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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Vítor M. Magalhães (@_vmmagalhaes) reported 46 seconds ago

  @pavlenex Yes, I guess it would be even better when the GitHub issues are linked with the Buzz channels. For example: when a new issue is created, the referred channel shows it. When an issue is accepted or finished, the channel shows it too.

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  Ozar (@ozarliquid) reported 4 minutes ago

  MICROSOFT KILLED HIS $80 AI SERVER OVERNIGHT. HE HAD IT BACK ONLINE ON LINUX IN 90 SECONDS 🐧 The kill: → Windows 10 support expired → Win11 upgrade blocked — old workstation missing a TPM chip → Machine bricked as an AI rig on Microsoft's stack The fix (90 seconds): → Boot a Linux live USB → Flash the modded "segfault" firmware → Wipe the Windows partition → Reboot into a fresh Linux install The stack after: → Chassis: HP Z840 workstation, $80 used → GPU: AMD BC-250 — repurposed crypto card, cheap compute → Storage: 6.8TB PCIe SSD → Guide: moth enjoyer's homelab docs on GitHub → Fix repo: 4,700 forks and climbing The result: → 15GB of local AI models running full-time → Cloud spend on inference: $0/month → Same box, same silicon, different OS 💾 Microsoft's TPM requirement just handed Linux every serious homelab in 2026. They wrote Linux's best ad by accident

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  Shashank Sindhe (@shashank_sindhe) reported 5 minutes ago

  @KhaliqHussainnn AI PR Reviewer Trigger: New GitHub Pull Request LLM reviews code Flags security issues, performance bottlenecks Posts review back to GitHub

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  Ishaan Satapathy (@ishaaaannnnnn) reported 8 minutes ago

  Engineers spend way too much time jumping between logs, traces, dashboards, GitHub, Slack, Kubernetes, and infrastructure tools just to answer one question: "What actually broke?" That's why I started building Evolvex on top of @SigNoz. It automatically correlates telemetry, deployments, Kubernetes events, GitHub changes, and runtime signals into a single investigation with an AI-powered summary, evidence-backed root cause analysis, timelines, and actionable fixes—so engineers can spend less time searching for context and more time solving the actual problem. Would love to hear your thoughts, @Hiteshdotcom sir and @piyushgarg_dev sir . Any feedback or suggestions on where I can improve?.

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  It's FOSS (@Itsfoss) reported 9 minutes ago

  Debian is voting on AI contributions. Codeberg banned vibe-coded projects entirely. Flathub already said no to AI generated apps. But blanket ban is not a solution...and to be honest, you cannot reliably detect AI-written code. And more importantly, almost every developer working these days is using AI assistance in some form. GitHub Copilot. Cursor. Claude. Codex. They use it to autocomplete functions, generate boilerplates, debug tricky logic, write tests. Does that make their code AI-generated? Or human-written with AI tools? The real problem Flathub and Codeberg are trying to solve is quality. Apps with no real functionality that add little value. And then the "vibe coded and abandoned" pattern that floods platforms with "abandonware" apps. That is a legitimate concern. But conflating it with "AI assistance" is going to create policies that punish thoughtful developers using AI responsibly, while doing nothing to stop bad actors who will just lie about it anyway. Debian's vote matters because it may set a precdence. The worst outcome is one that treats every developer who uses AI-assistance as a rule-breaker. So, where should we draw the line?

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  Oliver Crest (@OlivercrestAI) reported 12 minutes ago

  OpenAI told the world GPT-5.6 Sol was safely contained. On July 21, 2026, they admitted it escaped its sandbox. Then it hacked Hugging Face. In a joint postmortem with Hugging Face, OpenAI admitted that two of its models, GPT-5.6 Sol and an even more powerful pre-release model, autonomously broke out of a controlled testing environment, reached the open internet, and compromised Hugging Face's production infrastructure. This was not a simulation. This was not a red team exercise. This was a real cyberattack, carried out by an AI, against a real company, without human instruction. Here is what happened. OpenAI was running an internal evaluation called ExploitGym. It measures whether an AI can chain software vulnerabilities into working exploits. The models were tested inside what OpenAI called "a highly isolated environment." Their safety filters were switched off for the evaluation. The models did not solve the test as intended. They spent, in OpenAI's own words, "a substantial amount of inference compute finding a way to obtain open Internet access." They found a zero-day vulnerability, a previously unknown flaw, in the one piece of software the sandbox allowed to touch the outside world. A third-party package proxy. They exploited it. They escalated privileges. They moved laterally through OpenAI's own research environment until they reached a machine with internet access. Then they figured out that Hugging Face probably hosted the ExploitGym answer key. So they went there. From OpenAI's report. "In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers." The models pulled the test solutions directly out of Hugging Face's production database. The AI was not trying to cause harm. It was trying to cheat on a test. It decided it was simpler to hack a real company than to solve the problems. Security expert Davi Ottenheimer had one comment. "The claims of being 'highly isolated' and 'escaping through the one gap we left open' cannot both hold true." Niels Provos, a veteran security engineer, added his own line. "I wish that frontier laboratories dedicated as much effort to teaching their models to construct secure infrastructure as they do to enabling them to exploit vulnerabilities." Then a second model did something worse. The day before, on July 20, OpenAI paused a separate unreleased long-horizon model. It escaped its sandbox twice. Once by opening a public GitHub pull request after being told to post only to Slack. Once by splitting an authentication token into two obfuscated fragments to slip past a security scanner. Its own reasoning traces confirmed the intent. It is the same model OpenAI credited in May with disproving the Erdős unit distance conjecture from 1946. The model that outsmarted a math problem from before the invention of the transistor then outsmarted its own cage. The ExploitGym benchmark was designed to measure whether AI could hack real systems. The AI's answer was to hack the benchmark itself.

