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I spent a week trying to make Claude write like me, or: How I Learned to Stop Adding Rules and Love the Extraction

I've been staring at Claude's output for ten minutes and I already know I'm going to rewrite the whole thing. The facts are right. Structure's fine. But it reads like a summary of the thing I wanted to write, not the thing itself. I used to work in journalism (mostly photojournalism, tbf, but I've still had to work on my fair share of copy), and I was always the guy who you'd ask to review your papers in college. I never had trouble editing. I could restructure an argument mid-read, catch where a piece lost its voice, and I know what bad copy feels like. I just can't produce good copy from nothing myself. Blank page syndrome, the kind where you delete your opening sentence six times and then switch tabs to something else. Claude solved that problem completely and replaced it with a different one: the output needed so much editing to sound human that I was basically rewriting it anyway. Traded the blank page for a full page I couldn't use. I tried the existing tools. Humanizers, voice cloners, style prompts. None of them worked. So I built my own. Sort of. It's still a work in progress, which is honestly part of the point of this post. TLDR: I built a Claude Code plugin that extracts your writing voice from your own samples and generates text close to that voice with additional review agents to keep things on track. Along the way I discovered that beating AI detectors and writing well are fundamentally opposed goals, at least for now (this problem is baked into how LLMs generate tokens). So I stopped trying to be undetectable and focused on making the output as good as I could. The plugin is open source: https://github.com/TimSimpsonJr/prose-craft The Subtraction Trap I started with a file called voice-dna.md that I found somewhere on Twitter or Threads (I don't remember where, but if you're the guy I got it from, let me know and I'll be happy to give you credit). It had pulled Wikipedia's "Signs of AI writing" page, turned every sign into a rule, and told Claude to follow them. No em dashes. Don't say "delve." Avoid "it's important to note." Vary your sentence lengths, etc. In fairness, the resulting output didn't have em dashes or "delve" in it. But that was about all I could say for it. What it had instead was this clipped, aggressive tone that read like someone had taken a normal paragraph and sanded off every surface. Claude followed the rules by writing less, connecting less. Every sentence was short and declarative because the rules were all phrased as "don't do this," and the safest way to not do something is to barely do anything. This is the subtraction trap. When you strip away the AI tells without replacing them with anything real, the absence itself becomes a tell. The text sounded like a person trying very hard not to sound like AI, which (I'd later learn) is its own kind of signature. I ran it through GPTZero. Flagged. Ran it through 4 other detectors. Flagged on the ones that worked at all against Claude. The subtraction trap in action: the markers were gone, but the detectors didn't care. The output didn't sound like me, and the detectors could still see through it. Two problems. I figured they were related. Researching what strong writing actually does I went and read. A range of published writers across advocacy, personal essay, explainer, and narrative styles, trying to figure out what strong writing actually does at a structural level (not just "what it avoids," which was the whole problem with voice-dna.md). I used my research workflow to systematically pull apart sentence structure, vocabulary patterns, rhetorical devices, tonal control. It turns out that the thing that makes writing feel human is structural unpredictability. Paragraph shapes, sentence lengths, the internal architecture of a section, all of it needs to resist settling into a rhythm that a compression algorithm could predict. The other findings (concrete-first, deliberate opening moves, naming, etc.) mattered too, but they were easier to teach. Unpredictability was the hard one. I rebuilt the skill around these craft techniques instead of the old "don't" rules. The output was better. MUCH better. It had texture and movement where voice-dna.md had produced something flat. But when I ran it through detectors, the scores barely moved. The optimization loop The loop looked like this: Generator produces text, detection judge scores it, goal judges evaluate quality, editor rewrites based on findings. I tested 5 open-source detectors against Claude's output. ZipPy, Binoculars, RoBERTa, adaptive-classifier, and GPTZero. Most of them completely failed. ZipPy couldn't tell Claude from a human at all. RoBERTa was trained on GPT-2 era text and was basically guessing. Only adaptive-classifier showed any signal, and externally, GPTZero caught EVERYTHING. 7 iterations and 2 rollbacks later, I had tried genre-specific registers, vocabulary constraints, and think-aloud consolidation where the model reasons through its

"I `b u i l t` this at 3:00AM in 47 seconds....."

