ScaleGlide’s Post

Starting April 24, GitHub may use inputs, outputs, and interaction context from Copilot for model training, unless you manually opt out. In practice, that includes: prompts we write suggested/accepted code repository context All of this can feed back into the model improvement loop. The point isn’t the policy itself (this is fairly standard in AI), but the fact that the flow is passive: you keep coding as usual, and you’re already contributing to training. If you’re working on open side projects, it might not matter. If you’re dealing with proprietary code or sensitive environments, it’s probably worth making an explicit choice. #AI #GitHub #Copilot #SoftwareDevelopment #MachineLearning #DataPrivacy #DevTools #Engineering #TechAwareness

Does your business use Github CoPilot? If so prepare for Usage Based Billing coming in June. You may want to take this chance to pro-actively set quotas. https://lnkd.in/eJqN-TJb #github #development #ai #pricing

GitHub Copilot is moving to usage-based billing by June 2026. Base plan prices stay, but now you'll get monthly AI Credits. Seems like those powerful "agentic" coding sessions are what's driving the change – makes sense for compute costs. Good thing basic code completions are still included! Businesses will appreciate the pooled credits and new admin budget controls. Smart move to offer a preview bill too. 💻💰 #GitHubCopilot #AI

⚠️ Addictive tech warning for developers. Once you add a 🦆rubber duck to your AI agent pipeline, you’ll start feeling uncomfortable without it. This is exactly what happened to me. I no longer want to rely on a single model’s opinion for important technical decisions, and I definitely don’t want extra manual steps just to get a second perspective. That’s where “Rubber Duck”, an experimental feature in the GitHub Copilot CLI, really worked for me: - enable it with: "copilot --experimental" (Rubber Duck is the 1000th reason for you to switch to terminal-first development) - watch one LLM actively criticise another’s decisions right at the moments where it matters most, pushing towards a better solution - everything happens automatically, no extra friction, no context switching It is a targeted reviewer that steps in at high-value moments such as after drafting a plan, after a complex implementation, and after writing tests before execution. That feels like a very practical way to reduce compounding errors early, especially in long-running or multi-file tasks. So having AI challenge AI has quietly become part of how I build now. Would you trust critical technical decisions to a single model, or is multi-model critique the new baseline for serious AI-assisted development? Ready to try Rubber Duck? I warned you :) More details: https://msft.it/6044Q4Zs2 Morten Stange Bye, Haakon Hasli, Christian Tryti, Else Tefre, Francesco Manni, Jaime De Mora, Martin Woodward, Lee Stott, Christoffer Noring, Daniel Meppiel, Joel Norman, Ömür Sert, Adil I., Sebastien Le Calvez, 🥑 Aaron Powell, Nick McKenna, Burke Holland, Cornelia Bjørke-Hill #GitHubCopilot #GitHubCopilotCLI #CopilotCLI #DeveloperTools #AIAgents #CopilotRubberDuck #msftadvocate

Is the era of "all-you-can-eat" AI coding officially coming to an end? GitHub has announced a fundamental shift for Copilot, moving from its long-standing flat-rate subscription to a token-based consumption model starting June 2026. While the monthly fees remain nominally the same, they will now function as a pre-paid credit balance. This change is driven by the rise of "agentic" workflows—complex, multi-step autonomous tasks that consume significantly more compute power than simple autocomplete suggestions. For developers and enterprise leaders, this marks a transition from predictable seat-based expenses to variable operational costs. While basic code completions remain exempt, high-intensity tasks like architectural planning and deep-dive debugging will now require careful credit management. This shift reflects a broader market trend where AI is maturing from an experimental add-on into a metered utility, much like electricity or water. How will this change your team's approach to AI integration? Will "prompt optimization" become a financial necessity rather than just a technical skill? #GitHubCopilot #GenerativeAI #SoftwareDevelopment #TechTrends #CloudComputing Read more: https://lnkd.in/gbhtiATq

The AI coding stack is quietly consolidating. In the last two weeks alone: Cursor rebuilt its interface around orchestrating parallel agents. OpenAI shipped an official plugin that runs inside Claude Code. GitHub launched /fleet in Copilot CLI. Every major player now assumes you're running multiple agents at once, not picking one. The industry has already answered "which agent should I use?" The answer is "all of them, depending on the job." The unanswered question is the one teams are actually stuck on: how do you coordinate a team of people running a farm of agents — across machines, repos, and credentials — without losing track of who's doing what? That's the problem Coord was built for. Your agents. Our coordination. Start building free → coord.io #AIAgents #AgenticDevelopment #DevTools

