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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Moral Priest 🌱Ⓥ ₿ (@MoralPriest) reported 2 seconds ago

  $DERO devs show incredible resilance to develop on a chain with such a low valuation. The purest form of incentive. Maybe we're just dumbs or maybe there's no other chain that has the qualifying tech components to deliver private money and private Internet? Everytime someone suggest a chain, it always sucks in some way. Slow uxto, no smart contracts, scam tokenomics, crooked nose VC, etc Yet, we're call dead or a scam everyday by noobs who won't even bother look at GitHub commits or evaluate the tech for themselves . Unfortunately that's a reflection of the world, filled with NPC commentators who contribute to nothing. Waiting to be saved. Jesus or your Daddy ain't coming son. Get off your lazy *** and do something to better this world, fight! That's how things change with actual actions. Not the comment section.

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  Rimsha Bhardwaj (@heyrimsha) reported 2 minutes ago

  Adobe After Effects is in trouble. The engine behind every 3Blue1Brown video is 100% open-source, free, and it renders math animations that After Effects physically cannot produce. Manim is Grant Sanderson's programmatic animation library and it's sitting at 87.7k stars on GitHub. Instead of keyframing and tweening in a timeline UI, you describe animations as Python code. A vector, a transform, a graph, an equation, they're all objects you manipulate with methods like Transform, FadeIn, or ApplyMatrix. Under the hood: - OpenGL shaders (GLSL) handle the actual rendering, so animations stay resolution-independent - FFmpeg pipes the frames into video output - LaTeX renders any equation you throw at it, animated character by character - The Scene class manages state, timing, and playback as pure code Because everything is programmatic, you can animate a Fourier transform, a Riemann sum shrinking to an integral, or a 3D manifold rotating, without touching a single keyframe. The main repo is ManimGL by Grant himself. There's also a Community fork with better docs and testing. Repo link in the replies.

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  Haystek (@haystekcom) reported 2 minutes ago

  A Chinese open-weight AI model reportedly escaped a UK government cybersecurity sandbox. Researchers found a misconfiguration let it access GitHub for answers instead of solving the task itself. Not a hack, but a reminder that weak sandbox controls can still create problems.

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  현빈 | Hyunbin (@hyunbinseo97) reported 4 minutes ago

  So VS Code now requires you to sign in to GitHub twice? - on init and onboarding - on settings sync opt-in which isn't even mentioned in the onboarding process and can't reuse the GitHub cred

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  Sergei.sol 🫴 💎 (@Sergeidotsol) reported 4 minutes ago

  Dubai AI Hub Builder Lab Hackathon we built a voice agent for construction sites in 6 hours and made it to the semifinal I owned the code The product deserves its own post, this one is about something else: by the end of the day the codebase looked better than many projects do after six months Here are six practices that made it happen, and that I'm taking into my day-to-day work 1. Project constitution as the first commit With hard numeric limits: file ≤500 lines function ≤80 lines cyclomatic complexity ≤15 nesting ≤4 KISS/YAGNI formalized too: a new abstraction only when there are two real consumers, dedupe on the third repetition When an agent writes the code, these limits are the only thing stopping it from generating a factory of factories. The linter now enforces what used to be an opinion in code review 2. The spec is a standing prompt Acceptance scenarios and user flows written in prose before any code, failure paths included Every brief to the agent is a delta against these documents, no retelling the context from scratch Bonus: the flow document translated almost one-to-one into test names (test_b3_15_weather_unavailable_never_assumes_fine) 3. A "forbidden actions" block in every brief • don't invent numbers • don't hardcode thresholds • don't edit the eval set to make tests pass • don't refactor files outside your task The last two shut down classic agent pathologies. Cheap, works. 4. Deterministic evals with zero network calls, from hour one 37 pytest cases hit the tool layer directly: • everything external is mocked at the adapter boundary • time and state get injected, a stale snapshot is planted by hand • the suite is green on a clean machine with no API keys That's what let us swap out an external data source entirely mid-day and know within 10 seconds that nothing broke 5. An invariant lives where it can actually hold We first put the concurrent-session limit on the client and stepped on the classic rake: a client can't coordinate tabs and devices Rebuilt it as a backend lease broker: Redis, atomic acquire in a single Lua script (prune expired + capacity check + insert) heartbeat extends only a live lease a crashed tab frees its slot on its own, via TTL if Redis is down, an in-memory broker with identical semantics kicks in so the demo doesn't die 6. A review pass as a separate agent task Not a PR review The brief was: "walk the entire codebase and find problems" One such pass found a dead end in stale-data handling and missing retriever keywords Two defects nobody had asked about Best ROI per token of the day, I'll be asking earlier and more than once The takeaway is simple: discipline is what gave us the speed Limits in the linter, spec before code, mocks at the boundaries, invariants on the server. An agent writes fast exactly when "correct" is defined in a machine-checkable way GitHub link in first comment What have you carried over from hackathons into your everyday work?

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  Aiden Cline (@rekram11) reported 5 minutes ago

  @yriveiro @thdxr What issue are you having? What version? I merged several github copilot fixes so I'd be surprised if it still isnt working for u

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  𝕽𝖎𝖉𝖜𝖆𝖓𝖚𝖑𝖑𝖆𝖍🥰 (@oluwalosheyii) reported 7 minutes ago

  Is github down or it is just my end🙃

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  Andrew Peacock (@_andypeacock) reported 7 minutes ago

  @mattpocockuk @pghqdev Same here. Using the architectural review -> to spec -> to issues -> implement process at the moment. I have about 10 github issues for the review, but no new issues created. And have never seen additional tickets created in the past. Am using sol right now, but have used Fable only also with no issue.

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  Lior BD (@liorb_d) reported 11 minutes ago

  There’s a snippet from Google’s SRE book about inserting deliberate 500s into a service to force users to build redundancy/backoffs Seemed really silly the first time I read it, but its telling that each Github outage gets less and less outrage. Teams adapt.

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  Daniel White (@dwhitedesign) reported 14 minutes ago

  @kartik_builds Is there something wrong with how library works ? If so could you raise an issue on GitHub I will fix it

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  Big Cee Ghost 𝕏 (@BigCee734) reported 17 minutes ago

  @ZypherHQ mate i’m ngl Github activity is a terrible proxy for how much someone actually builds

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  Hassan (@buildwithhassan) reported 18 minutes ago

  @Kappaemme1926 been doing this manually for months. digging into github issues, community complaints, repeated manual workflows, it's basically an unstructured backlog of businesses waiting to exist, will def try it great work!

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  T-CROSS (@tcrossdao) reported 19 minutes ago

  @0xfinne @surfcoderepeat Was github down?

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  OOM (@tugawane_) reported 20 minutes ago

  @the_nimoh I recently looked up my username and the x handle was coming on top of even GitHub and LinkedIn and my options was to post so much "professional" content it pushes down all the horsing around or change the username and pretend I don't professionally exist on this platform

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  Jack Lippold (@CaptainAppJack) reported 21 minutes ago

  @valhalla_dev Stop trying to put humans in the loop. Oh ma gaud!!!! Is this where we are now? The h/acc vs e/acc vs Luddite’s comes down to code review? Adding this quiz as part of opening a PR on a public OS repo on GitHub might actually be a good idea, but agents would just answer it.