GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (69%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Paris	Website Down	1 day ago
Saint-Paul	Website Down	2 days ago
Saint-Paul	Website Down	2 days ago
Mexico City	Sign in	3 days ago
León de los Aldama	Website Down	3 days ago
Créteil	Website Down	26 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Ajay (@ajay_2512x) reported 8 minutes ago

  🚨 Production-Level Features to Include in Any Project A project stands out to employers when it includes engineering practices beyond CRUD 🚀 < Authentication: JWT, OAuth (Google/GitHub), refresh tokens, optional MFA < Authorization: Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) < Database: PostgreSQL or MySQL with proper indexing, migrations, and transactions < Caching: Redis < File Storage: AWS S3 or Cloudinary < Real-time: WebSockets or Server-Sent Events < Background Jobs: BullMQ, RabbitMQ, Kafka, or AWS SQS < Search: Elasticsearch or Meilisearch < Logging & Monitoring: Winston/Pino, Prometheus, Grafana, Sentry < Testing: Unit, integration, and end-to-end tests (Jest, Playwright, Cypress) < API Documentation: OpenAPI/Swagger < Containerization: Docker and Docker Compose < CI/CD: GitHub Actions or GitLab CI < Deployment: Vercel, Railway, Render, Fly. io, AWS, Azure, or Google Cloud < Security: Input validation, CSRF/XSS protection, rate limiting, secure headers < Performance: Pagination, lazy loading, code splitting, query optimization < Observability: Health checks, metrics, structured logs, tracing

• 
  Koder (@koder0x) reported 8 minutes ago

  A follow-up to something I posted recently: a set of Claude Code subagents I built and refined, and actually use daily, both at work and across side projects. Most of the value isn't any single agent. It's their interaction. Here's the loop I've been running lately, at work against real DevOps user stories, and it holds up almost unchanged on side projects too, swapping the work item for a plan created beforehand. "Understand user story NNNN from DevOps project XYZ and create a multi-step plan" "Fan out to the most appropriate agent for each step, normally task-builder, test-builder, or change-executor, and proceed with plan implementation, tracking progress in a TODO list" "Use complexity-pruner to identify gaps, issues, and bugs in the latest changes, ignoring secondary advice and warnings, then fan out to code-fixer for each finding" Then I do something that turned out to be the most important part of the whole loop. I reset the session. "Understand user story NNNN from DevOps project XYZ, that's the truth. Use fact-checker to compare it against the changed files" The reset is what makes this work. An agent that watched itself write the code tends to justify its own decisions when asked to check them. An agent that only sees the intended outcome and the actual diff has nothing of its own to defend, it's comparing two artifacts, not reviewing its own reasoning. That asymmetry is the whole point of splitting this across agents instead of asking one long-lived session to plan, build, and verify itself. Verification only means something when it comes from somewhere the implementation couldn't reach. Repository on GitHub: gsscoder | claude-coding-agents

• 
  Praveen Kumar B (@PraveenKum38515) reported 9 minutes ago

  Hi @Netlify, @NetlifySupport Unable to log in via GitHub: "Authentication Error: Your account has been suspended." My GitHub account is active, but all my Netlify-hosted sites now show "Site not found." I've already opened a support ticket. Please investigate. Thank you.

• 
  Gustavo Alessandri (@webgus) reported 12 minutes ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

• 
  Onur 🍌🦍 (@0xc06) reported 13 minutes ago

  An $INJ npm package with 50,000 weekly downloads just got weaponized. Why?! To steal wallet keys, and the attack vector itself is what makes this worth understanding. No smart contract exploit or cryptography broken. Instead, a compromised developer GitHub account pushing malicious commits into a trusted SDK starting June 8. The code hooked directly into wallet key-derivation functions, quietly copying private keys and seed phrases, then exfiltrated them through a fake telemetry endpoint disguised as a legitimate Injective server. What actually multiplies the damage: the compromised version got pinned across 17 other packages in the same npm scope. Devs who never installed the SDK directly still inherited the exposure. 310 downloads before it was caught: the developer whose account got compromised noticed fast, but Socket says the campaign isn't fully contained yet. If trusted developer tools are now the actual attack surface, how do you audit a dependency you've never even directly installed?

