I maintain several open-source projects and was spending hours every day triaging GitHub notifications — context-switching between repos, deciding what's urgent, reviewing PRs without missing breaking changes.
Ossue pulls issues, PRs, and discussions from all your repos into one local inbox. AI analyzes each item automatically: structured triage for issues (bug vs feature, priority, area), code review verdicts for PRs (with breaking change detection), and discussion summaries. You can customize the AI's review criteria per project.
It also has a "notes to issues" workflow, brain dump a rough idea, AI structures it into a proper GitHub issue with title, labels, and priority, then publish directly.
AI coding agents are powerful but they don't hesitate before running rm -rf /, kubectl delete namespace production, or terraform destroy. shellfirm adds a safety layer that catches risky commands before they execute.
How it works with AI agents:
One command to set up: shellfirm connect claude-code
This gives you two layers of protection:
1. Hooks – automatic pre-execution checks that block dangerous commands before the agent can run them. The agent can't bypass or ignore these.
2. MCP tools – the agent can proactively ask "is this command risky?" and get back structured risk assessments with severity, blast radius, and safer alternatives.
shellfirm covers 9 ecosystems (git, k8s, terraform, databases, filesystem, etc.) with pattern matching, context detection (SSH, root, production branch), and team
policies via .shellfirm.yaml.
Supports Claude Code (hooks + MCP), Cursor, Windsurf, Zed, and Cline.
Tried Intuvo — it transforms CVs into strategic interview blueprints.
You upload a resume + job description, and it uses LLMs to:
- Reveal strengths & gaps
- Flag red flags
- Generate tailored interview questions
Feels like a superpower for recruiters or hiring managers.
Curious, would you use something like this in your workflow?
What would make it more useful or trustworthy?
We're thrilled to announce a powerful new version of Loco, packed with features, fixes, and improvements designed to supercharge your development experience!
When I maintained the https://loco.rs project, I often encountered issues with documentation not keeping up with code changes.
To address this, I developed the snipdoc tool, which significantly improved the project's ease of maintaining and validating the docs as part of our CI.
Now that we're pretty much solid with Loco (https://loco.rs), the "Rust on Rails" framework, we're taking feedback from the community on what they would expect to see on the UI part.
Should it be React based? Dioxus? Yew?
Happy if anyone would like to chip in with their opinion here:
We've just released an initial version of Loco, a Rails inspired framework for Rust. Looking to get feedback, ideas, and contributions! (also, feel free to AMA here)
shellclear is a security tool that provides a simple and fast way to secure your shell command history.
Sensitive data can be stored in your history file when exporting a token of something or running a script with a token.
Sometimes those secrets can be visible to others when sharing your terminal / autocomplete command and more.
* Shellclaer can be a shell plugin that shows a summary of sensitive data when opening a new terminal
* Get a list of sensitive data found in the history shells
* Clear the sensitive information
shellfirm not execute the command.
it's just get interrupt when you run the command, check the pattern and prompt your a verification. your shell is the executer
In base you need a hook that get the command before your shell execute the command.
when the shell get the command interrupt this project execute shellfirm before summation in your shell
Hey this project is very small, and not execution command! only prompt you a verification when detect a risky pattern from a yaml pre-define (default or by you)
Ossue pulls issues, PRs, and discussions from all your repos into one local inbox. AI analyzes each item automatically: structured triage for issues (bug vs feature, priority, area), code review verdicts for PRs (with breaking change detection), and discussion summaries. You can customize the AI's review criteria per project.
It also has a "notes to issues" workflow, brain dump a rough idea, AI structures it into a proper GitHub issue with title, labels, and priority, then publish directly.