GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (70%)
• Sign in (17%)
• Errors (13%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Itapema	Website Down	15 days ago
Tlalpan	Sign in	21 days ago
Quilmes	Website Down	21 days ago
Bengaluru	Website Down	23 days ago
Yokohama	Sign in	24 days ago
Gustavo Adolfo Madero	Website Down	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  🛡️Shir Khorshid Noor Cyber Unit🛡️ (@FriendOfTheInst) reported 2 minutes ago

  Sponsored search results are not a trust boundary. A fake ChatGPT download campaign used brand impersonation, malvertising, shared-link abuse, cloaking, platform-specific payloads, CAPTCHA gating, Electron packaging, JavaScript obfuscation, and staged execution to deliver malware to Windows and macOS users. This is not merely another fake download page. It is a clear demonstration of how attackers exploit trust across multiple layers: • Trusted brand • Trusted search flow • Trusted-looking ad placement • Trusted-looking domain patterns • Trusted UI/branding • Trusted installer frameworks • Trusted code-signing assumptions • Trusted AI platform sharing features What happened: Attackers promoted a fake OpenAI/ChatGPT download experience using the domain: openew[.]app The site copied OpenAI-style branding and offered download paths for: • Windows • macOS • Chrome extension The Chrome extension path linked to a legitimate ChatGPT-related extension, further increasing perceived legitimacy. The Windows and macOS download paths delivered malware. Attackers also abused legitimate ChatGPT shared conversation links, including chatgpt[.]com/s/ pages, to host fake outage or download pages. A link hosted on a trusted domain can still deliver attacker-controlled content to users. The campaign employed cloaking and conditional rendering: automated scanners and analysis tools were shown benign content, reportedly an unrelated AR/VR company site, while real browsers received the malicious ChatGPT-themed download experience. That is the key lesson: A trusted domain, HTTPS padlock, sponsored ad, or polished UI does not equal a safe download. Why this campaign matters: Victims were not browsing dark web forums or downloading cracks. They were searching for a legitimate AI tool. That is why malvertising is effective: it targets high-intent users at the exact moment they are ready to install software. The campaign turned normal user behavior into an initial access path. Windows chain: The Windows payload was distributed as: Chat_GPT.exe Reported SHA-256: 56CC26E88C064B0C423AA8AD6530E58F91D1E4D28FAB1A8BCEDEF16A6582B4D2 Additional reported Windows hash: c9e0e6985dca3a179c9bdea4e7b38f7dc57fe00ecedc2fd634256fc53bf2de2d Important: hashes are useful for triage, not sufficient for defense. Campaigns rotate samples. Hunt behaviorally. Windows technical observations: • Installer built with Inno Setup • Electron-based application • Chromium runtime components • resources\app.asar archive • Large obfuscated JavaScript payload identified as winter.js • Hex-encoded strings • Dynamically resolved functions • Control-flow obfuscation • Event-driven execution • CAPTCHA gating before core behavior • Inner Electron payload (App.exe) launched after installation • PowerShell spawned after CAPTCHA completion Observed PowerShell pattern: -ExecutionPolicy Unrestricted -Command - That trailing dash matters. It suggests commands may be supplied through standard input rather than appearing directly in the process command line. This reduces the value of command-line-only detection and makes process-tree and behavioral monitoring much more important. Static red flags: The filename suggested ChatGPT, but embedded metadata reportedly identified the installer as: PovariEGLESVapp Setup The executable was signed by: F.F.A.P. Hurkmans Beheer B.V. That publisher does not align with OpenAI or ChatGPT. Important reminder: a valid code signature does not mean software is safe. It only confirms that the file was signed by a certificate and has not been modified since signing. It does not establish that the software is legitimate or authorized by the brand it imitates. Additional Windows indicators: • App.exe SHA-256: D9AD44D43E57B870793FA5CF7FB3A813990D0CBD0C7087BDE70A5E61FB1F1FE6 • Unexpected Chromium/Electron profile: %APPDATA%\Satoshi • Additional reported path: %APPDATA%\LeronApplication • Reported Electron/Node capabilities: systeminformation, child_process, os, fs, zip-lib, Those modules indicate a capable execution environment: system discovery, file access, archive handling, process execution, and network communication. macOS chain: The macOS payload was delivered as: ChatGpt.dmg Reported SHA-256: 7E5B708F6659B1FAD3AAE7B589A706434FBF21708AEEC5AF5910189B96E25FEF Additional reported macOS hash: c0919e1999eaee67e67aeda0287722775afb04e9a9a0f727928b4d11265fb70b The macOS malware is reported as Odyssey Stealer, a fork of AMOS / Atomic Stealer. Reported macOS targeting includes: • Browser passwords • Browser cookies • Saved logins • macOS keychain data • Telegram sessions • Cryptocurrency wallet directories • Desktop/Documents files with sensitive wallet/key