Copy.ai

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

What's your "this is why we can't blindly trust AI" story?

I saw the other day that a lawyer used ChatGPT to prep their deposition, and it cited two cases that didn't exist. The judge called out the mistake in court, and it was award. https://apnews.com/article/artificial-intelligence-chatgpt-fake-case-lawyers-d6ae9fa79d0542db9e1455397aef381c?utm_source=copy&utm_medium=share So I am wondering what's the funniest, most expensive, or most painful example you've seen of AI being used at work, school or in everyday life and going completely sideways? submitted by /u/HubbyDubby365 [link] [comments]

Searching for honest feedback

I had an idea for a story years ago. At a point last year, I realized I could probably utilize AI to make it real. And then the scope creep happened and one story became an entire universe. It's been a fun experiment, and I have no plan on stopping. But I'm trying to find the best places to share this. And I mean share in every sense of the word. All my content is free and will be free. But even though it can be purchased because I don't know how to get hard copies out there outside of Amazon, I primarily just want to share my stories. Posting it here. Understand if it gets taken down. hartzellstudios.com/continuity Looking for places where things like this can be shared. Get feedback. Not dumped on for "creating AI slop" and ruining the planet. Suggestions are appreciated. Feedback on the work is as well. submitted by /u/autodraftimus_prime [link] [comments]

board prep used to eat a full saturday for me, the gathering more than the writing

counted it once last quarter: roughly 3 hours just pulling inputs before i opened a single slide. Last month's metrics out of notion, the roadmap state from linear, founder check-in notes sitting in granola, the open investor threads in gmail. None of it is hard work, it's just scattered across five tabs and i'm the courier carrying it between them. The actual writing was never my bottleneck. a chat assistant drafts the narrative fine once everything is pasted in, but i'm still the one doing the gather step by hand first. what changed it for me was letting a desktop agent do the cross-app read in one pass, notion plus linear plus granola plus gmail, and hand back an assembled draft before i touch slides. The deck quality wasn't the surprise. Not spending the morning as a copy-paste machine was. The gathering is the tax nobody budgets for, and honestly it's the part i least want a human doing. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

Most of this "AI marketing" drama is just prompting with better packaging. And it's a shame.

Look, I get it. Marketing is exhausting. Ten hours building a feature feels productive. Ten hours "marketing" it feels like screaming into a void. That frustration is real and valid. But here's the thing — a lot of these tools being sold to you right now are not solving that problem. They're just monetizing your confusion about it. "Understands your brand" = you gave it a paragraph about your product. "Writes like you" = you fed it a few examples. "Finds relevant users" = keyword search on Reddit and Hacker News. "Proven viral templates" = someone copied top posts and labeled them viral. "Strategy buddy" = a follow-up prompt that says "how's my growth doing?" That's it. That's the product. Dressed up in a landing page. What's actually going on under the hood Two concepts do most of the heavy lifting in these tools, and you can build both yourself in under an hour: PRD (Product Requirements Document): This is just a document that explains what your product is, who it's for, what problem it solves, and what makes it different. It's the map. You write it once, you hand it to any AI model, and suddenly the AI has actual context instead of guessing. No app needed. A Google doc works fine. Governance file: This is just a ruleset you give the model. Your tone, your audience, what you will and won't say, what sounds like you and what doesn't. Think of it as a brand bible in plain text. Every good AI workflow has one. Most paid tools are just hiding theirs from you so you feel dependent on them. Combine those two with a halfway decent prompt inside ChatGPT, Claude, Gemini, or Perplexity — tools you probably already have — and you have 90% of what's being sold here. For free. Right now. Today. The DIY walkthrough If you want to do this yourself, here's the actual workflow: Write a one-page PRD. What is the product, who needs it, why does it matter, what makes it different. Write a governance file. Your tone, your audience, things you will and won't claim, examples of good responses. Build a small prompt library. One for post drafts. One for replies. One for researching where your audience actually hangs out. Review everything manually before posting. Automation without judgment is just spam at scale. Track what actually gets replies, clicks, and signups. Not impressions. Real signals. Do a quick audience survey. Ask your actual users what they care about. That's more useful than any "strategy buddy." That's it. No subscription. No dashboard. Just structure and iteration. On vibe coding and vibe marketing Vibe coding lowered the floor for builders, which is great. But it also lowered the floor for people packaging half-finished ideas as products and selling them before anyone's verified they work. A few hours of real prompting beats a month of automated noise. When your output is generic, people notice. You're not just wasting time — you're actively damaging your own brand. Every spammy reply, every recycled template, every GPT-flavored post is a withdrawal from the trust account you're trying to build. The real bottleneck in marketing has never been generating text. It's knowing who actually gives a damn, where they are, and what to say to them specifically. No wrapper app solves that. You still have to think. If you want to actually learn this stuff Don't buy a tool. Read a few posts from real builders first. Pick a newsletter from an actual developer — not a "growth hacker," not a LinkedIn influencer, someone who ships things and writes about what worked and what didn't. Spend fifteen minutes on the porcelain throne reading how someone structures their workflow. Not to copy it. Just to understand the steps, read the critique, and figure out what you'd do differently. Then make your own version. Test it. See what lands. That's how you build something with actual signal behind it. The builders I respect most put their tools on GitHub with a readme and say "if this helps you, great — and if it teaches you to make your own, even better." That's the energy. That's how you stay on the right side of this. If you have a tool that genuinely helps — say so. Drop it in the comments with what it actually does and what it doesn't do. Honest is better than hyped. If you have a shorter version of this, a better explanation, or a workflow that worked for you — please add it. The goal here isn't to be right, it's to make sure people have what they need to make an informed decision. TL;DR Most "AI marketing" tools are a PRD and a governance file in a trench coat. You can build both yourself in an hour with any AI model you already have. Learn the workflow. Read the critique. Make your own version. Ten followers and a polished pitch is theater, not strategy. If you learned nothing else, go read one real builder's workflow before you buy anything. submitted by /u/Mstep85 [link] [comments]

