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AI coding tools increase developer output. But growing pull request backlogs can easily overwhelm engineering teams, contributing to what’s known as “review fatigue.” Here’s how to calibrate your workflows for agentic development. Improve prompts, use stacked pull requests, and recognize how the developer role is changing.

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GitHub Enterprise

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11,426 görüntüleme • 5 gün önce •via X (Twitter)

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Madeactual4 gün önce

The bottleneck is not output; it is review bandwidth. If AI doubles PR volume without changing ownership, test depth, and merge policy, the “productivity” metric is just work sitting in a queue.

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94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.

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🆕The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect Cognition cofounder + CPO Walden and Open-Inspect creator explain why engineering is moving from local IDEs to cloud background agents, how Devin went from 16% to 80% of commits across Cognition repos, why spec-to-PR workflows became real after the December model inflection, why testing is harder than computer use, how Devin separates the brain from the machine, why MCP is not enough for production agent integrations, and how PMs, support teams, and SRE workflows are starting to turn Slack messages into pull requests.

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Excited to share that I’ve joined Anomaly as founding designer. I’m thrilled to work with Jay dax Frank Wang and Adam — a team stacked with talent! I’ve always been fascinated by challenges that influence the way people work. Today, AI is reshaping software development at a pace we’ve never seen. From solo developers to global enterprises, the way we write code has shifted dramatically in the past year, and will continue to change in the years ahead. This is an opportunity for me to help shape these experiences for everyone, regardless of frameworks, IDEs, or tools. I want to explore how they’re adopted and how they can have the greatest impact. The team is hard at work on OpenCode — the open source AI coding agent for the terminal. With over 23K stars on GitHub, it’s already changing how thousands of developers write code every day. Follow OpenCode as we go beyond the terminal and design and build world-class AI developer experiences ✌️

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📢 It's official! The MicroGPT token is launching on November 20th! 📢 Get ready to fuel the future of coding with MicroGPT’s powerful suite of tools: 💡 Real-time coding suggestions 🛠️ IDE integration for seamless workflows 📲 Mobile app, Chrome extension, and more Join us as we redefine the developer experience and make software creation faster, smarter, and more accessible. The countdown is on! 🚀 #MicroGPT #TokenLaunch #AI #CodingInnovation

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1:07

How can HR help organizations navigate the people side of AI transformation? Paula Goldman, Salesforce’s chief ethical and humane use officer, argues that HR functions have an important role to play in shaping how AI changes people management. AI can help give managers more evidence to inform performance decisions, identify employees’ skills, and connect people with projects or learning opportunities that prepare them for what’s next. The opportunity for HR is to use AI intentionally to improve how organizations manage and develop their people. Listen to the full IdeaCast episode wherever you get your podcasts, or here:

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In this episode of On Rails, Jason Meller (VP of Product at 1Password) talks to Robby Russell about how Rails has become one of the most token-efficient architectures for LLM-assisted development, and why that matters as token costs increasingly shape what’s practical (and profitable) to build. They also dive into security in agentic workflows, an area getting harder to ignore. Jason breaks down what 1Password’s open-source SCAM benchmark reveals about how LLMs handle credentials, where risks emerge when models act autonomously in developer environments, and what teams can do now to stay ahead. Find the full episode here or on Spotify/Apple/YouTube:
0:56

In this episode of On Rails, Jason Meller (VP of Product at 1Password) talks to Robby Russell about how Rails has become one of the most token-efficient architectures for LLM-assisted development, and why that matters as token costs increasingly shape what’s practical (and profitable) to build. They also dive into security in agentic workflows, an area getting harder to ignore. Jason breaks down what 1Password’s open-source SCAM benchmark reveals about how LLMs handle credentials, where risks emerge when models act autonomously in developer environments, and what teams can do now to stay ahead. Find the full episode here or on Spotify/Apple/YouTube:

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28:07

GitHub has a front-row seat to how code is changing now that everyone—and their army of agents—can ship code. In March alone, agents created 17 million pull requests on the platform. That’s why I was thrilled Mike Taylor was on hand to interview GitHub COO Kyle Daigle at Microsoft Build for a behind-the-scenes look at how the platform is helping developers manage the influx without dictating which pull requests they should trust or merge. This week on Every 🪨’s AI & I, Mike gets into: - The 14x commit explosion. GitHub hit 1 billion commits last year. Kyle says they’re on pace for 14 billion this year—and he doesn’t think that curve is plateauing. - GitHub is committed to letting open-source maintainers set their own standards. Agent PRs are flooding communities, but GitHub’s philosophy is to leave code maintainers in control. - The developer/non-developer distinction is collapsing. GitHub’s own legal and finance teams are using Copilot to build apps, one example of how AI has expanded the definition of who counts as a developer. - Per-seat pricing doesn’t survive a world where agents run while you sleep. Kyle thinks automatic model routing—swapping in Haiku for simple tasks instead of always calling the expensive model—is the best way to make the economics make sense. - Daigle runs a daily self-improvement loop with an AI he named Baxter. Every day, Baxter reads 7 days of his emails and Slack messages, flags his communication patterns, and checks whether Kyle followed last week’s advice. This is a must-watch for anyone running agents in their dev workflow—and curious how GitHub is handling the explosion of commits on its platform. Watch below! Timestamps: Introduction: 00:00:52 The agentic PR flood: 00:03:27 GitHub’s approach to helping open-source maintainers manage the surge: 00:04:33 What 14 billion commits means for code quality: 00:06:15 Moving from per-seat licensing to usage-based pricing: 00:08:03 Kyle's dual role as GitHub COO and Microsoft's chief marketing officer for developers: 00:09:45 Developer choice as competitive moat: 00:13:03 How to balance dogfooding your own tools with staying honest about the competition: 00:14:57 Hill climbing, frontier tuning, and solving the model-routing problem: 00:19:45 Kyle's agentic communication hack: 00:24:45

Dan Shipper 📧

18,569 görüntüleme • 3 ay önce

You’re wasting insane amounts of money on all of your agentic workflows. I calculated the cost, it is staggering. You could be saving over 100x in tokens by changing the workflow’s orchestration loop by just adding one extra step into your agentic workflow setup. This completely changes the economics of deploying agentic systems for both simple and complex agentic workflows. I am writing an article on this right now to teach you how to do it so you can save money on your agentic workflows. Errrrrm… back to typing I guess? Turn my notifications on as I’m aiming to post it later on tonight. Speak to you shortly.
0:29

You’re wasting insane amounts of money on all of your agentic workflows. I calculated the cost, it is staggering. You could be saving over 100x in tokens by changing the workflow’s orchestration loop by just adding one extra step into your agentic workflow setup. This completely changes the economics of deploying agentic systems for both simple and complex agentic workflows. I am writing an article on this right now to teach you how to do it so you can save money on your agentic workflows. Errrrrm… back to typing I guess? Turn my notifications on as I’m aiming to post it later on tonight. Speak to you shortly.

hoeem

16,427 görüntüleme • 3 ay önce

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:
3:27

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:

Andrew Ng

182,119 görüntüleme • 1 yıl önce

Episode 5 of How I AI with some practical AI hacks for managers from hilary gridley, Head of Core Product at WHOOP. This one is all about feedback: how to scale it, how to make it specific, how to give it to AI tools. You’ll learn how to: - build rubrics for what “good” looks like - create GPTs to “think like you” and use them to scale quality feedback to your team - use prompting techniques to improve the insights you get from AI - improve your quality of writing and strategic thinking by bringing it all together Plus we gab about getting more women in AI, girly use cases for chatGPT, and whether or not getting invited to meetings is a good thing. Check it out in the links below 👇🔗 As always, a big thanks to our sponsors: 🔀 Orkes enterprise platform for genetic workflows ☑️ Vanta automate compliance and simplify security
0:41

Episode 5 of How I AI with some practical AI hacks for managers from hilary gridley, Head of Core Product at WHOOP. This one is all about feedback: how to scale it, how to make it specific, how to give it to AI tools. You’ll learn how to: - build rubrics for what “good” looks like - create GPTs to “think like you” and use them to scale quality feedback to your team - use prompting techniques to improve the insights you get from AI - improve your quality of writing and strategic thinking by bringing it all together Plus we gab about getting more women in AI, girly use cases for chatGPT, and whether or not getting invited to meetings is a good thing. Check it out in the links below 👇🔗 As always, a big thanks to our sponsors: 🔀 Orkes enterprise platform for genetic workflows ☑️ Vanta automate compliance and simplify security

claire vo 🖤

19,127 görüntüleme • 1 yıl önce

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!
0:55

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Hurley

33,232 görüntüleme • 4 ay önce