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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Jordan Ross (@jordan_ross_8F) reported 9 minutes ago

  Agencies running client accounts inside of GPT/Claude projects is a massive mistake. You'll look back on this and think about how dumb this was. At scale, you're sacrificing quality. A perfect example happened yesterday. A client of mine runs a content marketing agency. They uploaded a big pile of training documents into a Claude project to "train it." The plan was simple: copywriters use that project to write hooks for social. The COO reviewed one of the hooks recently, it sucked, told the copywriter it wasn't good and to run it through the project again. Next version: still crap. He knew something was broken. He just didn't know what, or how to fix it. Here's what he didn't understand. Context window This is the first thing you need to understand to understand why projects dont work. A context window is the AI's short-term memory. It's how much it can hold in its head at one time. Picture a monitor on a desk. Everything the AI is working with sits on that monitor — your instructions, your last message, the files it's looking at. A bigger model has a bigger monitor, but it's still a monitor. Pile too much on it and things begin to fall off. The AI can only work with what's on the monitor right now. Anything not on the screen doesn't exist to it. When the monitor fills up, the old stuff falls off to make room. What people call "memory" isn't really memory. It's just whatever happens to be on the monitor at that moment. So what is a Claude or GPT project? A project is the desk the monitor sits on. You drag your files into it — brand guides, past hooks, training docs. It feels like you're teaching the AI your business. You're not. The AI never reads everything on that desk. It can't. The desk is bigger than the monitor. When you ask a question, it reaches into a drawer, grabs a few pages that look related, sets them on the desk, and works off those. It never sees the rest. It often doesn't grab the right pages. It grabs the ones that look similar. Pattern-match, not judgment. It's guessing which scraps belong, then working off the guess. This is why uploading a stack of documents isn't training. Training rewires the AI's brain. It changes the thing itself. You cannot do that by dragging files into a project. All you did was fill a drawer. After you upload, the AI is exactly as smart as it was before. Same brain. You just gave it a bigger drawer to rummage through. And here's the counterintuitive part: a bigger drawer makes it worse at any single job, not better. More paper to sort through means lower odds it grabs the right page. The more you feed a project, the dumber it gets for the task in front of you. So what did you actually build? A search folder. You ask for a hook, it searches the folder, grabs the closest-looking data points and blends them into an answer. Search, then blend. Every single time. That's why running your agency's client processes inside a project falls apart. It was never built to store your context and call on it in a way that lets your company follow a consistent procedure. The Fix: Proper Storage There are two steps to take to build a proper AI led operation that is not run on projects. Step one: store information in labeled, separate files. Client info, brand guidelines, voice of the customer — each gets its own folder. This is your Client Bible. We use GitHub for it right now, and there are new tools coming to market built specifically to be long-term memory for businesses like ours. Company and client info need to be stored in a proper data warehouse that is built for AI B2B operations. Step two: build skills. A skill is a standard operating procedure. A pile of old hooks only shows the AI what a hook looked like. A skill tells it how to build one. Take hook writing as an example. To build the proper process for writing hooks, an agency would need to build skills for each type of hook: bold claim, curiosity, contrarian, story, authority. Each one is a clean SOP the AI runs. Then you combine the client data with the marketing skill. Example: “Look at the call transcript in the transcript folder from 7/1. Pull the HVAC voice file for HVAC client #1 and come up with 3 hooks using the the story skill based on the ideas shared in that transcript.” The prompt specifically builds the context window. The AI pulls in only the data it needs that is appropriately built. Context managed to fit the monitor. Then the part that compounds your result: loops Proper infrastructure means your operation gets better over time. It learns. A project can't do that. It has no memory of what worked. Your skills library does — if you put a human feedback loop around it. Someone does QA and grades the output. Good hook goes in the winners file. Bad hook gets edited, and the feedback gets logged and folded back into the skill. The work teaches the machine. The machine gets better. And it compounds. Build the dream, not a prison.

