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I used to live in VS Code with GitLens pinned — the branch graph, heat-mapped blame, the lens annotations. That was my git workflow. Then 2026 happened. With Cursor, Claude Code, and Codex doing the actual editing, the editor itself became optional. The only thing dragging me back was GitLens. That felt wasteful — booting an entire IDE just to peek at commit history. The agent runs the git commands now; I only need to sanity-check the result, occasionally, when something looks off. So I built gitwink — the smallest possible tool for that loop. A tray icon that expands into a glance, hands the commit off as AI context, and gets out of the way. Read-only by design. It cannot commit, push, merge, or modify anything. If I need git surgery, I tell the agent. The 0.5-second confirm loop agent commits → tray click → inline expand → "Copy as AI context" → paste into Claude/Codex/Cursor → "did the agent do this right?" No window switching. No IDE boot. The whole loop fits inside a glance. What's in it Tray-resident (Windows tray / macOS menu bar) — click to toggle, global hotkey Ctrl+Shift+G to summon from anywhere. Right-click the tray icon for Reset position / Open settings file / Quit. First-run discovery that walks your usual code dirs ( source , Documents , Projects , Code , Dev , repos , Desktop , every non-system drive on Windows; ~/Projects , ~/Code , ~/Developer on macOS) and caches the result in SQLite. No "add repo" friction. Unified commit timeline across all repos, with chips above for filtering by Repo (search + pinning), Time range (24h / 3d / 7d / 30d / All), and Authors (multi-select with counts). Per-row markers — ● commit · ◆ merge · ★ tagged — and branch label badges when a commit isn't on the currently checked-out branch. Single-repo DAG view — pick a repo and the panel switches to a per-branch graph with a custom SVG lane drawer (eight-colour palette, hashed from branch name; main / master / develop kept neutral). Inline expand on click — commit body +

What if a development board could be as friendly as an Arduino, yet powerful enough to drive industrial-grade robots? That is exactly the gap the new G2 Nano sets out to close. Most hobby boards handle simple robot builds with ease, but they hit a wall once a project demands tight, simultaneous control of several motors. Embedded systems engineer Ryan Strace noticed that the custom controllers built for these complex machines tend to look remarkably alike, with motor coordination as the recurring headache. Rather than reinventing that hardware on every project, he designed a single accessible platform to handle it, and the G2 Nano is the result. Precise motor control usually leans on closed-loop techniques like PID, but real-world gremlins such as integrator windup, sensor noise, mechanical saturation, and phase delay can all degrade performance. Robots also need smooth multi-axis motion with managed acceleration to avoid jerky, stressful movement, plus solid fault handling so an unexpected state does not wreck expensive parts. Strace is tackling all of this with a low-cost motion-control IC he is developing, and the G2 Nano is the high-performance platform built to prove out that future chip. What's under the hood Processor: NXP Arm Cortex-M7 clocked at a brisk 1 GHz Wireless: u-blox MAYA-W1 module with dual-band Wi-Fi and Bluetooth Motion sensing: six-axis IMU (3-axis accelerometer plus 3-axis gyroscope) and a dedicated magnetometer for compass heading Form factor: just 0.8 by 3 inches, breadboard-friendly, on a six-layer PCB stackup for clean high-speed signals On the software side, the board targets native micro-ROS and the Zephyr real-time operating system, with planned MicroPython support so you can prototype in Python without paying the usual speed penalty, thanks to that unusually high clock. Every design file and document is open-source and published on GitHub. Build it yourself If you want to follow along, the core ingredients are clear: an NXP Cortex-M7 a

Cybercriminals used the Glassworm botnet to infect open source software projects with malware, and in turn hack the developers and companies that use that software.

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I...

Google's SynthID, designed to embed imperceptible signals into AI-generated content, is adding a new Content Detection API on Google Cloud's Gemini Enterprise Agent Platform, after gaining adoption by several industry players including Nvidia and OpenAI. By Sergio De Simone

This week's Java roundup for May 18th, 2026, features news highlighting: GA releases of WildFly 40, Micronaut 5.0, Maven Embedded GlassFish Plugin 8.0 and Apache Fory 1.0; the May 2026 edition of Open Liberty; point releases of Gatherers4j, Apache and Kafka; and the seventh milestone release of Spring AI 2.0. By Michael Redlich

The Trust Identity Protocol is a free, open, post-quantum, patented standard for verified human identity and AI content provenance on the public internet. Built by The AI Lab Intelligence Unobscured, Inc.

Two stories landed in the same news cycle. Microsoft cancelled most internal Claude Code licenses....

Check out these 10 open source tools that help game developers create art, animation, levels, audio, dialogue, debug UIs, and engine-ready assets. The post Beyond the engine: 10 open source projects shaping how games actually get made appeared first on The GitHub Blog .

Explore our update on GitHub’s accessibility strategy, and learn how you can join us in building a culture of accessibility. The post Building GitHub’s next chapter in accessibility appeared first on The GitHub Blog .

See how data science teams can use Codex to build root-cause briefs, impact readouts, KPI memos, scoped analyses, and dashboard specs from real work inputs.

See how sales teams can use Codex to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

See how business operations teams can use Codex to create initiative briefs, strategy updates, leadership decision packets, progress updates, and more from real work inputs.

See how finance teams can use Codex to build MBRs, reporting packs, variance bridges, model checks, and planning scenarios from real work inputs.