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OpenScreen is a free, open‑source screen‑recording application positioned as a lightweight alternative to the commercial product Screen Studio. Licensed under the MIT License, it permits unrestricted personal and commercial use, modification, and distribution. Core capabilities include full‑screen or window‑specific capture, microphone and system‑audio recording, customizable automatic or manual zooms, cropping, background selection (wallpaper, solid, gradient, custom), motion‑blur smoothing, annotations (text, arrows, images), clip trimming, segment‑wise speed adjustment, and export in various aspect ratios and resolutions. The app is built with Electron, React, TypeScript, Vite, PixiJS, and dnd‑timeline. Platform support: - macOS 13+ (system audio requires macOS 14.2+ permission; older versions lack system audio); - Windows (audio works out‑of‑the‑box); - Linux (requires PipeWire; older PulseAudio may lack system audio). Installation uses GitHub releases: macOS app bundle (requires Gatekeeper bypass via `xattr -rd com.apple.quarantine` and screen‑recording/accessibility permissions), Windows installer, and Linux AppImage (may need `--no-sandbox`). Contributions are welcomed via GitHub issues and roadmap.

The post describes how Imbue’s **mngr** CLI is used to launch and coordinate hundreds of Claude agents for self‑testing. A tutorial script ( tutorial.sh ) is written with commented blocks; each block is turned into one or more pytest functions. Agents generate these tests, annotate each function with its source block, and a validator ensures the correspondence. Tests use a thin wrapper over Python’s subprocess to run CLI commands, capture stdout/stderr, and produce transcript files; a custom connect_command (`mngr‑e2e‑connect`) records tmux sessions via asciinema. Orchestration proceeds by collecting test names (`pytest --collect-only`), spawning an agent per test (via `mngr create` primitives), and having agents fix failing tests, improve passing ones, and emit JSON result artifacts. Results are pulled, and an “integrator” agent merges non‑implementation fixes wholesale, ranks implementation fixes, and creates a linear branch for review. The workflow scales from a local machine (using Git worktrees) to remote Modal sandboxes (via `mngr create [email protected]`), with identical primitives for listing, pulling, and stopping agents. The architecture is framed as a map‑reduce pipeline: map each test to an agent, then reduce the outputs into a single PR. mngr’s design emphasizes low upfront cost, easy scale‑down, and platform‑agnostic primitives, supporting both small‑team local development and large‑scale remote execution.

- Emphasizes that life is a series of choices; authentic living requires accepting responsibility and the anxiety that freedom brings. - Describes “luck” as a broader perception of opportunities rather than external chance, urging continual awareness beyond narrow goals. - Argues most jobs are obtained through referrals, networking, and demonstrating high agency; merit alone demands top‑tier performance, while interpersonal qualities and willingness to help are equally decisive. - Recommends concise, value‑adding outreach (e.g., fixing a typo, offering relevant resources) and framing oneself as a problem‑solver, likening this to a plumber’s role. - Highlights confidence as the cumulative memory of past successes; confidence builds through repeated, challenging practice in domains such as jiu‑jitsu, freediving, and public speaking. - Suggests learning follows “on‑season/off‑season” cycles, with progressive overload needed to overcome plateaus; mastery requires sacrifice and sustained effort. - Advises writing to externalize thoughts, act despite fear (“do it scared”), and avoid unnecessary complexity—simpler systems reflect clearer understanding. - Concludes that one’s identity is distinct from their work; recognizing this facilitates feedback, growth, and healthier self‑valuation.

