Ask HN: What are you working on? (September 2026)

Contextify (https://contextify.sh)

It is an application for Linux, macOS and Windows that files your AI chats into a database your AI can read using a skill or MCP.

It backs up the valuable content of your Claude Code and Codex conversations (JSONL transcripts) and turns them into a complete history you don’t have to manage.

Use Contextify during normal flow of CLI AI conversations:

   1. Resume unfinished work → "where did we leave off on that?"
   2. Recover the intent/scope → "what was the actual goal of this whole effort?"
   3. Verify it got done → "did we ever finish that, and which session proves it?"
   4. Recall a fix → "how did we fix this the last time it broke?"

You can self-host and keep all data local or use the source available Contextify Cloud sync server so your history on all machines in your cluster or desktop and laptop are always backed up and immediately available for use.

I have not been a Windows user since the days of the Hackintosh. But urging here and from users to cover the platform pushed me into it and I ended up really enjoying the challenge.

Right now I’m working on an expanded version of the windows app and support for integrating a 3rd party local LLM.

1. Constructivist rather than propositional logic, so propositions can be true or false or unknown.

2. Compiler which targets the Linux ABI (in progress). An interpreter exists and acts as an oracle for tests.

3. Built-in row polymorphic effect model.

4. Fine-grained capability and resource-based security.

I aspire for it to have a comprehensive first-party software catalog.

https://github.com/swe-toolkit/ken

(is it for perf reasons because bottleneck becomes verification? Though in that case perhaps it's better to just run a Pytorch reference on the GPUs, which should be good enough. Though, perhaps if you could get this current design to the point where `overlap(CPU, GPU cuda) < GPU cuda + GPU reference` that would be pretty awesome.