Booking.com AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Booking.com AI AI

Claude made this Roast comic generator to roast my friends and family.

I decided a couple of months ago to dabble in AI comic and book generators. Then an idea came to me a few weeks ago to make comics with my friends picture so I could roast him about something XD (Sorry Timo I put you on blast XDD. (It's okay he knows)) And the results were hilarious. I used Claude Code in VScode to build everything and it helped me make the proper logic. This thing is fully vibe coded, I am not a developer. Im using Gemini 3.1 flash for image generations (Gemini 3 pro is too expensive and doesn't have that much higher quality output). But I'm thinking of switching to GPT image 2.0 maybe for some consistency issues. Claude Code is still the best for everything coding and logic. So far I have garnered 186 users. For those curious there's free samples on the site when you visit. I made multiple styles from realistic to puppet styles. Here's the site: www.draftmybook.com And feel free to roast Claude or me here for making this! submitted by /u/ChargeAdventurous751 [link] [comments]

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. --- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: ~1 weekend. Cost: $0/month. --- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. --- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: * decisions * objections * follow-ups * action items * CRM-ready notes * client context * internal operating memory That is where the value is. --- https://github.com/albertshiney/utter_public submitted by /u/Tabani897_YT [link] [comments]

Adaptive Markdown

I’ve been working on an open-source document format / viewer idea I’m calling Adaptive Markdown. The basic idea is: instead of a document being static text it's controlled by coding agents. You interact with the document more like a live workspace. This has different implications depending on what you are doing. I made a short video demo here: https://youtu.be/H4MnFs8irm8 The thing I’m most excited about is academic / technical reading. In a few years I don’t think people will just read papers passively. I think they’ll translate passages, ask questions, generate examples, explore alternate proofs, run code, attach notes, convert math to Lean when possible, and keep all of that inside the document instead of scattered across chats and notebooks. This is trivial to do inside a browser with coding agent that has access to JS, CSS etc. Some possible use cases I’m thinking about: -Turning articles and books into personalized learning objects - lecture notes with automatically maintained structure -documents with embedded code, tables, consoles, images, audio, or video -AI-generated alt text and descriptions Incorporate Adaptive Markdown into automated work flows eventually, things like automatically recording audio in lectures and taking a picture of a blackboard and turning it into LaTeX notes inside the document It’s very early, but the workflow already feels surprisingly useful to me. GitHub: https://github.com/SemiSimpleMath/Adaptive-Markdown Curious whether this seems useful to anyone else, or whether I’m just overexcited because I built it. So far it's only configured for Anthropic coding-agent SDK, but in couple of days we will have it running on Codex as well. submitted by /u/IDefendWaffles [link] [comments]

Adaptive Markdown

I’ve been working on an open-source document format / viewer idea I’m calling Adaptive Markdown. The basic idea is: instead of a document being static text it's controlled by coding agents. You interact with the document more like a live workspace. This has different implications depending on what you are doing. I made a short video demo here: https://youtu.be/H4MnFs8irm8 The thing I’m most excited about is academic / technical reading. In a few years I don’t think people will just read papers passively. I think they’ll translate passages, ask questions, generate examples, explore alternate proofs, run code, attach notes, convert math to Lean when possible, and keep all of that inside the document instead of scattered across chats and notebooks. This is trivial to do inside a browser with coding agent that has access to JS, CSS etc. Some possible use cases I’m thinking about: -Turning articles and books into personalized learning objects - lecture notes with automatically maintained structure -documents with embedded code, tables, consoles, images, audio, or video -AI-generated alt text and descriptions Incorporate Adaptive Markdown into automated work flows eventually, things like automatically recording audio in lectures and taking a picture of a blackboard and turning it into LaTeX notes inside the document It’s very early, but the workflow already feels surprisingly useful to me. GitHub: https://github.com/SemiSimpleMath/Adaptive-Markdown Curious whether this seems useful to anyone else, or whether I’m just overexcited because I built it. So far it's only configured for Anthropic coding-agent SDK, but in couple of days we will have it running on Codex as well. submitted by /u/IDefendWaffles [link] [comments]

I tested whether a cold Claude agent could discover and use my site's llms.txt. Here's what actually happened.

