Show HN: Chatolia – create, train and deploy your own AI agents https://ift.tt/wiOd4bS

Show HN: Chatolia – create, train and deploy your own AI agents Hi everyone, I've built Chatolia, a platform that lets you create your own AI chatbots, train them with your own data, and deploy them to your website. It is super simple to get started: - Create your agent - Train it with your data - Deploy it anywhere You can start for free, includes 1 agent and 500 message credits per month. Would love to hear your thoughts, https://ift.tt/dYeK7wv https://ift.tt/dYeK7wv November 3, 2025 at 02:38AM

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) https://ift.tt/JEGnaBD

Show HN: I built a Raspberry Pi webcam to train my dog (using Claude) Hey HN! I’m a Product Manager and made a DIY doggy cam (using Claude and a Raspberry Pi) to help train my dog with separation anxiety. I wrote up a blog post sharing my experience building this project with AI. https://ift.tt/3LId1GA November 3, 2025 at 05:34AM

Show HN: Give your coding agents the ability to message each other https://ift.tt/qm12auG

Show HN: Give your coding agents the ability to message each other I submitted this earlier but it didn’t get any traction. But it’s blowing up on Twitter, so I figured I would give it another shot here. The system is quick and easy to setup and works surprisingly well. And it’s not just a fun gimmick; it’s now a core part of my workflow. https://ift.tt/iuRy7c5 November 3, 2025 at 03:09AM

Show HN: Carrie, for what Calendly can't do https://ift.tt/gklnWTU

Show HN: Carrie, for what Calendly can't do Hey everyone, Through my career, I've spent too many hours and too much mental load on busywork like scheduling and following up on people's availabilities. So, I built Carrie. You simply cc her into your emails, and she sorts out meeting times across time zones, finds what works best for everyone, confirms the meeting and sends the invite. She handles scenarios beyond what Calendly can handle and it’s been freeing me up from the back-and-forth of juggling different meeting requests. I’ve been testing this with a beta group of users and am now looking to expand the user pool (please feel free to join the waitlist if you're interested). Would also love feedback on whether this seems useful and what seems to be missing to make this part of your workflow. Thanks! https://getcarrie.com/ November 2, 2025 at 08:10PM

Show HN: UnisonDB – Log-native KV database that replicates like a message bus https://ift.tt/7D9rmIy

Show HN: UnisonDB – Log-native KV database that replicates like a message bus Hi HN, For the past few months, I’ve been building UnisonDB — a log-native database where the Write-Ahead Log (WAL) is the database, not just a recovery mechanism. I started this because every time I needed data to flow — from core to edge, or between datacenters — I ended up gluing together a KV database + CDC + Kafka. It worked, but it always felt like overkill: too many moving parts for even small workloads, and too little determinism. What is it? UnisonDB unifies storage and streaming into a single log-based core. Every write is: • Durable (appended to the WAL), • Ordered (globally sequenced for safety), • Streamable (available to any follower in real time). It combines B+Tree storage (predictable reads, no LSM compaction storms) with WAL-based replication (sub-second fan-out to 100+ nodes). Key Ideas 1. Storage + Streaming = One System — no CDC, no Kafka, no sidecar pipelines 2. B+Tree-Backed — predictable reads, zero compaction overhead 3. Multi-Model — KV, wide-column, and large objects (LOB) in one atomic transaction 4. Replication-Native — WAL streams via gRPC; followers tail in real time 5. Reactive by Design — every write emits a ZeroMQ notification 6. Edge-Friendly — replicas can go offline and resync instantly Performance & Tradeoffs 1. Write throughput is lower than pure LSM stores (e.g. BadgerDB) — because writes are globally ordered for replication safety. Deliberate tradeoff: consistency > raw write speed. 2. Still ~2× faster than BoltDB with replication enabled. Tech Details Written in Go FlatBuffers for zero-copy serialization gRPC for streaming replication GitHub: https://ift.tt/ozuGlhe https://unisondb.io November 2, 2025 at 12:31AM

Show HN: Just vibe coded a HN TV dashboard https://ift.tt/1AvJU7a

Show HN: Just vibe coded a HN TV dashboard https://ift.tt/HqnLDFs November 2, 2025 at 12:11AM

Show HN: Proxmox-GitOps: Container Automation Framework https://ift.tt/MV9iOnI

Show HN: Proxmox-GitOps: Container Automation Framework By encapsulating infrastructure within an extensible monorepository - recursively resolved from Git submodules at runtime - Proxmox-GitOps provides a comprehensive Infrastructure-as-Code (IaC) abstraction for an entire, automated, container-based infrastructure. Core Concepts: - Recursive Self-management: Control plane seeds itself by pushing its monorepository onto a locally bootstrapped instance, triggering a pipeline that recursively provisions the control plane onto PVE. - Monorepository: Centralizes infrastructure as comprehensive IaC artifact (for mirroring, like the project itself on Github) using submodules for modular composition. - Single Source of Truth: Git represents the desired infrastructure state. - Loose coupling: Containers are decoupled from the control plane, enabling runtime replacement and independent operation. https://ift.tt/Dmcqrha November 1, 2025 at 11:19PM