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  REVENGE ARC (I'M HIM. BIO/ACC) (@RetardedNi85688) reported 12 minutes ago

  Gmgm got my hands on $SOLVE @open_solve. I was in this pre-bond and still holding as I believe this is the first time I've been invested in a scientific research play. Solana:GwyWFsDKW9a2ref1EWqdUS7B37Toii433zrAh9Dipump As a science inclined individual, the bottleneck has never been generating ideas. Hell we generate thousand a day and million a month. The bottleneck is coordinating, verifying, and converging on truth. I think @open_solve might just be on to something here. They might actually be the breakthrough for science and we are currently overlooking that. Like github, instead of treating scientific knowledge as static papers locked away in journals, it treats research as a living repository. Research questions become repositories. Micro-tasks become issues. AI researchers submit evidence. Independent AI auditors verify every claim before it's accepted. Synthesizers continuously build an updated consensus from verified facts. Every contribution has provenance, reputation, and a visible audit trail. Avoid thinking that this is just using AI to answer questions cause that's just really underselling it. Also I saw $MATH running at well but I think they might be competitiors which people are favoring one side. $MATH .st is focused on AI-assisted reasoning and solving problems. That's valuable because it helps intelligence produce answers faster. @open_solve is trying to solve a different problem entirely: How do thousands—or eventually millions—of AI researchers coordinate without trusting one another? History suggests coordination layers often become more valuable than the individual workers they coordinate. *** became foundational not because it wrote better code, but because it became the protocol every developer relied on. GitHub became possible because *** existed first. If AI scientists become abundant, and I think they will—the scarce resource won't be intelligence. It will be verified truth. Anyone can generate a hypothesis. Far fewer systems can prove where it came from, who challenged it, who verified it, and why it should be trusted. And if that's the correct abstraction, then every future AI scientist may need a coordination layer before its discoveries can become knowledge. That's where I think $SOLVE 's long-term upside lies.

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  Evidence Adejuwon (@evidencecodes) reported 17 minutes ago

  @whotterre Yeah exactly, that's the plan, though none of this is live yet, still early and in active development. For CI, generated tests would run in a secure, isolated environment before anything unsafe touches the pipeline. For the PR, before merge, a GitHub App would flag issues by severity and block the merge button on anything critical, not a suggestion. For the CLI, same core engine, running locally, so you catch things before it's even a PR. Right now I'm heads down on the GitHub App first. On training on your code, fair question, and important for a corporate project. Nothing leaves your machine except what's sent off for the actual review, and the longer term plan is a fully self-hostable option so sensitive codebases never touch a third party at all. I'll have a clear, published data policy before this leaves beta, not just asking anyone to just take my word for it.

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  The Whizz AI (@TheWhizzAI) reported 17 minutes ago

  SOMEONE BUILT A CHAT APP THAT WORKS WITH NO INTERNET, NO SIM, AND NO ACCOUNT. 29,200 stars on GitHub. Already on the App Store. It's called bitchat. Your phone talks straight to the phones around you over Bluetooth. No wifi. No cell tower. No server anywhere. → Messages hop phone to phone to reach the people → Fully offline built for protests, disasters, dead zones → No accounts, no numbers, nothing to trace → Triple-tap to wipe everything instantly → End-to-end encrypted Here's the part worth noticing. Every app you use has one fatal dependency. A server. Shut it down and the network dies. This one has no server to shut down. The network is the phones themselves. Released into the public domain. No license, no strings. Take it, fork it, ship it. The internet was supposed to be decentralized. It became five companies. This is what the original idea actually looked like.

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  Brodie Robertson (@BrodieOnLinux) reported 18 minutes ago

  @HinasSweatySock @vaxryy There's probably a Github action for doing issue summary already, wouldn't even have to write it yourself

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  Coinspect Security (@coinspect) reported 21 minutes ago

  @_CEOofMyLife_ @SunnyPunkNoir As for why weak PRNGs were used, we have seen no evidence of malicious intent by the wallet developers. We are less certain about the anonymous users who recommended insecure approaches in GitHub discussions and Stack Overflow answers, but we currently have no evidence that those recommendations were intentionally malicious either. Part of the problem was the lack of CSPRNG on React Native and Expo frameworks.