I've been building [CielStay](https://www.cielstay.com) — a semantic discovery platform for vacation rentals that finds properties by personality and vibe rather than checkboxes using a matching concept I call "Resonance". It's in alpha mode, but we have ~64K listings across 61 countries, cross-linking OTA (Airbnb, Vrbo, Booking.com) and direct sites. This service is currently 100% free. I set up llms.txt at [cielstay.com/llms.txt](https://www.cielstay.com/llms.txt) with full API documentation so Claude agents could search our inventory. Then I tried to actually use it. **What I expected:** Agent reads llms.txt → calls /api/search → returns results. **What happened:** Claude couldn't fetch the URL at all. Not because the file was broken — it returns 200 fine. Because cielstay.com hasn't appeared in any search results yet, so it wasn't in Claude's authorized URL list. The domain was effectively invisible. I had to warm it up by searching for the farmhouse listing on Booking.com and Airbnb first (which are indexed), then Claude could eventually find the CielStay URL as a secondary reference. But it couldn't cold-bootstrap from llms.txt the way the spec intends. The underlying issue: llms.txt discoverability depends entirely on your domain being in Google/Anthropic's index. For a new site, there's a catch-22 — you need indexed pages to get llms.txt discovered, but llms.txt is supposed to help agents find your pages. **Partial fixes we landed on:** - Add llms.txt to your XML sitemap (Google will crawl it directly) - Link to llms.txt from a crawlable page (we added it to the footer + /ai-agent-guide) - in every page's The real fix is just time + inbound links. But it's an interesting bootstrapping problem for the llms.txt spec. The API is public if anyone wants to test: https://www.cielstay.com/llms.txt. Thanks for feedback and shared experiences! submitted by /u/ajfa [link] [comments]

Mahoraga - Stop paying Anthropic and OpenAI so much

Are you sick of paying a million credits per month?!?!? I'm joking, i aint that enthusiastic. But really, this saves me a ton of credits by routing simple tasks to local agents. Clone the repo, fork the repo, star the repo, whatever you want. github.com/pockanoodles/Mahoraga This is Mahoraga, an open-source orchestrator that routes tasks across local and cloud AI agents using a contextual bandit (LinUCB) that learns from every decision. Context (skip): I only started integrating AI into my workflows in late 2025, so I came on the scene broke with no credits. This left me with local models. However, many students and employees also receive credits from their institution to work with. (I got claude yippee) I wanted to be able to flawlessly route between models when credits ran out, which made me build an orchestrator. I used to use claude more as a chatbot/complete workflow engine, which made it difficult to use local models due to the context window, reasoning, etc. Opus 4.5 running open-source "superpowers" ate my usage every month. Now I realize that wasn't an effective way to use claude, or AI in general. I was using claude for both heavy planning/brainstorming and minor tasks. How about tasks specifically for code generation? Code generation is a relatively constrained task, with correct answers and short outputs. Surely local models can compete in tasks that don't need cloud? So I switched Mahoraga to an adaptable router. I ran 192 tasks across 8 agents (4 local Ollama models, 4 cloud CLIs) on a 16GB MacBook Pro, forcing round-robin so every agent got every prompt. Quality is scored by a 4-layer heuristic system (novelty ratio, structural checks, embedding similarity, length ratio). Zero API cost for evaluation, and no LLM-as-judge. Qwen3 4B in nothink mode dominates code and refactor at 33.8 t/s and 6.1s average latency. Cloud agents cluster around 0.650 on code. The local model isn't just cheaper; it's measurably better for this task class. Other findings: LFM2 hits 77.1 t/s but trades ~5 quality points vs Qwen3 4B DeepSeek-R1 averages 123.5s per task on 16GB. The reasoning overhead makes it unusable as a default Security scores are flat at 0.650 across all agents due to my human error—the scorer doesn't capture security-specific signals well. The bandit (LinUCB) is the only routing strategy with sublinear regret (β=0.659) across a 200-task simulation—it actually converges The routing works in two stages: the keyword classifier puts the task in a capability bucket (code, plan, research, etc.), and then the bandit picks the best agent within that bucket. 9-dimensional context vector, persistent state across sessions, warm-start from the compatibility matrix. All local inference, all free. Cloud escalation exists but only fires on retry. Why pay for cloud when a local model handles it better? Looking for any feedback, any input. Feel free to be critical: I appreciate everyone who interacts on this subreddit. I will continue to work on this in the future. Again, this is open source and free. (Mods, please. i'm not making any money off this. submitted by /u/Own-Professional3092 [link] [comments]

