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Threat Detection in Kubernetes with Falco

Threat Detection in Kubernetes with Falco

Finding out there is "suspicious activity" in your infrastructure is enough to make any DevOps engineer's heart rate spike. If you’re running containerized workloads, you need a way to see exactly what’s happening inside those isolated environments in real-time. Falco, the open-source standard for cloud-native runtime security. In this guide, we'll walk through a hands-on scenario: investigating a suspicious Nginx container by detecting unauthorized spawning processes. A team member reports odd behavior in a specific container. Our goal is to use Falco to monitor the execve system call—which is triggered whenever a new process is started—and log those events to a report for analysis. Falco uses a flexible YAML-based syntax for defining security rules. We need to create a rule specifically targeting our Nginx container. Create a new rules file: vi nginx-rules.yml Paste the following configuration: - rule: spawned_process_in_nginx_container desc: A process was spawned in the Nginx container. condition: container.name = "nginx" and evt.type = execve output: "%evt.time,%proc.name,%user.uid,%container.id,%container.name,%container.image" priority: WARNING Save and exit (Esc, :wq, Enter). Breakdown of the Rule: Condition: We are filtering for events where the container name is exactly "nginx" and the event type is execve (process execution). Output: This defines the format of our log, capturing the timestamp, process name, user ID, and container metadata. Why is this rule important? If a hacker successfully exploits a vulnerability in your Nginx web server, the first thing they will often try to do is open a reverse shell (bash or sh) or run malicious scripts to look around. Because execve catches any new process being spawned, this rule will instantly catch an attacker attempting to run commands inside your container. Now, we run Falco using our custom rule. We’ll use the -M flag to run the scan for a set duration (45 seconds) and redirect the output to a log file for fu

dev.to

How Developers Are Actually Using AI at Work in 2026: A Brutally Honest Analysis of 10,000+ PRs, Real Productivity Data, and What Nobody's Talking About

Everyone claims AI makes them 10x more productive. I measured it. The results are more nuanced — and more interesting — than anyone admits. There's a lie circulating through tech Twitter, LinkedIn, and every developer meetup in 2026. It goes like this: "AI makes me 10x more productive." You've heard it. You've probably said it. I certainly did — until I actually measured it. Over the past 6 months, I've been running a controlled experiment. I deployed AI agents across my entire development workflow — code generation, code review, bug bounty hunting, documentation, testing, and deployment. I tracked every metric I could: lines of code, PR merge rates, time-to-merge, bug introduction rates, and actual revenue generated. The results? AI didn't make me 10x more productive. It made me differently productive. And that distinction matters more than any headline number. Let me show you exactly what I found — with real data, real code, and real numbers that nobody else is sharing. I ran three parallel workflows from January to June 2026: Manual workflow — I wrote code myself, reviewed it myself, submitted PRs manually AI-assisted workflow — I used GitHub Copilot + Cursor for generation, but I reviewed everything AI-agent workflow — I deployed autonomous agents (Claude, Gemini, and custom models) to find issues, write fixes, submit PRs, and respond to reviews Each workflow handled similar tasks: bug fixes, feature additions, documentation updates, and security patches across 50+ open-source repositories. Here's the raw data: Metric Manual AI-Assisted AI-Agent PRs submitted 47 89 312 PRs merged 38 (81%) 61 (69%) 47 (15%) Avg time to submit 4.2 hours 1.8 hours 12 minutes Avg time to merge 3.1 days 4.7 days 8.2 days Bugs introduced 2 7 23 Lines of code (avg/PR) 142 89 340 Review comments (avg/PR) 1.3 2.8 7.4 Read that table carefully. The AI-agent workflow submitted 6.6x more PRs than manual — but merged only 23% more. The merge rate dropped from 81% to 15%. The bug introduction

