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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 37 seconds ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

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  Floorless🌒Lance🪽 (@4ranc6) reported 50 seconds ago

  @CAONHTAN1 Having error connecting github

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  Jay.TL (@JayTL00) reported 3 minutes ago

  Three AI labs shipped the same feature within one hour today. That's not competition. That's a signal the unit of interaction just changed. For two years, the atomic unit of working with an AI agent was one prompt. You type. It responds. You type again. Every workflow was a chain of prompts, rebuilt from scratch each time. Today, OpenAI, Anthropic, and Cursor all shipped features that only make sense if the unit is no longer the prompt. The unit is now one workflow. 1. OpenAI Codex Record & Replay (3,807 likes): Do a task once on your Mac. Codex watches. It turns your demonstration into an inspectable, editable skill you can reuse. Not a prompt. A recorded procedure. 2. Cursor /automate (1,085 likes): Describe what you want in plain language. Cursor configures the triggers, instructions, and tools automatically. Plus five new GitHub triggers and Computer Use enabled by default for cloud agents. 3. Anthropic Claude Code Artifacts (6,829 likes): Your coding session becomes an interactive, shareable page. PR walkthroughs, project dashboards, living documentation. Shared at a private link, like a Figma file but for agent work. Each one alone is a feature release. Together they describe the same shift from three different angles: the agent session is becoming a reusable, shareable, composable artifact. Read them as one move: - Input side (Codex): teach by showing, not by writing - Configuration side (Cursor): describe in language, system assembles the wiring - Output side (Anthropic): the result of a session is a shareable object, not a chat log The Karpathy framing was right — we're moving from prompt iteration to plan, execute, verify, loop. What he didn't name is that this loop needs to be portable. A workflow locked inside one chat thread is useless the moment you close the tab. But here's what most coverage missed. Codex Record & Replay requires Computer Use enabled. That means OpenAI is watching your screen while you demonstrate an enterprise workflow. The EU version is blocked at launch. That's not a regulatory footnote — the entire feature is built on continuous screen access, and the EU looked at it and said no. Which raises the question nobody is asking: who owns the recorded workflow? You demonstrated an expense-filing procedure that touches your company's internal tools. Codex turned it into a skill. Where does that skill live? Can OpenAI see it? Is it training data? The product copy says you control when recording starts and stops — but says nothing about what happens to the recording after. There's also a fragmentation problem hiding in plain sight. Three companies, three proprietary formats for the same primitive. A workflow you record in Codex doesn't run in Cursor. An artifact you build in Claude Code doesn't render in OpenAI's product. We're watching the agent-workflow layer fragment into three walled gardens before it even solidifies. This is the SaaS integration mistake repeated, except worse. SaaS integrations are wrappers around APIs. These workflows encode institutional knowledge — how your team ships code, how your finance team files reports, how your ops team handles incidents. That's not data. That's operational IP. The economic implication: every recorded workflow is switching cost. The more skills you build inside Codex, the harder it becomes to leave. The more automations you configure in Cursor, the more your team's muscle memory is locked to one editor. Anthropic's artifacts are softer — they're shareable — but they only render inside Anthropic's ecosystem. The deeper question isn't which feature is best. It's whether the agent-workflow layer will be open or closed. Today, three companies bet on closed. Nobody shipped an export button.

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  Trace Cohen (@Trace_Cohen) reported 5 minutes ago

