Show HN: Ellipticc Drive – open-source cloud drive with E2E and PQ encryption https://ift.tt/Fum8H1w

Show HN: Ellipticc Drive – open-source cloud drive with E2E and PQ encryption Hey HN, I’m Ilias, 19, from Paris. I built Ellipticc Drive, an open-source cloud drive with true end-to-end encryption and post-quantum security, designed to be Dropbox-like in UX but with zero access to your data, even by the host. What’s unique: Free 10GB for every user, forever. Open-source frontend (audit or self-host if you want) Tech stack: Frontend: Next.js Crypto: WebCrypto (hashing) + Noble (core primitives) Encryption: XChaCha20-Poly1305 (file chunks) Key wrapping: Kyber (ML-KEM768) Signing: Ed25519 + Dilithium2 (ML-DSA65) Key derivation: Argon2id → Master Key → encrypts all keypairs & CEKs Try it live: https://ellipticc.com Frontend source: https://ift.tt/8CGQWey Would love feedback from devs and security folks — particularly on encryption flow, architecture, or UX. I’ll be around to answer every technical question in the comments! https://ellipticc.com October 31, 2025 at 01:00AM

Show HN: Meals You Love – AI-powered meal planning and grocery shopping https://ift.tt/d0cUy9g

Show HN: Meals You Love – AI-powered meal planning and grocery shopping Meals You Love is a meal planning app that creates weekly meal plans tailored to your tastes and dietary preferences. It integrates with Kroger and Instacart's APIs so you can add your meal plan groceries directly to your cart. You can also import your own recipes to include alongside AI suggestions. I originally built this to help my wife with meal planning and grocery shopping. We were always struggling to decide what to make and inevitably forgot ingredients. Most meal planners felt too rigid or generic, and few handled the grocery side well (or at all). We've also used meal kits like Home Chef in the past but they end up being quite expensive and produce a comical amount of packaging waste, plus you still wind up needing to purchase groceries anyway. In all honesty, I also wanted an excuse to try building something "real" using AI and to see if it could be used in an actually useful manner. Would love feedback from anyone interested in food, meal planning, or product design! Tech stack: - Cloud Run - Firestore - Vertex AI / Gemini https://ift.tt/vGu8c70 https://ift.tt/vGu8c70 October 27, 2025 at 11:27PM

Show HN: I made CSV files double-click to open in Google Sheets instead of Excel https://ift.tt/dbfY0B9

Show HN: I made CSV files double-click to open in Google Sheets instead of Excel I built my first macOS app to automatically open csv, xls files in Google Sheets. I work as marketing, revops person and often have to combine data from different platforms for reporting purposes. Google made the import flow super broken with too many clicks in between. So I built a simple solution that saves me some time. Sharing it here, you can test it out for free. No subscription bullshit, one time payment to get unlimited usage if you like it. Happy double clicking! https://csvtosheets.com October 30, 2025 at 11:25PM

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