Show HN: Coder Words – An offline-first PWA word puzzle for programmers https://ift.tt/MLcPz2O

Show HN: Coder Words – An offline-first PWA word puzzle for programmers It's a clone of 7 Little Words, but with topics from computer science and programming. No sign-up, no app install, no tracking. It's a PWA and works offline, also as a home screen app. Tech: js, no libs, Canvas API, Web Audio, AI-aided but not vibe coded, puzzles curated by hand. https://ift.tt/WgnwsVX May 23, 2026 at 12:30AM

Show HN: Quit All, an iOS app with an SOS mode for cravings https://ift.tt/KEexT4L

Show HN: Quit All, an iOS app with an SOS mode for cravings I built Quit All, an iPhone app for breaking bad habits. The main idea is that most habit trackers help after relapse, but cravings happen before that. Quit All has an SOS mode with a timer, GIFs/prompts, streak tracking, relapse logging, savings, milestones, danger-time stats, and iOS widgets. YouTube demo: https://www.youtube.com/watch?v=qwNK4rqOY88 App Store: https://ift.tt/VmoU6gN Website: https://quit-all.com May 22, 2026 at 11:31PM

Show HN: CoreMem – Portable context for AI agents https://ift.tt/iAmdEja

Show HN: CoreMem – Portable context for AI agents CoreMem lets you build collections of context, called a mem, and share it with any AI agent via URL, a Chrome extension, MCP, Cursor/VS Code plugins, a skill, and more. Instead of re-explaining your project or goal when you switch agents or start new sessions, CoreMem keeps your context centrally organized so that any AI tool can read it. This originally started as a CLI I built that kept pieces of context (Project A/B/C details, my writing style, preferred tech stacks, coding style, etc) in a SQLite database. I could instruct various agents to “use my `coremem` CLI to retrieve details about [project A] before we get started.” It solved a problem for me b/c I am continually bouncing around between different projects and chat agents, and having to re-explain myself every time became an exercise in either repeating myself or copy/pasting summaries I’d saved from previous sessions. I decided to make this a little more robust and portable, so I turned that original CLI into a SaaS. Tl;dr: You can create a “mem”, which is a collection of 1 or more pieces of related context, and share that mem with any agent to quickly get them up to speed. Right now I’ve got integrations in the form of revokable share links, a Chrome Plugin, Cursor Plugin, Cursor/VS Code extension, Claude Code plugin, ChatGPT/Claude/Gemini/et al via MCP. Since I mostly work from the CLI, I use the Claude Code plugin or create 5-min share links I can drop into a chat, but I’ve tried to make this useful to people who mainly work from a browser or an IDE. I’ve been coding for 30+ years, and I vibed most of this. I was able to use CoreMem to help it built itself as I jumped between various coding agents, having them grab context then start a new task. I’m sure my architecture and engineering experience helped, but building this in a few weeks confirmed for me that the barrier for someone to build a tool they need to solve a problem is incredibly low. The rush I used to get from coding has mostly faded, but I’m getting similar rushes managing different agents to build things now. https://coremem.app May 22, 2026 at 09:52PM

Show HN: I Made a Claude Skill for Spec-Driven Development (SDD) https://ift.tt/lDFJiyd

Show HN: I Made a Claude Skill for Spec-Driven Development (SDD) At my work they provided a single Claude subscription for everyone on the team. To be honest I like kiro better as it provides a way better SDD management. But the company can't provide it and I can't afford it yet. Turns out I had the skill creator skill in my claude instance so I made use of it to create this Skill. I made it fully by using Claude but I wanted to make it open source, so I asked it to help me make tests and preparations for it, even a CI to run python tests. Well, we got this results with it: - Phase 2A: 67 static assertions (Python script, runs in CI) - Phase 2B: 15 behavioral tests (live Claude Code session) - Phase 2C: 53 generation quality checks across 3 end-to-end flows All of these passed and the CI also passed (after a few tries). I made it to suit my way of prompting and coding and based it off kiro's SDD management, but I want it to be publicly available and used by many people. According to claude some of the testers need to fit the following criteria: 1. Developer starting a real new project from scratch 2. Solo dev with an active side project (greenfield or partial codebase) 3. Team lead whose team uses multiple AI tools 4. Developer with an existing codebase and no written specs 5. Developer who actively uses 3+ AI coding tools It's actually a blind test, no guiding, just try it if you can, I'd really appreciate your help. The repo is here: https://ift.tt/MqrGupe https://ift.tt/MqrGupe May 21, 2026 at 04:49PM