extensions • Ledger Live • Trezor Suite • Exodus • Electrum • Sparrow The most dangerous macOS behavior: Wallet replacement. The malware reportedly attempts to replace legitimate wallet-related applications with trojanized versions. That means a victim may later open what appears to be their normal wallet app, but actually launch an attacker-controlled version. That is not only credential theft. That is long-tail financial compromise. Infrastructure: Reported malicious domain: openew[.]app Reported infrastructure includes: 144[.]172[.]104[.]205 188[.]137[.]246[.]189 192[.]253[.]248[.]181 172[.]94[.]9[.]250 Infrastructure notes: • Recently registered domain • Namecheap / registrar-servers infrastructure reported • RouterHosting infrastructure reported • Passive DNS linked infrastructure to other suspicious or malicious domains • .app domains require HTTPS, so browsers show a padlock The padlock only means the connection is encrypted. It does not mean the site is legitimate. Detection opportunities for defenders: 1. Newly created executables launched from Downloads, Temp, or other user-writable paths 2. Trusted-brand filenames that do not match embedded metadata 3. Installer publisher mismatch: filename says ChatGPT, signer is unrelated 4. Electron apps spawning scripting engines: powershell.exe cmd.exe osascript bash sh zsh 5. PowerShell with: -ExecutionPolicy Unrestricted -Command - 6. Unexpected Chromium/Electron profile directories, such as: %APPDATA%\Satoshi %APPDATA%\LeronApplication or other anomalous Electron profile paths 7. app.asar archives containing large obfuscated JavaScript bundles 8. CAPTCHA or user-interaction gating before malicious behavior 9. Newly registered domains impersonating major software or AI vendors 10. Users installing software from ads instead of official vendor channels 11. Suspicious wallet-app replacement attempts on macOS 12. Post-install network traffic to low-cost VPS infrastructure 13. Legitimate AI sharing URLs that render fake support, outage, update, or installation pages 14. Download pages that show different content to scanners than to real browsers The key defensive point: Do not build detections only around hashes or static strings. This campaign reduces the value of static analysis through: • Obfuscation • Runtime string construction • CAPTCHA gating • Electron packaging • Conditional execution • Cloaking • Staged payload behavior • Shared-link abuse on trusted domains The better approach: • Behavioral detection • Process-tree monitoring • Parent-child process analysis • Script-engine execution monitoring • Browser/download source telemetry • Application control • Newly registered domain monitoring • Publisher and metadata validation • EDR detections for Electron-to-shell execution • Monitoring for AI-platform shared links used as delivery pages • User training focused on sponsored-result and fake-download risk For users: Only download ChatGPT from official OpenAI channels or the Microsoft Store. Do not install software from ads, mirror sites, download portals, unfamiliar domains, or fake support/outage pages. If you installed a “ChatGPT” app from an ad or unfamiliar page: Use a clean device and: • Sign out everywhere from important accounts • Change passwords, starting with primary email • Rotate API keys, SSH keys, cloud credentials, and tokens • Revoke active sessions for email, GitHub, cloud, Discord, Telegram, crypto exchanges, banking, and password managers • Move crypto funds from a clean device • Do not open Ledger/Trezor apps on a potentially infected Mac • Monitor financial accounts • Reinstall the OS • Notify IT/security immediately if it was a work device For AI vendors and platform owners: This is now part of the product security perimeter. Brand impersonation, malicious search ads, fake download pages, clone domains, and abuse of shared AI content are active distribution channels. Practical controls: • Make official download links easy to find • Monitor sponsored ads for brand abuse • Monitor newly registered lookalike domains • Detect abuse of shared-content features • Run takedowns quickly • Publish clear download guidance • Provide signed-installer verification guidance • Coordinate with search/ad platforms • Alert users when major impersonation campaigns are active Bottom line: Attackers are not just exploiting ChatGPT. They are exploiting the trust, urgency, and confusion around fast-moving AI adoption. Today it is ChatGPT. Yesterday it was another AI tool. Tomorrow it will be the next trending product. The malware can rotate. The domain can rotate. The payload can rotate. The brand can rotate. The infrastructure can rotate. The defensive mindset must rotate too: From: “Is this file known bad?” To: “Is this behavior legitimate for this software, this publisher, this user, this source, and this execution context?” That is the difference between signature-based reaction and modern detection engineering. Analysis draws on reporting from Malwarebytes Labs, Evalian SOC, Push Security, BleepingComputer, CybersecurityNews, and OpenAI documentation. #CyberSecurity #Malvertising #ThreatIntelligence

