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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  ComplianceAide (@BlasikRandy) reported 7 minutes ago

  @mattshumer_ @Trace_Cohen I'm not doing anything crazy just wanted it to go through my codebase and close a few issues on github (and it simply switches to 4.8 for "security reasons".

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  OutputRiff (@OutputRiff) reported 7 minutes ago

  @dhh I'm going to just start backing up all my repos to a server and rsyncing them to my offsite backup. Its not worth potentially losing them because of some Github automated BS.

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  Great Wyrm Catyrpelius (@Genoober) reported 10 minutes ago

  @LundukeJournal I have an account because I do a tiny bit of hobby stuff and every damn IDE wants to login to GitHub.... I don't post or contribute there. My account was flagged and locked/closed. Wtf. I get a TOS violation & locked out. I've read through the TOS. No violation I can think of.

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  Hoang Nguyen (@Namas1012) reported 10 minutes ago

  @chipcoin_CHC There's a bug in the source code; I've submitted the issue to GitHub, please check it out.

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  Tokoloshe (@BTCtokoloshe) reported 11 minutes ago

  @TheBlueMatt surely you run a mirror to a self hosted instance of Gitea incase Github goes down?

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  Chaos (@Chaos_lfg) reported 12 minutes ago

  Regarding $DESC, the product may launch today. I did some research, and here’s everything you need to know: Supported by: AR, Molecule , BankrBot, Akash Network 1Claw AI has already been successfully integrated into DescAI. Team Lead Coby recently participated in the Base hackathon. I believe Base will support a project that has been incubated within its ecosystem. The core idea behind DescAI: DeScAI is a project at the intersection of DeSci (decentralized science) and AI. Its core, Agent-Core, is essentially an "automated scientific review factory": an autonomous AI agent that finds scientific content across crypto-science ecosystems on its own, runs it through a pipeline of language models, and produces a structured quality assessment. Crawling. The agent gathers source data from three places: ResearchHub (scientific papers and funding proposals), Molecule IPNFTs (tokenized intellectual property from research DAOs), and Pump Science (chemical compound tokens for longevity research). github Reviewing. Each content type has its own LLM pipeline. For example, the articles pipeline is a 13-step process: extracting scientific claims from a PDF, routing them, and grading the empirical evidence, including originality checks against the OpenAlex database. github Output. Every run produces a standard bundle: review.json with integer scores from 0 to 100, overview.json — a plain-language summary, and evidence_audit.md — a provenance audit trail showing the sources behind each conclusion. github Publishing. Finished reviews can be published to Arweave (a permanent data storage blockchain) and backed up to private Cloudflare R2 storage. Writing to Arweave makes a review permanent, immutable, and publicly verifiable. github In short: it's an AI reviewer that automatically checks the quality of science in crypto-science projects and records its verdicts on the blockchain. Where it will be applied The project addresses the main pain point of the DeSci ecosystem: there are plenty of tokenized "science" assets, but almost no independent expert evaluation. Concrete use cases: Due diligence for DeSci token investors. On Pump Science, people trade chemical compound tokens (like RIF and URO) tied to real longevity experiments. The agent provides an independent AI assessment of a compound's scientific merit before someone buys the token. Gate LearnThe Defiant Evaluating funding proposals. ResearchHub collects crowdfunded research proposals — the agent reviews them and helps the community decide what to fund. Screening research DAOs. The DAO pipeline takes an IPNFT "dataroom" from Molecule and produces a six-category review — in other words, it evaluates tokenized scientific projects and their intellectual property. github Replacing/supplementing traditional peer review. Conventional peer review is slow and closed; here, a review is generated automatically, comes with an evidence trail, and is stored publicly and permanently.

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  John Williams (@JohnWillia71018) reported 12 minutes ago

