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startup-projects

Show HN: Natel, a self-hosted contacts editor for CardDAV servers in 20MB of RAM

Hi HN, I decided to move away from Google Contacts and self-host my contacts two months ago. After installing a Radicale server, I was really missing a way to edit my contacts on the desktop. Nextcloud is overkilled for my needs, and InfCloud is the only other web contacts editor but it hasn't had a release in more than 10 years.So I built what was the missing piece in my setup : Natel, a lightweight self-hosted contacts editor for a CardDAV server. The goal is to edit your contacts on the web, be robust enough to not make you lose anything when there's an edit conflict with the server, and be super light in resources on the server. I came up with building a docker image of just a proxy caddy server and a go process that stores your credentials only in-memory, for a compressed image of less than 30MB and it uses less than 20MB of RAM when idle.Tested against four CardDAV servers : Nextcloud, Radicale, Baïkal and Xandikos. I already use it myself and it syncs with my server and DAVx5 back-and-forth, but I consider it still in beta and want to add more testing to prove compatibility for features I don't (yet ?) use. Note that any field that isn't displayed is never lost, it's just written back as-is.Demo: https://demo.natel.app/contacts Website: https://natel.appI wouldn't have done this side-project if I couldn't delegate writing the code to agents. We iterate on architectural decisions together, and once I'm satisfied with the direction, they write the result in a file (find it in docs/DECISIONS.md in the repo), then write the code to meet the expectations and independently review each other's iterations. I drive the project's direction and decisions, but I don't review the code myself. I keep an eye on the tests to know what's covered, and will read and review code when an agent draws my attention to it. The e2e tests were able to find a bug in SoGO, a CardDAV server : https://bugs.sogo.nu/view.php?id=6269 (and I didn't add that server in the compatilibity list yet because that bug breaks one the promises of the app, which is to be warned if we're trying to delete a contact that is modified elsewhere ; the server doesn't correctly enfore the If-Match instruction)As I don't have that many users yet, I welcome feedback on any part of the project but especially the parts that I have not yet tested like Contacts on a Mac ! Comments URL: https://news.ycombinator.com/item?id=50439845 Points: 1 # Comments: 0

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Hacker News Show HN
startup-projects

Show HN: Show HN: Sowbot – Open-hardware agricultural robot 8months progress

8 Months ago I made this post https://news.ycombinator.com/item?id=47123894Since then we've done stuff, the software is much more mature, we've done some basic field testing on small dev platforms.We've moved from ESP32 for the microcontroller to a STM32.Volunteers have come, gone again, new ones arrived.We're getting ready for full scale field trials now.Been putting some videos of sim up on https://www.youtube.com/@rosmo-robot/videosPlease critique again, and come join us! Comments URL: https://news.ycombinator.com/item?id=50437978 Points: 1 # Comments: 1

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Hacker News Show HN
enterprise-apps

Single-shot and agentic retrieval with Amazon Bedrock Managed Knowledge Base

Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside agentic retrieval. This post explains how the maxAgentIteration parameter matches retrieval effort to each request, using an illustrative internal banking policy assistant.

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AWS Machine Learning Blog
datasets

An Anatomy-guided 3D Segmentation Pipeline for Prostate Surgical Phantoms from CT Scans

arXiv:2611.08652v1 Announce Type: new Abstract: Full autonomy has been a long-term goal in robotic surgery and a near-to-medium-term goal for prostatectomy. Due to the relatively closed structure of the procedure, its standard-of-care robotic/laparoscopic solutions, and procedure economics, prostate procedures are an optimal early target for surgical automation. Prototyping and testing of possible solutions is done in constructed laboratory phantoms, either synthetic tissues or animal phantoms. To enable measurement of perceptual and robotic motion error, phantoms undergo volumetric imaging; these images are critical to both a potential deployed automatic system, used in planning motion and treatment (cutting, ablation, suturing, etc.), as well as post-hoc assessment of system performance. Rapid iteration on these phantom models yields small, inconsistent datasets, while the current workflow for human annotation is often anatomically unreliable and prohibitively time-consuming, rendering deep learning unsuitable at reasonable scales. To address these limitations, we present an automated 3D segmentation pipeline that uses classical medical image-processing techniques to encode the known anatomy of the phantoms as explicit geometric and intensity priors, which requires no training corpus and is transferred to each new phantom revision without re-annotation. Across 57 scans spanning multiple phantom revisions, our pipeline recovers the capsule interior at 0.83 median Dice on 11 contrast-suppressed scans and detects all 32 annotated focal lesions without false positives, completing a full scan in 2-8 minutes on commodity CPU hardware versus 1-2 hours by hand.

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arXiv cs.CV
training-alignment

Dual-Stream LLM-Guided Reward Design for Reject-Inference-Aware Reinforcement Learning in E-Commerce Payment Fraud Detection

arXiv:2611.08706v1 Announce Type: new Abstract: Production fraud-detection models learn from a censored slice of their own traffic: transactions a legacy system blocked never receive fraud labels, so models train on a biased, approved-only distribution. We extend LLM-guided reward design, the framework of Qu et al. (2025) in which an LLM evolves the reward of a two-step REINFORCE fraud-blocking policy, to attack this reject-inference problem inside the reward. A single LLM designs a dual-stream reward: stream 1 applies the standard dollar-weighted precision objective to labeled transactions; stream 2 scores legacy-blocked, unlabeled transactions by contrasting the newest gradient-boosted risk signal against the legacy scores that drove the original block. Rather than prescribe which signals matter, we expose a superset of evidence and let the LLM decide for itself which to use, guided by evaluation feedback alone; because it writes the reward as code, that choice is directly auditable. On a held-out four-month out-of-time test, the dual-stream agent beats the prior labeled-only agent at every target recall, by +4.7, +2.4 and +3.0 points of dollar-weighted precision at 80%, 85% and 90% recall, and the production baseline by +6.8 points on average. The resulting policy is architecturally identical to that of Qu et al. (2025) and adds no inference cost.

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arXiv cs.LG