip sync, and low-latency orchestration all have to work together in real time. The open question is whether this becomes the next interface for entertainment AI, or if latency and uncanny valley issues keep text chat dominant for now. submitted by /u/DonutRare5633 [link] [comments]

Leaked files detail Russia's Social Design Agency building fake reference platforms to contaminate AI training data and search indices

Leaked planning documents obtained by Bloomberg describe a Russian state-linked operation called "Project 2026," run by the Social Design Agency (SDA), with the stated goal of seeding the information layer that AI chatbots and search engines draw from. This is a structurally different threat than the bot and social media campaigns practitioners have long accounted for. The documents describe three components. A German-language Wikipedia clone is designed to look like legitimate reference material while embedding Russian narratives, on the explicit theory that AI systems trained on publicly available text would absorb and repeat those narratives in generated answers. A second component is an AI-driven "self-filling knowledge base" also targeting Germany, for which the documents state that servers are already running and the database already contains over 200,000 pages. A third initiative targeting Western think tanks launched in English, with German, French, and Spanish versions planned. Our coverage: https://aiweekly.co/alerts/russias-project-2026-targets-ai-and-search-leaked-files-show submitted by /u/Justgototheeffinmoon [link] [comments]

What I learned from $8,960 worth of Claude usage in 30 days

I’ve been pushing Claude pretty hard over the last 30 days. According to CodexBar, I ran through about 7.4B tokens, with an estimated API-rate equivalent of $8,960.31 What I actually paid was about $220/month for the subscription. TL;DR: I ran about 7.4B tokens through Claude in 30 days. CodexBar estimated that at $8,960.31 in API-rate equivalent usage, while I paid about $220/month. As u/ShelZuuz pointed out, caching plus different input/output token pricing means the real API equivalent is hard to calculate and likely lower. Either way, my main takeaway is the same: the leverage is not using AI for everything. It is using AI to map your business/workflows, then deciding what should be AI, normal software, automation, or human work. Here’s what I learned: 1: AI gets much more interesting when it stops being a chatbot Some of the most useful things I’ve built are scheduled agents. I have agents that run every hour, perform lead generation or enrichment, then add and index contacts into my CRM. Other agents can pick those up later to help with lead scoring, qualification, disqualification, pipeline movement, and initial outreach. That’s where AI starts to feel less like “asking a model questions” and more like building an operating system around the business. 2: Not everything should be an LLM call This was probably the biggest lesson. A lot of the token usage went into building systems that are actually more traditional software: deterministic workflows, scripts, checks, queues, indexes, and rules. The LLM is useful, but it should not be the entire system. The best results came when I used AI where judgment, language, or ambiguity mattered, then used normal software everywhere else. 3: Vendor lock-in is getting vicious Every provider wants you inside their agent, skill, workflow, or automation ecosystem right now. And I get why. If your daily business processes are running through bloated AI workflows, you become very expensive to leave. But a lot of those workflows should not stay as AI workflows forever. In many cases, the better answer is a simple automation, a small internal tool, or a more efficient deterministic process. That can take something from costing tens of dollars per run down to under $0.50, depending on what the process actually is. 4: Use AI to map the business before you automate the business This is where I think AI is incredibly useful right now. Use it to document your processes, clean up messy context, identify repeatable steps, map decision points, and expose the parts of the business that are currently living in people’s heads. Once the business is mapped, you can make better decisions. Some parts need traditional software. Some parts need basic automation. Some parts are good fits for AI agents or skills. And some parts should probably stay human. 5. AI subscriptions are wildly subsidized right now The gap between what this usage would cost at API rates and what I paid through a subscription is pretty insane. I don’t know how long this “free buffet” lasts. That’s why I’ve been using it to build durable systems now. Things that can still serve the business if or when the economics change. 6. The client value is the real unlock This subsidized gap means I can deliver work that would have been too expensive or too time-consuming before. For clients, that might mean better research, cleaner documentation, deeper process mapping, faster implementation, better QA, or more thoughtful automation. A lot of that does not need to show up as a direct line item. It just shows up as better work. My takeaway: the leverage is in using AI to understand your business deeply enough that you can decide what should be AI, what should be software, what should be automation, and what should stay human. AMA. I’m happy to share workflows, examples, or anything else that helps anyone here. \edited to improve readability* submitted by /u/Onemightymoose [link] [comments]

I built a fairly detailed medieval peasant sim by directing Claude Code agents in parallel. Write-up on how.