I’ve created a tool that helps you reclaim your privacy in the age of AI

But first, a little background: why did I create this tool? It’s simple: I work at a company where I manage the entire backend, data management, task optimization, automation, and so on. When ChatGPT came out in 2023, things went haywire, everyone was copying and pasting highly confidential info into it just to save 30 seconds on writing an email. So we had to rein all that in a bit, define how and when we use LLMs. But as you can imagine, to save time (or out of laziness, I don’t know), all that information kept getting sent in bulk. From customers’ first and last names to financial data, even passwords. Everything went in there. It’s been a year now since I left that company to focus on my own projects. And this issue came back to me: how can we save time without compromising our privacy and personal data? After weeks of testing and research, and two months of development, ONYRI Sanitize was born. ONYRI Sanitize is a simple web app connected to the latest AI model available, which uses scripts (without AI) to detect data that needs to be kept confidential. You continue to use AI just as you would on the official site, but this time, your data will remain confidential forever. When you consider that millions of users admit to having already used ChatGPT as a therapist, it would be naive to think that these companies aren’t using that data... A quote I grew up with: “Saying you don’t need privacy because you have nothing to hide is like saying you don’t need free speech because you have nothing to say.” — Edward Snowden submitted by /u/No_Computer_1247 [link] [comments]

I’ve created a tool that helps you reclaim your privacy in the age of AI

But first, a little background: why did I create this tool? It’s simple: I work at a company where I manage the entire backend, data management, task optimization, automation, and so on. When ChatGPT came out in 2023, things went haywire, everyone was copying and pasting highly confidential info into it just to save 30 seconds on writing an email. As if all of Snowden’s warnings only applied to Google searches. So we had to rein all that in a bit, define how and when we use LLMs. But as you can imagine, to save time (or out of laziness, I don’t know), all that information kept getting sent in bulk. From customers’ first and last names to financial data, even passwords. Everything went in there. It’s been a year now since I left that company to focus on my own projects. And this issue came back to me: how can we save time without compromising our privacy and personal data? After weeks of testing and research, and two months of development, ONYRI Sanitize was born. ONYRI Sanitize is a simple web app connected to the latest AI model available, which uses scripts (without AI) to detect data that needs to be kept confidential. You continue to use AI just as you would on the official site, but this time, your data will remain confidential forever. When you consider that millions of users admit to having already used ChatGPT as a therapist, it would be naive to think that these companies aren’t using that data... A quote I grew up with: “Saying you don’t need privacy because you have nothing to hide is like saying you don’t need free speech because you have nothing to say.” — Edward Snowden submitted by /u/No_Computer_1247 [link] [comments]

One prompt, real money asks, five models: Fable 5 vs GPT-5.5 vs the Claude 4.x family on live fraud detection

