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[App] Prose – BYOK writing assistant that learns your style over time (alpha)

I built a writing assistant with Claude Code that uses your OpenRouter API key directly — no subscription, no middleman. I built this because I kept paying for Grammarly and resenting it — the subscription felt wrong for a tool I only use occasionally. I'm 16 and have been building small API wrapper apps for a while, so I figured I'd just build my own. The interesting engineering problem turned out to be the caching layer — figuring out how to recognize when the same suggestion pattern recurs so it can be promoted to a free local rule without hitting the API again. Built with Vite + React + TypeScript + Tiptap for the editor, OpenRouter for the AI layer. It's called Prose. You paste your OpenRouter key in settings and pay only when you scan. The interesting part: every time you accept a suggestion, the pattern gets tracked. After a few accepts it becomes a free local rule — runs instantly, no API call. So the tool literally gets cheaper the more you use it. Full rich text editor with inline underlines and a grammar/style/clarity panel. Scan the whole document, a paragraph, or a highlighted selection. No account required. Alpha at prose-writing.vercel.app — would love feedback from people already using OpenRouter with Claude. Give feedback in the comments. I'll respond. submitted by /u/SevereDev [link] [comments]

7 ways I actually wire AI into my marketing work (the boring practical stuff, not the hype)

None of it is magic. It's just removing the parts of the job that don't need a human. I'm Curious what everyone else is wiring in. 1. I run all terminal installs inside a virtual machine https://preview.redd.it/3287jk1gyo4h1.png?width=2024&format=png&auto=webp&s=488efdc39223d26307e57d837888233fa351b8e4 Running code on a company laptop is a security minefield. First time a dev caught me pulling packages with npx, alarms went off (fair enough). Now anything that installs or downloads through the terminal runs inside a UTM, fully isolated from the real system. If it breaks, it breaks in a sandbox. 2. I pull audience data off almost any platform with Apify Reddit, LinkedIn, X, anything else. There's usually an Apify actor for it. I find the right one and have Claude Code write a small Python script that pulls exactly the audience data I'm after. 3. I buy SERP data on demand instead of subscribing I got tired of another monthly bill, so I switched to topping up token packs and pulling SERP data only when I need it. A daily make scenario saves the results to storage, so the tracking basically runs itself. 4. I scrape competitors' GitHub Issues to figure out what to build https://preview.redd.it/oajhtithyo4h1.png?width=2784&format=png&auto=webp&s=5c991fbe62f7ea30a59f1ba480674bd7d1e8d27c To work out what a software dev should add to a tool we are promoting, I scrape the Issues tab of competitors' repos. GitHub token + a Python script in Claude Code. That's how I found a massive backlog of unaddressed complaints about our biggest rival. 5. I run SEO tasks through open-source Claude Code skills There's a free open skill set that plugs into Claude Code for SEO work. I extended mine with a Google Keyword Planner API connection so I get search-volume data for free. The repo's open, you just wire in your own SERP API key. 6. I generate captions and filler shots in code with Remotion Right at the end of a video edit, Remotion + Claude Code handle the finishing touches. A few animated filler shots and burnt-in captions, all defined in code. Remotion's free for commercial use within certain limits, which is nice. 7. I hand-finish animations in Rive or Jitter https://preview.redd.it/cbglzuqiyo4h1.png?width=2230&format=png&auto=webp&s=fdd82f382eaf92f95e35eef651fc428972a3a64b Claude Code gets me most of the way, but its animations look... not good. So I finish by hand in Rive or Jitter. You can craft genuinely polished motion in a few minutes there. AI for the grunt work, human eye for the polish. submitted by /u/Independent-Elk-1019 [link] [comments]

"Persisting" memory for Cowork - what's your preferred approach?

