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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Bok (@BokLocks) reported 2 minutes ago

  @therealDeFlock hello, there is a pumpfun token with all the fees going to your github currently to support the deflock movement FaL1PFQhNo4JAGaQKSnKurWeNtpexqEAduQjR4H6pump you just need to login to pump through your github and claim! it's completely safe and I can help you with it if needed

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  Dima (@UniqueDima) reported 2 minutes ago

  I think I can finally formulate something that makes me more of an engineer than ... a non-engineer. It is no longer that I want my processes to be deterministic. That has been gone for a couple of months now. AI agents are far too powerful to disregard, and there is evidently not much to be won by forcing their workflows to be 100% reproducible. It is possible, yes; it is just pointless. The correct approach, I believe, is to focus on good harnesses: build systems where one misstep does not derail the whole thing, but is quietly taken care of down the road. Call this one of my engineering-minded maxims if you wish; for me, it is just common sense. Either I can prove something is 100% correct, like arithmetic, or I know for a fact that a mistake in a particular non-deterministic step has a) a very small blast radius, and b) is self-healing in the grand scheme of things. Kind of how I have worked with people my entire life. There are very few folks you can trust 100%. With virtually everyone else, you act in good faith, but the bigger the decision becomes, the more checks and balances you should both be interested in introducing. So what makes me more of an engineer is not determinism. It is checkpointing. I want my processes to always support some form of “Undo”. To the point that I can meaningfully reason about it. For instance, with my AI-assisted coding, I simply have two GitHub accounts. I create private repos in one of them, configure branch protection, and invite the other one. And this other one is the account that agents have have full access to it. But it is me, the human being me, who needs to log into a different browser and confirm with the passkey — my fingerprint! — that I endorse a certain pull request to be merged. Or to kick off a production deployment. For me, this way of designing processes is second nature. Because this is the only way that makes sense at scale. AI agents did not create new attack surfaces. They just helped us understand how much of what we chose to ignore is actually full of holes. People as paranoid as me — we did see most, if not all, of these holes for years. We were just not listened to. And rightly so, I must say. Since listening to us would have broken the “move fast and break things” paradigm, which was quite effective for a long time. But not any more. So, all in all, I personally am quite happy with what is going on in the industry. Because it is both moving much faster and returning to sanity. The sanity people like me have been preaching for a long, long time. And we are finally being heard. So, it is not really about guarding against vendor lock-in or potential data loss. It is about defining the fine line between “this is a sustainable way to do business” and “this is almost guaranteed to blow up.” Ten or even five years ago, it was a relatively safe call for most businesses to ignore those crying wolf. But AI is setting the record straight as we speak. In the meantime, if you will excuse me, I will continue making sure my code is backed up on three devices in two locations. Because if, for instance, GitHub or Amazon is wiped off the face of the Earth tomorrow, I do not want to lose more than a couple of minutes of productivity. Not exactly a standard risk profile, I will grant you that. But that is my personal path to staying informed, safe, and sane. And I plan to stick to it, because so far, it has not let me down.

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  Vaibhav | Data Say (@vaibhavs28) reported 3 minutes ago

  Last week I wrote about how we are using AI to ship product with a very small team. One thing I did not expect when we started working this way was how many tools I would personally start using, which I never thought would become part of my day. GitHub is one of them. I am not a coder, and for most of my career GitHub was something the technical team used. I understood product, customers, business problems, data, dashboards, commercials, and operations. But code repositories, branches, PRs, conflicts, checks, and merges were not part of my normal working language. Even this chart is not perfect. I later realized I was using two different GitHub accounts for some of this work, so the activity is split and there are gaps. That probably says enough about how new this world was for me. But that has changed quite a bit now. I am still not pretending to be an engineer. That would be wrong. But I am much closer to the product build than I was earlier. If I see a product issue, I can now think through the expected behaviour, work with AI to scope the change, understand what files or flows are getting touched at a high level, create or review the PR, run checks, resolve smaller conflicts, and merge low-risk changes. The larger or more technical changes still go to our technical partner. That boundary is important. But a lot of product work is not always a deep architecture decision. Sometimes it is fixing labels, improving how something is shown, cleaning a flow, making the dashboard easier to understand, or removing confusion that a customer may face. Earlier, these small things could easily wait because the technical queue was always full. Now, many of them can move much faster. That changes product thinking itself. When the distance between noticing a problem and trying a fix becomes shorter, you start observing the product differently. You become more specific. You do not just say “this page is confusing.” You start saying “this metric label can be misunderstood,” “this table should not show empty channels,” “this filter needs to behave differently,” or “this issue is small enough to fix now.” For a small company, this matters a lot. We do not have large teams for product, QA, analytics, documentation, and engineering. The same few people are speaking to customers, understanding the problem, thinking about the product, and trying to ship improvements. AI has not removed the need for technical judgment. But it has made the loop tighter. Customer issue to product thought to implementation to review can now happen much faster for the right kind of problem. That is the biggest change for me. Not just speed, but proximity. I am closer to the product, closer to the details, and closer to the actual act of shipping than I ever expected to be. More on this later, because we are still figuring this out as we build.

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  Keeta Github Tracker (@KeetaCode) reported 7 minutes ago

  🐆 Keeta GitHub PR Opened 📦 Repo: node-rs 🔀 PR #33: Release: v0.4.0 🌿 Branch: process/v0.4.0 → main 👤 Opened by: @sephynox 🧠 Overview: Keeta’s node software appears to be moving to version 0.4.0, which matters because it bundles several behind-the-scenes improvements into one release. This pull request is a release update that groups four earlier changes: better typing, cleanup of crypto-related issues, centralizing some internal tables, and reducing repeated code. The public description does not explain user-facing impact, so this appears to be a technical/internal update with limited public details. - Likely takeaway: this is more about making the software cleaner and easier to maintain than announcing a major new feature.

