The Git Times

Firecrawl, the API for converting websites into LLM-ready markdown and structured data, has leveled up with v2.8.0. This update targets agent-driven applications, introducing parallel agents that handle thousands of /agent queries simultaneously. Powered by new models like Spark 1 Fast for instant retrieval and Spark 1 Mini for complex research, it features automatic failure handling and intelligent fallback execution.

The release also debuts the Firecrawl CLI, installable via npm install -g firecrawl-cli, supporting full scrape, search, crawl, and map commands. Developers can now pipe web data directly into local pipelines without API roundtrips. Complementing this is the Firecrawl Skill, enabling AI agents like Claude Code and OpenCode to invoke Firecrawl autonomously via CLI integration—npx -y firecrawl sets it up across detected tools.

Core strengths remain: >80% benchmark reliability on dynamic sites, JavaScript rendering via proxies, and outputs in clean markdown, JSON, screenshots, or HTML. Customization options—tag exclusion, auth walls, depth limits, media parsing from PDFs/images, and pre-extraction actions like clicks or scrolls—make it robust for real-world scraping. Batch processing and change tracking further solidify its role in production.

For builders, v2.8.0 matters now as AI agents demand scalable web context. Parallelism cuts latency on high-volume tasks, while CLI and skills embed Firecrawl into agentic loops without custom glue code. Platform tweaks, including faster search, Zod v4 SDK compatibility, and webhook-enhanced agents, optimize Redis-heavy deployments.

curl -X POST 'https://api.firecrawl.dev/v2/scrape' \
-H 'Authorization: Bearer fc-YOUR_API_KEY' \
-d '{"url": "https://example.com"}'

Yields instant markdown with metadata, as before. Self-hosting lags—still local-run only amid mono-repo refactoring—but API users gain immediate velocity. Firecrawl powers apps needing fresh web data without brittle parsers, outpacing rivals on JS-heavy sites.

Developers tweaking advanced driver assistance systems (ADAS) now have fresh tools in commaai/openpilot's v0.11.0 release. This Python-based robotics operating system, which retrofits 300+ car models, introduces a new driving model fully trained via a learned simulator. The upgrade sharpens longitudinal performance specifically in Experimental mode, addressing acceleration and braking precision on real roads.

Power efficiency sees major gains too. Standby consumption on the comma four hardware drops 77% to just 52 mW, easing long-term viability for always-on deployments. This matters for builders integrating openpilot into fleets or custom robotics, where battery drain has long been a pain point.

Vehicle compatibility grows with support for the 2017 Kia K7 (via contributor royjr) and 2018 Lexus LS (thanks to Hacheoy). These additions push the tally past 275 models documented in the README, focusing on popular sedans ripe for upgrades. Installation remains straightforward: plug in a comma 3X device, harness it to the car's OBD port, and flash via URLs like openpilot.comma.ai for the stable release-tizi branch on comma 3X.

For non-plug-and-play setups, openpilot runs on custom hardware, inviting robotics experimentation. Developers can fork branches like nightly-dev for bleeding-edge features, including experimental car-specific tweaks. The project emphasizes community input—pull requests and issues on GitHub, plus Discord for collaboration.

Technically, openpilot processes camera feeds and CAN bus data to override stock systems, enabling lane centering, adaptive cruise, and more. The simulator-trained model represents a shift: it leverages synthetic training data to refine behaviors without endless road miles, accelerating iteration for contributors.

Builders should care because this release lowers barriers to production-grade ADAS hacking. Whether forking for drones, industrial robots, or further car ports, v0.11.0 delivers measurable gains in performance and efficiency. Check the release blog for model benchmarks, and dive into the repo's roadmap for upcoming longitudinal overhauls.

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The community-maintained ProxmoxVE helper-scripts repository has rolled out its March 18, 2026 release, targeting pain points in Proxmox VE environments with targeted enhancements. Originally tteck's creation, this collection of shell scripts now thrives on global contributions, focusing on one-command deployments for LXC containers, VMs, and self-hosted apps across Debian-based Proxmox 8.4.x to 9.1.x.

Key additions include Alpine-Ntfy, a lightweight notification server from contributor @MickLesk, and Split-Pro, a new partitioning tool. These join over 5000 supported apps, emphasizing quick setups for homelabs running Home Assistant, Docker, or smarthome stacks.

Bug fixes sharpen reliability: Tdarr now uses curl_with_retry for robust downloads and proper exit codes; reitti gets v4 compatibility; Paperless-NGX defaults to 3GB RAM for document processing; Plex restarts post-update to ensure version activation. These address common deployment hiccups in resource-constrained nodes.

New features shine in hardware integration. A centralized setup_hwaccel function manages GPU groups across scripts, simplifying NVIDIA/AMD acceleration for media servers like Plex or Tdarr. Termix gains Guacamole daemon builds with systemd integration for remote access.

