Feedback Loop Customization Options

♾️ Claude Code just got a quiet but important upgrade. /loop and /schedule turn it from a reactive tool into something that can run continuously, even when you are not there. Here is how to use that properly with RuFlo. Think of /loop as your sensing layer. It runs inside an active session and keeps checking reality. Use it to watch tests, monitor deploys, track swarm health, or detect performance drift. It is fast, temporary, and focused on what is happening now. Think of /schedule as your continuity layer. It runs in the background, persists across sessions, and builds knowledge over time. Use it for nightly audits, daily summaries, weekly architecture reviews, and long running analysis. On their own, these are just timers. With RuFlo, they become a system. RuFlo acts as the control plane. When a /loop or /schedule trigger fires, RuFlo decides what happens next. It selects agents, retrieves relevant patterns from memory, applies guardrails, executes the task, and stores the outcome. Over time, this builds a feedback loop that starts to reflect how you think and work. To make this a true second brain, use three methods. Bounded reasoning. Every task has a clear goal, limits, and expected output. This prevents runaway loops and keeps results usable. Memory accumulation. Store summaries, decisions, and patterns after each run. This is how tone, preferences, and judgment get learned. Guardrails. Use hooks to enforce security, formatting, and safe execution. Now the edge. A /loop that detects subtle performance drift before it becomes a problem. A /schedule that rewrites your docs daily in your voice. A system that critiques your architecture decisions and surfaces blind spots. A continuous agent that tracks signals and adjusts strategy suggestions. It starts as automation. It becomes a system that thinks alongside you.

🧠 New Essay + Feaures: How To Use AI To Build and Scale Product Intuition “Your gut is the world’s most sophisticated ML model ever created.” - David Lieb (GP at YC) That is an awesome quote. Beneath the clever analogy is a very important insight about something that is becoming very important for product managers and teams… Full details -> https://lnkd.in/gWHkDn3T Product Intuition Many of the skills that made product managers or product teams gain an edge in the past are becoming commoditized. In the AI era, you can’t follow process and best practices and expect to win. You have to take bets based on strong judgment. Enough of those bets have to be right in order to win. 1. Building product intuition is like building a machine learning model. You need: High quality training data - Direct, unfiltered, raw customer feedback connected (the inputs) to product decisions and outcomes (the outputs). 2. Lots of Cycles - Fast feedback loops between the inputs and outputs to build pattern recognition and intuition. Traditional user research doesn’t serve this purpose. It’s too high friction and prohibitively expensive to scale. More quant metrics tell you more about the what, but not the why. Sachin Rekhi introduced me a while back (and many others via his Mastering Product Management course) to a great tool - Feedback Rivers. (link in comments) Feedback Rivers establish a continuous stream of customer feedback to the product team in Slack/Teams. They were great as a way to help build intuition but had a few issues as we grew: 1. They were a firehose of info. 2. Weren’t personalized to team/individual. 3. Fuzzy connection with product outcomes 4. Reactive vs proactive We changed all that with some recent releases on Reforge Insights. The first step was enabling easy aggregation, analysis, and action on feedback. But now we’ve added: ⚡️ Ability to personalize feedback rivers based on team, initiative, goal, etc. 💬→📊 Create reports/dashboards directly from AI chat. 🔁 Integrations w/ JIRA, Linear, etc to close the loop on product outcomes. As a result, you can set up AI-powered feedback rivers that are personalized and directly connected with product outcomes. Full product details -> https://lnkd.in/gXu4ek9D Even better, the team can now go from: Personalized feedback rivers → explore with chat → dashboard tied to product outcomes in minutes rather than days. Links to some demo videos and more thoughts on product intuition below 👇

I used to tweak landing pages, ads, and CTAs endlessly… Until I realized the problem wasn’t the funnel. It was the lack of signal. Most founders think they need a new campaign. But what they actually need— → is to understand why the last one didn’t work. Here’s what changed the game for us: → We stopped guessing. Started listening. The system: 1. Use Typeform to collect post-interaction feedback 2. Send responses to GPT via OpenAI API 3. Analyze for friction points, objections, and drop-off cues 4. Rewrite copy & UX using actual user language No more “conversion best practices.” Just actual voice-of-customer data on repeat. 💡 When your feedback loop is tight, your funnel self-optimizes. Faster learning cycles → Better messaging → Better performance You don’t need 10 more hooks. You need the right signal to sharpen the one that works. Fix the loop, not just the output. What’s one overlooked insight you found in your customer feedback? #VoiceOfCustomer #FunnelOptimization #GrowthMarketing #ConversionRateOptimization #MarketingStrategy

When you create question slides in your authoring tool of choice (Storyline, Captivate, etc.), do you use the default feedback layers (Correct / Incorrect / Try Again)? By default in Storyline, feedback layers usually have: - A gray or white box in the center - A “Correct” or “Incorrect” label - Some explanation text - A Continue or Try Again button Here’s why I don’t recommend using those default designs: ❗ The learner can’t see how they answered compared to the correct answer. ❗ The default design (in my opinion) is… not great. ❗ Learners may skip the carefully written explanation in the box. On top of that, the white box is often too small, the text shrinks to “fit,” and things can get cramped—especially for drag-and-drop feedback. Instead, here’s one simple approach I like (and there are many others): 1️⃣ Go to Feedback Master and clear one layout so it’s completely blank. 2️⃣ Remove any leftover default items from the feedback layers on your question slide. 3️⃣ Create a clean white bar along the bottom of the slide. 4️⃣ In that bar, add a label (e.g., ✅ Correct / ❌ Incorrect) on the left and a Continue button on the right. Use the middle area for a short explanation if needed. 5️⃣ Apply this layout to all feedback layers (Correct / Incorrect / Try Again). Why this helps: The learner can still see the entire original question slide: the question, their selected answer, and the correct answer. 🌞 It solves the “What did I even click?” problem. 🌞 It reduces dev time and review cycles because you’re not fighting the default feedback boxes and text behavior. You can also move that bar somewhere else on the slide so it doesn’t cover important content (and avoid the very bottom if you’re using closed captions). 🔍 What about you? Do you use the default feedback designs or customize them? Any favorite patterns for feedback layers? #eLearning #InstructionalDesign #SharingKnowledge #eLearningbyDana

Overlapping text. Overflow. Too dense. No variety. Every AI slide deck I've made in our TechWolf style has had the same issues. So I stopped fixing them manually and built a feedback loop where the AI rates itself. The setup is pretty simple: a critic agent takes screenshots of generated slides and scores them on a rubric covering all those dimensions. The main agent reads that feedback, adjusts its approach, and the critic re-evaluates. If the score improves, the change sticks. If it drops, it rolls back. First pass: 7 out of 10. Sounds decent until you see the worst slide at 5.1/10. After 14 iterations with no human input: 9.2. I now have TechWolf slides that need minor tweaks, not rewrites. Andrej Karpathy calls this pattern autoresearch. Instead of you sitting in the feedback loop giving notes on every iteration, you give the model a way to evaluate itself. A numerical score. A rubric. Something it can optimize against without waiting for your judgment. The results compound in a way manual iteration can't. Each round fixes its own issues. And because the critic is isolated, it stays honest. No drift, no fatigue, no gradually lowering the bar. The best feedback loop these days is one you're not in.