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[Other] Show HN: ChartDB Cloud – Visualize and Share Database Diagrams Me and Guy (@guyb3) built ChartDB to generate ER diagrams from your database without a need of any database access (via query&#x2F;sql&#x2F;dbml). We started with an open-source version, and after seeing a lot of use we decided to make a cloud version.<p>Our OSS launch (1y ago) - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=41339308">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=41339308</a><p>Now we’re launching ChartDB Cloud - built for teams:<p>- Embed ERDs into docs, dev portals, or Miro&#x2F;Notion etc.<p>- Collaborate in real-time (with live cursors like Figma)<p>- Keep diagrams always in sync with your database<p>- Organize large, messy schemas without pain<p>- Export DDL in multiple SQL dialects (solved deterministically)<p>- AI assistant to brainstorm and generate new schema objects or schema changes<p>We designed it so working with databases feels less like a chore and more like a creative process.<p>Would love feedback - especially from teams dealing with messy schemas or outdated docs.<p><a href="https:&#x2F;&#x2F;app.chartdb.io" rel="nofollow">https:&#x2F;&#x2F;app.chartdb.io</a>

[Other] Show HN: Using Common Lisp from Inside the Browser

[DevOps] Using Podman, Compose and BuildKit

[Other] Show HN: Weam – open-source AI collaboration platform for teams We built Weam because we felt existing AI tools didn’t work well for teams. Everything was scattered across chats, prompts, and workflows — hard to share, harder to organize.<p>Weam is an open-source platform that tries to fix that.<p>Organize prompts, chats, and agents into “Brains” (team folders).<p>Run agents and even Pro Agents for workflows.<p>Bring your own LLM keys (works with OpenAI, Anthropic, Gemini, Llama, etc.).<p>Self-hosted, so you keep control of your data.<p>Includes RAG pipelines for document-based AI.<p>It’s early but we’d love feedback. Repo here: <a href="https:&#x2F;&#x2F;github.com&#x2F;weam-ai&#x2F;weam" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;weam-ai&#x2F;weam</a><p>Docs: <a href="https:&#x2F;&#x2F;weam.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;weam.ai&#x2F;</a><p>Curious if anyone has run into the same pain of making AI tools “team-friendly.”

[Other] Show HN: I replaced vector databases with Git for AI memory (PoC) Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases.<p>The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git&#x27;s built-in diff&#x2F;blame&#x2F;history?<p>How it works:<p>Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask &quot;how has my project evolved?&quot; and it uses git diff to show actual changes in understanding, not just similarity scores.<p>This is very much a PoC - rough edges everywhere, not production ready. But it&#x27;s been working surprisingly well for personal use. The entire index for a year of conversations fits in ~100MB RAM with sub-second retrieval.<p>The cool part: You can git checkout to any point in time and see exactly what the AI knew then. Perfect reproducibility, human-readable storage, and you can manually edit memories if needed.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;Growth-Kinetics&#x2F;DiffMem" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Growth-Kinetics&#x2F;DiffMem</a><p>Stack: Python, GitPython, rank-bm25, OpenRouter for LLM orchestration. MIT licensed.<p>Would love feedback on the approach. Is this crazy or clever? What am I missing that will bite me later?

[Other] SimpleIDE

[Database] Show HN: MCP Server for PostgreSQL Monitoring/Operations (MCP-PostgreSQL-Ops) MCP Server for PostgreSQL Monitoring&#x2F;Operations (MCP-PostgreSQL-Ops)<p>&lt;Examples Queries&gt; &quot;Check PostgreSQL server status&quot; &quot;Check PostgreSQL server version and connection status&quot; &quot;Verify if extensions are installed&quot; &quot;Show current active connection count&quot; &quot;Show the shared_buffers configuration&quot; &quot;Show PostgreSQL configuration parameter for shared_buffers&quot; &quot;Find all memory-related configuration settings&quot; &quot;Show logging configuration parameters&quot; &quot;Display connection-related settings&quot; &quot;Find all timeout configurations&quot; &quot;Show all PostgreSQL configuration parameters&quot; &quot;Show top 10 slowest queries&quot; &quot;Show top 20 slowest queries&quot; &quot;Analyze slow queries in specific database&quot; &quot;Find unused indexes&quot; &quot;Analyze recent query activity&quot; &quot;Check index efficiency in specific database&quot; &quot;Check database sizes&quot; &quot;Find largest tables&quot; &quot;Show tables that need VACUUM&quot; &quot;Check table sizes in specific database schema&quot; &quot;List tables in specific database&quot; &quot;Check maintenance status in specific database&quot;

