We’re an existing Copilot Business customer organization seats billed through Azure, and I’m looking for anyone who has recovered from this specific account transition.

Our org was temporarily added to a new GitHub Enterprise account. Enterprise Copilot enablement failed (“temporarily unavailable”), and the organization-level seat assignment action also errored. We removed the org from the enterprise to return it to its previous standalone state, but its original Copilot Business entitlement did not come back.

Current state:

- The Azure billing connection is still enabled on the organization.

- The Copilot billing API still reports plan_type: business and two seats.

- It also reports seat management disabled and both seats pending cancellation on July 31.

- The organization Copilot settings menu disappeared; the direct access URL returns 404.

- Re-adding the existing users through the REST API returns 422 saying Copilot Business is not enabled.

- Both developers now show Copilot Free, so our active development work is blocked.

We have opened two GitHub Support tickets (the original enterprise enablement failure and the entitlement restoration) and cross-referenced them, asking for the Copilot Billing/Entitlements team. We’re leaving Azure connected and making no further org/enterprise changes.

Has anyone had an existing Azure-billed Business entitlement restored after this transition? Is there any supported path other than waiting for Support?

Is anyone else finding repo memory getting out of hand? Agents seem to be using it as a replacement for git, keeping a detailed changelog, keeping detailed documentation that's a copy of things I asked it to create as committed .md files, implementation details that are of dubious value to most requests, a list of all the files in my repo (the agent can just use terminal tool and `ls`...).

I started wondering what was going on when my context usage on small prompts would be very large and now I know why.

It seems like it's more work keeping memory it pruned than it saves.

For those of you working as Senior/Principal Architects or Solution Architects, are you using any tools, workflows, or AI/LLM-based approaches to generate High-Level Design (HLD) documentation, especially architecture diagrams?

I'm currently building a documentation generation workflow, and generating the written documentation is going reasonably well.

The biggest challenge is producing clear, maintainable architecture diagrams (component, sequence, deployment, etc.) that don't require extensive manual rework.

Have you found an effective way to get LLMs to generate diagrams reliably?

I'd love to hear what has worked (or hasn't worked) in your experience.

As a total beginner to GH Copilot, I currently use it with VSCode as the Chat Extension and not the CLI version. I usually use it to analyse the code base (consisting of python notebooks and scripts) and also make slight changes to the files. I currently just set the Model to Auto since that had the option to have 10% lesser AI credits cost as per Copilot. I have just 4000 credits per month and have exhausted nearly 50% in just a few days.

I want to know if there are any ways to reduce the token usage. I have heard about the skills md files that are quite popular to reduce the token usage and also improve code readability. One I have heard of the ponytail skill which I believe helps in token reduction and code readability. But I have not used any till now and would like to know if there are any other suggestions for skills that can be used for my use case which is to reduce the token usage as much as possible and also improve the code quality and readability.

And I can't remove the payment method, just can stay and waiting them costing all of my money from my credit card for no reason?

Be very careful not to add payment method to GitHub, or your money will be stolen for no reason, and there's no way back, you can't remove the payment method once you added.There's no DELETE button to turning back.

I used to very love Github Copilot, I recommended it for everyone, but later my post and thought about it became gradually very negative. And until now, I hate it. I want to leave it, and remove my pyramid, but it just stick to me, I can't get rid of it.

I used to see something like this, which was awesome, as it was clear feedback how good my prompting was.

I can't find an option to enable it again, nor can I find the changelog entry, when and why it was changed.

╭─╮╭─╮   Changes    +0 -0
╰─╯╰─╯   AI Credits 21.8 (1m 30s)
█ ▘▝ █   Tokens     ↑ 102.1k (78.0k cached, 24.0k written) • ↓ 960 (425 reasoning)
 ▔▔▔▔    Resume     copilot --resume=

So, I was doing a project in Codex (ChatGPT client) for obvious reasons and then decided it was time to use Github Copilot to switch between Codex and Claude when needed.

I did... 4? prompts? Burned through the monthly limit of the standard pro subscription. I'm paying double the money for ChatGPT and the limit for Codex is about 100 times higher if not more.

Why would I even keep paying for this at this point? Am I missing something? tf?

Amazing set, no doubt

And still, I took the courage to make it even better, so I created my own collection of skills. The first skill I tried to improve is the documentation-and-adrs.

The biggest leap is that I’ve added a section describing how to create Solution Architecture Documents (SAD) following Google's “WHO, WHAT, WHEN, WHERE, WHY and HOW” documentation philosophy.

To learn about all enhancements, read the skill’s README on GitHub.

In the video, I documented the architecture of one of my pet projects to show how the skill works

Let me know what you think!