The document proposes a workflow where an LLM continuously builds and maintains a personal knowledge base as a persistent, interlinked wiki of markdown files. Raw source documents (articles, papers, images, data) remain immutable; the LLM reads each new source, extracts key information, and updates entity, concept, and summary pages in the wiki, flagging contradictions and revising syntheses. A schema file defines wiki structure, conventions, and ingestion/query workflows, allowing the LLM to act as a disciplined wiki maintainer rather than a generic chatbot. Core operations include: * **Ingest** – LLM processes a source, creates/updates 10‑15 wiki pages, logs the action, and updates an index file (index.mdis) that catalogs pages with one‑line summaries. * **Query** – LLM searches the index, reads relevant pages, synthesizes answers with citations, and optionally writes new result pages (e.g., tables, slides, charts). * **Lint** – Periodic health checks identify contradictions, orphan pages, missing cross‑references, and data gaps, prompting further investigation. Supporting tools (Obsidian, local search engines like qmd, Dataview, markdown slide decks, image clipping) facilitate editing, graph visualization, and version control via Git. The human curates sources and guides analysis; the LLM handles summarization, cross‑referencing, and bookkeeping, enabling a compounding, up‑to‑date knowledge repository.

Microsoft’s “Copilot” branding now covers roughly 75 distinct items across its portfolio. The term applies to a wide mix of offerings—including standalone apps, embedded features, entire platforms, a dedicated keyboard key, a specific line of laptops, and a development tool for creating additional Copilot solutions. No single Microsoft source aggregates these instances; the author compiled the list by cross‑referencing product pages, launch announcements, and marketing materials. The resulting visualization groups the items by category and illustrates their interconnections, allowing users to explore relationships among the various Copilot‑named products. This mapping highlights the breadth and overlap of Microsoft’s branding strategy around the Copilot name.

- An AWS engineer reported that PostgreSQL throughput on a Graviton‑4 server drops to roughly 0.51× when running the Linux 7.0 development kernel, with latency increasing due to longer time spent in a user‑space spinlock. - The regression was traced to Linux 7.0’s change that removed PREEMPT_NONE as the default preemption mode, a modification previously discussed on Phoronix and upstreamed to the kernel. - A patch to restore PREEMPT_NONE as the default was posted to the Linux kernel mailing list, but its acceptance is uncertain. - Kernel maintainer Peter Zijlstra suggested the practical fix is to adapt PostgreSQL to use the Restartable Sequences (RSEQ) time‑slice extension, which was also upstreamed for Linux 7.0. - If PostgreSQL is not updated, Linux 7.0 stable—scheduled for release in about two weeks and slated for Ubuntu 26.04 LTS—may cause significant performance degradation for PostgreSQL workloads.

The page describes “isseven,” a web API that validates whether a submitted numeric value equals seven. Users send a POST request to /api/isseven with a JSON payload containing a field named number. The API returns a JSON response with a Boolean isseven field indicating the result and an ad field containing promotional text. Successful validation (value 7) yields {"isseven": true, ...}; any other value yields {"isseven": false, ...}. Three pricing tiers are offered: * **Free** – 1 check per month, responses include ads, community support only. * **Pro** – $77 per month, unlimited checks, ad‑free responses, email support with a 72‑hour SLA, and a 77.7 % uptime SLA. * **Enterprise** – $777 per month, dedicated infrastructure, ad‑free responses, a dedicated engineer, and a “seven‑figure” SLA guarantee. The service emphasizes minimal compromise: a single number, a single truth, and optional upgrades for ad‑free usage.

The author, a DevOps engineer, uses agentic AI (primarily Claude via OpenRouter) for code generation. When attempting to build an RSS‑reader conversion tool in Lisp, they encountered severe productivity issues: AI struggled with REPL interaction, required costly token usage, and produced low‑signal output compared to Python. To ease REPL access, they created a Python utility tmux‑repl‑mcp that forwards commands and returns output, but the AI still consumed $10–$20 in minutes with modest results. Experiments with cheaper models (DeepSeek, Qwen) performed worse. The author notes that AI favors languages with abundant training data (Python, Go) because high‑latency API calls conflict with REPL workflows, forcing batch code generation rather than incremental testing. Tooling preferences (e.g., OCICL over QuickLisp) also needed explicit instruction to the AI. Consequently, they consider rewriting the project in Go, observing that AI‑driven development makes high‑usage languages economically preferable, while Lisp remains “AI resistant” despite its historical resilience.