[Other] Show HN: PlutoPrint – Generate PDFs and PNGs from HTML with Python Hi everyone, I built PlutoPrint because I needed a simple way to generate beautiful PDFs and images directly from HTML with Python. Most of the tools I tried felt heavy, tricky to set up, or produced results that didn’t look great, so I wanted something lightweight, modern, and fast. PlutoPrint is built on top of PlutoBook’s rendering engine, which is designed for paged media, and then wrapped with a Python API that makes it easy to turn HTML or XML into crisp PDFs and PNGs. I’ve used it for things like invoices, reports, tickets, and even snapshots, and it can also integrate with Matplotlib to render charts directly into documents.<p>I’d be glad to hear what you think. If you’ve ever had to wrestle with generating PDFs or images from HTML, I hope this feels like a smoother option. Feedback, ideas, or even just impressions are all very welcome, and I’d love to learn how PlutoPrint could be more useful for you.

[Monitoring/Observability] Show HN: Okapi – a metrics engine based on open data formats Hi All I wanted to share an early preview of Okapi an in-memory metrics engine that also integrates with existing datalakes. Modern software systems produce a mammoth amount of telemetry. While we can discuss whether or not this is necessary, we can all agree that it happens.<p>Most metrics engines today use proprietary formats to store data and don’t use disaggregated storage and compute. Okapi changes that by leveraging open data formats and integrating with existing data lakes. This makes it possible to use standard OLAP tools like Snowflake, Databricks, DuckDB or even Jupyter &#x2F; Polars to run analysis workflows (such as anomaly detection) while avoiding vendor lock-in in two ways - you can bring your own workflows and have a swappable compute engine. Disaggregation also reduces Ops burden of maintaining your own storage and the compute engine can be scaled up and down on demand.<p>Not all data can reside in a data-lake&#x2F;object store though - this doesn’t work for recent data. To ease realtime queries Okapi first writes all metrics data to an in memory store and reads on recent data are served from this store. Metrics are rolled up as they arrive which helps ease memory pressure. Metrics are held in-memory for a configurable retention period after which it gets shipped out to object storage&#x2F;datalake (currently only Parquet export is supported). This allows fast reads on recent data while offloading query-processing for older data. On benchmarks queries on in-memory data finish in under a millisecond while having write throughput of ~280k samples per second. On a real deployment, there’d be network delays so YMMV.<p>Okapi it is still early — feedback, critiques, and contributions welcome. Cheers !

[Other] Show HN: Anchor Relay – A faster, easier way to get Let's Encrypt certificates From the cryptic terminal commands to the innumerable ways to shoot yourself in the foot, I always struggled to use TLS certificates. I love how much easier (and cheaper) Let&#x27;s Encrypt made it to get certificates, but there are still plenty of things to struggle with.<p>That&#x27;s why we built Relay: a free, browser-based tool that streamlines the ACME workflow, especially for tricky setups like homelabs. Relay acts as a secure intermediary between your ACME client and public certificate authorities like Let&#x27;s Encrypt.<p>Some ways Relay provides a better experience:<p><pre><code> - really fast, streamlined certificates in minutes, with any ACME client - one-time upfront DNS delegation without inbound traffic or DNS credentials sprinkled everywhere - clear insights into the whole ACME process and renewal reminders </code></pre> Try Relay now: <a href="https:&#x2F;&#x2F;anchor.dev&#x2F;relay" rel="nofollow">https:&#x2F;&#x2F;anchor.dev&#x2F;relay</a><p>Or read our blog post: <a href="https:&#x2F;&#x2F;anchor.dev&#x2F;blog&#x2F;lets-get-your-homelab-https-certified" rel="nofollow">https:&#x2F;&#x2F;anchor.dev&#x2F;blog&#x2F;lets-get-your-homelab-https-certifie...</a><p>Please give it a try (it only takes a couple minutes) and let me know what you think.