Refactors modernize dependencies: Podman ditches deprecated commands for Quadlets, aligning with upstream shifts for container orchestration. Partial Jell refactors hint at ongoing code hygiene.

Installation remains dead simple. Paste a bash one-liner from helper-scripts.com into the Proxmox shell, or run the local UI manager:

bash -c "$(curl -fsSL https://raw.githubusercontent.com/community-scripts/ProxmoxVE/main/ct/pve-scripts-local.sh)"

This embeds a script menu in Proxmox's interface, with auto-updates, secure practices, and post-install config scripts. Optimized for performance, it handles networking, authentication, and security out-of-box.

For builders scaling homelabs, this release matters: it reduces manual config for GPU-accelerated workloads and Podman pods, while community Discord and GitHub Discussions ensure rapid iteration. In an era of rising self-hosting, these scripts cut setup time from hours to minutes, letting developers focus on apps over infrastructure.

Microsoft's Windows Terminal repository has long unified the modern Terminal app, legacy conhost.exe console host, and shared components under one C++ codebase. Builders on Windows 10 build 19041 or later rely on it for tabbed, customizable shells including PowerShell, CMD, WSL, and SSH.

The v1.24.10621.0 stable release—held an extra cycle for polish—delivers targeted enhancements amid ongoing Preview channel iterations. Key updates address extensibility and security gaps that developers flagged.

Command Palette now searches in English alongside the user's display language (skipping English-only locales), streamlining shortcut discovery for non-native speakers. A new Settings page centralizes extensions management, listing auto-profile detectors and user-installed fragment extensions for toggling.

Asset handling sees relative paths for icons, background images, pixel shaders, and sounds—working for local settings and fragments alike. This simplifies packaging without absolute paths. However, web URIs (http, https, ftp) and certain network shares are now barred for icons to block information leakage, tracking pixels, and unauthorized "phoning home" by extensions.

These changes follow Preview builds (v1.24.2372.0 through v1.24.3504.0), which ironed out SSH host auto-detection (deferred here). Bug fixes abound, ensuring stability for daily workflows.

Installation remains straightforward: Microsoft Store for auto-updates (recommended), GitHub .msixbundle downloads, winget, Chocolatey, or Scoop. Canary channel offers bleeding-edge via Store or GitHub.

For contributors, the repo welcomes hacks via good-first-issue labels, Hacktoberfest, and clear build guides in PowerShell or CMD. Prerequisites include Visual Studio; shared components like ColorTool and Cascadia Code tie into Console APIs.

Why it matters now: As Windows ecosystems fragment with WSL2, Azure, and remote dev, Terminal's controls prevent extension risks while boosting usability. Relative paths cut deployment friction; the extensions page aids auditing in enterprise setups. Builders gain signal on evolving threats—web assets were a vector—without sacrificing customization.

SSH detection's pause underscores deliberate pacing, prioritizing reliability over rush. With last pushes into 2026 territory, this repo evolves as Windows' command-line cornerstone, far beyond the "old console" look users once mistook it for.

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The realsenseai/librealsense repository, home to the RealSense SDK 2.0, has rolled out beta release v2.57.6, focusing on expanded platform compatibility and firmware upgrades for Intel RealSense stereo depth cameras. Developers working with depth and color streaming now benefit from updated MacOS support (High Sierra 10.13.2 and later), detailed in fresh installation docs, alongside NVIDIA JetPack 7.1 for USB SKUs on Jetson platforms.

This source-only beta—available via release assets and a PyPI beta package (pyrealsense2-beta)—introduces early access firmware for the D555 camera. The new FW improves native ROS2 integration and multicamera stability, critical for robotics and multi-sensor setups. Various bug fixes are documented in the release notes.

A key shift: the project has migrated from IntelRealSense to the realsenseai organization. Redirects remain, but devs are urged to update clones and links promptly. Branch policy now funnels beta releases to master, enabling early access to features before official tags. Validated releases stay on the GitHub Releases page.

Technically, the C++ library delivers intrinsic/extrinsic calibration, high-res depth at varied ranges, and cross-platform streaming on Ubuntu 24.04/22.04/20.04 (kernels 6.x/5.x), Windows 10/11, Jetson JetPack 5-7, MacOS, Android 7-14, and Docker. Wrappers for Python, ROS, C#, Unity extend its reach.

For builders, this matters in texture-light-invariant stereo depth computation, powering de facto 3D sensing in robotics. The open-source nature, active community, and rich examples lower barriers for integrating depth into drones, scanning, or tracking pipelines. JetPack 7.1 support aligns with edge AI pushes on newer Jetsons, while D555 enhancements aid ROS2 migrations in autonomous systems.

No Debian/PyPI collaterals this round, but beta binaries loom for APT. Devs should pin to releases for production; master for cutting-edge.

This evolution keeps librealsense as the go-to for hardware-accelerated 3D perception, especially as multi-camera and embedded deployments scale.