git11 is an AI workspace for GitHub engineering teams
Hey HN, I built git11 - an AI workspace for engineering teams that connects to your GitHub org. What it does: - Auto-generates structured documentation for any repository - Lets you ask natural language questions about your codebase ("how does auth work?", "where is payment logic?") - Manages org-wide access with role permissions and audit logs - Official GitHub App integration, read-only by default I built this because I kept seeing teams with zero docs and developers wasting hours just navigating unfamiliar codebases. Tech stack: React, Supabase, GitHub API, Resend e-mail, Google auth, Would love feedback - especially from anyone who's dealt with documentation debt on a growing codebase. https://git11.xyz Comments URL: https://news.ycombinator.com/item?id=47611889 Points: 1 # Comments: 0
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integrationgithubrepository![[P] Trained a small BERT on 276K Kubernetes YAMLs using tree positional encoding instead of sequential](https://images.unsplash.com/photo-1677442135703-1787eea5ce01?w=600&q=80)
[P] Trained a small BERT on 276K Kubernetes YAMLs using tree positional encoding instead of sequential
I trained a BERT-style transformer on 276K Kubernetes YAML files, replacing standard positional encoding with learned tree coordinates (depth, sibling index, node type). The model uses hybrid bigram/trigram prediction targets to learn both universal structure and kind-specific patterns — 93/93 capability tests passing. Interesting findings: learned depth embeddings are nearly orthogonal (categorical, not smooth like sine/cosine), and 28/48 attention heads specialize on same-depth attention (up to 14.5x bias). GitHub: https://github.com/vimalk78/yaml-bert submitted by /u/vimalk78 [link] [comments]
Rusty Flying Robots: Learning a Full Robotics Stack with Real-Time Operation on an STM32 Microcontroller in a 9 ECTS MS Course
arXiv:2604.00032v1 Announce Type: cross Abstract: We describe a novel masters-level projects class that teaches robotics along the traditional robotics pipeline (dynamics, state estimation, controls, planning). One key motivational part is that students have to directly apply the algorithms they learn on a highly constrained compute platform, effectively making a robot fly. We teach nonlinear algorithms as deployed in state-of-the-art flight stacks such as PX4. Didactically, we rely on two core concepts: 1) avoidance of provided black-box software infrastructure, and 2) usage of the safe and efficient programming language Rust that is used on the PC (for simulation) and an STM32 microcontroller (for robot deployment). We discuss our methodology and the student feedback over two years with
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