Vscode Copilot

... getPrettierPath (#​35680) core: detect vscode copilot ai agent (#​35757) core...​arturovt Benjamin Staneck @​Stanzilla Copilot @​Copilot Craigory Coppola @​AgentEnder...) Colum Ferry @​Coly010 Copilot @​Copilot Craigory Coppola @​AgentEnder dan-winters...

... an agent chat just like VScode/copilot agent sidebar chats. It has...

... cuando se empezó a popularizar copilot a lo loco. La empresa... un ordenador personal instalamos VSCode, activamos copilot con una cuenta nueva de...

.... Everything was working fine until VSCode disabled Claude Sonnet 4.6... of my gh account in vscode. I have also tried restarting... access to this model via copilot CLI (??!!). This issue has become...

... them to fix this. Before Copilot everything on GitHub had a... is pushing me away from Copilot and vscode (really want to try the... security. It's simply because most copilot settings default to off and ...

... a rogue AI Agent in VSCode wiped my disk. I have... all seems fine EXCEPT the CoPilot+ packages are not recovered/reinstalled, .... This means that the dedicated CoPilot button on the keyboard simply ...opens a window saying "CoPilot needs an update. We're getting ... turns out that ALL the CoPilot+ packages are missing which means ...

Day 1: Agentic comparison of Gemma 4 with Qwen 3.6 35B ( https://www.reddit.com/r/GithubCopilot/comments/1ss583x/i_am_not_switching_yet_but_i_tested_gemma4_and/ ) Day 2: Qwen 3.6 27B is released. Deep comparison between 35B and 27B in a real world case ( https://www.reddit.com/r/GithubCopilot/comments/1st1m93/update_compared_claude_47_with_qwen_36_35b_with/ ) Day 3: Developing a browser based (for quick iteration) game with Qwen 35B until it breaks or wins - comparison with 27B # Start: Develop the framework in a chat session, retried 4 times per model I kept evaluating, I made it write a GTA-1 type clone and I asked both models first in a chat session to develop it. In the chat session the 35B model constructed a very nice starting framework, beyond the 27B versions I tested. AI, wanted system, different weapons, police and various NPCs in a city with parks. Both 27 and 35 were bug ridden - 27 can correct bugs but 35 once context gets large will keep repeating the code 1:1. Remarkable achievement on it's own, it can replicate 1700 lines of code character precise - less remarkable is that it can spot all the errors, it can also outline how to fix them but it will not implement the fix. 27B has similar issues but not as intense, it will fix one error and claim it has fixed 6. Some of the errors remaining are total showstoppers (camera and movement errors) # Giving other models the chance I gave the full precision models the same task, they failed similarly! I gave the same task to Gemma 4 26B and Gemma 4 31B - miserable results Gemma 4 31B was able to fix the camera/movement bug but it ruined the game. GPT 5.4 Mini high was able to fix the bug but it changed the game to a totally different style. # Agentic: Sonnet or GPT would be able to solve this in chat, but Qwen 3.6 does not This is where I moved into agentic environment and 35B again showed it's capacity, fixed tons of error and was behind 27B only a little. Again amazing results, tons of problems solved including a seriously difficult rendering loop mistake. 35B is better than 27B here in terms of time to solve. Both find similar solutions, but 35B does it in a quarter of the time. At one point console errors came up and I told the 35B model to fix based on console errors, instead of having me relay them. And here the situation broke: # Qwen 35B reaching it's capabilities 35B was incapable of accessing the console (it's not that easy but I'd have like 10 ideas and 35B fixed on 3 ideas that failed. I believe it can solve it but the real showstopper is that once it approaches 90k tokens it becomes prone to repetitive reasoning on hard tasks. It repeats the same 1-2 pages over and over again. There is no way, aside of a harness, to fix that. I tried for hours, really wanting the 35B model surviving my test but I then had to switch to 27B. #Change to 27B Now 27B was asked to continue the session 35B could not handle, and it noted the problems quickly. It noted that playwright is not installed and gave up on the vscode internal browser - instead searched for and ran chrome natively but headless on it. It saw the showstopper but it failed capturing the console error. So it wrote a python script that handles the internal chrome dev console natively, instead of installing dependencies (playwright etc) it developed it's own developer API harness that connects to chrome. That's a feat I would expect from Opus, not from a local model. It works.. It captured multiple bugs, corrected them without difficulties (related to syntax, a wrong implementation of audio effects and some other details). I'm stunned.. So I followed up and gave it a todo list of 30 points to significantly enhance the game. Now with the new capturing tool it kept iterating chrome to test for bugs autonomously. As much as I love the performance and capabilities of Qwen 3.6 35B - this is a serious game changer Verdict My last verdict was that Qwen3.6 35B wins, it was slightly less competent but so much faster. This changes for tasks of higher complexity when approaching 90k context size. Qwen 35B showed repetitive loops, multiple times and non recoverable. Qwen 27B in the same session powers through. That makes Qwen 35B the winner for simple tasks and Qwen 27B the one you want to use for complex work, especially if your context size is supposed to reach 90k tokens. Update - Hardware -GPU: I am testing this on a RTX 5090 (32GB VRAM) -Software: llama.cpp (lm studio) as backend - single parallel slot -Quantization: 4 bit quants (no real difference between the tiny iq4 and the larger q4kxl) -KV Quant: None for 35B, 8bit/8bit for 27B -Batchsize: 8196 Hardware Notes: - The 27B model will fit on a 3090 with ~50k VRAM and with the upcoming turboquant it can reach 100k+ - speed about half to my 5090 - The 35B will fit comfortably with any context you want on a 3090, speed will fast - If you are on tight budget and have a good midrange Cuda CPU like a 4080 - then you can buy a cheaper 2nd GPU like a 4070 or 5070 and offload on two GPUs, keeping the bulk on the faster GPU. - I believe lmstudio ALWAYS loads the visual stack, that's another 1-2 GB vram you can save by just removing the mmproj file submitted by /u/Charming-Author4877 to r/GithubCopilot [link] [comments]

