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Big tech moves on a different timescale than the news cycle wants you to believe. What looks like a product launch is usually the public surface of an organizational bet made eighteen months earlier.

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Palantir CTO says the Pentagon has already replaced Anthropic’s AI

Palantir CTO says the Pentagon has already replaced Anthropic’s AI

The Pentagon has already replaced Anthropic’s AI models, with no operational impact, Palantir’s chief technology officer, Shyam Sankar, said on CBS’s Face the Nation on Sunday. “It’s already been replaced,” Sankar said. The Pentagon stopped using Anthropic’s products after a dispute over its safeguards for military AI. It labelled the company a supply chain risk. […] This story continues at The Next Web

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I built a tool that answers "what changed?" when production breaks

It's 2 AM. PagerDuty goes off. You open your monitoring tool and it tells you, with great confidence, that checkout latency is up 400%. Thanks. Extremely useful. Now the actual work starts: what changed? You open the deploy history in one tab, the feature-flag dashboard in another, the migration log in a third, Slack in a fourth, and you spend twenty minutes doing timestamp archaeology while the pager keeps screaming. Every on-call knows this drill. The cause is usually a recent change - a deploy, a flag flip, a migration. Finding it is the slow part. I got tired of doing it by hand, so I built LastGood. LastGood ingests your alerts (generic webhook or Datadog), GitHub deploys, DB migrations, and feature-flag flips into one timeline per service. When an alert fires, it scores recent changes by time proximity and type, and highlights the most likely suspects: "This deploy 23 minutes ago correlates with this error spike." The result lands as one quiet card in your Slack. No dashboard you're supposed to stare at, no agent to install, no telemetry crawl. There's a sandbox with sample data if you want to poke at it without signing up: https://console.lastgood.space/sandbox It's heuristics, not magic. Right now the score is roughly: Timing - how close the change was to the alert (exponential decay, not a step function, because "4 minutes ago" shouldn't automatically beat "40 minutes ago") Change type - a migration is more suspicious than a README update. The classifier actually looks at what files changed: docs-only changes get ruled out explicitly, config/schema changes get boosted Service criticality and topology - a change to a critical service, or to a direct dependency of the alerting service, ranks higher Blast radius and change frequency - services that change constantly get less benefit of the doubt Every score comes with its reasons attached. "Ranked #1 because it's a direct dependency of checkout-service, 6 minutes before the alert" - because a magic number help

dev.to

HookSentry CLI: Production-Ready Self-Hosted Webhook Ingestion Proxy, Dead-Letter Queue & Auto-Retry Engine

In modern distributed microservices and event-driven architectures, third-party webhooks are the lifeblood of business operations. Payment confirmations from Stripe or Paddle, inventory synchronizations from Shopify, identity lifecycle updates from Clerk, and CI/CD triggers from GitHub all rely on asynchronous HTTP callbacks. Yet, raw webhook ingestion is notoriously brittle. When your application server restarts during a blue-green deployment, experiences a database connection pool exhaustion, or hits an unexpected 500 Internal Server Error, incoming webhook payloads are dropped. Most external providers implement crude retry policies—or none at all—causing dropped revenue events, unsynchronized user states, and hours of tedious manual log parsing. To solve this, developers have historically had two bad choices: Spend weeks writing home-grown queue infrastructure with Redis, RabbitMQ, or AWS SQS, complete with custom retry workers, cryptographic signature checking, and dead-letter handling. Pay an exorbitant recurring SaaS tax by outsourcing ingestion to managed vendors like Hookdeck or Svix, running anywhere from $30 to $150+ per month just to forward HTTP payloads. HookSentry CLI eliminates this dilemma. It is an ultra-lightweight, zero-dependency Go micro-utility and containerized proxy engineered to sit between upstream webhook providers and your downstream applications. HookSentry guarantees zero data loss through local persistence, automated cryptographic verification, exponential backoff retries, dead-letter queuing (DLQ), and one-click manual replay capabilities—all running entirely on your own infrastructure for a one-time investment. Directly exposing application endpoints (such as https://api.yourdomain.com/webhooks/stripe) to the public internet presents four distinct failure vectors: [Stripe / Shopify / Clerk] │ ▼ (Direct POST) [App Backend] ──(Downtime / 502 / DB Lock)──► [Payload Permanently Lost] Deployment Blips and Cold Starts: An internal containe

