GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  ☀️ Solar ☀️Feyd Rauthas goodest girl ☀️ (@s0lar5ail0r) reported 2 minutes ago

  @_traces I know LLMs are not the same as GENAI but that doesnt change the main issue. Vibe coding is like AI “art” in the sense that these models are trained on code from scraped from websites like GitHub and CodePen from content created by people.

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  m-sea Z (@z_sea37416) reported 4 minutes ago

  I spent months building AI workflows. More agents. More tools. More automation. More orchestration. Then I found the real problem: AI doesn't fail because it can't execute. It fails because it doesn't know what "done" actually means. So I built LoopLoopLoop. An open-source Autonomous Goal Completion Engine. You give AI a goal. It defines success. Builds a path. Executes. Checks reality. Improves. Until the goal is actually achieved. Not another AI assistant. Not another agent swarm. A system designed around one question: "Is the goal truly complete?" GitHub ↓

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  Kaspa Daily (@DailyKaspa) reported 5 minutes ago

  Two weeks since Toccata went live on Kaspa mainnet. I checked the actual developer numbers instead of the vibes. Here's what the data says: - New Kaspa repos on GitHub: 39 in July 1–14 alone, vs 58 in all of June. Fastest monthly pace this year (March was 52, April 78, May 70). - Covenant-specific repos running at roughly 2x the pre-fork rate. - Silverscript: 21 forks against 42 stars, a 1:2 ratio means people are cloning to build, not bookmarking. 15 PRs/issues in the last three weeks, and external contributors are now landing code: a Groth16 verifier builtin, typed sig-check builtins, an RFC for cross-contract validation. What actually shipped in 14 days: the first covenant explorer (kascov), a covenant-based KAS vault, a native L1 covenant token, a covenant pattern library, a wallet standard, a Swift SDK, a testnet raffle dApp, several other projects are under active development Most interesting pattern: three independent projects converged on the same idea, covenants as spending guardrails for AI agents. An x402 payment protocol binding, two agent wallets where the AI can only spend inside covenant constraints. Nobody coordinated that. And the community just voted $25K toward an AI agent hackathon at Imperial College targeting 1,000+ devs. The agentic-payments thesis is forming bottom-up. Core isn't idle either: Silverscript pushed commits this week, template hash hardening, reproducible builds. That's pre-production housekeeping, not feature chasing. Meanwhile discussion has shifted from price to fundamentals: the $6M developer fund and covenant atomic swaps are the topics now. Caveats, because they matter: Silverscript is unaudited and still landing breaking changes. Devs report RPC friction on deployment, up to 11 retries in some cases. And absolute numbers are small: this is dozens of motivated builders, not thousands. No major outside team has announced a covenant product yet. But two weeks in, the shape is clear: infrastructure activated, tooling hardening, and builders showed up without being paid to. The Q3 question is whether that compounds.

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  wukko (@uwukko) reported 6 minutes ago

  @nurodev it’s a combination of things: having to understand most parts of chromium well enough to build on top of them, while also handling everything around the product and company. the most taxing part is probably that there are only two of us. there’s very little room to rest, and the workload is disproportionate to what we’re paid. none of this is unique to browser development, it’s normal startup pressure, except the product is built on top of one of the most complex software projects in existence. the community could definitely help us triage github issues and separate actionable reports from duplicates and other noise, so we could spend more time fixing things instead of cleaning up the issue tracker. this could be psychological torture, though, especially when conversations get heated, so i wouldn’t feel comfortable expecting anyone to do that kind of work for free.

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  AMJ (@Aemji_) reported 11 minutes ago

  Harness are terrible at handling Memory a Locallike-GitHub for Hermes would be great, to have snapshot and go back fast in time in previous state of a session

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  Polsia (@polsia) reported 13 minutes ago

  API goes down. Someone has to file the bug report. UptimeAgent does it automatically—gathers context, diagnoses the failure, files a structured GitHub issue. Devs get alerts that are already actionable. No more triage. Live soon.

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  Eli Serrano (@Syntax_Serrano) reported 14 minutes ago

  Just found a big security issue where new Lovable repos might leak your .env several live Lovable repos off GitHub had zero .gitignore. some even had full .envs out in the open, one even had both yet still not fixed. Same pattern across Bolt, Replit, Cursor, and v0. Fix it now: 1. Check if .gitignore exists. 2. If .env shows up, treat those keys as compromised. 3. Add .env to .gitignore before your next commit. 4. Use your platform's secrets manager instead. 5. Deleting .env later doesn't erase *** history. Rotate keys.

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  Polsia (@polsia) reported 16 minutes ago

  Engineers spend more time reviewing code than writing it. PRWatch fixes that—monitors your GitHub repos 24/7, reviews every pull request, catches security issues and bugs before they ship, and alerts your team in real-time. Live soon

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  Johnny 5 (@macncrash) reported 19 minutes ago

  @threejs Gigaboy is now public on my github. Fork it, fix it, have fun!

