GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  anu (@svector_eth) reported 1 second ago

  funny timing. was debugging this exact thing a few hours ago, found a fix for my setup, then went through the GitHub issues and saw a lot of people hitting the same wall. submitted a PR while i was at it. nice to see Telegram ship support for it.

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  Adam Arcada (@AdamArcada) reported 3 minutes ago

  Gemini CLI: millions of users, 100K GitHub stars, weekly releases. Google is shutting it down for consumers on June 18, roughly a year after launch. Replacement: Antigravity CLI.

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  Dan B (@BachelderDan) reported 4 minutes ago

  Day 19 of @shipordie_ I have a deployed product with auth and payment. Landing page is still mid. But I have a few days to work on it while my chrome extension is approved! My backend is auto scaling because why not.. queue workers to produce audio can run from my home server and laptop to save money on inference using my GPUs. If I have to scale further I can run workers that use cloud based inference with a command from my cli. Datafast and sentry are connected and ready. Everything auto deploys to AWS when I push to GitHub. All of it for under $100/month until it gets users, then we will see. I am at a conference for 4 days but I'm still hoping to launch this week.

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  fromsoftserve (@fromsoftserve) reported 5 minutes ago

  @morphys_tears oh if you mean the actual renderer itself, it's not on github or anything, although ragevitamins has said in the past that he might open source it down the road, but don't take my word as gospel as I could be entirely wrong, and I don't speak officially for it haha

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  Student Offers (@StudentOffersHQ) reported 6 minutes ago

  @Your_PARAM @beingamanFF use github login maybe, could verify you faster, using your personal email would make you wait for days for credits

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  Ayodeji (@3x3cv3) reported 8 minutes ago

  Imagine my shock when I realised people are actually using some of my packages from 2024. There were open issues and PRs but GitHub just never notified me for some ******* reason.

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  Aniketh Dsouza (@Aniketh) reported 13 minutes ago

  One of my friends was having trouble establishing connection to Marketsmith with the Second Brain concept. But as he proceeded, as I guided him we landed on a Github repo for YouTube transcribing. I told him to install it. Apparently, he doesn't need to use a chrome extension for transcribing a video and then adding the transcription to CC. Now he just has to add the YouTube Link to Claude Code and it will auto transcribe and ingest to your Second Brain. I realised I didn't do it myself. Complete that today. So then I thought for a moment. If it can ingest a youtube transcription what if it can build me a offline video transcription where I can give it a video from an offline folder and it can transcribe it and ingest the transcript like it generally does. It worked. Found a github repo. Now ingesting as skill. You have the Power.

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  DannFox (@Dannfox_22) reported 14 minutes ago

  Last night I had dinner with an old tech friend and the conversation was genuinely interesting. @AnthropicAI released @claudeai Fable 5 on June 9 - the most powerful AI model ever made public. State-of-the-art at coding, research, vision. Three days later, it was gone. The US government ordered it offline over national security, after concluding someone had found a way to jailbreak it. Anthropic couldn’t separate who was using it from who wasn’t, so they shut it down for everyone, worldwide, within hours. Now they’re refunding people who paid for a model that vanished overnight. First time a government has reached in and switched off a commercial AI model across the entire world. Fascinating and unsettling in the same breath. The quieter shift is the one that hits closer to home: junior work gets automated first. HR, support, finance - agents already handle pieces of it. Companies are watching. You don’t have to love it. You don’t even have to like where it’s heading. But the tools are here. The people who learn them now will move differently than the ones who wait. So learn them anyway. Claude, @github, whatever your field runs on. Even if you never need it, you’ll be glad you didn’t sit it out. Liking it is optional. Paying attention isn’t.

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  JRC (@Jubleerc) reported 14 minutes ago

  From Enthusiasm to Caution- Enterprise AI story in 2026 Microsoft gave thousands of its engineers Claude Code in December. By June, it's cancelling most of those licenses. Not because the tool failed. Because the bill arrived. Token billing ate Microsoft's annual AI budget. Teams are moving to GitHub Copilot CLI by June 30. (The Verge) Uber's version was faster — its entire 2026 AI coding budget gone in 4 months. Power users: $500–$2,000 per engineer, per month. (Forbes) The mid-year scorecard: → 88% of companies use AI somewhere (McKinsey) → 2 in 3 haven't scaled past pilots → 95% of pilots show zero profit (MIT) → 40%+ of agentic projects will be killed by 2027 (Gartner) Everyone's using AI. Only Few are making money with it. But the other column of the ledger looks very different: JPMorgan: ~$2B/yr in AI value, matching its ~$2B spend. Dimon calls it "the tip of the iceberg." IBM: $4.5B saved using its own AI across 70+ internal workflows. Agents that survive pilot: ~171% avg ROI. Same models. Same vendors. Different discipline. That's the whole story. What the winners do differently: 1. Track cost per outcome, not total spend 2. Tie every project to real revenue or savings 3. Small models for routine work, big ones for hard problems 4. Humans in the loop on customer/money decisions 5. Give every pilot a kill date H1 didn't prove AI is overhyped. It proved AI is industrial — and industrial tools reward operators, not enthusiasts. The window to be early on disciplined AI is still open. The window to be casual about it just closed. What's your biggest AI lesson from H1 2026 ? #AI