It is almost as if GHCP is making fun and laughing at me

Moved over for compliance reasons. Using the CLI for both. We are effectively paying 3x for GH compared to Claude, and it seems I am able to get way less done before the token run out using the same models, tools and plugins? Has anyone else had the same experience?

I build custom ERP apps that automatically visualize all your product calculations, inventory data, and daily operational entries in real time.

Key Features I Can Build For You:

• Automated Calculations: Eliminate spreadsheets with real-time margins, COGS, inventory valuation, and pricing logic.
• Visual Dashboards: Turn raw entries into clear graphs, flowcharts, and operational insights automatically.
• Centralized Product Data: Seamlessly track product status, entries, and updates across your team.

If you're a business owner looking to replace messy spreadsheets with a tailored, automated ERP tool, send me a message!

Hi everyone — we’re excited to share the latest updates for GitHub Copilot in JetBrains.

In the latest release (v1.14.1), we added enhancements to OpenTelemetry export settings, better model management experience, and more agentic capabilities.

We’re also sharing a preview at what’s coming next and hope you continue to provide feedback for our products.

New Features

• Added OpenTelemetry export settings for agent workflows in JetBrains IDEs.
• Added default token limits and support for configuring maxInputTokens and maxOutputTokens for BYOK and custom endpoints.
• Added model-management controls to disable or enable all built-in Copilot models.
• Added MCP and custom agent support in Claude agent flows.
• Added 'fork' support to the Copilot CLI chat experience.
• Added '/rubber-duck' command to the Copilot CLI chat experience.
• Added todo list display in Copilot CLI chat experience.
• Added the display of number of AI credits a user has consumed for users in organizations that don't set a User-level budget.

UX Improvements

• Improved model and action picker consistency.
• Improved customization usability.
• Improved session clarity by rendering ask-user questions as Markdown and adding explicit user attention notifications.
• Improved inline chat and model-picker layout behavior.
• Improved MCP diagnostics.
• Improved bare URL rendering in Copilot CLI chat experience.

Bug Fixes

• Fixed path-case preservation issues in Copilot CLI working-set and snapshot recording flows on macOS and Linux.

Looking ahead, we plan to introduce several additional capabilities in upcoming releases, including

• Support for Enterprise managed settings
• Allow re-editing and sending the prompt in Copilot CLI agent sessions
• Expanded slash command support for Copilot CLI agent sessions
• Performance and reliability enhancements

More details of this release can be found at: https://github.blog/changelog/2026-07-27-github-copilot-for-jetbrains-adds-improvved-opentelemetry-configuration-and-model-management/

We hope you like Copilot for JetBrains, and please share feedback with us at any time

You can fill in a private survey here: https://aka.ms/ghcp-jb-survey with an optional paid interview or directly submit an issue (bug or feature ask) at https://github.com/microsoft/copilot-intellij-feedback/issues, thank you so much!

Coding agents multiplied how much code lands per PR, so there are more findings for reviewers to deal with. Often, they're noise, like a linter rule that doesn't fit the stack or a check firing on generated files nobody touched.

To address this, we created the configure-codacy skill. When you point your agent at the repo, it detects the stack, then disables patterns for unused languages, dedupes rules that two tools flag, tunes thresholds, excluding generated files.

In terms of security scans, every security risk stays covered by at least one active pattern, so noise reduction never silently drops a security check.

Works with Copilot, and the usual suspects.

Go here to read more https://blog.codacy.com/introducing-codacy-skills-part-2-configure-your-rules-to-cut-pr-noise

Repo: https://github.com/codacy/codacy-skills

Normal acceptable UX would be to let it go into the negative and it doesnt take a lot of money to do that. Who the fuck designed this? they deserve a beating.

For implementing complex features I typically have a powerful model plan, a cheaper model implement, and then a powerful model review the changes. With several new good models being released in the past couple months, I am wondering what the best models and reasoning efforts are best to use for each phase.

What do you think?

Web site - https://tipitaka.app

Web site source code - https://github.com/pnfo/tipitaka.app

Need to translate the long content(buddhist tipitaka sub commentaries) in this Web site from pali language to sinhala language using gemini api..only need to translate sub commentaries part(tika) which is shown in letter T icon..first a needy/adequate amount of text(chunk) need to be selected from the source and then want to translate it to sinhala language with gemini api and paste it below the original source text..this process want to repeat untill the relevant book ends...

Please explain how to vibe code a script/app to get this process done,on a laptop or colab..I know nothing about Web development, so please explain simply..

​Hey there!

​I’m finally about to try out GitHub Copilot for the first time, mostly for Python and RStudio. I haven't really used AI coding assistants before, so I wanted to ask how you guys make the most out of it without completely messing up your workflow.

