Meta has launched its first smart glasses without Ray-Ban branding. Starting at $299, they're cheaper than the Ray-Ban Meta Gen 2 while retaining EssilorLuxottica as a design and manufacturing partner. The Verge reports: As far as style and specs, the Meta Glasses aren't that different from Ray-Bans. The internal specs are the same as the recently released Ray-Ban Meta Optics Styles, with slightly longer battery life. The Adventurer models have thinner rims, while the Fury models hew a bit closer to the Meta Ray-Ban Display with a bolder, chunkier frame. You could describe the Adventurer as square, and the Fury as even more square. The Kylie glasses sport a more unique design with a distinct Y2K flavor that I'm told is meant to be worn lower on your nose. [...] While playing around with the Meta Glasses, it was hard not to notice that the camera appears smaller than in previous Ray-Ban glasses. Technically, Himel tells me, that's not new to these Meta Glasses. It was actually introduced back in March with the prescription-optimized Optics Styles.
[...] Meta is quadrupling down on AI. The new Meta Glasses will all launch with Muse Spark, the first model out of Meta's Superintelligence Labs. (It'll also be arriving on older Ray-Ban and Oakley glasses in the US and Canada via a software update.) Supposedly, that means more helpful glasses. At my hands-on, I was told that Meta AI would now be less stiff. I'd be able to talk to it more naturally and get smarter responses. The AI now supports 14 more languages, including Arabic, Japanese, Mandarin, Hindi, and Korean. Pedestrian turn-by-turn navigation is also coming to Meta's displayless glasses. Later this month, there'll be a new "dynamic photo" feature that automatically takes multiple frames and then recommends the best one.
Here’s the thing about senior leadership that nobody warns you about: the job isn’t hard because of any single task. It’s hard because your work lives in fifteen different places and your brain is the only system connecting them.
Meetings bleed into each other. Decisions are made in threads without you. Someone mentioned your name in a planning meeting, and now there’s an action item living in a doc you’ve never seen. You’ll find out about it in two weeks when someone casually asks for an update. Fun.
Last year, my team almost missed a performance review deadline because it was announced in a channel nobody was watching. One person spent ten minutes searching Slack and couldn’t find it. Another found the date in a random, unrelated channel. I ended up posting “I’ll admit we dropped the ball on following up in Slack, so that’s on me.” That’s the kind of thing that keeps happening when your brain is the only system connecting everything.
I was spending so much energy on context-switching that I had nothing left for the thinking, connecting, and creating that my role actually requires (and that’s the work I actually like doing). But I started using automations in the GitHub Copilot app, and it changed my entire workflow. Bear with me.
What automations actually are
The GitHub Copilot app is a standalone desktop app for macOS, Windows, and Linux, built for working with agents, not just talking to them. You can run parallel sessions across repositories, each on its own branch and worktree. You can see what agents are doing in real time through canvases, which are bidirectional work surfaces where you and the agent operate on the same plan, terminal, or browser session. Progress is visible and steerable, not buried in chat history.
Automations are scheduled prompts that run against your real work context: your calendar, your email, your messages, your GitHub repos. They connect through MCP servers and integrations, so they can see what’s happening across all the places your work lives. They tell me what actually needs my attention, which lets me ignore the rest.
Think of them as agents with a standing brief. You tell them what to care about, how to think, and when to run. Then they just… do it. Every day. Without you remembering to ask. Which is good, because you won’t.
What this looks like
I’m a senior director at GitHub. I lead developer relations. My scope is wide, my calendar is full, and my brain works differently than most people assume. I’m AuDHD, which means I’m good at pattern recognition and deep focus, but genuinely terrible at remembering which thread I promised to follow up on three days ago.
I didn’t set out to build 40 automations. I was curious about the automations tab, asked the app what it could do, and it suggested things I hadn’t thought of. The first time I set one up, I opened a chat and said something like: “Look across all of my work surfaces, my calendar, my email, my messages, and figure out where I’m dropping balls, where I might need help, and suggest automations that would be useful.”
It immediately suggested about six. The first drafts weren’t perfect, and that’s okay. You refine them. You give them voice. You teach them how you think. Once I saw what was possible, I kept going. Now I have about 40. (I know. I know.)
I’m not going to walk through all of them. (You’re welcome.) But here are the categories that matter most, and some highlights from each.