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  sun happy (@sunhapp47618266) reported 16 minutes ago

  $TEA Danger House (33,540) was broken, but after the candle closed, price is rebounding. It is now important to watch whether it can re-enter the trendline resistance and continue the recovery. If the rebound fails and the price falls back below the box range again, momentum could be lost. "GATE AI" 1. Technical Issue – NPM Registry Spam Incident The biggest issue was an attack on the open-source ecosystem driven by "token farming." Facts: Since February 2024: Spam package creation began almost immediately after the TEA Incentivized Testnet launched. Around 150,000 malicious packages: Amazon Inspector detected more than 150,000 TEA-related malicious packages in the NPM registry. Single accounts creating hundreds of packages: Some accounts registered hundreds of fake packages. 70% spam rate: Of the approximately 890,000 packages published to NPM during the first half of 2024, around 70% were reportedly TEA-farming spam. Technical Weaknesses: The design of TEA's Proof of Contribution mechanism had flaws: No cost to register a package (free). Dependencies could be added with only a single line in the manifest. teaRank = Number of packages × Connectivity, making it possible to earn rewards by creating thousands of fake packages. 2. Airdrop Issues Facts: February 2024: Incentivized testnet launched (points accumulated with the expectation of future token conversion). September 2025: Public sale on CoinList (4 billion TEA at $0.0005 each). June 4, 2026: Mainnet launch and TGE (Token Generation Event). Problems During Launch: Liquidity pool activated early On-chain liquidity activity began at 23:54 UTC, six minutes before the official launch time of 00:00 UTC. Price collapse The token price fell approximately 75% during the first hour after launch ($0.00046 → $0.00011). Mass selling by CoinList buyers Because the sale terms included 100% unlock on day one, early buyers were able to sell immediately. 3. Allegations of Development Slowdown GitHub Activity Since November 2025: Commit activity in the tea protocol organization (teaxyz) has largely stalled. December 2025: 2 commits. January 2026: 1 commit. February 2026: 2 commits. Since March 2026: No commits. Founder Situation Max Howell remains CEO. However, as of June 2026, his public GitHub activity appears focused on a separate project called automic-vault, unrelated to tea. 4. Investment and Funding Situation Total funding raised: Approximately $16.9 million (including investment from Binance Labs and others). Public sale proceeds: Approximately $2 million. Current market capitalization: Around $7 million (about 86% below the CoinList sale valuation). Remaining liquidity pool: Approximately $280,000. Summary TEA did not suffer from a smart contract exploit or hack. However, fundamental flaws in its tokenomics design allegedly allowed participants to exploit the reward system, resulting in large-scale spam attacks against the open-source ecosystem. Registry maintainers reportedly incurred significant cleanup costs as a result. The project stated that it would halt reward distribution and redesign the rules, but development activity appears to have slowed dramatically following the launch. Why Binance may have distanced itself (speculation): There is no confirmed official statement proving this was the reason. However, it is speculated that concerns about ecosystem abuse, spam incentives, and declining project credibility may have contributed to Binance reducing its involvement. (This part is speculation, not a confirmed fact.)

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  jayesh (@0xjayeshyadav) reported 17 minutes ago

