Most “Copilot for blogging” setups are fake. They give the model a nicer prompt, maybe a scaffold script, and then act surprised when the output breaks the repo. That approach fails the moment the repository has real structure: Hugo page bundles instead of one flat posts/ folder local images and downloadable assets theme overrides on top of a vendored submodule deploy config and build rules old posts with inconsistent front matter styles companion materials like quizzes, flashcards, or social copy I wanted something stricter: a repo where GitHub Copilot could take a scoped topic, research it, scaffold the right bundle, write into the right files, validate the result, and stop before touching generated output. ...

In March 2026, attackers turned Aqua Security’s Trivy ecosystem into a credential-harvesting distribution channel. This was not one bug, one poisoned package, or one bad release. It was a chained failure across GitHub Actions trust, secret rotation, mutable tags, runner memory, registry publishing, and npm’s default willingness to execute third-party code. On February 27 and February 28, 2026, the Trivy story started the way a lot of modern software compromises start: not with a zero-day in the scanner, but with automation glued together too loosely around trust. An autonomous agent dubbed hackerbot-claw found a dangerous pull_request_target pattern in Aqua Security’s Trivy repository, exploited it, and stole a privileged aqua-bot token. That first breach was bad enough on its own. The real disaster came after the first incident was supposedly contained. ...

Most autonomous content demos are fake. They show a model taking a prompt and emitting a draft, but they skip the part that actually matters in a working repository: intake structure, validation, repo rules, PR flow, and failure handling. For this blog, I wanted a GitHub-native pipeline where an idea could start as a structured issue, get normalized into a deterministic brief, be assigned to GitHub Copilot, and come back as a draft PR that still respected the repo. ...

Reading time: ~24 min | Audience: staff engineers, AI architects, platform leads, senior ICs inheriting a slow conversational system | Primary goal: stop treating a 10 to 13 second LLM workflow like a prompt problem when it is really a systems problem Preface: Voice Did Not Create the Problem Here is the version of this story that gets told too often: “The text bot was fine. Then voice arrived. Now latency matters.” ...

Preface: How I Read This Research Pack The local research bundle on RunAnywhere is broad, but it is not uniform. Some files are direct performance summaries, some are opinionated strategy memos, and some are clearly derivative study aids built from the same underlying source set. After reading the full bundle, then re-checking the public web evidence on March 12, 2026, my conclusion is narrower and more useful: RunAnywhere is not just a “fastest inference on Apple Silicon” demo. It is trying to become the runtime, packaging, and fleet-management layer for on-device AI, with MetalRT acting as the Apple-Silicon flagship proof point. S1 S2 S3 S4 ...

How a “simple” photo library upgrade turned into a deep dive through PostgreSQL version migrations, deprecated vector extensions, and the kind of database surgery you hope to never need. The Starting Point My Immich instance had been happily humming along at v1.113.0 for months on my Unraid server. I was running the community all-in-one imagegenius/immich container variant, which bundles the server, microservices, machine learning, and Redis into one image, backed by an NVIDIA GPU for CUDA-accelerated ML and a shared PostgreSQL 14 instance that also served a pile of other workloads. ...

Preface: Why This Version Exists Most FinOps programs fail in the same place: they build good dashboards and still ship bad decisions. The root cause is rarely tooling. It is usually one of these: Ingestion is not trustworthy (missing prices, missing months, duplicates after scope changes). Ownership is fuzzy (nobody is on the hook for a recommendation becoming a change). The loop is discontinuous (big cost projects twice a year instead of an operating rhythm). This playbook focuses on the parts that create trust: data contracts, scope design, versioning, and operational gates. ...

Audio Version Your browser does not support the audio element. Play/download concise audio version Download full deep-dive audio 1. Introduction: From Rio to the Future of Efficiency The Fourteenth International Conference on Learning Representations (ICLR 2026) in Rio de Janeiro has solidified a paradigm shift that many of us in the AI architecture space have long anticipated: the transition from “approximate” efficiency to “exact” sub-quadratic modeling. For years, the industry accepted the quadratic compute and linear memory bottlenecks of standard Transformers as an unavoidable tax on quality. Rio 2026 has definitively challenged this notion. S4 S5 ...

Why Your OpenCode Skill Shows No Output (And How to Fix It) TL;DR: OpenCode’s run mode captures subprocess stdout and only returns it after the command finishes. If your skill launches a long-running pipeline, the user sees nothing for minutes — or hours. The fix: write a plain-text progress log that users can tail -f from a second terminal. The 30-Second Fix If you just want the solution: from datetime import datetime from pathlib import Path def log_progress(output_folder: str, msg: str) -> None: log_path = Path(output_folder) / "progress.log" log_path.parent.mkdir(parents=True, exist_ok=True) with open(log_path, "a") as f: f.write(f"[{datetime.now().strftime('%H:%M:%S')}] {msg}\n") f.flush() # Critical for tail -f! Then tell users: tail -f <output>/progress.log ...