Impli reveals the exact APEX method professionals use to optimize AI prompts. The article shows the complete system specification—from analyzing requests to executing optimized prompts that work across all platforms.
Web scraping has quietly become the backbone of AI training data. But legal gray areas and sophisticated anti-blocking measures make success tricky. This guide reveals what works in 2025.
Sean Grove from OpenAI says coding is dead. Instead of writing code, developers should write specifications that generate software. AWS just launched Kiro to make this real, while GeneXus claims they've done it for 35 years
Talk about a productive week off. The gamble worked. Within weeks, two million people jumped on the waitlist for Notion AI. Users now save over 70 minutes weekly through automated summaries and content generation. The company has morphed from a simple note-taking app into what co-founder Akshay Kothari calls their users' "second brain."
But this isn't your typical Silicon Valley AI publicity stunt. Notion rebuilt their entire platform around OpenAI's technology, integrating GPT-4 and other models into every corner of the workspace. The result? A system that doesn't just store information – it helps you use it.
The transformation runs deep. Notion's engineering team can now evaluate new AI models in half a day. Their Q&A feature lets users interrogate their entire workspace like a knowledgeable colleague. Response times have dropped from minutes to milliseconds.
The numbers tell a compelling story. Two-thirds of AI feature adopters become more active users. A whopping 86% would be "very disappointed" if the AI features vanished. Translation: they've become essential tools, not just fancy add-ons.
The partnership between Notion and OpenAI has evolved beyond a simple vendor relationship. They've shaped each other's development, with Notion's real-world feedback influencing OpenAI's technical roadmap. It's less David and Goliath, more Rogers and Hammerstein – if they wrote code instead of musicals.
The future looks ambitious. Notion aims to transform their AI from a writing assistant into something closer to a chief of staff – a digital partner that helps users "operate at the top of their game." Given their track record, they might just pull it off.
Why this matters:
A rare tech success story where "adding AI" actually worked: Notion proved that deep integration, not superficial features, makes the difference
The 70-minute weekly time savings shows AI can deliver real productivity gains – when companies take the time to build it right
Tech translator with German roots who fled to Silicon Valley chaos. Decodes startup noise from San Francisco. Launched implicator.ai to slice through AI's daily madness—crisp, clear, with Teutonic precision and deadly sarcasm.
Chinese startup Moonshot AI released Kimi K2, an open-source model that matches GPT-4.1 performance while costing five times less. Silicon Valley's response? OpenAI delayed their planned open-source release hours after K2 launched.
Grammarly bought email app Superhuman for an undisclosed sum, part of its plan to build an AI productivity empire. With $1 billion in fresh funding, the grammar company wants to put AI agents at the center of your workday.
While Congress debates TikTok's future, ByteDance quietly built America's #2 education app. Gauth helps 200 million students cheat on homework by solving problems from photos. Same company, same data concerns, zero scrutiny.
Programming computers in English sounds impossible. But Andrej Karpathy built working apps without knowing code, using only natural language prompts. He calls it Software 3.0. These AI systems think like humans, complete with superhuman memory and distinctly human mistakes.