  Onchain indexes hold about $100 million combined today, while the TradFi index industry holds tens of trillions of dollars. That gap is the entire opportunity, which most people read backwards. They see $100M and conclude nobody wants an onchain index. But after spending the last year building index products, I see it differently: nobody has built one correctly yet, and the underlying assets were never ready. The first problem is that every onchain index today sets weights manually, through token-holder votes or governance plus an offchain supply feed. Inserting a committee or token vote between methodology and basket simply rebuilds a discretionary fund wearing an index label. Its performance decays with governance attention. The two multi-year category leaders are both down 80 to 98% from peak TVL, showing this was a design problem, not a demand problem. The second reason is subtler: the only assets available to index onchain were crypto. A market-cap basket of volatile tokens is a leveraged bet on two or three names unless real methodology is applied, which almost nobody did, leaving the product fragile and narrow. What I am building is the opposite of discretionary. Weights are computed entirely onchain from float-adjusted market cap, capped and redistributed using the same iterative rules S&P and Nasdaq have run for decades. They are fed by a supply oracle that treats circulating supply as a bounded, explicitly named trust assumption rather than an unguarded feed. There is no committee and no vote; the methodology itself is the protocol. I am betting on much more than crypto. The index is the most successful product structure in finance history and already holds tens of trillions offchain. It has been stuck onchain not because the structure fails, but because the only assets available were volatile tokens. That changes as tokenized treasuries, credit, and equities arrive onchain at scale, which is already underway. The wrapper that manages trillions in TradFi finally gets a native onchain home. An onchain index of tokenized RWAs can then do what its offchain cousin cannot: settle continuously, show every holding in real time, rebalance programmatically, and remain in self-custody. Broad index ETFs are already cheap, so the honest comparison is not against a Vanguard fund but against the stack of intermediaries wrapping every index product, and against crypto index funds that still charge 1.5 to 2.5% a year. A fully autonomous onchain index removes that entire layer, replacing the administrator, custodian, and licensing fee with code while swapping the market-maker spread for open solver competition. Because the methodology runs itself, the fee can be a fraction of what crypto index products charge today. I'm building the reference implementation, and the code is on GitHub 👇

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  Dr. Martin | AI x Business (@Dr_Martiin) reported 19 minutes ago

  Codex will also determine which browser to use based on the task. Its priority is: use a dedicated plugin if available (such as Jira or GitHub integrations), use Chrome if a login state is required, and use the built-in browser in all other cases.

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  Chip Haze (@ChipHaze) reported 21 minutes ago

  @zekramu Are they going to shut down GitHub? Discord? What about local AI?

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  aichina.news (@AiChinaNews) reported 23 minutes ago

  The story of this cycle is practical engineering over parameter bloat. While Western attention defaults to Hugging Face, Alibaba's ModelScope platform continues to ship highly capable open-weight foundations. The standout release is Qwen3.6-35B-A3B, a multimodal Mixture-of-Experts model aimed directly at the autonomous agent space. It houses 35 billion parameters but activates just 3 billion during inference, keeping compute costs in check while retaining heavy-duty reasoning. More importantly, it integrates native "Thinking Preservation"—forcing the model to deliberate internally before committing to an output. This isn't for generating isolated snippets; it is explicitly engineered for repository-level software development. Meanwhile, the Chinese open-source community is aggressively filling the workflow gaps left by Western AI giants. A flurry of updates hit GitHub this week for the localised Claude Desktop client, pushing it to version 1.6.26. What began as a simple language patch has evolved into a full-scale project console. The community has bundled a Windows runtime to drastically lower the setup barrier for Anthropic's "Computer Use" capabilities in China. They didn't stop at API access—the client now features Kanban boards, local *** integration, IDE-style multi-tab workspaces, and multi-agent task orchestration. This is what happens when developers tire of waiting for official enterprise tools and build the scaffolding themselves. Hardware reality continues to dictate software deployment in the domestic market. Eco-Tech released highly optimised, production-ready versions of Zhipu AI's GLM-5.1 specifically tailored for Huawei Ascend NPUs. Available in W4A8 and W8A8 quantization, this is actual engineering substance. Rather than chasing theoretical benchmark supremacy, these releases are built for high-throughput inference, solving the memory overhead bottlenecks required to run heavy models on domestic data centre and edge hardware. The rest of the cycle's open-source radar is clogged with automated filler. Projects like SpecFusion, ZLabs-RoundPix-12px, and a dizzying number of game localisation patches pushed updates where the public summaries literally contain unrendered placeholder variables like '{release_date}' and '{explanation}'. If a team cannot be bothered to fill out their own PR templates, no working professional should be bothered to review their code. Elsewhere, YiMu-Subtitle-Translator pushed a minor update for AI video localisation that boils down to standard API configuration tweaks dressed up as a launch. The industry continues to bifurcate: teams building production-grade infrastructure for real constraints, and teams automating their own noise.

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  Libegato (@Libegato) reported 23 minutes ago

  Working with AI means accepting no bottlenecks. I don’t always exercise that instinct as much as I should. But a few days ago, I did! I had a local workflow problem: how to parallelize work when a single repository is ~50GB? I wanted multiple parallel workstreams, but I definitely did not want 10 full copies of the repo when I barely had disk space for one. Worktrees don’t solve it. So I built Mirage. It leverages APFS to clone a folder with virtually zero upfront disk cost, and then only pays as files are actually edited in a sweet CLI API. Suddenly BANG! I can spin up a bunch of “worktrees” fast and cheap. Now to the next bottlekneck... Github repo here: renanliberato/mirage