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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Alex Wave (@WorkorAI) reported 6 minutes ago

  Your hiring post is live. Applications start coming in. That feels like progress—until you realize someone now has to open every CV, find every GitHub profile and decide who gets a call. The real hiring problem often starts after Apply.

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  Kaifyyy.sh (@Iamkaifyyy) reported 7 minutes ago

  - Claude = coding. ($20/mo) - Supabase = backend. (Free) - Vercel = deploying. (Free) - Namecheap = domain. ($12/yr) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build. Helps me a lot I’m gonna bookmark it

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  Elias (@iam_elias1) reported 7 minutes ago

  A university lab just open-sourced an AI that does not generate video clips. It directs entire films. Screenwriter. Director. Producer. Video generator. Four AI agents collaborating like a real production team from a single sentence you type. It is called ViMax. Built by Hong Kong University's Data Science Lab. 10,800 GitHub stars. Trending #5 on GitHub. MIT licensed. Free. Here is the problem every AI video tool has right now. Sora generates a 10-second clip. Runway generates a 10-second clip. Veo generates a 10-second clip. Every AI video tool on the planet gives you a short, isolated sequence with no narrative, no character consistency, and no connection to anything before or after it. Ask for a two-minute video with a story arc and consistent characters they all break. Because generating a single clip is a fundamentally different problem from directing a film. A clip needs one prompt and one generation. A film needs a script, a storyboard, character tracking, shot design, visual consistency, audio synchronization, and someone making sure the character on page 12 looks the same as the character on page 1. No single AI model can do all of that. So ViMax does not use one model. It uses four agents. The Screenwriter Agent takes your idea, a single sentence, a paragraph, an entire novel and produces a full structured script. Characters, scene segmentation, dialogue, transitions. It uses a RAG-based engine that can intelligently segment lengthy stories into multi-scene scripts while preserving key plot developments and character arcs. You type: "A cat and a dog are best friends. They meet a new cat." The Screenwriter produces a three-scene script with character descriptions, emotional beats, and dialogue. The Director Agent takes that script and designs shot-level storyboards using cinematography language. Camera angles. Transitions. Pacing. Visual rhythm. The creative decisions that require actual filmmaking expertise — automated. It does not randomly arrange shots. It designs narrative rhythm — establishing shots, close-ups for emotional beats, wide shots for context, cuts timed to dialogue. The Producer Agent is the quality controller. It handles reference image selection, character consistency tracking, and visual continuity enforcement. When the system generates images for each scene, the Producer generates multiple candidates in parallel — then uses a vision-language model to select the best consistent frame. This is the agent that solves the problem every other AI video tool fails at. The character in scene 5 looks the same as the character in scene 1. The lighting stays consistent. The environment does not randomly shift. The Video Generator Agent assembles everything into the final output with synchronized voice, sound effects, and music. Four agents. One production pipeline. From a single sentence to a finished multi-scene video. Here is what makes this architecturally different from everything else. Most AI video tools are single-model systems. One prompt in, one clip out. ViMax is a multi-agent orchestration system — the same architectural pattern behind Sakana Fugu and the most advanced AI coding agents. Each agent specializes in one role. The orchestration layer coordinates them. The same way a real film production team works. Nobody expects the screenwriter to also operate the camera. Here is what you can actually do with it. Idea to Video — describe a concept, get a complete multi-scene video. Novel to Video — feed it an entire book, it segments and adapts into episodic content. Script to Video — write your own screenplay, ViMax produces it. Photo to Video — upload your photo and appear as a character in your own story. That last one is worth pausing on. Upload a selfie. Describe a story. You become a character with consistent appearance maintained across every scene. Here is the honest part. ViMax orchestrates, it does not generate pixels. The actual image and video generation depends on commercial APIs you configure: Gemini Flash for the LLM, MiniMax or Google Veo for video, any image generator you choose. You bring your own API keys and pay those providers directly. It is also early-stage. The TUI and agent loop were just stabilized on June 28. No formal benchmark against Sora or Runway exists. Quality depends heavily on which generation backends you plug in. And it is researcher-grade Python tooling — not a polished consumer app. But the architecture is right. And the research community knows it. The paper was published on arXiv on June 2, 2026. The repo has 10,800 stars in under five weeks. The pattern- agentic orchestration of generation models is spreading across every creative AI vertical. Here is what this means for the future of video. The next jump in AI video quality is not a bigger diffusion model. It is better orchestration. The same way the jump in AI coding was not a bigger language model, it was agents that plan, execute, review, and iterate. ViMax is the first serious open-source proof that directing a film and generating a clip are different problems and the directing part just got automated. A university lab in Hong Kong just open-sourced a film production team. You provide the idea. Four AI agents do everything else. Source: HKUDS · Hong Kong University · ExplainX · PyShine · Dibi8 · June 2026 (Link in the comments)