🚨 GitHub Copilot is no longer “unlimited” GitHub Copilot has moved to a usage-based model — and the key concept now is model multipliers. 👉 The same request can cost 10x more depending on the model you use. 💡 What it means in practice simple task → 1× advanced model → 5×–10× 👉 using the most powerful model for everything = burning your budget fast 🧠 This is no longer just about coding It’s about managing: budget resources efficiency GitHub essentially turned Copilot into a delivery cost driver, not just a dev tool. 🚀 Takeaway AI is no longer “the more powerful, the better” 👉 it’s “the more optimal, the more efficient” 🔗 Details here: https://lnkd.in/dwau2dzG 💬 Are you already controlling AI usage cost in your team — or still defaulting to the most powerful model?

AI is about to crush inefficient workflows everywhere – and GitHub's the latest giant feeling the pain. GitHub's facing a massive uptime crisis, but dig deeper and it's clear AI's the real culprit behind the chaos. Developers hammering the platform with AI-generated code and endless Copilot requests are overwhelming servers, causing outages that hit thousands of repos and teams worldwide. This isn't just a glitch; it's a sign of how AI tools are scaling so fast they're breaking the infra they rely on. Benchmarks show models like the new GPT 5.5 smashing records – scoring 82.7 on terminal command benchmarks, leapfrogging rivals like Anthropic's Opus at 47 – while image gen hits top spots with huge jumps over Gemini variants. But when everyone piles on with agentic coding, platforms buckle under the load. This signals a huge shift for dev teams and businesses. AI's no longer a nice-to-have; it's flooding pipelines with output that exposes weak spots in legacy systems. Companies ignoring this will waste hours on downtime, while smart ones automate smarter – chaining agents that handle CRM, calls, and analysis without crashing the stack. We're heading to a world where AI doesn't just code, it runs entire ops seamlessly, but only if your setup can handle the firehose. This is exactly the mess Katy at Gitwix fixes for our clients – one dashboard keeping everything humming. How's AI disruption hitting your workflows right now? #AI #AIAutomation #FutureOfWork

The era of unlimited AI coding tools is quietly coming to an end. 🚨 Both Claude Code and GitHub Copilot hit major turbulence this week, and the reasons tell us a lot about where AI is headed. 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: • GitHub Copilot froze new signups for Pro, Pro+, and Student plans • Anthropic briefly pulled Claude Code from its $20/month Pro tier • Usage limits tightened. Premium models quietly removed from lower plans. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? Agentic AI. Developers aren't just asking for code snippets anymore, they're running autonomous agents that execute long, complex workflows for hours. A handful of user sessions can now cost more than an entire monthly subscription. Flat-rate pricing was built for a world that no longer exists. 𝗪𝗵𝗮𝘁'𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗻𝗲𝘅𝘁: • Token-based billing (Microsoft has already planned this for June) • Tiered access to powerful models based on what you pay • Potential removal of agentic features from entry-level plans • Pricing models that reflect actual compute costs The uncomfortable truth: the tools developers have come to rely on daily are about to get more expensive, or more restricted. The companies that adapt their workflows now will be far better positioned than those caught off guard when the pricing hammer drops. Are you rethinking your AI tooling strategy? 👇 #AI #DeveloperTools #ClaudeCode #GitHubCopilot #AgenticAI #SoftwareDevelopment

GitHub Adds “Rubber Duck” Review Agent to Copilot CLI GitHub has launched an experimental “Rubber Duck” mode in Copilot CLI, bringing a second AI model into the loop to review, challenge, and validate the primary agent’s work before execution. What’s interesting isn’t just the feature - it’s the pattern. 🔹 Second Opinion by Design: A separate model from a different AI family evaluates plans before they run. 🔹 Focused Review Layer: It flags missed assumptions, edge cases, and hidden risks. 🔹 Better Outcomes on Complex Tasks: Especially effective on multi-file, high-step problems where errors compound. 🔹 Agent + Reviewer Pattern: Introduces a structured “builder + critic” dynamic inside AI workflows. As agents become more autonomous, the risk isn’t that they can’t execute - it’s that they execute flawed plans too confidently. Rubber Duck introduces friction in the right place: before things break. At GlenFlow, we see this as a natural next step in agentic development. Not just more powerful agents but systems of agents that challenge each other. Because in an AI-native workflow, quality won’t come from a single smarter model - it’ll come from orchestrated disagreement. Read more: https://lnkd.in/dUwd5dms #AI #GitHubCopilot #AICoding #AgenticAI #DevTools #SoftwareEngineering #FutureOfWork #GlenFlow