#GitHub Copilot

[orthogonal-slut]

what's the fucking point then

[saurabhshukladigitalcreator]

A collection of IT resources. #tumblr#google

[freshyblog07]

10 Powerful GitHub Copilot Tools for Smarter Development

GitHub Copilot: AI Coding Assistant

GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It helps developers write code faster by suggesting entire lines, functions, and even solving complex problems in real-time. Integrated directly into popular IDEs like VS Code, Copilot learns from context and generates smart code completions, saving time and boosting productivity. Whether you’re debugging, learning a new language, or building large-scale applications, Copilot makes development smarter and more efficient.

[teguhteja]

Discover how GitHub Copilot custom instructions can revolutionize your coding workflow! This guide shows you how to write less while getting better, more tailored code suggestions. Perfect for developers looking to boost productivity. #GitHubCopilot #VSCode #DeveloperTools #AICodeAssistant #Producti

[pier-carlo-universe]

Scott Guthrie in visita al campus Microsoft di Hyderabad: confronto ispirante su AI, sicurezza e innovazione

È stato un vero privilegio ospitare Scott Guthrie, Vicepresidente Esecutivo di Cloud + AI, nel nostro campus di Hyderabad per coinvolgenti interazioni con dipendenti e leadership. I suoi interventi non sono stati solo stimolanti, ma profondamente motivanti. Ecco i miei tre principali spunti: La sicurezza e la qualità non sono negoziabiliScott ha sottolineato che sicurezza e qualità sono le…

[govindhtech]

Agent Mode In GitHub Copilot For Your VS Code Workflow

More context for your tools and services makes GitHub Copilot more agentic, driven by the finest models.

For MSFT's 50th anniversary, Microsoft Azure is releasing Visual Studio Code's agent mode to all users, which now supports MCP and lets you access any context or feature. Microsoft is also thrilled to provide a local, open-source GitHub MCP server that lets you incorporate GitHub features into any MCP-capable LLM product.

To fulfil its promise to offer a range of models, it is adding Anthropic Claude 3.5, 3.7 Sonnet, 3.7 Sonnet Thinking, Google Gemini 2.0 Flash, and OpenAI o3-mini to all paid Copilot levels through premium requests. All base model paying subscriptions feature unlimited agent mode, context-driven chat, and code completion requests. Premium requests add to them. With the new Pro+ tier, developers may use Copilot's latest models.

More to the agent awakening. The Copilot code review agent is also being released via Microsoft Azure. Over 1 million GitHub engineers have used the preview in a month. The next change recommendations are public, so you may tab tab tab your way to coding greatness.

VS Code agent mode

Agent mode will be gradually made accessible to VS Code users in stable in the following weeks to ensure total availability. It may now be manually activated. Agent mode can put your thoughts into code, unlike chat or multi-file modifications, which enable you suggest code changes across several workspace files. Agent mode challenges Copilot to go beyond simple prompts. To ensure your goal is fulfilled, it completes all subtasks across automatically discovered or created files. Agent mode may propose tool calls or terminal instructions. Additionally, it evaluates run-time defects and self-heals.

Since February, VS Code Insiders has allowed developers to tweet contributions, create web apps, and automatically fix code generation bugs in agent mode.

OpenAI GPT-4o, Google Gemini 2.0 Flash, and Claude 3.5 and 3.7 Sonnet power agent mode. Agent mode currently passes SWE-bench Verified with Claude 3.7 Sonnet 56.0%. As chain of thought reasoning models improve, agent mode should get stronger.

Model Context Protocol (MCP) public preview is currently available

Developers must research, navigate telemetry, manage infrastructure, code, and debug all day. They use engineering stack tools to achieve this. MCP gives agent mode context and tools to help you, including a USB port for intelligence. When you input a conversation prompt in agent mode in Visual Studio Code, the model can utilise numerous tools to understand database structure or do online searches. More interactive and context-sensitive coding is available with this option.

Agent mode could ask an LLM what to do with the list of MCP tools and the prompt to “Update my GitHub profile to include the title of the PR that was assigned to me yesterday”. The agent would repeatedly call tools until the job was done.