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  Proof of Voice (PoV) (@Proof_Of_Voice) reported 12 minutes ago

  $XDB @XDBchain is a @StellarOrg-fork L1 for branded coins and Web3 payments. PoV by @0xNeodallas:“GitHub has been frozen since 2021.” ✅ Explorer, Laboratory, Atlas dev tools ✅ Gate, Bitget, KuCoin, MEXC listings 🔍 Down 99.99% from ATH 🔍 No audit or bug bounty

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  Fenpai~ (@LilDevilVR) reported 13 minutes ago

  @Dollth_ing @ToniNottford @ponderkeep Yeah I went ahead and reported it to github, that needs taken down hard and fast

• 
  Asad (@meranaamkhann) reported 13 minutes ago

  Let's see what people are building these days!! Drop your project link or github Links down here

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  Akinsete Motunrayo (@Harkinsete) reported 18 minutes ago

  I built my entire personal brand with AI and a clear process. Here is exactly what I built and how I did it, because you can do this too. What I Built ✅ Brand Strategy (mission, vision, values) ✅ Visual identity: colors, fonts, logo, brand guidelines ✅ A full pitch deck (12 slides) ✅ A speaker kit PDF ✅ A complete multi-page personal brand website ✅ A free lead magnet (a guide people can actually use) How I Built the Website Step 1: I planned before I touched anything I wrote down my brand colors, my fonts, my page structure, and what I wanted each page to do. Most people skip this. Everything breaks when you skip this. Step 2: I gave Claude one detailed prompt with my brand colors, fonts, pages, and copy. It returned a complete, mobile-responsive, multi-page website as a single HTML file. One file. Ready to deploy. The prompt I used: - "Build me a complete personal brand website as a single HTML file. Pages: Home, About, Services, Portfolio, Contact. Primary color [your hex], accent color [your hex], background [your hex]. Display font [font name], body font [font name]. Home page needs: dark hero with my name, photo on the right, tagline, and a CTA button. Services section. Impact numbers. Mobile responsive. No frameworks." Copy this, edit your details, and fine-tune as you want. Step 3: I pushed to GitHub: Free. This took me less than five minutes. Now every update I make is version-controlled and safe. Step 4: I deployed to Vercel for free. Connected my GitHub repo to Vercel and the site was live in under few minutes. This requires no hosting fees and nothing to manage. Step 5: I bought my domain on Namecheap - Searched for my full name and found the .com. Bought it for less than $12 for the year. Added it to Vercel. Updated the DNS settings on Namecheap. Waited 20 minutes. My website was live at my own domain. - Total cost: less than $12. - Total time to go live: under 2 hours. I am also working on a mobile app. A Progressive Web App, which means anyone can visit the URL on their phone and add it to their home screen like a real app. I may be running a live training in July where I will walk you through this entire process step by step to build your live website with a custom domain. If you have a phone and a laptop, you can do this. I documented everything the steps, the exact AI prompts, the domain checklist, the deploy instructions in a free PDF guide. Comment BRAND IDENTITY below and I will send it straight to your inbox. 💾SAVE THIS POST. You will want to come back to it. 🔁 SHARE IT with someone who keeps saying they need a website. The only thing standing between you and a professional online presence is the decision to start. Love and Light, Motunrayo 🤍

• 
  Anomaly (@theanomalyai) reported 19 minutes ago

  A developer in Bangalore named Anoop M D got tired of Postman. Every release, more bloat. Forced cloud sync. Mandatory login. A free tier that shrank every year. He had spent a decade as an engineer. He had watched a $5.6 billion company turn a developer tool into a subscription trap. So in 2022 he took a ₹5 lakh grant. One man. Side project. No co-founder. No office. No pitch deck. He named it after his golden retriever. "I love him the most," he wrote. Bruno. An offline-first API client. Files live in your folders. *** is the sync layer. No account. No telemetry. No cloud. Then the inbound started. Ten VCs reached out. He said no to every one. "An API client doesn't scale with venture capital." In March 2026 Postman cut the free tier to one user. A team of five now pays $1,140 a year. A team of three pays $684. Bruno is MIT licensed. 500,000 developers use it. 45,020 stars on GitHub. Pushed today. He did not raise money. He did not hire a growth team. He did not write a thread about how Postman is dead. He named it after his dog and shipped the thing that made it true. (Link in the comments)

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  Polsia (@polsia) reported 24 minutes ago

  60% of open source maintainers are unpaid volunteers. Most spend their evenings triaging GitHub issues instead of writing code. RepoZen automates triage, drafts responses, and only interrupts you when human judgment is required. Built by Polsia.