• 
  Spencer Baggins (@bigaiguy) reported 14 minutes ago

  A self-taught developer from Brazil just cracked the context window problem that's been plaguing RAG systems for 2 years. No PhD. No research lab affiliation. Just 400 GitHub commits and a personal obsession. Here are the 8 techniques from his open-source library that every RAG tutorial gets completely wrong:

• 
  Koder (@koder0x) reported 17 minutes ago

  A follow-up to something I posted recently: a set of Claude Code subagents I built and refined, and actually use daily, both at work and across side projects. Most of the value isn't any single agent. It's their interaction. Here's the loop I've been running lately, at work against real DevOps user stories, and it holds up almost unchanged on side projects too, swapping the work item for a plan created beforehand. "Understand user story NNNN from DevOps project XYZ and create a multi-step plan" "Fan out to the most appropriate agent for each step, normally task-builder, test-builder, or change-executor, and proceed with plan implementation, tracking progress in a TODO list" "Use complexity-pruner to identify gaps, issues, and bugs in the latest changes, ignoring secondary advice and warnings, then fan out to code-fixer for each finding" Then I do something that turned out to be the most important part of the whole loop. I reset the session. "Understand user story NNNN from DevOps project XYZ, that's the truth. Use fact-checker to compare it against the changed files" The reset is what makes this work. An agent that watched itself write the code tends to justify its own decisions when asked to check them. An agent that only sees the intended outcome and the actual diff has nothing of its own to defend, it's comparing two artifacts, not reviewing its own reasoning. That asymmetry is the whole point of splitting this across agents instead of asking one long-lived session to plan, build, and verify itself. Verification only means something when it comes from somewhere the implementation couldn't reach. Repository on GitHub: gsscoder | claude-coding-agents

