Brevlin’s Post

9x more tests = faster wins — if the system can handle it. The Brevlin AI SOP increases testing velocity without creating data chaos. Testing framework • Generating multiple variations for titles, creatives, and ads • Isolating variables for clean results • Prioritizing high-impact tests only Control layer • Avoiding simultaneous conflicting changes • Tracking performance at granular levels • Scaling only validated winners Outcome • Faster learning cycles • Reduced guesswork • Compounded performance gains Follow the page, drop AI below, and we’ll share the SOP.

What do you *really* hope to achieve using AI? Are you prioritizing well-defined outcomes over outputs? Experimentation and iterative learning are golden - for learning, but as a strategy? Make sure you're focusing on what's key to any success - regardless of how you're using and what you've learned with AI...

Your team tripled their ship rate with AI. Congratulations. So did everyone else. Every product team, every engineering org, every marketing function will have the same tools inside 18 months. The learning curve is short. The pressure is massive. The models underneath are identical. What you spent the last year building, that fluency, that speed, that velocity chart that looks like a hockey stick, your competitors have it too. Or they will. Soon. Speed was never the advantage. The teams winning right now aren't the ones shipping fastest. They're the ones who figured out that execution was never the constraint and started investing in the thing AI can't give you. Judgment. → This week's newsletter is about exactly that: https://lnkd.in/eizbRW6J

"The teams winning right now aren't the ones shipping fastest. They're the ones who figured out that execution was never the constraint and started investing in the thing AI can't give you. Judgment."

Most businesses are choosing LLMs the wrong way. They pick based on headlines. GPT-5.5 has the biggest brand. Claude Opus 4.7 leads on coding benchmarks. Gemini 3.1 Pro costs 60% less per token. DeepSeek V4 is the wildcard everyone is watching. The truth is: the right model depends entirely on your use case. Customer service automation requires different capabilities than financial analysis. Lead qualification needs different strengths than document processing. Voice agents have completely different latency and accuracy requirements than back-office workflows. EngineVult AI evaluates every deployment against the current model landscape, matching the right LLM to the right function. No single-model lock-in. No overpaying for capabilities you do not need. This is why our clients achieve better performance at lower cost than companies committing to one provider and hoping it covers everything. Curious what the optimal AI stack looks like for your business? Let's talk. Link in bio.

The best AI tools will start to feel less like software and more like creative direction. Less dashboards. Less complexity. Less “operate the machine.” More atmosphere. More emotion. More intention. We’re moving into a world where: -Prompting becomes the interface -Interfaces become cinematic -Workflows become conversational With product design becoming a competitive moat again.

I tried something new this week, and it changed how I think about AI in production. Not another image generator. Not another prompt workaround. Something you can actually build with. Luma just turned Uni-1 into an API. Which means: 👉 It’s no longer just a model you use 👉 It’s infrastructure you can plug into your stack Here’s what stood out: → You stop fighting prompts You give direction like a human: “Make this cleaner, keep the style, adjust lighting” – it holds context. → Consistency actually holds Same character. Same style. Same brand – across outputs. → Editing is controlled You change one thing, the rest of the composition stays intact. What matters isn’t just the model, it’s how this fits into real workflows: • Plug into products as a creative engine • Build multi-step pipelines with consistent outputs • Ship tools that rely on it in production This isn’t a beta experiment. Teams are already using this in real pipelines. We’ve seen AI generate. This is the shift to systems that can reason before they generate, and actually hold up at scale. If you’re building or shipping anything in this space: 👉 https://lumalabs.ai/api #LumaPartner

Switching models won’t fix a broken workflow. Your advantage isn’t the algorithm; it’s the system you run around it. Here’s how I turn “okay” AI into reliable output without juggling apps or chasing magic prompts: - Draft a Working Brief: purpose, audience, constraints, tone, 3 positive examples, and what to avoid. This becomes the contract. - Add a scoring rubric: criteria for quality (clarity, accuracy, structure, fit-for-purpose). Ask the model to self-grade and revise until it meets the bar. - Enforce evidence: require citations, link to sources, and label confidence. No source? Mark as hypothesis. - Version your prompts: name, version, change notes, and a small test set (“golden samples”). Ship v0.1, not a masterpiece. - Plan routing: define when to use which tool, acceptable latency, and fallback steps. Quality breaks? Roll back to the last good version. - Start in shadow mode: run AI alongside your current process for a week, compare results, then automate the steady parts. This isn’t about longer prompts; it’s about repeatable decisions. If you made your own “AI Working Brief” today, what would go in it first? #AIWorkflow #PromptEngineering #Productivity

My 2026 AI Stack for a One-Person Marketing Team The tool usually isn't the problem. The system around it is. If I had to rebuild from scratch today, here's how I'd think about it: **Fast drafting.** One LLM for writing. Not three. Pick it, learn its quirks, build prompts that work. **Clean research.** A tool that can pull and summarize — not just hallucinate confidently. Perplexity or a grounded model with citations. **Simple automation.** One workflow layer connecting inputs to outputs. Make, Zapier, n8n — doesn't matter. What matters is it runs without you. **Reusable prompts.** A small library you actually maintain. Ten good prompts beat a hundred mediocre ones. **One queue.** Notion, Linear, a spreadsheet — pick one place where work lives and moves. The tool doesn't matter. The habit does. The practical rule I keep coming back to: Clear input. Clear output. Clear fallback. That's where the real gains show up — not in adding more tools, but in making the ones you have predictable. Where is this breaking down for you right now? Follow Prompts & Tools for more. --- #AI #AITools #Automation #AIAutomation #Productivity #SoloOperator #MarketingOps #SystemsThinking #OnePersonBusiness #AIStack #SmallTeams #promptsandtools

💭 Before: Manual content creation consuming 15+ hours/week ✨ After: AI automation handling production in 30 min/week Here's what changed when I self-hosted my AI stack: ✅ Script generation runs locally — no API costs ✅ Image creation is automated via ComfyUI pipelines ✅ Multi-format repurposing happens automatically ✅ Everything queues to publishing — I just review The transformation isn't about better AI. It's about owning the infrastructure. → The exact playbook: https://lnkd.in/eXdedWRB Your turn — what's one workflow you'd automate if you had the stack set up tomorrow?