Rust for Morello ARM CHERI: always-on memory safety, even in unsafe code [pdf]
2 points 0 comments on Hacker News · drops.dagstuhl.de
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2 points 0 comments on Hacker News · drops.dagstuhl.de
You all know the drill: you want to write prose using an AI because it can draft faster than you can type your ideas out. Next thing that happens, you find yourself in an editing loop: change this, fix there, explain your world in more detail. The chat window scroll bar shrinks to 2 pixels and the chapter gets worse every turn. You wonder if your prompts got bad or if the AI has forgotten your world. What actually happens is the context window starts working against you. A context window works fine if you keep talking about ever-changing topics. When you write prose and ask for endless changes, the context fills up with copies of the same text in slightly different colors. Your actual changes become noise, not focus. My solution: a clean chat with zero context to fix one piece in one scene. Instead of spilling all my chapters into the prompt, I split my world setup into small Markdown files. When I start a new chat, all I put into context are mentions of those world files, the actual chapter, and relevant cast sheets. I let the agent do the heavy lifting. That's what Cursor is best for: finding its way around tons of files and making a code change like a surgeon. Side effect: your entire book stays as local files on your disk. My tool does not take your work hostage. When you ask for a change, you don't get back a full rewrite in a browser window—you get a targeted DIFF view on a text file. The chat context stays crisp and focused on exactly what you ask. So the paradigm change I promote is: go away from long-term chat sessions. Forget about "the next prompt will fix it." Start working in a controlled environment where rules are fetched on demand. You do not prompt anymore—you have a creative meeting. Since I don't trust anyone, not even myself, I added two mean critics to the system: 1. The AI Slop Critic: looks for where something sounds like generic AI prose. 2. The Story Critic: finds contradictions or plot holes in your story. The first time I ran the Slop Critic on a chapter, it came back and crushed my heart. It read so bad! But it gave a "fixable" verdict. To fix it, you right-click on the critic's finding and start a chat with your writer. A clean chat opens with the relevant context and the brief inside. All you do is hit return. Because these are plain text files, it also includes Git integration. It commits the chapter state upfront before each agent edit, showing you a diff to all historical versions with a few clicks. If you're still here, I will finally give you a name and a link to play with: Buckram Studio: https://open-vsx.org/extension/buckram/buckram-studio Or search for "Buckram Studio" inside Cursor. It's freemium so you can test the writing/editing process up to 50k words. Post on X: https://x.com/TorrenBramley/status/2079210475066114326 Ask me any question you want, or crush me like my mean critics :D
64 points 39 comments on Hacker News · fzakaria.com
85 points 19 comments on Hacker News · armaangomes.com

39 points 32 comments on Hacker News · instructables.com
234 points 41 comments on Hacker News · github.com
44 points 22 comments on Hacker News · tokyodev.com
Hi, I designed and developed a spatial index that maps museum artefacts as connecting arcs/hyperlinks from their origin site to institutional location/sources. The Index specifically looks at western/euro-american institutions and maps their collection categorised by them under Islamic art, Asian/African art, ethnological collections, Middle East, South America etc. Started as my MA thesis in 2022, kept building since, mostly solo with a little funding. Fully open source, self-hosted, AGPL-3.0, Next.js + Deck.gl on the frontend, Strapi backend, Python ETL pulling from museum open-access APIs. Currently indexing over 100k artifacts from all over the world, across 8 collections (Met, V&A, SMB Berlin). Recently added an md export so researchers can download provenance data for a filtered set of artifacts. The infrastructure is conceptualized as an indexer, not a hoster. Even images are kept as URLs pointing to the source institutions. It's connective tissue between archives that were never designed to speak to each other, with an origin-first search UI concept. The data model is on purpose flat, to avoid encoding problematic taxonomies, and routes researchers directly to the source giving responsibility to the source institutions rather than duplicating institutional data. It indexes the relationship between origin sites and destination collections. Coming from a design background, taking this project from prototype to production app has been incredibly rewarding, but honestly it’s getting a little bit overwhelming to scale alone. I would love community’s feedback on performance scaling, any code contributions, data pipelines for missing museum apis or general feedback. Repo: https://github.com/hburakyel/ex-situ Live: https://exsitu.app
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