惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
The Last Watchdog
The Last Watchdog
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
T
Troy Hunt's Blog
L
LINUX DO - 最新话题
C
Check Point Blog
T
Threat Research - Cisco Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
罗磊的独立博客
V
Vulnerabilities – Threatpost
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
J
Java Code Geeks
Apple Machine Learning Research
Apple Machine Learning Research
大猫的无限游戏
大猫的无限游戏
S
Security @ Cisco Blogs
IT之家
IT之家
T
The Exploit Database - CXSecurity.com
The GitHub Blog
The GitHub Blog
D
Docker
Engineering at Meta
Engineering at Meta
AWS News Blog
AWS News Blog
S
Security Affairs
U
Unit 42
P
Palo Alto Networks Blog
V
Visual Studio Blog
Y
Y Combinator Blog
D
DataBreaches.Net
Forbes - Security
Forbes - Security
阮一峰的网络日志
阮一峰的网络日志
美团技术团队
Security Latest
Security Latest
aimingoo的专栏
aimingoo的专栏
Simon Willison's Weblog
Simon Willison's Weblog
A
Arctic Wolf
博客园_首页
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
H
Hacker News: Front Page
博客园 - 司徒正美
博客园 - Franky
宝玉的分享
宝玉的分享
TaoSecurity Blog
TaoSecurity Blog
Latest news
Latest news
Scott Helme
Scott Helme
MongoDB | Blog
MongoDB | Blog
量子位
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
C
Cisco Blogs
P
Privacy International News Feed
Application and Cybersecurity Blog
Application and Cybersecurity Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
I gave Claude six months of our retros. It found three things I'd missed.
Matt · 2026-05-18 · via DEV Community

There's a thing PMs do that nobody puts on a job description. Every couple of weeks, after a retro, you flag three or four items in your head as things to watch. Then a sprint goes by. Then another. Three months later you're in a leadership review, somebody asks "is X getting better or worse?", and you answer from gut feeling because there's no time to read 12 retros before the meeting.

The retros are sitting there. The data is fine. The labour of reading them isn't.

I work on Kollabe, so take this with the appropriate grain of salt. The patterns generalise; if your retro tool exposes an API or an MCP server, the workflow below works against whatever you've got. I'll keep the prompts vendor-shaped so they're easy to swap.

What I changed

For the last two months I've been driving most of my retro-adjacent work through Claude with three MCP servers connected: Kollabe (for the retro and action-item surface), Atlassian (for Jira), and GitHub. The Kollabe MCP exposes about 50 tools that map 1:1 to the public REST API, so anything I can do in the UI, Claude can do as me. That includes reading every retro and action item in the spaces I have access to.

The Monday-morning workflow is one prompt. It does four things in order, then stops and waits for me.

  1. Reads the last 26 weeks of retros across the spaces I own.
  2. Pulls every open action item, including ones quietly older than the people who created them.
  3. Reads the last sprint's standups for additional signal.
  4. Writes a structured brief: what's getting better, what's getting worse, what action items are stale, and a one-line "here's what I'd ask the team about this week."

Then it shuts up and lets me think.

Monday morning Grief

The action item half: the part that quietly fails

Here's the embarrassing thing nobody says out loud about retros: most action items don't close because they fall out of working memory. The retro happens, somebody writes "investigate flaky CI", it gets assigned, two sprints pass, the person who owned it changed teams, and the item is technically open in the tool forever. Read a clean dashboard of "open action items" on most teams and you'll find the median age is something like 47 days.

The fix isn't a better tracker. The fix is somebody reading every open action item once a week and asking three questions: is it still relevant, did it actually get done quietly, who really owns it now.

I have Claude do that. The prompt is short:

For each space I own, use `action_item_list` (status = PENDING).
For every item older than 21 days:
1. Use `search` (Kollabe MCP) to look for activity on the item's keywords across
   retros, standups, and Jira via the Atlassian MCP — last 30 days.
2. If the item appears resolved or superseded, propose marking it COMPLETED with
   a one-line note explaining what closed it.
3. If the assignee changed spaces or hasn't been active in standups for >14 days,
   flag it for reassignment.
4. Otherwise, propose a one-sentence nudge comment from me, via
   `action_item_create_comment`.
Show me a table. Wait for me to approve each row before any writes.