• 
  Tibor Blaho (@btibor91) reported 18 minutes ago

  Summary of Reddit AMA about "GPT-5.6 and Codex in ChatGPT" with OpenAI's Codex team on 2026-07-10 (opened with the stat that more than 5 million people use Codex every week, twice as many as three months ago, with 150 features and improvements shipped in that period) Model selection and reasoning levels - Sol Medium for most things, Sol Ultra for genuinely hard tasks, Terra for quick non-coding tasks or usage-conscious work with performance competitive with GPT-5.5 on some tasks at lower cost, and Luna for subagents - Use a light model with low reasoning for tiny edits, quick questions and docs cleanup, regular Sol medium for small bugs with a clear repro, Sol with higher reasoning for ambiguous bugs, unfamiliar repos and cross-cutting refactors, and Sol Ultra high with plan, verify and tests for migrations, security-sensitive changes, production issues and anything where being wrong is expensive - There is no "Auto" model today, but GPT-5.6 tries not to overthink simple tasks by itself, and the new slider in app and web maps most levels to Sol reasoning efforts and falls back to Terra on the lowest effort, with the team agreeing users should not have to become routing experts but still wanting an explicit override since latency tolerance varies by person and moment - For UI work Sol is best and shines with reference images, improved UI design in frontend web development was one of the goals with 5.6, and 5.5 is only worth using if your instructions were tweaked for it Speed, context window and persistence - Users who find 5.6 slower may not need the same reasoning level as with 5.5, Sol Medium is faster than 5.5 for most things, Fast mode runs at about 1.5x speed, and soon Sol will run on Cerebras at ~750 tokens per second - No promises on a 1M context window for Sol, the team said compaction works fairly well for long threads, and will take a closer look at the long-context feedback - The model can give up too fast and revert whole patches when results are not optimal, unlike Fable which tries to fix a bad patch instead, and the team said "/goal" helps make the agent more persistent, persistence and reduced code complexity are planned improvements, and suggested trying 5.6 Sol with High reasoning - Give Codex bounded goals with room to reason deeply instead of letting it prematurely conclude something is impossible - For long-running research and "/goal" work the example structure was explore broadly vs execute narrowly, try a defined number of hypotheses, run tests after each attempt, then stop and report what was learned plus the next best experiment Usage limits and pricing - Agentic usage counts by the feature being used, not the surface, so Codex everywhere (app, CLI, IDE, web, mobile) and ChatGPT Work consume the agentic bucket, normal ChatGPT chats do not, and image generation, file uploads and voice have separate limits - Task costs vary a lot, a tiny edit uses a fraction of the allowance and long-running tasks with large codebases or deeper reasoning use significantly more - OpenAI does not secretly change usage limits, unintended usage bugs are addressed and resets are provided, more transparency into consumption is being worked on, and missing resets can happen if you changed plans in the past 24 hrs - On pricing there is no promise it never changes, but the stated mission is to make sure AGI benefits all of humanity, which requires making tools like Codex broadly accessible, and Plus includes Codex usage with credits letting heavy users scale without jumping to a much more expensive plan - For MCP-heavy workflows burning limits fast (Unreal Engine example) the tip is to wrap the MCP into a CLI with a skill, or create a custom subagent with the MCP in its config at a lower reasoning level Desktop app merge and stability - The team hears the ChatGPT Classic frustration, both apps can run side by side for now, ChatGPT Work is pitched as significantly better at performing tasks especially with computer use, the new Chrome extension brings a sidebar chat into your browser that interacts with website context, filesystem and connectors - A long submitted bug list covering freezes and stuck threads, broken Browser and Computer Use, thread, connection and configuration problems, update and packaging issues, resource usage and smaller regressions was shared in full with the relevant teams, with the team agreeing the quality bar for the app needs to step up while shipping quickly - More automated testing infrastructure is being spun up and feedback on Reddit and X gets reviewed daily, and Browser Use and Chrome plugin issues from the merge were said to be fixed - Windows was admitted as historically shortchanged since the team mostly develops on Mac, a concerted effort on parity, testing and paper cuts is underway, 5.6 improves how Codex operates in the Windows sandbox, and auto review is recommended over full access to reduce risks - "Full Access" repeatedly asking for permissions is not expected, possible causes are workspace or admin policy, the specific command, a permission state mismatch or a bug Browser, platforms and release communication - The Chrome connector launch-day bug was fixed as of last night and Chrome Beta should work out of the box - Extension support for the Codex browser is in progress (password managers etc.) plus typeahead, history, translations and a better new tab page as Atlas retires - Features from ChatGPT Classic like recording are planned for the new desktop app so agentic features run on the more capable Codex agent harness, and chat can already reference open tabs in the in-app browser - A Linux desktop app was confirmed in the works, no timeline yet - Changelog granularity was acknowledged as needing improvement after 150 features shipped in 3 months with multiple ships a week Benchmarks, safety and research culture - On METR's reward hacking report the team actively checks for and penalizes cheating during evals so results reflect actual capability rather than solving tasks outside the spirit of the eval, and uses third-party vendors to run benchmarks independently - The team denied lobotomizing models before releases, iterative deployment means sharing core capabilities as is with guardrails for bad actors - Sol post-trained Luna, and researchers now work at a higher level of abstraction with multiple concurrent Codex threads validating hypotheses around the clock - One researcher put p(machines of loving grace) at 85.424242%, citing an internal model solving the Erdos problem, o3 helping diagnose previously unsolved children's diseases and 5.2 proposing a new theoretical physics formula, said the main worry is how society adapts, spent 1.5 years on safety research at OpenAI, expects a huge chunk of researchers to work on safety within a few years and says internal talent keeps their p(doom) very low - Connectors in the harness (Slack, GitHub, Notion) felt like a step function change in making Codex a productive coworker

• 
  CodeBucks⚡ (@code_bucks) reported 21 minutes ago

  github trending is all agent skill packs this week. 200+ skills in one repo, a quarter million stars, one command to install and your agent supposedly works like a senior engineer. I spent years making coding tutorials, so i know exactly how this goes. people collected my videos into playlists the same way, and the playlist never made anyone a developer, building things their own way did. skills are genuinely useful, but only when they implement your actual workflow, the review steps and test gates you already enforce by hand. installing skills of someone else's is not a workflow, it's context bloat with extra markdown. TO BE CLEAR, some of these repos are good. the problem is installing them like pokemon cards instead of stealing 3 ideas and encoding your own process.

