Show HN: I built a daily game that tests if you can tell 1999 apart from 2005 https://ift.tt/zpwDh0J

Show HN: I built a daily game that tests if you can tell 1999 apart from 2005 https://yeartobeat.com/ March 7, 2026 at 09:44PM

Show HN: Mantle – Remap your Mac keyboard without editing Kanata config files https://ift.tt/g7vOsUX

Show HN: Mantle – Remap your Mac keyboard without editing Kanata config files I built Mantle because I wanted homerow mods and layers on my laptop without hand writing Lisp syntax. The best keyboard remapping engine on macOS (Kanata) requires editing .kbd files which is a pain. Karabiner-Elements is easy for simple single key remapping (e.g. caps -> esc), but anything more wasn’t workin out for me. What you can do with Mantle: - Layers: hold a key to switch to a different layout (navigation, numpad, media) - Homerow mods: map Shift, Control, Option, Command to your home row keys when held - Tap-hold: one key does two things: tap for a letter, hold for a modifier - Import/export: bring existing Kanata .kbd configs or start fresh visually Runs entirely on your Mac. No internet, no accounts. Free and MIT licensed Would love feedback, especially from people who tried Kanata or Karabiner and gave up https://getmantle.app/ March 7, 2026 at 12:26AM

Show HN: Mog, a programming language for AI agents https://ift.tt/JStrFOI

Show HN: Mog, a programming language for AI agents I wrote a programming language for extending AI agents, called Mog. It's like a statically typed Lua. Most AI agents have trouble enforcing their normal permissions in plugins and hooks, since they're external scripts. Mog's capability system gives the agent full control over I/O, so it can enforce whatever permissions it wants in the Mog code. This is even true if the plugin wants to run bash -- the agent can check each bash command the Mog code emits using the exact same predicate it uses for the LLM's direct bash tool. Mog is a statically typed, compiled, memory-safe language, with native async support, minimal syntax, and its own compiler written in Rust and its own runtime, also written in Rust, with `extern "C"` so the runtime can easily be embedded in agents written in different languages. It's designed to be written by LLMs. Its syntax is familiar, it minimizes foot-guns, and its full spec fits in a 3200-token file. The language is quite new, so no hard security guarantees are claimed at present. Contributions welcome! https://gist.github.com/belisarius222/203ac5edbc3306c34bf0481f451d4003 March 6, 2026 at 10:46PM

Show HN: VaultNote – Local-first encrypted note-taking in the browser https://ift.tt/ledpwmA

Show HN: VaultNote – Local-first encrypted note-taking in the browser Hi HN, I built VaultNote, a local-first note-taking app that runs entirely in the browser. Key ideas: - 100% local-first: no backend or server - No login, accounts, or tracking - Notes stored locally in IndexedDB / LocalStorage - AES encryption with a single master password - Tree-structured notes for organizing knowledge The goal was to create a simple note app where your data never leaves your device. You can open the site, enter a master password, and start writing immediately. Since everything is stored locally, VaultNote also supports import/export so you can back up your data. Curious to hear feedback from the HN community, especially on: - the security approach (local AES encryption) - IndexedDB storage design - local-first UX tradeoffs Demo: https://ift.tt/H0TeADm Thanks! https://ift.tt/DRFLpfv March 6, 2026 at 11:22PM

Show HN: Cognitive architecture for Claude Code – triggers, memory, docs https://ift.tt/w3azeZ7

Show HN: Cognitive architecture for Claude Code – triggers, memory, docs This started as a psychology research project (building a psychoemotional safety scoring model) and turned into something more general: a reusable cognitive architecture for long-running AI agent work. The core problem: Claude Code sessions lose context. Memory files live outside the repo and can silently disappear. Design decisions made in Session 3 get forgotten by Session 8. Documentation drifts from reality. Our approach — 12 mechanical triggers that fire at specific moments (before responding, before writing to disk, at phase boundaries, on user pushback). Principles without firing conditions remain aspirations. Principles with triggers become infrastructure. What's interesting: - Cognitive trigger system — T1 through T12 govern agent behavior: anti-sycophancy checks, recommend-against scans, process vs. substance classification, 8-order knock-on analysis before decisions. Not prompting tricks — structural firing conditions. - Self-healing memory — Auto-memory lives outside the git repo. A bootstrap script detects missing/corrupt state, restores from committed snapshots with provenance headers, and reports what happened. The agent's T1 (session start) runs the health check before doing anything else. - Documentation propagation chain — 13-step post-session cycle that pushes changes through 10 overlapping documents at different abstraction levels. Content guards prevent overwriting good state with empty files. Versioned archives at every cycle. - Git reconstruction from chat logs — The project existed before its repo. We rebuilt git history by replaying Write/Edit operations from JSONL transcripts, with a weighted drift score measuring documentation completeness. The divergence report became a documentation coverage report. - Structured decision resolution — 8-order knock-on analysis (certain → likely → possible → speculative → structural → horizon) with severity-tiered depth and consensus-or-parsimony binding. All built on Claude Code with Opus. The cognitive architecture (triggers, skills, memory pattern) transfers to any long-running agent project — the psychology domain is the first application, not a constraint. Design phase — architecture resolved, implementation of the actual psychology agent hasn't started. The infrastructure for building it is the interesting part. Code: https://ift.tt/9tXGuTF Highlights if you want to skip around: - Trigger system: docs/cognitive-triggers-snapshot.md - Bootstrap script: bootstrap-check.sh - Git reconstruction: reconstruction/reconstruct.py - Documentation chain: .claude/skills/cycle/SKILL.md - Decision resolution: .claude/skills/adjudicate/SKILL.md - Research journal: journal.md (the full narrative, 12 sections) Happy to discuss the trigger design, the memory recovery pattern, or why we think documentation propagation matters more than people expect for AI-assisted work. https://ift.tt/9tXGuTF March 5, 2026 at 10:05PM

Show HN: Gobble – Yet Another OSS Alternative to Google Analytics/PostHog, etc. https://ift.tt/hmcXVxf

Show HN: Gobble – Yet Another OSS Alternative to Google Analytics/PostHog, etc. https://ift.tt/O8rMZ9l March 5, 2026 at 01:12AM

Show HN: Qlog – grep for logs, but 100x faster https://ift.tt/I9cqWmY

Show HN: Qlog – grep for logs, but 100x faster I built qlog because I got tired of waiting for grep to search through gigabytes of logs. qlog uses an inverted index (like search engines) to search millions of log lines in milliseconds. It's 10-100x faster than grep and way simpler than setting up Elasticsearch. Features: - Lightning fast indexing (1M+ lines/sec using mmap) - Sub-millisecond searches on indexed data - Beautiful terminal output with context lines - Auto-detects JSON, syslog, nginx, apache formats - Zero configuration - Works offline - Pure Python Example: qlog index './logs/*/*.log' qlog search "error" --context 3 I've tested it on 10GB of logs and it's consistently 3750x faster than grep. The index is stored locally so repeated searches are instant. Demo: Run `bash examples/demo.sh` to see it in action. GitHub: https://ift.tt/0u6ZSFj Perfect for developers/DevOps folks who search logs daily. Happy to answer questions! https://ift.tt/0u6ZSFj March 5, 2026 at 12:17AM