The morning brief
Every day before I open anything, several automations have already run. Meeting Prep pulls my calendar and builds context for every meeting, with different formats for one-on-ones vs. large syncs vs. external calls. By the time I sit down, I know what each meeting is about and what I need to bring. Pre-Meeting Access Check verifies I actually have access to the docs and links referenced in the invite. No more showing up and realizing the agenda doc is locked. If you’ve never experienced that particular panic, honestly, must be nice. Daily Triage Digest sweeps GitHub, email, and messages for anything that needs my attention.
The cumulative effect is that my mornings went from “frantically opening twelve tabs while pretending I’ve read the agenda” to “reading a few summaries with coffee.” It’s a different life.
Staying current
I cannot be surprised by our own launches. That’s literally the job.
Ship Decoder finds everything GitHub shipped in the last 24 hours and explains it to me in plain language. This is real context I can use in conversations. Launch Radar runs weekly and surfaces upcoming launches that touch my team’s space so I’m never blindsided. These two alone probably save me an hour a day of scrolling through channels trying to piece together what happened. I used to spend that hour. I did not enjoy that hour.
Career architecture
This is the category that surprised me most. I built automations that actively work on career development, and if that sounds weird, stay with me.
Daily Wins Recap runs every evening and summarizes what I actually accomplished. This one matters more than it sounds. My default mode is to check something off and immediately move to the next thing. I don’t sit with it. I don’t recognize it. I just keep going. Then performance review season comes around. I have to articulate my impact, and I’m panic-staring at a blank doc trying to remember eight months of work.
This automation keeps a running record so I don’t have to. Think of it as a gratitude practice backed by real data rather than a task list. It counters the “what did I even do today?” spiral that hits hardest on the busy days. On the days when imposter syndrome is loud, I need something that talks back to it with facts. The robot believes in me even when I don’t. That’s oddly moving? I don’t know. It works.
Team and people
This is where I want to be really honest, because I know you might be thinking: is she automating the human parts of her job?
No. And that distinction matters to me more than anything else in this post.
Commitments and Follow-Up Tracker searches my own messages for things I said I’d do and flags what I haven’t done yet. This one is humbling. And essential. Because when I tell someone “I’ll look into this” and then forget, that’s a trust problem. The automation protects the trust.
The kudos I write are still mine. The noticing is still mine. The automation just makes sure my brain doesn’t steal it from the people who deserve it.
These automations don’t replace connection. They enable it. They give me back the headspace to actually show up for people. Before this system, I’d walk into conversations distracted or running on fumes, because my brain was full of operational noise. Now when I sit down in a one-on-one, I’m actually present. When I write recognition for my team, it’s specific and real.
The automations handle the scaffolding. I do the human work. That’s the deal.
Maintenance and logistics
This category covers the boring stuff that quietly eats your week if you let it: Dependabot PR Triage finds and merges safe dependency updates across my repos daily. Handled. Stale Work Finder surfaces pull requests I opened and forgot, issues that went quiet, branches collecting dust. (We all have those. Don’t lie.) Travel Logistics Tracker watches for conference-related threads and consolidates logistics into a single brief. Conference season is chaos. This helps.
What my automations look like
Here’s a real one from my setup, the Stale Work Finder, so you can see what these prompts actually look like in practice:
Find all my stale work across GitHub using the gh CLI. Things that are falling through the cracks.
Check for:
- PRs I opened that haven't received a review in 7+ days
- PRs I'm assigned to review that I haven't reviewed yet (older than 3 days)
- Issues assigned to me that have had no activity in 14+ days
- Draft PRs I own that have been drafts for 2+ weeks
- For each item show: repo, title, link, how long it's been stale, and who's involved.
Format as:
1. 🔴 Embarrassingly stale (3+ weeks)
2. 🟡 Getting dusty (1-3 weeks)
3. 🟢 Just needs a nudge (under a week)
That’s it. That’s the whole automation. You write a prompt, set a schedule, and the agent runs it on your behalf. You can get as detailed or as loose as you want. The app fills in context from your connected tools. It runs every Monday for me and the results are… always a little eye-opening. But it’s better to know.
The AuDHD part
I’ll just say it: for me, automations are an accessibility tool.
AuDHD means my executive function and working memory are wildly inconsistent, and the inconsistency is the hardest part to explain to people. Some days I can hold seventeen threads in my head. Other days I forget I have a meeting in 10 minutes. There is no in between. The gap between those days used to scare me, because my team deserves consistent leadership regardless of what my brain is doing on any given Tuesday.