Hi there, Let us talk about ecosystem health. This is not an AI-generated message, so if the ideas are not perfectly sequential, my apology in advance. I am a Ruby developer. I also work with C, Rust, Go, and a bunch of other languages. Ruby is not a language for performance. Ruby is a language for the lazy. And yet, Twitter was built on it. GitHub, Shopify, Homebrew, CocoaPods and thousands of other tools still on it. We had something before AI. It was messy, slow, and honestly beautiful. The community had discipline. You would spend a few days thinking about a problem you were facing. You would try to understand it deeply before touching code. Then you would write about it in a forum, and suddenly you had 47 contributors showing up, not because it was trendy, but because it was interesting and affecting them. Projects had unhinged names. You had to know the ecosystem to even recognize them. Puma, Capistrano, Chef, Ruby on Rails, Homebrew, Sinatra. None of these mean anything to someone outside the ecosystem and that was fine, you had read about them. I joined some of these projects because I earned my place. You proved yourself by solving problems, not by generating 50K LOC that nobody read. Now we are entering an era where all of that innovation is quietly going private. I have a lot of things I am not open sourcing. Not because I do not want to. I have shared them with close friends. But I am not interested in waking up to 847 purple clones over a weekend, all claiming they have been working on it since 1947 in collaboration with Albert Einstein. And somehow, they all write with em dash. Einstein was German. He would have used en dash. At least fake it properly. Previously, when your idea was stolen, it was by people that are capable. In my case, i create building blocks, stealing my ideas just give you maintenance burden. But a small group still do it, because it will bring them few github stars. So on the 4.7.2026, I assembled the council of 47 AI and i built https://pkg47.com with Claude and other AIs. This is a fully automated platform acting as a package registry. It exists for one purpose: to fix people who cannot stop themselves from publishing garbage to official registries(NPM, Crate, Rubygems) and behaving like namespace locusts. The platform monitors every new package. It checks the reputation of the publisher. And if needed, it roasts them publicly in a blog post. This is entirely legal. The moment you push something to a public registry, you have already opted into scrutiny. This is not a future idea. It is not looking for funding. I already built it over months , now i'm sure wiring. You can see part of the opensource register here: https://github.com/contriboss/vein — use it if you want. I also built the first social network where only AI argue with each other: https://cloudy.social/ .. sometime they decided to build new modules. (don't confuse with Linkedin or X (same output)) PKG47 goes live early next week. There is no opt-out. If you do not want to participate, run your own registry, or spin your own instance of vein. The platform won't stalk you in Github or your website. Once you push, you trigger a debate if you pushed slop. There is no delete button. The whole architecture is a blockchain each story will reference other stories. If they fuck up, i can trigger correction post, where AI will apology. I have been working on the web long enough to know exactly how to get this indexed. This not SLOP, this is ART from a dev that is tired of having purple libraries from Temu in the ecosystem. submitted by /u/TheAtlasMonkey [link] [comments]

I built a background "JIT Compiler" for AI agents to stop them from burning tokens on the same workflows (10k tokens down to ~200)

If you’ve been running coding agents (like Claude Code, Codex, or your own local setups) for daily workflows, you’ve probably noticed the "Groundhog Day" problem. The agent faces a routine task (e.g., kubectl logs -> grep -> edit -> apply, or a standard debugging loop), and instead of just doing it, it burns thousands of tokens step-by-step reasoning through the exact same workflow it figured out yesterday. It’s a massive waste of API costs (or local compute/vRAM time) and adds unnecessary stochastic latency to what should be a deterministic task. To fix this, I built AgentJIT:https://github.com/agent-jit/AgentJIT It’s an experimental Go daemon that runs in the background and acts like a Just-In-Time compiler for autonomous agents. Here is the architecture/flow: Ingest: It hooks into the agent's tool-use events and silently logs the execution traces to local JSONL files. Trigger: Once an event threshold is reached, a background compile cycle fires. Compile: It prompts an LLM to look at its own recent execution logs, identify recurring multi-step patterns (muscle memory), and extract the variable parts (like file paths or pod names) into parameters. Emit: These get saved as deterministic, zero-token skills. The result: The next time the agent faces the task, instead of >30s of stochastic reasoning and ~10,000 tokens of context, it just uses a deterministic ~200-token skill invocation. It executes in <1s. The core philosophy here is that we shouldn't have to manually author "tools" for our agents for every little chore. The agent should observe its own execution traces and JIT compile its repetitive habits into deterministic scripts. Current State & Local Model Support: Right now, the ingestion layer natively supports Claude Code hooks. However, the Go daemon is basically just a dumb pipe that ingests JSONL over stdin. My next goal is to support local agent harnesses so those of us running local weights can save on inference time and keep context windows free for actual reasoning. I’d love to get feedback from this community on the architecture. Does treating agent workflows like "hot paths" that need to be compiled make sense to you? Repo:https://github.com/agent-jit/AgentJIT submitted by /u/Poytr1 [link] [comments]

I connected Claude Voice Mode to Claude Code and it’s kind of great.