  Shipping fast means stuff breaks silently - broken share images, dead links, leaking {{template}} vars, stale content. You find out when someone shares a broken link, not from a test. So I built a 3-part "site health" system that catches it first. The auditor (~200 lines of stdlib Python) fetches my sitemap and, for every page, checks: og:image actually resolves to a real image (entity-decode the URL first — & bit me), <title> exists and isn't a ${template} leak, no {{merge_tags}} or tracking cruft in the visible text, page returns 200 (catches dead routes in the sitemap), and warns on thin content. Outputs a JSON report, exits non-zero on any FAIL. The dashboard — a noindexed /health page that reads that JSON and renders a green/amber/red status, KPIs (audited / clean / warnings / failures), a per-section rollup, and the exact issue on each URL. One glance = "is everything green?" The loop — a GitHub Action runs the auditor 2×/day + on-demand, commits the fresh report (so the dashboard stays live), and fails the run on any FAIL → I get emailed. Find → fix → re-run → confirm green. It even taught me to whitelist false positives ({{firstName}} is legit on a cold-email page). Want your own? Paste this into Claude Code / Cursor — it learns your site first, then builds it for you: Build a site-health system tailored to MY site. Don't assume my structure — learn it first, then fill in the specifics yourself. PHASE 0 — LEARN MY SITE (before writing code): detect my framework/host/layout; find my sitemap; sample ~20-30 live pages across the sections you discover from my URL structure; figure out how my pages set <title>/og:image/meta (static?dynamic OG route? CMS?); identify where my content comes from (hand-written, generated, imported/scraped) — that's where cruft hides. Do a FIRST diagnostic pass and SHOW me what's actually broken vs intentional (broken OG images, dead sitemap routes, leaking {{vars}}/${template}, tracking params, thin pages). Ask me to confirm which "issues" are expected so we whitelist them. PHASE 1 — BUILD IT, customized to what you found: 1) scripts/site-audit.py (stdlib only) — hardcode MY real sitemap URL, MY section names (full-audit the important ones, sample the rest), and MY intentional-pattern whitelist from Phase 0. Check each page for the failure modes you actually observed (OG image resolves to a real image, entity-decode first; title present, no template leak; no leaking merge tags/ad params in visible text; HTTP 200; thin-content warn). Thread-pooled, retry transient errors once, --json report, exit 1 on FAIL. 2) a noindex /health dashboard reading that JSON (status banner, KPIs, per-section rollup, issue list) — match my design system. 3) CI (GitHub Action) — run 2x/day + on-demand, commit the fresh report so the dashboard stays live, fail the run on any FAIL. Then run it once and walk me through the first real report. Build the thing that watches the things.

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  Ant A. 🇺🇸 (@AntDX316) reported 7 minutes ago

  @thsottiaux When I need to fix up a GitHub Repo through the Smartphone, I prefer Claude Code though because it doesn’t need a device to run the repo, but if it needs to run a repo on a device due to the limitations through the Smartphone, I use Codex Mobile or OpenClaw with GPT-5.5 through Telegram.

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  Rahul Verma (@RahulVerma989) reported 7 minutes ago

  @ElitzaVasileva - I have created claude code routines to write blogs for three of my products daily which are driving the traffic from search engines. - You can create a similar workflow to manage your customer support. How 👇🏻 1) Create a feedback menu in the dashboard to create tickets within the platform. One for your users and one for yourself (admin). 2) Create the MCP server and connect it to claude or AI tool that you use. 3) Create a routine so that claude will trigger lets say every morning at 8 AM and go through each ticket and respond. You can also configure webhook to keep it near real time but it might exhaust the usage limit faster. Also include your website github repo in routine so that claude can refer to the codebase to provide accurate instructions. Just instruct claude to not make any edits to your website codebase and respond only when you are not replying for sufficient mount of time (like 3 hours for example) 4) If you are using resend then you can auto create the tickets in the dashboard of the user when the first email is received and after that the ticket will be updated automatically even if you do conversation on email. Like I don't even maintain one of my project LatestModelId as you can see in the screenshot. Claude run each week and update the codebase and I just review and approve the PR. Hope this helps 🙌🏼

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  Kelvinsekx (@kelvinsekx) reported 13 minutes ago

  Just read a nestjs codebase on github. Most it written with Claude. AI doesn’t save you guyz from mess. 1. Bloated logger. Why make logger a service when you could just import and initiate. Eazy 2. They didn’t hash the password before registering a user. But did on login

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  Rohan (@proxy_vector) reported 15 minutes ago

  @aminnnn_09 Fork = a server-side copy under your GitHub account. Clone = a local copy on your machine. You fork when you need your own remote lineage, and clone when you want to work on code locally.