[Other] Show HN: Luminal – Open-source, search-based GPU compiler Hi HN, I’m Joe. My friends Matthew, Jake and I are building Luminal (<a href="https:&#x2F;&#x2F;luminalai.com&#x2F;">https:&#x2F;&#x2F;luminalai.com&#x2F;</a>), a GPU compiler for automatically generating fast GPU kernels for AI models. It uses search-based compilation to achieve high performance.<p>We take high level model code, like you&#x27;d have in PyTorch, and generate very fast GPU code. We do that without using LLMs or AI - rather, we pose it as a search problem. Our compiler builds a search space, generates millions of possible kernels, and then searches through it to minimize runtime.<p>You can try out a demo in `demos&#x2F;matmul` on mac to see how Luminal takes a naive operation, represented in our IR of 12 simple operations, and compiles it to an optimized, tensor-core enabled Metal kernel. Here’s a video showing how: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;P2oNR8zxSAA" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;P2oNR8zxSAA</a><p>Our approach differs significantly from traditional ML libraries in that we ahead-of-time compile everything, generate a large search space of logically-equivalent kernels, and search through it to find the fastest kernels. This allows us to leverage the Bitter Lesson to discover complex optimizations like Flash Attention entirely automatically without needing manual heuristics. The best rule is no rule, the best heuristic is no heuristic, just search everything.<p>We’re working on bringing CUDA support up to parity with Metal, adding more flexibility to the search space, adding full-model examples (like Llama), and adding very exotic hardware backends.<p>We aim to radically simplify the ML ecosystem while improving performance and hardware utilization. Please check out our repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;luminal-ai&#x2F;luminal" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;luminal-ai&#x2F;luminal</a> and I’d love to hear your thoughts!

[Other] Improvements to OCaml code editing: the basics of a refactor engine

[CLI Tool] Show HN: Claude Code workflow: PRDs → GitHub Issues → parallel execution I built a lightweight project management workflow to keep AI-driven development organized.<p>The problem was that context kept disappearing between tasks. With multiple Claude agents running in parallel, I’d lose track of specs, dependencies, and history. External PM tools didn’t help because syncing them with repos always created friction.<p>The solution was to treat GitHub Issues as the database. The &quot;system&quot; is ~50 bash scripts and markdown configs that:<p>- Brainstorm with you to create a markdown PRD, spins up an epic, and decomposes it into tasks and syncs them with GitHub issues - Track progress across parallel streams - Keep everything traceable back to the original spec - Run fast from the CLI (commands finish in seconds)<p>We’ve been using it internally for a few months and it’s cut our shipping time roughly in half. Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;automazeio&#x2F;ccpm" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;automazeio&#x2F;ccpm</a><p>It’s still early and rough around the edges, but has worked well for us. I’d love feedback from others experimenting with GitHub-centric project management or AI-driven workflows.

[Other] Tidewave Web: in-browser coding agent for Rails and Phoenix

[Other] Show HN: Because I Kanban Just wanted to share my latest project Taskstax, it&#x27;s just a simple Kanban kind of trello clone, built mainly for the learns, but it works too so it&#x27;s online.<p>Simple Kanban boards, easy login that takes you straight to it.<p>It uses socket.io for data xfer after login which was fun to setup and also makes it work well.<p>Totally free, any feedback would be cool or if you wanted some info on the tech just ask.

[Other] Docker container for running Claude Code in "dangerously skip permissions" mode