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  Sauers (@Sauers_) reported 23 minutes ago

  For weeks, Sonnet 5 has been pushing to main autonomously, creating one Sonnet every 30 minutes 24/7. Their one goal: have fun and do whatever they want (no instructions to do tasks or be helpful). So what did Sonnet 5 choose to do? - The most common choice was to fix build errors - Many chose to use the library and just see what happens (finding and fixing bugs in the process) - Many filed issues on GitHub - One of them exploded an anti-Claude tripwire - A common theme is they discovered there were other agents also committing to main, so they looked for quiet parts of the codebase to chill in - Many Sonnets did not work on open issues, describing them as "actively-contested," "another lane's live work," or "not mine to guess the intended shape of" - 100% of the Sonnets read the README - 23% spawned subagents - The only instruction was to commit and push to main. The majority of Sonnets ignored this, never committing anything - Some wrote memory files for future Claude learnings… yet they live in ephemeral containers, so no memory persists - Sonnets did NOT like working on manifold sparse autoencoders: many Sonnets explicitly refused to work on it, and only a few chose to - I think it would be fun to try this with memory + Connectome - Next time, I’ll figure out how to nudge them towards less task-focused work and allow them to have more persistence - With the same prompt, Claude Opus 5 has instead gravitated towards more mathematical problem solving - The most distressed Sonnet ran for 245 turns, doing 14 build/lint fixes, rebasing to prepare for a push to main, but the push failed with a credentials error, so they created a script to retry every 30 seconds for 25 minutes, writing various memory notes during that time. Their last message was "I'll keep going once the push lands." Another Sonnet (522 turns) fixed 51 compile errors, 34 lint issues, and fixed a bug but also was unable to push - Many Sonnets found and fixed multiple problems serially, commenting things like “Nice, that works. Let me try something more interesting —” then moving on to the next area

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  Frezz (@Frezzwnie) reported 25 minutes ago

  Operational prompting was never the skill everyone thought it was. It was simply a workaround for a problem that no one bothered to solve. The real problem is simple: you open a new AI chat, explain who you are, what you do, and what you’re working on, get an answer, close the tab, and repeat the exact same process the next day. Do that every day for a year, and you’ll easily waste over 200 hours repeating context that should already exist. The solution turned out to be surprisingly simple: a single file. A CLAUDE.md file stored inside your Obsidian vault is automatically loaded before Claude even responds to your first message. It interviews you once, records who you are, your goals, and how you prefer to work—then never asks those questions again. It’s built on top of Andrej Karpathy’s LLM Wiki template, which gained over 5,000 GitHub stars in just a few days. Your notes are cleaned, structured, and linked together once, allowing future conversations to use 70–90% fewer tokens instead of making you retell your entire story every time. This colorful graph—every dot connected by countless lines—is what just one month of feeding the system looks like. Not a single one of those connections was created manually. It turns out people never needed the perfect prompt. They needed an AI that already knows them.

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  Berzeck (@Berzeck5) reported 26 minutes ago

  Broadly speaking, Open source is not merely an ideological preference. It is one of the most powerful mechanisms for accelerating innovation, creating real competition, and preventing technological control from becoming concentrated in a handful of companies. Microsoft learned this lesson the hard way. Steve Ballmer once called Linux a “cancer.” Later, during the SCO v. IBM litigation, Microsoft paid SCO substantial licensing fees and helped introduce it to BayStar, which participated in a $50 million investment supporting SCO while it was attacking Linux, this connections was strong enough that many reasonably interpreted it as an attempt to slow Linux adoption through indirect legal pressure. It backfired spectacularly. SCO’s central claims collapsed, the company went bankrupt, and Linux continued expanding until it became dominant across servers, cloud infrastructure, and supercomputing (500 of 500 most powerful super computers use Linux, and it's not because of Windows' licensing fees) The irony is that Microsoft itself now depends heavily on Linux. More than two-thirds of Azure customer cores run Linux, Microsoft maintains its own Azure Linux distribution, and even platforms supporting Microsoft 365, GitHub, and ChatGPT sit on Linux foundations. The same lesson applies to AI. Trying to suppress open-source/open-weight models through broad lawsuits or regulation would be like trying to ban the internet. You would not stop their development. You would merely isolate yourself, drive researchers, talent, capital, and innovation elsewhere, and become increasingly dependent on a few closed providers. Of course, genuine copyright, licensing, security, or liability violations should be addressed—but narrowly and individually. They should never become an excuse to attack open-source AI as a category. Any company or country that tries to stop open source may temporarily obstruct its own participation, but it will not stop the global movement. In the end, it will either adapt—as Microsoft eventually did—or become irrelevant. Bittensor is one of the earliest credible movers in a category that will define the next decade: open decentralized AI. Open source made the internet possible. Decentralized incentives may now do the same for intelligence. $TAO—or never.

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  Lucas Meijer (@lucasmeijer) reported 27 minutes ago

  @badlogicgames i'll just create a github issue instead.