Tahleel Shaikh

Everyone wants to build the next breakthrough AI model. Nobody wants to clean the data. The glamorous part of AI gets all the attention. The reality? -> Fixing missing values. -> Removing duplicates. -> Standardizing formats. -> Handling noisy labels. -> Debugging data pipelines. -> Repeating the process... again. The truth is simple: Your model can only be as good as the data you feed it. A 1% improvement in data quality often beats chasing the latest benchmark or swapping to a newer model. The best AI engineers don't just optimize models. They optimize the entire pipeline. What's the most frustrating data issue you've dealt with? 👇🏻 #AIEngineering #MachineLearning #DataScience #ArtificialIntelligence #MLOps

Benchmarks are for vanity. Architecture is for production. Everyone is chasing model leaderboards, but a smart model is just a brain. Without a robust system around it, it's useless in the real world. The real breakthrough isn't the model, it’s AI Engineering. Check out my full breakdown on this below.

Benchmarks are for vanity. Architecture is for production. Everyone is chasing model leaderboards, but a smart model is just a brain. Without a robust system around it, it's useless in the real world. The real breakthrough isn't the model, it’s AI Engineering. Check out my full breakdown on this below.

Everyone's talking about the benchmark scores. But the reason developers are paying attention to Z.AI's GLM-5.2 goes beyond a leaderboard. What's interesting isn't that GLM-5.2 has clearly surpassed Claude. It's that it performed well enough to make people ask: "Is GLM-5.2 strong enough to challenge Claude?" A few things that caught my attention: • Competitive coding benchmark performance • Strong agentic and long-horizon coding capabilities • Massive context window • Open-weight ecosystem • Aggressive pricing strategy Right now, I'd still choose Claude for production-critical systems. But GLM-5.2 is one of the few recent models that has genuinely shifted the conversation. Developers aren't asking whether it's good. They're asking whether it's good enough to become a real alternative. And that's a much bigger deal. My take: GLM-5.2 doesn't need to beat Claude. It just needs to get close enough. What's your pick today? ▪︎ Z.AI GLM-5.2 ▪︎ Claude Looking forward to hearing different perspectives. #ArtificialIntelligence #ClaudeCode #ZAI #GenerativeAI #SoftwareEngineering

Someone asked me: “What’s your session persistence strategy when your agent fails mid-task?” That’s where AI engineering gets real. Building agents is easy. Making them reliable is hard. Your agent is fetching data, calling APIs, reasoning… …and suddenly it crashes. Now what? Restart everything? Or resume from where it stopped? That’s where things like checkpointing, persistent state (SQLite / Redis), retries and idempotent steps come in. Instead of one-shot agents, build systems that: → remember progress → recover from failure → continue intelligently Want me to break down how to actually implement this? Comment have you faced this before? #AI #AIEngineering #LLM #MachineLearning #BuildInPublic