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  SoEmailSecurity (@Soemailsecurity) reported 7 minutes ago

  Researchers at Noma Security found a GitHub issue can trick workflows into leaking private repo data with no stolen credentials needed, will your org be next if you have agent read access enabled? Don't wait until it happens to you? #GitHubSecurity #RepoSecurity #DataLeak

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  Julian Goldie SEO (@JulianGoldieSEO) reported 13 minutes ago

  There's a free tool that makes Claude read 92% less text and still fix the same bugs. It's called RTK. 68,000 GitHub stars. Here's how it works: → It sits between you and your AI agent like a filter → Your agent runs the same commands it always runs → RTK strips out the padding, headers, and fluff before Claude reads it → One test went from 373,000 characters down to 29,000 → Same output. Same quality. A fifth of the tokens That means your Claude subscription lasts 5x longer. Install takes one line. The delay is 14 milliseconds. Most people pay more for tokens. Smart people just stop wasting them. Want the SOP? DM me. 💬

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  Alexey Samoylov (@metalagman_dev) reported 15 minutes ago

  @shengzheyao Good job, 454 github issues left

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  vorty (@vorty279) reported 19 minutes ago

  on screen they run a huge model on hardware that by every chart cannot handle it. the trick is called airllm and it is open source the usual logic. a 70 billion parameter model wants tens of gigabytes of memory at once. you do not have that much vram, the model will not load, done, goodbye what airllm does. it does not load the whole model. it loads one layer at a time from disk takes the input prompt, tokenizes it pulls the first transformer layer from disk computes, unloads it, loads the next and so on through all 27 layers in turn at any moment only one layer sits in memory, not the whole model. so a big model fits where the compatibility chart shows a red cross the honest downside. it is slow. the disk is read for every layer, speed drops several times over. this is not for realtime autocomplete, it is for tasks where you can wait but the fact itself. the barrier is no longer how much vram you have, it is how much patience. a model you could not even start now runs on your hardware and this is not a secret tool. airllm is open, sitting on github. the pickaxe is handed out for free

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  The Whizz AI (@TheWhizzAI) reported 20 minutes ago

  Every AI coding agent has the same expensive habit: showing itself the same file 40 times. That was the story of June. HN and r/LocalLLaMA threads kept circling back to it. It's called Headroom. Not a smaller model. Not a shorter prompt. A compression layer that sits between your agent and the LLM and cuts 60-95% of the tokens before they ever get billed. Here's the difference: Code search normally costs 17,765 tokens. With Headroom: 1,408. Same result. Here's what it does: → Compresses tool outputs and logs → Library, proxy, or MCP server → One command wraps Claude Code → Shared memory nothing re-explained Real numbers from real workloads: SRE incident debugging goes from 65,694 tokens to 5,118. GitHub issue triage drops 73%. 100% Open Source ( Comments have it )

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  chrisreedbates (@chrisreedbates) reported 23 minutes ago

  @HansStuffer @benlsage Actually it handles technical work pretty well as well. Example: Did a walkthrough session with an actual user yesterday, recorded granola notes. Sent the notes the Head of Product + the CTO + the CMO. The Head of Product generated a bunch of issues on GH (after research + convo with me), then DMd the CTO that he just sent issues for him to check. The CTO checked them, and greenlit some, discussed others with me. Then DMd the Eng Manager who woke up, saw the issues, generated the PRs, put them through testing and indi reviewer, then sent to the release captain, DMd him, release captain reviewed, checked they were safe to merge, sent to the Merge captain with a dm who did his final checks, then merged the PR. Sometimes they need architecture work, and there's a "platfrom operator" who they can also message. When i say DM, it means "Hey, CTO, it's Head of Product, read your Github inbox issue, and my comment on PR 1234". That way the actual convo stays in GH. Does that make sense?

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  Sauers (@Sauers_) reported 25 minutes ago

  @NostaIgicGareth Oh no is that a seam on the cylinder or sphere? Did you use native cyclic fit? If so you should open an issue on GitHub since that looks like a bug. Expected if you used non-cyclic spline though

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  Kenton Varda (@KentonVarda) reported 26 minutes ago

  @m_chirculescu Oh, so sorry about this, but the release has been delayed. I had originally planned to basically present this side project I'd been working on at AI Engineer and then yeet it onto GitHub during the talk... but in the week before Cloudflare decided to make a bigger bet on it and that meant yeeting no longer felt like the right move. Plan is still to open source but with a more careful release! Sorry for the broken promises. You are right that we're looking to create a new paradigm of AI use here.

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  Jaco (@jacoveldsman) reported 28 minutes ago

  The ecosystem is younger and messier than the hype suggests: · 2,442 servers (16%) have a verified problem · 1,672 point at GitHub repos that are GONE (deleted or private) · 61 repos are claimed by 5+ different registry entries — one by 126

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  █████ (@CrunkComputing) reported 28 minutes ago

  In *** web interfaces, README.md should always be visible above the fold, in the initial viewport. README is naturally the first thing anyone would want to read first. No one should have to scroll down to read it. There, I said it. @gitlab @github

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  Chris Klumph (@chrisklumph) reported 29 minutes ago

  An interesting side effect of GitHub Copilot showing everything in tokens now is that you can get instant feedback on how expensive a feature it. You can spend 15 mins having Copilot implement a new feature, and then see that is cost $3.64 for that feature. sometimes it's a steal, sometimes it's a waste. Either way, it's interesting to now have more than just TIME to throw at a problem. It used to be "spend 4 hours to code this" but now it's "spend 1 hour to code this if you pay $5 for increased productivity." It's a very weird paradigm shift.