AI - IT TAKES ONE TO CATCH ONE: ChatGPT as Sheriff in the Digital Wild West

Mixed feelings abound surrounding Artificial Intelligence (AI). I am a fan and use it daily to search. I rarely seek an opinion but rather pose questions that I could likely answer myself if I had the time and access to more databases. This is the story of coming uncomfortably close to being scammed by AI and then being saved by AI from (at the very least) embarrassment and (potentially) financial and reputational harm. As someone who meets definitions of both 1. “Old and At Risk” – i.e., 79, and 2. “Educated and Should Know Better” – i.e., Having successfully completed 23rd Grade I offer this cautionary tale: The Story: As the 2024 author of an underappreciated (and definitely under-selling) book, I was surprised to receive an extremely well-written email via my book’s website. Politely describing my less-than-bestselling effort as “in the early stages of reaching that wider audience,” the writer offered a “brief conversation to share a few ideas on how books like yours often gain traction with the audiences who need them most.” They described themself as one who “works with authors . . . helping position them so they connect more effectively with readers who are actively looking for answers.” Sucked in with well-written flattering prose, I replied, agreeing to the suggested “brief conversation”. After a second prompt and flattering email and offering availability to talk, the words “Perhaps too good to be true” crossed my mind. I searched for the writer on Google and found nothing, so I queried ChatGPT: MY QUESTION to ChatGPT: “A writer and their ‘team’ have emailed offering to help me with promoting my book. What can you find about them?” Milliseconds later came the: “SHORT ANSWER”: "I cannot find any credible, verifiable person or legitimate company behind [this letter writer] doing book promotion—and that’s actually a red flag in itself. What I can find strongly suggests this fits a very common, ongoing scam pattern targeting authors.” “FINAL ASSESSMENT" by ChatGPT after reviewing the letter. If I had to categorize this: • High likelihood: mass outreach template • High likelihood: paid marketing pitch coming next • Moderate risk: scam or zero-value service • Low likelihood: legitimate, high-quality publicist The Safest Option is: “IGNORE COMPLETELY.” Reference Provided: Anne R. Allen blog on AI book marketing scams submitted by /u/ResearchAware7810 [link] [comments]

Auris: an offline ebook reader

Built an offline audiobook reader called Auris using Claude and Claude Code during development. https://github.com/nikhilprasanth/Auris The project reads EPUB, PDF, and TXT books using fully local OmniVoice TTS with character aware voices, narrator control, synced text highlighting, subtitle export, and voice cloning support. No API keys or hosted TTS services required after setup. Claude and Claude Code were heavily used during development for debugging TTS pipeline issues, restructuring parts of the Flask app, refining parsing logic, experimenting with voice workflow UX, and speeding up iteration on local inference integration. A lot of the rapid prototyping and cleanup work happened through conversational coding sessions instead of traditional searching and boilerplate writing. https://preview.redd.it/723acldm9izg1.png?width=1264&format=png&auto=webp&s=cfe337db13e90c67bdd302ab6bbdfbf4ebb71222 https://preview.redd.it/i7o7rldm9izg1.png?width=1264&format=png&auto=webp&s=6e0ab28715b9bf022ebdec85aa1756d351eb6fa0 https://preview.redd.it/37e1qidm9izg1.png?width=1264&format=png&auto=webp&s=536fe1a482db89fc23ed73508ab3cb8ebc9af27f https://preview.redd.it/knbyfldm9izg1.png?width=1264&format=png&auto=webp&s=7ceb30651bceda626a62469f52199f0f2678a021 https://preview.redd.it/iaxlykzm9izg1.png?width=1264&format=png&auto=webp&s=39dacb9f2186743d55ca2c8b734e4d0b225fd1a0 One area I’m currently experimenting with is using lightweight local LLMs through OpenAI compatible endpoints for emotion tagging before speech generation to improve narration quality. The project is fully open source under MIT and free to try locally. Still very experimental, so I’d genuinely appreciate feedback, criticism, or ideas from people working on local AI, TTS, audio tooling, inference optimization, or ebook workflows. Also open to collaborators if anyone wants to contribute or experiment with the codebase. submitted by /u/nikhilprasanth [link] [comments]