Enter fullscreen mode Exit fullscreen mode

It produces a table. Most weeks, I approve about 80% of it and reject the rest. The rejected ones are the most interesting. They're items the AI thought were resolved but I know aren't, which usually means we have an undocumented decision that should become a docced one.

Two months in, the median action-item age in our spaces is 14 days, not 47. Nobody had to nag anyone. Nobody had to build a dashboard.

The six-month delta: the part that's new

This is the bit I underestimated when I wired it up. The Kollabe MCP has a search tool that runs semantic search across every retro, standup, action item, and round in your spaces, backed by pgvector embeddings. Faster-than-scrolling isn't really the point. The point is the questions you'd never have asked because the labour wasn't worth it.

What I ask, every Monday, in the same prompt:

Using the Kollabe MCP `search` and `retro_list` + `retro_get`, scan the last
26 weeks of retros in my spaces. Produce:

1. The five themes that appeared in the first 3 months but have NOT appeared
   in the last 6 weeks. (Things that quietly got fixed.)
2. The five themes that have appeared in 3+ retros over the last 6 weeks
   and weren't a problem 6 months ago. (Things that quietly got worse.)
3. Any theme that appeared, was resolved, and has come back.
4. Two questions worth asking the team this week given (1) - (3).

Enter fullscreen mode Exit fullscreen mode

The first time I ran it, the AI surfaced three things that genuinely surprised me. One was a fix I'd forgotten the team had quietly made. The deploy flow had been a chronic complaint for the first quarter and had simply stopped being a complaint, which meant somebody's January work had paid off and I hadn't thanked them for it. One was a slow drift in code review wait times, too quiet to feel like a problem in any single retro, obviously a problem when you saw three retros in a row mention it without escalation. The third was a recurring frustration about meeting overload that had been resolved once and was creeping back.

It was uncomfortable. It was also the most useful single data point I'd gotten about my team in a year. I sent the dev who fixed deploys a public kudos that afternoon.

The 26 week scan

Why MCP and not "I'll just build a script"

I work on this stuff for a living and I have, in fact, built the script version. It works. It is also rigid: every time I want to ask a slightly different question, I edit code. The MCP version lets me write the question in English on Monday morning and have it run against the same API surface my Python script would have used.

The thing that makes this real, not a parlour trick, is that the public API and the MCP server are the same surface. Every MCP tool is registered against a /api/v1/* handler, with the same Zod schemas and the same access checks. So when I prototype something in chat that turns out to be useful weekly, I can lift the same calls into a scheduled Worker, run it Friday at 4pm, and email myself the brief. The prompt and the script share an interface.

That matters for technical PMs specifically. You're going to want to graduate the things that worked into something that runs without you. With an MCP that mirrors a real REST API, you can — without translating between two surfaces or waiting for a vendor to ship Zapier support.

The honest caveats

A few:

  • It works to the depth of your retro content. If your team writes "deploy bad" as a retro item, the search and summary surfaces are not going to mine wisdom out of that. The first thing it convinced me of was that we needed better retro write-ups. We added a templated "what happened, who was affected, what changed" body to two columns. The signal jumped a month later.
  • Semantic clusters are smart, not psychic. The AI will sometimes group two superficially-similar items that are actually about different things. A CI complaint and a release-process complaint, say. I read the cluster headers, not just the takeaway.
  • The action-item triage prompt makes writes. Always. I've never let it write without a per-row approval step. The day I trust it to update without me is the day it'll mark an unresolved compliance item as fixed because somebody mentioned the word "fixed" in a tangent. The prompt stays in the "propose then wait" shape.

A rule of thumb you can steal

If you're a PM who feels like the data exists but the labour doesn't, here's the question to ask before you build anything:

What's the question I'd ask my team if I'd just spent two hours reading the last six months of retros?

Write that question down. That's your weekly prompt. If your retro tool has an MCP server or a public API with semantic search, you can have an AI do the reading and bring you back the question's answer with citations. If it doesn't, that's a procurement question. The next generation of agile tooling is going to assume an AI is reading the history, not just a human.

I run my version on Monday at 9am. Whatever your version is, time saved isn't really the win. The win is questions asked that weren't being asked.

rule of thumb

If you want to try this against Kollabe's MCP, the connection takes about a minute and is included on Premium plus all trials. The MCP page has the OAuth flow and a one-click setup. If you want the script version, every tool I mentioned is also a documented REST endpoint, same auth, same shape.