Show HN: SQLite Graph – Graph database with Cypher queries (alpha, but working) https://ift.tt/lAMG0NU

Show HN: SQLite Graph – Graph database with Cypher queries (alpha, but working) I've been working on adding graph database capabilities to SQLite with support for the Cypher query language. As of this week, both CREATE and MATCH operations work with full relationship support. Here's what it looks like: import sqlite3 conn = sqlite3.connect(":memory:") conn.load_extension("./libgraph.so") conn.execute("CREATE VIRTUAL TABLE graph USING graph()") # Create a social network conn.execute("""SELECT cypher_execute(' CREATE (alice:Person {name: "Alice", age: 30}), (bob:Person {name: "Bob", age: 25}), (alice)-[:KNOWS {since: 2020}]->(bob) ')""") # Query the graph with relationship patterns conn.execute("""SELECT cypher_execute(' MATCH (a:Person)-[r:KNOWS]->(b:Person) WHERE a.age > 25 RETURN a, r, b ')""") The interesting part was building the complete execution pipeline - lexer, parser, logical planner, physical planner, and an iterator-based executor using the Volcano model. All in C99 with no dependencies beyond SQLite. What works now: - Full CREATE: nodes, relationships, properties, chained patterns (70/70 openCypher TCK tests) - MATCH with relationship patterns: (a)-[r:TYPE]->(b) with label and type filtering - WHERE clause: property comparisons on nodes (=, >, <, >=, <=, <>) - RETURN: basic projection with JSON serialization - Virtual table integration for mixing SQL and Cypher Performance: - 340K nodes/sec inserts (consistent to 1M nodes) - 390K edges/sec for relationships - 180K nodes/sec scans with WHERE filtering Current limitations (alpha): - Only forward relationships (no `<-[r]-` or bidirectional `-[r]-`) - No relationship property filtering in WHERE (e.g., `WHERE r.weight > 5`) - No variable-length paths yet (e.g., `[r*1..3]`) - No aggregations, ORDER BY, property projection in RETURN - Must use double quotes for strings: {name: "Alice"} not {name: 'Alice'} This is alpha - API may change. But core graph query patterns work! The execution pipeline handles CREATE/MATCH/WHERE/RETURN end-to-end. Next up: bidirectional relationships, property projection, aggregations. Roadmap targets full Cypher support by Q1 2026. Built as part of Agentflare AI, but it's standalone and MIT licensed. Would love feedback on what to prioritize. GitHub: https://ift.tt/WhYPINH Happy to answer questions about the implementation! https://ift.tt/WhYPINH October 30, 2025 at 12:22AM

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/gBlEYnH

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/tBIVLP7 October 29, 2025 at 11:48PM

Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime) https://ift.tt/oIlJuUs

Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime) Built a native Scratch 3.0 runtime in Lua that runs .sb3 projects without a browser. Why? Browser sandboxing prevents access to hardware features (haptics, sensors, fine-grained perf controls). Native runtime gives you direct hardware access and lets you deploy to consoles, handhelds, embedded devices. Also means much smaller binaries (LÖVE is ~7MB vs 50-100MB for Electron). How it works: - Scratch blocks compile to IR, then optimize, then generate Lua - LuaJIT executes the compiled code - Coroutine-based threading for concurrent scripts - Lazy loading + LRU cache for memory management - SVG support via resvg FFI ~100% compatible with Scratch 3.0 blocks. Extensions that need JavaScript won't work (no Music, TTS, Video Sensing), but core blocks are there. Built on LÖVE framework, so it's cross-platform (desktop, mobile, gaming devices). Still rough around the edges (user input not implemented yet, cloud variables only work locally), but it runs real Scratch projects today. https://ift.tt/mBwHjDG October 28, 2025 at 11:08PM

Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) https://ift.tt/vUSMZbC

Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open standard under MIT: language spec, runtime, API server, editor extensions, MCP server, n8n node. - Composable: pipes can call other pipes, created by you or shared in the community. Why a domain-specific language? - We need context, meaning and nuances preserved in a structured syntax that both humans and LLMs can understand - We need determinism, control, and reproducibility that pure prompts can't deliver - Bonus: editors, diffs, semantic coloring, easy sharing, search & replace, version control, linters… How we got there: Initially, we just wanted to solve every use-case with LLMs but kept rebuilding the same agentic patterns across different projects. So we challenged ourselves to keep the code generic and separate from use-case specifics, which meant modeling workflows from the relevant knowledge and know-how. Unlike existing code/no-code frameworks for AI workflows, our abstraction layer doesn't wrap APIs, it transcribes business logic into a structured, unambiguous script executable by software and AI. Hence the "declarative" aspect: the script says what should be done, not how to do it. It's like a Dockerfile or SQL for AI workflows. Additionally, we wanted the language to be LLM-friendly. Classic programming languages hide logic and context in variable names, functions, and comments: all invisible to the interpreter. In Pipelex, these elements are explicitly stated in natural language, giving AI full visibility: it's all logic and context, with minimal syntax. Then, we didn't want to write Pipelex scripts ourselves so we dogfooded: we built a Pipelex workflow that writes Pipelex workflows. It's in the MCP and CLI: "pipelex build pipe '…'" runs a multi-step, structured generation flow that produces a validated workflow ready to execute with "pipelex run". Then you can iterate on it yourself or with any coding agent. What’s included: Python library, FastAPI and Docker, MCP server, n8n node, VS Code extension. What we’d like from you 1. Build a workflow: did the language work for you or against you? 2. Agent/MCP workflows and n8n node usability. 3. Suggest new kinds of pipes and other AI models we could integrate 4. Looking for OSS contributors to the core library but also to share pipes with the community Known limitations - Connectors: Pipelex doesn’t integrate with “your apps”, we focus on the cognitive steps, and you can integrate through code/API or using MCP or n8n - Visualization: we need to generate flow-charts - The pipe builder is still buggy - Run it yourself: we don’t yet provide a hosted Pipelex API, it’s in the works - Cost-tracking: we only track LLM costs, not image generation or OCR costs yet - Caching and reasoning options: not supported yet Links - GitHub: https://ift.tt/IrmCW5e - Cookbook: https://ift.tt/ZaRJdO3 - Starter: https://ift.tt/xB1nmiY - VS Code extension: https://ift.tt/f95BayD - Docs: [ https://ift.tt/N1mErG6 ]( https://ift.tt/4Sgv2LH ) - Demo video (2 min): https://youtu.be/dBigQa8M8pQ - Discord for support and sharing: https://ift.tt/ps34ZgL Thanks for reading. If you try Pipelex, tell us exactly where it hurts, that’s the most valuable feedback we can get. https://ift.tt/IrmCW5e October 28, 2025 at 09:49PM

Parking Management Meets Breast Cancer Awareness

Parking Management Meets Breast Cancer Awareness
By Melissa Culross

Parking Control Supervisors Natalie Laval and Jonathan Yu. Sometimes colors are personal. The color pink is very personal at this time of year for Parking Control Supervisor Natalie Laval. October is Breast Cancer Awareness Month. And Laval is one of dozens of our parking control officers, or PCOs, and supervisors who are sporting specially designed patches to support breast cancer prevention and research. Laval was a child when her mother died of breast cancer in 1989. And her grandmother succumbed to the disease when Laval was in high school. “[My mother] was 37 when she died, and my...

Published October 27, 2025 at 05:30AM
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Show HN: JSON Query https://ift.tt/rbUTB9R

Show HN: JSON Query I'm working on a tool that will probably involve querying JSON documents and I'm asking myself how to expose that functionality to my users. I like the power of `jq` and the fact that LLMs are proficient at it, but I find it right out impossible to come up with the right `jq` incantations myself. Has anyone here been in a similar situation? Which tool / language did you end up exposing to your users? https://ift.tt/kBMLHWA October 27, 2025 at 09:52PM

Show HN: MNML – Android Launcher (Open Testing Release) https://ift.tt/cCmikNj

Show HN: MNML – Android Launcher (Open Testing Release) Just got the first open testing release approved on google play store for a minimal android launcher i'm developing. I recently switched to a phone with an e-ink display and was missing features in current minimal launchers, so i decided to make my own. Would love to hear your opinion, improvements that could be made or features to change/add. Thanks! === Links === - https://ift.tt/5jdfJP0 - https://www.youtube.com/watch?v=6gcF9lSDBPY (video demo) === Current Features === - Multiple rows of apps - Material Font icons can be set as app names - When reducing or increasing apps you can re-order and select which apps to keep (this is missing from a lot of minimal launchers imo) - App shortcuts (long press on an app to show that app's own shortcut menu) - Backup your launcher settings with Export/Import - App drawer list (swipe up) - You can hide apps from the app list - You can uninstall apps from the app list - App rows can have their size individually set. === Planned features === - Background images or static colors - Show weather information - Screen time usage information - Possibly some limited widgets - google search bar on the bottom? - Notes on the main screen - App folders (so you can assign multiple apps to one slot) - App slot assignment other than apps - URL's - Files - Intents - Contacts - etc. - More gestures that you can assign actions to - 3 or 4 finger swipe - triple tap - pinch zoom - etc. - Find contacts and files in the app list - Localization October 28, 2025 at 12:50AM