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[Other] Browser extension and local backend that automatically archives YouTube videos

[IDE/Editor] VSCode extension for syntax highlighting multi-line YAML strings

[Other] Unikernel Guide: Build and Deploy Lightweight, Secure Apps

[Other] Show HN: WebGPU enables local LLM in the browser – demo site with AI chat Browser LLM demo working on JavaScript and WebGPU. WebGPU is already supported in Chrome, Safari, Firefox, iOS (v26) and Android.<p>Demo, similar to ChatGPT <a href="https:&#x2F;&#x2F;andreinwald.github.io&#x2F;browser-llm&#x2F;" rel="nofollow">https:&#x2F;&#x2F;andreinwald.github.io&#x2F;browser-llm&#x2F;</a><p>Code <a href="https:&#x2F;&#x2F;github.com&#x2F;andreinwald&#x2F;browser-llm">https:&#x2F;&#x2F;github.com&#x2F;andreinwald&#x2F;browser-llm</a><p>- No need to use your OPENAI_API_KEY - its local model that runs on your device<p>- No network requests to any API<p>- No need to install any program<p>- No need to download files on your device (model is cached in browser)<p>- Site will ask before downloading large files (llm model) to browser cache<p>- Hosted on Github Pages from this repo - secure, because you see what you are running

[Other] Show HN: NaturalCron – Human-Readable Scheduling for .NET (With Fluent Builder) Hi HN!<p>I built NaturalCron because I was tired of writing and debugging CRON syntax like:<p><i>&#x2F;5 </i> * * 5<p>Now you can write something human-readable in .NET:<p>var expression = new NaturalCronExpression(&quot;every 5 minutes on friday&quot;);<p>Or use a Fluent Builder for strong typing and IDE support:<p>var expression = NaturalCronExpressionBuilder .Every().Minutes(5) .On(DayOfWeek.Friday) .Build();<p>Great for: - Code-based scheduling in .NET apps - Overriding schedules from configs or databases - Displaying easy-to-read rules in UIs<p>NuGet: <a href="https:&#x2F;&#x2F;www.nuget.org&#x2F;packages&#x2F;NaturalCron" rel="nofollow">https:&#x2F;&#x2F;www.nuget.org&#x2F;packages&#x2F;NaturalCron</a> GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;hugoj0s3&#x2F;NaturalCron">https:&#x2F;&#x2F;github.com&#x2F;hugoj0s3&#x2F;NaturalCron</a><p>Would love your feedback on syntax, builder design, and what features you&#x27;d like to see next!

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[Other] Termagotchi – A terminal-based Tamagotchi simulation written in Go

[Other] AI-Powered Shopify Liquid Code Generator Stop writing repetitive Liquid — just describe what you need, and LiquidDaddy creates production-ready Shopify themes & components on demand.

[Testing] Defend What Matters : Web Application Penetration Testing Penetration testing is more than basic testing, as it helps identifying complex business logic vulnerabilities to prevent unauthorized access to sensitive information, operational disruptions, or data theft. Qualysec is ready to protect you from threats.

[API/SDK] Next-generation TypeScript HTTP client Next-generation TypeScript HTTP client with built-in retry, smart caching, and seamless authentication. Effortlessly handle API requests with advanced features for modern web development.

[Other] Show HN: Agentic AI Frameworks on AWS (LangGraph,Strands,CrewAI,Arize,Mem0) We’ve published a set of open-source reference implementations on how to build production-grade Agentic AI applications on AWS.<p>What’s in the repo: • Agentic RAG, memory, and planning workflows with LangGraph &amp; CrewAI • Strands-based flows with observability using OTEL &amp; Arize • Evaluation with LLM-as-judge and cost&#x2F;performance regressions • Built with Bedrock, S3, Step Functions, and more<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;aws-samples&#x2F;sample-agentic-frameworks-on-aws">https:&#x2F;&#x2F;github.com&#x2F;aws-samples&#x2F;sample-agentic-frameworks-on-...</a><p>Would love your thoughts — feedback, issues, and stars welcome!