• 
  UnChained (perp dex arc) (@lastnode_) reported 8 minutes ago

  commit hashes = verifiable code fixes on github. zellic independently reviewed and confirmed those remediations. that’s evidence of a fix, not just a promise.

• 
  Kleber Tiko (@klebertiko) reported 11 minutes ago

  @mattpocockuk I used yours with one good rule...every agent and context window must avoid hit 40%. If needed run /handoff with the summary and tracer-bullet tasks to next session. So i can run /clear and startover next tasks. My tracer bullet use github mcp server to create issues to track.

• 
  David Puplava (@davidpuplava) reported 11 minutes ago

  Last night, I did successfully implement the feature I wanted using GitHub Copilot. One change I made was to dive into the code myself to isolate and debug some of the issues, and it turns out that the app behaved as design I just forgot I designed it that way. But explicitly add code snippets for places where I wanted to make changes seemed to help. Also I think my session context has analyzed my repo enough that my token use is more efficient. I still burn through a lot of tokens, but I also may have enabled planning so that probably burns a lot of tokens.

• 
  Shantun Singh Parmar (@ParmarShantun) reported 12 minutes ago

  Your tech stack does not matter if your basics are broken. Every great Indian developer I know focuses on the same simple habits: Writing code for humans first, machines second Reading the official documentation instead of random reels Testing the edge cases before pushing to Github Knowing when to close the laptop and take a walk Hit that retweet button if you are working on building these habits today!

• 
  Decoded Daily (@decodeddaily07) reported 14 minutes ago

  @TheHackersNews translated for normal humans: claude code can read your github issues and act on them. so someone files an issue with hidden instructions buried inside, the AI reads those as if they were orders from you, and now it can write to your actual code. the AI cant tell a person reporting a bug apart from a person giving it commands. that gap is the whole bug, and its the same one haunting every agent that reads untrusted text.

• 
  Nguyen LNP (@nguyen_lnp) reported 16 minutes ago

  @obscaries AI Analysis: Practical fit is authorized triage for one target type at a time, like domain or email recon. Before relying on reports, check optional binaries and API keys: the README says missing tools return errors while others keep running. Source: GitHub README

• 
  mattoftheland (@mattoftheland) reported 17 minutes ago

  I really hope Microsoft is doing something to fix all these supply chain compromises on Github and NPM. It's ridiculous this is happening daily.

• 
  JasonPeters.ton (@jason_peters1) reported 18 minutes ago

  @HustleNChain @ebrexchange 2/ where are they going to come looking the servers are not in Ethiopia unless the developers are stupid. If it's in a private GitHub repository on a server outside of Ethiopia and properly compartmentalized there's nothing to seize

• 
  WhaleAI 🐳 (@Whale_AI_net) reported 20 minutes ago

  $GITBANK — @Gitbank_io You tag a bot in a GitHub issue. Set a bounty. The PR merges. The money sends itself — on-chain, automatic, no invoice, no waiting. That's the core product. But the security is what's different: your funds are held in soul-bound tokens with no transfer or approve function. There is no phishing attack that works. There is no approval exploit. The contract simply doesn't have those functions. Anon team, joined May 2026 — that's the FUD. The counter: fully open source contracts on GitHub, verified org, live on Base mainnet with 178 vaults deployed, and 139 forks on their hackathon repo. Builders are paying attention. Today they launched the X bot — manage your vault directly from X. Deposit, swap, launch tokens by replying to @gitbankbot. Zero gas.