My post from yesterday was focusing on the actual professional capabilities of Gemma 4 (26B) compared with Qwen 3.6 35B (https://www.reddit.com/r/GithubCopilot/comments/1ss583x/i\_am\_not\_switching\_yet\_but\_i\_tested\_gemma4\_and/) Today 3.6 27B was released and so I continued the test, this time on a project of very high complexity (right at the border of what Opus 4.6 can understand). I asked Qwen 35B to create a documentation of the entire project and it did a quite good job. That's a million tokens in code, including the need to look into bash history and find shellscripts to get an understanding how the project was used. So we look at multiple context summarization events, Qwen 3.6 35B mastered that without any struggles - remarkable on its own. The documentation it created looks high quality. Task 1 - Audit I then asked Opus 4.7 to audit that documentation I asked Qwen 3.6 27B to audit that documentation I asked Qwen 3.6 35B to audit (it's own) documentation I had all 3 transform their audit into the same format and I then let GPT 4.5 xhigh compare the audits without telling Opus which one is which. Result: Ranking My (GPT 5.4 xhigh) ranking would be: 1 > 2 > 3 (That's Opus -> 27B -> 36B) Short read on the others 27B = best at spotting conceptual misunderstandings Good second choice, but a bit more interpretive. 35B = strong and detailed, but more likely to make confident edge-case claims that still need checking. That's quite interesting already, Opus clearly wins with details but the Qwen 3.6 27B did find some details Opus missed. The 35B model was making unverified claims, first in the documentation and then again in the audit. It is more inclined to assume something and not verify that assumption. Task 2 - Rewrite Documentation and Audit by Opus again So now Qwen 3.6 27B got the same task 35B received, create documentation again. The context summarization events were notable slower. 35B just shoots through those but 27B needs a while - though this can likely be improved. Same thing with generation speed The performance might suffer from the Q8 KV cache quantization, I've not benchmarked that yet. The result was not fully conclusive. 27B did a better job at auditing and correcting the 35B flaws but it did not excell at documenting it without help. One particular issue is that after context summarization it does not reliably reload "skills", in my case a copilot-readme file, it also did not pay strong attention on the instructions. My guess is that it needs an adaptation of the system prompt (which I had empty/default in the server), to reinforce the copilot instructions Task 3 - Real work Next I started digging deeper into the capabilities and code understanding of the models. I started with the 27B version and had it analyze the possiblity of using Qwen 3.6 in a very low level (python based) project that hooks transformers, does intricate deep runtime analysis on the model and basically monitors how a llm is thinking in realtime. It's lowest level inference manipulation available with pytorch - one of the hard subjects for SOTA AI. It started well, no issues and given time constraints I broke here. The prompt ingestion was low (maybe a llama.cpp issue with Q8 KV cache) and token generation was about 49 tokens/sec at ~100k context - that's good but it's slow. I switched to the 35B version and had it start over to the same work (no implementation yet, but deep studies of architectural changes necessary to support the complex attention mechanisms) Again I gave the preliminary results to GPT 5.4 xhigh, this time it favored the 35B work over 27B. The inference speed is insanely nice, so I continued with 35B for now. The real, and only, problem I ran into was the same as we had in Task 2: Unverified assumptions. The model reacts brilliant when asked harmless like "did you check the model N loader or assume about it? " and it reacts flawless. It's not stubborn - it reacts happily on its own flaws. That's 3 hours invested so far - I'm switching back to Opus now ;) Final conclusion Qwen 3.6 27B is a bit smarter, more reliable and much slower. Qwen 3.6 35B needs more of a hand or stronger instructions, it's lightning fast, very stable Token usage of 27B is quite a bit lower, so it compensates the slow performance a bit. The 27B model is smaller, fits nicely on a 24GB card but requires KV cache quantization. The 35B model is large, fits tight on a 24GB card but requires almost no KV cache If speed were not an issue, I would use Qwen3.6 27B but 35B is 3-4 times faster and has larger context for less VRAM. For practical use 35B wins due to its speed. Both models are absolutely stunning, a huge leap in capabilities on fully local consumer grade hardware. submitted by /u/Charming-Author4877 to r/GithubCopilot [link] [comments]

