Like most people, I use a lot of models, which makes searching for specific chats a headache sometimes. So I came up with a pattern to stay better organised by leveraging the strengths of each model. Preferences differ for most people, but here’s mine: - ChatGPT: brainstorming, general productivity - Claude: creative writing, code generation - Qwen: deep research, code generation - Gemini: basic questions and source verification - Perplexity: broad research & information gathering Feel free to share yours. I think it would be nice to have a product that could use the API keys of these models to pull relevant chats based on provided keywords.

Hey guys! I feel like "hours saved" is the obvious metric but it's rarely enough to get decision-makers to approve budget I'm trying to figure out what actually moves the needle in those conversations 🔵 What metrics do you use beyond "time saved" to justify AI projects? 🔵 🔵 What's actually convinced a client to say YES to your proposal? 🔵 I want to get better at showing real business impact, not just cool tech demos Would love to hear what's worked for you!! 😁😁 Thank you!!!

Watching a swarm of agents spin up a landing page, draft copy, generate images, run A/B tests, and spit out analytics in minutes, feels quite mesmerizing. It feels like we've cracked the code on infinite scale with zero headcount. The temptation is real: spin up 50 agents, assign them tasks, and declare yourself a productivity god. Here's the part we often miss: even as AI accelerates everything at breakneck speed, real quality still takes time. If we're building products and services that actual humans will love or the ones they'll pay for repeatedly and recommend without being asked, human taste, judgment, and iteration remain non-negotiable. I've seen the pattern play out too many times. Most people boasts about orchestrating dozens of agents like an orchestrator, cool setup… until you zoom in. Every agent needs clear instructions, context, guardrails, and most critically, inspection. One subtle misalignment cascades: off-brand tone here, factual drift there, weird edge-case behavior everywhere. Suddenly you're not managing a lean machine; you're babysitting 50 toddlers with keyboards. What looked like leverage becomes a resource sink. There's only so much cognitive bandwidth one person (or even a small team) has. Reviewing outputs at scale isn't heroic, it's exhausting. And exhaustion leads to shortcuts, which leads to mediocre work dressed up as "fast iteration." The flex isn't how many agents you can juggle. The real flex is ruthless efficiency: using just enough automation to remove drudgery, while keeping humans firmly in the driver's seat for taste, coherence, and soul. Be efficient, not busy. Prioritize depth over breadth. Ship fewer things, but ship things that feel unmistakably human-crafted in the best way. AI is an incredible multiplier. It lets us explore more ideas faster than ever. But the moment we confuse speed with quality, or volume with value, we start building noise instead of products people actually care about. So yeah, I'm still betting on the man in the loop, deliberate human layer. Not because I'm nostalgic, but because the stuff worth building usually is.

AI automation is changing how we think and approach work, turning complexity into clarity and enabling people to focus on what matters most. With intelligent systems handling repetitive tasks, teams can invest their energy in problem-solving, relationship-building, and strategic creativity. This shift not only boosts efficiency but also increases human contribution. For businesses, the key lies in harmonizing AI with culture. When automation augments rather than replaces, organizations unlock both technological power and human potential. The best form of automation is the one optimised for people and not machines.

Are you guys using Claude code everyday for your projects?