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Most "Debezium vs managed tool" articles get the question wrong. They frame it as a product bake-off, feature grid included, and declare a winner. But if you've actually run change data capture in production, you know the real decision isn't which tool captures a transaction log better. They mostly read the same logs the same way. The real decision is who operates everything that sits around the capture, and whether that work is a good use of your team's time. That's a build-vs-buy question, not a product question. This post is a framework for answering it for your own situation. First, let's kill an outdated assumption A lot of Debezium criticism floating around is two or three years stale, and if you repeat it in 2026 you'll get corrected fast. So let's set the record straight before we compare anything. Debezium is no longer just “the thing you run with Kafka Connect.” In recent Debezium 3.x releases, the project has become much more flexible than the old tutorials suggest. Today, you have several deployment options: Kafka Connect , the classic setup, which gives you the Kafka ecosystem, distributed fault tolerance, durable schema history, and access to Kafka Connect sink connectors. Debezium Server , a standalone application that streams changes to systems like Amazon Kinesis, Google Cloud Pub/Sub, Apache Pulsar, Redis Streams, or NATS JetStream without requiring Kafka. Debezium Management Platform , which builds on Debezium Server and the Debezium Operator to provide a higher-level way to configure and manage CDC pipelines in Kubernetes-style environments. Embedded usage , where you run Debezium Engine inside your own application. Recent Debezium releases also added framework support such as the Quarkus extension. A few more things are worth knowing so the comparison is fair: Kafka 4.x runs in KRaft mode, and ZooKeeper mode has been removed. “You need to babysit ZooKeeper” is no longer true for a modern Kafka deployment. Debezium's default remains at-least-once

I hate presenting. Not the prep, not the content, the actual moment of unmuting, sharing my screen, and narrating 20 slides to a wall of black camera squares, having no idea if anyone's actually listening or just quietly making lunch. So a couple weekends ago I went down a rabbit hole and built something to get me out of that. It's called Meeting Presenter. It's an AI skill that joins the call and presents the deck for you. You just... sit there. Steer it if you feel like it. Or don't. What it actually does You hand it a deck, it joins the meeting, shares its screen, and talks through the slides on its own. Not in a flat text-to-speech way either, it walks through the content more like a person explaining it than a bot reading bullet points. The part that actually got me hooked, though, wasn't the presenting, it's that you don't even need a finished deck to use it. If you've got a PowerPoint or PDF already, it'll just present that. If you've only got some rough notes, it'll turn those into slides first. And if you've got nothing but a vague idea, you can hand it a single sentence and it'll build the deck from scratch before presenting it. Which means the laziest possible version of this is: think up a topic five minutes before standup, type it in, and let it build and present the thing while you drink your coffee. I'm not proud of how often I've already done this. Setting it up Took me less than 10 minutes, most of which was making coffee while it installed. Grab a free API key from agentcall.dev - no lengthy signup, just a few seconds. Install it. Two options depending on how hands-on you want to be: Recommended: paste the GitHub repo link to your coding agent and tell it to install it. It clones and sets everything up for you. Or clone it manually and run it with your meeting link and deck. No config files to hand-edit. What it's actually like on a call It joins like any other participant, and it asks before it starts presenting rather than just barging in mid-mee

According to the PLG AI SaaS Benchmarks 2026 report , SaaS companies lose an average of 5–7% of revenue every month to churn , a rate that quietly compounds into nearly half of annual revenue erosion if left unchecked. Most teams don’t realize churn is already happening long before the cancellation click. It starts as subtle behavioral drift, lower engagement, feature abandonment, and delayed logins and only shows up in dashboards when it’s too late to act. That’s where AI changes the equation. Instead of reacting to churn, modern SaaS teams now try to intercept it through real-time behavioral detection, automated interventions, and continuous experimentation inside the product. Here are the best AI tools for SaaS customer retention (also called churn prevention tools) in 2026, compared by category, pricing, and key limitation. Why Traditional Churn Prevention Fails Most churn prevention strategies fail for three predictable reasons. First, they rely on lagging indicators. By the time dashboards show declining engagement, the user has already mentally churned. The decision didn’t happen when they clicked cancel; it happened days or weeks earlier during silent disengagement. Second, interventions are batch-based. Many lifecycle tools still operate on schedules like “send email after 7 days of inactivity.” But churn signals don’t wait for weekly jobs. The best intervention window is the moment behavior changes. Third, messaging is too generic. A user abandoning reporting features needs a completely different response than one abandoning collaboration workflows. Yet most tools treat both cases the same. The result is simple: teams react too late, too slowly, and too generically. Churn Signal Framework (What Predicts Churn) Churn doesn’t appear randomly; it follows patterns that can be detected in product data before cancellation ever happens. Churn Signal What It Looks Like Intervention Window Best Response Login drop Daily user becomes inactive within 7–14 days 1–7 da

Verity Harding tells WIRED that the US government’s nationalistic attitude toward AI is evidence that a worst-case scenario is taking shape.