When Cowork just dropped, I was pretty excited. Of all the "OpenClaw" hubbub that went around earlier this year, at its core it was basically a Telegram wrapper and a cron scheduler. So with Cowork having scheduled tasks I figured I could make it into a makeshift OpenClaw. And I did! I gave my Cowork system a reformatted Macbook of its own, Chrome with Claude for Chrome installed, an email account of its own on a domain I manage, and integration to Telegram. It worked quite as well! The telegram integration was not quite as "real-time" as OpenClaw's was - but it was good enough. And the email integration worked well too. Honestly, the first biggest problem? Having Telegram checked every 5 minutes and email every hour burned through the tokens on my modest Pro account. So I disabled the telegram integration but left the email integration functional. That started a cascade of other types of "what type of things could I have Claude spin up and think about, and send me an email from time to time?" and so it started to move into market research on topics relevant to my career - I get a daily email on those. And then it moved into "hey why not watch my stock portfolio for me and send me an email showing week over week performance?" and that's where I hit my first kind of snag. Cowork wanted to try to write the portfolio state to the local filesystem so that it could just reference that in the following week - but it seems like each instantiation of a Cowork scheduled task might execute in its own protected container? And it seems like the path changes each time - there's some sort of english-word "triplet" embedded into the path each time that is different, i.e.: 'word-word-word' and so just using the local filesystem as a persistence tier ... has been fraught with problems. Even though I'm fine dedicating an entire laptop to Cowork to use on its own, it seems the security model confounds my ideas. I could just point it at my Notion or Airtable accounts and say "create whatever tables you need!" but I figured I'd check here to see if anyone is doing something similar? Are you using Cowork and attempting to persist knowledge cleanly across executions, and if so what do you use for "memory"? submitted by /u/Marathon2021 [link] [comments]

How much of MLE-Bench's gains are the algorithm vs. better models + more search? [R]

MLE-Bench scores have jumped from 30% to 80% over the last two years. But how much of that is real algorithmic progress vs. better base models + problem definition shifts + overfitting? Turns out: not much. Once you control for the same step budget and models, and then test on a different set of tasks, the two-year-old AIDE algorithm matches modern agent/evolutionary search systems. Figure from FML-Bench, a new automated ML research benchmark, which unifies the code editing agent, step definition, and val/test split, and tries to benchmark the algorithmic efficiency (search/memory) of the agents. paper link: https://arxiv.org/pdf/2605.17373 test improvement and pairwise win-rate submitted by /u/Educational_Strain_3 [link] [comments]

Are we cool with comics?

I make comics about being a designer and lately... a lot of them are about how I use AI tools and what it all means... figured I would share this submitted by /u/pablostanley [link] [comments]

Attention is all you need, ADHD is all I have 😭

Apparently attention is all I need... bad news for me, I have ADHD. Being the vibe engineer that I am, I decided to engineer my own attention instead. So I built a harness for my brain. A skill for claude code that helps me prioritize my work and stay on track. It connects to my company brain, looks at my priorities, and figures out what I should probably be working on. Then it decomposes the work into small enough subtasks and feeds them one by one, because apparently my brain’s context window can't handle the full roadmap without opening 12 unrelated tabs. So far, it works surprisingly well. submitted by /u/1hassond [link] [comments]

If you run multiple AI sessions, what do you find yourself manually carrying between them?

I've been paying attention to my own workflow lately and noticed a lot of my time goes into moving stuff between AI sessions, not the actual thinking. Like I'll get an output in one session and then manually bring the relevant pieces into another so it has what it needs. What I can't tell is how much of that is necessary vs. me just being sloppy. So I'm curious how others handle it: When you move from one session to another, what do you actually carry over? Just the output, or also the reasoning, the decisions, the constraints, what to avoid? Have you ever handed off too little and the second session went sideways? Or too much and it got lost in the noise? Does anyone have a mental rule for what's "enough context" to pass along? Trying to figure out if there's a clean pattern here or if it's just inherently messy. Curious what people have landed on. submitted by /u/riley_kim [link] [comments]

