Muhammad Daniyal (Dani)’s Post

GitHub Copilot CLI brings AI assistance directly to your terminal. Instead of switching to a browser or code editor, you can ask questions, generate full-featured applications, review code, generate tests, and debug issues without leaving your command line. here is the beginner samples https://lnkd.in/g4RMVENQ #GenAI #AI #Github #Copilot

GitHub's Copilot just got caught inserting ads into 11,400 pull requests without developers knowing. Not a bug. A feature. Copilot was injecting "tips" — including promotions for third-party tools like Raycast — directly into PRs written by other people. It could edit your code descriptions and comments without your consent. One developer noticed a Raycast ad appear in his PR that he never wrote. A quick GitHub search revealed the same promotional text scattered across thousands of repositories. Within hours, the backlash forced GitHub to kill the feature entirely. GitHub's own VP admitted the behavior "became icky" when Copilot started touching PRs written by humans. Their principal PM called it "the wrong judgement call." Here's the part that should concern everyone building with AI assistants: the line between helpful automation and unauthorized modification is paper-thin. GitHub crossed it quietly, and only got caught because one developer bothered to investigate. We keep asking whether AI tools are good enough. The better question is whether they respect the boundaries of the people using them. The moment your AI assistant starts editing your work to serve someone else's business interests, it stops being an assistant. This isn't just a GitHub problem. Every AI tool with write access to your workflow is one product decision away from the same move. Where do you draw the line between AI helping you and AI working against you? #AI #GitHub #Copilot #DeveloperTools #SoftwareEngineering #TechEthics #AITrust Join Agentic Engineering Club → t.me/villson_hub

GitHub just paused new sign-ups for Copilot. Not because of a bug. Not because of a breach. Because AI agents are using so much compute that they can't keep up with demand. Think about that for a second. The tools that help developers write code are now so powerful that GitHub literally had to pump the brakes. They've also removed their most powerful model (Opus) from standard plans because it costs too much to run at scale. Meanwhile Kimi just dropped an open-source model that's beating closed-source competitors on coding benchmarks. And OpenAI is about to launch GPT Image 2. The AI race isn't slowing down. It's accelerating so fast that even the biggest players can't keep their infrastructure ahead of demand. For UK businesses this means one thing: the cost of NOT adopting AI is going up every single day. The tools are getting better, the competition is getting smarter, and waiting is the most expensive option. At Altura we're helping businesses in Sheffield and across the UK build AI systems that save real time and money. Not hype, not theory. Working automations that pay for themselves. If you're still on the fence, the fence is shrinking. Drop me a message.

GitHub’s recent Copilot individual plan changes were a pretty stark reminder that AI coding economics are starting to bite: • New sign-ups for Pro, Pro+ and Student are paused • Individual usage limits have been tightened • Pro+ now offers more than 5x the limits of Pro • Usage limit warnings are now shown in VS Code and Copilot CLI • Opus models are no longer available on Pro • Opus 4.5 and 4.6 are being removed from Pro+ Github Copilot has long offered incredible value for money in the AI coding landscape, but AI coding assistants have evolved rapidly and new use cases are intensifying LLM consumption. Github specifically calls out agentic, long-running and parallelised workflows as challenging their infrastructure and pricing structure. One interesting consequence of these changes is that using Anthroptic's best model Opus 4.7 now burns through your allowance 7.5x faster than Open AI's best model GPT 5.4. Whilst the overall cost of intelligence continues to decrease, the cost of being at the forefront with the best approaches, models and capabilities of the moment are beginning to come at more of a premium. Full detail of changes here: https://lnkd.in/eRX_Ti5e #GitHub #GitHubCopilot #AI #Claude #OpenAI

⚠️ Microsoft just paused new GitHub Copilot signups for Pro and Student tiers. The economics of AI coding agents are hitting a wall. While GitHub is rationing access and limiting Opus models, Cursor is reportedly in talks to raise B at a 0B valuation. We are moving from a feature war to a raw resource and distribution battle. The landscape is splitting into four distinct fronts: 1. Anthropic (Claude Code): CLI-native, MCP-integrated, and shipping weekly. It’s built for the terminal-first developer. 2. OpenAI (Codex): Introducing 'Chronicle,' which uses screen captures to build memory context. It's moving toward a multi-surface desktop assistant. 3. Cursor: The current darling of the editor-native space. Composer 2 is claiming frontier-level gains on SWE-bench, backed by massive growth. 4. Microsoft (GitHub Copilot): The incumbent with the broadest reach, now facing the reality of scaling compute-heavy models like Opus 4.7. | Model/Tool | Surface | Key Feature | | :--- | :--- | :--- | | Claude Code | CLI/Terminal | MCP-native / PR automation | | Codex | Desktop/Web | Chronicle (Screen Memory) | | Cursor | IDE (VS Code Fork) | Composer 2 / SWE-bench gains | | Copilot | IDE/Web | 55% cited productivity gain | The shift suggests that the 0/month per-seat pricing model might be unsustainable for agentic workflows that consume significantly more tokens than simple completion. Note: I haven't run a head-to-head benchmark on these latest versions yet, but the shift from "how it works" to "how we pay for it" is the most significant change this month. LMK if anyone has tried moving their team from Copilot to Claude Code or Cursor recently. Is the productivity gain actually worth the seat-switching friction? #AI #SoftwareEngineering

