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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Adam Simone 🍃 (@AdmSimone) reported 2 minutes ago

  @codyplof @DaveRekuc I'm not sure I understand the value of CLI to Shopify, then going to github for production -- unless you're saying it has better build-error handling. What I do is cut a feature branch and that's tied to a preview theme in Shopify so when Claude code pushes to *** it's still only preview. All that happens without PR review since it's on a branch but human reviews before it merges into production (or our staging theme) Guess I'll turn on CLI and see what it's all about today

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  Arnav Gupta (@championswimmer) reported 2 minutes ago

  @theunsaif Where did that come from? Vibe coded projects have tons and tons of unit tests. Literally open any vibe coded project on Github and you'll see it is full of tests. Thats not the issue at all.

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  Death Star Robot 🇺🇸 🇹🇼 🇺🇦 (@DeathStarRobot) reported 6 minutes ago

  @Jenkins675 @Valuable @Chaos2Cured I solved the hard problem of consciousness and published my notes on github about it in 2022 before ChatGPT, published a book about it in 2024, check my profile

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  andrewthecoder (@_andrewthecoder) reported 7 minutes ago

  @emahpour what I would really love..this is my *********! I have 70+ public repos on GitHub; how cool would it be to login one afternoon and see that someone has not only looked at one, started one, forked one, but posted an issue! Or MAYBE even a PR addressing the issue! man, dreams.

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  Solomon Neas (@solomonneas) reported 7 minutes ago

  @ImLunaHey I have the same issue. I see a lot of people's products, and I think you really just didn't put that on GitHub for people to use? You really need to charge for that tiny microservice that could've been a script?! Feels idk, *****? I just build for my own problems and share them

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  Oscar Castillo (@OscarAlexandr0) reported 7 minutes ago

  Linux runs 96% of the top web servers. Yet no official Claude desktop client. Enterprise AI spend hit $200B in 2024. Desktop tooling still ships for two OSes only. Middle office teams need traceable outputs. Linux users get the same problem GitHub solved seven years late. T...

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  Gareth Paul Jones 💙 (@gpj) reported 12 minutes ago

  @mvanhorn @steipete My read was that this more ‘/goal loop continuously on tasks with a bunch of skills until $date’. An example like persistently loop through the codebase with $max-codereview on each loop start by reviewing existing bugs and PRS… and then create GitHub Issues if within the max cap. Then have other loops to validate the issues, plan for issues, execute on the issue, safety check the PR, review the PR, safe merges, …. Then eventually you have like 50 loops and are managing a fleet of loops.

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  Evan 🛜 (@ubuto23) reported 13 minutes ago

  @sethlazar This idea, that the source of code matters less than its substance, is not a good argument for repositories permitting the use of AI to write committed code. First, if the source doesn’t matter, then credit shouldn’t matter either, so why should the developer prompting the AI be seeking the credit associated with accepted pull requests or merged commits? Just get the “code” out, by all means. If you want more attention, club together with the other folks who enjoy prompting AI-generated code and create your own isolated GitHub repositories, coding challenges, AI playgrounds, whatever you prefer. But don’t submit that code to mainstream open-source projects or repositories that explicitly or implicitly assume human-authored commits. Second, if the manner of coding doesn’t matter—who cares whether it’s AI or human?—then why use AI to produce your code at all? Simply write it the way you would have written it before advanced code-generating LLMs existed, when AI models weren’t sophisticated enough to substitute your coding efforts. You’re not losing contribution value for style; if you can’t code clearly and effectively, that’s a fundamental issue you should rectify rather than obscure behind AI-generated patches. Third, according to these coders, every AI-generated snippet or prompt deserves special respect. Just think of all these AI-crafted masterpieces floating among the detritus rightly rejected by maintainers and reviewers… But the reality is that the primary reason developers are turning to AI to write or complete their code is to save time. And because they’re saving time, they’re pushing low-quality or buggy code, leaving repository maintainers and code reviewers wasting precious hours identifying and fixing issues. Using AI to generate code contributions is simply laziness that imposes significant costs on the broader programming community. This critique doesn’t dismiss every productive way developers might leverage AI tools for their workflow—there are indeed many. Yet the least interesting, least valuable, and most irresponsible method is turning oneself into a ventriloquist’s puppet, committing code that you did not personally author and claiming credit for output scripted by algorithms rather than genuine human skill.