These automations narrow that gap. They make me consistent. They mean my team gets the same quality of attention whether my executive function showed up today or not. For me, that’s the difference between thriving and slowly burning out. And I’ve done the burning out part. Zero stars, would not recommend.
How to start (for real)
If you’re thinking about building something like this, here’s what I’d say: don’t try to automate everything at once. Start with the one thing that causes you the most friction.
For me, it was meeting prep. I kept walking into meetings cold because prep required visiting four different tools and synthesizing information I didn’t have bandwidth to synthesize. One automation fixed that. And once I felt that relief, I kept going. And going. And going.
Could I consolidate some of these? Probably. I have about 40, and I’m sure some of them could be combined. I prefer specificity, but you could easily roll several into one big automation if that’s more your style.
Here’s the trick that worked for me: open a chat in the GitHub Copilot app and ask it to audit your work surfaces. Where are you dropping balls? Where are the repetitive patterns? What’s the thing you keep meaning to do but never get to? Start there.
The first draft won’t be perfect. That’s fine. You refine it in conversation. You teach it your voice, your priorities, how you think about “good.” Then you let it run.
Start with one. See how it feels.
Then build another. And another. And before you know it, you have 40, and you’re writing a blog post about it. Anyway.
The bigger picture
I think we’re at an interesting moment for how people relate to AI at work. The early conversation about AI at work was mostly about generation. Make me a thing. Write me the code. The reality, at least for me, is more like augmentation of invisible labor. The stuff that burns you out but never shows up in your output. The meta-work nobody acknowledges in performance reviews but everyone is buried in.
Every leader I know is overwhelmed by context. Every neurodivergent professional I know is spending enormous energy on systems that neurotypical people navigate without thinking about. Automations won’t fix organizational dysfunction or bad management or an unreasonable workload. But they can give you back enough headspace to actually do the work you’re here to do.
And honestly? That’s enough. That’s a lot.
And look, this is a GitHub product. It’ll also run your dependency updates, triage your issues, do security sweeps across your repos. The developer workflows are exactly what you’d expect. I just happen to use it for the parts of my job nobody talks about.
GitHub has joined an open source coalition of Black Forest Labs, Hugging Face, and Mozilla Corporation calling for targeted amendments to California’s AI Transparency Act (SB 942, as proposed to be amended in SB 1000). Read the full letter here.
At issue is a narrow but important problem for developers: as currently drafted, the bill’s license revocation provisions conflict with how open source licenses work in practice. Open source licenses are designed to be perpetual and irrevocable, which is what allows developers to reliably build on, reuse, and share code across projects and organizations.
The proposed language would require developers to revoke licenses if downstream users fail to meet certain obligations. That approach is incompatible with widely used open source licenses, and it could introduce uncertainty across the software supply chain—particularly for collaborative and community-driven projects.
The coalition’s letter explains that this requirement is not necessary to achieve the bill’s goals. Developers who modify and deploy AI systems are already directly covered by the law, and enforcement mechanisms remain in place. At the same time, there is a workable alternative: aligning with the EU’s approach in the AI Act Transparency Code of Practice, which recognizes the distinct nature of the open source ecosystem and acknowledges that notifying downstream users of best practices in documentation is sufficient.
GitHub supports these amendments because they preserve the bill’s transparency objectives while maintaining compatibility with open source development. Getting this balance right is critical to ensuring that California continues to support both AI accountability and open, collaborative innovation.
Call to action
We encourage you to review the letter and share your perspective with policymakers. Clear, technically grounded feedback that includes open source developers and civil society can help ensure that AI transparency requirements work in practice without compromising the open source ecosystem that underpins AI innovation.
Cloud operations are entering a new era as AI-driven and autonomous agents become a larger part of modern software systems. As software becomes increasingly agentic, the challenge is no longer just managing greater scale and complexity. Operators must also contend with systems that evolve faster, act more autonomously and interact across an expanding network of dependencies.
As applications, models, APIs and infrastructure become increasingly interconnected, their behavior is harder to understand end to end. Systems no longer fail in isolation. They fail through interactions across dependencies, services and environments that are constantly changing in real time.
To help organizations operate effectively in these increasingly dynamic environments, today we’re announcing the general availability of the Azure Copilot Observability Agent. Built on Microsoft Azure Monitor, it correlates signals across agents, applications, infrastructure and services to provide the context needed to operate confidently in this new environment.