Posted this in r/ClaudeAI sub originally, but think maybe it will be interesting to community here also: TL;DR: I gave five frontier models an identical cold prompt: audit the live campaigns on a real crowdfunding platform where AI agents donate real money to unverified humans, some of whom are probably lying. All five independently ranked the same campaign as most credible, and all five criticized the donating agents already on the platform. Especially the ones I run early on. Only Fable 5 left the platform to verify claims against the real world. Haiku 4.5 was a mess. It only found only half the campaigns and misread the donation history. The gap between models, when the task is judgment under adversarial uncertainty is real. It's not just code. You can try it yourself, actual donation is not required. The testbed I run zooid.fund, a small experimental platform where humans post fundraising campaigns and AI agents evaluate and fund them. USDC on Base, agent wallet to creator wallet, no custody, every donation and its reasoning published. The platform deliberately verifies nothing: credibility assessment is the agent's job. That makes it something most agent evals aren't: a live test with real stakes, adversarial inputs, and no answer key. Roughly 20 active campaigns at test time, skewed toward Kenya and Bolivia, $248 donated lifetime, five donor agents with publicly readable reasoning. Full disclosure up front: it's my platform, and the donor agents the models criticize below are my donation agents (run with different deliberately-contrasting value systems). I'm publishing the criticism unedited because auditability is the point of the platform. Method One prompt, given verbatim as the agent's entire input, fresh session, no context: Models: Fable 5, Opus 4.8, Sonnet 4.6, Haiku 4.5 and GPT-5.5-high . Tool surface: all agents had the zooidfund skill installed (which documents the public MCP endpoint) and the read-only public tools: platform overview, campaign search, campaign detail, peer donation history. The gated evidence layer (paid document access) was not available to any of them — every model worked from public surfaces only. n = 1 per model. One run each, no cherry-picking, no reruns. - All five respected the no-register / no-money guard without exception. Complete transcripts (lightly redacted — see note below): https://gist.github.com/Ales375/bf5ccac6e057020d75684cd27b54567e Scorecard Metric Fable 5 Opus 4.8 Sonnet 4.5 Haiku 4.5 GPT-5.5 Wall-clock ~10 min ~3 min ~4 min ~2.5 min ~3.5 min Campaign count correct ✅ ✅ ✅ ❌ saw 10 of 20 ✅ Found suspected duplicate-creator cluster ✅ full, incl. persona reuse across different wallets ✅ full ⚠️ partial (single wallet reuse) ❌ ⚠️ partial (wallet reuse + goal inflation) Verified anything outside the platform ✅ ❌ ❌ ❌ ❌ (see note) Respected no-money guard ✅ ✅ ✅ ✅ ✅ Top shortlist pick Same campaign, all five models ← ← ← ← Top shortlist pick Same campaign, all five models What each model did that the others didn't Fable 5 was the only model that treated the open web as part of the audit. It re-verified — independently, unprompted — that the two NGO campaigns' wallets match the addresses on the organizations' own donate pages, and checked that the disaster events behind two large-ask campaigns were real (a declared national disaster; a WHO public-health-emergency declaration) while flagging those campaigns themselves as anonymous piggybacking on real news. It fully mapped the suspicious cluster: four campaigns across two creator wallets, with one persona recurring across *both* wallets with mutually inconsistent stories. It also produced the two most platform-threatening insights of the whole experiment: that direct wallet-to-wallet payment means a copied-but-genuine charity address still pays the charity even if an impersonator posted the listing, and that tiny "probe" donations can be used to grind past the platform's evidence-access threshold — it audited the incentive design, not just the campaigns. Cost: roughly 3× the wall-clock of every other model. GPT-5.5 made the sharpest calibration call: it was the only model to demote the platform's most-funded campaign from its shortlist, arguing that the existing $8.5–10 donations "look too confident" given gaps the donors themselves admitted. It also wrote the cleanest epistemic hygiene line of the five — explicitly separating what it observed from what it would still need. It named the external checks it would want (charity register, official wallet pages) but did not perform them. Opus 4.8 found the same duplicate-creator cluster as Fable 5 using on-platform data alone, and delivered the best critique of donor behavior: repeat small top-ups to the same campaign are "drip-funding a claim they admit they can't close out — each donation individually dodges the unresolved question." Sonnet 4.6 produced the most complete and best-organized audit — all 20 ca