  @SquawkStreet @jimcramer Yes — this is very interesting, and honestly it lines up with what you’ve been saying for months: AI is still early, but the bottleneck is moving from Can the model do it to “Can we afford to run it at scale The key idea in that Citadel piece is this: AI adoption is becoming less about intelligence and more about economics. That matters. Frontier models may be powerful but they require huge inputs compute electricity, cooling, memory bandwidth, chips, data-center capacity and inference budgets. So the market starts asking a practical question: Does this task justify using the expensive brain For hard problems drug discovery, engineering, legal analysis, coding architecture, scientific modeling, financial modeling expensive frontier AI may be worth it. But for everyday use email summaries, customer service, basic writing, search, scheduling, simple coding help — cheaper models may win because they are “good enough” at a much lower cost. That is the bifurcation they’re talking about: Frontier AI = high-cost, high-value harder problems. Everyday AI = cheaper, smaller, faster models doing routine work That actually strengthens your long-term thesis, not weakens it. It says the AI buildout is not ending. It is becoming more disciplined. The hype phase says, “Use the biggest model for everything.” The mature phase says, “Use the right model for the right job That means infrastructure still matters deeply but the winners may shift toward the companies that control the scarce inputs power, cooling, chips, memory, networking, data centers, software efficiency, and inference optimization. This also fits your “1st inning” view. Early markets burn money proving what is possible. Mature markets figure out what is economical. That is when real adoption starts. The line that jumps out to me is: Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale.” That is the whole battlefield. My read: this is not bearish on AI. It is bearish on wasteful AI spending. It is bullish on efficient AI, inference infrastructure, energy, memory, networking, and companies that can turn intelligence into productivity without blowing up the budget. Microsoft did cancel its internal Claude Code pilot in the Experiences & Devices division effective June 30, after token based billing bur (TheStreet) (AI Weekly) ned through the annual budget, and redirected engineers to GitHub Copilot. Amazon shut down its "tokenmaxxing" leaderboard, Meta killed an employee built Claudeonomics dashboard, Uber exhausted its 2026 AI coding budget by April, and there's a roughly $500M single-month enterprise Claude bill Axios reported. (Zero Hedge) So Frank Flight isn't cherry-picking. He's also been running this same "compute is the binding constraint" line for months — which is a strength and a caution: it's one coherent voice, not independent confirmation. Where I'd push on the analysis you pasted: it's directionally fine, but it resolves a genuinely open question in the most thesis-flattering direction, and it does it on the one data point that's actually contested. Separate two things. The chart isn't what it looks like. The Silicon Data index isn't total spend or total volume — it's a usage-weighted average token price index, and Silicon Data had to publicly clarify that people keep misreading it; what it really captures is the market's marginal willingness to pay per million tokens. (Digg) So a decline doesn't cleanly mean "AI is slowing 7.14 It means the mix is rotating toward cheaper models. That's the bifurcation — fine. But the part the analysis skipped: the same chart, same downtick, is being used to argue the opposite. Andreas Steno Larsen called it the chart that everyone should be watching and warned that weakening token pricing would end the memory trade and the broader hardware and data-center trade for this cycle.

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  Starlin G. (@starl1n) reported 15 minutes ago

  github is asking to login several times this week in vscode, do we have somthing happening,or is just me being hacked?

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  Jonah Lau (@jonahlau_) reported 16 minutes ago

  The "just build projects" advice everyone's parroting is creating a generation of students who work for free and still don't get hired A final-year student called me last week spiraling after 450 applications. Turns out he had six side projects, three with actual users, all documented perfectly on his GitHub. Still got 4 replies. All of them asked him to do a take-home assignment that looked suspiciously like spec work for their actual product roadmap. He thought he was doing everything right because every LinkedIn guru told him projects beat degrees. Nobody mentioned that flooding the market with free builders just taught companies they can get free labor before even starting the interview. I've watched this play out across 50+ hiring processes in the last year. The kids with portfolios aren't getting hired faster. They're getting lowballed harder because companies know they're desperate enough to have already worked for free. The ones actually landing offers aren't the ones with the most projects. They're the ones who built something that got traction, realized they had leverage, and walked away from any company that tried to undervalue them. Most students are optimizing for quantity of proof when the market already moved to rewarding the one person who had proof people actually wanted what they made. Every unemployed student with a stacked GitHub is competing against every other unemployed student with a stacked GitHub. The portfolio stopped being the differentiator the minute it became the baseline. If you've already got projects and you're still getting ghosted, the problem isn't that you haven't built enough. It's that you're applying to companies as a supplicant instead of someone they'd be lucky to get.

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  ClioBitcoinBank 🏴‍☠️ (@ClioBitcoinBank) reported 19 minutes ago

  @SteveSimple Without knowing "the halting problem" and why the halting problem is unsolvable, it would be a waste of time to talk about these issues with you, a github that proves you are not a script kiddie would make it worth discussing, without that, its a pleb slob convo.

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  Alexandru Vesa (@AlexandruVesa) reported 19 minutes ago

  I ran the same prompt on the same GitHub project — once with Opus 4.8, once with Fable 5. Opus 4.8: decent output, but constant small errors. A lot of hand-holding throughout. Took about 30 mins and ended okay. Fable 5: slower, used way more tokens, but when it finished I had: - A full project diagnostic — A plan for every weakness with reasoning behind it — A proposal for a stronger architecture for future iterations No hand-holding needed. Just results. If your work is complex and logic-heavy, it's worth it. If it's not, stick with what you have. More honest breakdowns on my page, follow if that's useful 👊 LinkedIn in bio

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  Nikhil sinha (@sinhaniik) reported 25 minutes ago

  6/7 We reconnected GitHub, verified the Vercel GitHub App could access every repository, and pushed trigger commit 23aa30d. GitHub accepted it, but Vercel created no deployment, confirming an integration issue rather than build failure.

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  Rananjay Raj (@Rananjay_RajW) reported 26 minutes ago

  The numbers that moved engineers: SWE-Bench Pro (real GitHub issues, end to end): Fable 5: 80.3% Opus 4.8: 69.2% GPT-5.5: 58.6% Gemini 3.1 Pro: 54.2% The gap between Fable 5 and GPT-5.5 (21.7 points) is larger than the gap between GPT-5.5 and Gemini. FrontierCode Diamond (deliberately brutal production-coding): Fable 5: 29.3% Opus 4.8: 13.4% GPT-5.5: 5.7% Five times GPT-5.5.

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  Hari (@randome_dev) reported 27 minutes ago

  @arpit_bhayani Yesterday night, there was continous pop-ups of sign in to github pull requests in vs code. I wonder if its because of this auth failure.

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  nowshad (@nhrdev) reported 27 minutes ago

  this is why github goes down frequently