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In early-stage startups, documentation often feels like a luxury. Everyone is focused on shipping features, signing customers, and raising the next round. Writing down decisions, system flows, or API contracts? That can wait.
Until it can’t.
Six months later, no one remembers why a certain database schema was designed that way. Onboarding a new engineer takes three weeks instead of three days. A critical bug reappears because the “fix” lived in one developer’s head and never got documented.
Most startups don’t fail because they lack ideas or talent. They fail because they can’t scale knowledge across their team. And that’s exactly what documentation solves.
Here are the common patterns we see when working with fast-moving startups at TLVTech:
Documentation doesn’t fail because it’s unnecessary. It fails because it’s invisible until it’s too late.
A good CTO doesn’t make documentation a “nice-to-have”—they make it part of the culture. Here’s how:
Good documentation doesn’t slow you down—it speeds you up:
Startups don’t fail because they move too fast. They fail because they can’t repeat their success consistently. Documentation is the bridge.
If you’re a CTO at a startup, documentation isn’t bureaucracy—it’s leverage. Build it lean, keep it simple, and tie it into your development process.
At TLVTech, we’ve seen how strong documentation can turn chaotic engineering teams into scalable, predictable machines. And that’s what separates startups that stall from startups that scale.
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- AI plays a crucial role in computer vision by processing images and recognizing their contents. - It's trained with extensive data to help it recognize various elements in new images. - Real-world applications include spotting defects in production lines, healthcare scans analysis, security enhancements, and more. - Different industries utilize AI vision, like healthcare for disease detection, retail for inventory management, and agriculture for crop monitoring. - Models such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) are utilized in AI vision processing. - Future trends include more accurate image tracking, dark object detection, and faster, detailed understanding of images due to tech advancements like higher resolution and improved processing speeds. - AI's impact on computer vision will improve efficiency, potentially enabling automatic shopping through visual identification.
- Agile methodology is a flexible, user-oriented approach to software development, emphasizing teamwork, feedback, and short work cycles called sprints. - Adopted in 2001, Agile's principles prioritize people and interactions over tools, working software over documentation, embracing change, and sustainable work pace. - Agile's lifecycle includes defining a vision, developing a roadmap broken into features, then allocating features to a backlog for development during sprints. User stories help shape features from a user's perspective. - Agile differs from traditional waterfall and CMM methodologies, focusing on adaptability and continuous iteration. - Agile methods include Scrum, Kanban, Lean, Extreme Programming, and Feature-Driven Development. - Real-life examples of Agile implementation include Spotify and Philips in healthcare. Amazon uses Agile in developing their AWS services. - Transitioning to Agile involves training, starting small and communicating continually. For scaling Agile, practices like the Scrum of Scrums and frameworks like SAFe are effective. - Agile tools aid in tracking progress and fostering teamwork. They utilize techniques like Test-Driven Development and aid in creating estimates using burn-down charts. - Agile proves effective in improving product quality, reducing risks, increasing customer satisfaction, and providing faster ROI.