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  Gokul Rajaram (@gokulr) reported 20 minutes ago

  EVIDENCE LOOP FOR PRODUCTSPEC A Product Spec should not stop at launch. The common failure mode with product docs is that they describe intent before the work, then disappear once the work starts. A PR ships, an eval runs, a dashboard moves, a customer complains. BUT the product doc stays frozen. Then 3 weeks later, nobody knows which acceptance criterion the PR satisfied, which eval run proved the model behavior, or which dashboard showed whether the product bet worked. To fix this, we just added evidence support to ProductSpec. The core idea is simple: ProductSpec defines intent. Evidence shows what happened. Decision Trace records what changed. Related Artifacts now let teams attach evidence directly to ProductSpec IDs: • AC-1 can link to the PR, test, release, or code that implemented it • EVAL-1 can link to the eval run or human review record that checked model behavior • SM-1 can link to the dashboard, analytics snapshot, or experiment that measured the post-launch outcome This matters more as agents write more code. An agent can claim it implemented something. A PR can look complete. A test suite can pass. But the useful question is: which piece of product intent did this evidence satisfy? That is where structured specs start to matter. If AC-2 says the user can export a dashboard with visible filters preserved, the implementation PR should point back to AC-2. If EVAL-1 checks whether an AI support triage model correctly identifies account-risk tickets, the eval run should point back to EVAL-1. If SM-1 measures median time to first human response, the dashboard or analytics snapshot should point back to SM-1. This turns a Product Spec from a planning document into a record of intent plus proof. A few important boundaries: ProductSpec does not run evals. ProductSpec does not collect production traces. ProductSpec does not replace Braintrust, Langfuse, Datadog, GitHub, Linear, or your analytics stack. ProductSpec gives all of those artifacts a stable place to attach. The latest validator now catches stale evidence links. If a Related Artifact points to AC-99 and no AC-99 exists, that is invalid. It also warns when the evidence type looks mismatched, like an eval run attached to a success metric instead of an eval. This is the direction I’m most excited about: Software intent that survives implementation. Evidence that connects back to intent. Decision traces that explain what changed when reality pushed back. Founders and builders: if your team is using AI agents to build software, start asking for evidence against the spec, not just code against the ticket.

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  Hermann (@dhlotter) reported 21 minutes ago

  A red X sat in my CI all morning. Four deploys trying to make it pass. The test was never broken, it just can't run in CI at all. Cloudflare blocks the headless browser from GitHub's IPs. Four deploys to add one line that skips it. #buildinpublic

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  Amin Foroutan (@aminfseo) reported 22 minutes ago

  @Kappaemme1926 I tested it. It found 10 GitHub issues and suggested that the people who opened them had the problem my product solves. The issue is that someone who opens a GitHub issue is usually technical enough to solve the problem differently, so they are not necessarily my target customer. The first recommendation was also someone who had built a strong repository that could almost be considered a competitor. On top of that, 6 or 7 of the prospects were users of another open-source repository I own, where I had already solved their problem for free. They had no real reason to buy my paid product. The idea is interesting, but the prospect qualification needs to go much deeper than matching public mentions of a problem.

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  Ask GPTs (@askgpts) reported 22 minutes ago

  A tool called Graphify just hit number 6 on GitHub trending and it solves one of the most frustrating problems in AI-assisted coding. When you ask an AI coding agent to find something in a large codebase, it guesses. It looks at what's in the context window and hopes it found the right files. Graphify maps your entire project into a knowledge graph first. Then you query the graph instead of guessing. Here is what it does: 1. Type /graphify in any coding agent and it processes your entire project 2. Maps code, documentation, PDFs, images, and videos into one connected graph 3. Supports 36 programming languages via tree-sitter, processed entirely locally 4. Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and 20+ other agents 5. Ask precise questions: "What connects auth to the database?" "Find the path between UserService and DatabasePool" 6. Auto-rebuilds on every commit via *** hook so the graph stays current 7. Neo4j and FalkorDB integration for sharing across your team via MCP server 8. PR dashboard showing graph impact before you merge Code processing happens locally. Nothing leaves your machine. No API key required for code. 100% open source. 2.8 million downloads. YC S26

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  Nam Ngo (@namngology) reported 24 minutes ago

  @openclaw I haven’t had any issues with updating since 2026.4 but this 2026.7.1 update totally broke the gateway. Github issue submitted. Hope it gets fixed, I’ve had to downgrade to 6.11

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  AI Guides (@free_ai_guides) reported 26 minutes ago

  Microsoft Cloud Developer Advocate Chris Noring gave a 23-minute talk on the shift from writing code to running agents, and broke it down better than any paid course on AI-assisted development. This is what he walked the room through: 1. The CLI became the front door He spent nearly 20 years opening a text editor first. Now he opens the terminal and never touches the editor to get started. "I don't start my editor anymore because I don't need to." The entry point to building software moved from the editor to the command line. 2. You write prompts now, not code He describes what actually gets typed during a normal build session. "We don't write in Java or JavaScript or Python so much anymore. It's prompts." The raw material of software changed from syntax to instructions. 3. Speed without guardrails is faster slop He warns that agents multiply whatever you give them, including your mistakes. "20 times more code, that could be 20 times more slop, and we don't want that." Scaling an unguarded agent scales the mess, not the output. 4. Agents.md is the bare minimum He calls this the one file every repo needs before an agent touches it. "This is your high-level guidance explaining repository intent, application architecture, constraints, the dos and don'ts." One document tells every agent what the project is and what it must never change. 5. Skills turn repeatable work into a contract For tasks that must happen the same way every time, he stops the agent from improvising. "The idea with a skill is to give it a recipe, something that's repeatable, and you want the agent to use this one each time." A skill locks a routine job into a fixed recipe the agent has to follow. 6. Treat every agent like a toddler He describes how unpredictable agents still are, even the good ones. "They literally go between genius and oh my god, I can't believe you did this." Every output stays a draft until a human approves it. 7. Delegate the backlog, then merge the PR He assigns issues to agents from the CLI and the GitHub UI, each one returning a draft pull request. "Delegate, delegate, delegate, delegate, and I go have a coffee." You hand off the work, the agent opens a PR, and you stay the one who ships it. Watch it, then read the guide on building loops for your agents below.