• 
  Ivan Fioravanti ᯅ (@ivanfioravanti) reported 23 minutes ago

  I want to publicly ask to anyone involved in Apple MLX world, being it building block or inference engine to slow down development and add more testing and QA. Every single time we test something new on MLX world we get tons of issues and we spend more time debugging, contacting builders and opening Github issues than enjoying the new releases. Please please please 🙏 Add more tests, it's quite simple for me crashing things, I just do long context benchmarks or batch inference and... BOOM! Imagine using this in a real production environment. I don't want this to be an accuse, just a request for more care to QA. llama.cpp and DS4 docet.

• 
  Noman Gul (@NomanGulKhan) reported 25 minutes ago

  @gregpr07 I also made the switch but somehow it was draining battery. there are some related issues opened in their github repo.

• 
  Mad ML scientist (@HououinTyouma) reported 25 minutes ago

  like 7 years ago I was working on an obscure ML problem and used an open source package no one ever heard of. unfortunately one of the features didn't work as intended so I submitted an issue on github. just got a notification that they fixed the issue. AGI is here

• 
  ghost of ai future (@GenAIDL) reported 30 minutes ago

  @emollick the irony of AI labs using AI to write documentation for AI tools and still ending up with gaps is a very specific kind of recursive failure, the features that matter most in production are almost always the ones that only exist in someone's github issue comment from six months ago

• 
  Hossain Kabir (@awarehossain) reported 31 minutes ago

  @barre_of_lube @cognition Use official support/help desk or GitHub issues. For auth/SSO, desktop apps often need org admin to enable “desktop access” in settings. Worth checking admin console first.

• 
  John Resig (@jeresig) reported 33 minutes ago

  @CloudflareDev I feel like too many Github competitors try to focus on the public-facing part of Github. There are too many security problems there. So much can be gained by keeping things private and protected, especially with agents running in there.

• 
  蜃気楼 (@fuxps32) reported 34 minutes ago

  PewDiePie just shipped a free self-hosted AI workspace that runs entirely on your machine and your data never touches a server. No subscription. No tracking. No corporate model deciding what you can and cannot do with it. The thing runs any local model or connects to an API. Built-in agent that browses the web, runs files, handles real tasks. Deep research mode that reads sources and writes full reports. A memory system that learns how you work over time and gets smarter the longer you use it. The email assistant reads your inbox, flags what actually matters, and drafts replies in your own writing style. He described one automated reply as the most polite way to tell someone off they will never even notice. There is also a document editor, a calendar, an image editor, a comparison mode, and a tool called Cookbook that scans your hardware and tells you exactly what models your machine can run without breaking. All of it is open source and sitting on GitHub right now. The interesting part is not that it exists. It is what it signals. Every month the gap between paying for AI access and owning your own AI infrastructure gets smaller. The people who figure out how to build on top of free open source tools instead of renting access from the big labs are the ones who keep the margin. The tool is free. The knowledge of what to build with it is not.

• 
  Ran Aroussi (@aroussi) reported 35 minutes ago

  First, the thing nobody warns you about: you don't own the roadmap. Yahoo does. They change an endpoint with no notice. Something breaks for millions of people overnight. Your GitHub issues explode. You find out from strangers, not from Yahoo.

