The Surface Slim Pen 2 can work as a tiny Slidev clicker. The trick is not to make Slidev understand a pen. It is to make the pen emit the same keyboard actions Slidev already understands: single click -> Right Arrow or Space -> next animation or slide double click -> Left Arrow -> previous animation or slide long press -> F -> fullscreen That is the whole setup. The annoying part is that the Slim Pen 2 is not a presentation remote by default. Windows treats the top button as a Bluetooth shortcut button for Windows Ink-style actions, while Slidev listens for keyboard navigation. So you need a small remap layer between the two. ...

The speech stack has split into three very different shapes. One shape is a model family: Voxtral. It is Mistral’s audio line, with text-to-speech, speech-to-text, realtime transcription, and API-centered voice workflows. Another shape is a native Apple SDK: FluidAudio. It is not one model. It is a Swift/CoreML pipeline for local transcription, voice activity detection, diarization, and TTS on macOS and iOS. The third shape is a recognition engine: Parakeet. It is NVIDIA’s ASR family, built around FastConformer/TDT variants, optimized for very fast and accurate speech-to-text. ...

How a single Telegram message can spin up a swarm of AI agents, decompose your work, run a TDD pipeline in isolated git worktrees, and ship a PR — all while you’re asleep. TL;DR Hermes Agent v0.15.0 (codenamed The Velocity Release) landed on May 28, 2026 with 747 PRs, 1,302 commits, and 321 contributors. It transforms Hermes from a smart chat-driven coding agent into a production-grade autonomous multi-agent orchestration platform. The big-ticket items: ...

I do not trust agent demos where the same model context can read a public issue, inspect a private repository, and post back to the internet with one broad token. That is not autonomy. That is a breach waiting for a better prompt injection. Prompt injection is usually framed as a model problem: the model read hostile text and followed it. I think that framing is too small. The real problem is authority. We keep putting hostile text, private data, and privileged tools into the same execution loop, then asking the model to behave. ...

Microsoft 365 Copilot agents are crossing the line from demo artifacts into software products. Once that happens, manual spot checks are not enough. A production agent needs a release discipline: evaluation datasets, judge configuration, thresholds, CI/CD gates, evidence packages, and regression memory. Not as governance theatre. As the shortest safe path from “nice demo” to “we can ship this and explain why.” This is the blueprint I would use to move a Copilot agent from vibe-based confidence to governed delivery. ...

As of May, 2026, the strongest pattern in AI coding is not “give the agent a bigger context window.” It is the emergence of a controlled agent operating layer around the repository. That layer has a few recognizable parts: canonical instructions in version control, path-scoped rules near the code they govern, task specs before implementation, bounded subagents, MCP/tool allowlists, sandboxing, audit logs, cost-aware model routing, and a verification loop that does not confuse “the agent says it passed” with evidence. ...

As of April 18, 2026, the latest revision in my local clone of microsoft/skills-for-copilot-studio is commit 5c1cc83, tagged v1.0.8, with recent release-process work merged on April 14-16, 2026. Microsoft also labels the repository an experimental research project, which is the right framing: this is not a finished product so much as a serious attempt to make Copilot Studio agents behave like code instead of opaque portal artifacts. S1 S3 S4 S17 That distinction matters more than it sounds. Copilot Studio has always had a tension between low-code authoring and engineering discipline. This repo picks a side. It says an agent should live as a folder-backed YAML bundle, should be editable by AI coding tools, should be checked against schema and runtime constraints, and should move through a repeatable clone -> author -> validate -> push -> publish -> test loop. S1 S2 S5 S8 ...

On April 16, 2026, I replaced my earlier local Gemma run with a heavier stack: llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q8_K_XL --jinja The result was not “a slightly better chat model.” The result was a qualitatively different local agent loop. This time the agent did not stop at repo reconnaissance, rough planning, or code scaffolding. It wrote a research narrative, generated a slide deck module by module, rebuilt after failures, converted the deck to PDF, rasterized slides for visual inspection, read the resulting PNGs, ran text extraction against the .pptx, checked for placeholder residue, and then closed the loop with targeted repairs. That is not AGI. But it is no longer a toy local demo either. ...

Most research-agent failures are not model failures. They are context-budget failures. You give the agent a large context window, then quietly burn it on bootstrap files, tool schemas, verbose search results, stale heartbeat history, and repeated prompt reinjection. The run still looks healthy for a while, but quality has already started to slide. The agent drifts, repeats searches, forgets what it just learned, or falls into overflow recovery loops. As of April 15, 2026, OpenClaw already exposes the right primitives to prevent that: detailed context introspection, isolated cron sessions, lightweight bootstrap context, session pruning, compaction, and heartbeat isolation. The engineering problem is not missing features. It is composing those features into a system that keeps the model’s active working set small, fresh, and task-specific. S1 S2 S3 S4 S5 ...