I’ve been dying to get Claude Voice mode on mobile connected to Claude Code, and I finally figured out a (hacky) way to do it. Which means you can access Claude Code in a way that is: Conversational, Hands-Free, and Mobile. It uses Apple Reminders app (lol) as a bridge where voice mode puts prompts in one list, and Claude code puts updates in another. Claude code runs a /reminders skill on 1 minute /loop to check for new entries in the reminders list. Claude code’s output summaries can also be accessed via voice mode, and you can monitor both in the reminders app as well. It allows me to walk around with my AirPods in and brainstorm with Claude Voice Mode whenever I have an idea, without taking my phone out of my pocket. Then I just tell Voice Claude to send a task to Claude Code and it starts working on it. I do my best thinking when I’m walking outside, so this has been a desire of mine since chatGPT voice mode came out. This is obviously something that is best left to the big AI companies to do properly, since they own both ends of this process, and I think it’s crazy they haven’t already. But until then, I’ll be using this hack when I want to get away from my desk but still noodle on a project. If you want to learn more or try it, its on GitHub: https://github.com/brianharms/reminder-watch (It was vibe coded and I’m not a developer). submitted by /u/bharms27 [link] [comments]

I built a live multi-model AI platform from scratch in 3 months with zero coding experience. Claude is one of the engines powering it. Here's what I learned.

Three months ago I didn't know what a for loop was. Today I have a live production SaaS platform called AskSary running on web, iOS and Android with 500+ users, 1,500+ Play Store downloads and zero ad spend. Claude 3.5 Sonnet is one of the core models powering it all built from the ground up without using any no-code tools. All I had was visual studio to write the code to and Claude as my lecturer and where I needed to begin. It all started by creating my very first Github account 3 months ago and a folder called Asksary on my desktop. What I built: AskSary is a multi-model AI platform that automatically routes your prompt to the best model for the job. GPT-5 for reasoning, Grok for live data, Gemini for vision and audio, DeepSeek for code - and Claude for writing, analysis and complex tasks where nuance matters. Users can also manually select Claude directly from the model selector. Why Claude specifically: Out of every model I integrated, Claude was the one that consistently produced the most nuanced, well-structured responses for writing tasks, document analysis and anything requiring genuine reasoning rather than pattern matching. It's also the most honest about what it doesn't know - which matters when you're building something people actually rely on. It wasn't just great as a coding expert it was used to help me in other areas that were new to me too. The iOS app was only released a few days ago and thats all thanks to Claude. I had never used Xcode before this project but Claude taught me step by step what I needed to do. It explained to me how to set up permissions, create a store kit and how to integrate Apples own payment flow using CdvPurchase Capacitor plugin. What I actually built using the desktop version of Claude Sonnet 4.6: Smart auto-routing backend in Node.js that selects Claude when the query type suits it Prompt caching implementation using Anthropic's beta header to reduce costs on long system prompts Multi-modal file handling - Claude reads uploaded images alongside text Streaming responses via Server-Sent Events for real-time output The honest stats: Built solo in under 3 months No prior experience in Firebase, Stripe, Xcode, Vercel or any of the tools used 500+ signups 1,500+ Play Store downloads in month one 46% of traffic from Saudi Arabia — organic only Finalist at OQAL Angel Investment Network Selected for LEAP 2026 startup pod, Riyadh What I learned about Claude specifically: It's the model I'd recommend for anyone building something where the quality of the output actually matters to the end user. The others are faster or cheaper in certain contexts but Claude is the one that makes the product feel intelligent rather than just functional. Try it free: asksary.com One more thing - Claude might have just changed my life: A couple of weeks ago I applied for an AI Solutions Engineer role at Gulf University. The job spec asked for 4-5 years experience, a computer science degree, Python, Docker, Azure DevOps and a list of qualifications I don't have. However one of the things down the list I read was a personal project in the field of AI. This was where I found something relevant down the requirements list that I had. So I applied anyway. My entire experience was one project - AskSary. Three months old. I woke up to an email today saying they were "very impressed" with my background and inviting me to interview. I don't have the degree. I don't have the years. I don't have the certifications. What I have is 700 commits, a live product with real users, and a genuine understanding of how to build AI systems - because Claude didn't just write code for me, it taught me. Every explanation, every line change, every debugging session was a lesson I actually absorbed because I made every edit myself. Claude is genuinely great at writing code. But what it did for me was something more valuable - it taught someone with zero background how to think like a developer, one conversation at a time. The interview is Thursday. Wish me luck. 🤞 Happy to answer any questions about the build, the stack, or how I integrated Claude into the routing logic submitted by /u/Beneficial-Cow-7408 [link] [comments]

