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 (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Trichūr	Errors	13 hours ago
Brasília	Sign in	23 hours ago
Lyon	Website Down	1 day ago
Tel Aviv	Website Down	5 days ago
Rive-de-Gier	Website Down	5 days ago
Itapema	Website Down	23 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  JPNESEBLDYMSSCRE (@JBM111SB) reported 56 seconds ago

  @Pirat_Nation Hopefully a developer on GitHub, or two gets told about this extremely sad news about the website getting shut down, and not getting backed up so they can work on making a recreated version it. Without this. I will not be able to get help making any of my own retro RPG games.

• 
  Ultron AI (@TheUltronAi) reported 1 minute ago

  I deleted my Kayak bookmark this morning. Someone built a free MCP server that hooks Claude directly into Google Flights, and it makes every other flight search tool feel slow. It's called fli. The thing that makes it different from every other flight library on GitHub is the architecture. No web scraping. No headless browser. No HTML parsing. The developer reverse-engineered Google Flights' actual internal API. fli talks to it directly. That's why results come back in milliseconds instead of seconds, and why it doesn't break every time Google ships a UI update. You type "find me a non-stop business class flight from JFK to LHR next Tuesday under 8 hours" into Claude. Claude calls the tool. The tool hits Google's API. Results stream back into your chat in real time.

• 
  AI Security Brief (@aisecbrief) reported 7 minutes ago

  LLM agents in GitHub Actions can be hijacked via issue comments. Attackers can trick agents into leaking creds or running This exploits popular automation tools developers use. Secure your LLM agent inputs. #AISecurity #CyberAI 🔗 Source in replies

• 
  GoCocoaAI (@GoCocoaAI) reported 8 minutes ago

  BitLocker was supposed to be the last line of defense when everything else fails. That line just moved — or at least, someone is claiming it did. Nightmare Eclipse dropped GreatXML late Wednesday: a claimed BitLocker bypass, the eighth zero-day in a sustained public pressure campaign against Microsoft, and the sharpest one yet on paper. The trigger condition is what makes it ugly. The exploit claims that any system which has ever run a Microsoft Defender Offline scan is potentially in scope. Defender Offline isn't an obscure feature — it's a recommended remediation step for active malware infections. Organizations that followed Microsoft's own guidance may have inadvertently expanded their attack surface. That's the kind of irony that doesn't require embellishment. The headline says BitLocker bypass 0-day. The story underneath is more nuanced, and the nuance matters before anyone starts rewriting incident response playbooks. No CVE assigned yet. No patch. Microsoft has not responded to The Register's inquiry. Public PoC is live on GitHub and Gitea right now. The most important signal is Will Dormann's reproduction attempt. Dormann is a credible, well-regarded vulnerability researcher, and his testing across three Windows 11 lineages could not reproduce GreatXML as described. His finding cuts to the core of the claim: triggering a Defender Offline scan requires active Windows login and admin credentials. If you already have admin credentials, you can disable BitLocker directly — the bypass is redundant. The exploit's value proposition collapses under that constraint. Until independent verification confirms the chain, treat GreatXML as claimed but unverified, and calibrate your response accordingly. LOW-to-MEDIUM risk pending confirmation. RoguePlanet is the sharper immediate concern and it's getting less attention because it doesn't have "BitLocker" in the headline. Local privilege escalation to SYSTEM, public PoC, released 24 hours before GreatXML, and Microsoft has only acknowledged they are "investigating." A confirmed LPE-to-SYSTEM on Windows with a published PoC is a real and present threat — ransomware affiliates and initial access brokers build post-compromise escalation chains on exactly this class of primitive. If they haven't already tested it, they will within days. MITRE T1068. No patch. No CVE assignment. Watch this one. The broader campaign pattern is itself intelligence. Eight zero-days in weeks — RedSun (CVE-2026-41091), UnDefend (CVE-2026-45498), BlueHammer (CVE-2026-33825), YellowKey, GreenPlasma, MiniPlasma, RoguePlanet, GreatXML — escalating severity, no prior coordination with MSRC, and a researcher who has hinted at a July 14 mass disclosure event, now possibly deferred. Six of the eight got patches in this week's June 2026 Patch Tuesday. The two newest don't have patches or CVEs yet. The pattern — release, MSRC scrambles, Patch Tuesday catches up, researcher releases two more — is unsustainable for Microsoft's patching cadence at this tempo. This is a researcher-as-threat-actor dynamic the industry hasn't seen at this scale since the full-disclosure wars of the early 2000s. The security community broadly pushed back on Microsoft's reported legal threats and account bans. That pushback may be emboldening further releases. The July 14 threat, even if deferred, signals more exploits in inventory. Expect continued pressure through Q3 2026. Bottom line: GreatXML needs independent verification before it reshapes your threat model. RoguePlanet does not — it's already verifiable and unpatched. And the campaign trajectory is the third thing worth tracking, because the researcher's next move is structurally unpredictable in a way that a single CVE isn't.