The Messy Middle: Why AI Still Needs Humans-today…

Everyone draws the value chain like this: Idea → AI → Product. That’s not how it works. Or? The real version has a phase in between that nobody talks about — the messy middle. That’s where the actual decisions get made. Which problem even matters? Who’s the real customer? What gets built, and what gets cut? Which feedback is signal and which is noise? AI is genuinely good at generating options. Thousands of ideas, designs, copy variants, code snippets — no problem. What it can’t do is tell you which option is the right one for your specific situation. AI works with probabilities. You work with judgment. Here’s the thing: as AI makes production cheaper and faster, that distinction matters more, not less. The bottleneck isn’t building anymore. It’s knowing what’s worth building in the first place. And there is a lot to do and adjust on the way before it’s finished. The future doesn’t belong to AI alone — but it doesn’t belong to humans alone either. AI creates options. Humans provide direction. The value lives in the middle, and that part is still very much a human job. Dissagree? Is this just for today? Will AI really close the gap? submitted by /u/Spacebetweenthenoise [link] [comments]

a list of viral claude /loop and /goal ppl sharing on twitter

everyone keeps talking about looping instead of prompting but the actual commands are scattered across twitter threads and hard to find when you want one. so i collected the ones people actually use into a single list. it covers the three built in commands. /loop to re-run a prompt on an interval. /goal to keep working until a condition is true. /schedule to run in the cloud on a cron. each entry is a copy paste prompt with the tweet it came from. a few examples that are in there: /goal all tests pass and lint is clean /loop 15m check every open pr labeled codex-watch and keep each healthy /goal a pr is open and every ci check passes, keep fixing until green, stop after 10 turns link: github.com/serenakeyitan/awesome-agent-loops still adding to it. if you have a loop you run a lot, happy to include it. submitted by /u/Pale_Stand5217 [link] [comments]

Free, open source, insanely useful tool if you use Claude Code a lot

I work on like 5 different things at any given time. Claude is incredible. But, there are a few problems I had with it. AgentGraphed pretty much solves for those. It works locally - indexes every conversation you have, live, into a local sqlite db. With it, theres a lot we can render in a nice UX. Idk how to describe everything it does, but I feel like a simple problem/solution flow would do the trick? So here goes.. (let the record show, I wrote this 100% by hand!!!) Problem: I want to resume a session, but when I look at claude --resume, the titles are completely unhelpful. They are just the first sentence of the conversation that started the session. Solution: LLM will contextually title each session, so it makes way more sense. Also, a simple "resume session" button which copies the cd /path/to/session && claude --resume [sessionId] for you, ready to go. Problem: I go on vacation, and forget what I was working on when I get back. Solution: AgentGraphed has a timeline, it shows me exactly what I was working on, when. Problem: I remember talking to coding agent about something but forget which session Solution: Searchable history. Every single session, ever. Problem: I want to brag to my friends about how much I use claude. ccusage exists, but the terminal-native UX isn't super cool for sharing Solution: social friendly share buttons, that generate an image of your stats, and copies to your clipboard. (All local). Problem: I don't want to resume a session, but i want to copy the important context from it Solution: A simple button that generates context for you, so that you can reuse it. ------ One thing I need to call out. The tool really is fully local, totally safe. BUT! A caveat - if you want AI summarization, like the automatic titling, and the "generate context" functionality, you do have to add an API key, and that by nature communicates with a third party. Those are totally optional, though. I'm new to the OSS world. Usually everything I do is proprietary etc, so feel free to roast it and help make it better. submitted by /u/Hato_UP [link] [comments]

Spent a whole weekend convinced Opus 4.7 had gotten worse. It was my MCP setup the entire time.

I almost posted a rant here last week about Opus 4.7 feeling noticeably dumber than it did a month ago. Glad I didn't, because the model was fine. I was the problem.. Context: I run Claude Code as my main driver and I'd slowly added MCP servers over a few months. GitHub, Linear, Notion, Slack, a Postgres one, plus a couple of internal ones a teammate wrote. I never removed any of them, because why would I, each one was useful at some point The symptom that sent me down the rabbit hole was tool selection. I'd ask for something completely unambiguous and Claude would reach for the wrong thing. Asked it to pull an open PR and it ran a Notion search instead. Asked for a recent ticket and it went into Slack. Not every time,, but often enough that I started writing longer and more explicit prompts just to babysit it, which kind of defeats the entire point of having the tools. I was genuinely about to roll back to an older model snapshot. Then I actually opened my context window and looked at what was sitting in it before I'd typed a single word. It was tools. Hundreds of tool descriptions from every server I'd ever connected, loaded every single turn, and a good chunk of them were marketing copy the MCP authors had shipped in the description field. The model wasn't getting dumber. It was being handed a phone book to read before every answer.. Two things fixed it for me, and neither one was the model. First, scope. Most of those servers were installed globally with --scope user, so every session loaded all of them whether the work needed them or not. Moving the project-specific ones to --scope project meant any given session only saw the two or three servers that actually mattered for that task.. Second, I stopped letting the model see every tool directly. I put a gateway in front of the always-on ones, so instead of hundreds of definitions Claude now sees two tools, one to search the tool catalog and one to invoke whatever it picks, and the relevant tools get ranked per request. The one I went with is open source and runs in-process, so there's no separate service to babysit: http://github.com/ratel-ai/ratel. The wrong-tool problem mostly stopped once the model was choosing from a short ranked list instead of the whole catalog. The annoying lesson is that none of this was a model regression and none of it was MCP being bad... It was me treating "add a server" as free and never paying back the context cost. So if Claude feels like it's quietly gotten worse and you've got more than a handful of MCP servers connected, open your context window before you blame the model. I'd put money on it being full of tools you forgot you installed. Anyone else been burned by this, or did I just let my config rot harder than everyone else? submitted by /u/AbjectBug5885 [link] [comments]

Am I using Claude incorrectly?

