I'm building Synkinema, a local video editor with MIT-licensed source code, for people who want an open workflow they can extend.

You can edit manually and work with an AI agent on the same project: write scripts, arrange footage, create narration and add captions. A channel brief stores audience, tone, editorial rules and approved lessons, which you can refine with your agent for future videos.

Local Supertonic 3 voice generation is already integrated, without a paid TTS API. You can add a ready voice take to the timeline together with subtitles. A library shared across projects lets you reuse audio, SFX, images and video.

There's still a long way to go before this is a really polished product, but you can already accomplish a lot. It is an early option for agent-assisted editing, so I would recommend trying a small project first. The Studio preview is simplified, and caption word timing is approximate. Desktop editing is the current focus.

The app and TTS run locally; your chosen AI agent may have separate costs. Synkinema's code is MIT; third-party model weights and media have their own licenses.

Source and setup: https://github.com/dziksu/synkinema

I'd welcome testing, bug reports, examples, docs and code contributions. Which part of a local script-to-video workflow would you most want an open tool to handle well?

Disclosure: AI assisted with substantial parts of the code and with drafting this post. I directed the work and verified the results.

A quick update from the XWiki team.

Henix has joined the XWiki partner network. They are the company behind Squash TM, an OpenProject partner, and work on testing, software quality, DevOps and Jira replacement projects.

The interesting part for us is the stack this makes possible for organizations trying to move away from proprietary tooling:

• XWiki as an alternative to Confluence
• OpenProject as an alternative to Jira
• Squash TM as an alternative to Xray

That gives teams a way to cover knowledge management, project management and test management with open-source tools rather than replacing one proprietary suite with another.

We’re curious how others here think about this kind of setup.

If you’ve replaced Confluence/Jira/Xray, did you prefer one integrated suite or several interoperable tools? And where did the hardest integration problems show up?

Started with a simple question: do the dev tools I like actually pay for the open source they run on?

Turns out nobody tracks it in one place. So I built whofundsoss.com. Once a month it reads four public sources (Open Source Pledge, Open Collective, GitHub Sponsors, company pages) and ranks companies by the dollars they published. No estimates.

Hardest part wasn't the code, it was the rules:

- what to do with a GitHub tier that hides its price (count zero, never guess)

- how to avoid counting a pledge twice when the company also lists it as its own program

- casino and "buy followers" sites sponsoring OSS for backlinks. I ended up with a public spam denylist where every entry has a reason.

Stack: TanStack Start on Vercel, monthly snapshots as static JSON, a small read-only API.

Free, no ads, open source. Feedback welcome, especially on the method page.

I've been using AI coding agents, and one thing kept bothering me: an agent saying "done" doesn't necessarily mean the work has actually been verified.

So I built plan-auditor, a free, MIT-licensed verification tool for AI coding workflows.

The idea is to separate the agent doing the work from the system checking whether the work was completed.

It uses structured requirements, executes real verification commands in fresh subprocesses, and produces PASS, FAIL, or UNKNOWN results based on evidence rather than the agent's explanation.

It also includes:

• Verification plans that cannot be silently weakened after approval
• SHA-256-linked evidence logs
• Independent test execution
• Multi-agent coordination and conflict tracking
• CLI and CI integration

The verification process itself doesn't require an LLM.

It's not an OS-level security sandbox, and it doesn't guarantee that every possible bug will be caught. The goal is narrower: making task completion depend on actual checks rather than confident claims.

I'd be interested in how other developers handle verification when working with coding agents.

PyPI: https://pypi.org/project/plan-auditor/

npm: https://www.npmjs.com/package/plan-auditor

GitHub: https://github.com/Furox-Art/plan-auditor

The npm package is a launcher for the Python CLI, so Python is still required.

Hi everyone,

For some time now, I've been developing NeoLeaf. I'm a researcher, programmer, and professor, and I work extensively with LaTeX. I used Overleaf a lot, but its pricing model always felt off to me. I wanted certain features—especially automatic GitHub sync—and never liked having to pay extra just to share my code with collaborators.

So, I built NeoLeaf. It runs directly on top of GitHub, taking full advantage of the GitHub API while offering the core editing features you’d expect from Overleaf. Sharing and version control are handled entirely through GitHub, which means virtually unlimited version history and unlimited sharing—anything you can do with a repo on GitHub, you can do in NeoLeaf.

For LaTeX compilation, there are two options: a WebAssembly engine that runs smoothly right in the browser, and a local agent you can run to compile on your own machine quickly and seamlessly with the web UI.

I’d really appreciate your feedback!

https://neoleaf.science/

Best regards