northstar702

Feels like AI on autopilot running OSS projects is the dream everyone is trying to get to. Tokens will never be free. Anthropic needs to go IPO. You make an interesting point about personal spend, but bad PRs are unlikely to be on purpose. But the discipline to review/clean up the slop might not be there?

there is an ongoing thread here on a similar topic from an AWS expert (former AWS CTO)

"Apparently, AI doesn't lead to positive results in all software development teams. Customers are asking me whether they should slow down the adoption of AI in their teams.

How do you respond to that? Yes? No? "

https://www.linkedin.com/feed/update/urn:li:activity:7510679...

Feels like part of it is a learning habit, getting proficient in use of the tools (AI agents) themselves, and better workflow around it, but it remains AI is not perfect yet?

Looking forward to that hiring. So far, just layoffs. That said, there reports that consulting firms are hiring to fuel the demand around AI adoption itself. LLMs are a breakthrough like machines were. We will create new business models around them, and perhaps drives more humans. Wrt software, LLMs can make learning very easy. But with all the AI generated code, who is the "architect"?

Fair. I suppose, the question behind my question is -- what else do we need. Thanks.

Definitely not unique. Thats said, given how "easy" it is to scale LLM output, compared to human output, this pattern could be messier in the LLM era? The whole tokenmaxxing motion assumed more output equals more outcomes. What do you think?

On September 8, 2025, NPM reported that several libraries were compromised in a user-side man-in-the-middle attack. This research dives into the details about how the attack works.

This is interesting. Do you find this simpler?

Curious which version you tried and what k8s environment did you explore?

Are you referring to redpanda.com or something else?

Data streaming emerged as a new software category. It complements traditional middleware, data warehouse, and data lakes. Apache Kafka became the de facto standard. New players enter the market because of Kafka’s success. One of those is Redpanda, a lightweight Kafka-compatible C++ implementation. This blog post explores the differences between Apache Kafka and Redpanda, when to choose which framework, and how the Kafka ecosystem, licensing, and community adoption impact a proper evaluation.

is your thesis available to read? Jepsen and these concepts for databases are certainly very different from those for kafa, as we heard from Kyle during the Jepsen testing for Redpanda. Someone needs to write about those perceptions!