标签：#r

I’m building an experimental HTML-to-PDF engine in Rust and would like technical feedback from people who understand browser engines, PDF generation, rendering, or high-volume document systems. The goal is to build a very low-RAM, highly parallel HTML-to-PDF engine for server workloads. Think invoices, reports, tables, receipts, business templates, generated dashboards, and API-based PDF rendering. The motivation is that Chromium/Puppeteer is very compatible, but expensive for high-volume PDF generation because each render can involve a heavy browser process/runtime. I want to explore whether a dedicated engine can cover common server-side HTML-to-PDF use cases with much lower memory and better multi-core scaling. Current architecture: HTML -> html5ever parser -> compact arena DOM -> cssparser stylesheet parsing -> cascade + computed styles -> box tree -> layout / pagination -> display list -> compressed PDF writer Some current design choices: Rust implementation no Chromium or browser subprocess compact arena-based DOM instead of pointer-heavy node graph independent render jobs so many PDFs can render in parallel across worker threads display-list boundary so layout is separate from PDF writing optional bounded pre-layout JavaScript stage behind a trait PDF backend supports compressed streams, selectable text, embedded fonts, Unicode/ToUnicode, and font subsetting work The long-term ambition is “lean and fast HTML-to-PDF with broad CSS and controlled JS support,” but I’m trying to be realistic. I know “full CSS/JS” is basically browser-engine territory, so I’m thinking the practical first target should be server-generated documents rather than arbitrary websites. Questions for the community: Is this technically realistic if the scope starts with business/document HTML rather than full browser compatibility? What are the hardest parts I’m likely underestimating? Which CSS/layout features are absolutely necessary for real-world adoption? Is pre-layout deterministic J

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The autonomous vehicle space is starting to feel like a repeat of the 2016 hype cycle. Travis Kalanick is back building a robotics company, and the talent wars and capital are heating up the same way they did the first time around. The money’s flowing back, and it’s the people who lived through that first […]

US lifts curbs on Anthropic’s advanced Fable and Mythos models.

Kobo recently raised the price of its Libra Colour e-reader to $259.99, but today’s deal effectively erases that hike. The company, Best Buy, and Target, are all selling it for its old $229.99 price as part of broader sales, making now one of the best times to grab my favorite alternative to Amazon’s Kindle. If […]

Penalty kicks are already proving critical to big wins at this year’s World Cup. But the advantage in penalty kicks has more to do with psychological effects than who kicks first.

Debug AI agents by replaying and forking runs Discussion | Link

Starliner's certification may be delayed to 2027, 10 years later than Boeing's original schedule.

The government has removed restrictions on Anthropic’s Fable 5 and Mythos 5 AI models—but there were strings attached.

These six free settings will not make your project unhackable. Nothing will. What they will do is close the easy doors. Turn these on, and your project will be meaningfully harder to attack than it was before. The post 6 security settings every GitHub maintainer should enable this week appeared first on The GitHub Blog .

AWS released Managed Knowledge Bases for Amazon Bedrock on 17 June 2026. The feature significantly reduces the operational complexity of building Retrieval-Augmented Generation (RAG) solutions by allowing Bedrock to manage the vector storage, indexing, embeddings, and retrieval infrastructure on your behalf. For teams looking to deliver an Agent Core proof of concept or their first production RAG workload quickly, this can be a compelling option. However, there are some important trade-offs to understand before committing to the managed approach. Traditionally, a Bedrock Knowledge Base required a customer-managed vector store such as: OpenSearch Serverless OpenSearch Managed Clusters Aurora PostgreSQL with pgvector Pinecone DocumentDB Other supported vector databases With a Managed Knowledge Base, Bedrock handles the underlying vector infrastructure and embedding model selection for you. Creating one from the AWS CLI is straightforward: aws bedrock-agent create-knowledge-base \ --name "my-managed-kb" \ --role-arn "arn:aws:iam:: ${ AWS_ACCOUNT_ID } :role/service-role/AmazonBedrockExecutionRoleForKnowledgeBase_ihv1p" \ --knowledge-base-configuration '{ "type": "MANAGED", "managedKnowledgeBaseConfiguration": { "embeddingModelType": "MANAGED" } }' Advantages Lower operational overhead There is no need to provision, secure, monitor, patch, or scale a separate vector database. Lower costs S3 storage is cheaper than database storage. Pay only for each ingestion and retrieval operation. Indexing and searching compute is free. No 24/7 server costs. Faster time-to-value Managed Knowledge Bases make it possible to stand up a RAG solution in minutes rather than days. Automatic embedding management Bedrock manages embedding selection and indexing, reducing the number of architectural decisions required from development teams. Cost-effective for smaller workloads The managed model can be attractive for: Proofs of Concept Departmental knowledge bases Agent Core pilots Workloads wi

A few weeks ago I was in Copenhagen for my first Codegarden, and one quiet thought has stuck with me since. It didn't come from a keynote. It came from the bit the keynote leaves out. I've worked with Umbraco for years, but I'd never been to Codegarden, and I turned up without much of a fixed idea of what the two days would be. I kept that open on purpose. I wanted to take it in rather than measure it against something I'd decided in advance. What struck me most was that the value came from two places at once. The sessions were a fantastic source of inspiration; everything from keynotes to guest speakers all seemed to resonate in some way or another. The conversations in between the sessions - drifting around the event space and finding common ground with anyone and everyone - proved just as valuable. I came home more energised than I've been in a while, with a notebook full of half-formed ideas and a better feel for the community I'm part of. But the thing I kept turning over afterwards was that bit the keynote leaves out. That's what I want to write about. The easy half and the hard half Every major Umbraco release gets the same treatment. A polished keynote, a clean demo, a feature that looks effortless on stage. There's plenty in 18, and which part matters most depends on what you're building. For me it's Elements: a new Library section where you manage reusable content and reference it through a new element picker. Create once, use everywhere. It's a genuinely good direction. Reusable content has lived awkwardly in the content tree for years, and Library finally gives it a proper home. What the demos don't show you is the part I've been playing around with for the past few weeks. Taking a real Umbraco 17 site, with content pickers threaded through block lists, block grids, rich text blocks and base document properties, and getting all of it to point at the new Library without an editor ever noticing anything moved underneath them. The feature is the easy half.