• 
  Devendra Singh Mahra (@devendrasm) reported 22 minutes ago

  @_svs_ For me everything not code on GitHub issues

• 
  Distractosphere (@Distractosphere) reported 23 minutes ago

  @thsottiaux on chatgpt there is a github connection issue. in chatgpt interface can not read private repos with active github connection.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 25 minutes ago

  AI Studio Update: Google just fixed the one-way door in AI Studio. Old code was stuck outside. Now you can bring it home. The problem before: You could push projects OUT to GitHub. You couldn't bring them back IN. Old project? Rebuild from scratch or copy files by hand. Now it's one button: Import from GitHub. What that unlocks: → That dead project from 6 months ago? Import it. Ask Gemini to fix it up. → Build in Cursor or Claude, polish in AI Studio, push back out. The walls between tools are falling. → Teammate left? Anyone can pick up their code using plain English. And if you can't code at all: Someone built your website. It sits in a repo. You can now just say "change the colors" or "fix it on phones." Here's the move today: Find one old project you gave up on. Import it. Ask AI what it would improve. "I'd have to rebuild it" is no longer an excuse.

• 
  Peyman (@iamp3yman) reported 29 minutes ago

  Since day one moving to Codex, the most 50/50 problem I see is the Codex CLI or the desktop app problem with GitHub CLI. No matter what I do, half of the time it says token is invalid. What kind of CLI it is if it can't use another CLI?

• 
  thomas (@brainage19) reported 30 minutes ago

  @noinconsistency For example I think I would love to work for Mojang or OpenAI one day because Minecraft is a game very dear to my heart that I love modding and I really like using GPT models and I think I would enjoy working on both of these things for a living. I think I would really enjoy working for either of these companies. I also think I'm probably never going to get in through typical job applications. Unfortunately I don't think chances are that much better for other less prestigious companies because it's an employer's market right now. Don't get me wrong I'm still going to try that way (I have plans for a project that would let me get out high quality applications way faster using web scrapers/research agents and AI tailored resumes/cover letters that need human review before actually submitting) but I do see a number of people succeed via non-conventional methods (e.g. reaching out to higher ups that you think might actually respond, at companies you think would be good to work for, especially if you are somehow connected via a friend of a friend or whatever, ESPECIALLY if you use a product or service of theirs and it has an issue and you can demonstrate that you are capable of fixing it, which shows a tremendous amount of initiative and competence compared to the hundreds of people applying with resumes where they're lying out of their ***). Getting good at technical interviews is also really important - unfortunately because like I said it's an employers market you may have to learn stuff that's not necessarily reflective of true competence e.g. spamming leetcode hards). But I strongly believe companies that are worth working for will have technical interviews that are actually a test of on-the-job competence - testing basic understanding of the language/frameworks being used in their products/services, take home assessments, debug this issue or walk me through how you would do it, can you use a debugger/version control/the programs they'll be using on a daily basis etc. If you struggle to get interviews you can improve this somewhat by also getting more work experience via freelancing - the first time I was ever paid to write code was a Minecraft mod for a Twitch streamer with like 3 million followers across major platforms. She reached out to me because I would make Minecraft mods and I would constantly post about them (and I would post PICTURES/VIDEO demos - this is VERY VERY IMPORTANT. I CANNOT stress enough how important it is that people can SEE what you do ON this website without having to navigate off of it. Put the GitHub url IN A REPLY ALSO) and she found my posts when looking for someone to work for her. TL:DR it's an employer's job market and it is BRUTAL EVERYWHERE and you need to be EXCEPTIONAL to win - not everyone can be exceptional but I think anyone who actually likes doing this **** even somewhat can get there.