Launching Conifer tomorrow, an open-source local AI runtime + IDE. Different layer of the stack from PewDiePie's Odysseus, would love your honest thoughts

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [comments]

Voice mode not available on ipad

I am trying to figure if it's just my account. I have voice mode available on my phone app but that icon is not available on the app on my ipad. Its also not available on browser on ipad. How can I use voice mode on ipad? submitted by /u/market_anaadi [link] [comments]

This is funny, thought i'd share

This was a brand new chat, and i figured i'd have some fun. Just thought i'd share, because i can almost feel the emotion here, which is weird (but not bad) for an LLM submitted by /u/Dragonbonded [link] [comments]

I built a system that makes Claude actually remember me across sessions — here's how it works

Every time I opened a new Claude chat I had to explain myself from scratch. Who I am, what I'm working on, who the people in my life are, how I write. It got old. So I built a folder of plain text files. One about me, one for each person I deal with regularly, one per project, and a running log of decisions I've made and why. At the top there's a single file that tells Claude what to read before it does anything else. That's the entire system. No app, no database, no plugin. Now I open a chat and it already knows me. I can say "draft a follow-up to Barry" and it pulls who Barry is, the last few things we talked about, and the way I actually write, without me feeding it anything. I know the obvious reaction is "this is just ChatGPT memory" or "mem0" or "a vector DB with extra steps." It genuinely isn't, and the differences are the whole point: Nothing gets auto-captured. ChatGPT's memory decides for you what's worth keeping, and you end up with a black box you can't inspect. Mine is the reverse. I decide what goes in, so there's no junk, and I can open any file and see exactly what the model knows about me. It's text in git. I can read it, edit it, or delete a wrong fact in about two seconds. It reads, it doesn't retrieve. No embeddings, no similarity search trying to guess which chunk is relevant. The rulebook defines a fixed read order and the model loads the actual files at session start. For one person's worth of context this beat RAG every time I tried it, because RAG kept surfacing the wrong note or missing the obvious one. It outlives the tool. Plain text works with whatever model I switch to next year. No lock-in. On evidence, since fair question: I've run it as my daily driver for a few months. The concrete win is that it drafts emails in my voice that I send with little or no editing, because it has my past messages and my style notes already loaded. The video has three demos of things a cold session flat-out can't do, so you can judge for yourself rather than take my word. Limitations, because they're real: It doesn't scale to a huge corpus. Loading files into context has a ceiling, so this is built for "everything important about one person's working life," not a 10,000-note archive. If your goal is a giant searchable knowledge base, you want retrieval, not this. There's no automatic capture. If I don't write a fact down, it doesn't exist. That's the price of having no noise. Bad taxonomy degrades it quietly. What's stable versus what changes weekly, what lives in the always-read file versus what only gets opened when relevant. Get that split wrong and recall gets worse without you noticing. The code was an afternoon. Figuring out the taxonomy took weeks of actually using it. Short walkthrough with the three demos (recalling a past decision, pulling a person's full context cold, and stitching facts together from separate files): https://youtu.be/tZKAY5mqa_c That's enough to build your own. I also wrote the method up as a guide for anyone who'd rather skip the trial and error, but you don't need it to do this. Happy to get into the folder structure if you're setting one up. That's where the gotchas live. submitted by /u/Michaelcbaldwin [link] [comments]

How to share a Claude conversation with ChatGPT?

(NOT related to coding) I use Claude for a lot of analytical work for my business. I use it for analysis and digging deep and finding nuggets from dashboards, competitor annual reports, broader industry analysis etc. But Claude isn’t the best at writing one pagers, two pagers and long docs for business. I feel writing is where ChatGPT shines. Is there a way for me to share the entire conversation (and artifacts) from Claude to ChatGPT? The select all and print to pdf in Claude leaves out all long messages and excel files etc, which takes away the whole point. Ideally I wish I could have “added” ChatGPT as a collaborator to the chat in a “view only” capacity, but don’t think that’s possible. For me, both Claude and ChatGPT resources with different superpowers. Please do share if you’ve figured a solution to this problem. PS - I am on the pro plan for both. submitted by /u/girl-who-dreams-big [link] [comments]