dev.to

The Open Source Illusion: Why "Free" AI Models Are Getting Expensive

The Open Source Illusion: Why "Free" AI Models Are Getting Expensive Everyone's watching Chinese open-source models. But the subscription costs are catching up to Western counterparts. GLM 5.1 — arguably the best open-source model available — just doubled subscription prices. Maximum tier now costs $160/month. For comparison: Claude Pro: ~$20/month ChatGPT Plus: ~$20/month Mid-tier API access: variable, but often lower The narrative around open-source models has been "free alternatives to expensive closed models." But: Inference costs scale with usage. Running GLM-5 at scale requires serious hardware or API credits. Chinese providers are monetizing aggressively. The open weights are free; reliable hosting and premium features are not. Local deployment isn't free either. A 70B+ parameter model needs 2-4x A100s or equivalent. That's $5-15/hour on cloud GPU instances. Model Access Cost Inference Cost (1M tokens) GPT-5.2 API $0 $10-30 Claude API $0 $3-15 GLM-5 (Z.ai) $0-160/mo Included in subscription Local 70B $0 $5-15/hr hardware What you're paying for with premium tiers: Consistent availability (local GPUs can be flaky) No setup maintenance (dependencies, updates, drivers) Multi-modal features (not always available in open weights) Context window guarantees (local setup may crash on 200K tokens) Hybrid strategy: Experiment locally — understand model behavior, validate approaches Production APIs — reliability and scale matter more than marginal cost savings Monitor burn — token consumption grows non-linearly with adoption More AI economics, model comparisons, and production insights from inside a bank — follow my Telegram channel: 🚀 https://t.me/ai_tablet (Russian, technical)

dev.to

Anthropic's run-rate revenue hits $47 billion

The most interesting thing about Anthropic's $65B Series H announcement is this line (emphasis mine): Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month. Anthropic have made a bit of a habit of sharing their "run-rate revenue" in this kind of announcement, which is an annualized projection of their current revenue - typically calculated by taking the most recent month and multiplying by 12. Earlier this year: Apr 6, 2026 in Anthropic expands partnership with Google and Broadcom : "Our run-rate revenue has now surpassed $30 billion —up from approximately $9 billion at the end of 2025." Feb 12, 2026 in Anthropic raises $30 billion in Series G : "Today, our run-rate revenue is $14 billion , with this figure growing over 10x annually in each of those past three years." I had Claude Opus 4.8 make me this chart using Matplotlib (Claude: "a data line chart is more straightforward matplotlib work—not really a design piece"): Back in April Axios CEO Jim VandeHei wrote that he could not find "any company — in any industry, in any era — that has scaled organic revenue this quickly at this level as Anthropic" - and that was when they were at a paltry $30 billion. (Also in Axios today is an anonymously sourced note that "An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees" - times that by 12 and you get an extra $6 billion in annualized run-rate!) Ed Zitron was extremely skeptical of that $30 billion number - I wonder if his skepticism will update for the new $47 billion figure. I've seen a few people dismiss this as untrustworthy, because the numbers come from Anthropic. That doesn't hold up: these numbers were included in announcements of their fundraises, and lying to investors who just put in $65 billion would be securities fraud. They're even le

simonwillison.net

Claude Opus 4.8: "a modest but tangible improvement"

Anthropic shipped Claude Opus 4.8 today. My favourite thing about it is this note in the release announcement: Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost. It's so refreshing to see an AI lab honestly describe a release as a minor incremental improvement over the previous model! Honesty seems to be a theme. Here's my other favorite note from that announcement: One of the most prominent improvements in Opus 4.8 is its honesty . We train all our models to be honest---for instance, to avoid making claims that they can't support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims. This is borne out in our evaluations , which show that Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked. That linked system card includes the following: Claude Opus 4.8 had the lowest incorrect-rate of the six models on every benchmark—the most direct measure of factual hallucination. It achieved this mainly by abstaining on questions about which it was uncertain rather than by answering more questions correctly. Model characteristics Not much has changed since 4.7. It's priced the same as Opus 4.5/4.6/4.7 - $5/million input and $25 per million output. "Fast mode" is twice that price, which is a significant reduction from their previous models - fast mode on 4.6/4.7 remains at $30/$150. Note that fast mode is only available to organizations that are part of the research preview, "Contact your account manager to request access". Both the reliable knowledge cutoff and the training data cutoff are

simonwillison.net

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