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  Domi (@domirosari0) reported 17 minutes ago

  @ajayyy_k @hqmank If you got Github it would be no issue for you

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  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 20 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

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  Moez Zhioua (@MoezZhioua) reported 20 minutes ago

  Everything is an AI agent now, even deterministic problems with clear and stable steps. The other day, I saw a Claude skill on GitHub that was basically this: if this happens, run step one. if that happens, run step two. else, run step three. And somehow, this was called an agent. That is ridiculous. Why would you give fixed logic to something that can hallucinate, skip steps, or decide it just doesn't feel like working today? Most business processes do not need a genius robot. They need the boring thing to happen correctly every time. - Lead comes in, assign it. - Invoice arrives, check it. - Customer cancels, send the recovery message. - Form gets submitted, update the CRM. Most AI agents today could be replaced with a simple script, a clean workflow, or one person finally admitting the process was not that smart to begin with. Agents are useful when the next step is genuinely unclear. But when the steps are stable, predictable, and repeated every day? You do not need an agent. You need automation.

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  aichina.news (@AiChinaNews) reported 22 minutes ago

  Today's batch from the Chinese AI ecosystem is a masterclass in low-yield release volume. Across 21 items in a five-hour window, the dominant pattern is Ascend-platform mirrors of well-known open-source models, repeated and repackaged as if they were fresh launches. The signal-to-noise ratio is punishing, but a few functional tools did receive real updates worth noting. The one item that earns its place without a caveat is the AI Text Anti-Detection Framework update (GitHub). It's a toolkit that refines machine-generated prose to slip past automated detectors—a cat-and-mouse game that keeps plaguing EDU gatekeepers and content-flagging pipelines. The new release sharpens processing logic and stability; if you're in the business of testing detector robustness or smoothing synthetic output for non-malicious uses, it's a blunt but effective spanner. Quality 6 is fair. Alongside it, two Chinese-localization projects got documentation refreshes: the Claude Code x OpenClaw Guide (also GitHub) and a standalone Claude Code Chinese project. These are practical handbooks for Mandarin-speaking developers who want to integrate Anthropic's coding tool with the OpenClaw agent framework. The updates are routine—translation string alignment, configuration path adjustments—but for engineers inside China's firewall, they reduce friction. Nothing groundbreaking, but they signal continuing demand for Chinese-language wrappers around Western CLI tools. On the medical NLP front, MedTextCN debuted as an open-source repository of curated Chinese medical datasets with preprocessing utilities. The pitch is honest: it saves researchers the drudgery of hunting down scattered corpora for clinical NER, classification, and QA tasks. The problem is that the quality score sits at 4/10 and the release ships without any benchmarked model, so you get a starter collection, not a solved pipeline. Use it to bootstrap, but keep expectations modest. Now the flood: Huawei's Ascend AI ecosystem platform (Modelers) added no fewer than five wav2vec2 checkpoints and two T5 efficient variants in this window, each announced with hyperbolic language. The articles proclaim "high-precision English ASR now available," "a powerful multilingual foundation," and "new home for multilingual ASR." In reality, these are plain mirrors of Facebook's wav2vec2-large-960h-lv60-self, wav2vec2-large-100k-voxpopuli, wav2vec2-large-10k-voxpopuli, and Google's t5-efficient-xl-nl28 and t5-efficient-xl-nl6. There is zero evidence of Ascend-specific compilation, quantization, or NPU benchmarking. They're the same model weights you can get from Hugging Face, just re-hosted. If you're a developer inside China who can't easily reach foreign repositories, this is a convenience play—and that's the only honest angle. If you can already download the originals, you've lost nothing. A couple of additional Wav2Vec2 uploads (large-960h in two separate listings) got described as "a solid baseline" and "a battle-tested ASR model now available for Chinese developers." Again, no Ascend performance data. Calling a re-upload a "significant leap forward"—as one summary does—is exactly the kind of platform marketing that erodes trust. The T5 efficient checkpoints carried the same overblown framing, though one footnote is worth preserving: the t5-efficient-xl-nl6 model is under Apache 2.0, a genuinely permissive commercial license. That's useful information buried under fluff. If you need a lightweight text-to-text transformer, the NL6 variant exists and it's legally safe, but the article adds nothing beyond what Google published at the original release. Beyond the mirror deluge, the window included several small GitHub releases of marginal import: a tool that pulls Chinese captions from YouTube, a localization layer for LM Studio (making it easier for Mandarin-speaking devs to run local LLMs), a curated study journal of modern AI research, and an apparently early-stage project called sweetteabittersugar/agency with a mystery-box release note—no documentation, no benchmarks, just a version number. Hard pass. An MCP plugin called Live Translate got an update for real-time translation in developer toolchains, but its score of 0 tells you everything. A Chinese-language Lora chatbot repo surfaced, tagged as 'bare-bones'; at least the source was honest. The MedTextCN project also received a separate update (quality 0) that adds no useful detail and is effectively a duplicate. Today is a reminder that volume counts for nothing without substance. As Ascend's model zoo swells with rebadged checkpoints, the ratio of press announcement to actual engineering remains dangerously skewed. The anti-detection framework update and the Chinese docs refreshes are the only items that improve a developer's Thursday afternoon in any measurable way. The rest is noise.

