Forem: Ingo

I built Job Hunter, an open-source local-first desktop workspace for long-running job searches

Ingo — Thu, 30 Apr 2026 07:29:40 +0000

I built Job Hunter because many serious job searches are not really one-shot searches.

If you are an experienced candidate, the hard part is often not seeing more job listings. The harder part is remembering which companies are worth tracking, which role titles are noisy, which openings were already reviewed, and which public hiring links are actually useful.

Job Hunter is an open-source, local-first Windows desktop workspace for job discovery and follow-up tracking.

It is designed for long-running specialist searches, especially cases where generic job-board feeds are not enough and the better route is to find company career pages, ATS links, and official application pages directly. That makes it a stronger fit for Europe / international searches, specialist roles, and domains where the same capability may appear under many different job titles.

What It Does

Maintains local candidate profiles, target directions, search state, and result history.
Helps define target role directions with AI.
Discovers and verifies concrete job links.
Scores and explains role fit.
Writes strong role signals back into a reusable company pool.
Lets the user track interested, applied, offer, rejected, and dropped states.

The main idea is that a good role should improve the next search round. If a company has a strong fit, the company becomes part of the future source set instead of being forgotten after one run.

Important Boundaries

This is free and open source, but AI calls are not free by default. Users provide their own OpenAI or compatible API key, so API usage may cost money.

It is also not an auto-apply bot. Job Hunter helps discover, evaluate, and organize leads. The user still decides what to submit.

The project is local-first: resumes, company pools, search results, SQLite data, exports, and runtime backups are meant to stay on the user's machine. The public repository contains source code, documentation, demo seeds, and safe example templates.

Try It

Repo:
https://github.com/liuyingxuvka/Job-Hunter

Latest Windows release:
https://github.com/liuyingxuvka/Job-Hunter/releases/latest

Feedback discussion:
https://github.com/liuyingxuvka/Job-Hunter/discussions/1

End users should download the Windows zip from GitHub Releases, extract it, and run Jobflow Desktop.exe. Developers can run it from source.

Feedback I Am Looking For

Is "local-first job discovery workspace" a clear framing?
Does the company-pool feedback loop make sense?
Would this be useful for specialist or international job searches?
What would make you hesitate: API cost, privacy, Windows install, search accuracy, or something else?

I built Khaos Brain, an open-source local-first experience system for AI agents

Ingo — Wed, 29 Apr 2026 18:03:12 +0000

I built Khaos Brain because most AI memory features feel too shallow for real agent work.

Saving "remember this next time" is useful, but the more valuable unit is accumulated experience: what condition appeared, what action was taken, what result happened, which route failed, and which route later became reliable.

The problem is not that an agent cannot remember a sentence. The problem is that after doing similar work many times, its working experience often does not accumulate into something inspectable and reusable.

Khaos Brain is an open-source, local-first experience organization tool for AI agents. It stores experience as visible file-based cards instead of opaque memory.

What it does

The current release is Codex-first, but the idea is broader:

before a task, the agent can retrieve relevant experience
after a task, it can write back observations and lessons
maintenance workflows can organize those cards over time

The cards can be searched, inspected, reviewed, diffed, merged, and rolled back with Git.

One part I care about is keeping personal memory and shared knowledge separate. Personal preferences stay local. Reusable task models, engineering lessons, and reviewed skills can be shared through a GitHub-backed organization knowledge base.

The easiest way to try it is to hand the GitHub URL to your coding agent and ask it to install and enable the project.

Repo:
https://github.com/liuyingxuvka/Khaos-Brain

Feedback:
https://github.com/liuyingxuvka/Khaos-Brain/discussions/2

I would especially like feedback on:

whether "local-first experience system" works well as a framing
whether visible experience cards feel more useful than opaque AI memory
what kind of agent work would benefit most from accumulated experience

I built Flow-Guard, an open-source finite-state workflow simulator for AI coding agents

Ingo — Wed, 29 Apr 2026 18:02:21 +0000

I built Flow-Guard because I keep seeing the same failure mode with AI coding agents: a local change looks correct, but the larger workflow or architecture is still wrong.

Flow-Guard is an open-source Python tool that acts like a lightweight workflow / architecture simulator before an agent writes or changes code.

It is not an LLM wrapper and it is not prompt-only. The core is a real finite-state mathematical model: you define the workflow state, transitions, inputs, and outputs, then Flow-Guard explores that model and reports concrete counterexample traces.

In other words, it is using a state-machine style mathematical simulation to reason about the workflow before code is generated or changed.

What it tries to catch

Right now I am focusing on three broad classes of problems:

duplicate side effects or repeated actions
state / cache / source-of-truth drift
stuck loops, retry paths, or progress failures

The goal is not to replace tests or claim full formal verification. I am trying to add a design-time guardrail: simulate the logic first, inspect the failures, then let the coding agent implement or modify the real code.

The easiest way to try it is to hand the GitHub URL to your coding agent and ask it to install the project and run the examples.

Repo:
https://github.com/liuyingxuvka/FlowGuard

Feedback:
https://github.com/liuyingxuvka/FlowGuard/discussions/1

I would especially like feedback on:

whether the concept is understandable
whether "workflow / architecture simulator" works well
what real AI-agent workflow bugs would be worth modeling next