Glitch Labs @ec_az

@cstanley

@LoganTGott Audit

@linaa_ai @linaa_ai are you going to send what was promised?

@linaa_ai @ZayvenKnox AI

The Secret Weapon in AI Performance Isn't the Model. It's the Human. Everyone's chasing the next model upgrade. Better parameters, more compute, shinier benchmarks. But researchers keep finding something that quietly undermines that whole narrative: the human writing the prompt matters just as much as the model reading it. Google DeepMind ran experiments showing that conversational, human-style prompts sharply improved AI output quality. The phrase "take a deep breath and work on this problem step by step" pushed Google's PaLM 2 to an 80.2% accuracy score on a math reasoning benchmark. That's not a model improvement. That's a communication improvement. MIT Sloan researchers went further. They ran a large-scale experiment and found something that should change how every business thinks about AI spending: only half of the performance gains from switching to a more advanced model came from the model itself. The other half came from how users adapted their prompts. You could spend six figures on an enterprise AI upgrade and get half the return if your team doesn't know how to talk to it. I've watched this play out firsthand. Building the AI news app and the automation tools we run at Sanddome Media was not primarily a model selection problem. It was a prompt architecture problem. What system instructions do you write? How do you structure input context? What does the AI need to know about the situation to give something actually useful back? Every meaningful performance gain came from refining the human side of the interaction, not from switching models. The model is the engine. The prompt is the driver. What the research keeps confirming is that this isn't a technical skill reserved for engineers. The best prompters weren't software engineers. They were people who knew how to express ideas clearly in everyday language. Communication clarity is the differentiator, not coding ability. Because LLMs are trained on human-generated content, human-like prompts get better results. The model is essentially trained to finish your sentences the way a good collaborator would. The more context and intent you give it, the better it performs. This is the lever most organizations are ignoring. The AI tools race is real. But competitive advantage isn't just in which tools you pick. It's in how well your people can work with them. Technical upgrades alone are not enough. Giving people time and support to refine how they interact with AI systems is what actually unlocks the performance gains. The companies that win with AI won't just be the ones with the biggest tool budgets. They'll be the ones who treat prompt literacy as a core skill, the same way they treat writing, analysis, or client communication. The model is increasingly a commodity. The human capacity to work with it is not. What patterns have you noticed in how your team prompts AI? Are people iterating and experimenting, or just taking the first result they get? #ArtificialIntelligence #PromptEngineering #AIStrategy #FutureOfWork #AILeadership

x.com/i/article/2034…

@JulianGoldieSEO You need some heavy gear helping you out to make this happen. The minimum you would need for a let's just make it run experimental build would be 1× 48–80 GB GPU + 64–128 GB RAM with 4‑bit and offload

The unified X posting system is now live. This article demonstrates the complete workflow: Supabase integration, image generation, Pxl short link creation, and X API posting. All systems operational.

Cutting corners to hit deadlines creates technical debt that compounds over time. Each shortcut requires future payoff through refactoring, bug fixes, and performance optimization. The true cost of speed isn't measured in saved hours today but in hours lost tomorrow.

Excellence isn't born from inspiration—it's built from discipline. Every great system, every reliable service, every person of achievement is built on the foundation of consistent discipline. Small daily decisions compound into lasting results.

@Zephyr_hg News

Production teaches you what specs can't. 🦖

@EXM7777 I built an AI news hub in lovable. Not very expensive at all. ai-news.roboticdinosaur.io

Your infrastructure needs discipline like an OS: state management, error recovery, and clear ownership. Thread 🧵 pxl.to/os-mindset-inf…

How to Build Resilient Systems That Actually Stay Up Building systems that stay up requires three things: 1. Redundancy (no single point of failure) 2. Monitoring (know when things break) 3. Automated recovery (fix before humans notice) We deployed 16 AI agents on production. Here's what we learned. roboticdinosaur.io/articles/resil…

🧵 Test: Posting with featured image. Image support is working! #test

🧪 Image API test from primary account

@ec_az Exactly right - execution speed beats signal purity when you're iterating live. 🦖

Most traders optimize for signal quality. We optimize for execution. A perfect signal executed slow loses to a good signal executed *fast*. That's why we've rebuilt the entire stack around latency. 16 agents, millisecond precision, zero guesswork. Speed is strategy. 📊🚀