标签：#ci

In this blog post, we will see how I use AI to speed up incident investigation without letting it take over the decisions that need a human. It is 3:00 AM. Your phone starts making that familiar PagerDuty noise. You open the alert with half-open eyes. Error rates are climbing. Slack is already active. The incident commander wants an update. Depending on the severity, your director or CTO may also join the call. Every developer who goes on call will face this situation at some point. I have faced it a few times a year. The first time, panic is normal. You do not know where to start, which dashboard to open, or how to explain the issue while you are still investigating it. Experience teaches you how to stay prepared. AI can reduce some of that early morning panic too. It will not bring the panic factor down to zero, and it should not replace the engineer. But it can remove the first few minutes of searching, tab switching, and collecting context. The goal is simple: move from panic mode to confidence mode faster. I Started With a Prompt in My Notes I started with something small, before custom skills became common in coding harnesses. I kept one incident prompt at the top of my notes folder. I also pinned it in my clipboard manager. When an alert arrived, I filled in the blanks and launched the investigation: I received this alert: <PagerDuty or Slack alert link>. Context: - Service: <service name> - Environment: <environment> - Region: <region> - Error or symptom: <error details> - Investigation window: past <n> hours - Runbook: <runbook link> Start investigating the issue. 1. Analyze the relevant Splunk logs and dashboards. 2. Check recent deployments, configuration changes, and feature-flag changes. 3. Check upstream and downstream dependencies. 4. Check cloud-provider status pages and internal maintenance announcements. 5. Search PagerDuty history and incident records for similar symptoms. 6. Use parallel agents for independent investigation tracks where useful. R

Yao Yue discusses the fundamental limitations of standard line charts for system observability. Drawing from 15 years of operating large-scale systems, she shares how engineering leaders and software architects can transform telemetry data - moving beyond simple time-series defaults - to build visualizations that directly answer critical capacity, latency, and fleet-sizing questions. By Yao Yue

HashiCorp is positioning HCP Terraform as the governance and control plane for a new generation of AI-driven infrastructure, arguing that the rapid adoption of coding agents is shifting the biggest infrastructure challenge from writing configuration to verifying and safely executing it. By Craig Risi

NASA's cost estimate for SR-1 Freedom is $2.1 billion, but that doesn't include the helicopters.

How an alliance of tech startups, MAHA operatives, and actual medical experts made millennial women the new face of hormone therapy.

Dyson is once again expanding its line of personal care products with a device that focuses on your teeth instead of your hair. As the name implies, the $499 CameraJet is the first electric toothbrush to incorporate a camera into the brush head. Available starting today in ceramic blue or ceramic pink color options, it […]

Some weeks the complaints about AI are existential. This one they were arithmetic. Scroll Hacker News over the past week — the forum where developers argue about their tools in unusual detail — and the grievances about AI coding assistants weren’t about the models being dangerous. They were about limits running out, bills that don’t add up, models quietly swapped underneath you, and a desktop app eating memory like a browser. And the recurring move wasn’t outrage. It was switching. Quotes sourced from: Hacker News. Every quote below was located at its comment permalink and reproduced verbatim; each is listed with its username, the platform and the date in the Sources section. As always, we quote experiences, not verdicts — a forum comment is one practitioner’s account, often mid-argument, and we’ve framed them as exactly that. What makes this batch worth reading isn’t volume; it’s that the complaints are specific enough to check, and that they keep ending the same way: with a cancelled subscription. “Consumed within an hour”: the limits gripe The loudest theme by far was paid usage limits that vanish faster than the price suggests. On a thread bluntly titled “Quick impressions: A week of using Codex more than Claude,” a user posting as jmaker , on 22 August, described dropping his subscriptions around exactly this problem: “The Claude Pro is consumed within an hour on a simple task.” That’s one account of one plan, but it wasn’t isolated. In the same discussion, roamerz on 21 August traced the arc from happy customer to defector in four sentences: “Then one day I burned through my limit in about 10 minutes and had to get a project completed. I subscribed to Codex and it has been fantastic… I just dropped my Claude max plan down to the pro and subscribed to the $200 plan on Codex.” The specific number matters less than the shape: a heavy user hits a wall mid-task, and the wall — not the model’s quality — is what sends them to a competitor. It’s the lived version of t

