Mentornity is mentoring and coaching program software for organizations that run structured programs and need to show what came of them.
It covers the full cycle. Participants enrol and are matched by an algorithm that scores every possible pair against weighted criteria the program defines, discarding pairs that fail a mandatory rule; administrators approve suggestions individually or in bulk, assign by hand, or open a mentor pool and let participants choose. Mentors publish availability, mentees book within it, sessions sync to Zoom, Microsoft Teams or Google Meet, and reminders go out automatically before and after each meeting.
Measurement runs alongside rather than after. Surveys and forms can be scheduled at any point in a cycle, and a program health score tracks participation, meeting frequency and completion against each program's own targets, flagging programs that drift before the cycle ends. Certificates are issued automatically on completion.
Each program runs under its own branding on its own domain, with role-based access and six interface languages. Free for up to 10 users with no expiry and every feature enabled; paid plans start at $289 per month and scale by participant count.
Running mentoring programs since 2015.
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gtechna’s smart parking management and enforcement solution is trusted by leading cities worldwide. Our cloud-based system empowers municipalities, transportation agencies, and universities with cutting-edge applications that increase parking revenue, cut operational costs, and improve the driver experience.
By choosing gtechna for your parking management and enforcement needs, you're partnering with an industry leader known for constant innovation. Cities like Washington, D.C., Boston, Pittsburgh, Toronto, and Vancouver have already transformed their parking infrastructure with gtechna—now it's your turn to experience the future of parking.
Upgrade to a scalable parking management and
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DarkRomanceWrite
DarkRomanceWrite is a dedicated writing platform designed exclusively for romance authors, seamlessly integrating tools for brainstorming, story development, outlining, scene planning, and drafting chapters all within a cohesive workflow. This innovative studio allows writers to meticulously arrange elements such as tropes, emotional arcs, attraction dynamics, power relations, character vulnerabilities, heat levels, psychological insights, boundaries, timeline specifics, and continuity across series as their manuscripts progress. Each phase of the writing process is easily accessible for review and modification, featuring options for version restoration and export to Word. Upon signing up, new users are granted 50,000 Somas without any immediate subscription commitments, ensuring that creative ownership remains intact and that your work will not be utilized for AI training purposes. Additionally, the platform encourages a collaborative environment where authors can refine their narratives, making the writing journey more engaging and productive.
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Muse Spark 1.2
Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.
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