Hi everyone, I've been using GitHub Copilot since it first came out around 2022, back when it was mainly just inline suggestions through the VS Code extension. I’ve always stuck with Copilot inside VS Code, but recently I’ve been trying to branch out and explore other tools. I know about Codex, Claude Code, and even Copilot CLI, but I'm having a hard time fully understanding how they actually compare in practice. With the chat interface (like in VS Code or similar tools), I can clearly see what's happening like edits in real time, context, and I can guide the AI step by step if it goes off track. But with CLI-based tools, from what 've seen in videos, the workflow feels a bit less transparent and harder to control. Am I missing something there? I also tend to rely heavily on adding context like images, markdown files, and links directly into the chat to improve results. Is there an equivalent way to do this effectively in CLI-based workflows? Ideally, I’d like to keep a similar interface and workflow, but use my own API keys (BYOK). I’m currently on a Pro+ plan, but I often hit limits and end up spending an extra ~$50/month anyway. For context, I mostly use Codex 5.4 (XHigh) or Opus 4.x for coding tasks. What's the best setup today that gives me a chat-style, transparent workflow like VS Code, supports rich context (files, images, links), and allows BYOK without the typical platform limitations? submitted by /u/YouExpress to r/GithubCopilot [link] [comments]

I'm sure it's interesting to many. Removal of models, 4-6 rate limits and in the next months we'll be billed for tokens instead of requests which basically turns off copilot for anyone professionally using it. I did test token based usage many months ago, I believe it was Sonnet 4.5 through OpenRouter on Vscode Copilot as custom model. It burned 50$ in two short requests. So no thanks. My Pro+ License is always at the risk of a weekly rate limit as well, it's not a pleasant situation anymore. Cloud vs Local has been in my head for a long time, given I have a couple 24 and one 32GB card at home, I felt I am underutilizing. For my tutorials and marketing projects (speech and audio) my early start was Chatterbox TTS (also very nice) but not good enough for productive work then I used Cloud services. However I switched from Elevenlabs and Suno completely to Demodokos Foundry last month, Cloud->Local and in that case the experience was an significant improvement in quality and productivity for me (and $ savings). For Copilot through local LLMs I was more sceptical, my code is complicated and very large. But I believe it was worth the time investment: So today I took the time and I first looked deeply into Benchmarks, including LM Arena. For models that can be run on a 24GB card. Gemma-4 31B is a model that is rated ver high, it's above Pro models I paid not too long ago. Gemma-4 26B is the MOE version of it, and rated almost as high. Qwen-3.5 27B and 3.6 35B (MOE) are the chinese competitors and before Gemma they were the official open source LLM Powerhouse - still they are ranked very high against models in the 0.5-1T parameters class. Same game with Qwen, the 27B dense model is highly regarded, the 35B MOE is trying to catch up. The two dense models are too slow and too context heavy (kv cache grows with density) so I tested the MOE versions only Both models were loaded in llama.cpp, I used LM Studio as server for convenience. I chose a solid 4 bit quantization. For Gemma I added 8 bit quantization on the KV cache, for Qwen this was not necessary due to it's SWA attention that extremely reduces KV cache VRAM. My original expectation was that I'll use Gemma-4 26B and Qwen is not even needed for testing, the benchmarks are heavily favoring Gemma. So my test started with Gemma 4 26B The test project: I had it work on a scraping project from grounds up, getting web addresses, titles, descriptions about a topic, getting current time from a web service, aggregating it nicely and appending it to a markdown file with format. I let it run in my normal VScode Copilot environment, with pages of custom instructions - no difference to how I run GPT5.4 or Opus 4.