Show HN: Freenet, a peer-to-peer platform for decentralized apps https://ift.tt/aXlqI9p

Show HN: Freenet, a peer-to-peer platform for decentralized apps For the past 5 years or so I've been working on a ground-up redesign of Freenet, my peer-to-peer project from the early 2000s (now renamed Hyphanet). The new Freenet has been up and running since December along with some early applications like River[1], our decentralized group chat and Delta - a decentralized CMS. Users have already started to build their own apps on Freenet including games, and we have some interesting apps in development like Atlas, a search/recommendation engine. Architecturally, this new Freenet is a global, decentralized key-value store where keys are webassembly contracts which define what values (aka "state") are valid for that key, how or when the values can be mutated, and how the state can be efficiently synchronized between peers. We've developed a unique (AFAIK) solution to the consistency problem, every contract must define a "merge" operation for the contract's associated state. This operation must be commutative, meaning that you can merge multiple states in any order and you'll get the same end result. This approach allows state updates to spread through the network like a virus[2], which typically achieves consistent global state in a few seconds or less. Like the world wide web, Freenet applications can be downloaded from the network itself and run in a web browser - similar to single-page apps on the normal web. However, rather than connecting back to an API running in a datacenter, the webapp connects locally to the Freenet peer and interacts with Freenet contracts and delegates over a local websocket connection. If you'd like to try Freenet we have convenient installers for the major desktop OSs but not yet mobile, and you can be chatting with other users on River within seconds[3]. Happy to answer any questions, you're also welcome to read our FAQ[4], or watch a talk I gave back in March[5]. [1] https://ift.tt/zKD8MVy [2] https://ift.tt/kF9e6fN [3] https://ift.tt/OjoYB3z [4] https://ift.tt/eOGjqxL [5] https://youtu.be/3SxNBz1VTE0 https://freenet.org/ May 21, 2026 at 06:34PM

Show HN: Dari-docs – Optimize your docs using parallel coding agents https://ift.tt/gL5MlxE

Show HN: Dari-docs – Optimize your docs using parallel coding agents It’s well known at this point that documentation needs to be optimized for AI agents - we’re all pointing our Claude Code / Codex / Pi agents at documentation, and expecting the models to figure out how to implement a product. This, however, changes the entire optimization problem when writing documentation. Good documentation now becomes more objective - you are solving the very concrete problem: can a dumb harness running the dumbest model implement this reliably? Humans can typically compensate for inconsistent terminology or scattered context across pages, but for agents, this often will waste time (or even just completely confuse the agent). We’ve been building a small project around this called dari-docs: users can upload their documentation via website or CLI and run agents across different providers to see where they falter. You can upload your documentation, feed a list of tasks, and ask agents with varying intelligence / cost levels to complete those tasks in parallel. When a run is complete, you get back a list feedback markdown files from each agent run and can apply changes based on agent feedback. Managed service: https://ift.tt/kR0TtBv , repo link: https://ift.tt/ZqAk2fl The agents actually try to use the product end-to-end. They search through the docs, follow instructions, run commands, try examples, and attempt to debug failures. Importantly, this is not a static LLM review of the documentation. The agents are actually attempting the integration. You can also enable live verification with test credentials so the agents can actually verify workflows against real APIs: dari-docs check . --live-verify --secret-env DARI_TEST_API_KEY --task "Create a checkout session" If you’re building a CLI, API, MCP server, or SDK and actively maintaining docs for humans or agents, we’d love to work with you and test this on real workflows! https://ift.tt/ZqAk2fl May 20, 2026 at 08:53PM

Show HN: IgniteMS – batch text embeddings at 253K msg/s on 8x A100 https://ift.tt/y19PZVG

Show HN: IgniteMS – batch text embeddings at 253K msg/s on 8x A100 https://ift.tt/m2ykXgD May 20, 2026 at 09:07PM