[Other] Show HN: Schematra – Sinatra-inspired minimal web framework for Chicken Scheme I started this project a couple of weeks ago because I was stuck on my side project and needed some motivation. For a very long time I wanted to get back to do something useful in lisp&#x2F;scheme, did a quick research and settled on CHICKEN mostly because it&#x27;s relatively well maintained, fast enough, it&#x27;s extremely easy to build&#x2F;install and very easy to write interop to pretty much any library.<p>Most of the projects that I&#x27;ve written on the side have been using some combination of Sinatra + Sequel + Postgres&#x2F;Redis&#x2F;Something else + HTMX. I love the simplicity of Sinatra&#x27;s API so I decided to focus on trying to have a similar experience but in scheme, trying to make it ergonomic for a scheme dev (that part might not be there yet since I&#x27;m not an experienced scheme dev).<p>The most fun part was the dev cycle: Emacs + NREPL + Aider (as a code reviewer &amp; rubber ducky. For codegen it&#x27;s mostly annoying but works great for documentation &amp; refactoring).<p>I hope to add full SSE &amp; WebSocket support some time this week. Anyway, hopefully this is interesting to some of you and might be a source of fun :)

[Monitoring/Observability] Show HN: TraceRoot – Open-source agentic debugging for distributed services Hey Xinwei and Zecheng here, we are the authors of TraceRoot (<a href="https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot">https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot</a>).<p>TraceRoot (<a href="https:&#x2F;&#x2F;traceroot.ai">https:&#x2F;&#x2F;traceroot.ai</a>) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents.<p>At the heart are our lightweight Python (<a href="https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot-sdk">https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot-sdk</a>) and TypeScript (<a href="https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot-sdk-ts">https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot-sdk-ts</a>) SDKs - they can hook into your app using OpenTelemetry and captures logs and traces. These are either sent to a local Jaeger (<a href="https:&#x2F;&#x2F;www.jaegertracing.io&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.jaegertracing.io&#x2F;</a>) + SQLite backend or to our cloud backend, where we correlate them into a single view. From there, our custom agent takes over.<p>The agent builds a heterogeneous execution tree that merges spans, logs, and GitHub context into one internal structure. This allows it to model the control and data flow of a request across services. It then uses LLMs to reason over this tree - pruning irrelevant branches, surfacing anomalous spans, and identifying likely root causes. You can ask questions like “what caused this timeout?” or “summarize the errors in these 3 spans”, and it can trace the failure back to a specific commit, summarize the chain of events, or even propose a fix via a draft PR.<p>We also built a debugging UI that ties everything together - you explore traces visually, pick spans of interest, and get AI-assisted insights with full context: logs, timings, metadata, and surrounding code. Unlike most tools, TraceRoot stores long-term debugging history and builds structured context for each company - something we haven’t seen many others do in this space.<p>What’s live today:<p>- Python and TypeScript SDKs for structured logs and traces.<p>- AI summaries, GitHub issue generation, and PR creation.<p>- Debugging UI that ties everything together<p>TraceRoot is MIT licensed and easy to self-host (via Docker). We support both local mode (Jaeger + SQLite) and cloud mode. Inspired by OSS projects like PostHog and Supabase - core is free, enterprise features like agent mode multi-tenant and slack integration are paid.<p>If you find it interesting, you can see a demo video here: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=nb-D3LM0sJM" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=nb-D3LM0sJM</a><p>We’d love you to try TraceRoot (<a href="https:&#x2F;&#x2F;traceroot.ai">https:&#x2F;&#x2F;traceroot.ai</a>) and share any feedback. If you&#x27;re interested, our code is available here: <a href="https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot">https:&#x2F;&#x2F;github.com&#x2F;traceroot-ai&#x2F;traceroot</a>. If we don’t have something, let us know and we’d be happy to build it for you. We look forward to your comments!

