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A practical guide to risk-based code review

Traditional code review no longer keeps pace with how much code teams are shipping. Risk-based code review is the response: instead of giving every pull request the same scrutiny, you route human attention by risk, letting low-risk changes ship with light or automated review and reserving deep human review for the changes that are expensive to get wrong.

What your AI SRE can't see (and what you can do about it)

AI SRE is having a moment. The category pulled in massive funding rounds over the last two years, Gartner published its first market guide, and vendors are promising everything from 90% faster resolution to fully autonomous incident response. If you run an engineering organization, someone has probably pitched you an AI SRE in the last quarter. And let’s be honest: faster triage, less alert fatigue, and automated frontline response are wins for understaffed teams.

When Your SQL Table Outgrows Itself: Lessons from Refactoring at Scale | Harness Blog

At Harness, we build an AI-powered software delivery platform, and test result data is core to how we help engineering teams ship faster. The table that stores it started small: one row per record, all the context right there on the row. Simple, readable, and it worked. Until it didn't. This is the story of how we refactored it, what we learned, and what I'd tell you to watch for in your own systems.

Zero Day to Fix: Why Security Response Speed-Not Discovery-Is Your Real Bottleneck | Harness Blog

Here's the uncomfortable truth about the Mythos era: knowing about a vulnerability and being able to neutralize it are two entirely different problems. AI models like Mythos are finding vulnerabilities 10x faster than humans ever could. Project Glasswing participants discovered over 10,000 high and critical vulnerabilities in their applications. Firefox alone had 271 previously unknown zero-days exposed by Mythos. That's the good news.

Junior developers have one big advantage right now

Everyone keeps saying we don't need junior engineers anymore. This episode makes the case that's exactly wrong. Rob sits down with two CircleCI colleagues at opposite ends of the experience spectrum: Hanabel Mengistu, a new grad less than a year into her first engineering role, and Michael Webster, who has been writing software professionally since 2012. Together they explore what it actually feels like to enter the industry right now, when AI has reshuffled the deck for everyone.

Open Models Are Closing the Gap

The frontier models have led the pack for a while now. It seems like the big players of Anthropic and OpenAI keep leapfrogging each other by a couple points in benchmark scores every other month. But, a trend we are starting to see is that open weight models are improving by leaps and bounds. They don’t hold the lead and probably won’t for a while, but the fact that open models are scaring the leaders is something to think about.

AI isn't a black box. It's Pandora's Box.

When CFOs talk about AI budgets, they tend to describe it the same way: it’s a black box, offering little or no transparency. The bill arrives at the end of the month, it’s bigger than last month, and nobody can really explain why. Meanwhile, engineering keeps asking to raise the token budget. I think that framing undersells what’s actually happening out there. If the black box is the bill, the Pandora’s box is what you opened when you brought AI into the company.

Generative AI ROI: benchmarks and how to prove it

Generative AI ROI measures the financial return on generative AI investments relative to their total cost. Benchmarks diverge sharply: Google Cloud's 2025 study found 74% of enterprises see ROI within the first year, while MIT's NANDA initiative found 95% of pilots deliver no measurable P&L impact. The difference is not the AI. It is whether the organization can actually measure cost and outcome at the use case level.

Shipped: Catch the S3 object-tag charge before it scales with you

There’s an S3 charge that stays invisible in a normal storage cost review. AWS bills S3 object tags per tag, per hour, so the cost scales with how many objects you have, not how much data you store. It gets its own line item, which is easy to miss when you’re scanning storage spend. It can sneak up on you. Tags get added in a dev environment to drive lifecycle rules, where object counts are small and the cost is nothing.