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[Other] Pdoc – Generate API documentation for Python projects

[Other] Show HN: Stochastic Gradient in Hilbert Spaces I wrote a free, proof-complete monograph on stochastic gradient methods in infinite-dimensional Hilbert spaces. It starts from the functional-analysis foundations (Riesz, Radon–Nikodym), gives five equivalent definitions of stochastic gradients, proves existence&#x2F;uniqueness of the dynamics, connects the continuum limit to gradient-flow PDEs, and derives explicit convergence rates under multiple assumption sets (convex, strongly convex, PL&#x2F;KL, heavy-tailed noise, proximal). It also covers special cases (Gaussian&#x2F;RKHS), extends to Hilbert manifolds&#x2F;Banach spaces, analyzes discretizations (stability&#x2F;consistency), and includes four applications (QM ground states, elasticity, optimal control, inverse problems) with pseudocode and explicit constants. Open problems at the end invite feedback. PDF is free to redistribute if cited.<p>Happy to answer questions on assumptions that are actually needed in infinite-D, how the spectral picture influences rates, and what breaks outside Hilbert structure. If you want a quick start, skim the convergence summary and the applications sections.

[Other] Show HN: ElevenLabs UI shadcn/UI components for audio Hey hn, I put together a collection of audio and agent components for Next.js built on top of ShadCN.<p>Hope you find this useful &amp; let me know if there’s ant feedback a (GitHub repo will be open sourced tomorrow am)

[Testing] Automated code reviews via mutation testing

[Other] CodeMender: an AI agent for code security

[Other] Apps SDK

[Other] Show HN: Tangled – Git collaboration built on AT Protocol

[Other] Show HN: A Digital Twin of my coffee roaster that runs in the browser I built this website to host a data-driven model of my coffee sample roaster.<p>I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!<p>The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT&#x2F;transformer-based). Buncha math.<p>The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.<p>My next steps are to add other roasters and the ability to practice control&#x2F;reference tracking.

[Other] OpenZL: An open source format-aware compression framework <a href="https:&#x2F;&#x2F;github.com&#x2F;facebook&#x2F;openzl" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;facebook&#x2F;openzl</a><p><a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2510.03203" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2510.03203</a><p><a href="https:&#x2F;&#x2F;openzl.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openzl.org&#x2F;</a>

[Other] Show HN: I'm building a browser for reverse engineers

[Other] Show HN: I built an open-source AI data layer that connects any LLM to any data Excited to share a project I’ve been building for months! Would love to receive honest feedback :)<p>My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong?<p>The product allows you to connect any LLM to any data source with centralized context (instructions, dbt, code, AGENTS.md, Tableau) and governance. Users can chat with their data to build charts, dashboards, and scheduled reports — all via an agentic, observable loop. With slack integration as well!<p>* Centralize context management: instructions + external sources (dbt, Tableau, code, AGENTS.md), and self-learning<p>* Agentic workflows (ReAct loops): reasoning, tool use, reflection<p>* Generate visuals, dashboards, scheduled reports via chat&#x2F;commands<p>* Quality, accuracy, and performance scoring (llm judges) to ensure reliability<p>* Advanced access &amp; governance: RBAC, SSO&#x2F;OIDC, audit logs, rule enforcement<p>* Deploy in your environment (Docker, Kubernetes, VPC) — full control over infrastructure<p>GitHub: github.com&#x2F;bagofwords1&#x2F;bagofwords<p>Docs &#x2F; architecture &#x2F; quickstart: docs.bagofwords.com

[Monitoring/Observability] GPU Hot: Dashboard for monitoring NVIDIA GPUs on remote servers

[Other] Show HN: Turn your OpenAPI spec into negative tests

[Other] Show HN: I've build a platform for writing technical/scientific documents

[Other] Build a VPN Tunnel with Wintun on Windows – Part 1

[Other] Django: One ORM to rule all databases

[Testing] QA-use-MCP: MCP for E2E testing

[DevOps] Show HN: Envirobly – Efficient App Hosting This is my, fully bootstrapped, take on an application hosting platform. I designed it primarily for myself, to meet the following goals:<p>* Utilize the best parts of the cloud, leave expensive value added services behind. * Host anywhere in the world, close to your users. * Batteries included: backups, logging, monitoring, auto-scaling, containerization, databases. * Simple and user friendly.<p>The journey was tough. It took me round about 3 years to get everything just right. This goes against the conventional wisdom of getting something simple out quick. With a hosting platform however, in my mind, reliability, stability and certain maturity are a must have from the start.<p>I definitely need to work on the presentation, it&#x27;s barely covering the basics. But you have to start somewhere :-)<p>I&#x27;d love to hear some critique from the community. Thanks!

[Other] Flightcontrol: A PaaS that deploys to your AWS account

[CLI Tool] Show HN: A Node.js CLI tool to generate ai.txt, llms.txt, robots.txt, humans.txt