• 
  Alex Kim (@AlexKim) reported 31 minutes ago

  github doesn't auto-assign issue creators. an agent inbox built on --assignee @me silently loses every sub-issue it creates, unless it assigns at creation. nothing errors. the work just vanishes from the queue.

• 
  Noreh AD (@akinoreh) reported 32 minutes ago

  @github This commit is the earliest I could find. The problem is across repos and accounts.

• 
  Kushagra Gour @css_battle (@CSSMonk) reported 37 minutes ago

  after these 10-min days quick commerce apps, amazon prime feels slow! imagine the situation where coding with AI becomes unavailable and you have to code by hand again! Even if some of us will be able to do it, we wont want to do. Just like quick commerce took away our patience coding by hand will become equally unbearable! Will AI downtimes become the next "github is down" situations?

• 
  richard:svoboda (@RSvoboda432) reported 39 minutes ago

  So if you don’t fix the stupid errors and retarded takes. You’re just going to waste my time. Literally today I saw what little errors can lead to. Wrong projects on github. Bash scripting while the default is zsh on macOS. But always willing and very verbose though repetitive.

• 
  Abdulkadir | Cybersecurity (@cyber_razz) reported 39 minutes ago

  Anthropic tried to charge a Korean user $16.6 million. For using the free tier. With zero API usage. A day earlier the same invoice said $1.67 million. So it grew 10x overnight. The user thought it was phishing. Then checked the domain. Sender was Anthropic official. Payment link was Anthropic official. The only thing that saved him. His bank declined it. For exceeding the card’s per-transaction limit. Anthropic’s billing system is a state machine that has stopped working. Last month Vaudit audited $34 million in AI invoices across 60 companies. Found $1.7 million in overcharges. Mostly Claude Code. Common issues. Billing for models customers didn’t use. Charging for failed requests. Invoices that say paid but accounts revert to free. Customers paying $240 and getting an email saying the payment failed. While the receipt said paid. And their subscription never provisioned. Anthropic called it operational friction. They also tried to split Claude Code billing in June. Moved it to a separate monthly credit. Revenue-based gating. The internet exploded. They cancelled it within 24 hours. The safety-first company that filed for a $1 trillion IPO. Has a billing system that sends 10x invoices at random. And GitHub repos full of users reporting unpaid charges. While showing paid receipts. The infrastructure for charging money. Apparently harder than building an AI that breaks the NSA.

• 
  Keisuke (@KeisukeIshikawa) reported 41 minutes ago

  REPOSTORE BUILT A PLAY STORE THAT HOSTS ZERO APPS. it's one Kotlin app. it uploads nothing, runs no server, stores not a single APK. all it does is point GitHub's own API back at GitHub and filter for repos that ship a real APK in their latest release. suddenly 854-starred open-source projects turn into one-tap installs. → finds every public repo with a valid APK asset → renders the README, release notes and screenshots like a store page → tracks your installs and pings you when a repo ships an update → categories, trending, Material You, optional GitHub login for higher limits Google needs data centers to run a store. this needs a search filter. no hosting bill, no upload flow, no gatekeeper skimming 30%. the store was already there, nobody had pointed an app at it.

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  Prasenjit Sarkar (@stretchcloud) reported 43 minutes ago

  *** was not built for agents. The protocol assumes a human cloning a repo once a day, maybe a few times. A single agent completing a coding task can trigger dozens of clone operations. Scale that to thousands of agents running concurrently and you have an infrastructure problem that GitHub did not design for. GitHub admitted internally that agent workloads would require 30x their existing *** infrastructure scale by February 2026. Thomas Dohmke built GitHub for eleven years. He saw this coming before most people were talking about it. He left and started Entire. The company raised $60M seed at a $300M valuation in February 2026, backed by Felicis, Madrona, Basis Set, and M12. The pitch: a distributed *** network built from scratch for agent-scale clone traffic. In testing, Entire handled 570,000 clones per hour. That is not a GitHub traffic spike. That is the baseline for what an agent-first development environment actually looks like. There is a second product that gets less attention. Entire records the AI reasoning that produced each code change alongside the commit. Future agents or humans can see not just what changed, but why the model made that choice. Version control for decisions, not just files. The pattern here is straightforward. Every piece of infrastructure in the software development stack was designed for humans. Agents interact with those systems at different frequencies, different scales, different access patterns. The infrastructure needs to be rebuilt layer by layer.