Offload routine Claude Code work to Gemma 4 through the Google GenAI API

The idea of offload-mcp is simple: instead of running hardware-hungry local models for routine work, let Claude offload that work to FREE model APIs and SAVE tokens. I’m using Gemma via the Google GenAI API because I like it in my processing pipelines, but running it locally on my MacBook Air is slow and resource-limited. The API path is much more practical for small jobs. I didn't find any other tool on GitHub or elsewhere to handle that. offload-mcp takes care of commit messages, PR summaries, translations, docstrings, source diff/file summaries, and freeform prompts. Freeform is what I use most: send almost any routine prompt to a cheaper model instead of burning expensive Claude Code or Codex context on it. The source-based mode can read local diffs/files directly through the MCP server and reports estimated primary input tokens avoided. The default model chain uses Gemma, but model IDs are configurable. Curious if this fits anyone else’s Claude workflow! GitHub: https://github.com/peterhadorn/offload-mcp submitted by /u/dd1100 [link] [comments]

I used Claude as my pair programmer to build a safe for kids generative coloring book app for my daughter!

Hey r/ClaudeAI Recently I’ve been having a hard time finding safe, kid friendly, easy to use coloring book apps for my child. Everything I found was overly complicated, overloaded with weird ads, no safeguards, and overly stimulating for a young kid. So I decided to build one myself in Swift UI. I wanted the app to feel simple, calm, and safe the moment a child or parent opens it. The app uses an API for image generation, but everything stays local on the device using Swift Data. I also built in robust parent protection across the app, so purchase links, external links, or even the terms page can’t be accessed without the parent lock. My goal was to use AI in a way that is actually useful and hopefully can add value to someone’s life instead of feeling gimmicky. I know this is an AI sub and everyone here cares about thoughtful products and the responsible use of AI, so any feedback from the Claude community is so much appreciated. If there are any other young parents out there or if you know someone in a time of need where an app like this could bring the a bit of joy, please message me and we can arrange completely free usage :) I’d really like to figure out a way to use this app to give back to those in need, if it ends up getting any traction. Here is the app link if you would like to check it out: https://apps.apple.com/us/app/imagine-coloring-for-kids/id6762320485 submitted by /u/pythononrailz [link] [comments]

I kept re-explaining my codebase to every AI tool I opened. So I built Carto.

While building my project (Emfirge), I wrote a module called cartography.py so what it does is it maps AWS resources into a structured graph so AI understands your infrastructure accurately. Halfway through, I switched AI tools. New session. Had to explain everything from scratch again. I thought that why doesn't this exist for codebases? So I built Carto. What it does > Watches your project. Every save → routes, models, functions, env vars extracted → AGENTS.md updated in 300ms. Cursor, Kiro, Copilot, Claude all read current truth. Zero explaining. Proof it works > Ran the same task on cal.com (800k lines) in two Claude sessions: "Add a notes field to the booking model." Without AGENTS.md: - Wrong API route - Wrong file paths - 20+ missing fields - Couldn't proceed without follow-up With AGENTS.md (generated by Carto): - Correct route ✅ - Correct files ✅ - All 35+ fields ✅ - One shot, no follow-up ✅ Also ships `carto impact` carto impact app/models.py → 5 files depend on this → 15 routes affected → Risk: HIGH Know what breaks before you break it. No AI. No cloud. Under a second. Free. MIT. No telemetry. Your code never leaves your machine. github.com/theanshsonkar/carto I made it so it can help me in my projects and i am Happy to answer questions still early, community contributions welcome (Go, Rust, Ruby parsers needed). submitted by /u/aspectop [link] [comments]

Four months building with Claude: a diagnostic framework for American constitutional history