Domesday is a bleak life-sim set in England just after the Conquest, running 1068 to 1086. I designed and directed it; the agents did the actual building. The bit that might interest this sub is how it was made to test itself. The game logic runs with no browser, so it can play through thousands of times headlessly and catch its own bugs, and there’s a separate AI step that looks at each rendered screen, since a coding agent can’t see a picture and tell you a sprite is floating off its tile. Mostly it was an experiment in running an AI build team without ending up with something I didn’t recognise as mine. Write-up: https://domesdaygame.vercel.app/how-domesday-was-made.html. Play it: https://domesdaygame.vercel.app/ Happy to go into any of the workflow. submitted by /u/pandulfi [link] [comments]

Is AI app development becoming easier or just more crowded?

I've noticed that AI app development seems more accessible than ever. Between open-source models, APIs, and no-code tools, it feels like almost anyone can launch an AI-powered product today. At the same time, the competition is intense. Every week there's a new AI assistant, chatbot, or productivity tool entering the market. It makes me wonder whether the challenge has shifted from building the technology to actually creating something people want to use. A friend of mine works at a startup and mentioned how teams like thedreamers often spend more time discussing user workflows than model selection. That perspective surprised me because I always assumed the AI component was the hardest part. For those actively building products, where do you spend most of your time? submitted by /u/No_Hold_9560 [link] [comments]

Why am I even in the middle of this?

Just an amusing anecdote from a user only about 10 days in to my AI awakening. If not allowed, delete me. I asked Claude to design an integration between my business's time tracking app and billing platform. It was confident what I wanted to do was very doable. It found the appropriate API's, guided me through the setup, and then started testing. Then, in the most urgent and concerned tone I've ever heard Claude express, it warned me that something had gone wrong and that a test invoice may have inadvertently been sent to a client. (It had not, only appeared that way on the platform, I know not to anthropomorphize, but when I told Claude I swear it was relieved.) The urgent issue settled, Claude began to troubleshoot. After some deliberation Claude states, your billing platform's API is bugged. It does not behave as the documentation states. I don't see a workaround. It writes a letter and suggest I send to the platforms technical support. This letter was 3 paragraphs of technobabble, nonsense to me. I sent it. Well, unsurprisingly, Level 1 technical support is an AI Chatbot. I paste it's response to Claude, then Claude back to it... I have absolutely no idea what they are talking about. After a few exchanges of not getting anywhere, I escalate to a human. Human reviews the chat, an hour later confirms Claude is right, that function is bugged. Promises a fix. Next morning, fix is tested and deployed, they thanked me for bringing it to their attention. Claude confirms all is good, the build accomplished all I hoped. Claude found the bug, chatted with a developer's AI chatbot about it, a human almost certainly used AI to summarize that exchange, then confirm, write and test a bug fix. 12 hours start to finish. I'm happy to have my integration and for maybe helping to fix a bug for other users. My only contribution? "Claude, I want this thing." submitted by /u/tylerdoubleyou [link] [comments]

I created a tank based game called “Scorched Steel” using Claude Code

Hey r/ClaudeAI! I recently built a small web game called **Scorched Steel**, inspired by the classic games I used to play in my childhood. It is completely free to try right in your browser. What the game is: Scorched Steel is a turn-based artillery game where you have to calculate angles and power to defeat the enemy tanks. How I used Claude Code to build it: I used Claude Code extensively to bring this idea to life. Since I was just starting out, Claude acted as my co-pilot and helped me with: Core Logic: Claude generated the math for the projectile physics and collision detection. UI & Graphics: It helped me structure the HTML/CSS and set up the game loop on the canvas. Debugging: Whenever a mechanic broke, I fed the error logs back into Claude to help me refactor the code and fix the bugs quickly. The process of going from an idea to a deployed game was incredibly fast thanks to Claude Code. The game is completely free to play. I’d love for you to test it out and give me any feedback on the gameplay, or let me know if you have any questions about the prompts I used! You can play it here: https://scorched-steel.vercel.app/ Thanks! submitted by /u/arpan171 [link] [comments]

How many AI tools do you actually pay for at the same time?

I use AI tools regularly, but I’m starting to question how many paid subscriptions make sense at once. A general chatbot covers a lot, but then there are research tools, coding assistants, image tools, transcription tools, and document tools. The overlap is getting harder to ignore. For people who use AI for real work or study, do you keep multiple paid tools active, or do you rotate based on the project? I’m trying to find a practical approach that balances capability, cost, and not spending half my time comparing tools. submitted by /u/RhubarbLarge2747 [link] [comments]

