How to evaluate NVIDIA NeMo Switchyard on Amazon Bedrock for routing coding-agent requests by task difficulty, and gate rollout on latency, pass rate, and cost per accepted patch instead of defaulting to the largest model.
A hands-on deep dive into Cloudflare's open-source Kitesurf browser for AI agents, comparing its isolation, session, and interaction primitives with a Playwright stack and weighing the operational costs of untrusted agent browsing at scale.
A deep technical walkthrough of how roughly 4,837 compromised GitHub accounts were used to reach CI secrets and cloud credentials, and how to audit GitHub Actions workflows for the same exposure using OIDC short-lived credentials, fine-grained PATs, secret scanning, audit-log detection, and…
Rust 1.100 stabilizes the never type after a decade of design work, giving diverging expressions a first-class bottom type that sharpens match exhaustiveness, generic return types, and FFI signatures.
A short defensive audit of GitHub Actions credential exposure: pull_request_target plus head checkout, branch-triggered workflow_dispatch with id-token write, and OIDC migration fixes. Full write-up: https://hackyjs.com/posts/auditing-github-actions-for-ci-secret-theft-oidc-migration-lessons-from-4837-compromised-accounts
Rust 1.100 reportedly stabilizes the never type after a decade of design work, giving diverging expressions a first-class bottom type. This short walks through what actually changes for your builds and what a one-line headline can't tell you. What the video covers: - Return-position `!` has been stable since 1.0; type-position `!` still fails on stable with error E0658. - Why the gate is positional, not semantic, and what that means for signatures like a return type carrying never. - The distinction that matters: return-position `!` lets you describe divergence, type-position `!` lets you reason about it, because inference has to pick a type. - The three open questions: which fallback policy ships, whether a blanket conversion impl comes with it, and whether the change is edition gated. The post treats the stabilization claim as reported rather than verified, and gives a reproducible probe for each behavior so you can confirm it on the toolchain in front of you. Full write-up: https://hackyjs.com/posts/rust-1100-stabilizes-the-never-type-match-exhaustiveness-generic-returns-and-ffi-signatures
Inference got cheap on paper: $0.10 per million tokens for a Google model, a Meta model at Haiku-tier pricing, and a claim that open-weight tokens cost about 7x less. None of that settles build versus buy, because almost nobody quoting those numbers is quoting the same unit. This video walks the JavaScript cost calculator from the post. The API side splits input and output tokens, applies cache hit rate and an amplification factor, and returns dollars per million total tokens. The self-hosted side divides a fixed monthly bill by the tokens the GPUs actually emit. Set the two equal and you get a break-even utilization. Numbers from the post's worked example: one rented H100 at 2,500 aggregate tokens per second, a $7,491.67 monthly bill split 56% ops, 24% GPU, 20% platform. Against a frontier-priced API that breaks even at about 1.59 billion tokens per month, roughly 24% utilization. Against a $0.10/M-class API, break-even is about 52.5 billion tokens per month while the same fleet tops out at 6.57 billion — unreachable at any volume it can physically serve. You'd need more than 6,900 tokens per second per $2.50 GPU-hour on raw GPU cost alone, before a single engineer-hour. The post is also explicit about what the price-collapse reports do not establish: the input/output split behind those rates, whether caching or batch tiers are included, what throughput or latency the price buys, and the methodology behind the 7x figure. Full write-up, calculator, and sweep harness: https://hackyjs.com/posts/a-javascript-cost-calculator-for-choosing-between-010m-apis-and-self-hosted-models