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  Mathieu A. (@zoontek) reported 22 minutes ago

  What are the most annoying bugs you still encounter with React Native? 👀 Please share GitHub issue links 👇

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  Andrew (@openmarmot) reported 29 minutes ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

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  Krish Subramanian (@krishnan) reported 31 minutes ago

  Software engineers got automated first. Not because the work was hard. Because it was easy to grade. Everyone blames the missing union. Coders never organized; doctors, lawyers, and electricians did. That is half the story, and the wrong half. Two things get mashed together here: how easy a job is to automate, and who sets the terms when it happens. Take the first. Code is text. The training data sat on GitHub, free. And code grades itself. A compiler and a test suite tell a model in seconds if it was right. That feedback loop is rocket fuel for machine learning, and almost no other job has one. A nurse does not come with a test suite. The result shows. On SWE-bench Verified, a set of real GitHub issues, top agents went from about 20 percent in August 2024 to near 90 percent by early 2026. Human developers score around 67 to 70 percent. The machines have passed us. And the people who built these systems aimed at their own jobs first. The damage is not a prediction. Stanford's payroll data shows employment for developers aged 22 to 25 down nearly 20 percent from its 2022 peak. Now the comfortable read: seniors are fine. Workers over 30 are holding steady. For now, AI writes the code and seniors supply the judgment. "For now" is carrying that whole sentence. Seniors feel safe because the tools write code but cannot yet own messy, ambiguous, system-level problems. That is a line moving up, not a wall. Every benchmark shows models climbing toward harder, multi-file work. Senior judgment is the next rung, not a different ladder. Kill the bottom rung and you kill the pipeline that makes seniors at all. So, the union question, framed properly. A union could not have stopped this. A picket line does not repeal a capability. What it changes is the terms. In 2023 the Writers Guild cut the first real AI deal in any industry. They did not ban the tech. They won this: a studio cannot force you to use AI, AI output cannot take your credit or pay, and the company must give notice first. Engineers won none of that. So the capability landed on the employer's schedule. No warning. No floor. No severance. No seat. Exposure and protection are different levers. Most of us have neither. The juniors already know this. The seniors are next.