Veritos
Centralized code intelligence for your whole team
Veritos launched on March 11, 2026 with 3 votes, #3,303 of 23,380 launches that month and more than 50% of that year's launches.
What it does
Centralize context, rules, and agents for AI coding workflows. Manage reusable AI resources (sub-agents, skills, MCPs, rules) in a unified library, then inject them as versioned files into multiple repositories. Keep teams aligned with organization hierarchies, permission-based access, and a Knowledge Base that feeds your AI context. Built for teams scaling AI-assisted development across projects.
Manage AI coding rules, skills, and context in one place. Veritos syncs your team
Define your AI coding standards once. Veritos distributes them to every connected repository through pull requests. Your team reviews AI context changes the same way they review code. Your AI agents are only as good as the context they get. But right now, most teams are copy-pasting config files between repos and hoping nothing falls through the cracks. Your .claude files live in 20+ repositories. When you update one, the rest fall out of date. Inconsistency builds up quietly. Who changed that rule last week? No idea. Something broke the AI output? Good luck figuring out what changed and where. Every rule change means opening repo after repo. It takes hours, it's easy to miss one, and…from veritos.io
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Git for AI AgentsMay 2026 · github.com · ▲129hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…

20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
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