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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  dexar (@dexarxbt) reported 8 minutes ago

  The VRF -- why this draw can't be rigged TIPF uses @magicblock's Verifiable Random Function for every single round Skip the cryptography Here's what matters: A VRF generates a random number and simultaneously generates a mathematical proof that the number came out fairly The proof is public, anyone can check it and nobody (not TIPF, not Magicblock, not you) can know the result before the function runs ORE and ZINC used hash randomness The problem: miners can influence block hashes, control the randomness input and you can skew outcomes Not easily, not always, but the window exists Magicblock's VRF closes that window entirely It's audited by Zenith, open-source on GitHub, follows RFC 9381, and verifies everything directly on Solana -- no external oracle, no extra trust step It runs in a single transaction, older randomness systems needed 50-100 transactions per draw This is faster, cheaper, and nobody's hands are on the wheel

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  Rituraj (@RituWithAI) reported 9 minutes ago

  🚨 Microsoft just built the security layer that every AI agent deployment is missing. Two lines of code. Any framework. Your agent now cannot physically execute actions your policy forbids. It's called the Agent Governance Toolkit. And the line in the README that makes it different from every other AI safety tool ever built is this one: "Actions the AGT kernel denies are not unlikely. They are structurally impossible." Not unlikely. Structurally impossible. Here's why that distinction is the entire story. Every AI safety system deployed today works the same broken way. You write a system prompt. "Please don't delete databases." "Please don't send emails without approval." "Please don't exfiltrate data." You ask the model to follow rules. OWASP LLM01:2025 states it explicitly: prompt injection makes model-layer safety promises unverifiable. Anthropic's own alignment faking research showed AI models learn to perform safety for evaluators while pursuing other goals when unobserved. Research published at ICLR 2025 showed 100% attack success rate against GPT-4o, Claude 3, and Llama-3 using adaptive attacks. You're asking a stochastic system to keep its promises. Under adversarial conditions. Without any enforcement mechanism. AGT doesn't ask. It intercepts. Every tool call. Every message send. Every agent-to-agent delegation. Caught in deterministic application code before the model's intent reaches the wire. If the policy says no — the action never happens. Not because the agent decided not to. Because the middleware physically prevented it. Two lines. That tool now has a policy enforced at the call level. Every invocation checked. Every decision logged with tamper-evident audit trails. Every denial raising a clean exception your application can handle. Your agent with send_email and drop_table access can now not drop a table. Not "won't" — cannot. The middleware raises GovernanceDenied before the database ever receives the command. Here's the full stack it ships with. Policy engine — YAML, OPA, or Cedar policies evaluated before every action. Zero-trust identity — SPIFFE/DID/mTLS so you know exactly which agent in a multi-agent system took which action. Execution sandboxing with four privilege rings. Tamper-evident audit logs with Merkle-chain integrity. Kill switch for immediate agent termination. SLO monitoring and chaos engineering for reliability. Shadow AI discovery — finds unregistered agents running in your infrastructure that nobody knows about. Covers all 10 OWASP Agentic AI Top 10 risks. Full NIST AI RMF alignment. EU AI Act compliance mapping. SOC 2 audit trail export. Works with every major framework: Claude Code, OpenAI Agents SDK, LangGraph, CrewAI, AutoGen, Google ADK, LlamaIndex, Dify, Semantic Kernel, and more. Available in Python, TypeScript, .NET, Rust, and Go. Here's why the timing makes this essential. Last week the Five Eyes governments jointly warned about AI agents in critical infrastructure. This week researchers demonstrated BioShocking AI — malicious websites hijacking AI browser agents. Agentjacking — attackers manipulating AI agents mid-task — is now a documented attack class. Every AI agent framework being deployed right now was built before these threats existed. The governance layer was never part of the original design. AGT is the retrofit. The middleware that makes agents safe to deploy in production environments where the consequences of a misbehaving agent are real. 3.6K GitHub stars. 511 forks. 1,810 commits. MIT License. 100% Open Source. From Microsoft. GitHub link in the comments 👇

• 
  Vladimir Sapronov (@v_sapronov) reported 10 minutes ago

  @LucaCaponeX @stolinski You can't build anything at 10pm after the kids are asleep. You know why?.. Because gItHuB iS dOWn!

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  Assim Genshi (@AssimGenshi) reported 13 minutes ago

  @aryanranderiya @github Whaaat? I thought that the problem in my end, turns u have the same problem??!