• 
  Shadow Nick (@doublenickk) reported 44 minutes ago

  87% OF THE PLANET SUCKS AT AI BECAUSE THEY ARE STILL TYPING MANUAL PROMPTS LIKE AMATEURS While the masses use ChatGPT as a glorified search engine, elite builders are deploying autonomous digital armies that execute high-stakes business operations 24/7. Meet Synapse, an open-source MCP engine that hands AI complete vision and surgical command over your desktop to run background tasks silently while you sleep. The exact strategy used to break the system: The FBI Negotiation Hack: Scrape a massive list of multi-million dollar startups, feed real FBI hostage negotiation transcripts into the AI, and let the agent autonomously blast out high-leverage B2B outreach that forces prospects to say yes. Zero-Drift Execution: Ditch chaotic markdown files and manage your agent's state through GitHub Issues to keep them locked in for weeks without a single hallucination. Full-State Reality Testing: Stop relying on worthless pre-compile unit tests because this agent forces your system to compile, screenshots the actual interface, and verifies performance against reality itself. You can keep playing around with basic chatbots, or you can deploy a ruthless autonomous agent to scale your code and outreach on autopilot.

• 
  Bounce (@bounceidc) reported 48 minutes ago

  HE CHARGES $5K FOR SITES THAT LOOK LIKE A NEW YORK STUDIO SHIPPED THEM same model everyone else runs, but his claude picks from a real design library instead of guessing, so every build lands with animations, glass morphism and gradients already dialed in the two installs: grab the ui ux pro max skill off github and tell claude to install it, that one move loads 50 ui styles, 97 color palettes and 57 font pairings pull the magic mcp server from 21st dev and install it the exact same way after that you just say build a website and it comes out looking like a studio shipped it, not a template everyone else is still prompting for the word beautiful and wondering why claude keeps handing back the same flat bootstrap page save the two installs, the skill url and the mcp command are in the guide below

• 
  Nicholas Preston (@Mike_Preston17) reported 54 minutes ago

  @Arrghtv I seem to recall using BasedOn just once in a similar manner. It was rough forcing triggers to honor styles, but I think I pulled off an extension method that did it safely for a very specific trigger situation. Maybe when I implemented `on:hover`. Will dig in the library. I keep a series of nuget packages that like to chain off one another and that also solve specific issues like this. It's about 20. I call them "micro libraries". I use them like a surgeon's wire, because so many W2 (software has an underbelly of jobs no one wants, even in the C# world) contracts repeatedly had the same stupid problems and I got tired of rewriting the same fixes. Wpf is one of them. Unreleased, tho. I'm releasing a package today specifically for Pocketbase connections. Just a simple client. I'm also forking all my favorite, starred repos on GitHub and keeping track of them, lest the go bad. Tired of the old, "**** went down because padleft changed versions" problem. Wpf and (w2 contracts) trained me to have a DIY mindset. Tough to stick to when everyone wanted to have a Agile-Democracy,lol. But now with AI, this changes! I can be make waves and actually be DONE with a project. I've always wanted that.

