I fixed a generative AI app for a client that was struggling with consistency and accuracy. The application was designed to handle flight bookings, car rentals, hotel reservations, and excursions - essentially a virtual travel assistant. However, it was failing to maintain

You offered $1 milion, but you can't use chatgpt anymore. Do you accept?

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I fixed an AI voice bot for a client, and it was a challenging task. The application was designed to qualify leads and schedule meetings, essentially acting as an AI-powered SDR. However, the original implementation was failing miserably, despite the previous company charging

Same functionality, two approaches: C# requires 110 lines following best practices, while Hyperlambda accomplishes it in 5 lines. This 22x difference highlights how specialized languages can offer more concise solutions than general-purpose OOP frameworks.

Day 1 of helping 1000 developers deploy AI tool for their clients - there are so few good AI devs - lots if demos and poc, very few apps in prod - i was asked to help a departament of dev to build, eval and deploy AI apps - i decided to do it in public - reply with + if you want

Day 4 of posting on tiktok to promote my future app Results: - 2883 views - 98% from US S t eps: - 1$ per month vpn - no us sim - no warmup - 800-1000 views each video - 3 video uploaded - images generated with shortsblink - text added inside up - generated 20 hooks text

I let Coursor Sonnet 4 improve my AI SDR app that i sale for 10k per client. In 2 hours the accuracy for question qualification went from 80% to 93% and schedule meetings from 95% to 97% AGI is not close but i can work 10x faster

🚀 I fixed a RAG system that was giving wrong answers 90% of the time. Here's how: Ever had an AI chatbot completely misunderstand your question? It's frustrating, right? Well, I recently tackled this exact problem for a client's technical support system. The Issue: - Users

I've analyzed 10+ AI products last month, and one thing stands out: technical execution isn't the bottleneck—distribution is. As an AI developer who can build functioning products in 1-3 days, I'm looking for a marketing co-founder who can match my velocity with rapid traffic

"Our HR bot kept hallucinating company policies until we fixed this" Ever deployed an AI chatbot that confidently gave wrong answers? You're not alone. Last month, I worked with a client whose HR bot was causing chaos. When asked about vacation days for senior engineers, it

🚨 I analyzed 10+ RAG applications last month, and here's a critical issue I found in 80% of them: Document chunking is breaking context in RAG systems, leading to incomplete or inaccurate responses. Here's how I fixed it: Problem: Standard 1000-token chunks often split crucial

RAG is dead, or is it? How many times have you heard it? As an AI developer who's been deploying RAG agents for almost 2 years now, I've seen countless companies struggle with common RAG problems: 1. Inefficient chunking strategies 2. Excessive API calls driving up costs 3.

🚀 I saved a startup $100K by fixing their "AI" feature in just 10 days! Here's the thing: ML is not just about calling an API. Sure, you can use APIs for small features, but if you want to truly differentiate your product with AI, you need to know what you're doing. The

🚨 Are you second-guessing which AI library to use, what to test, or how to implement Gen AI features? You're not alone! I've spoken with 10 managers who tried implementing RAG (Retrieval Augmented Generation) systems, and none of them worked as expected. Why? Because they

Most companies fail to implement AI for sales development. Why? They're using a one-size-fits-all approach. 🧐 Problem: Sales teams struggle with lead qualification, wasting time on unqualified prospects. Traditional chatbots are rigid and can't handle complex conversations. 💡

🔍 Evaluation is King: Are You Scoring Your AI Chatbot Correctly? Ever launched an AI chatbot only to find it's not performing as expected? You're not alone. Many companies implement Gen AI applications without proper evaluation methods, leading to subpar results. The problem?

Many companies rush to implement "all-knowing" AI systems, expecting 100% accuracy. This approach often leads to disappointment and wasted resources. 💡 The Solution: Embrace a "Human-in-the-Loop" approach. Create AI systems that learn from human input, allowing for verification

Many developers think tweaking LangChain parameters will magically fix their Retrieval Augmented Generation (RAG) issues. But here's the hard truth: it won't. The real problem? Lack of robust testing and unclear use cases. To build a reliable RAG system, you need: 1️⃣

Ever wonder why some AI projects drag on for months while others are completed in days? I recently encountered a startup that spent 6 months struggling with a Gen AI implementation. I rebuilt their solution in just 3 weeks. How? The problem: Inexperienced developers often: •

40% of AI agents fail unexpectedly in enterprise environments, exposing a critical gap in current AI deployment strategies. The Enterprise AI Complexity Unveiled: Implementing AI is far more than solving isolated tasks. The true challenge lies in creating intelligent systems