Show HN: Pocket2Linkding – Migrate from Mozilla Pocket to Linkding https://ift.tt/IwYJfju

Show HN: Pocket2Linkding – Migrate from Mozilla Pocket to Linkding With the Mozilla Pocket shutdown coming up in about two weeks, I thought I'd share this quick tool to migrate to linkding in case it's helpful to others. After reviewing self-hosted options to Pocket, I decided linkding has the best combination of features. (The creator/author of linkding has done a great job -- however, I plan to eventually create a new tool that is based on linkding but adds some new features that the author has indicated he doesn't want to include [I’m currently using a fork, but I want to expand on it further].) HN thread about shutdown announcement: https://ift.tt/J3RUqb6 Mozilla announcement: https://ift.tt/PUmEA85 linkding: https://linkding.link/ Note that Pocket is shutting down July 8, 2025, but the export service will remain available until October 8, 2025. [edit] fix typo in title & formatting https://ift.tt/gtaOyri June 26, 2025 at 10:33PM

Show HN: Zizmor, static analysis for GitHub Actions https://ift.tt/jNi4rT3

Show HN: Zizmor, static analysis for GitHub Actions https://docs.zizmor.sh/ June 27, 2025 at 12:30AM

Show HN: Chat with ChatGPT, Claude, Grok, Gemini and Llama on One UI https://ift.tt/CA10MrO

Show HN: Chat with ChatGPT, Claude, Grok, Gemini and Llama on One UI Chat with multiple AI models once and compare the results to pick the best one. This should help you with your research as different AI model can give you different answers and some might be better than others. https://instaask.ai June 26, 2025 at 11:12PM

Show HN: MCP Server for Tally – Create and Manage Forms with Claude https://ift.tt/IP4j73i

Show HN: MCP Server for Tally – Create and Manage Forms with Claude I've built an MCP server for Tally that bridges the gap between their complex API and simple natural language commands. As someone with ADHD, I built this because context-switching between documentation, form builders, and actual work destroys my flow. Now I can stay in one conversation and just describe what I need. The interesting technical challenges: 1. API Complexity Abstraction Tally's API requires deeply nested objects for simple fields. An email field needs ~10 nested objects with UUIDs. I built a translation layer so users can just say "add an email field" in natural language, and the server handles the complex structure behind the scenes. 2. Safe Bulk Operations For destructive operations, I implemented a preview-then-confirm pattern. The server generates a confirmation token during preview that must be passed back for execution. This prevents accidental mass deletions while keeping the conversation flow natural. 3. Smart Rate Limiting The server monitors API responses and adjusts its behavior dynamically. When hitting rate limits, it automatically reduces batch sizes and adds delays between requests. Added randomization to prevent multiple instances from hitting the API simultaneously. 4. Type Safety Throughout Full TypeScript with runtime validation for both MCP messages and Tally API responses. This caught several undocumented API quirks during development. Performance notes: - Batch creation of 100 forms: ~12 seconds with batched operations - Individual creation of 100 forms: ~5 minutes due to rate limits - Human creation of 100 forms: probably a full week of mind-numbing clicking - Submission analysis across 10K responses: ~3 seconds The code is ISC licensed: https://ift.tt/rjJyx17 This particularly helps when you need to create multiple similar forms but your brain rebels at repetitive tasks. Curious if others are building MCP servers and what workflows you're optimizing for. Also interested in thoughts on MCP vs traditional CLI tools. The conversational interface is slower for simple operations but much better for complex, multi-step tasks where you might forget the exact syntax. https://ift.tt/rjJyx17 June 26, 2025 at 02:24AM

Show HN: PLJS – JavaScript for Postgres https://ift.tt/JSwCFVM

Show HN: PLJS – JavaScript for Postgres PLJS is a new, modern JavaScript trusted language extension, bundling QuickJS, a small and fast JavaScript runtime with Postgres, providing fast type conversion between Postgres and JavaScript, fast execution, and a very light footprint. Here are bencharks that show how it compares to PLV8: https://ift.tt/d2aCqKv This is the first step toward a truly light-weight, fast, and extensible JavaScript runtime embedded inside of Postgres. The initial roadmap has been published at https://ift.tt/7rHbITM You can join the discussion by joining the PLV8 Discord: https://ift.tt/bfWYctu You can find PLJS at https://ift.tt/561oCVX June 26, 2025 at 01:06AM

Show HN: I rawdog a MCP server from scratch in Zig. No SDK https://ift.tt/eTtaOn9

Show HN: I rawdog a MCP server from scratch in Zig. No SDK Some time ago I wanted to write a MCP server in Zig but found out there's no real JSON-RPC support in Zig, which MCP needs for communication. I ended up developing a JSON-RPC 2.0 library in Zig and more [1], which had its challenges. So I finally was able to put together a MCP server in Zig. It's built from scratch implementing the protocol messages from the MCP JSON schema. It's actually quite magical to have the LLM calling my MCP server [2]. The work is not too bad. Most of the hard work has already been done in the JSON-RPC library. [1] https://ift.tt/WsOnua6 [2] https://ift.tt/2MRFg6a... https://ift.tt/LfCQN2J June 25, 2025 at 11:44PM

Show HN: Logcat.ai:AI-powered observability for Operating Systems(Android+Linux) https://ift.tt/b6IV74D

Show HN: Logcat.ai:AI-powered observability for Operating Systems(Android+Linux) Hello HN! I'm an Android OS engineer. I've worked with AOSP and Linux kernels all my career and always wondered about lack of sophisticated tools to debug and analyze system-level logs. Always had to resort to manually skimming through large log files to find something I needed to. With the rise of LLMs and the AI-age, I felt it was a great opportunity to build something for OS engineers, which is what led to logcat.ai! We are building the industry-first observability platform for system level intelligence. Think "Datadog for operating systems" instead of applications. Currently, we support Android and Linux - more platforms on the way. With Android we offer: 1. logcat analysis: Ability to analyze logcat logs for root cause analysis of system issues with natural language search. Unlike, Firebase which is an app-level observability, logcat.ai provides intelligence at OS level spanning bootloader, kernel and framework layer. 2. bugreport analysis: As you know a bugreport is a super-verbose snapshot of an Android OS collected at a point of time. Analyzing these logs takes hours and sometimes even days. We are working to bring this down to minutes! Analysis of memory, cpu, process stats to infer memory pressure levels, system stress, and nail down the processes responsible for it, identify performance bottlenecks and memory leaks across the system. For Linux we offer: dmesg (kernel log) analysis to help identify issues at Linux kernel level. We plan to add support for different Linux distros with their own logging pretty soon. Our goal is to build a single-pane-of-glass observability experience for operating systems worldwide, something that's never been done before. Our website may not reflect all the features a.t.m but we have a lot of things cooking! Ask us anything. We are providing free beta access for a period of time. We'd love your feedback and comments on what you think about logcat.ai! https://logcat.ai June 24, 2025 at 10:53PM