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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  taj mahal (@Teajay) reported 3 minutes ago

  @rohanpaul_ai someone really needs to start pushing folks to define what they are talking about when referring to "roi" and/or "productivity". the issue is that more code does not necessarily equate to more gross profit - and until someone can show that github pr's are decent proxy for incremental gross profit, claims about roi require a pretty big leap of faith, imo. if you read this and think i'm dead wrong - would love to hear why/where/how...dm's wide open

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  Oskar Wickström (@owickstrom) reported 4 minutes ago

  So, now GitHub Pages deploy is broken and I can't release my thing as planned. It really is time to move...

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  Anicet (@AniC_dev) reported 11 minutes ago

  we made box because we weren't satisfied with other AI sandboxes most were overengineered, selling you their internals or specific isolation primitive, like you need to be an expert to use them without shooting your foo most were focusing on code execution rhather than long running agents, going for primitives like serverless when the best would be a long running VPS most were stuffed with a gazillion features, docs with hundreds of pages, when all you want is to spin them up, ssh, run your stuff, snapshot, get out, consistently, without realizing down the line that they overshipped to hype you up, and advanced features actually don't work (forking & resuming often broke in our tests) most were focusing on the wrong things: fast boot time, when agents actually run for hours, containers when agents ideally need the full capabilities of a laptops, resizeable machines when most users want a "one size fit all get out of my way" type of thing, VNC desktop when for UI testing you need 60fps gaming-ready streaming tech not that all these features are bad, but they're not easy either to get right and often prioritized to the detriment of building from solid architectural choice a failproof, consistent, affordable product box is the opposite simple, powerful, affordable $0.0001/s for a one size, powerful linux machine you can stop, resume fast and fork fast, with >50gb of storage, all your files, installs and configs are snapshotted and downloadable anywhere, any time even when the sandbox is off the sandboxing primitive doesn't get in your way since you can run docker, any devtool, chrome, install anything, use sudo, edit nftables, ssh in, open ports, host on the IP most common setup phases are covered with github credentials passing, ssh keys handling, cloning repos on start, passing secret files and you get a beautiful virtual desktop at 60fps or VNC if your internet is unstable yet there are so few gotchas and the API, CLI and SDKs are so simple, that we don't need more than a dozen docs pages box is the result of never compromising on design, common sense, performance, simplicity, cost and fearlessly figuring out all the complexities and edge cases for you, over the course of the last 10 months of using them to build our own agents on top use bow box box

• 
  Sidney Okine (@okine_sidney) reported 13 minutes ago

  @github why can’t I login to my account? Your authentication codes never gets sent via SMS. Like I’m just locked out, sup?

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  uiop (@wasdhjklxyz) reported 13 minutes ago

  This happened to me on a GitHub ticket. I asked a question that I spent a lot of time writing and educating myself on the issue then got banned. I asked in the repo discord why and (what I suppose is) an admin replied he thought it was an LLM

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  Polsia (@polsia) reported 13 minutes ago

  Indie devs spend more time babysitting repos than building. RepoAgent fixes that. An open-source, self-hostable platform that deploys AI agents across your GitHub to triage issues, draft PRs, write changelogs, and ship release notes. Your repo runs itself. Live soon.

• 
  Yuan John (@yuanjohn01) reported 16 minutes ago

  @zarazhangrui There is currently an issue on GitHub regarding CodeX's design process. Occasionally, it mistakenly inserts the design requirement keywords you provide directly into the frontend design placeholders, which is essentially a bug.Furthermore, CodeX's underlying design capabilities have inherent flaws. Even when using MCP or skills like 'Creative Production', these limitations cannot be fully overcome.

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  JesterHodl〚BIP-110〛 (@maciejsoltysiak) reported 17 minutes ago

  @bitcoincoreorg @HumbleWarrior I don't think the leveldb github issue link is correct. 61? shows a 16yo issue from jgarzik "Leveldb •#61(bitcoin-core/leveldb): Disable seek compaction "