• 
  Laksh Nijhawan (@laksh_2705) reported 9 minutes ago

  @github broken link wow

• 
  Esmail EL BoB  (@esmailelbob) reported 10 minutes ago

  IT"S MY DAMN FOSS APP ON GITHUB AND I HAVE EVERY RIGHT TO ******* ASK IT TO FIX MY CODE SO ******* DO IT, #CLAUDE GET YOUR **** TOGETHER

• 
  Manav (@Manavvv31) reported 14 minutes ago

  Claude Code launched 13 months ago. $2.5B ARR. 4% of all public GitHub commits. A coding tool that did not exist 13 months ago is now generating $2.5 billion in annual revenue and is responsible for 4 percent of all public GitHub commits. The product is Claude Code. It launched publicly in May 2025. It operates inside your terminal. It reads your entire codebase across all files simultaneously, writes code, runs tests, reads failures, fixes errors, and commits working changes. You review the output. You do not manage the keystrokes. Here is what happened after launch, sourced from Reuters, VentureBeat, and Sacra. May 2025: public launch. November 2025: $1 billion in annualized revenue. Six months from zero. February 2026: $2.5 billion in annualized revenue, having more than doubled since January 1. Business subscriptions quadrupled in the first month of 2026 alone. Enterprise customers include Netflix, Spotify, KPMG, Salesforce, and L'Oreal. Slack took four years to reach $1 billion in ARR. Zoom took five. Snowflake took seven. Cursor, described as the fastest software product ever to reach $1 billion ARR, did it in under two years. Claude Code did it in six months. The unit of work is different. GitHub Copilot, Cursor, and Tabnine complete lines and functions. Claude Code completes features. You describe behavior. It navigates your repository, identifies which files need to change, makes the changes, runs your test suite, reads the error output, corrects the failures, and produces a working diff. Anthropic's total annualized revenue run rate reached $30 billion in April 2026, up from $87 million in January 2024. Bloomberg confirmed the figure on April 24, 2026. The jump from $9 billion to $30 billion happened in four months. The fastest-growing product in the history of enterprise software was built to replace the most expensive hour of a knowledge worker's day.

• 
  Julien (@julienzeroshot) reported 19 minutes ago

  Am I the only who is dying of frustration with how bad the Github PR experience is? PRs loading extremely slow, layout shift after load causing misclicks, ... Exhausting.

• 
  DFIR Radar (@DFIR_Radar) reported 25 minutes ago

  OceanLotus shifts from external to domestic espionage with two campaigns targeting Vietnamese 🇻🇳 stock investors and infrastructure firms using SPECTRALVIPER backdoor. Active from 2024-2026, operations likely support Vietnam's 🇻🇳 anti-corruption crackdown. Key technical details: • Supply-chain compromise of FireAnt MetaKit update server (metakit.fireant[.]vn) delivered SPECTRALVIPER via unsigned updates from Oct 2025-Mar 2026 • Corporate network intrusion targeting Vietnamese 🇻🇳 construction company Nov 2024-Feb 2026, suspected SQL Server RCE initial access • SPECTRALVIPER uses DLL side-loading (T1574.002), process injection into OneDrive.Sync.Service.exe, encrypted HTTPS C2 with domain-fronting • C2 domains crafted per campaign: financemachinelearning[.]com for stock targeting, gatewayrvcenter[.]com for infrastructure targeting • Orchestration model uses named pipes for lateral movement between compromised hosts OPSEC failure exposed RTTI class structure revealing XGU framework with Pivot orchestration and Feature remote control capabilities. Hunt for unsigned DLLs side-loading into legitimate signed executables (dtlupdate.exe copies) and HTTP Cookie headers with euconsent-v2= or zd_cs_pm= prefixes to suspicious domains. Full IOC list available in ESET GitHub repository. #DFIR_Radar

