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Ask HN: What Are You Working On? (April 2026)

344 pointsby david927last Sunday at 4:24 PM1160 commentsview on HN

What are you working on? Any new ideas that you're thinking about?


Comments

arionhardisonlast Sunday at 7:55 PM

Codify — democratic digital public infrastructure that turns your problems into structured, executable programs.

The idea: describe any problem in plain language (voice or text), and AI codifies it into a structured program with the right people, steps, timeline, and agents to get it done. It's a 5-step wizard: Define Problem → Codify Solution → Setup Program → Execute Program → Verify Outcome.

It runs across 50+ domains — codify.healthcare (EMR backend), codify.education (LMS backend), codify.finance, codify.careers (HRM backend), codify.law, plus 13 city domains (codify.nyc, codify.miami, codify.london, codify.tokyo, etc.). Each domain tailors the AI assessment and program output to that sector.

The platform is Project20x — think of it as the infrastructure layer. If Codify is the verb ("codify your healthcare problem into a care program"), Project20x is the operating system that runs it all: multi-tenant governance, AI agent orchestration, and domain-specific sys-cores for healthcare, education, city services, etc.

Every US federal agency and state-level department has a subdomain — ed.usa.project20x.com (Dept of Education), doj.usa.project20x.com, hhs.usa.project20x.com, etc. — with AI agents representing each agency's mandate. Same structure at the state level.

The political side: Project20x hosts policy management for both parties — dnc.project20x.com and rnc.project20x.com — where legislative intent gets codified into executable governance through a 10-step policy lifecycle. Right now I'm building out the multi-agent environment so agency agents can negotiate with each other, make deals, and send policy proposals up to the HITL (human-in-the-loop) politician for approval. Each elected official has a profile (e.g. https://project20x.com/u/donald-trump) where constituents can engage and where policy proposals land for review.

The name is a nod to structured policy frameworks, but the goal is nonpartisan infrastructure: democratically governed essential services delivered as AI-native social programs.

Stack: Nuxt 2/Vue 2 frontend, Laravel 10 API, Python/LangGraph agent orchestration, Flutter mobile app. Currently live across all domains.

https://project20x.com | https://codify.healthcare | https://codify.education | https://dnc.project20x.com | https://rnc.project20x.com etc...

Datagrouttoday at 7:35 PM

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TimCTRLlast Sunday at 9:12 PM

nothing

lam0x86last Monday at 10:46 AM

Yet another dual-panel file manager. FAR + vscode. https://dotdir.dev/

SFXTECHDEVtoday at 3:21 AM

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michioswlast Monday at 12:29 PM

https://github.com/michiosw/oamc

I built a local-first tool for turning research material into a maintained markdown wiki.

The idea is simple: instead of repeatedly querying raw notes or documents, sources get ingested into a structured wiki with source pages, concept pages, entity pages, and synthesis pages. Then questions are asked against that wiki, and useful answers get written back as new pages.

Everything stays file-based and Obsidian-friendly. There’s also a local dashboard and a macOS menubar app so it can keep running in the background.

I was trying to build something that feels more cumulative than chat, but much lighter than setting up a full RAG stack.

The original inspiration was Andrej Karpathy’s “LLM Wiki” idea. I also took some UI/product inspiration from wiki-os.

Curious if other people here have found wiki-first or markdown-first workflows more useful than pure retrieval for personal research and project memory.

thisisfatihlast Sunday at 11:32 PM

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nezharlast Monday at 12:48 PM

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arcbytelast Monday at 12:28 PM

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wangsjlast Monday at 1:36 AM

https://github.com/vince-0202/acgo

Over the past few weeks, I have been building an AI coding tool in Go. The core loop is straightforward: accept a natural-language instruction, let the LLM interpret intent, then execute coding work through tools such as file read/write, code search, and terminal commands.

As of now, I haven't come across any agent coding tools written in Go, but I have always thought that Go is an excellent language and is very suitable for building any CLI tools.

Currently, I have added harness constraints to the agent by exposing hooks and implementing monitoring during the agent's working lifecycle. I think this will enable a clear division of responsibilities between the agent and the harness. The agent is the smallest execution core, while the harness acts as the execution agent for the agent and imposes constraints on its behavior.

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