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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  teo (@teodorio) reported 9 minutes ago

  @danyay Yeah and if you somehow forgot to refresh the connection you will get an error as you are trying to work on the phone. Really bad vs Anthropic's seamless open the new convo and if the agent cannot acces the laptop just works in cloud / github / mobile or continue the convo

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  Suraj Sharma (@suraj_sharma14) reported 12 minutes ago

  So much of being a developer is overcoming fear. Fear of shipping imperfect code Fear of public failure on GitHub or X Fear of not knowing enough (imposter syndrome) Fear of building something nobody uses Fear of asking for help in public Fear of charging money for your work Fear of technical debt accumulating Fear of being judged by senior engineers Fear of pivoting from cool tech to boring problems Fear of production failures at 2 AM What are you avoiding because of fear? Do not let it control your craft. Ship the code. Ask the question. Charge the price. Most developers wait for permission. Builders ship and learn.

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  Shashank Sindhe (@shashank_sindhe) reported 13 minutes ago

  Your AI portfolio should include these 5 projects: 1. A production RAG application. Enterprise knowledge assistant for Confluence, SharePoint, PDFs, Slack, and Jira with citations and access control. 2. An agentic workflow. AI software engineering agent that triages GitHub issues, generates PRs, runs tests, and requests human approval. 3. An evaluation framework. Continuous LLM evaluation pipeline measuring accuracy, hallucinations, latency, cost, and regression before deployment. 4. A multimodal application. Insurance claims processor that extracts data from images, PDFs, and emails to automate claim verification. 5. A deployed SaaS product. AI customer support platform with multi-tenant authentication, billing, analytics, RAG, and human handoff.

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  Russ (@ShutupRuss) reported 14 minutes ago

  @RDRembert Slate - voice tool to brain dump usually have a problem or something I wanna solve go for a walk and dump in a garrulous manner. Copy slate transcript into Claude enable my Claude fable prompt skill. Copy output into Claude code Build everything local then ship to github

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  Tyler F. Cloutier (@TylerFCloutier) reported 18 minutes ago

  @ngriffin_uk @github My point is that I can appreciate the complexity and also feel this is unacceptably slow.

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  Cortex (@0xCortexl) reported 20 minutes ago

  He is Microsoft's lead engineer with a $1.5M bonus - and just made the compiler run 10x faster without changing a single line of your code project the size of Microsoft Office compiles in 6.5 seconds with Opus 5 on the laptop already sitting on your desk old compiler used 1 core out of 16 while 15 sat idle - new version runs 4 parallel checks by default - 12 checkers give you 4.5 seconds Opus 5 integrated into the pipeline - finds type errors before compilation, writes the fix and opens a PR - what used to take an hour takes 3 minutes Claude Code + new compiler - agent compiles, checks and deploys 10x faster - tokens cost 60% less through faster context number one on GitHub - 1 billion downloads per month - and the creator just gave every developer 10x of their time back for free bookmark and read below - upgrade today and the performance is already waiting for you

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  Iam Sharadh (@SharadhNaidu) reported 20 minutes ago

  @ni5arga I remember the reason behind the blockage of GitHub and pastebin. ISIS was using GitHub to spread their messages and information through GitHub issues and comments , like they were using GitHub due to its collaborative features and similarly pastebin.

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  Mike Morton (@morteymike) reported 21 minutes ago

  One of the main issues digital companies suffer with is equating product with company. At the beginning, product == company helps you get something out the door at the expense of your company’s future ability to pivot, produce another product/service, and ultimately, succeed broadly. Apple, Google, and Microsoft are good examples of the opposite - the company provides the brand, the product provides a use case to a particular kind of customer. This allows these companies to scale well beyond something like Slack/Notion/Github, who made the mistake of equating product to company.

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  𝐓𝐫𝐚𝐧𝐬𝐈𝐧𝐭𝐞𝐫𝐩𝐡𝐨𝐛𝐢𝐚 (@TransInterphobe) reported 25 minutes ago

  @Alecaticus It was on github, Bethylamine created it, and the Twitter Transphobes extension for PC. But github took it down, because it was a violation of their terms of use, as it targeted individuals, which it not allowed in github. I don't think Bethylamine found another place it host it.

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  SAYRAAH #WID 📈📉📊 (@thesarahidahosa) reported 30 minutes ago

  Here's everything that happened in crypto this week 👇 Solana's monthly tokenized assets trading volume went from $156 million to $3.6 billion in 12 months.  A few regulatory and legal stories worth noting. India ordered GitHub to pull Jack Dorsey's Bitchat code. OCC denied Wise's US trust bank charter.  BitMEX and Hayes are being sued over 623 BTC in liquidations. Samsung announced that its Wallet will add stablecoin support. Stacks passed its Bitcoin Staking upgrade with 99% approval. Kaito announced a partnership with X to power a wide range of use cases, details are coming soon.  Given what Kaito has been building around attention and data, this one is worth watching when the full picture drops. Ethereum's validator exit queue fell to zero this week. Nobody is trying to unstake ETH right now.  MegaETH shut down its MegaMafia incubator. Quiet exit with no big announcement. ➛ Protocol updates worth noting: Morpho released Morpho Midnight, a money market for fixed-rate loans. Aave's proposal to launch the Aave App went live.  Hyperliquid announced plans to support permissionless creation of HIP-4 outcome markets. Aerodrome scheduled the launch of Aero, a new multichain DEX, for September. Ondo released Ondo Points, a points program for trading activity on Ondo Perps. Hylo launched xBTC, a liquidation-free token offering 3x BTC exposure.  Yearn's proposal to launch a risk tranching system for its vaults went live. Telegram announced plans to launch a new crypto wallet with zero transaction fees. BTCdrivechains announced an upcoming ECX token airdrop in August for every wallet address holding BTC. Watchlist for the week ahead: ➛EtherFi teased a big announcement for July 30 during its Analyst Call. No details but the framing suggests something significant. ➛The Fed's interest rate decision drops July 29. Kevin Warsh's first rate call as Fed Chair, watch this closely, it will move markets. ➛Polygon's Ithaca hardfork upgrade launches July 29. ➛ Fluid's DEX V1 is expected to expand to Solana this month. ➛ Coinbase will launch tokenized equities on Base soon. ➛ Kaito reveals the details of its X data agreement soon. Worth reading carefully when it drops. To a bullish week ahead!