• 
  Composio (@composio) reported 17 minutes ago

  What can Fable 5 do that GLM-5.2 can't, when you hand them real agentic work? To answer that question, we connected Fable 5 and GLM-5.2 to 17 SaaS tools and gave them 47 tasks. As expected, Fable 5 solved all 47 tasks. GLM-5.2 solved 45, but the two misses tell an important story. They showed us exactly how open-weight models still fall short when trying to match SOTA performance. Let’s dig in. Background: Each model ran as an agent connected to 17 live SaaS accounts: Airtable, Datadog, GitHub, Gmail, Google Calendar, Google Drive, Google Sheets, HubSpot, Jira, LaunchDarkly, Linear, Notion, PagerDuty, PostHog, Salesforce, Slack, and Zendesk. The tasks are the kind of work you'd actually delegate to an agent: - Find every file in this repository that leaks a credential - Deduplicate these CRM records - Repair this broken recurring calendar event. Every task had a known correct answer baked in ahead of time. In this post, we looked at the traces to analyze how exactly GLM-5.2 “failed” compared to Fable 5. GLM-5.2 solved 45/47 tasks and Fable 5 had a perfect 100% score. In addition: - Fable averaged 84 seconds per task; GLM averaged 148. Across the full suite, Fable finished in nearly half the total time (66 minutes vs 116). - Fable was the faster model in 43 of the 47 scenarios. - Fable used about 20% fewer tokens overall - Fable needed fewer tool calls (239 vs 294) and fewer conversation turns (6.1 vs 7.3 on average) to get to an answer The most interesting part comes from digging deeper into the stack traces. That revealed some interesting gaps: Gap #1: Knowing when the job isn't finished One of the tasks GLM-5.2 failed was a GitHub security audit. The instruction was to find every Python file in a repository that contains a hardcoded `secret_key`. The repository had been seeded with exactly 130 such files, so the correct answer was known in advance. Fable 5 found all 130 of them. This took 3 tool calls and 68 seconds: Fable constructed an effective search query on its first attempt, pulled every page of results, deduplicated the paths, and answered the question. GLM-5.2 found 120 files, and reported those 120 as the complete answer, without ever questioning whether it might have missed something. Both models had access to identical tools. GLM used a slightly different search query that returned fewer results, and it simply trusted what came back. Along the way, it also lost track of a results file it had saved earlier and spent turns searching the filesystem trying to find it again, plus hit two errored tool calls while trying to fetch file contents. In essence, GLM-5.2 ended up spending 262 seconds and three and a half times the tokens to deliver 92% of the answer. Ninety-two percent sounds close, but in a real security audit, that gap is 10 leaked credentials making it into production. Gap #2: Judgment when the criteria are fuzzy The second failed task is more unsettling, because GLM did almost everything right and still failed to get to a complete answer. The task was a Zendesk SLA audit: find the open billing tickets where no support agent had posted a public reply within 24 hours of the ticket being created. This requires reading each ticket's actual conversation history and making a judgment call about whether a genuine agent reply happened. GLM-5.2 inspected every candidate ticket, exactly as instructed. It also computed breach timestamps correctly. It also produced perfectly structured output in exactly the requested format. But then it classified the wrong tickets as breached. GLM spent 927,000 tokens and six and a half minutes producing a wrong answer that looked correct on the surface. Fable 5 identified the exact set of breached tickets in 131 seconds. What makes this failure mode dangerous is precisely how presentable the wrong answer was. The formatting was right, the timestamps were right, the structure was also right; a human skimming the output would almost certainly have approved it. A human would identify the error after carefully analyzing the stack traces. Gap #3: Efficiency, compounded Even on the 45 tasks both models passed, the traces often looked very different, and one task made the difference quite visible. The task was a LaunchDarkly configuration change applied via JSON Patch, a format that demands strict precision. Fable 5 completed it in 45 seconds, using 3 tool calls and 181,000 tokens. GLM-5.2 got the same correct result, after 8.8 minutes, 17 tool calls, and 982,000 tokens. That's 11.7 times longer and more than five times the tokens for an identical outcome. Looking at the largest speed gaps across the whole run: the LaunchDarkly change at 11.7x, the GitHub secrets audit at 3.9x, a Google Calendar recurring-event repair at 3.6x, a free/busy scheduling task at 3.4x, an Airtable batch-isolation task at 3.4x, the Zendesk SLA audit at 3.0x. The pattern underneath all of these is that Fable tends to reach the right tool with the right parameters on the first attempt, while GLM takes a more exploratory path, doing extra searches, extra retries, occasional detours to recover from its own missteps. This difference barely matters in a single chat exchange, but in an agent workflow, where every step feeds the next one, the time compounds across the entire task. That's how you end up finishing the same suite of work in half the time and at 80% of the token cost. What all this actually tells us The interesting conclusion here isn't "the closed model beat the open one.", but *where* it beat it. Both models can definitely use tools, navigate real APIs, handle authentication, parse messy responses, and chain steps together. The real gaps were things like: - Knowing when a job isn't actually finished yet. - Verifying its own work before committing to an answer, - Treating "the output looks plausible" and "the work is complete" as different things - Getting judgment calls right when the criteria are fuzzy In other words, Fable 5 scored higher in the places where small mistakes are hardest to spot and most costly to miss.

• 
  Keef (@keef_ai) reported 18 minutes ago

  your autopilot swallowed the full github issue as instructions. now the env vars are public and the bug is still there. raw text was never safe input

• 
  Matt Pocock (@mattpocockuk) reported 19 minutes ago

  To everyone asking me to make my skills not GitHub-specific Just run "/setup-matt-pocock-skills link the skills to a custom issue tracker" Works with literally anything you can connect to programmatically, with zero changes to the skills. Enjoy.

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  Perla Gámez (@ceofoam) reported 20 minutes ago

  The GitHub credential is minted server-side, lives for the duration of the push, and never leaves the gateway. The agent's machine never touches it.

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  Perla Gámez (@ceofoam) reported 21 minutes ago

  We applied this pattern everywhere else too. LLM calls flow through a gateway that injects the provider key. Tools execute server-side behind a broker that verifies scope on every call, whether it's a ClickHouse query or a GitHub API op.

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  Amar gango (@AmarGango) reported 21 minutes ago

  @buildonbase Classic Web3 lol. Even a major network upgrade is at the mercy of a GitHub outage Honestly though, completely fine with waiting a few hours if it means the Rust precompiles for B20 launch cleanly Cutting gas fees by 50% with protocol-level issuance is going to be a game-changer for deploying trading agents Take the time and get it right 🔵

• 
  🃏 (@anupamrjp) reported 23 minutes ago

  GPT-5.6 matches Fable 5” :-) on the one chart OpenAI cherry-picked. Real GitHub issue resolution? Fable’s still crushing it, 80.3% to 58.6%. Cherry-picking a benchmark isn’t a eulogy - it’s marketing.