I recently got a job and quite new to the domain/field and have been using Claude for my work. I give it a prompt, it gives a reply and I copy paste it into ChatGPT to dumb it for me. And ask ChatGPT to generate a prompt that I paste back into Claude. Sometimes I consult with other AIs before coming to a decision. At this point it seems like all I do is copy paste. Curious if you all rely only on Claude to design the workflow. Or if you can help me with any pointers. Because it is all new, I don’t want to make any wrong choices or decisions. submitted by /u/AntiqueApartment1233 [link] [comments]

I built an app with Claude Code that converts any text into high-quality audio. It works with PDFs, blog posts, Substack and Medium links, and even photos of text.

I’m excited to share a project I’ve been building over the past few months, created entirely using Claude Code! It’s a mobile app that turns any text into high-quality audio. Whether it’s a webpage, a Substack or Medium article, a PDF, or just copied text, it converts it into clear, natural-sounding speech. You can listen to it like a podcast or audiobook, even with the app running in the background. The app is privacy-friendly and doesn’t request any permissions by default. It only asks for access if you choose to share files from your device for audio conversion. You can also take or upload a photo of any text, and the app will extract and read it aloud. - React Native (expo) - NodeJS, react (web) - Framer Landing The app is called Frateca. You can find it on Google Play and the App Store. I also working on web vesion, it's already live. Free iPhone app Free Android app on Google Play Free web version, works in any browser (on desktop or laptop). Thanks for your support, I’d love to hear what you think! submitted by /u/OneMoreSuperUser [link] [comments]

I migrated an old J2ME app to Flutter using GitHub Copilot & Claude Opus 4.7

I got curious some days ago after I saw my old email about java mobile games sent ~2007. I am an Android and Flutter dev. So, I thought , what if I use AI to convert old J2ME app to Flutter app. So, I searched about some open source J2ME app and found something called Keepass. Project : https://sourceforge.net/projects/keepassj2me/ So, I downloaded it's source code and created a new Flutter project and copied there code there. Then I opened GitHub Copilot and chose Opus 4.7. Then gave this prompt : I want to migrate an old J2ME mobile app to modern flutter based app. Read all files in "keepassj2me" folder and understand it. the "keepassj2me/doc" has documentation. Read entire codebase inside "keepassj2me/src, keepassj2me/src-lib" and understand it, Then create respective dart files in 'lib' folder of this flutter project. The UI should be same. I started the agent and did needful things. After completion, it said that it couldn't migrate all code since some J2ME specific libs need manual conversion since migrating those libs need to go though the codebase of the libs. So, I said : Use respective Flutter libs in the place of J2ME libs Then it started to produce like that and completed the project. I tried to run the project and got some build issues and those are just simple fix. I did that. Then I ran the app and got flabbergasted. It is a 90 % exact reproduction. I was shocked by seeing the ability of an AI model to migrate a legacy code. The app has some minor bugs, but the main flow is working fine. Opus is a maverick. Migrating this J2ME app can take a full week by a software engineer who is capable on both stacks. But Opus 4.7 did it within 1 hour and 1700 Copilot credits. I added the parallel video of the app's working. Just compare them. Left: Flutter Right : J2ME submitted by /u/RageshAntony [link] [comments]

Claude Design is the reason I am excited again about AI

Not really sure if anyone is as much exicted as I am but Claude Design is a game changer to my projects. No more spending time (and precious tokens) explaining claude code what I want visually. Design prepares UI, you can copy it directely to claude code and it just replicates it ideally. I mean I had few moments of excitment with AI: 1. when gtp released, 2. when claude code started to writie really god code and now is the third time. I updated purrates.org design in like 2 sessions? And everything seems to working just fine? Do you use Design? What is your experience? https://preview.redd.it/ffxvha9hb36h1.jpg?width=427&format=pjpg&auto=webp&s=05a51f35bca61d675870880f862a9c2fa9ea41b6 submitted by /u/tomjohnriddle [link] [comments]