A keynote recap outlined how LLM progress and agent tools in 2026 made coding agents noticeably more reliable and drove new “Claw” software adoption. The speaker said AI token spending went from under $50 in the past year to $1,000 in a day after coding agents became broadly useful. The result was product-market fit centered on coding agents, followed by companies tightening token/spend rules to manage costs.
GeckIt launched as a tool that organizes multiple Claude Code chats on a Kanban board to show their statuses. It offers a free test with 20,000 credits and runs your own Claude Code on your Claude subscription without an API key. This adds a task-management workflow for AI coding agents and AI code editors across Mac, Windows, and Linux.
The Official Microsoft Blog·2 weeks ago·
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● 7 sources
Microsoft rebuilt Copilot around persistent workplace agents by adding Home, Code, and Autopilot that connect chat, delegated task execution, solution building, and ongoing background work across Microsoft 365. Home and Code start rolling out in the Frontier program within the coming weeks, while Autopilot expands to private preview at the end of the month. Copilot also adds FinOps for AI tools to manage agent spend and credits, alongside new business context, plugin governance, and a Copilot Managed Runtime for safely running company-hosted code inside Microsoft 365.
Adrian Cockcroft discussed how performance engineers should move beyond relying on system metrics and percentiles by digging into kernel-level causes and then using new analysis techniques for response-time distributions. He said percentiles like P99 do not work for modern web service latency because a single number cannot show whether there are one or multiple latency peaks. As a result, he used an LLM to automate a custom R tool that identifies and tracks multiple peaks over time instead of collapsing data into averages or a single percentile value.
Squint launched a desktop tool that lets you press a hotkey, drag a box over anything on your screen, and get an answer beside the selection with follow-up questions. Its free tier allows 3 captures per day. This replaces the usual screenshot-and-tab-switch workflow with a one-step, on-screen interaction on Windows 10/11 and Linux (Wayland).
Arsaze launched as an AI video editor with a multi-track timeline that can be driven by users or an AI agent. The page describes it as “Launching today” on Product Hunt. As a result, developers and creators get a new tool positioned for agent-driven and AI-assisted video editing on a shared timeline.
OpenClaw launched PIP, an AI buddy for macOS that watches your screen and guides you by pointing to where to click and what to do. The page lists 29 followers. It shifts the result from generic launch info to a new Mac guidance product centered on AI screen assistance.
Speek launched as a macOS voice assistant that lets users hold a key to dictate into apps, edit selected text, and trigger actions in apps and accounts using voice. It runs tasks in the background and has a built-in flow where it notifies you when longer tasks are finished. The result is added hands-free dictation and on-screen pointing via circling, plus voice responses and follow-ups that remember details you request.
Chargebee launched AUDR, an open standard for tracking agent run cost details across billing and other systems. It is the 6th launch from Chargebee, and AUDR is used by 6,500+ businesses worldwide. Systems can now emit and ingest the same JSON schema to record who initiated each agent run and its cost, using shared IDs and rules for merges and corrections.
The tutorial demonstrates how Google Research’s MSEB Massive Sound Embedding Benchmark works by implementing two different SoundEmbedding encoders and running them through the benchmark’s classification, clustering, retrieval, and segmentation evaluators. It builds the example audio and data pipeline at a 16,000 Hz sample rate, then directly inspects what each evaluator’s metric rewards. As a result, the two encoders trade places depending on the evaluator being used, illustrating that leaderboard scores reflect the evaluator “surface” a submission is tested on.
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