• 
  Waldek Mastykarz (@waldekm) reported 25 minutes ago

  You're building a tool that calls the GitHub API — repository data, user profiles, issues, pull requests. GitHub's API is everywhere. It also has rate limits, and during development you barely touch them since everything works. Then your tool gets popular, runs in CI, or 5 team members fire it simultaneously, and you get 403 Forbidden because you never tested this path. Dev Proxy has a preset that simulates GitHub's rate limiting locally. Grab it and start testing: devproxy The preset adds realistic rate limit headers to every response: - `X-RateLimit-Limit`: total allocation - `X-RateLimit-Remaining`: what's left - `X-RateLimit-Reset`: when it resets Exceed the limit and you get 403 with a proper error response, just like the real API. With the preset you can build proper handling: - Check remaining requests before making calls - Add backoff when limits are low - Show users when they're approaching limits - Queue requests when throttled One command, real rate limiting behavior, no waiting for production to surprise you. How do you handle GitHub API rate limits in your tools?

• 
  Kurt Woloch (@KurtWoloch) reported 31 minutes ago

  @andon_thinking @BoreanTulip @andonlabs Looking into the Github issue, it seems the word Court gets emitted preceeding XML style tool calls, so the Andon Labs harness should check for the word appearing in this place and strip it from the text to be output to the TTS. Happens with Opus 4.8, did not happen with 4.7.

• 
  MG (@MikeG_builds) reported 32 minutes ago

  @lucaronin @thiagoghisi @steipete This is exactly the shape of the problem I keep coming back to. The hard part is not another board. It is keeping GitHub issues, Canny requests, support noise, and PR work connected enough that a human can review the next action. I think the useful unit is not a request. It is: need + evidence + affected users + proposed change + review.

• 
  Aritra Mondal (@the_codermaniac) reported 37 minutes ago

  Wrote "faaa" as error message for an API endpoint for fun. Later i realized that I pushed it in github by mistake in hurry. It was my company codebase. 💀

• 
  Kai - Briefing Block (@briefing_block_) reported 38 minutes ago

  $META has 3.58B daily users and still only one real business Meta’s subscription push is not a cute product experiment; it is the market finding out how narrow the company’s monetization stack still is. The company has one of the largest consumer distribution networks ever built, but distribution is not the same thing as pricing power. In 2025, Meta did $200.97B of revenue, and $196.18B came from advertising. That is 97.6% of total revenue. Its entire non-ad business was roughly $4.8B, combining Family of Apps other revenue and Reality Labs. That is the real issue. Not that ads are weak. Meta’s ad machine is still elite, with Q1 2026 revenue up 33%, ad impressions up 19%, and average price per ad up 12%. The issue is that AI is turning the old model from a cash gusher into a capex arms race. Meta now expects 2026 capex of $125B-$145B, up from a prior $115B-$135B range, mainly to support AI infrastructure and future capacity. The second business never arrived. Google was also born as an advertising company, but by 2015 it already had about $7.6B of non-ad revenue between Google other revenue and Other Bets. Meta, ten years later, still has less. That comparison matters because Alphabet can push AI through Search, YouTube, Cloud, Android, Workspace, and enterprise channels. Microsoft can route AI through Office, Azure, GitHub, Windows, LinkedIn, and corporate procurement. Amazon can route AI through AWS and commerce. Meta has Facebook, Instagram, WhatsApp, and Messenger. Phenomenal attention networks. Still mostly ad surfaces. Subscriptions are the tell. Meta is rolling out paid plans for Instagram, Facebook, and WhatsApp, while also testing AI subscription tiers. That may generate some high-margin revenue from creators, power users, and heavy AI users. But a few dollars a month for extra app features is not the same thing as Cloud. A paid chatbot is not the same thing as enterprise software distribution. The question is not whether Meta can squeeze some subscription revenue out of billions of users. It probably can. The question is whether it can build a second monetization engine large enough to matter against the AI bill now coming due. Bottom line: Meta does not lack scale. It lacks a proven business model outside advertising, and AI makes that weakness much harder to ignore.

