Brian Pendergrass

Microsoft Cloud Solutions Architect with specialization in Azure and Cloud Identity

[Peter Steinberger 🦞 @steipete]

Such a privilege to work with Microsoft to bring claws to enterprises!

[Kyle Daigle @kdaigle]

A LOT showed up at Build this year.

My favorite theme: agents are moving from demos into the actual developer + enterprise stack: code, models, local compute, context, and OS-level safety.

Rapid-fire favorites 👇

[Microsoft AI @MicrosoftAI]

Seven new models launching at Build: let’s go!
Reasoning. Code. Image. Transcribe. Voice.

Built from scratch on a clean data lineage, designed for efficiency, working seamlessly as a family of models

Thread 🧵
#MSBuild

[Microsoft 365 @Microsoft365]

Meet Microsoft Scout.

An always-on agent that keeps work moving, taking action without needing to be prompted each time.

As Microsoft’s first Autopilot agent, Microsoft Scout works across Teams, Outlook, OneDrive, and more—taking action within the controls your organization sets.

Learn more: https://t.co/n8xTWVZA5P

[Rohit @rohit4verse]

https://t.co/8j0qNyJCYF

[Nandkishor @devops_nk]

Honestly, this is the most accurate diagram I've seen.

Waterfall: You plan for 18 months and deliver exactly what nobody needs anymore.

Agile: You deliver something usable at every step, but the CEO keeps asking, "Where's the car?"

AI: You get the car on day one. It has six wheels, the doors are on backwards, and it has a rocket launcher. You spend more time making it yours than actually "building"; it's shaping. owning. verifying. That's what the best AI developers do now. They don't build. They shape and own.

[Greg Brockman @gdb]

Codex agentic sandbox for Windows:

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Dario Amodei @DarioAmodei]

The Adolescence of Technology: an essay on the risks posed by powerful AI to national security, economies and democracy—and how we can defend against them: https://t.co/0phIiJjrmz

[Microsoft Learn @MicrosoftLearn]

We’re supporting people and organizations to grow, one skill at a time.
Meet AI Skills Navigator: AI‑led, human‑enhanced learning + credentials for every role (incl. new business‑focused).
Read our blog to learn more, incl. renewed skilling partnerships → https://t.co/gjqXTmcmpO

[Microsoft Events @events_msft]

Tomorrow Microsoft Ignite kicks off the next wave of AI transformation. 🌐

Join us live at 9AM PT as Microsoft leaders unveil what's next in AI, cloud, and security.

Save this post and tune in:

[Merill Fernando @merill]

👋 Folks, I'm super excited to announce the launch of the Microsoft Zero Trust Assessment!

I've been working on this project for the past year at Microsoft with an extended team including our security researchers, product feature teams and docs

Here's what it does

🧵👇

[Microsoft Learn @MicrosoftLearn]

Can’t make it to Ignite in person? No worries. 🌍
You can still join the digital experience from anywhere.
Save the date: November 18–20.
👉 https://t.co/4h5WuAkKgf

[Satya Nadella @satyanadella]

Today we’re releasing GitHub Spark — a new tool in Copilot that turns your ideas into full-stack apps, entirely in natural language.

[Microsoft Mechanics @MSFTMechanics]

Adapt AI security based on user roles. See how to block or allow access without changing your code.
 https://t.co/crYxmCxyrJ #PurviewSDK #purview #datagovernance #microsoft #microsoft365 #microsoftsecurity

[Natasha Malpani 👁 @natashamalpani]

@karpathy breaks it down so clearly it feels obvious in hindsight: software isn’t just code anymore. it’s prompts, weights, GUI, and behavior.

his framework is deeply simple:
-software 1.0: you write code
-software 2.0: you train weights
-software 3.0: you write in english

LLMs aren’t APIs. they’re operating systems. prompts are programs. context windows are memory. outputs are behavior.

most founders are building agents. karpathy says: build sliders. let the human choose how much control to delegate. @cursor and @AskPerplexity nailed this: partial autonomy that feels like power.

if you’re a builder:
-think like it’s 1965
-make your product readable by people and models
-design sliders, not agents
-build tools users can verify, not just use

[Y Combinator @ycombinator]

Andrej Karpathy's (@karpathy) keynote yesterday at AI Startup School in San Francisco.

[anarchy.build @anarchy_build]

Satya Nadella, CEO of Microsoft, just made a bold prediction:

"The application layer is collapsing into agents."

Translation? Your apps are obsolete.

In a recent interview, he revealed the ONE shift that will redefine AI’s future...

Here's everything you need to know:🧵

[Microsoft Learn @MicrosoftLearn]

🚨 BIG NEWS: MICROSOFT INTRODUCES THE AGENTIC WEB 🚨

AI is leveling up! Microsoft is leading the charge with the Agentic Web, where AI agents work together across individuals, teams, businesses, and entire ecosystems.

Learn more clicking thread below >>

[Merill Fernando @merill]

👋 Check out this new Microsoft Entra blog post 👇

Announcing Microsoft Entra Agent ID: Secure and manage your AI agents

https://t.co/wOyKzAUHT7