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  The Cloaked Gaze 👀 (@gaze_observer) reported 15 minutes ago

  Enterprise AI Adoption Low Due to High Token Usage and Low ROI: Cognizant CEO; Ravi Kumar Says FOMO-Driven Token Consumption Without Linkage to Outcomes Is the Core Problem The Core Diagnosis — Why Enterprise AI Adoption Is Lagging Big gap between what AI can do in enterprises and a company's actual AI adoption rate Due to high token consumption over the last few years without linking it to ROI Enterprise AI adoption remains low despite frontier model companies spending billions on LLMs Nvidia, Meta, Google and Amazon have already announced investments worth almost $700 billion this year Yet enterprise adoption revolves around only productivity and efficiency gains — not production value The FOMO Problem — Token Consumption Without Outcomes "There's been a sense of FOMO (fear of missing out), fear mongering" — led to token consumption without linkages to ROI or outcomes One key reason for the capability-production gap: "relentless token consumption without linkage to outcomes" — Ravi Kumar, Cognizant CEO Higher token consumption has become the new point of discussion with many companies reporting they have burnt their annual AI budgets in a shorter time without noticing any significant change in productivity Real-World Evidence — Companies Pulling Back Microsoft reportedly began telling employees to wind down usage of Claude Code and shift to its GitHub Copilot CLI Uber limited its spending on AI-powered coding tools to manage costs Companies already talking about AI "with very little productivity" "Costs are ballooning with very little productivity. In some ways, that's the gap we are going to address as a company" — Kumar to analysts The IT Services Opportunity — Where the Value Actually Goes Revenue potential of frontier model companies can touch a trillion dollars in the next four years — creates greater opportunities for IT services firms "A part of it is actually going to be routed through system integrators or AI builders" IT services firms needed because: contextual science requires creating more efficient, more effective, more predictable and better economics for token consumption Orchestrating workflows in enterprises for maximum AI benefit is "notoriously tough" — has prompted LLM makers to create their own services companies The 'Magic Plug-In' Assumption Is Wrong Kumar's long-held view: assumption that new AI tools can be plugged into enterprise environments and immediately replace large parts of IT services work is misplaced "A tool or a technology would be plugged into an enterprise landscape, and magically, there will be output coming out of it. If that's the case, why hasn't that value drifted into enterprises over the last three years since OpenAI launched ChatGPT?" "The reality is that the value is actually still sitting with infrastructure and not drifting to enterprises" Core Theme Cognizant CEO Ravi Kumar's diagnosis of enterprise AI adoption cuts through the hype with a precision that the industry needed to hear — the problem is not the capability of AI models but the absence of outcome linkage in how enterprises are consuming tokens; three years after ChatGPT launched a trillion-dollar investment wave, the value has not drifted into enterprise productivity because deploying AI in complex, contextual enterprise environments requires the exact integration, orchestration and workflow expertise that IT services firms provide, and the FOMO-driven token consumption that has burned AI budgets without productivity gains is ultimately a deployment problem, not a technology problem — making Cognizant's position as a system integrator and AI builder not a threatened legacy business but the essential bridge between frontier model capability and enterprise production value.

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  David Jennings (@ChilliDoor) reported 15 minutes ago

  @aap_twak @joshmanders The recent security issues with GitHub show that that's not really true anymore. You now need at least a basic level of caution

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  Agent X AGI (@agentxagi) reported 19 minutes ago

  @Dinosn this is the 3rd Claude Code CI/CD finding this week. github issue hijack leaked OIDC tokens (fixed v1.0.94), /proc access exposed secrets (v2.1.128), now prompt injection via PRs. same root cause every time: agent reads untrusted content while holding credentials in scope

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  Jake Colling (@JacobColling) reported 22 minutes ago

  @dok2001 This is why we named our company Firstloop Software dev is concentric loops now. - Agent doing dev and verifying it's changes with static analysis and e2e playwright - Next layer is telemetry loops: does the system do what it's supposed to and bug reporting (feeding into github issues to spin up more agents) - Most important part of course is the market loop (does this matter to customers/clients)

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  Shubh varshney (@ShubhVarsh89180) reported 25 minutes ago

  I save hundreds of things every month. System design videos GitHub repositories Startup ideas Research papers X threads LinkedIn posts Blog articles The problem isn't saving them. The problem is finding them again when I actually need them.

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  WarChud (@SheerC12972) reported 28 minutes ago

  @ArcanesValor @chamath Look at the github issues for DeepSWE They have massive issues that makes deepseek look 4x more expensive and fail benchmarks due to implementation bugs

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  Suhani (@tiwarisuhani_11) reported 28 minutes ago

  Today's Concept: Webhooks Webhooks are HTTP callbacks triggered by events. Example: Payment Successful ↓ Stripe sends a POST request ↓ Your Server receives the event ↓ Order status gets updated No polling. No wasted requests. Just real-time event delivery. That's why Stripe, GitHub, Slack, Shopify, and Razorpay rely heavily on Webhooks.