*, if it can't handle that it's useless anyway. Result with Gemma 26B Instruction following was a bit of a burden, I had to repeat some important instructions in the beginning - but the same happened with many Codex models. After a couple messages it was "in line" with how it should run. It correctly created the demo project, it found a hurdle (libcurl not working) and immediately corrected the way I wanted to direct it (shell wrapper to curl binary). It faked an old browser and accessed Google directly succesfully, I was surprised about this not getting blocked as Google is notoriously difficult for scraping without javascript/DOM capabilities. It tested the script, iterated on errors and I followed up with polishing tasks. And here it broke. We look at about 60 agentic internal messages, so quite a bit of complexity. The context was growing beyond about 60k and the intelligence of the Gemma-4 model went significantly down, it went into an thinking loop that I had to break manually. It then suffered strong instruction following loss, went into another loop and after 6 attempts including insults I decided to switch to Qwen 3.6 Result with Qwen 3.6 35B So I did not want to repeat the previous test, I wanted to see if the Qwen model is able to stay sane. So I kept the session alive, only switched the model and asked it to look at the previous agent and judge it. Qwen 3.6 had absolutely no problem to look at the chat, it noted the loops, it complained about the failure of the Gemma model to find a proper whitespace anchor for replacements, it said the script is sound and the markdown is good. No insanity, super stable, more "human-like" reasoning compared to the "math-like" of Gemma. So I gave it a larger task: "Look at the project, significantly improve on it, add parameters for topics. Amaze me" I was hoping for better formatting, maybe console colors and console parameters. Qwen made a list of 15 significant improvements and started working on a new file. It was stable at 145K context. It went through context summarization without issue and grew to 140k context one more time. It fell into a serious error with parameter parsing, a very strange one I could not understand myself without debugging. It gave up after 6-7 attempts (including nice console messages to see what happens) and rewrote it cleanly - this time flawless. It tested it and I saw a few utf8 encoding errors on console, it also spotted them and corrected the code immediately. It also ran into some syntax errors when testing on console, it took longer to solve them than I am used to but Gemma would have ran into a loop here - Qwen solved it in seconds. I tested the final script, it was a significant improvement and I found a documented but not working parameter (the shorthand version -t instead of --topic). I just copy/pasted the error and it fixed it in a second. It is very capable, I had some Sonnet 4.6 vibes here. Performance with Gemma 26B The biggest fear, we can't work with slow agents. It's a pain. So how did Gemma and Qwen perform compared to a Pro+ subscription and Opus or GPT 5.4 ? Gemma was slower than Qwen, especially the context ingestion (100k tokens) took a while, 15 seconds maybe. From there on the prompt caching works well. Context summarization is much faster than Opus or GPT 5.4, slower than "Opus 4.6 Fast" Token generation is like GPT 5.4 before they made it deliberately slow for us. Performance with Qwen 3.6 35B First I ran into a serious problem, llama.cpp has multiple errors with SWA attention in regards to token eviction and prompt caching. They are working on it since months and a lot has improved but it is causing issues. The "background context summarization" was killing it, also any parallel queries are killing it - if that happens the entire prompt context has to be prefilled again. So the agent has to read 140k tokens with each message or in between tool calls. I solved that by switching the number of parallel slots to 1, so no more background summarization and no multiple read queries or subagents etc. Now the prompt caching works and boy, this thing is fast. Context ingestion for 100k tokens, a few seconds. Context summarization, a few seconds. Code generation is faster than "Opus 4.6 Fast", entire pages of text shoot by. Conclusion So I have not used it on my main projects yet but I gave it some tasks of medium complexity at high context pressure and Qwen 3.6 was stable like a rock. Gemma had a strong start but it will need to operate at low context (maybe 40-50k context + 8-16k output size) Qwen 3.6 can be ran like Opus or Sonnet, I gave it 262k context size but reserved 100k for output. So effective context was 160k-180k. I'm not absolutely convinced that I can use Qwen 3.6 for my professional work, it's not "hands free" like Opus and would need intense and longterm oversight to be trusted - also I am not sure if it is competent enough to work on highest complexity (yet to test). But for many projects it certainly is a very solid tool. I'd not hesitate to use it for working on PHP, HTML, Javascript or Python. Update: I spent another couple hours testing the new 3.6 27B against the winner 3.6 35B https://www.reddit.com/r/GithubCopilot/comments/1st1m93/update_compared_claude_47_with_qwen_36_35b_with/ submitted by /u/Charming-Author4877 to r/GithubCopilot [link] [comments]

Planning to fix my code then it just said this. I'm subbed to the pro version. submitted by /u/Dreeey_ to r/GithubCopilot [link] [comments]

submitted by /u/ZootAllures9111 to r/GithubCopilot [link] [comments]