Sharing a project I built with Claude over four months. Free to try, no signup, runs in the browser: https://www.papercutslibrary.com/explore/constitutional-reality-framework/ It's an interactive learning module that maps 236 years of American constitutional history onto a two-dimensional analytical grid measuring accountability and proactivity by branch. The goal: let people see how American constitutional power has actually behaved over time, not how civics class describes it. https://preview.redd.it/56v5y0egx4yg1.png?width=1354&format=png&auto=webp&s=9cbcb6aa3499ab8b8a378b411447e2f1dbd21ae0 I want to be clear about what the collaboration actually looked like, because I think that's the more useful conversation. How the framework came to be. This started as research on the Supreme Court. I noticed the 1937 switch in time and wanted to track the kind of institutional movement it signaled. The framework idea emerged from that. Early work was one-off mappings and thematic analysis, building the framework's two-dimensional logic by testing it against specific cases. At some point I got the idea of mapping the full sweep of American history through it, and a two-month grind to produce the learning module began. The initial idea was much smaller than what it became. The framework grew, and so did the scope, through the process. I wrote a short book on AI arguing that one of its most important practical uses could be helping people level-set reality, particularly during periods of heavy misinformation. This project applies that idea to history, through a diagnostic framework. Claude wouldn't have proposed any of that. The originating ideas and the module's design are mine. What Claude contributed. Almost all of the historical and editorial content. I'm not a historian. Producing 29 mapped eras with placement-level evidence across 236 years was beyond what I could do alone. The work depends on AI's handle on historical context, and the info modal in the tool is explicit about this. I'm also not a coder. I have enough past programming experience to follow what I'm looking at, but I did not write a single line of code in this build. I reviewed specs and briefs, ran tests, and made architecture decisions. One day I spent four hours getting four captain threads to agree on a re-architecture brief. The code itself is Claude's work. The framework documentation grew complex enough that I couldn't track every internal consistency point either. Claude tracked it. I directed it. How I structured the work. Multi-thread architecture, with specialized Claude threads running in parallel: • Project Captain: coordination and sequencing • Design Captain: UI decisions • Editorial Captain: voice and style standards • Era/Audit Captain: placement integrity across the timeline • Editorial Execution and Editorial Review: separate drafting and review threads The roles weren't strict walls. Project Captain wrote and coded when needed. The discipline was in the processes between threads: editorial runs, placement setups, structured handoffs. Over a hundred briefs and specs moved between threads across four months. That structure is what kept the work coherent and prevented the drift that happens when a single context handles everything. Captains had to be retired when context degradation set in. That was a constant challenge. The methodology I held to throughout: batch tasks, take time with everything, prefer high-quality results over speed. All 29 maps went through an execution and review cycle against a dedicated style guide. Every placement is backed by tiered evidence (Tier 1 primary sources, Tier 2 secondary), documented with explicit confidence levels. Coding. The build itself ran through the same structured pattern. Captains wrote briefs and prompts for Cowork to do work on the modular codebase. Cowork was given a verification checklist in most cases, and the associated Captain would review the standalone HTML build that resulted. The current build is nearly 15,000 lines in a 1.6MB single standalone HTML file, which is what's online. Cross-model verification. Recent events fall past Claude's training cutoff, so I used GPT and Gemini for independent verification through systematic web research. One unexpected finding worth reporting: some 2026 developments, particularly recent military actions, were so far outside the other models' priors that they flagged them as likely hallucinations. They weren't. The events were just genuinely unprecedented. Validating that gap was its own piece of work. Disclosure. Full AI collaboration disclosure is in the tool's info modal. Claude (Opus and Sonnet, 2025 to 2026) for the analytical and editorial work. CC BY-NC-SA 4.0. Try it: https://www.papercutslibrary.com/explore/constitutional-reality-framework/ submitted by /u/papercutslibrary [link] [comments]

Qwen3 4B outperforms cloud agents on code tasks—with Mahoraga research [R]

