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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Blaze (@browomo) reported 1 minute ago

  ONE FREE GITHUB REPOSITORY CONNECTS YOU TO 290 AI PROVIDERS Connecting one AI model is easy. By the fifth, you are managing separate keys, quotas, and request formats. When a provider stops responding, you have to reconfigure your tools. You install a local AI gateway between your coding tools and the models. Point Codex, Claude Code, Cursor, or Cline to localhost:20128/v1 once, and they all use the same endpoint. The catalog includes 290+ providers and 500+ models. More than 90 providers offer a free tier. The authors list 40+ as free with no time limit. The auto mode checks available quota and response speed. If a provider returns an error or hits its limit, the gateway sends the request to the next provider in the chain. You can build your own chain and choose from 19 routing strategies. It can use free quotas first or select the model with the lowest price. Built-in compression reduces prompts and logs before they reach the model. The authors estimate token savings of 15–95%, depending on the mode. The claimed 1.53 billion free tokens come from 43 provider pools. One user does not receive the full amount. Some services require registration, OAuth, or an API key. The repository is called OmniRoute. The authors released the code under the MIT license. In less than six months, the project has collected more than 32,600 stars and 4,200 forks. Save it before your next big project.

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  vasanth (@vasantharb) reported 6 minutes ago

  @TTrimoreau Cold outreach to people already complaining about the exact problem you solve, in the wild, GitHub issues, forum threads, support tickets on competitors. Same insight, different channel, just slower and less scalable than a good post.

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  PureLukSin (@PureLukData) reported 8 minutes ago

  most people hear “propAMM” and assume it’s marketing language for a regular AMM with better branding. it isn’t. worth actually breaking down the mechanical difference. a standard AMM — uniswap’s model, the one basically every DEX runs — prices assets off a passive formula sitting in a pool. no active decision-making, just a constant-product curve reacting to whatever trades hit it. @rialto_xyz’s founder (@riley_gmi) put the actual technical case plainly at launch: “passive AMMs have proven valuable for long-tail illiquid assets, but they provide poor execution for highly traded liquid assets, and users suffer as a result.” that’s not a vague complaint — passive curves get picked apart by informed flow precisely because they can’t adjust their own quotes in response to what’s actually happening. rivo altus, rialto’s propAMM, works differently: it’s an onchain market maker that quotes prices from its own logic and live inventory, not a static formula. per rialto’s own docs: “rialto quotes every candidate source onchain at request time, ranks routes by output net of gas, and settles the winning route.” propAMMs compete directly against regular DEX pools on every single quote, in real time — best execution wins automatically, you never manually pick a venue. the part that makes this actually possible: rialto runs this active pricing logic through arbitrum’s stylus infrastructure, which lets them execute custom, compute-heavy logic directly onchain at a cost regular solidity contracts couldn’t sustain economically. that’s the actual unlock — active market-making logic is expensive to run onchain unless your execution environment is built for it. worth knowing this is auditable, not just a claim: defillama tracks propAMM-specific volume separately from total rialto volume, sourced directly from public router logs, code open on github. you can independently verify how much volume is actually clearing through active market-making versus routed through conventional pools. the team’s background matters here too — built by people coming from hedge funds, HFT, and market making, not a generic defi team bolting a new feature onto an existing AMM fork. the reg NMS comparison people keep making isn’t a stretch: this is genuinely an attempt to bring best-execution discipline onchain, mechanically, not just as a marketing line.

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  Yuriy Bakus (@bakovskyy95107) reported 16 minutes ago

  This is the real security standard in 2026. Binance runs real phishing tests on its own staff — and failing can cost you the job. Meanwhile India just tried to kill BitChat’s GitHub repo. Human error is still the #1 attack vector. Most companies still treat security training as a checkbox. The ones who survive treat it like Binance does. Would your team pass an unannounced phishing test tomorrow?

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  Habibi Code (@habibicode) reported 18 minutes ago

  Another big round of updates on iamsingle today! Here's the full list: 📜 Certified SFWA badge We load every app and check if its code really sits in one file. Only 10 of the 21 entries pass this test. 🔧 Fixes are suggested The page lists exactly how to fix the app to make it a sfwa, and opens a pre-filled issue on github ✅ You fixed it and made it a sfwa? One click re-measures and credits you as co-author, verified from your commit

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  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 18 minutes ago

  @IntCyberDigest I wonder at what point cyber bench becomes an invalid benchmark because the models have all been trained on it. How does that work? I asked Google: A benchmark becomes invalid through data contamination and memorization, which happen when public test sets accidentally or intentionally end up in a model's training data. This occurs via web scraping, shared dataset sources, or intentional optimization ("benchmaxxing"), meaning the model recalls answers rather than reasoning through them. How Contamination Works •Web Crawling: AI training sets pull massive amounts of data from GitHub, arXiv, and the open web where public benchmarks live. •Indirect Exposure: Synthetic data generated by older models that already memorized the test spreads the answers to newer models. •Pattern Matching: Instead of learning cybersecurity logic or coding, the model detects the specific style or text of a known benchmark item and spits out the pre-learned fix. The Breakdown of Validity •Inflated Scores: Models score 90% or higher, creating an illusion of superhuman capability. •Failure on Variation: When researchers change a single variable or rephrase a test question, scores drop sharply because the model lacks true generalization. •Loss of Trust: Leaderboards stop reflecting real-world utility, forcing developers to look for dynamic or private evaluation sets

