Show HN: High-fidelity, compact, and real time rendering of university campus https://ift.tt/fFmNjbt

Show HN: High-fidelity, compact, and real time rendering of university campus Technical thread: https://ift.tt/RktKbnN https://hoanh.space/aalto/ October 6, 2025 at 05:21AM

Show HN: Calcetra – an interactive geometry explorer for math visualization https://ift.tt/wav03Ru

Show HN: Calcetra – an interactive geometry explorer for math visualization https://ift.tt/HTFNbgW October 5, 2025 at 05:22PM

Show HN: A Vectorless LLM-Native Document Index Method https://ift.tt/JTGMq2R

Show HN: A Vectorless LLM-Native Document Index Method The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents. Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans. PageIndex is a new step that "looks back to move forward". It revives the original, human-oriented idea of an index and adapts it for LLMs. Now the index itself (PageIndex) lives inside the LLM's context window: the model sees a hierarchical table-of-contents tree and reasons its way down to the right span, much like a person would retrieve information using a book's index. PageIndex MCP shows how this works in practice: it runs as a MCP server, exposing a document's structure directly to LLMs/Agents. This means platforms like Claude, Cursor, or any MCP-enabled agent or LLM can navigate the index themselves and reason their way through documents, not with vectors/chunking, but in a human-like, reasoning-based way. https://ift.tt/chLuOx5 October 5, 2025 at 10:09PM

Show HN: An open-source, RL-native observability framework we've been missing https://ift.tt/xwHyLfS

Show HN: An open-source, RL-native observability framework we've been missing The RL ecosystem is maturing— verifiers are standardizing how we build and share environments. However, as it grows, we need observability tooling that actually understands RL primitives. Running RL experiments without visibility into rollout quality, reward distributions, or failure modes is a waste of time. Monitor provides live tracking, per-example inspection, and programmatic access—see what's happening during runs and debug what went wrong afterward. https://ift.tt/7onTK1Y October 5, 2025 at 03:05AM

Show HN: World Amazing Framework: Like Django for Civilization https://ift.tt/fgl5hed

Show HN: World Amazing Framework: Like Django for Civilization Any initial thoughts? This framework is meant to be a tool for construction, so if you want to play around with it for creating potential specific implementations, you can drop the contents of the website, the GitHub README, and the entire overview.md into an AI chat, and that should be enough to use the framework, at least conceptually. Would y'all want me to pre-prime a chat in Google AI Studio with the full context of the plan and some basic direction for discourse? I can share a link to a ready-to-go environment. The core documentation should answer most mechanical questions. And if you feed the docs into an AI chat, you can ask it any question you may have, or to simply ask it to explain something in different ways, or hypothesize solutions to any world issue, either systemic or regional. Gemini Pro 2.5 can take the full doc in one prompt, and its ability to co-create ideas is remarkable. I've been using it mostly through the AI Studio interface. Much of the overview is as much my work as it is a synthesis of my collaboration with Gemini Pro 2.5, ChatGPT-4o, and some early contributions from GPT-4 about a year ago. Before LLMs, I was building out pamphlet-style pages on a website (that are up at whomanatee.org, which is the base wrapper implementation of the framework), and I was planning to use them as talking points. I was anticipating that much of the deep thinking would have to happen in slow, public discourse. With LLMs, I've been able to stress-test these ideas from every possible angle, using any past event or theory to see if the framework could withstand scrutiny. At one point, a model argued that Adam Smith would have rejected this idea as fantasy. So I worked with it to develop an economic plan that "synthetic Adam" praised. It's incredible that we now have the ability to get synthesized thoughts from almost any perspective. You could ask it, "What would Barack Obama think of this plan? And using the framework, what would be your response to any hesitations he may have?" And it responds with incredible analysis, synthesis, and feedback. https://ift.tt/Q6BEXph October 5, 2025 at 03:44AM

Show HN: Run – a CLI universal code runner I built while learning Rust https://ift.tt/6RXytrx

Show HN: Run – a CLI universal code runner I built while learning Rust Hi HN — I’m learning Rust and decided to build a universal CLI for running code in many languages. The tool, Run, aims to be a single, minimal dependency utility for: running one-off snippets (from CLI flags), running files, reading and executing piped stdin, and providing language-specific REPLs that you can switch between interactively. I designed it to support both interpreted languages (Python, JS, Ruby, etc.) and compiled languages (Rust, Go, C/C++). It detects languages from flags or file extensions, can compile temporary files for compiled languages, and exposes a unified REPL experience with commands like :help, :lang, and :quit. Install: cargo install run-kit (or use the platform downloads on GitHub). Source & releases: https://ift.tt/4L3d6eV I used Rust while following the official learning resources and used AI to speed up development, so I expect there are bugs and rough edges. I’d love feedback on: usability and UX of the REPL, edge cases for piping input to language runtimes, security considerations (sandboxing/resource limits), packaging and cross-platform distribution. Thanks — I’ll try to answer questions and share design notes. https://ift.tt/4L3d6eV October 5, 2025 at 12:04AM

Show HN: Beacon (open source) – Built after AWS billed me 700% more for RDS https://ift.tt/zPuS6o7

Show HN: Beacon (open source) – Built after AWS billed me 700% more for RDS I've been hosting my side project on AWS. I was paying an okay price for not managing infrastructure at all. I moved everything to AWS Ligthsail after my startup credits run out. The project was initially a success and made several thousand euros per month in revenue. Then came covid with new regulations, and suddenly my customers were non existent (the problem it solved was no longer there). After that it was not making me money, I was paying it from my own pocket to maintain it, thinking maybe it will come back. Then one day, after some ignored spam AWS emails, I got a huge charge on my card, along with a bill from AWS. The charge was orders of magnitude higher than the previous charges."WTF??" I said to myself while rushing to log into the dashboard to see what the issue was. No DDoS, no misconfiguration, nothing unusual. I logged into the root account to look at the billing page, and there it was:RDS PostgreSQL legacy fee ~€200 because I did not upgrade to Postgres 16 (from 13). I was baffled. I paid monthly €25 (27% tax included) for the smallest RDS instance, then I see this monster fee for something I think should cost maybe €2. I mean AWS just has to run it in a different environment. For €200 I could buy them a new server to run it for me. That's when I had the realization: "I have a spare Raspberry Pi 3, I'll just host everything on that. That will be for free." But self-hosting came with it's own challenges, especially on a resource-constrained device. I needed better tools to deploy and monitor my application. SSH-ing into the Raspberry Pi every time I wanted to deploy a newer version was a pain in the ass. So was debugging issues. Existing deployment and monitoring solutions were either too expensive, too complex, or didn't work well with resource-constrained devices like Raspberry Pi. Examples: * Grafana/Prometheus for monitoring: Over-engineered for my needs. * OpenSearch/ELK for logs: A nightmare on low-resource devices. * Metabase for dashboards: A ram hungry monster that eats up more resources than if I hosted 100 applications. And to access the db remotely opening a port and putting it behind Cloudflare Zero Trust is much easier than setting up Metabase. So I decided to build my own deployment and montitoring agent, and why not make it opensource? The agent can currently deploy applications from github by polling release tags, monitor device metrics, alert when thresholds are reached, forward logs to cloud dashboard. It's still in development, with features improving every week. If you are interested, give it a start on Github. https://beaconinfra.dev October 4, 2025 at 01:52AM