Automated Feedback Solutions

What if a user could report a bug or feature request and all you'd have to do is say "yes let's fix that", and you'd have a pull request ready for review, or, better: it got auto-deployed? That’s exactly what I’ve built for an app I'm working on (more on *that* soon too). I’ve set up a fully autonomous feedback pipeline that takes a user's bug report or feature request and turns it into a deployed fix, powered by Claude Code. It runs on a Mac Mini, and the workflow is pretty slick: * Human Approval: I review the feedback (the only human gate!) * The Agent: Claude Code picks up the ticket, creates a branch, writes the code and does a pull request. * Automated Review: GitHub Copilot reviews the PR. If it’s clean or Claude can fix the nitpicks, it moves forward. * Auto-Deploy: The pull request is merged and deployed to production automatically if the systeem deems it safe, if not, it's assigned to me for review. When the queue is empty, the agent doesn't just sit idle; it goes into "optimization mode," hunting for server errors, dead code or DRY violations across the repository. As a solo developer on this project, this isn't just about saving time; it's about shortening the loop between "problem identified" and "problem solved" to almost zero. The web is changing, and the tools we use to build it are becoming our collaborators. I’ve written a deep dive into the technical stack and the lessons learned (like why timeouts and signal handlers are your best friends when running agents). How are you using AI agents in your development workflow? Are we ready for autonomous PRs, or do you still want a human looking at every line of code? #AI #Claude #Development

I just built an automation that sends me AI coaching feedback after every sales call. I started doing some AI consulting lately. After every call with prospects, I wanted feedback, hence this workflow was born. Here's what it does: When a call finishes, it pulls the transcript, sends it to Claude, and delivers structured feedback to Slack. Scores the call, highlights what went well, points out what I missed, and suggests specific improvements. A few thing that worked well: I designed the prompt for the AI to give specific feedback on actual moments from the call which has been great: - i.e. "At 11:32 when the prospect mentioned budget concerns, you moved on instead of digging deeper." Sometimes the webhook-based trigger doesn't come through, so it's good to build a backup option. In my case, I built a manual trigger to do polling. The tech stack is pretty simple, but works well. Fathom API, n8n for orchestration, Claude for the analysis, Slack for delivery. If you take a lot of sales calls and want consistent feedback, this is worth building. I made a quick video walking through how it works, so you can do it too.

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

From raw feedback to actionable insights: My AI-powered workflow. I'm running an AI-Native PM training and for each cohort I like to close the feedback loop in a more dynamic, engaging, and collaborative way. Here’s the 3-step, AI-powered, collaborative process I use. Step 1: Capturing the raw feedback with Google Forms. It starts with a simple Google Form to gather candid feedback on the training. Step 2: Transforming raw feedback into an engaging video with Notebook LM. This is where the magic happens. Instead of manually combing through the feedback and creating slides, I took a different approach. I uploaded all the raw, anonymized feedback directly into Notebook LM and then prompted it to act as a product manager synthesizing user research, asking it to identify the core positive themes, the most critical areas for improvement, and to structure these findings into a concise video. Step 3: Uploading the video to Loom for sharing and collaboration. Numbers are great, but a video is more personal and engaging. This final step is key because Loom transforms a one-way summary into a two-way conversation. By sharing a Loom link with my stakeholders, they can: • Watch the summary on their own time. • Leave comments and reactions tied to specific moments in the video. • Engage in threaded discussions right on the video timeline. This workflow didn't just save me time but created a richer, more collaborative way to understand and act on valuable feedback. It’s a simple and fun example of how we can use AI tools not just to build products, but to improve how we communicate and share learnings.

🚀 User reviews are the compass for how well our product truly performs. But getting those reviews? Yep… usually a painful process. Either you dig through your own database, or you integrate multiple sources just to collect scattered insights. And if you want to monitor competitor products too? Even more painful. Right? 😅 So I built a smart automated workflow to solve this once and for all. I’m using Google Sheets as a central URL database, making it super easy for other teams to add or update product URLs without touching n8n. Then comes the fun part: Using Decodo, the workflow scrapes the reviews and structures them cleanly. This one is breakthrough brooooo. After that, AI sentiment analysis kicks in, giving me high-level insights and summaries in seconds. ✨ What this solves: - No more manual digging through reviews - Zero engineering overhead for data updates - Shared access for cross-team collaboration - Fast understanding of customer sentiment I’ve published this workflow so you can try it too. If you’ve already used it, I’d love to hear: How did this automation improve your productivity? https://lnkd.in/g2nhkiV9 Let’s make review monitoring smarter, not harder. 💡

Imagine this. A sales rep has just finished drafting an important proposal. Before sending it, they submit it to an AI coach that immediately provides specific feedback on structure, value proposition, and alignment with the prospect's known priorities. Within minutes, they've improved their work and boosted their chances of success. This is happening right now in forward-thinking organizations. Timely, specific feedback is perhaps the most powerful driver of performance improvement. Yet traditional L&D approaches make it nearly impossible to deliver at scale: ▪️ Managers are too busy to provide consistent, quality feedback ▪️ L&D teams can't possibly coach thousands of employees individually ▪️ Formal feedback cycles happen too infrequently to drive real-time improvement ▪️ External coaches are prohibitively expensive for most employees This is where AI is creating a genuine breakthrough in how we develop talent. Here's how I've seen companies implement AI-powered feedback systems.

Oh noes. I just keep making work for myself. 😅 I performed an analysis of my PR review feedback (over 100 comments across 7 PRs) for the recent Agent Skills project I worked on. Now I’ve created 12! new issues on the ‘skill-validator’ repo for checks to automate or semi-automate detection of specific patterns of issues I commented on repeatedly. Six are structural checks: - Platform-specific tool names in the skill (not cross-platform portable) - Shell-specific commands without multi-shell handling - Flag/version/preview/beta features without stability notes - Near-duplicate text blocks within a skill - More specific token budget analysis within a skill to help authors be more efficient - Requiring eval *results* when an eval testing directory is present And six are LLM-as-judge scoring candidates: - Decision tree completeness scoring - Vague instruction detection - Reference file routing quality - Flagging external links and abstract verbs without actionable next steps - Cross-file consistency checking - Detecting conditional content in SKILL.md that belongs in reference files Once I get these checks implemented, passing the ‘skill-validator’ quality threshold will get harder. And I bet some people will be unhappy with that because skills that are passing in CI will start failing. But these all relate to real instructional quality and other quality issues that will negatively impact agent outputs. In for a penny, in for a pound! If I can automate some of this stuff successfully, it will reduce the human review burden *and* improve skill efficacy for teams that don’t have a “me” in the loop 😉