Persona’s biometric ID verification: what’s happening / why it matters

I run an R&D consultancy in Norway. Part of my work involves GDPR and EU AI Act compliance. I’m not here to be alarmist, there’s enough of that already, but I do want to lay out what’s going on with Persona verification and why the concerns are legitimate. Persona Inc. is a third-party identity verification company. When Anthropic or OpenAI require “ID verification,” they’re outsourcing it to Persona. The process typically involves uploading a government-issued ID and a live selfie. Persona uses biometric comparison to match your face to the document. Under the EU AI Act (Regulation 2024/1689), biometric identification systems are classified as high-risk (Annex III) or outright prohibited (Article 5), depending on context. Under GDPR, biometric data processed for identification is special category data (Article 9), the highest protection tier. Processing it requires explicit consent and must meet strict necessity and proportionality tests. The question regulators will ask is simple: is biometric verification necessary and proportionate for the stated purpose? For accessing a coding assistant or chatbot API, that’s a hard case to make. Your government ID and biometric data go to Persona, not Anthropic (or OpenAI). Persona’s retention and security practices become your problem. You’re trusting a company you didn’t choose and may never have heard of. Email verification, payment verification, and phone verification already establish identity to a reasonable standard. Biometric verification is a significant escalation with no clear justification beyond “we want to.” Requiring a face scan and government ID to use a developer tool creates a ‘surveillance-adjacent’ dynamic. People in sensitive roles, journalists, researchers in authoritarian contexts, and privacy-conscious users are disproportionately affected. If verification becomes mandatory, e.g. for API access, the choice is comply or lose access to tools that are increasingly essential for professional work. This isn’t Know Your Customer (KYC) for financial services, where biometric verification has clear legal grounding. This also isn’t about preventing CSAM, (where targeted measures can be justified). I see it as general-purpose access to AI tools. the verification being demanded is wildly out of proportion to that purpose. I’d like to see Anthropic and OpenAI explaining specifically why existing verification methods are insufficient, publishing a Data Protection Impact Assessment (DPIA) for this processing (required under GDPR Article 35 for biometric data), and offering meaningful alternatives for users who reasonably object. We can disagree on the severity of this, but the facts are straightforward: biometric ID verification via a third party with a shoddy history (study Rick Song’s journey via his LinkedIn - certainly a fast paced rise to fame. He has a bachelors in computer science from Rice Uni 2013, 5 years of work experience as an engineer then co-founder / CEO of persona, handling extreme amounts of the most sensitive global biometric data. Add on to that a few breaches / exposures and cash injection by Peter Thiels founders fund, it is no wonder the pubic are sceptical. persona engage in significant sensitive personal data processing operations, and users deserve more than a checkbox consent screen. Edit: This post is getting more traction than I expected so I want to point people toward the primary source work that informed a lot of the technical detail here. Celeste (vmfunc) published “The Watchers,” a detailed investigation into Persona’s exposed codebase and its capabilities, including the 269 verification checks, adverse media screening, and federal reporting infrastructure. Part 2 covers the direct correspondence with Persona CEO Rick Song, who to his credit engaged directly and in writing. Whatever your view on this, their work is thorough, transparent, and worth reading in full. Part 1: https://vmfunc.gg/blog/persona/ Part 2: https://vmfunc.re/blog/persona-2 Credit where it’s due this conversation is better because people are doing the actual research. submitted by /u/FiveNine235 [link] [comments]

My personal experience from last 4 years about AI

Hey everyone, i don't know it will approve or not btw Im Akash I’ve been building in the AI space for the last 4 years pretty much since ChatGPT first dropped and blew everything up. During that time, my team and we have built a ton of stuff: custom AI chatbots, SaaS platforms, automated customer support systems, and a lot of tailored products. ​ In the beginning, crafting the perfect prompt felt like finding a secret cheat code. If you didn't phrase things exactly right, the output was hot garbage. ​ But honestly? Looking at the landscape right now, using AI has become incredibly common and, frankly, pretty easy. The llms have gotten so smart that they understand terrible, poorly formatted prompts shockingly well. You don’t need to be a "prompt wizard" anymore to get a decent result. ​ So, if prompting isn't the competitive advantage anymore, what is? ​ From my experience building these products for actual business use cases, the real bottleneck and the real moat is your data. ​ AI doesn’t just need a clever question; it needs deep, accurate context. The businesses that are actually winning the AI transition right now aren’t the ones with a secret library of prompt templates. They’re the ones focusing on: ​ Data Volume Across Sectors: Collecting and organizing data from every single corner of the business (sales, support, logistics, ops). The more touchpoints you actually map out, the better the AI can understand the business ecosystem. ​ Clean Data & Context: If your data is messy, fragmented, or siloed, the AI is just going to spit out generic answers. Clean, rich data gives the model the exact context it needs to deliver hyper-tailored, actually useful outputs. ​ If you want your AI tools to actually drive ROI, stop spending weeks tweaking your system prompts. Go fix your data pipelines instead. Context is king, but data is the kingdom. ​ Curious to hear from other devs and founders building right now are you guys seeing the same shift? Are you spending more time on data ingestion or still tweaking prompts? submitted by /u/itsjhakash [link] [comments]