On GitHub, you may already use the enormous and growing MCP ecosystem. This repository is a great community inventory with top MCP servers. The GitHub local MCP server makes agent mode a powerful GitHub platform user by searching code and repositories, resolving problems, and producing PRs.

Configure local and remote MCP servers using Visual Studio Code's agent mode. See the repository to use the GitHub local MCP server, now natively enabled in Visual Studio Code.

Requests for premium models

Since GitHub Universe, Microsoft Azure has included discussion, multi-file editing, and agent mode models. Since these models are generally available, it is creating a new premium request type. Premium requests are included on all basic model paying plans (currently OpenAI GPT-4o) along with unlimited agent mode, context-driven chat, and code completions.

Starting May 5, 2025, Copilot Pro members will receive 300 monthly premium requests. From May 12 to 19, 2025, Copilot Business and Enterprise users will receive 300 and 1000 monthly premium requests. These premium models are uncontrolled till then.

It also offers a $39-per-month Pro+ subscription with top models like GPT-4.5 and 1500 monthly premium requests.

For extra premium request use, Copilot paying members can pay as they go. Individuals and organisations can utilise more requests than the maximum supplied to conveniently track spending. Copilot Admin Billing Settings lets GitHub Copilot Business and Enterprise administrators manage requests. Extra premium requests cost $0.04 apiece.

You can use Copilot's base model without restrictions while employing a more powerful or efficient model when needed. Premium models consume a set number of requests.

[willcodehtmlforfood]

sajnos a copilot eleg jo

ma meloban megoldottam egy haverom problemajat front end app installt winfosra hozzavetoleges tudassal (csak keves fogalmam van angular alkalmazasokrol / npm (fnm) / node lattam mar stb) + copilot (+gugli, mert valamiert ki kellett kapcsolni ipv6-t h npm rendesen lefusson...)

a copilot teljesen rendben megadott node / cmd parancsokat amiket gyakorlatilag csak vegre kellett hajtanom.... ( "@workspace what do i need to do to run this application locally?")

[infydeva]

GitHub CoPilot

Are you new to GitHub Copilot? I recommend watching this video, which provides a comprehensive guide from setup to coding examples. The video demonstrates how the AI assistant integrates with various IDEs, such as VS Code, and includes an example of a Python email validator. For sure it will improve your coding experience, and you will enjoy it!!

[fayazdev]

GitHub Copilot just added a ton of cool features!

Next Edit Suggestions.

o3-mini model.

Copilot Chat now has Vision capability - so adding a screenshot to a prompt will now work!

Copilot Edits now has agent mode - cool! 🥳

Add extra context with PROJECT/.github/prompts/*.md prompt files. Very helpful for creating new DB models.

[yahoo0messenger]

GitHub Copilot LLM

[goblinwee]

I will never get bored of making GitHub CoPilot speak like a wizard

[kazifatagar]

DataStax Enhances GitHub Copilot Extension to Streamline GenAI App Development

DataStax has expanded its GitHub Copilot extension to integrate with its AI Platform-as-a-Service (AI PaaS) solution, aiming to streamline the development of generative AI applications for developers. The enhanced Astra DB extension allows developers to manage databases (vector and serverless) and create Langflow AI flows directly from GitHub Copilot in VS Code using natural language commands.…

[laurentgiret]

GitHub Copilot will soon let developers leverage Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s o1-preview.

[knowledgegraphs]

[stxalq]

if ai is here to refactor regex expressions, i'm all for it

[fullyautomated]

AI Code Assistants, Developer Happiness at all costs?

I took a glance at a white paper and associated research that my boss pointed me to regarding AI assistants for code generation. It was not only an eye-opening moment for me (as I had not thought about the implications of coding assistants - most likely because I've been focused on the good ways AI can help test and quality engineers), but it was also very scary.

The white paper and accompanying research were commentary about a blog post from June 2023 posted to the GitHub blog written by Thomas Dohmke. The full article can be found here: https://bit.ly/3u5yMA6 with all the gory details including a link to download the PDF of the full research conducted. There are three main findings that are being reported as a result of this research. I will cover 2 of those here along with my opinion on them.