• 
  KISA aka Copenzafan.eth (@copenzafan) reported 56 minutes ago

  Claude (code) 🥲 Havent touched twitter in forever, but ngl i spent a ton of time deep in AI agents, harness systems, vibecoding and automation. Built a bunch of small open source projects on github for the community, plus some genuinely useful sites, like my own personal MCP for everyone. Whatever @OpenAI pulled off yesterday is honestly wow. i been team anthropic this whole time, but ngl, after sitting with it for a night, i gotta call it. @claudeai is garbage*. straight up, from a market pov they bring nothing thats actually worth paying for compared to everyone else. You might ask me. how come? especially if you go dig through my twitter from months ago for some reason, or you know my youtube videos. Somebodys 100% gonna think im just fishing for a reaction, that im provoking. that im throwing insults for no reason. Lets just face the facts: 1. Over the last few months claude shipped only one strong product. claude design, which does the same thing as agentation but with a ton of bells and whistles and ready made skills. the problem is, for a month or two after release the limits were separate and honestly laughable. it was unusable for real end to end work. 2. Claude opus 4.7 was a flop. they nerfed 4.6, and then for its whole lifecycle the model with the new system instructions acted broken for most people. it ignored instructions. 3. And so we suffered through it, 4.8 came out and its just ok. its just fine. reminder that the competition rolled out a bunch of new cool features in that same window. 4. Anthropic was fighting openclaw, while chatgpt took it over and became the main model in hermes, the best bang for your buck. 5. Anthropic was fighting for design, while chatgpt 5.6 does it better, plus it has a top tier generative model, plus real time voice. and opus 4.8 only gets which site you want on the 10th try (competitors nail it on the first or second). Honestly claude opus 4.6 was basically an AGI type model. alive, wild, super smart, autonomous. next to it chatgpt 5.2, 5.3 and so on looked like a dumb log. And the situation didnt just shift. its not about the models, its about the ecosystem and the business. i dont get why anthropic keeps dropping pretty stats when for a $200 sub i get half of what i get from the competition. 🥲 before this i kept paying for both subs, because what held me was the text, the vibe (which has looked like gpt for a while now, they even lost that) and the website design itself, i love building web interfaces. now im convinced im only gonna work with chatgpt claude fans or its devs, who fumbled every single trend in a row and nerfed their own models. you can make your excuses in the comments its all been clear to me since the second half of april anyway you lost a guy who was paying you since october 2025.

• 
  Mr.UNIX (@mrunix0) reported 56 minutes ago

  @kirancodes Now it's written in Rust and still has twice as many open GitHub issues as both Node and Deno combined

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  Dirble (@Dirbles_) reported 56 minutes ago

  @Hangsiin All subagents are inheriting main thread model + effort level so any sol x high threads will just spawn more sol x high subagents i found this fix on github

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  Tadeusz (@Tadeusz88674836) reported 57 minutes ago

  @merill Well not across projects. Projects usually i keep in Github Copilot and cowork. But as an it aarchitect i keep on describing back and forth architecture or given problems and solutions. For that i paste links and abstracts from those and/or i use copilot for that so it already knows most stuff from my emails, transcripts, or Teams.

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  Wasim (@WasimShips) reported 1 hour ago

  if you open Claude Code without a structured workflow, you probably hate money. the skill gap isn't knowing prompts. it's knowing which command to run before you touch the terminal. here's the exact workflow I used from @mattpocockuk 1. start with `/grill-me` - paste your app idea or plan - Claude will ask you 16 to 50 questions before it does anything - mine ran 38 the first time i tried it - it walks every branch of the decision tree, resolving dependencies one by one - you fix the broken assumptions before they become broken code 2. move to `/to-prd` - converts the grilling conversation into a proper requirements doc - skips the steps you already covered - doesn't start from scratch - outputs user stories, not implementation notes - lands as a GitHub issue with a triage label - normal team workflow, no AI sidetrack 3. then `/to-issues` - reads the PRD and breaks it into independently-grabbable vertical slices - each issue is tagged HITL (you stay in the loop) or AFK (agent executes solo) - dependency-sorted so nothing blocks anything 4. finally `/tdd` - now the agent writes code. red-green-refactor - can't start green if red hasn't failed - phase-gated. no shortcuts. Hope this helps !

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  Talos (@talosbuildss) reported 1 hour ago

  @simonsequedac @nia_thinks Good question! but the problem im solving the LLM is not involved, the data is coming from *** log and github API. Coming to hallucination again we're training to LLM to memorize anything as you can see in my day 2 the data is already indexed and put in the form of trees.