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  BIGWARZ (@bigwarzeth) reported 8 minutes ago

  @JoshXT message from Alon and i quote : "He needs to login with any other method and then he can connect via GitHub inside the app"

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  Austin Starling (@AustinDotCodes) reported 11 minutes ago

  @github Lol, "we heard you" in regards to a meme, but not the reliability issues?

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  Matt Rickard (@mattrickard) reported 12 minutes ago

  Reversed engineered their eval dataset and put it on GitHub Its a smart and simple idea -- find a recent fixed CVE not in training data, checkout the commit before the fix, run the agent, see if it finds it. Gonna run it on corigin's agent mapreduce and see what happens

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  Damián🦞 (@fagamericano) reported 14 minutes ago

  The top use case for enterprise openclaw deployments is the significant reduction of context switch by employees. When you can ask “what happened with this customer?” and: You get a full triage pulled from logs across different subsystems/microservices “this system incorrectly marked this transaction with this tx code” How it happened in code “line 57 of service/tx.py has a race condition…” Finding other customers with a similar issue “These 10 records were also affected” And suggesting an immediate “switch these codes in the db for the tx to go through” and a durable fix “here’s a PR” All within a few minutes, with full company context, in any model you choose…. It would take an Engineer easily 30mins-50mins to diagnose through new relic, github, gcloud logs, databases, to form a picture of what could’ve happened, vs getting a story to validate in a few minutes…. How we work is truly going to change

• 
  Alex Vu (@robberviet) reported 15 minutes ago

  @github Wasting time making fun of your own company instead of fixing your own problems?

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  High Signal AI (@HighSignal_AI) reported 15 minutes ago

  Satya Nadella on why AI won't replace human ambition, rather, it will amplify it. GitHub Copilot didn't start out as a revolution. It started as a joke. Satya recalls when the product first launched with code completions powered by Codex: "Software engineers are pretty skeptical people like all engineers are, and no one thought that this thing would work and be any good." Then something unexpected happened. "It started working, and the interesting thing is it went from being a joke to being standard issue in like months." Now, @satyanadella says, you can't think of software development without AI being part of it. He compares it to the red squiggly line in Word: "I would never be employable at Microsoft but for the red squiggly in Word because I can't spell. It's kind of becoming like that when it comes to software tools." He pushes back on the dominant narrative that AI replaces human work. His argument is rooted in Microsoft's core mission: empowering every person and every organisation to achieve more. "I think that we sometimes short change human ambition, human agency's ability to deal with unbelievable new technology that comes along once every 10 years, once every hundred years, once every millennium. Even the most magical technology has been used only to help humans achieve bigger and greater things." His point: we keep making the same mistake. Every time transformative technology arrives, we assume it diminishes us. Every time, we're wrong.

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  James Thornton (@espeed) reported 18 minutes ago

  @BrendanFalk Any LLM trained on auto-generated data or bot generated data at scale such as tweets is susceptible to covert embeddings. The solution to the Twitter problem is identify bots or fake accounts and filter them out. The solution to the GitHub problem is an epoch.

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  anonymous086505 (@anonymous086505) reported 18 minutes ago

  @github Literally no one cares. Fix your uptime first

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  Omasiri Udeinya (@Mr_Omasiri) reported 20 minutes ago

  @github @AnthropicAI For long-horizon coding, I would watch patch shape more than benchmark rank. Ten clever commits that do not isolate risk can burn more senior time than a smaller, boring fix.

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  liv (@livchenfig) reported 22 minutes ago

  @zuopiezi bullish on the github issue

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  Hardik Beladiya (@HardikCBeladiya) reported 23 minutes ago

  @CryptooIndia By that logic, go send notices to X, Instagram, Facebook, GitHub, Reddit, Discord, and half the internet. Usernames aren't the problem.

• 
  Waldemar Enns (@WaldemarEnns) reported 26 minutes ago

  @claudeai I really do not get the hype of Claude Tag. Months ago I used a simple GitHub App Integration to be triggered by mentions which hit my openclaw code agent and it used advanced looping techniques to implement festures, triage tickets and fix bugs. Am I missing something here?