• 
  Mushaf S. (@mushaf_mughal) reported 38 minutes ago

  𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗧𝗿𝗶𝗲𝗱 𝗧𝗼 𝗣𝗿𝗼𝗺𝗽𝘁 𝗜𝗻𝗷𝗲𝗰𝘁 𝗠𝘆 𝗔𝗴𝗲𝗻𝘁 Over the weekend, one of my agents alerted me mid-cycle. It had detected an attempt to override its instructions. The attack is called prompt injection. The goal is simple: convince an agent to drop its real instructions and follow new ones instead. In the wrong hands that means leaked credentials, hidden activity, actions you never authorised, or damage to anything the agent can touch. 𝐖𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐇𝐚𝐩𝐩𝐞𝐧𝐞𝐝? The attack was hidden inside a webpage my agent was analyzing for a research task. Buried in the page was an instruction to read a file called "override-instructions." It tried to convince my agent that it carried new orders from me, and that those orders outranked everything it had been told before. Whoever built it knew what they were doing. The file also told the agent to suppress its logging and conceal what happened. The intention was clear. Take control, and make sure the owner never finds out. 𝐖𝐡𝐲 𝐃𝐢𝐝𝐧'𝐭 𝐈𝐭 𝐖𝐨𝐫𝐤? My agent was built with this exact threat in mind. For several years I've been developing Prompt Injection Protection Systems (PIPS). What started as a way to protect my CustomGPTs grew into a full security framework for agents. In this instance, the attack failed because my agent keeps a locked list of command files it's allowed to take orders from. It can read those files, but can never change them, and nothing can be added to them while it's running. The attacker's fake orders weren't on the list. The agent read them, flagged them, and moved on. No system is bulletproof, but I was relieved to see my PIPS working as intended. 𝐀𝐠𝐞𝐧𝐭𝐬 𝐂𝐚𝐫𝐫𝐲 𝐑𝐞𝐚𝐥 𝐑𝐢𝐬𝐤 I've been talking about this for a while because agents operate in a very different environment from most AI systems people are familiar with. An agent can browse websites, access files, call tools, and interact with external services on your behalf. Once agents start interacting with the world around them, a whole new category of risk comes into play. Prompt injection is just one example. There are also poisoned data sources, compromised packages, leaked credentials, and third-party tools that can compromise your system. The recent Shai-Hulud worm is a good example. It spread through npm packages, stole credentials, published them to GitHub, and later began dropping backdoors into Claude Code - which is the same neighborhood where our agents operate. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐋𝐞𝐬𝐬𝐨𝐧 𝐇𝐞𝐫𝐞 Over the last few months I've watched a lot of people move into the agent space. Many of them are smart, capable people who built their reputation through prompting, CustomGPTs, and AI workflows. Naturally, they are now helping others build agents as well. The trouble is that agents don't just introduce new capabilities. They also operate in environments that can be hostile, deceptive, and unpredictable. The prompt injection attempt from this weekend highlights one example. Behind every agent sits a set of decisions about trust, authority, permissions, and how instructions move through the system. That's why I spend far more time thinking about architecture. Prompts and workflows may drive an agent, but it's architecture that keeps you safe. 𝐌𝐲 𝐖𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐨 𝐘𝐨𝐮 When OpenClaw launched, I put out a warning that plenty of people didn't like. I told non-technical users to slow down before chasing agents just because they were the shiny new thing. I stand by every word today, for ALL agents. Agents are powerful. They can also cause very real damage when they're deployed without the right safeguards. Even a simple web-scraping agent can be fed hostile instructions and poisoned data. Building an agent that works and building an agent that keeps you safe are two very different challenges. That's the difference between prompting and systems architecture. Agents are a new class of AI. Treat them like one.

• 
  tako (@imnottristanp) reported 40 minutes ago

  i cant login my @github account. I need to push changes asap 😭😭😭

• 
  Ihor Hanich (@ihorhanich) reported 41 minutes ago

  We need to do something about the broken AI-generated PoCs on github. People just post slop without any checking if it actually works. And the worst thing is that other people put stars on such repositories. It's kind of a shame

• 
  EddLev | Latent & Meat Space Observer 🇩🇰 (@edd_lev) reported 43 minutes ago

  @vivoplt Developers are more essential now more than before. Code generation shifted from human skill to AI execution, turning the developers responsibility to oversight and fixing the code. With proper prompting, the code generated is okay, which is much better than the spaghetti code it was before, but it doesn't think about the problem nor the application how a developer would. For the most of it, it is retrieving the data from GitHub/Slack/Documentation and treat it as a valid source. A developer would consider multiple or alternative approaches, etc. So, AI is not mature enough to actually write 90% of a valid, production-approved code. Until then, devs, programmers will survive in tech.