[Other] Show HN: Pontoon – Open-source customer data syncs Hi HN,<p>We’re Alex and Kalan, the creators of Pontoon (<a href="https:&#x2F;&#x2F;github.com&#x2F;pontoon-data&#x2F;Pontoon">https:&#x2F;&#x2F;github.com&#x2F;pontoon-data&#x2F;Pontoon</a>). Pontoon is an open-source data export platform that makes it really easy to create data syncs and send data to your enterprise customers. Check out our demo here: <a href="https:&#x2F;&#x2F;app.storylane.io&#x2F;share&#x2F;onova7c23ai6">https:&#x2F;&#x2F;app.storylane.io&#x2F;share&#x2F;onova7c23ai6</a> or try it out with docker: <a href="https:&#x2F;&#x2F;pontoon-data.github.io&#x2F;Pontoon&#x2F;getting-started&#x2F;quick-start&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pontoon-data.github.io&#x2F;Pontoon&#x2F;getting-started&#x2F;quick...</a><p>While at our prior roles as data engineers, we’ve both felt the pain of data APIs. We either had to spend weeks building out data pipelines in house or spend a lot on ETL tools like Fivetran (<a href="https:&#x2F;&#x2F;www.fivetran.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.fivetran.com&#x2F;</a>). However, there were a few companies that offered data syncs that would sync directly to our data warehouse (eg. Redshift, Snowflake, etc.), and when that was an option, we always chose it. This led us to wonder “Why don’t more companies offer data syncs?”. It turns out, building reliable cross-cloud data syncs is difficult. That’s why we built Pontoon.<p>We designed Pontoon to be:<p>- Easily deployed: we provide a single, self-contained Docker image for easy deployment and Docker Compose for larger workloads (<a href="https:&#x2F;&#x2F;pontoon-data.github.io&#x2F;Pontoon&#x2F;getting-started&#x2F;quick-start&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pontoon-data.github.io&#x2F;Pontoon&#x2F;getting-started&#x2F;quick...</a>)<p>- Support modern data warehouses: we support syncing to&#x2F;from Snowflake, BigQuery, Redshift, and Postgres.<p>- Sync cross cloud: sync from BigQuery to Redshift, Snowflake to BigQuery, Postgres to Redshift, etc.<p>- Developer friendly: data syncs can also be built via the API<p>- Open source: Pontoon is free to use by anyone<p>Under the hood, we use Apache Arrow (<a href="https:&#x2F;&#x2F;arrow.apache.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;arrow.apache.org&#x2F;</a>) to move data between sources and destinations. Arrow is very performant - we wanted to use a library that could handle the scale of moving millions of records per minute.<p>In the shorter-term, there are several improvements we want to make, like:<p>- Adding support for DBT models to make adding data models easier<p>- UX improvements like better error messaging and monitoring of data syncs<p>- More sources and destinations (S3, GCS, Databricks, etc.)<p>- Improve the API for a more developer friendly experience (it’s currently tied pretty closely to the front end)<p>In the longer-term, we want to make data sharing as easy as possible. As data engineers, we sometimes felt like second class citizens with how we were told to get the data we needed - “just loop through this api 1000 times”, “you probably won’t get rate limited” (we did), “we can schedule an email to send you a csv every day”. We want to change how modern data sharing is done and make it simple for everyone.<p>Give it a try <a href="https:&#x2F;&#x2F;github.com&#x2F;pontoon-data&#x2F;Pontoon">https:&#x2F;&#x2F;github.com&#x2F;pontoon-data&#x2F;Pontoon</a>. Cheers!

