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[Other] Show HN: Ducktape – a tiny HTTP/2 wrapper around DuckDB's Appender API Hi HN! I’m an engineer at Artie where we do real-time data replication.<p>We were adding MotherDuck as a destination and the first version just used DuckDB’s Go driver directly. It worked great on my machine… until we wired it into our Transfer service (<a href="https:&#x2F;&#x2F;github.com&#x2F;artie-labs&#x2F;transfer" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;artie-labs&#x2F;transfer</a>).<p>Because the driver requires CGO, our cross-compiles to amd64 and arm64 started failing, we lost our easy static binaries, and our Docker images had to pull in C toolchains and system libraries just to support one dependency. We tried isolating the CGO bits in a separate module, but it still caused CI failures and forced us to rewrite chunks of our build pipeline. At that point it was clear we didn’t want CGO anywhere near our main service.<p>So I built ducktape: a tiny standalone microservice that wraps DuckDB’s Appender API behind HTTP&#x2F;2 streams. Clients stream NDJSON over HTTP&#x2F;2, and ducktape appends directly into DuckDB on the other side. No CGO in the main codebase, and we keep our cross-platform, pure-Go build story.<p>The overhead was surprisingly low in benchmarks: ~757 MiB&#x2F;sec over HTTP&#x2F;2 vs ~848 MiB&#x2F;sec in-process — about 90% of native performance but over the network.<p>ducktape is open source and MIT licensed: <a href="https:&#x2F;&#x2F;github.com&#x2F;artie-labs&#x2F;ducktape" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;artie-labs&#x2F;ducktape</a><p>I’d love feedback, especially if you’ve tackled CGO isolation differently or have ideas to squeeze out more performance!

[CLI Tool] AIsbom – open-source CLI to detect "Pickle Bombs" in PyTorch models

[Other] Gh-actions-lockfile: generate and verify lockfiles for GitHub Actions

[Database] Biscuit is a specialized PostgreSQL index for fast pattern matching LIKE queries

[Other] Show HN: Misata – synthetic data engine using LLM and Vectorized NumPy Hey HN, I’m the author.<p>I built Misata because existing tools (Faker, Mimesis) are great for random rows but terrible for relational or temporal integrity. I needed to generate data for a dashboard where &quot;Timesheets&quot; must happen after &quot;Project Start Date,&quot; and I wanted to define these rules via natural language.<p>How it works: LLM Layer: Uses Groq&#x2F;Llama-3.3 to parse a &quot;story&quot; into a JSON schema constraint config.<p>Simulation Layer: Uses Vectorized NumPy (no loops) to generate data. It builds a DAG of tables to ensure parent rows exist before child rows (referential integrity).<p>Performance: Generates ~250k rows&#x2F;sec on my M1 Air.<p>It’s early alpha. The &quot;Graph Reverse Engineering&quot; (describe a chart -&gt; get data) is experimental but working for simple curves.<p>pip install misata<p>I’d love feedback on the simulator.py architecture—I’m currently keeping data in-memory (Pandas) which hits a ceiling at ~10M rows. Thinking of moving to DuckDB for out-of-core generation next. Thoughts?

[Other] Rust GCC backend: Why and how

[CLI Tool] Show HN: Open-source Markdown research tool written in Rust – Ekphos Hi! I just made an obsdian alternative in terminal after searching for an Obsidian like TUI and got nothing. So I built one myself.

[Other] A proof of concept of a semistable C++ vector container

[Other] VS Code deactivates IntelliCode in favor of the paid Copilot

[Other] Clean up messy text and formats in seconds - free text tools Free developer tools for clean text and data: JSON formatter, URL/Base64 tools, regex tester, case converter, remove duplicates, and more. Client-side processing (works offline) and privacy-focused. Try now!

[API/SDK] Easiest solution to deploy multimodal AI to mobile NexaSDK for Mobile lets developers use the latest multimodal AI models fully on-device on iOS & Android apps with Apple Neural Engine and Snapdragon NPU acceleration. In just 3 lines of code, build chat, multimodal, search, and audio features with no cloud cost, complete privacy, 2x faster speed and 9× better energy efficiency.

[Other] Show HN: Autograd.c – a tiny ML framework built from scratch built a tiny pytorch clone in c after going through prof. vijay janapa reddi&#x27;s mlsys book: mlsysbook.ai&#x2F;tinytorch&#x2F;<p>perfect for learning how ml frameworks work under the hood :)

[Other] A kernel bug froze my machine: Debugging an async-profiler deadlock

[Other] I used Claude Code to write a piano web app

[Monitoring/Observability] Show HN: Cordon – Reduce large log files to anomalous sections Cordon uses transformer embeddings and density scoring to identify what&#x27;s semantically unique in log files, filtering out repetitive noise.<p>The core insight: a critical error repeated 1000x is &quot;normal&quot; (semantically dense). A strange one-off event is anomalous (semantically isolated).<p>Outputs XML-tagged blocks with anomaly scores. Designed to reduce large logs as a form of pre-processing for LLM analysis.<p>Architecture: <a href="https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;docs&#x2F;architecture.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;docs&#x2F;architec...</a><p>Benchmark: <a href="https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;benchmark&#x2F;results&#x2F;README.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;calebevans&#x2F;cordon&#x2F;blob&#x2F;main&#x2F;benchmark&#x2F;res...</a><p>Trade-offs: intentionally ignores repetitive patterns, uses percentile-based thresholds (relative, not absolute).