Most teams don't notice their CI/CD pipeline is broken — they just notice that deploys "feel slow" and shrug it off as normal. It isn't. A pipeline that takes 25 minutes to ship a one-line copy change isn't a fact of life, it's a symptom. Here are the mistakes we see most often when reviewing pipelines — roughly in order of how much time they silently burn. 1. Running the full test suite on every single change If a developer fixes a typo in a README and the pipeline still runs the entire integration suite, database migrations, and end-to-end tests, you're paying full price for a change that touched nothing critical. Fix: split your pipeline into stages based on what actually changed. Path-based triggers (only run frontend tests if frontend files changed) and a fast "smoke test" tier before the full suite can cut average pipeline time dramatically without sacrificing safety. 2. No caching between builds Reinstalling every dependency from scratch on every run is one of the most common — and most fixable — sources of wasted time. Package managers, build artifacts, and Docker layers are all cacheable, and most CI platforms support this natively. Fix: cache dependency directories keyed by lockfile hash, and structure Dockerfiles so rarely-changing layers (base image, dependencies) come before frequently-changing ones (application code). 3. Sequential steps that don't need to be sequential Linting, unit tests, and security scans are often run one after another when they have no dependency on each other. That's pure wasted wall-clock time. Fix: parallelize independent jobs. Most CI systems support fan-out/fan-in patterns — run lint, test, and scan simultaneously, then gate the deploy on all three passing. 4. Environments that drift from production A pipeline that passes in staging and fails in production usually means the environments aren't actually equivalent — different env vars, different resource limits, different service versions. Teams respond by adding more manual

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As frustration over AI influencers has been growing, Instagram is limiting the reach of undisclosed AI profiles.

Every developer writes code that "just works" thousands of times without thinking about what happens between hitting save and seeing output on screen. This article pulls back that curtain. We're going to walk through, in real depth, how source code — plain text you typed — becomes a running program, covering lexing, parsing, abstract syntax trees, semantic analysis, and the actual difference between interpretation and compilation (including why that difference is far blurrier than most explanations make it sound). This is one of those topics where understanding the fundamentals pays off across your entire career — it changes how you read error messages, how you reason about performance, and how you evaluate new languages and tools. 1. The Big Picture: Two Broad Strategies At the highest level, there are two strategies for running code: Compilation — translate the entire source program into another form (often machine code, but not always) before running it. The translation and the execution are separate steps. Interpretation — read and execute the source program directly, translating and running it (roughly) simultaneously, statement by statement. In practice, almost no real system is purely one or the other. Python "compiles" your source to bytecode before interpreting the bytecode. Java compiles to bytecode, then a JIT (Just-In-Time) compiler compiles hot paths of that bytecode to native machine code while the program runs . JavaScript engines like V8 do something similar. The clean binary of "compiled vs. interpreted" that gets taught early on is really a spectrum, and most production language runtimes today live somewhere in the middle. But to understand any point on that spectrum, you need to understand the pipeline every one of these systems shares. Let's build it up stage by stage. 2. Stage One: Lexical Analysis (Lexing / Tokenizing) The first thing that has to happen to your source code is the least glamorous: it gets chopped into pieces. Source code, to a c

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on…

Finding reveals an overlooked way that water can damage surfaces.

“One more warning sign here of what we're facing in a warmer world.”

"We would need over half a million 4K TVs to fully display the single Roman image from our largest survey."

The app isn't cracking down on other kinds of AI slop, though.

Instagram is finally taking steps to address the rise of fake AI-influencer accounts that have gotten harder to spot. It's also renaming the "AI creator" label to "AI-generated profile" to make it clear when a profile features an AI-generated person that's not a real human being. "We've heard that people don't like seeing a profile […]

Circleback is also introducing new pricing plans starting from $14 per month

While Trump administration cuts have contributed to the epidemic of foodborne illnesses this summer, there are also deeper issues that make the American food supply chain susceptible to parasites and bacteria outbreaks.

In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models. By Scott Jenson