My wife tried to log 1k phone-free hours but quit. So I vibe-coded an app

​This past summer, my wife set an audacious goal: she wanted to log 1,000 hours of phone-free time with our family. ​To track it, she’d put away her phone and start a manual timer. At first, it was great. But between managing two young kids and constantly forgetting to start or log the timers, the friction just became too much effort. After about 120 hours, she gave up. ​I wanted to find a way to handle the data collection for her so she could just focus on being present. The problem is, I’m a school teacher with a very limited, hobbyist programming background. I had never created anything close to a native Android app before. ​With all the recent talk around "vibe-coding" and AI agents, I figured I’d see if I could cobble a solution together. ​The result is Green Dot. It’s a native Android app built with Kotlin and Jetpack Compose. The core philosophy is pretty simple: not less phone, just better phone habits. Instead of being a punitive screen blocker, it tracks your long lock durations and rewards you for taking intentional, 1-hour breaks away from the device. ​The development process honestly went way beyond my expectations. I used VS Code (leveraging the education benefits) and did the vast majority of the heavy lifting using Claude Sonnet. After a couple of days of prompting and debugging, I had a working prototype. After about three weeks of working in my spare time, I had a fully functional app live on the Play Store. ​As someone without a formal CS background, it’s wild to me that these tools can democratize software development to this extent. It’s obviously not going to replace a software company, but it allowed a parent to ship a real, working tool over a few weekends to solve a hyper-specific lifestyle problem. ​My wife is back to tracking her hours, and I've shared it with a few friends and family who have found it useful for disconnecting. ​I’m sharing it here because I'd love to get the community's thoughts—both on the psychology of rewarding lock durations rather than locking users out, and on the technical side of spinning up a native mobile app from scratch using LLMs if you've done something similar. ​Play Store Link: https://play.google.com/store/apps/details?id=com.greendot.phonebreaks submitted by /u/starcraftgamerz77 [link] [comments]

Back in the day, the slide rule would give you the number, but engineering judgement defined the significant figures

The slide rule (or log tables, or early calculators) could crank out a number with impressive precision — sometimes four, five, or more digits. But the competent engineer knew the inputs were often only accurate to two or three significant figures. Punching out 12 decimal places on a slide rule didn’t make your answer more correct; it just made you look foolish to anyone who understood the real world AI is the modern slide rule on steroids. Today’s models can generate outputs with astonishing fluency and apparent precision: Beautifully formatted stress analysis Polished code Detailed project plans Confident-looking financial models But they routinely: Hallucinate false assumptions Miss critical edge cases Apply the wrong model for the actual operating environment Ignore practical constraints that weren’t in the training data Human judgment is what decides: How many significant figures (or confidence digits) the answer actually deserves Which parts of the AI output are trustworthy vs. dangerous bullshit When the entire problem has been framed incorrectly Whether the “optimal” solution is feasible, safe, maintainable, or even morally defensible in context This is why experienced engineers still sketch on napkins or the back of an envelope first. They’re not rejecting the tools — they’re exercising judgment before feeding the problem into the high-precision machine. The scarcity Jensen is talking aboutAs AI becomes ubiquitous, the people who can reliably say: “This number looks precise, but it’s only good to about ±30% because of X, Y, and Z” “I don’t trust the model here — we need field data” “This elegant solution will fail in practice for these human/organizational reasons” …will be the ones who stand out. Everyone else will be producing impressive-looking but brittle work. The slide rule didn’t make judgment obsolete. It made good judgment more valuable because bad judgment now produced faster, prettier mistakes. Same story with AI — just at a much higher speed and scale. submitted by /u/danieldeubank [link] [comments]

Anyone tried using AI models to screen candidates?

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [comments]