[IDE/Editor] Show HN: Kanban-style Phase Board: plan → execute → verify → commit After months of feedback from devs juggling multiple chat tools just to break big tasks into smaller steps, we re‑imagined our workflow as a Kanban‑style Phase Board right inside your favourite IDE. The new Phase mode turns any large task into a clean sequence of PR‑sized phases you can review and commit one by one.<p>How it works<p>1. Describe the goal (Task Query) – In Phase mode, type a concise description of what you want to build or change. Example: “Add rate‑limit middleware and expose a &#x2F;metrics endpoint.” Traycer treats this as the parent task. 2. Clarify intent (AI follow‑up) – Traycer may ask one or two quick questions (constraints, coding style). Answer them so the scope is crystal‑clear. 3. Auto‑generate the Phase Board – Traycer breaks the task into a sequential list of PR‑sized phases you can reorder, edit, or delete. 4. Open a phase &amp; generate its plan – get a detailed file‑level plan: which files, functions, symbols, and tests will be touched. 5. Handoff to your coding agent – Hit Execute to send that plan straight to Cursor, Claude Code, or any agent you prefer. 6. Verify the diff – When your agent finishes, Traycer compares the diff to the plan and checks compatibility with upcoming phases, flagging any mismatches. 7. Review &amp; commit (or tweak) – Approve and commit the phase, or adjust the plan and rerun. Then move on to the next phase.<p>Why it helps?<p>* True PR checkpoints – every phase is small enough to reason about and ship. * No runaway prompts – only the active phase is in context, so tokens stay low and results stay focused. * Tool-agnostic – Traycer plans and verifies; your coding agent writes code. * Fast course-correction – if something feels off, just edit that phase and re-run.<p>Try it out &amp; share feedback<p>Install the Traycer extension (<a href="https:&#x2F;&#x2F;traycer.ai&#x2F;installation" rel="nofollow">https:&#x2F;&#x2F;traycer.ai&#x2F;installation</a>), create a new task, and the Phase Board will appear. Add a few phases, run one through, and see how the PR‑sized checkpoints feel in practice. If you have suggestions that could make the flow smoother, drop them in the comments - every bit of feedback helps.

[API/SDK] Show HN: WhiteLightning – ultra-lightweight ONNX text classifiers trained w LLMs Hey HN,<p>We’re Volodymyr and Volodymyr—two developers from Ukraine building WhiteLightning. It’s a tool that turns large LLMs (Claude 4, Grok 4, GPT-4o via OpenRouter) into tiny ONNX text classifiers that run anywhere—even on drones at the edge.<p>Why we built this: Many developers want custom models (spam filters, sentiment analysis, PII detection, moderation tools), but don’t want to deal with constant API calls or deploy heavy models in production.<p>How it works: WhiteLightning uses LLMs to generate training data and distills it into KB-sized ONNX models you can run on any device and in any language. Just describe your task in a sentence, grab the ONNX model, and run it locally—Python, JS, Rust, Java, Swift, C++, you name it.<p>Try it instantly in your browser: <a href="https:&#x2F;&#x2F;whitelightning.ai&#x2F;playground.html" rel="nofollow">https:&#x2F;&#x2F;whitelightning.ai&#x2F;playground.html</a><p>Code &amp; docs: <a href="https:&#x2F;&#x2F;github.com&#x2F;Inoxoft&#x2F;whitelightning">https:&#x2F;&#x2F;github.com&#x2F;Inoxoft&#x2F;whitelightning</a><p>Community model library: <a href="https:&#x2F;&#x2F;github.com&#x2F;Inoxoft&#x2F;whitelightning-model-library">https:&#x2F;&#x2F;github.com&#x2F;Inoxoft&#x2F;whitelightning-model-library</a><p>We’d love your feedback—what works, what doesn’t, and what to improve.

[Other] Recommend new arxiv papers of your interest daily according to your Zotero libarary.

[IDE/Editor] Powerful visual editor environment for building WebGL, WebGPU, WebXR apps

[Other] Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.

[DevOps] The container platform tailored for Kubernetes multi-cloud, datacenter, and edge management ⎈ 🖥 ☁️