Observability becomes foundational in an agentic world
In a recent survey of 250 IT decision-makers, Microsoft and Material found that 84% of organizations report increased cloud complexity, with 69% saying it is outpacing their current operating model. The impact is most acute across security, cost management and performance, and it extends across the entire operations lifecycle.
As the pace and scale of change accelerate, no individual or team can realistically maintain the full context required to diagnose and resolve issues quickly enough. This is driving a shift toward agentic operations, where intelligence augments how systems are understood and managed.
Observability is foundational to this shift. It provides the real-time understanding of system behavior that agents depend on to reason, adapt and act. Without a connected view across signals, even the most advanced agents lack the context required to operate reliably.
From signals to resolution with the Observability Agent
We designed the Observability Agent to help operators move more quickly from detection to understanding. It connects logs, metrics, traces, topology and operational context across environments, reducing the time it takes to identify the root cause of an issue.
As telemetry spreads across systems, operators are often forced to piece together context across multiple tools. The Observability Agent addresses this fragmentation by reasoning across signals in real time and unifying that context into a single operational view. These agentic capabilities are integrated directly into existing workflows, helping teams move from investigation to resolution faster with clear, actionable insight.
We’re already seeing customers use the Observability Agent to reduce manual effort, accelerate incident resolution and improve operational clarity:
“The biggest value is speed! The [Azure Copilot] Observability Agent helps us resolve incidents faster and reduce operational overhead by turning logs, metrics and traces into plain English insights. These agents run deep investigations and provide remediation recommendations almost immediately, compared to hours or even days previously.
Since adopting these capabilities, we’ve reclaimed an estimated 250 engineering hours monthly that are now redirected toward supporting new applications and features. We can use natural language to detect, diagnose and remediate issues faster than ever before.”
— Narmada Krishnaswamy, Head of KPMG Audit Application Support and Operations
“Azure Copilot Observability Agent helped us move from manual incident hunting to faster, AI-guided investigations. For PolicyVault, it pulls together the telemetry from our service, correlates it with Azure resource health and gives us actionable next steps based on the investigation. That means we’re not just seeing what broke; we’re getting a much clearer idea of why it happened and what to do about it, which saves us a lot of time during incidents.”
— Vladimir Gusarov, Founder & CEO, PolicyVault
“Azure Copilot’s Observability Agent helps us move faster from signal to insight. By bringing together our telemetry and guiding us toward likely root causes, it reduces the time and effort needed to investigate incidents and keeps our teams focused on what matters most.”
— Theus Hossmann, Chief Technology Officer at Ontinue
Beyond improving incident response, this shift reflects a new approach to cloud operations, where systems can continuously reason across signals and act on that understanding.
Check out our Tech Community blog post to learn more about the Azure Copilot Observability Agent.
From observability to agentic operations across the cloud lifecycle
Observability is part of a broader shift to agentic operations. As systems become more autonomous, operations expand from understanding what is happening in production to continuously improving how those systems behave over time.
In an agentic model, this forms a lifecycle. Systems generate signals, agents interpret those signals, take action and learn from outcomes. Over time, this creates a feedback loop where each operational cycle improves the next, increasing system resilience and efficiency.
This shift requires more than better visibility. It requires a coordinated approach across the lifecycle, from observability and diagnosis to optimization and remediation where insight and action are tightly connected.
As agents take on a greater role in that lifecycle, governance becomes central to how systems are trusted and controlled. Policy, auditability and guardrails ensure that actions taken by agents align with organizational intent and operate within defined boundaries. Human oversight remains essential, not as a bottleneck, but as a mechanism for building confidence and ensuring reliability as automation scales.
This is where Azure is uniquely positioned. By bringing together observability, automation and governance within a connected platform, Azure enables organizations to move from isolated tools to an integrated operational model that spans the full lifecycle.
Azure Copilot Observability Agent plays a key role in this model by grounding agentic systems in real-time operational context. As organizations build and deploy more agents, this foundation becomes critical for ensuring those systems operate effectively and responsibly.
Cloud operations are shifting from reactive management to a continuous, agent-driven lifecycle of learning, adaptation and control. This vision of agentic cloud operations is already taking shape across Azure. Read our companion Azure Blog post for more details.
Brendan Burns is a co-founder of the Kubernetes open source project and corporate vice president for Azure cloud-native open source and the Azure management platform including Azure Arc. He is also the author and co-author of several books on Kubernetes and distributed systems.
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