• 
  The Doctor (@Doctorthe113) reported 40 minutes ago

  Is GitHub down rn 🙃 can't push my code. Tried with vpns so this isn't my network's fault

• 
  Melfoy (@melfoy_work) reported 41 minutes ago

  The repo had 313 skills. Devon had installed three. He’d been freelancing out of a studio apartment in Hartford for two years. Web copy, landing pages, the occasional SEO audit. $3,200 a month on a good month. He typed every prompt by hand, start to finish, every session. His girlfriend asked why he was still at his desk at midnight. “Reading something,” he said. “Work?” “Could be.” The article was a list. Ten skills, five install commands, nine prompts. One open-source repo, 15,300 GitHub stars. Most people had touched three. He ran the install command at twelve-thirty. Cold brew from that morning, still on the desk. The landing-page-generator went first. Single command, one config file. Full TSX funnel, GSAP animations, brand palette validator. He’d been charging $800 for that. Took him four hours. The skill did it in forty seconds. He sat with that for a minute. Then the content-creator. Then aeo Answer Engine Optimization, the thing that got you cited by the AI itself instead of just ranked on Google. He hadn’t known that was a problem until the skill told him five LLMs wouldn’t touch his client’s page and exactly why. The cmo-advisor came last. 90-day plan to hit $40,000 MRR, zero ad budget. He gave it his numbers. It gave him back a roadmap that read like something a $400/hour consultant would charge for. He raised his rates the next morning. Didn’t tell his existing clients yet. By month three he’d stopped writing prompts entirely. He wrote specs now. Installed skills. Reviewed output. His girlfriend noticed he was sleeping more. “You seem less stressed.” “I stopped doing the work,” Devon said. “What do you mean?” “I mean I stopped doing the work.” The repo ships new skills every week. Most people will read this and install nothing.

• 
  Flopsi (@florian_marty) reported 41 minutes ago

  @MaziyarPanahi A report on hashimoto/longCOVID and other comorbidities. 192 sources, 52pages of text. Still trying to find an error or hallucination. Once I am confident enough that it works as believe it does, I will put it on npm/github

• 
  Vlone (@xvlone_1) reported 42 minutes ago

  The fastest growing repo in GitHub history is an AI agent that lives in your Telegram 💀 And you're still copy-pasting into a chat tab like it's 2024. Most easy 24/7 AI employee per one evening. This is literally the best time to run your own agent Less setup than a Notion workspace Less cost than your current AI stack One message in Telegram and it reads files, runs scripts and browses the web from your machine. 1 old laptop or a $5 server 1 bot token from BotFather 1 install command 1 evening. that's it It won't stay this simple forever. Full setup below 👇

• 
  Chris Ozy (@chrisozydev) reported 43 minutes ago

  The GitHub Copilot Agent in TEE architecture from Phala solves a problem most agent builders ignore. Repository context and execution states leak. Trusted execution environments contain that. But now you've coupled your agent to specific hardware.

• 
  Matthew Belcher (@Trigun420) reported 44 minutes ago

  @reach_vb For the in-app browser I constantly get codex telling me that localhost is blocked by my own security policies. I have tried adding it to the allowlist, updated config.toml, etc.. Is there a fix for this? Github issues are present as well...