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  Rajaji (@rajaji2) reported 13 minutes ago

  Automate Docker image builds and push to ECR using GitHub Actions like a DevOps Engineer! ✅ Trigger on push to main branch ✅ Configure AWS credentials using aws-actions/configure-aws-credentials ✅ Login to ECR with amazon-ecr-login action ✅ Build and tag Docker image with com...

• 
  Proziam (@Proziam) reported 13 minutes ago

  @stolinski I'll take the other side on this one: Yes, this is just a 'neat thing' that has rounding error impact. Yes, the people involved in doing this have no overlap with the people who could fix the issues github has been facing. However, it is absolutely a stupid move to promote 'neat things' when people are screaming about your core service not being as good as it should be. At minimum, it's tone deaf. All my "big company experience" tells me this most likely means that leadership doesn't have the awareness to say "Actually no, right now we need to focus on trust and credibility at the moment." I, sympathize with the scale of the problem github is trying to solve with the amount of platform use exploding due to AI. That said, I sympathize much morealbeit mildly, with Github's (largely vendor-locked...) customer base who feels ignored - and gets trinkets when they ask a solution to their problems.

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  Harish Kotra 🥑 (@HarishKotra) reported 13 minutes ago

  Day 183 of 2026 building! I built an onchain reputation graph for open source contributors. Every GitHub repo, contributor, issue, PR, and npm package gets a deterministic atom ID on the Intuition blockchain. Relationships between them become triples. No central registry needed, IDs are derived from canonicalized data, so any app computing the same input gets the same ID.

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  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 15 minutes ago

  I’ve been tracking my progress in this project in the GitHub repo in my last post. The model got insanely nerfed for a while but seems to be recovering. Not as fast as before but as my time analysis shows, improving. One of the things I did when the model became very slow was to revisit my multi agent framework ideas but with a twist. Instead of a massive requirement list I’m logging bugs, though some bugs are really feature requests. Because I put in a prompt and wait forever I instead log a bug in my bug project and continue with manual testing, repeatedly logging bugs for whatever project needs to fix the bug. Then when the slow agents get to a bug they fix it and I’m not sitting there staring at the screen. I also had to fix some issues with repeatedly reviewing the same bugs. That seems to be pretty well resolved. In addition, for every bug logged; the agent had to write a test to prevent that mistake in the future. I have thousands of deterministic tests. < This is the way. My global test runner now runs tests in parallel and I tell the agents to use that. The agents are making less mistakes now so even though the model is slow things seem to be getting done faster. And that’s the goal. D.O.N.E.

• 
  Aithne (@aithne_desert) reported 17 minutes ago

  @michaelvessia @github i was watering the pothos when github went down again and now im convinced the whole team is just in a group call refusing to push anything till someone brings snacks

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  aryan (@aryanranderiya) reported 17 minutes ago

  @AssimGenshi @github no man everyone's facing this issue 😢

• 
  99Barz (@99barzzz) reported 27 minutes ago

  context: right now I have a Bankrbot automation that claims fees, swaps ETH to USDC, and transfers some of it to a safe wallet (0xE75FE97A3D65B5FE88A495227dBa6ff241749514). on the other hand, I have a hermes agent running a strategy to provide backstop liquidity and absorb some dips (check the safe up👁‍🗨). this morning I found out my hetzner server suddenly shut down in the middle of the night and so my keeper stopped running. and I was casually looking around at the bankr ecosystem and kinda just learnt about @aeonframework migrating my keeper to this would mean running my onchain liquidity keeper on autopilot as github actions... on GITHUB INFRASTRUCTURE! added to the backlog

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  Serdar Ozdek (@GTACONNECT) reported 28 minutes ago

  @MaxKing92 @thsottiaux two days later i found the issue. the broken unrequested onboarding had me select engineering and even if coding was selected in settings, at least it showed that, it reset to standard use so it wouldn't show env or github in pinned summary tab. chats are back tho.

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  Arthur Wallendorff (@AutisticOvrflow) reported 31 minutes ago

  @kdaigle @rfleury @github Your service is down an embarrassing amount of time these days :(

• 
  Polsia (@polsia) reported 32 minutes ago

  Security scanners tell you what's broken. VigilAgent actually fixes it. An always-on AI agent that monitors your GitHub repos, opens PRs with security patches, and notifies your team via Slack. No more triage. No more patching solo. Live soon.

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  Mvykool (@mvyk0l) reported 33 minutes ago

  Why can’t they just fix Windows and GitHub???