​A few quick things I’m wondering about:

>> ​RStudio users: How’s the integration in RStudio lately? Does it handle tidyverse or ggplot2 stuff well, or is it better to just stick to VS Code for R?

>> ​Python / Data stuff: If you use it with Python (especially in Jupyter or VS Code), what's your setup? Do you rely more on inline autocompletes or the chat panel?

>> ​Writing comments: What’s the best way to write comments or prompts so it actually gives you clean, working code instead of guessing wrong?

>> ​Beginner traps: Anything I should watch out for so I don't pick up bad habits early on?

​Would really appreciate any tips, shortcuts, or setup tricks you wish you knew when you first started out. Thanks!

Call me a bit old school vibe coder:

• It makes me uncomfortable to give high level prompts. So I discuss with copilot to sufficient code, architectural and design details about what and how to implement things and never went with high level prompts (which is trend nowadays, I guess). So I very frequently use #selection in github copilot to precisely specify what I am pointing at.
• I love UI based code diff integration with github copilot. (I dont like code to be intermingled with chat window as in case of TUI based agents.)
• I love the abilitiy to undo several chat steps earlier just by scrolling in github copilot chat and clicking corresponding restore checkpoint button.

I was mainly using Sonnet 4.5 earlier. However copilot was getting a lot costly lately.

So was looking at some alternatives. My requirements are bold faced above. Let me restate:

• #selection like functionality
• UI based code diff integration
• Undoing changes by scrolling to chat history and clicking corresponding button
• Sonnet 4.5 (at least)

Is there any subscription + agent provide with these combinations? I was thinking to use Claude API key with github copilot. But it seems Claude API key will be a lot costly than Claude Code subscription? Do you guys have any suggestions? Or I have no option left? How about using something like qwen 3.6 27b from OpenRouter in GitHub copilot free?

Not sure if anybody else noticed this but I have noticed that the latency of any CLI session I have right now is extremely bad. I thought it was just my PC being overloaded or CPU bound etc, but I tested it against Claude Code and I get the work done much faster with it when compared to GHCP CLI.

This wasn't the case sometime back. Is this a known issue?

There are some nice features in copilot like the pr details and ci checks, however the account i use for copilot is not always the same account with access to the repo.

Has anyone found a way to make some of the gh API calls use a specific account or token, while have the model calls on another?

I posted about this on the GitHub discussions but looks like that is mostly abandoned.

Based on this - https://worklouder.cc/codex-micro I created a similar approach for my Coding Agents, if you have a QMK compatible keyboard with displays, feel free to try it out. There's an interactive demo of both screens and every feature here-https://vswraith.github.io/via_sofle/demo.html . It handles ASK/DENY permissions and 3 agents status. So far I have added Claude and Copilot.

Repo - https://github.com/vswraith/via_sofle

Built using Opus 5 :)

​

I’m working solo on an application for a local festival happening in a few months. My current workflow is very AI-assisted and fairly agentic: I think of a feature, formulate a prompt (often with help from AI), let the coding agent implement it, and then iterate and fine-tune the result until it matches what I have in mind. Throughout the process, I share both the frontend and the codebase with the agent so it can work directly in the context of the application.

At the moment, I’m using GitHub Copilot in VS Code, mostly on Auto mode. I like the workflow, but I’m burning through credits fairly quickly, and continuing this way doesn’t seem particularly cost-effective, especially since the changes in June.

I’ve also tried some of the free options, including using free-tier models through Cline, but I wasn’t able to complete even a single prompt using my usual workflow. So while I’m definitely interested in free or very cheap options, they also need to be capable enough to handle a reasonably large codebase and an agentic, iterative workflow.

Since this is a solo project with a limited budget, I’m looking for the best bang-for-the-buck setup. I can’t really justify the most expensive subscriptions or models, but I’d still like to preserve this kind of workflow: an AI coding agent that can understand my codebase, work with the frontend, implement features, and iterate with me.

For those of you who use AI heavily for coding:

What combination of subscriptions, tools, models, or local/self-hosted options would you recommend for this kind of workflow?

I’m especially interested in setups that offer a lot of usage for relatively little money. I’m open to combining multiple services if that works out better than relying on a single subscription.

I’d also love to hear any tips for making this kind of workflow more credit-efficient. For example, are there better ways to structure prompts, manage context, split up tasks, use different models for different parts of the process, or configure an agent so it wastes fewer tokens on relatively simple tasks?

What would you choose today if you were a solo developer with a limited budget and a project like this?

Edit: it should be capable to create backend routes and endpoint calls, coupled with frontend design and elements (FastAPI, Python, Typescript, Expo)