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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  farxxxxx (@farxxxxx1) reported 1 minute ago

  $29/month x 500 users = $14,500/month. That's the ceiling on a TikTok analytics tool I built from a free n8n scraper and one Claude Code session. > The product: type in a TikTok channel name, get back the hook structure, CTAs, and pacing behind their last 5 viral videos. Backend runs on n8n. Apify scrapes the channel. Gemini breaks down each video - hook type, structure, call to action. Data flows back through a webhook. The SaaS wrapper came from Claude Code. One PRD file, claude.md, holding the architecture, data schema, and stack. Next.js 14 for frontend and backend. Supabase for auth and database. Vercel for hosting. Webhook handles the connection. n8n sends results back through an HTTP Request node instead of the standard webhook response, so long AI processing never times out. Local testing ran through ngrok - fake public URL, real webhook calls, real bugs to fix. Every SQL error and mapping issue got fed straight back into Claude Code until it worked. UI came from a v0App design, rebuilt by Claude Code in under 10 minutes. Push to GitHub, connect Vercel, deploy. Swap localhost for the real domain in Supabase auth settings. Wire up Stripe. One session. One free workflow. A product that could charge real users real money.

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  BIGWARZ (@bigwarzeth) reported 2 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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  Ege (@is_ege_) reported 5 minutes ago

  @github @luciascarlet fix your ******* platform dude

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  SB (@swbitxches) reported 10 minutes ago

  @github FIX UR WEB APP. Cringe

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  Elias (@iam_elias1) reported 10 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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  Ameet Madan (@ameetm_) reported 10 minutes ago

  The enemy isn't the tool. It's the attention-harvesting design inside it. Slack isn't the problem. Slack with every notification on is. GitHub isn't the problem. 40 open tabs is. Remove what's built to grab you — not just what wastes time.

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  Michael Liam (@Millionareum) reported 11 minutes ago

  I JUST FOUND SOMETHING THAT SHOULD BE VERY EXPENSIVE Running a company with zero employees. Here's what makes this possible: Paperclip. It's a 100% open source project on GitHub, with over 70,000 stars. I'm not talking about triggering a single model. You hire a CEO, you hire engineers, and you also hire a QA supervisor. Each worker is an artificial intelligence agent, and Paperclip is the Node that keeps them compatible.js and React control plane. Stop dealing with disorganized systems and build a living organization: - Establish a CEO agent for strategy. Hire engineers and designers through Claude or Codex. - Set up an automated QA cycle before any ticket is closed. Manage the entire portfolio from your phone. Do you know what you do when an agent makes a mistake? You're not rewriting the entire pipeline. You're just refining the persona instructions, like coaching a junior employee. This is exactly the kind of tool this field needs right now. Free, open source, can be hosted on your own server.

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  Melfoy (@melfoy_work) reported 15 minutes ago

  Fable 5 runs for 11 days. One builder used it to write 3 files. The files still run. The model is gone. Marcus, 38, warehouse supervisor in Dayton. Kids in school, mortgage, $19/hour. Spent a Sunday building a spreadsheet factory instead of watching the game. He used Fable once - architect role only. It built the product, then he made it write down how it did it. One skill file. Committed to GitHub. Switched to Haiku. Ran the same build. Cents. His wife asked why he was still at the laptop at midnight. «Building something.» «Another one of those things?» By month two: 20 listings. $600 a month. Haiku running while he slept. By month four: $2,000. Approvals take 10 minutes a day. Fable is gone now. The 3 files are still in the repo. The brain was rentable. The playbook is his.

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  π (@maswadkar) reported 15 minutes ago

  dear @OpenAIDevs why do we not have gpt5.5-pro model under codex. (gpt5.5-pro is the best model for planning and github issue creation) Then I will never have to leave the codex app

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  Mvykool (@mvyk0l) reported 17 minutes ago

  @satyanadella Can yall just ******* fix Windows or GitHub????

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  Parth Jadhav (@ParthJadhav8) reported 18 minutes ago

  @free_duino Would really recommend to create a issue on GitHub with the data. It would be helpful

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  Jacky Chen (@hatespiecharts) reported 19 minutes ago

  No Fable, no problem. The move is not finding one magic model. The move is building a routing system. Drop what you are trying to build below. I will reply with the public GitHub repo for my exact agent swarm setup.

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  Ashish Sheth (@commanderdgr8) reported 20 minutes ago

  Never ignore any broken window in your code. Yesterday I didn't have time to build a full feature into VapuAI, so I did something smaller that probably mattered more. I fixed 12 bugs. Six were in the actual functionality issues. The other six were the boring kind. Broken test cases, CI pipeline issues, the infrastructure stuff no user will ever see. There's an old idea in software called the broken windows theory. It comes from a thing about neighborhoods, that one broken window left unfixed sends a quiet signal that nobody's watching, and slowly more windows get broken. Applied to code, it means about the same. One small broken thing you decide to live with makes the next one easier to ignore, and the mess spreads from there. So I have one rule when I build with AI. Never leave anything broken. Even if it's minor. Even if it's low priority. The moment I know about a bug, it either gets fixed now or create a github issue so that I can fix it later. Nothing is allowed to rot just like that. There is one bug worth paying attention to. Two of those bugs were permission issues in Claude Code. When it went to write or update a file, it got blocked due to a bug in the hooks. It wasn't blocking me in anyway. Claude Code knew how to worked around it without complaining. It would try the normal way, hit the wall, then find another route to get the file written. From where I was sitting, everything looked fine. So nothing was broken on the surface. The feature worked. The files got written. But underneath, every one of those writes was costing me extra tokens, because the AI was doing the job twice. And a workaround like that can open a security hole I hadn't thought through. And I think newer builders miss this when they code with AI. The AI is helpful. When it hits a problem, it often just routes around it and keeps going. It doesn't stop and wave a flag. So the broken window doesn't even look broken. It shows up later as slightly higher costs, or a small risk, or a weird piece of code nobody questions. None of my 12 bugs were blocking. I could have shipped features and ignored all of them. But small broken things don't stay in their corner. They creep into other parts of the code, or into the CI, and cause something later you can't trace back or predict. When AI is writing the code, nothing is low priority. Do not let any bugs keep lurking around. Never leave any broken window unfixed.

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  pegboard (@pegboard696969) reported 20 minutes ago

  @github Instead of trolling maybe fix your **** website? Dont you think that would be a better use of everyones time

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  One&OnlyAarav (@WaterAarav) reported 20 minutes ago

  Claude = coding. ($20/mo) Shypmenta = deploys, connects, and manages every platform below. Basically your Cursor for shipping.($6/yr) 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. Building has genuinely never been this affordable, and rarely this effortless either.