Hey everyone in ML. I've been working on Mahoraga, an open-source orchestrator that routes tasks across local and cloud AI agents using a contextual bandit (LinUCB) that learns from every decision. Context (skip): I only started integrating AI into my workflows in late 2025, so I came on the scene broke with no credits. This left me with local models. However, many students and employees also receive credits from their institution to work with. (I got claude yippee) I wanted to be able to flawlessly route between models when credits ran out, which made me build an orchestrator. I used to use claude more as a chatbot/complete workflow engine, which made it difficult to use local models due to the context window, reasoning, etc. Opus 4.5 running open-source "superpowers" ate my usage every month. Now I realize that wasn't an effective way to use claude, or AI in general. I was using claude for both heavy planning/brainstorming and minor tasks. How about tasks specifically for code generation? Code generation is a relatively constrained task, with correct answers and short outputs. Surely local models can compete in tasks that don't need cloud? So I switched Mahoraga to an adaptable router. I ran 192 tasks across 8 agents (4 local Ollama models, 4 cloud CLIs) on a 16GB MacBook Pro, forcing round-robin so every agent got every prompt. Quality is scored by a 4-layer heuristic system (novelty ratio, structural checks, embedding similarity, length ratio). Zero API cost for evaluation, and no LLM-as-judge. Forced round-robin, no bandit selection. 4-layer heuristic quality scoring. Hardware: MacBook Pro 16GB M-series (Nov 2024). Qwen3 4B in nothink mode dominates code and refactor at 33.8 t/s and 6.1s average latency. Cloud agents cluster around 0.650 on code. The local model isn't just cheaper; it's actmeasurably better for this task class. Other findings: LFM2 hits 77.1 t/s but trades ~5 quality points vs Qwen3 4B DeepSeek-R1 averages 123.5s per task on 16GB. The reasoning overhead makes it unusable as a default Security scores are flat at 0.650 across all agents due to my human error—the scorer doesn't capture security-specific signals well. The bandit (LinUCB) is the only routing strategy with sublinear regret (β=0.659) across a 200-task simulation—it actually converges The routing works in two stages: the keyword classifier puts the task in a capability bucket (code, plan, research, etc.), and then the bandit picks the best agent within that bucket. 9-dimensional context vector, persistent state across sessions, warm-start from the compatibility matrix. All local inference, all free. Cloud escalation exists but only fires on retry. Why pay for cloud when a local model handles it better? Looking for any feedback, any input. Feel free to be critical: I appreciate everyone who interacts on this subreddit. I will continue to work on this in the future. A star would be appreciated: github.com/pockanoodles/Mahoraga submitted by /u/Own-Professional3092 [link] [comments]

Thought I’d have to get rich or become a programmer to build my dream tool. 47 days later, I’m launching it thanks to Claude - here’s what I learned

I’m a former non-technical PM that now does startup consulting. Figured out a pretty great workflow as someone who can’t code at all, and wanted to share it in the hopes that it helps someone else on the fence about exploring what’s possible. I’ll share my tips first, and then a little bit about what I built at the end! While I’m not a coder, I’ve worked with engineers and creative teams my entire career, so I’m familiar with the time-honored process of writing strong stories and keeping track of scope. It’s been a while since I shipped something, but I have 11 software launches under my belt. Now it’s time to make it a dozen! I approached the relationship as me as the PM, and Claude as my super fast, over eager engineer who needed some coaching. Takeaways: My biggest tips from this process: Sky is the limit – if you can describe it. You don’t need to be a coder to build now; you don’t have to understand the ins and outs of every technical decision; but you DO have to have intent, a vision, and a reasonable willingness to understand how the parts relate to the whole. Claude needs to have as little space as possible in which to bounce around. What I mean by that is what I started hitting at with #1 – if you have a clear vision of what you want to build down to the ins and outs of specific features, it will be dramatically easier to build. On Day 1 of development, I had a basic list-style PM tool built after 3 hours. That wasn’t me being a wizard at prompting – it was leaning on my 16 years of domain knowledge and knowing exactly how to describe what I wanted. And that brings me to my next tip… You must learn to reign Claude in, and catch it when it starts to bounce around. There were several instances, particularly with respect to visual bugs (fades, visual location, tooltips, etc.), where Claude just could not understand what I was asking. I developed a rule: Claude gets two chances to fix it, and then if that doesn’t work, we roll back and change approach, usually doing a diagnostic with logs. This always ended up ultimately solving the problem. Claude needs specifics – and if you can’t provide them, you will eventually hit a wall. Having another contributor who could give advice was immensely valuable. A good friend of mine who is an SWE helped me out at a top level. They wanted to learn more about Claude Code and what was possible, and I needed help understanding specific architectural implications of what was being done. It ended up being great – the constraints (limited time on their end) helped us use the tool powerfully to solve key issues, rather than having to do it by hand. My friend was also the first to help me ask better questions of what Claude was doing, and developing that instinct to go from “it just works, good enough” to asking “Explain in detail how this affects feature X” was critical. Use Claude Desktop App for planning and strategy, and Claude Code to execute. You hear of this process a lot, but specifically what I did was have a core chat session in “Chat Claude” where I designed features and talked it through, got it to challenge ideas, and iterate. Then, when I was happy with a feature design, I got Chat Claude to write a feature spec with the explicit instruction that it should be a document Claude Code could read and then implement. This process ended up working enormously well; features that were very complex ended up being quick builds once I handed off to Claude Code, and I needed less time for back-and-forth implementation guessing because it had a “source of truth” to operate from. The exact workflow was: 1) I tell Chat Claude what I want to build in a core chat session that’s top-level strategy and planning, 2) we iterate back and forth, and then 3) it summarizes what we did and then builds a “spec” document that I then 4) hand over to Claude Code, and tell it to read the spec, ask questions, and propose a plan before building, and then we’re off to the races! The velocity can be mind-bending. I vividly recall my first week building – I was so mentally exhausted! It was hard to wrap my head around going from “This has been in my head for 8 years” to “It’s now being built before my eyes.” I do startup consulting, and this has changed my perspective on how these tools get used – we can accomplish a lot this way, yes, but the other side of that is we may be creating a loop of “hyperproductivity” where instead of freeing up our time from tools like Claude, we’re just filling that additional time with more work instead. Gotta be careful or we’ll just create more work for ourselves instead of gaining time back. Claude was most vicious about human decisions it couldn’t qualify. For example, when I was coming up with a name, everything it came up with was taken or bad. It just couldn’t nail the vibe. 30 minutes the old fashioned way (using the Thesaurus, referencing books I’ve read recently) got me a unique name – and Claude hated it. Claude also hated when that contr