[IDE/Editor] Show HN: I built an open-source alternative to the "brainrot IDE" that YC funded VSCode extension with games (Snake, Plinko, slots), HN&#x2F;LessWrong reader, and Pomodoro timer for AI coding wait times. Fake currency only.<p>Marketplace: <a href="https:&#x2F;&#x2F;marketplace.visualstudio.com&#x2F;items?itemName=TouchGrassIDE.touch-grass-ide" rel="nofollow">https:&#x2F;&#x2F;marketplace.visualstudio.com&#x2F;items?itemName=TouchGra...</a><p>Built in a week with Claude Code after seeing Chad IDE get ratio&#x27;d for integrating real gambling. Happy to answer questions or get roasted on the code.

[Other] Chafa: Terminal Graphics for the 21st Century

[Database] Show HN: Sqlit – A lazygit-style TUI for SQL databases I work mostly in the terminal but found myself constantly switching to bloated GUIs like SSMS only for the simple task of browsing tables and run queries. And I didn&#x27;t find Existing SQL TUIs intuitive, having to read documentation to learn keybindings and CLI flags to connect. Given I had recently switched to linux, I found myself using vs code&#x27;s sql database extension. Something was awfully wrong.<p>I wanted something like lazygit for databases – run it, connect, and query and frankly just make it enjoyable to access data.<p><pre><code> Sqlit is a keyboard-driven SQL TUI with: - Context-based keybindings (always visible) - Neovim-like interface with normal and insert mode for query editing - Browse databases, tables, views, stored procedures - Adapters for SQL Server, SQLite, PostgreSQL, Turso &amp; more - SSH tunneling support - Themes (Tokyo Night, Nord, Gruvbox etc.) Inspired by lazygit, neovim and lazysql. Built with Python&#x2F;Textual. </code></pre> Feedback welcome – especially on which adapters to prioritize next. My vision of sqlit is to make a tool that makes it easy to connect and query data, and to do that, and that thing only, really well.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;Maxteabag&#x2F;sqlit" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Maxteabag&#x2F;sqlit</a>

[Other] Show HN: Sub-microsecond (890 ns) trading execution research system I am sharing a research-grade, open-source trading execution framework that achieves a median end-to-end decision latency of 890 nanoseconds on commodity hardware.<p>The project is designed for education, systems research, and latency instrumentation, not for live trading. It focuses on understanding exactly where every nanosecond goes in a trading execution path.<p>Key features:<p>- Kernel-bypass networking: Direct userspace access to NICs via custom drivers, 20-50 ns RX latency - Lock-free SPSC&#x2F;MPSC queues: Zero-copy architecture - SIMD feature extraction: About 40 ns per update using AVX-512 - Deterministic replay: Bit-identical execution paths, SHA-256 verified - Nanosecond-level metrics: Full audit logs and performance dashboard<p>Technical stack: C++17 and Rust, NUMA-aware memory allocation, cache-line alignment, inline assembly for hot paths.<p>The framework is modular, allowing experimentation with different NIC drivers, feature extraction pipelines, or order-flow models such as Hawkes processes or Avellaneda-Stoikov logic. Everything is open source and documented.<p>Links:<p>Live demo: <a href="https:&#x2F;&#x2F;submicro.krishnabajpai.me&#x2F;" rel="nofollow">https:&#x2F;&#x2F;submicro.krishnabajpai.me&#x2F;</a> Source code: <a href="https:&#x2F;&#x2F;github.com&#x2F;krish567366&#x2F;submicro-execution-engine" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;krish567366&#x2F;submicro-execution-engine</a> Bare-metal NIC drivers: <a href="https:&#x2F;&#x2F;baremetalnic.krishnabajpai.me&#x2F;" rel="nofollow">https:&#x2F;&#x2F;baremetalnic.krishnabajpai.me&#x2F;</a><p>I would welcome feedback from anyone working on low-latency systems, networking, or HFT research.<p>Some questions for discussion:<p>- Which part of the execution path is typically hardest to optimize? - What measurement techniques do you trust for sub-microsecond systems?<p>This project is for research and educational purposes only. It does not connect to exchanges or execute real trades. It is intended as a sandbox for understanding ultra-low-latency execution.<p>I am happy to answer questions about methodology, performance, or design trade-offs.

[DevOps] Show HN: Open-Source Notion MCP Server (TypeScript, SSE, Apify) Hello HN,<p>I built this because I wanted to give Claude Desktop access to my Notion workspace without running a flaky local Python script via stdio.<p>This is a Node.js&#x2F;Express implementation of the Model Context Protocol (MCP) that uses SSE (Server-Sent Events) for transport. It’s designed to be stateless and deployable as a container (I&#x27;m hosting it on Apify, but it works anywhere with Node).<p>The Stack:<p>TypeScript + Express<p>@modelcontextprotocol&#x2F;sdk<p>Zod for input validation<p>Bearer Auth for security (since it exposes an HTTP endpoint)<p>Capabilities: It allows the LLM to search pages, read database properties, and append blocks. I use it primarily to have Cursor&#x2F;Claude summarize documentation and create tasks in my sprint board directly from the chat context.<p>Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;piskunproject&#x2F;notion-mcp-server&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;piskunproject&#x2F;notion-mcp-server&#x2F;</a> One-click deploy (Apify): <a href="https:&#x2F;&#x2F;apify.com&#x2F;piskunlab&#x2F;notion-mcp-server" rel="nofollow">https:&#x2F;&#x2F;apify.com&#x2F;piskunlab&#x2F;notion-mcp-server</a><p>Feedback on the SSE implementation is welcome!