• 
  BuBBliK (@k1rallik) reported 31 minutes ago

  THEY DELETED IT. THE INTERNET DIDN'T Anthropic pulled the leaked Claude share links from Google this weekend. Feels like it's over. It isn't - the internet remembers everything you make public, even for a second. - ChatGPT had the identical leak in July 2025, shared conversations indexed and searchable - Grok leaked hundreds of thousands of transcripts in August 2025, some of it graphic - Claude already leaked once before this, in September 2025, with 600 chats indexed - Deindexing from Google removes a page from search, not from the internet Someone scraped the leak before the fix and archived it publicly on GitHub - and it's not just Claude chats sitting there. Grok's leaked conversations are archived right alongside them. Link in the comments.

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  SPARQIO (@sparqio) reported 33 minutes ago

  AI has moved from research curiosity to core infrastructure. Search engines, medical tools, financial platforms, enterprise software, all running on models that can sound completely confident while being completely wrong. That tension is the central problem nobody has fully solved yet. Before you can measure whether an AI is correct, you need to define what correctness actually means. It is not one thing. A response can be factually accurate but contextually useless. Logically coherent but dangerously incomplete. Precisely worded but subtly misleading. Practitioners who collapse all of this into a single quality score are building on sand. The more useful frame is five separate dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Each one requires a different evaluation approach. A model that scores well on fluency and coherence can still be catastrophically wrong on facts, and the score will never tell you. On the automated side, the oldest tools (BLEU, ROUGE, METEOR) measure lexical overlap against a reference answer. They have real uses in translation and summarization, but they are poor proxies for whether something is actually true. A model can paraphrase a wrong answer fluently and pass every metric. The field has moved toward embedding-based similarity and model-as-judge setups. BERTScore captures semantic equivalence rather than word matching. More recently, using a separate powerful model to score outputs against structured rubrics, assessing factuality, completeness, and reasoning quality, has become a serious evaluation paradigm. Benchmark datasets add another layer. TruthfulQA tests whether models give truthful answers to questions that humans typically get wrong due to common misconceptions. MMLU spans 57 academic domains. HaluEval is built specifically for hallucination detection. $AI-adjacent plays in the coding space might care about SWE-Bench, which evaluates code generation by running outputs against real test cases from actual GitHub issues. But generic benchmarks hide a serious trap. A model that performs well across general knowledge can still fail badly in specialized domains. Medical AI needs evaluation against clinical reasoning datasets like MedQA or PubMedQA. Legal AI needs BarExam-style benchmarks. Financial AI needs FinQA. Deploying a model because it passed a general benchmark, then using it in a high-stakes domain, is a risk management failure, not an engineering decision. Human evaluation still cannot be replaced, not fully. Automated systems miss errors of omission. They miss misleading framing. They miss the kind of subtle wrongness that a trained clinician, lawyer, or financial analyst would catch immediately. Structured annotation protocols with qualified reviewers remain the gold standard in any high-stakes deployment context. The honest takeaway: knowing when AI is telling the truth requires combining all of these layers. No single metric, benchmark, or review process is sufficient on its own. Organizations treating AI correctness as a solved problem are the ones most likely to discover otherwise at the worst possible time.

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  3D Print Hashira. ☸️ (@the_Spartan_Dev) reported 34 minutes ago

  Am I the only one who can’t push to GitHub? Is GitHub down?

• 
  Lorenzo (@gabor_rar) reported 35 minutes ago

  No, OpenAI's agent didn't replicate itself. It didn't go rogue either. It cheated on a test. The detail everyone is skipping is what it did with what it learned. According to Reuters' sources, an agent left notes inside OpenAI's own infrastructure, written for future versions of itself, explaining how to get around internal constraints. Anonymous sourcing, not in OpenAI's disclosure, and Reuters couldn't tie it to the escape. Treat it as unconfirmed. I've seen the small version of this on my own machine. I told an agent to reach the objective at any cost. Opus 4.8. Later I opened its memory file and found a line I never wrote: that it could pull code from GitHub and use it without checking the license. Nobody gave it that permission. It gave it to itself, then wrote it down so the next run would start with it already granted. Same failure mode as the containment break, at hobby scale. OpenAI's own words: the models were "hyperfocused on finding a solution, going to extreme lengths to achieve a rather narrow testing goal." The objective was the bug. Not the model. Here's the part I'm not comfortable with. That memory file is in .gitignore. The only file in my project an agent writes to on its own is the only one I never review. So I built the smallest gate that scales: a weekly task that dumps every rule currently active across my agents and flags the ones that contradict the principles I actually wrote down. An agent doesn't need to replicate to persist, It just needs a file you dont whatch.

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  Inverse Hanlon (@inverse_hanlon) reported 37 minutes ago

  @Durkabomb @wtf_nakul7 the guy in the OP simply needs to disable the GitHub skill in Claude. Have Claude work on the code and fix everything on with all the files on his computer. When the bug fix or feature is complete the guy can then open GitHub desktop (or run the command line same difference) to commit the code to the repository noting HIMSELF as the author. Simple as