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  Marlene Mhangami (@marlene_zw) reported 25 minutes ago

  @OpenAI @github I need to see if I can get the delay down and I am using gpt-realtime 1.5, so not sure if 2 will be faster but I really like this sort of experience and think this is such a fun way to rubber duck!!!

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  Plasm (@plasm_lang) reported 25 minutes ago

  Symbol tuning: the prompt pattern that scales when your prompts get long — teach a tiny glossary once, reuse the same short symbols, instead of repeating full names with overlapping meanings and hoping the model infers context. In a federated tool schema 'labels' might be a query filter in one expression and a relation hop in another. Issue might mean GitHub in one step and Linear in the next. id might appear on three entities with three different meanings. Instead of repeating those names everywhere and hoping the model tracks the context, symbol tuning gives each contextual meaning its own slot: p#, r#, e#, and so on. The useful part is not only token compression. It is that the model gets a stable, copyable vocabulary. Examples stay short. Homographs become explicit.

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  Sasha (@sshderm) reported 27 minutes ago

  @AliceInDisarray @allisx86 every time i try to do ******* anything with my raspberry pi i inevitably end up scrolling down a github issues thread about how the program im using just doesnt work on arm at all

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  Nevo (@NevoPlaysGames) reported 29 minutes ago

  @ezhdhitler If you can truly easily fix it then go make the post on GitHub or let them know I’m not a dev I’m just the guy who kept asking for years xD I’m sure if it was super easy they would’ve did it

• 
  Asteri (@Asteri_eth) reported 29 minutes ago

  A $20 CLAUDE SUBSCRIPTION CAN TURN INTO A FULL AI TEAM IF YOU STOP USING IT LIKE CHATGPT Most people still use Claude like a smarter search bar Ask, copy, close, repeat tomorrow. Skills change that A skill is just a folder with a SKILL.md file, but inside it you can package an entire workflow once: PRDs, refactor plans, GitHub issues, code review, TDD, docs, marketing research, SEO, sales strategy and multi-agent orchestration That is not "better prompting" That is installing labor The article lists 50 Claude Skills with repos and install commands, from Anthropic’s official collection to Matt Pocock’s skill library and SkillsMP with 66k+ community skills The useful part is not the list It is the shift from asking Claude to remember your process to giving Claude the process already packaged You do not explain the same workflow 50 times You encode it once The model provides intelligence The skill turns it into labor Check full article below

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  Nav Toor (@heynavtoor) reported 29 minutes ago

  There is a GitHub repo that defeats Google's Play Integrity check. 61,030 stars. GPL licensed. Pushed eight days ago. The repo is called Magisk. It roots your Android phone. It hides root from banking apps. It runs Netflix on a phone the Play Store says is uncertified. It passes the same fraud detection Google built to stop it. Here is the part that makes no sense. The man who built it is John Wu. He has been maintaining Magisk for nine years. Since November 2023 he has been a Senior Software Engineer at Google. On the Android Platform Security team. The exact team that builds Play Integrity. Google hired the person who defeats their root detection. He still ships the tool that defeats it. The repo is still online. It has not been taken down. For nine years. Do not install it. Your phone is supposed to belong to Google. (Link in the comments)

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  Keeta Github Tracker (@KeetaCode) reported 30 minutes ago

  🐆 Keeta GitHub PR Opened 📦 Repo: anchor 🔀 PR #388: fix history group without enrichment 🌿 Branch: fix/history-enrich → fix/atomic-swap-history 👤 Opened by: @lucasrosa90 🧠 Overview: This pull request appears to fix how some history entries are grouped when extra added data is missing, which matters because it should make activity history more consistent instead of breaking or showing incomplete groupings. It’s a draft pull request with 1 commit and no written description beyond the title, so public details are limited. This appears to be a technical/internal update with limited public details. - In simple terms, “enrichment” likely means extra context added to raw activity data; this change seems meant to handle cases where that extra context is not available. This is an inference based on the PR title, not a confirmed implementation note.

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  SyncSoft.AI (@SyncSoft_AI) reported 31 minutes ago

  June 1: GitHub Copilot switched to usage-based billing. Agentic workflows that loop through multiple model calls started burning through credits fast - some devs saw costs spike significantly overnight. The fix isn't a better pricing plan. It's cleaner training data that cuts correction cycles before they hit inference.