CLAUDE.md is the most underused feature of Claude Code — I built a full knowledge management system around it

I've been using Claude Code daily for a few months. Mostly writing code, reviewing PRs, the standard stuff. Then I read Karpathy's brief note about LLM-Wiki and it reframed what I thought Claude Code was actually capable of. The standard pattern: paste context in, get output, session ends, nothing persists. The pattern I've been using: Claude has a permanent role in a specific directory that persists across sessions — not via memory, but via a CLAUDE.md file at the root of the folder that Claude reads at the start of every session. My CLAUDE.md for my Obsidian vault covers: What Claude's role is ("wiki maintainer, not chatbot — never write in a way that requires the human to edit it") The vault folder structure and immutable zones (raw sources are read-only, wiki pages only go in Projects and Areas) Exact page formats for different page types (entity, concept, synthesis, person, summary) The ingest workflow — 7 steps, executed in sequence every time I say "ingest [filename]" The query workflow — read the index first, read relevant pages, synthesise with citations The lint workflow — audit for orphaned pages, dangling wikilinks, missing person pages, stale synthesis pages Session startup ritual — read the schema, read the last 5 log entries, confirm ready With this in place, the experience is different from normal Claude usage: I drop a YouTube transcript into the Resources folder I say "ingest this" Claude asks one classification question (project or area?) Writes a structured summary page Updates all existing concept/entity pages that relate Creates person pages for any significant people mentioned Ensures every [[wikilink]] resolves to an actual file (creates stubs if not) Updates the master index and appends to the activity log After 5 weeks: 148 structured wiki pages. Roman history, architecture, furniture design, client projects, language learning. All cross-referenced. I can ask "what do I know about ergonomics" and get an answer pulling from a furniture design source, a restaurant architecture project, and a book excerpt — because Claude linked them during ingest, not me. The interesting thing about CLAUDE.md vs a system prompt: it's version-controlled with your vault. It's shareable. It evolves like code. Mine is at schema v1.3. When I change the schema, every subsequent session picks up the new behaviour. You can git blame your AI's instructions. I packaged the whole setup — CLAUDE.md schema, PARA vault structure, Claude Code skill — at github.com/Hi7anshu/polymath-vault (npx polymath-world to install). But the pattern is more interesting than the package. Is anyone else building persistent-role systems with CLAUDE.md? Curious what you're using it for and what you've put in yours. I feel like this is one of those things that's in the docs but nobody talks about. submitted by /u/notanaverageindian [link] [comments]