Show HN: 24-hour Halloween radio station hosted by Dr. Eleven https://ift.tt/QRHYpEj

Show HN: 24-hour Halloween radio station hosted by Dr. Eleven I built a 24h Halloween radio stream hosted by Dr. Eleven, using ElevenLabs and HLS for delivery. Excited for you to hear it + open to any feedback. https://ift.tt/3WQFxsj November 1, 2025 at 04:10AM

Show HN: First5Minutes, Your first 5 minutes decide your day https://ift.tt/63emKdH

Show HN: First5Minutes, Your first 5 minutes decide your day Hi everyone I have been experimenting with a simple idea. What if the first five minutes of your day decided the rest? I built First5Minutes, a small web app that helps you start strong. You choose one meaningful mission for the day and complete it with quick photo, video or text proof. I created it to fix my own habit of overplanning and not starting. The focus is on doing one real thing each day, not maintaining long to do lists. Key features: • One mission per day for focus • Quick proof capture with photo, video or text • Optional partner verification for accountability • Streaks based on proof, not checkmarks Try it here → https://ift.tt/6WDEtSx No install or sign up wall. Just start a mission. I would love your feedback on: • Is this level of simplicity helpful or limiting • What part made or failed to make you feel you actually did something • Any friction in completing your first mission Built with Next.js, Supabase and Clerk. Thanks for checking it out. I appreciate your time and thoughts. https://ift.tt/iXRNZg1 November 1, 2025 at 01:42AM

Show HN: A chess middlegame trainer so I can stop blundering https://ift.tt/8aWDtdj

Show HN: A chess middlegame trainer so I can stop blundering https://dontblunder.com November 1, 2025 at 01:00AM

Show HN: Ellipticc Drive – open-source cloud drive with E2E and PQ encryption https://ift.tt/madTX1b

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/9pKdm8Q 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: ekoAcademic – Convert ArXiv papers to interactive podcasts https://ift.tt/V8Uu2Fo

Show HN: ekoAcademic – Convert ArXiv papers to interactive podcasts https://ift.tt/wOejzvn October 31, 2025 at 02:33AM

Show HN: Meals You Love – AI-powered meal planning and grocery shopping https://ift.tt/QOAx1ou

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/w2Erji8 https://ift.tt/w2Erji8 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/CTRv5ku

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: Run Independent ATProto Networks in Docker or Kubernetes https://ift.tt/AcVn09w

Show HN: Run Independent ATProto Networks in Docker or Kubernetes https://ift.tt/QSs9jCh October 30, 2025 at 12:26AM

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/6Qc3aT0

Show HN: I Built an LSP and CLI for Ron (Rusty Object Notation) https://ift.tt/CN6wSaQ October 29, 2025 at 11:48PM

Show HN: Rewriting Scratch 3.0 from scratch in Lua (browser-free native runtime) https://ift.tt/Vp2gSFA

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/zpj0QZi October 28, 2025 at 11:08PM

Show HN: Pipelex – declarative language for repeatable AI workflows (MIT) https://ift.tt/nYv3XW5

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/NkvGegq - Cookbook: https://ift.tt/m0JYX4S - Starter: https://ift.tt/JFh2xq1 - VS Code extension: https://ift.tt/gtS74jY - Docs: [ https://ift.tt/yInqr05 ]( https://ift.tt/WoAdSVl ) - Demo video (2 min): https://youtu.be/dBigQa8M8pQ - Discord for support and sharing: https://ift.tt/o2WSZzN 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/NkvGegq October 28, 2025 at 09:49PM

Show HN: Easily visualize torch, Jax, tf, NumPy, etc. tensors https://ift.tt/dCQ4O3B

Show HN: Easily visualize torch, Jax, tf, NumPy, etc. tensors hey hn, wrote a python library for myself to visualize tensors. Makes learning and debugging deep learning code so much easier. Works seamlessly with colab/jupyter notebooks, and other python contexts. It's built on top of the graphics backend, chalk ( https://chalk-diagrams.github.io/ ). why? Understanding deep learning code is hard—especially when it's foreign, because it's hard to imagine tensor manipulations, e.g. `F.conv2d(x.unsqueeze(1), w.transpose(-1, -2)).squeeze().view(B, L, -1)` in your head. Printing shapes and tensor values only get me so far. tensordiagram lets me quickly diagram tensors. Other python libraries for creating tensor diagrams are either too physics and math focused, not notebook-friendly, limited to visualizing single tensors, and/or serve a wider purpose (so have a steep learning curve). https://ift.tt/4IdkX59 October 28, 2025 at 03:00AM

Show HN: Action Engine — An API/Agent Buildkit Putting Flexibility First https://ift.tt/R84lmQ3

Show HN: Action Engine — An API/Agent Buildkit Putting Flexibility First https://ift.tt/84pGkWA October 28, 2025 at 01:56AM