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AI is everywhere—featured in demos, sales decks, and investor calls. Founders feel the pressure to “add AI” as if it’s just another feature to check off. But here’s the reality: most AI features either don’t work reliably in production or fail to deliver real value.
At TLVTech, we’ve partnered with dozens of startups to integrate AI—sometimes with great success, other times with hard lessons learned. Before you invest your team’s time, money, and energy into building an AI product, here’s what every founder should know.
Don’t chase the latest model just because it’s trendy. Focus on the real user pain you’re solving. You don’t need GPT-4 to deliver value; you need clarity on what your users actually need.
The best AI is invisible. If users notice the AI, it’s either broken or unnecessary. Ask yourself: “Would anyone use this feature if it didn’t have AI under the hood?” If the answer is no, rethink your approach.
It sounds counterintuitive, but your first version shouldn’t be “smart.” Before investing in models or APIs, validate your assumptions manually or with simple rules. If a human can handle the task in 30 seconds, do it manually behind the scenes. If users find value, then—and only then—should you automate.
For example, instead of building a custom AI support agent, route key queries to your team and tag them. If you see strong user engagement, you’ll know it’s worth productizing.
The AI feature that impresses in a demo isn’t always what keeps users coming back. Users don’t care about novelty—they care about reliability and usefulness.
Test for failure early. What happens when your model gets it wrong? What’s your fallback? Users will forgive limited features, but not unpredictable or nonsensical results.
Plugging into OpenAI or another LLM provider doesn’t solve everything. You’re still responsible for:
You need guardrails, error handling, and a user experience that anticipates mistakes. Otherwise, you’re shipping a prototype—not a product.
Unless you’re building deep tech, off-the-shelf models will get you 90% of the way. Focus on user experience, feedback loops, and smart prompting.
Only train custom models if:
You’ve pushed off-the-shelf solutions to their limits
You have unique, proprietary data
The potential impact justifies the investment
The best AI products we’ve seen didn’t start with fancy models. They started with tight user feedback loops, clear success metrics, and relentless focus on utility.
AI is a tool—a powerful one. But it won’t rescue a weak product or a fuzzy value proposition.
If you’re building something real and want AI to be part of it, let’s talk. We help teams launch AI features that actually work.
• APIs, or Application Programming Interfaces, are four primary types: Open APIs (public), Partner APIs, Private APIs (internal), and Composite APIs. • Open APIs offer visibility and audience growth potential, Partner APIs help establish business relationships and paths to income, Private APIs enhance internal efficiency, and Composite APIs save time by bundling data fetch tasks. • The audience plays a critical role in choosing an API, with private APIs used internally, partner APIs for strengthening business alliances, and open APIs to reach a wide audience. • Different API protocols cater to unique situations, with REST being a favorite due to its simplicity, scalability, and stateless servers, while SOAP is fit for enterprise-level web services. • Examples of API application include banking APIs for secure data connection and handling transactions, Selenium WebDriver APIs for testing web application interfaces, and weather monitoring APIs for guiding shipping routes. • An effective enterprise API strategy is crucial in the digital age; it fosters innovation and collaboration while potentially opening new revenue streams. Comprehensive understanding of different APIs can assist in formulating an apt enterprise strategy.
- A Fractional CTO is a part-time tech executive who creates tech strategies aligned with business visions, oversees system upgrades, audits, staff training, and ensures effective communication within the company. - Ideal hiring times include the scaling-up stage, when a full-time CTO isn't affordable, or during business transitions or significant projects. - Fractional CTOs differ from full-time CTOs by offering flexible expertise across multiple businesses rather than consistent oversight in one. - Cost of a Fractional CTO varies, with the median wage around $10,000 to $15,000 per month, influenced by experience, expertise, and time requirements. - Fractional CTOs can be found via online platforms like LinkedIn, Indeed, and CTO Academy, as well as networking events. - Benefits include fresh perspectives, fostering innovation, leading in product development and technology adoption, and boosting business success. - To become a Fractional CTO, one needs robust tech knowledge, business strategy insight, significant people skills, continuous learning, leadership experiences, and wide networking.
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- Domain-Specific Languages (DSLs) are designed to manage a defined set of tasks effectively in the tech world, like Markdown for formatting, MySQL for managing databases, and CSS for styling web pages. - Domain-Specific Modelling (DSM) uses DSLs to speed up software production. - Tools such as Antlr, Xtext, and Xtend help in crafting and implementing DSLs. - DSLs enhance productivity, better communication among teams, and consistency in software development. However, they require time to learn and limit the flexibility to carry out an extensive range of tasks due to their specific nature. - DSLs are used in app development and offer specific advantages like SQL for interacting with databases and regex for text operations. - There is a balance between DSLs and General-Purpose Languages: DSLs are specialized for specific tasks, while general-purpose languages offer more flexibility. - The future of DSLs includes increased use in AI, data science, Internet of Things, and the growth of visual DSLs.
