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February 28, 2026

I spent ten years trying to write a novel. Every time I sat down, I'd write a sentence, decide it wasn't good enough, and rewrite it.

The problem wasn't discipline — it was that I could always see what I'd written and go back to change it.

I tried other approaches. Apps that delete your words when you stop typing — they fight fear with fear. That just made me panic. I wanted the opposite: not punishment, but permission.

"Tomoshibi" is Japanese for a small light in the dark — just enough to see what's in front of you.

You write on a dark screen. Older lines fade, but not when you hit return. They fade when you start writing again. If you pause, they wait. You can edit the current line and one line back — enough to fix a typo, not enough to spiral. The one-line-back rule also catches my own practical issue: Japanese IME often fires an accidental newline on kanji confirmation.

Everything is saved. There's a separate reader view for going back through what you've written. Tomoshibi is for writing over months, not just one session. When you come back, your last sentence appears as an epigraph — as if it always belonged there.

No account, no server, no build step. Your writing stays in your browser's local storage — export anytime as .txt. Vanilla HTML/CSS/ES modules.

Try it in your browser. A native Mac app (built with Tauri) with file system integration is coming to the store.

I've been writing on it for two months.

https://tomoshibi.in-hakumei.com/app/

Comments URL: https://news.ycombinator.com/item?id=47197735

Points: 8

# Comments: 2



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February 26, 2026

I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem

When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work

I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment.

The Solution

Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans.

The killer feature is the autonomous daemon. It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates.

The Architecture

- Local-first: Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API: The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency: Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests: This thing has to be reliable. It's doing unattended work.

The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue.

Why Release This?

A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained.

What's Next

- Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles)

If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken.

GitHub: https://github.com/MeisnerDan/mission-control

Comments URL: https://news.ycombinator.com/item?id=47165602

Points: 9

# Comments: 1



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February 12, 2026

Hi HN,

Been hacking on a simple way to run agents entirely inside of a Postgres database, "an agent per row".

Things you could build with this: * Your own agent orchestrator * A personal assistant with time travel * (more things I can't think of yet)

Not quite there yet but thought I'd share it in its current state.

Comments URL: https://news.ycombinator.com/item?id=46992136

Points: 5

# Comments: 0



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February 06, 2026

Hey HN, I built an automated system that tracks malicious Chrome/Edge extensions daily.

The database updates automatically by monitoring chrome-stats for removed extensions and scanning security blogs. Currently tracking 1000+ known malicious extensions with extension IDs, names, and dates.

I'm working on detection tools (GUI + CLI) to scan locally installed extensions against this database, but wanted to share the raw data first since maintained threat intelligence lists like this are hard to find.

The automation runs 24/7 and pushes updates to GitHub. Free to use for research, integration into security tools, or whatever you need.

Happy to answer questions about the scraping approach or data collection methods.

Comments URL: https://news.ycombinator.com/item?id=46914974

Points: 7

# Comments: 3



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February 03, 2026

What PII-Shield does: It's a K8s sidecar (or CLI tool) that pipes application logs, detects secrets using Shannon entropy (catching unknown keys like "sk-live-..." without predefined patterns), and redacts them deterministically using HMAC.

Why deterministic? So that "pass123" always hashes to the same "[HIDDEN:a1b2c]", allowing QA/Devs to correlate errors without seeing the raw data.

Key features: 1. JSON Integrity: It parses JSON, sanitizes values, and rebuilds it. It guarantees valid JSON output for your SIEM (ELK/Datadog). 2. Entropy Detection: Uses context-aware entropy analysis to catch high-randomness strings. 3. Fail-Open: Designed as a transparent pipe wrapper to preserve app uptime.

The project is open-source (Apache 2.0).

Repo: https://github.com/aragossa/pii-shield Docs: https://pii-shield.gitbook.io/docs/

I'd love your feedback on the entropy/threshold logic!

Comments URL: https://news.ycombinator.com/item?id=46873308

Points: 4

# Comments: 0



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