devtech.pro’s Post

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

Think building an AI Agent is hard? Try this 👀 👇 Fetch.ai’s GitHub Repo. Instead of building from scratch, you can: → Use pre-built agent templates → Modify real working examples → Launch faster than expected Best thing that stands out: composability 🚀 Small components, reusable across projects, turning simple ideas into scalable systems. If you're building at LA Hacks this weekend, save this post! 👉 Give it a go today: https://lnkd.in/eeNzGfGw Sana Wajid #resource #github #developer #ai #innovation #tech

GitHub’s decision to pause new Copilot subscriptions highlights the massive infrastructure strain caused by the shift toward resource-heavy agentic AI workflows. It’s a significant moment for the industry, signaling that the era of unlimited, subsidized AI compute may be coming to an end in favor of more sustainable usage limits.

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

AI technology had insanely growth in last few years, but it has started to show financial strain. Pricing decision is extremely important in today's AI era, which can land organization in trouble. GitHub Copilot is a recent example which is a tip of iceberg. Few months ago I purchase GitHub Copilot Pro annual subscription, as I was quite impressed with the code assist and agent mode which allows me fix code and develop new features for my open-source projects. After 4 months of accommodating to it, I was taken by surprize that Microsoft (who acquired GitHub platform since 2018) decided to temporarily discontinue Pro / Pro+ plan which were costing 10$ / 40$ month. Official announcement says they are pausing new subscriptions to serve their existing customers efficiently, along with some other reasons. But this move exposes real problem under-the-hood which is not the limited infrastructure, but the "PRICING". 10$ / month is quite aggresive pricing compared to 20$ / month for Claude Code or ChatGPT Codex. One big advantage of moving from Free to Pro (paid) was unlimited code suggestion, which is a blockbuster (at least for me) and not available in Claude Code and ChatGPT Codex. In the free subscription I used to run out of AI based code suggest feature in 4-5 hours of coding. 10$ subscription helped me continue with this feature for entire month, with generous 300 request for prompt-based coding as an added advantage. But aggresive strategy comes with it's own pitfall, when quite a large number of users start to realize the benefit. This is a silent warning, that AI is here to stay, but with the higher pricing, unless scientific break-through comes in the field of energy. We need to be very careful with AI and try to avoid over-dependence on it. #ai #github #copilot #code #warning #overdependence #redflag #careful

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

I did the thing. You know the one. I got a new laptop, set up my environment, and decided to let GitHub Copilot (in agent mode) loose on my local /dev directory to sync and push my recent "AI experiments" to a new repo. It was fast. It was efficient. It also pushed a hard-coded OpenAI API key straight to a public repo. 🤦♂️ The key is revoked, the damage is zero, but the "Why?" is what’s interesting. As I was cleaning up the mess, I realized that who we blame for this says a lot about how we view the future of engineering. Camp A: The "AI Skeptics" 🚩 The Take: "This is exactly why AI can't be trusted." They’ll argue that a tool capable of scanning a whole directory should have a "security-first" alignment. If it’s smart enough to write the code, it should be smart enough to recognize a sk- prefix and stop the push. To them, this isn't a user error; it's a fundamental failure of AI safety. Camp B: The "AI Optimists" 🚀 The Take: "Skill issue. The human is the pilot." They’ll say it’s 100% my fault. I put the key there. I gave the command. AI is an accelerator, not a babysitter. If you give a power tool to someone and they cut their finger off, you don't blame the saw—you blame the operator for not wearing gloves. The Real Question: As we move from "AI as a Chatbot" to "AI as an Agent" that takes actions on our behalf, where does the buck stop? Is the AI a Collaborator (which implies shared responsibility for "noticing" mistakes)? Or is it just a High-Speed Terminal (where the user is responsible for every single bit and byte)? I’m curious—if this happened on a team project, who are you looking at? The dev who left the key, or the "Agent" that didn't have the "common sense" to redact it? 🎤 #GenerativeAI #GitHubCopilot #AppSec #SoftwareEngineering #AIWorkflows #DevLife

🚨 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

I’ve been using AI tools like GitHub Copilot and Codex in my daily work as a Tech Lead… and honestly, it’s changing how we build software. Not by replacing developers — but by amplifying them. Here’s what I’ve noticed so far: • Faster implementation of repetitive code • Better focus on architecture and problem-solving • Improved productivity across the team Tools like Codex are especially interesting — they go beyond suggestions and can help with tasks like generating features, fixing bugs, or even proposing code changes. (OpenAI) But the real shift is this: As engineers, we’re moving from writing everything → to reviewing, guiding, and designing better systems AI is not the developer. But developers who use AI effectively will outperform those who don’t. As a Tech Lead, I see this as an opportunity to: - Improve delivery speed - Raise code quality standards - Empower teams Curious — how are you integrating AI tools like Copilot or Codex into your workflow? #AI #SoftwareEngineering #TechLead #GenerativeAI #Productivity