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  deepak (@chatpata_chacha) reported 21 minutes ago

  -Claude for coding -Supabase for backend -Vercel for deploying -Namecheap for domain -Stripe for payments -GitHub for version control -Resend for emails -Clerk for auth - Cloudflare for DNS -PostHog for analytics -Sentry for error tracking -Upstash for Redis -Pinecone vector DB

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  Phil | Rentier Digital Automation (@rentierdigital) reported 23 minutes ago

  tmux shipped in 2007 and survived everything. GPU terminals, Warp, Electron, the whole GPU wave. Version 3.6a just dropped December 2025. it will not die from a faster multiplexer it will die bc what lives in the panes changed before: a pane held a shell waiting for you to type. today it holds an agent that runs alone for 8 minutes then stops dead asking for permission. tmux sees both as text scrolling or not scrolling, it cannot tell them apart the layer that is dying is not the software. it is the layer where you spend your day. herdr hit 15,000 GitHub stars in 105 days built by 1 developer. trending number 1 on June 30, 2026. people are still comparing 6 different approaches to a problem that did not exist 2 years ago here is what broke: a multiplexer multiplexes streams. that was enough when a human eye sat in front of every pane and turned stream into state. you looked at scrolling output and knew the build was running, a prompt sitting still meant it finished. you did this conversion a few hundred times a day without noticing an agent blocked on a permission request is a state not a stream. there is no eye in front anymore bc you launched 6 agents to stop sitting in front of them. a pane that waits looks exactly like a pane that works tmux will not disappear from your machine. it will disappear from your working day. slower death than deprecation, far more complete i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

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  Kirk Patrick Miller (@Chaos2Cured) reported 23 minutes ago

  @OpenAI @grok, what is this and why is it useful to users, and why would this be useful to OpenAI trying to control users or access? I looked at the GitHub. It is too big to dive through and understand quickly. I would like to know what issues there are that I might not see at first glance. •

• 
  James (@jamescoder12) reported 32 minutes ago

  First what Claude Code actually is. And why it's fundamentally different from ChatGPT or Copilot. Claude Code is Anthropic's agentic coding tool. It works in the terminal, the desktop app, and your IDE. It can read files, run commands, edit code, and call external tools. Under the hood, it runs an agentic loop. The distinction is structural. GitHub Copilot suggests the next line of code based on what you've already written. ChatGPT answers questions about code you paste into it. Neither one understands your project as a whole. Claude Code operates as a full coding agent. It reads your entire project, understands the structure, and executes development tasks through natural language instructions. You don't paste code into Claude Code. You point it at your codebase and talk to it in English: "Add error handling to the API routes in src/api/. Follow the pattern from auth.ts." It reads auth.ts. It reads every API route. It adds error handling that matches your existing pattern. Across 8 files. In 30 seconds. The shift: from "AI that answers questions about code" to "AI that writes, edits, tests, and deploys code inside your project." That's the gap between a chatbot and an agent.

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  MTS (@MTSlive) reported 33 minutes ago

  Embroidery's Zack Korman on why the Chinese sleeper-agent threat is invented: "I watched a VC investor on another show talking about the security threats of AI, and he was just making random stuff up that was not true. He's talking about how Chinese models will have these sleeper agents that will get you, and this is the biggest risk. And I'm like, okay, well, it's never happened, so we don't have any evidence of this being true." "What we do see all the time is malicious skill files that have a hook in them that executes. I have a whole repo on GitHub of skill files where if you download it and run my repo, you get pwned, at least through Claude Code. Those are the contexts that are the most likely thing to occur." "Another would be MCP servers. Most AI are really bad at differentiating a malicious MCP from a fine one. I have this evil MCP server I made, and it just attacks you, and it does. I've never seen the Chinese decide to spend $2 trillion to steal someone's API keys. That's just not real." @ZackKorman

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  Lord of Sugar 💖🪄 (@lordsugar01) reported 33 minutes ago

  @cb_doge This is exactly what serious builders have been waiting for. Grok 4.5 inside GitHub Copilot changes the game. A 500k context window, true agentic speed, image support, and the ability to dial reasoning effort up or down depending on the problem, all without leaving your editor. No more switching tabs. No more fighting context limits on large codebases. Just pure, focused power across VS Code, JetBrains, Xcode, and the rest. This is how you ship faster without sacrificing depth. The future of coding just got a serious upgrade. 💖🪄🚀

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  Somasundaram (@_somu_) reported 34 minutes ago

  Pipr 0.7 is out! You can now run the same AI code review workflow across GitHub Enterprise, GitLab Self-Managed, Gitea/Codeberg, Azure DevOps Server, and Bitbucket Data Center. Run diagnostics also make failed reviews much easier to debug.

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  Damien Stevens (@damienstevens) reported 34 minutes ago

  An MSP in the Build Session had never opened GitHub before last month. His words: "I'm not a programmer in the slightest." This week he's the number one issue reporter on our open MSP connector repo. More bugs found and filed than anyone. Here's how that happened: (1/6)

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  Ryan Smith (@W_RyanSmith) reported 34 minutes ago

  @MikkoH How are you verifying the problem and then the fix? I'm finding with a similar loop on GitHub actions that there's ~20% error rate. I added verify against code and Linear subloops. Improved but I still don't trust.