Climate change is helping invasive species to not only establish themselves across the continent but to stay alive for longer. Scientists suggest we get used to it.

Cable and satellite aren't your only options when it comes to local channels. There are free ways to get the same content.

X and the World Federation of Advertisers say they're resetting their relationship.

I'm building Kedge, a globally distributed platform for stateful serverless apps. Here's how you make a simple static site: `echo '# Hello world!' | ssh kedge.dev` I helped build Fly.io for 4 years and shared enthusiasm for the founders' vision of a 'global Heroku'. While there, I wrote "The Serverless Server" ( https://fly.io/blog/the-serverless-server/ ) as a study of Lambda and a sketch of a modern serverless product built around lightweight VMs. That essay was the initial inspiration for Ked

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Dependabot keeps your dependencies current, but its defaults can flood your repository with pull requests. Here's how grouping updates, slowing the cadence, and keeping security fixes fast cut the noise on a Microsoft open source project. The post Tame Dependabot: Group your updates, slow the cadence, keep security fast appeared first on The GitHub Blog .

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Hi HN, Rohit here from Tokenless (usetokenless.com), which I’m building alongside co-founders Andrew and Kev. We’re building an API gateway which routes agent traffic dynamically turn-by-turn between different models to save on AI spend. The cost of AI tokens is top-of-mind for many. Companies like Uber and Salesforce have been complaining about blowing their yearly AI spend faster than expected. Frontier models are amazing for dev work, but are so expensive. Open-source models are cheap and rap

Download my Automation Testing Interview Questions from ⬇️ Apple AppStore - https://apps.apple.com/us/app/qa-automation-interview-prep/id6786760948 👈 ⬇️ Playstore - https://play.google.com/store/apps/details?id=com.app.seleniuminterviewquestions 👈 The standard advice for passing a QA Engineering interview is broken. If you ask a forum how to prepare, you will be told to "learn Playwright," "memorize XPath," or "know how to write a basic API GET request in Postman." That advice worked in 2021. Today, engineering teams do not want manual testers who learned basic syntax. They are hiring Software Engineers in Test (SDETs) who understand system architecture, CI/CD pipelines, and data state. If you are failing technical rounds, it is rarely because you forgot a WebDriver command. It is because you are testing the syntax instead of the system. Here are the two architectural concepts you are actually being judged on in a modern QA interview, and how to approach them. 1. The API Race Condition & Idempotency Trap In a technical round, a senior engineer will rarely ask you to "test a login endpoint." Instead, they will give you a scenario like this: "We have a microservice that processes payments. The user clicks 'Submit', but the network drops, so they click it again. How do you automate a test to ensure they aren't charged twice?" The Junior Answer: "I will write an automated script that clicks the button twice quickly and checks the database." The Senior Answer (What they want to hear): "I will write a test that validates the API's idempotency . I will intercept the first request, capture the unique idempotency key from the header, and fire a duplicate POST request with the exact same payload and key. The test must assert that the backend returns a 409 Conflict or a 200 OK with the original transaction ID, verifying the database state didn't duplicate the charge." If you do not understand idempotency, payload validation, and race conditions, your API automation is just che

Six months ago I wrote up my local AI setup and a reader bookmarked it, tried to reproduce it last week, and emailed me confused because half of it no longer matched what I actually run. Fair. Stacks rot quietly. So here's the mid-2026 state of mine: what's still earning its place on disk, what I deleted, and where I quietly went back to the cloud. Context for the numbers and opinions below: I do smart contract security work, I run everything on WSL2 on a machine with a modest GPU, and I've been doing the local-model thing daily for over a year, not as a hobby but as part of shipping. Still here: Ollama as the runtime Ollama remains the center of the local stack and honestly it's not close. I've tried the alternatives, llama.cpp directly for control, a couple of the newer serving layers for speed, and I keep coming back for one boring reason: the API is stable and everything I've built talks to it. My audit tooling, my shell scripts, my editor config, they all point at localhost:11434 and they've pointed there for a year without breaking. That stability matters more than a marginal tokens-per-second win. When a model update lands, ollama pull and I'm done. The day something meaningfully better appears with the same API shape, I'll switch in an afternoon, which is exactly the position you want to be in. Still here: qwen2.5-coder, both sizes, different jobs I run two models and the split has stayed remarkably stable: qwen2.5-coder:1.5b is the reflex model. It handles anything where speed matters more than depth: quick "what does this diff do" summaries, commit message drafts, pre-filtering files before a heavier pass, and the small classification jobs inside my pipelines ("does this file handle user input, yes or no"). It's fast enough on my machine that I never think about invoking it, and that's the whole point. A model you hesitate to call is a model you stop calling. qwen2.5-coder:7b is the thinking model. Code review, security triage, structured findings extracti

Microsoft is on a mad dash behind the scenes to patch exploits before hackers find them.

Hey everyone, Whenever I start scaling out a new Rust service or protocol, I always find myself hitting the same wall: testing gets messy fast. You end up juggling basic cargo test unit checks, hacking together ad-hoc integration scripts, and manually setting up coverage tools every single time. I put together RustForge to solve that headache for my own projects, and figured it might save a few of you some time too. It’s a clean, zero-bloat starter template designed to take you from simple unit tests all the way to compiler-style UI snapshots and coverage tracking without having to reinvent the harness every project. https://github.com/rwilliamspbg-ops/RustForge

AI agents often begin as application code: a prompt, a model call, a few tools, and enough control flow to make the first example run. As the agent grows, the definition and the environment tend to collapse into each other. Model configuration lives beside credentials. Tool access is mixed with prompts. Persistence assumes a particular process. Deployment choices become part of the agent itself. That coupling makes an agent harder to inspect, test, move, and recover. We built Clear Ideas Agent Runtime around a different boundary: the agent definition should be portable, while the infrastructure that executes it should remain under host control. The Agent Manifest is the portable contract An Agent Manifest is a versioned YAML or TypeScript definition. It can describe: prompts and structured outputs; typed variables; conditions and loops; tools and MCP connections; approvals and webhooks; sandboxed code steps; sub-runs; limits and final outputs. A separate Agent Run Manifest supplies the values and execution choices for one invocation. That keeps the reusable agent definition distinct from the inputs and operational decisions associated with a particular run. Here is a small example: schemaVersion : " 1.0" name : research-brief variables : topic : type : string researchNotes : type : string briefDraft : type : string steps : - id : research type : prompt prompt : | Research {{ topic }} and return concise notes. outputVariable : researchNotes - id : draft type : prompt prompt : | Draft a brief using these notes: {{ researchNotes }} outputVariable : briefDraft The manifest describes the agent. It does not contain the credentials, infrastructure account, or persistence implementation that happens to run it. The host controls the operational boundary The host application supplies and controls: models and provider credentials; connections and tool authorization; persistence and artifact stores; local or remote compute; sandbox providers; telemetry; concurrency and resource