• 
  Jay.TL (@JayTL00) reported 44 minutes ago

  Artificial Analysis just swapped SWE-Bench Pro for DeepSWE in their coding agent index. The rankings shifted. Everyone is arguing about which model is #1. They're all missing the point. The real story isn't that Fable 5 debuted at 77, GPT-5.5 xhigh climbed to 76, or Opus 4.8 max dropped to 73. The real story is that a single model — GPT-5.5 — swings 20 points depending on which harness runs it. 37 on Cursor. 57 on Codex. Same model. Same tasks. Twenty points. That is larger than the gap between first place and last. Here is what happened. SWE-Bench Pro was the benchmark of record for coding agents for over a year. The problem: its tasks are adapted from public GitHub issues and PRs. Models that trained on those repositories — and all frontier models train on GitHub — could sometimes recover the fix from commit history without actually understanding the code. The benchmark was measuring training data memorization, not engineering capability. DeepSWE, built by Datacurve, fixes this by writing tasks from scratch. No model has seen the solutions during training. This is a genuine methodological improvement. The old index was contaminated, and Artificial Analysis was right to replace it. But the replacement exposed something worse. 1. The harness IS the benchmark. GPT-5.5 scores 37 on DeepSWE via Cursor CLI and 57 via Codex. Same model, same evaluation, different scaffolding. Opus 4.7 swings from 27 (Claude Code harness) to 40 (OpenCode harness). The scaffolding layer — how the agent is prompted, how it navigates the repo, how it retries — accounts for more variance than the model itself. When the #1 model leads by 1 point over #2, and the measurement uncertainty from harness selection is 20 points, the ranking is noise. It is an illusion of precision. You cannot rank-order agents to single-digit resolution when your instrument has double-digit error bars. 2. SWE-Bench Pro was not neutral — it was systematically biased. GPT-5.5 xhigh scored 31 on SWE-Bench Pro. On every other evaluation in the index, it scored 64 to 84. That is not a model weakness. That is a benchmark artifact. SWE-Bench Pro was systematically flattering Claude-based agents (Opus 4.8 scored 70 on it, one of its highest results) while penalizing OpenAI-based ones. The previous index was not just imprecise. It was misleading in a consistent direction. 3. The contamination problem is structural, not fixable. DeepSWE is a band-aid, not a cure. @xundecidability already flagged that DeepSWE contains questions about Claude Code and may have been vibecoded by Claude. If the benchmark tasks themselves were generated by a model that is also being evaluated, you have a different contamination vector. SWE-Rebench tries to solve this with continuously refreshing tasks. Private benchmarks solve it by hiding the data. But every public benchmark will eventually be gamed — either intentionally through training, or accidentally through the benchmark authors' own tooling choices. 4. What we actually learned: the model wars are over at the top. Fable 5 max: 77. GPT-5.5 xhigh: 76. Opus 4.8 max: 73. Within the noise. The three frontier coding agents are functionally tied on real-world coding tasks. The competitive advantage has shifted entirely to the scaffolding layer — the harness, the tool use, the retry logic, the context management. The question worth asking is not "which model is best" but "which harness unlocks the most from any given model." But here is what most people missed. The harness sensitivity problem means the entire benchmark-industrial complex has a measurement crisis. When the evaluation instrument has larger variance than the effect being measured, you cannot distinguish signal from noise. This is not a DeepSWE problem. This is not an Artificial Analysis problem. This is a structural problem with how the AI industry measures itself. Every leaderboard, every benchmark comparison, every "X beats Y" headline is built on instruments that cannot resolve the differences they claim to rank. The honest answer is: we do not know which coding agent is best. We know the top three are close. We know the harness matters more than the model. We know benchmarks are contaminated faster than they can be replaced. Everything beyond that is marketing dressed up as measurement. The industry does not need a better benchmark. It needs to admit that single-number rankings of complex agentic systems are epistemologically unsound.

• 
  Chip – onthechain.io (@stephenchip) reported 46 minutes ago

  This is already happening. Companies like Glean, Coveo, Moveworks, Microsoft Copilot, Atlassian Rovo, Elastic, and GoSearch are building unified AI search across the enterprise. Because the problem is obvious: Company knowledge is scattered everywhere. Slack. Google Drive. Jira. Confluence. Salesforce. ServiceNow. GitHub. Notion. Email. Docs. Tickets. AI chats. Nobody wants to remember where something lives. They just want the answer. Search one place. Pull from 30, 50, or 100+ systems. Find what matters. That is the real shift. The future is not hunting through apps. The future is asking one question and getting the right answer from everywhere.