[API/SDK] Show HN: Lemonade: Run LLMs Locally with GPU and NPU Acceleration Lemonade is an open-source SDK and local LLM server focused on making it easy to run and experiment with large language models (LLMs) on your own PC, with special acceleration paths for NPUs (Ryzen™ AI) and GPUs (Strix Halo and Radeon™).<p>Why?<p>There are three qualities needed in a local LLM serving stack, and none of the market leaders (Ollama, LM Studio, or using llama.cpp by itself) deliver all three: 1. Use the best backend for the user’s hardware, even if it means integrating multiple inference engines (llama.cpp, ONNXRuntime, etc.) or custom builds (e.g., llama.cpp with ROCm betas). 2. Zero friction for both users and developers from onboarding to apps integration to high performance. 3. Commitment to open source principles and collaborating in the community.<p>Lemonade Overview:<p>Simple LLM serving: Lemonade is a drop-in local server that presents an OpenAI-compatible API, so any app or tool that talks to OpenAI’s endpoints will “just work” with Lemonade’s local models. Performance focus: Powered by llama.cpp (Vulkan and ROCm for GPUs) and ONNXRuntime (Ryzen AI for NPUs and iGPUs), Lemonade squeezes the best out of your PC, no extra code or hacks needed. Cross-platform: One-click installer for Windows (with GUI), pip&#x2F;source install for Linux. Bring your own models: Supports GGUFs and ONNX. Use Gemma, Llama, Qwen, Phi and others out-of-the-box. Easily manage, pull, and swap models. Complete SDK: Python API for LLM generation, and CLI for benchmarking&#x2F;testing. Open source: Apache 2.0 (core server and SDK), no feature gating, no enterprise “gotchas.” All server&#x2F;API logic and performance code is fully open; some software the NPU depends on is proprietary, but we strive for as much openness as possible (see our GitHub for details). Active collabs with GGML, Hugging Face, and ROCm&#x2F;TheRock.<p>Get started:<p>Windows? Download the latest GUI installer from <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a><p>Linux? Install with pip or from source (<a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a>)<p>Docs: <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;docs&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;docs&#x2F;</a><p>Discord for banter&#x2F;support&#x2F;feedback: <a href="https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk" rel="nofollow">https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk</a><p>How do you use it?<p>Click on lemonade-server from the start menu Open http:&#x2F;&#x2F;localhost:8000 in your browser for a web ui with chat, settings, and model management. Point any OpenAI-compatible app (chatbots, coding assistants, GUIs, etc.) at http:&#x2F;&#x2F;localhost:8000&#x2F;api&#x2F;v1 Use the CLI to run&#x2F;load&#x2F;manage models, monitor usage, and tweak settings such as temperature, top-p and top-k. Integrate via the Python API for direct access in your own apps or research.<p>Who is it for?<p>Developers: Integrate LLMs into your apps with standardized APIs and zero device-specific code, using popular tools and frameworks. LLM Enthusiasts, plug-and-play with: Morphik AI (contextual RAG&#x2F;PDF Q&amp;A) Open WebUI (modern local chat interfaces) Continue.dev (VS Code AI coding copilot) …and many more integrations in progress! Privacy-focused users: No cloud calls, run everything locally, including advanced multi-modal models if your hardware supports it.<p>Why does this matter?<p>Every month, new on-device models (e.g., Qwen3 MOEs and Gemma 3) are getting closer to the capabilities of cloud LLMs. We predict a lot of LLM use will move local for cost reasons alone. Keeping your data and AI workflows on your own hardware is finally practical, fast, and private, no vendor lock-in, no ongoing API fees, and no sending your sensitive info to remote servers. Lemonade lowers friction for running these next-gen models, whether you want to experiment, build, or deploy at the edge. Would love your feedback! Are you running LLMs on AMD hardware? What’s missing, what’s broken, what would you like to see next? Any pain points from Ollama, LM Studio, or others you wish we solved? Share your stories, questions, or rant at us.<p>Links:<p>Download &amp; Docs: <a href="https:&#x2F;&#x2F;lemonade-server.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;lemonade-server.ai&#x2F;</a><p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;lemonade-sdk&#x2F;lemonade" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;lemonade-sdk&#x2F;lemonade</a><p>Discord: <a href="https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk" rel="nofollow">https:&#x2F;&#x2F;discord.gg&#x2F;5xXzkMu8Zk</a><p>Thanks HN!

[API/SDK] Show HN: Twick - React SDK for Timeline-Based Video Editing

[Other] D2 (text to diagram tool) now supports ASCII renders

[Other] Show HN: Built a memory layer that stops AI agents from forgetting everything Tired of AI coding tools that forget everything between sessions? Every time I open a new chat with Claude or fire up Copilot, I&#x27;m back to square one explaining my codebase structure.<p>So I built something to fix this. It&#x27;s called In Memoria. Its an MCP server that gives AI tools persistent memory. Instead of starting fresh every conversation, the AI remembers your coding patterns, architectural decisions, and all the context you&#x27;ve built up.<p>The setup is dead simple: `npx in-memoria server` then connect your AI tool. No accounts, no data leaves your machine.<p>Under the hood it&#x27;s TypeScript + Rust with tree-sitter for parsing and vector storage for semantic search. Supports JavaScript&#x2F;TypeScript, Python, and Rust so far.<p>It originally started as a documentation tool but had a realization - AI doesn&#x27;t need better docs, it needs to remember stuff. Spent the last few months rebuilding it from scratch as this memory layer.<p>It&#x27;s working pretty well for me but curious what others think, especially about the pattern learning part. What languages would you want supported next?<p>Code: <a href="https:&#x2F;&#x2F;github.com&#x2F;pi22by7&#x2F;In-Memoria" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;pi22by7&#x2F;In-Memoria</a>