Photorealistic human video, powered by compact AI Discussion | Link

A key delay from a state agency means robotaxi rides in the company’s new Ojai vehicle might be free for a few more months.

I have a simple rule when I work with a team: if a vulnerability makes it into main, the process...

ZML, a hot French AI startup endorsed by Turing Award winner Yann LeCun, has now released ZML/LLMD, software that could make running AI less costly.

"HalluSquatting" weaponizes LLMs' inability to say "I don't know."

id CTI-2026-0708-KAKAO title "KakaoTalk Source Code for Sale" — Anatomy of a Dark-Web Claim, the Real Target, and the Blast Radius If It's True subtitle Not a data breach but a leak of intellectual property and infrastructure. Title says KakaoTalk, repo names say ZigZag — an extortion pitch built on a misattributed target author Dennis Kim / HoKwang Kim email gameworker@gmail.com github gameworkerkim date 2026-07-08 classification TLP:GREEN severity HIGH (conditional — CRITICAL if verified) lang en tags Source-Code-Sale · Dark-Web · Extortion · Target-Misattribution · Kakao-Style · ZigZag · Supply-Chain · LLM-Weaponization · DPRK-APT · Secret-Sprawl threat_actors ExtortionLord (unidentified dark-web forum seller) frameworks MITRE ATT&CK · NIST SP 800-207 (Zero Trust) · SLSA / SSDF (SP 800-218) · PIPA Art. 34 (Korea) license CC BY-NC-SA 4.0 "KakaoTalk Source Code for Sale" — Anatomy of a Dark-Web Claim, the Real Target, and the Blast Radius If It's True Report ID CTI-2026-0708-KAKAO · Published 2026-07-08 · Classification TLP:GREEN · Severity 🔴 HIGH (conditional) Author Dennis Kim / HoKwang Kim · gameworker@gmail.com · @gameworkerkim 🌐 한국어 · English (this document) Not a data breach but a leak of intellectual property and infrastructure. The title says KakaoTalk, the repository names say ZigZag — an extortion pitch built on a misattributed target. Table of Contents Executive Summary (TL;DR) Framing — Not "What's Being Sold" but "What's at Stake" The Claim — Anatomy of the Listing The Real Target — Title Says KakaoTalk, Fingerprints Say ZigZag Credibility Assessment — Why We Can't Trust It Yet Blast Radius If True — Why It Exceeds TVING and CU Three-Breach Severity Comparison — Data Leak vs. Code/Infra Leak Korean Context — The Next Stage of the B2C Breach Chain Detection, Mitigation, and Response Recommendations Conclusion References 1. Executive Summary (TL;DR) In early July 2026, a threat actor calling itself "ExtortionLord" posted a dark-web forum listing offering

Software architecture debates have a problem that most other engineering disciplines don't: the alternative was never built. When a bridge fails, the failure is physical, attributable, and measurable against every other bridge that didn't. The engineering decisions that caused it can be isolated, traced, and corrected — not just in theory, but in the next bridge, because the material itself produces feedback that no amount of professional opinion can override. Steel deflects. Concrete cracks. Physics doesn't care what the architect believed. Software produces no equivalent feedback. A system built around the wrong abstractions compiles, runs, ships, and passes its tests just as readily as one built around the right ones. A bug introduced by a misaligned domain model looks identical, from the outside, to a bug introduced by a typo. A feature that took three times longer than it should have, because the structure made it harder than the business logic warranted, produces no artifact that distinguishes it from a feature that was simply difficult. The cost is real. The cause is invisible. This is the unfalsifiability problem, and it runs deeper than "we can't measure everything." It means that when a system becomes expensive to change, the diagnosis almost always lands on the wrong variable. The domain is complex. The requirements changed. The previous team was careless. Almost never: the structure was wrong, and the structure was wrong because nobody ever built the other version of it to compare against. That version doesn't exist, it never will, and every architectural argument in the industry is conducted in its absence. This would be a purely philosophical problem if there were nothing to do about it. There is something to do about it — but it requires accepting that the standard metric for software quality, whether it works, is measuring the wrong thing entirely. The Metric That Hides the Problem The natural substitute for "is this good engineering" is "does it wor