Finding #1

Less than a year after its general availability, GitHub Copilot is turbocharging developers writing software.

This finding is based on an analysis of a sample of current GitHub Copilot users of around 934,533. The claim is that, on average, the users of Copilot accept nearly 30% of code suggestions thereby reporting an increase in productivity. When comparing senior to junior developers, the latter has greater benefits.

And therein lies the problem I perceive. Junior developers will undoubtedly accept recommendations presented by an Intelisense-type prompt potentially without thinking about the ramifications downstream of their actions. Another problem is that we seem to focus on "speed" instead of "quality". Speed is but one aspect of the Iron Triangle (faster, better, cheaper). With the advent of AI assistance for actually producing the code, we are taking care of "faster" and "cheaper" (two birds, one stone), but let's not neglect better! Invest the time and money we are saving by paying close attention to the quality of the code being generated as well as ensuring that it is in line with our organization's code quality guidelines (DRY principles, for example).

Finding #2

We estimate these generative AI developer productivity benefits could boost global GDP by over $1.5 trillion USD by 2030 by helping to meet growing demand for software.

The main reason being touted for this $1.5 trillion boost in GDP by 2030 is again linked to "productivity gains" based on the estimation that approximately 30% of code being developed is being contributed by Copilot. Further, based on their data, productivity increases will be greater based on developers getting over the learning curve along with projected Copilot improvements.

The Copilot improvements are the key here, as well as the part that is not being covered by the data or the research. The analysis only covered productivity over an initial period of time and seems to ignore what happens after the code is developed and checked in. Based on the GitClear analysis of over 153 million lines of code aimed at answering the questions:

a. Are there measurable side effects to committing AI-generated code?

b. What are the implications of the widespread adoption of AI programming assistants?

They concluded that based on the 6 metrics they tracked and analyzed, "the output quality of AI-generated code resembles that of a developer unfamiliar with the projects they are altering. Just like a developer assigned to a brand new repository, code generation tools are prone to corrupting the DRY-ness of the project". I'm not surprised by this at all, and it points to some potentially serious risks in the realm of Quality.

The rationale behind this is that the suggestions from Copilot seemed to be biased toward adding code (like a junior developer might do) as opposed to activities related to refactoring (as a senior developer might do), like moving, updating, or deleting existing code.

From my perspective, adding more code faster without thinking about future maintenance can create more tech debt as time goes on. I reckon that using Copilot for MVPs, POCs, and other green field experiments where normally net new code is always added is a good use case for it. It would be interesting to see how Copilot behaves when dealing with existing and legacy code bases.

Conclusion/Suggestions

It remains to be seen if the power of Copilot can be harnessed via the deployment of organizational policies and guidelines that drive the incorporation of Copilot into the development landscape. Some examples of this:

Junior developers can use AI assistants for their own learning and upskilling but should pair with more seasoned developers when dealing with production code.

Senior developers are exposed to the guidelines and encouraged to provide code faster and maintain the engineering organization's coding standards.

Limit the use of Copilot and other assistants for greenfield projects like POCs, MVPs, and limited-scope low-risk projects aimed at getting products into customers' hands faster to facilitate rapid feedback.

Encourage the initial use of AI assistants to create lower-risk code that can be utilized for in-sprint test automation.

Encourage the use of AI assistants for infrastructure as code where the scope is more limited and can be used as a learning platform for all software developers as they go through "the learning curve" to get to know the tool.

Create a Community of Practice within the organization where users of GitHub Copilot and other AI assistants can share lessons learned and good practices.

The above community can also be harnessed to collect feedback that can be aggregated and categorized to provide feedback to GitHub Copilot for product improvements and feature suggestions.

In general, I feel optimistic about GitHub Copilot, not because it generates code faster, but because (just to name a few):

It is a new tool that has harnessed broad support from the development community.

It has the capability, via this captive audience that has embraced it, to include all of the Code Quality features that are currently lacking and rely on experience.

It can help the fellows in the testing community or other adjacent communities to learn proper coding as well as participate in the numerous automation initiatives throughout an organization. Pipelines, test automation, etc.

What other suggestions do you have? Feel free to leave them in the comments or ping me directly if you'd like to chat about it.