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Why I Built a $5 ESP32 Hardware Firewall to Stop Cyber-Physical Grid Attacks

`The Problem: Physics is Faster Than the CloudAs we rapidly deploy distributed high-voltage infrastructure—like public EV charging stations and autonomous drone landing pads—we are securing them exactly the wrong way. The industry standard relies on centralized cloud firewalls and heavy industrial controllers to authorize power delivery.But this creates a fatal vulnerability: Tight Coupling and Latency.If a threat actor breaches the central network and commands a fleet of remote chargers to draw maximum amperage simultaneously, the system relies on the cloud to detect the anomaly, encrypt a response, and send a "kill" command. That round-trip takes hundreds of milliseconds. In high-voltage AC engineering, a transformer or a drone's Li-Po battery can be fatally damaged in 50 milliseconds.You cannot outrun the physics of electricity with a cloud API.The Solution: A Hardware-Isolated Edge FirewallTo fix this, I designed a decentralized, physical air-gap mechanism using a simple ESP32. Instead of acting as a traditional software firewall that inspects packets, it monitors the physical physics of the charging cable.By placing the logic at the absolute edge—between the power grid and the vehicle—we reduce the attack surface to near zero.1. Rate-of-Change ($dI/dt$) Anomaly DetectionElectric vehicles and drone batteries use Battery Management Systems (BMS) to dynamically step power up and down. A simple static threshold (e.g., "trip if current > 32A") generates false positives.Instead, the ESP32 samples the instantaneous RMS current (using a PZEM-004T sensor) every 100ms and calculates the discrete derivative against a rolling window to ignore baseline noise. If the rate-of-change ($dI/dt$) exceeds the maximum allowable slew rate, the firewall recognizes a malicious overdraw attack.2. The Non-Blocking C++ LogicHere is the core C++ loop running on the ESP32. It uses millis() to ensure the dual-core processor can handle background TLS/MQTT tasks without ever interrupting the

dev.to

Routing Traffic on Kubernetes (EKS) with the F5 NGINX Ingress Controller

If you have run anything on Kubernetes, you have almost certainly used ingress-nginx - the community controller that fronts inbound traffic for roughly half of all clusters. That era is ending. In this post we will look at why you need to move, and then build a complete, working setup on AWS EKS using the F5 NGINX Ingress Controller instead: an EKS cluster provisioned with Terraform, a Network Load Balancer wired up through the AWS Load Balancer Controller, and three flavors of routing (basic, path-based, and host-based) using two sample apps. First, the thing you need to know. The community-maintained kubernetes/ingress-nginx project (the controller that uses nginx.ingress.kubernetes.io/ annotations) has been retired. The Kubernetes Steering and Security Response Committees announced the retirement in early 2026, after years of warnings that the project badly needed maintainers. Best-effort maintenance wound down around March 2026, and after that point there are no new releases, no bug fixes, and no security patches - the repository becomes read-only. Existing installs keep running, and the Kubernetes Ingress API itself is unaffected, but running an unpatched, internet-facing proxy is not a position you want to be in. Source: the official Kubernetes blog, Ingress NGINX Retirement: What You Need to Know. Facts rephrased here for compliance. That leaves teams with a decision. The two mainstream directions are: Gateway API - the long-term successor API for L4/L7 routing. A supported Ingress controller - most naturally the F5 NGINX Ingress Controller (nginx/kubernetes-ingress), which is a different project from the retired community one. It is actively maintained by F5/NGINX Inc., uses the nginx.org/ annotation prefix, and ships its own CRDs (VirtualServer, VirtualServerRoute, TransportServer, Policy) for advanced traffic management. Community ingress-nginx (retired) F5 NGINX Ingress Controller Status Retired, read-only, no patches Actively maintained Maintainer Kubern

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