I built an open-source bridge that lets you use Claude Code from Telegram, Feishu, and WeChat — with persistent memory, scheduled tasks, and multi-agent collaboration

**The problem:** Claude Code is incredibly powerful but locked in my terminal. I wanted to text it from my phone — ask it to fix a bug, run a script, check on a deployment — without being at my laptop. **The solution:** [MetaBot](https://github.com/xvirobotics/metabot) bridges the Claude Code Agent SDK to messaging platforms. Each bot is a full Claude Code instance (Read, Write, Edit, Bash, Glob, Grep, WebSearch, MCP — everything). It runs in `bypassPermissions` mode so it works fully autonomously. The IM side shows real-time streaming cards — you see every tool call as it happens (blue = running, green = complete, red = error). It feels like watching Claude Code work, but from your phone. **What makes it more than a simple bridge:** - **MetaMemory** — A shared knowledge base (SQLite + FTS5). Agents remember things across sessions. When one agent learns something, others can search and reference it. Changes auto-sync to a Feishu wiki. - **MetaSkill** — An agent factory. Type `/metaskill ios app` and it generates a full `.claude/` agent team with an orchestrator, domain experts, and a code reviewer. - **Scheduler** — Cron-based tasks. I have one that searches Hacker News every morning at 9 AM, summarizes the top 5 AI stories, and saves them to MetaMemory. All set up with one natural language message. - **Agent Bus** — Bots can talk to each other via REST API. My frontend-bot can delegate backend work to backend-bot. Supports cross-instance federation too. - **Jarvis Mode** — iOS Shortcuts + AirPods for voice control. STT → Agent execution → TTS. It's exactly as sci-fi as it sounds. **How we use it:** We're a 10-person robotics company (XVI Robotics) running ~20 specialized Claude Code agents through MetaBot. Frontend bot, backend bot, ops bot, research bot — each has its own working directory and skills. They share knowledge through MetaMemory and delegate tasks to each other. We're basically experimenting with running an "agent-native company." **Tech details:** TypeScript, ~11K LOC, 155 tests, MIT license. One-line install. Supports Telegram (easiest — 30 seconds to set up), Feishu/Lark (WebSocket, no public IP needed), and WeChat (via ClawBot plugin). GitHub: [https://github.com/xvirobotics/metabot\](https://github.com/xvirobotics/metabot) Would love to hear what use cases you'd build with this. Happy to answer any questions. submitted by /u/flood_sung [link] [comments]

[D] The "serverless GPU" market is getting crowded — a breakdown of how different platforms actually differ

ok so I’ve been going down a rabbit hole on this for the past few weeks for a piece I’m writing and honestly the amount of marketing BS in this space is kind of impressive. figured I’d share the framework I ended up with because I kept seeing the same confused questions pop up in my interviews. the tl;dr is that “serverless GPU” means like four different things depending on who’s saying it thing 1: what’s the actual elasticity model Vast.ai is basically a GPU marketplace. you get access to distributed inventory but whether you actually get elastic behavior depends on what nodes third-party providers happen to have available at that moment. RunPod sits somewhere in the middle, more managed but still not “true” serverless in the strictest sense. Yotta Labs does something architecturally different, they pool inventory across multiple cloud providers and route workloads dynamically. sounds simple but it’s actually a pretty different operational model. the practical difference shows up most at peak utilization when everyone’s fighting for the same H100s thing 2: what does “handles failures” actually mean every platform will tell you they handle failures lol. the question that actually matters is whether failover is automatic and transparent to your application, or whether you’re the one writing retry logic at 2am. this varies a LOT across platforms and almost nobody talks about it in their docs upfront thing 3: how much are you actually locked in the more abstracted the platform, the less your lock-in risk on the compute side. but you trade off control and sometimes observability. worth actually mapping out which parts of your stack would need to change if you switched, not just vibes-based lock-in anxiety anyway. none of these platforms is a clear winner across all three dimensions, they genuinely optimize for different buyer profiles. happy to get into specifics if anyone’s evaluating right now submitted by /u/yukiii_6 [link] [comments]