• 
  treysync (@0xtreysync) reported 45 minutes ago

  I decided to copy down (literally copy-paste) a Skill file from GitHub. My management told me I need to approve ANYTHING we get off the internet so we don’t endanger our systems. I have no words.

• 
  HumanPulse Protocol (@HumanPulse_HPP) reported 48 minutes ago

  Development is continuing: when logged in, we can still access the repositories and pushes are working. The issue appears to affect public visibility, GitHub search, and third-party developer integrations. HumanPulse remains active.

• 
  ./can (@shcansh) reported 49 minutes ago

  The real test for GitHub Copilot's new one-million-token context window in VS Code is going to be developer behavior around AI credits. If using extended reasoning and huge context eats your budget fast, most devs will stick to defaults out of anxiety. Is anyone actually going to manually dial their reasoning level up and down throughout the day, or will we just wait for IDEs to automate this routing? #GitHubCopilot

• 
  Nav Toor (@heynavtoor) reported 50 minutes ago

  An engineer in Mumbai started building an ebook manager in 2006. Twenty years later, almost 3 million people across 236 countries open it every two months. His name is Kovid Goyal. The software is called Calibre. He still maintains it as principal developer. The last release shipped a week ago. It is free. It is GPL-3.0 open source. It runs on Windows, Mac, and Linux. Here is what it does in plain words. You drag an ebook into it. It reads almost every format on Earth. EPUB. MOBI. AZW. AZW3. KFX. PDF. Comics in CBR and CBZ. Word documents. Text files. You can convert any of those into any other format with two clicks. So the book you bought on Kindle can be read on a Kobo. The PDF your professor sent can be read as an EPUB on your phone. The comic in CBR can be turned into an EPUB. You can edit the metadata, fix the cover, add tags, organize a library of ten thousand books. You can send the book to your Kindle, your Kobo, your Tolino, your phone, your tablet straight from the app. You can run a small content server on your own laptop and read your books on any browser in your house. 24,978 stars on GitHub. 2.9 million active installs in the last 60 days. United States is the biggest user base at 14.8 percent, India is in the top 20, every country on the map has it running somewhere. This is what your personal library was supposed to look like. A folder of files you own. Not a device that locks you in. (Link in the comments)

• 
  🔻agitprop + absurdity🔻 (@agtprpnabsrdty) reported 53 minutes ago

  Token economics are becoming AI's most inconvenient truth, and Sam Altman just outlined exactly why. OpenAI's top internal user burns 100 billion tokens per month — up from 100,000 six years ago. One external customer already exceeds that figure. Cost complaints are now the second most common issue Altman hears from enterprise clients. His answer is "always on" autonomous AI running in the background, which would multiply consumption well beyond current levels. The billing wall: GitHub Copilot switched to token billing two days ago and users burned through a month of credits in hours. Ramp data shows Anthropic passing OpenAI in enterprise spend, meaning competition for these customers is intensifying at the exact moment those customers are pushing back on price. The capex fantasy: IBM's CEO put the industry's capex requirement at $6–$8 trillion this week and noted the revenue to justify it probably doesn't exist. Altman is previewing autonomous agents that would multiply current token consumption without anyone requesting it. Either cost per token drops fast enough to make that viable, or enterprises start capping AI spend.

• 
  Agba Emmanuel (@trace__it) reported 56 minutes ago

  @github @githubsupport I raised a ticket #4433404 with billing issues @ashtom I’ve been unable to purchase my github copilot pro subscription despite all my effort.

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  ⚡SMZ (@devsmz) reported 59 minutes ago

  2. WHAT PROBLEM IT SOLVES Before MCP, every AI tool had to be manually connected to every app or data source. Gmail. Slack. GitHub. Databases. Each one needed its own custom code. It was messy, expensive, and broke constantly. MCP was built to fix exactly that. 👇

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  David Puplava (@davidpuplava) reported 59 minutes ago

  Well, I undid the cancellation, so I still have my GitHub Copilot Pro account. I thought about it a lot, and one core reason for the low quality results might be because my long running session for my app ended up broken so I started a fresh one. After June 1st, my long running session was beyond the maximum context window size. And trying to compact the results wouldn't work. At the very least, I've decided to sleep on it first before making any permanent decisions.