Built an open-source CLI to make job hunting feel more like a developer workflow.

pushcv lets you:

• 📋 Track applications on a terminal Kanban board
• 🔎 Scrape LinkedIn job postings into a local SQLite database
• ✍️ Generate ATS-optimized resumes and cover letters using a local LLM
• 💰 Estimate salary ranges
• 📝 Keep notes and follow-up timelines for every application
• 📦 Export everything to JSON or CSV

The project is local-first by design:

• No accounts
• No telemetry
• No cloud backend
• Your application data stays in a local SQLite database
• Resume generation runs against your own local model

Built with Python, Typer, Rich, SQLModel, and SQLite.

I'd love any feedback, feature requests, or contributions!

GitHub: https://github.com/notnotparas/pushcv-cli

It's an open-source, local-first CLI that helps you manage your entire job search from the terminal.

Features

• 📋 Track applications on a terminal Kanban board (Drafting → Applied → Interviewing → Closed)
• 🔎 Scrape LinkedIn job postings with a single command
• ✍️ Generate ATS-optimized resumes and cover letters using a local LLM
• 💰 Estimate salary ranges for jobs
• 📝 Keep a timeline of notes and follow-ups for every application
• 📦 Export everything to JSON or CSV

Everything is stored in a local SQLite database.

No accounts.
No telemetry.
No subscriptions.
No API keys required.

The only network calls happen when you explicitly ask to scrape a job posting or fetch salary information.

A typical workflow looks like this:

pushcv init

pushcv fetch <linkedin-url>

pushcv status

pushcv draft 1

pushcv move 1 applied

pushcv note 1 "Recruiter replied today"

The project is still early, and I'd really appreciate feedback from the community.

I'm especially interested in hearing:

• What features are missing?
• Does the CLI workflow feel natural?
• What would make this genuinely useful during a job search?

GitHub:

https://github.com/notnotparas/pushcv-cli

Thanks for taking a look!

I have been working on an AIOps/LLMOps course and decided to use AI tools end-to-end for the first video and not just for writing, but for the full production pipeline

The stack I used:

Claude: course content, lecture structure, Jupyter notebook labs, video transcript [Hands-On Lab video coming up next week]

NotebookLM: fed it the transcript + source PDF to generate a video overview and fact-check the structure

ElevenLabs: audio narration from the transcript Claude helped write

Canva + Adobe Firefly: thumbnails, graphics, and editing

The audio overview sounded great on first listen, but when I went through it carefully for a technical video, it was full of invented filler that added nothing. For a casual explainer this probably doesn't matter. For a course where accuracy is the whole point, it meant a full manual edit pass to strip out the garbage. Ended up costing me way more time than I thught.

Did anyone find a way to get cleaner, tighter audio output without the padding? Or is manual editing just the cost of using it for anything technical? [The workflow still compressed maybe 3-4 days of content work into a few hours 😄 ]

For your feedback: https://youtu.be/Uiqy52KY1VM?si=l8-gs3M1FQbB6KBe

For anyone doing educational content in the ML/AI space: what's your production stack?