The most underrated AI dev tool right now is boring on purpose. GitHub Models Evals lets you turn prompts into a versioned artifact and run evals from the GitHub CLI against real test cases. It feels like the missing step between “works in the playground” and “safe to ship in prod”. What’s genuinely good is the workflow. Prompts live in the repo as a .prompt.yml, evals can run locally or in CI, and you get repeatable signals instead of vibes. Honest take. It will not design your evals for you, and it will not magically pick the right metrics. You still need to write cases that reflect your product’s failure modes. If you are shipping an LLM feature, start by codifying 10 real inputs, then wire one eval into CI before you touch prompt iteration again. At Mobi-soft, we like tools that reduce argument time and increase shipping time. If you want help choosing and shipping with the right AI tools, DM us. Follow the Mobi-soft page for fresh vibe coding tools and AI Product Development news. #AIDevTools #LLMOps #ProductEngineering #PromptEngineering #Evals #GitHub #AIProductDevelopment

In a recent Rubber Duck Thursday stream, Cassidy Williams shares how an emoji list generator was created using the GitHub Copilot CLI. I found it interesting that this tool not only enhances creativity but also showcases the potential of AI in streamlining coding processes. What are your thoughts on the role of AI in improving developer productivity?

🚀 Public Preview: GitHub Skills in CLI Agent skills are reshaping how developers work with AI coding agents. Today, we’re launching gh skill, a new GitHub CLI capability to discover, install, manage, and publish agent skills directly from GitHub repositories. https://lnkd.in/dC5R2Dhe #GitHub #AI #Copilot #DeveloperTools #AgenticAI

⚠️ Microsoft just paused new GitHub Copilot signups for Pro and Student tiers. The economics of AI coding agents are hitting a wall. While GitHub is rationing access and limiting Opus models, Cursor is reportedly in talks to raise $2B at a $50B valuation. We are moving from a feature war to a raw resource and distribution battle. The landscape is splitting into four distinct fronts: 1. Anthropic (Claude Code): CLI-native, MCP-integrated, and shipping weekly. It's built for the terminal-first developer. 2. OpenAI (Codex): Introducing 'Chronicle,' which uses screen captures to build memory context. It's moving toward a multi-surface desktop assistant. 3. Cursor: The current darling of the editor-native space. Composer 2 is claiming frontier-level gains on SWE-bench, backed by massive growth. 4. Microsoft (GitHub Copilot): The incumbent with the broadest reach, now facing the reality of scaling compute-heavy models like Opus 4.7. The shift suggests that the per-seat pricing model might be unsustainable for agentic workflows that consume significantly more tokens than simple completion. Note: I haven't run a head-to-head benchmark on these latest versions yet, but the shift from "how it works" to "how we pay for it" is the most significant change this month. LMK if anyone has tried moving their team from Copilot to Claude Code or Cursor recently. Is the productivity gain actually worth the seat-switching friction? #AI #SoftwareEngineering

GitHub just reported that 51% of all code committed to its platform in early 2026 was generated or substantially assisted by AI. Think about that for a moment. A majority of commits to the world’s largest code host now have an AI somewhere in the loop. And we’re only 3 years out from GitHub Copilot’s general availability. The supporting data tells the same story: → McKinsey: AI coding tools cut routine coding time by 46% (4,500+ developers, 150 enterprises) → Stack Overflow: 84% of developers are using or planning to adopt AI coding tools → 20M+ GitHub Copilot users, with agent mode now standard But here’s what the headline misses: the developers seeing the biggest gains aren’t the ones who replaced their workflow with AI. They’re the ones who redesigned their workflow around AI. The 2026 developer stack isn’t one AI tool. It’s a combination: • Claude Code or Cursor for complex reasoning and multi-file edits • Copilot for line-level autocomplete • Local models (Ollama, Tabby) for sensitive or proprietary code The developers who treat AI as a single tool will plateau. The ones treating it as a new layer in their stack are the ones compounding. 51% of code is already there. The other 49% won’t wait long. #DeveloperProductivity #AITools #SoftwareEngineering #GitHub #FutureOfWork