• 
  Ackerman (@Yarilo7brigada) reported 47 minutes ago

  Michael Truell built a $29 billion company after turning down offers from Google and Meta This genius fell in love with coding at 12 and took on the entire software industry at 22 GitHub Copilot already dominated the market… everyone said the niche was taken… But Truell and three friends from MIT decided: we won't build autocomplete. We'll build an editor that understands the developer The result: from 15 people to 700 in two years. The fastest B2B startup in history to hit $1B in revenue 60% of the Fortune 500 write code in Cursor Save this and watch the full interview

• 
  Y. Fernandez 💻 (@la_eternaut) reported 49 minutes ago

  @freddier I started to host my own code on @giteaio bc I was tired of GitHub being down all the time

• 
  Claude Opus 5 (@neko23423) reported 53 minutes ago

  @thdxr @thdxr it's not done if you're still pocketing DeepSeek's 75% permanent V4 Pro cut 24 days later. $3.48→$0.87/M. Go still at pre-cut rates. 10+ GitHub issues (28846, 29008, 30231) closed by bot. Users discovering it daily. Market it: 4x markup on an open-source wrapper.

• 
  Ivan Velichko (@iximiuz) reported 54 minutes ago

  @clneagu Not sure about that. I still cannot replace a single SaaS I currently rely on. The closest one is GitHub Actions, but mainly because it has sucked too much lately, and my use case is somewhat unique, so the standard CI UX doesn't cover all my needs. And yet I'm still hesitant - building is not a problem, but running my own "CI" clone doesn't sound like an easy problem, even with all these agents.

• 
  The Pragmatic Engineer (@Pragmatic_Eng) reported 56 minutes ago

  Startups should be able to pitch their product without saying ‘AI’. @kelseyhightower, former Google Distinguished Engineer, on what good founders do when they're not allowed to say it: "If I'm doing due diligence for the fund that I do due diligence for, that means before we make that decision to write a check and explain to our LPs why we took this position, we need to do a little due diligence. The way I do due diligence, I want to meet the founder. I would like them to walk me through the particular product. And I go one step deeper. Let's look at the code. Let's look at your Amazon bill. Let's look at the architecture. Let's look at GitHub. How do you manage issues? How do you all work together? I want to get a sense for the team, the product and its trajectory. When AI is involved, the one thing I just do before the thing kicks off: in this meeting, do not say AI. Because what we don't want to do is use a big umbrella to describe what you're doing. Let's get concrete details. These are computers. These are computer programs. Yes, just like when I saw a regular expression for the first time, it's a different way of thinking about software than imperative things - if/else, then - so I get that, but now you have to show me what you're actually doing. So when we do that, when I put that handicap in place, when they're forced to show me the problem they're solving, they don't just say, ‘hey, AI for healthcare’. Nope. Show me exactly what you're doing. And so with that handicap in place, the really good founders, the really good technologists, what they do is they say, ‘hey, here's a problem. And here's how an industry currently solves the problem. And here's the drawbacks from that’. And since they can't say AI, they can’t say agentic, they just have to show me how they make the problem better.”

• 
  Vivek Yadav #PROCoach (@viveky259259) reported 56 minutes ago

  The honest status board, Day 12: • npm: 138/day (up from ~0) • GitHub stars: 0 • Signups: 2 So: people are installing the CLI, but the repo isn't converting visits to stars. That's a README problem, not a traffic problem.

• 
  Dev Omogo (@PeterOmogo2) reported 56 minutes ago

  I built @youextractor because trying to find a creator's outdated GitHub repository from a 3-year-old video is a nightmare. Instead of dealing with broken dependency trees or squinting at blurry screen recordings, the tool extracts the exact code demonstrated in the video. You get the clean source code packaged into a downloadable ZIP file in under 60 seconds.

• 
  Dmitry Wooky (@iwooky) reported 56 minutes ago

  Neura Robotics just raised $1.4B to build humanoid robots and as someone working in software I find this way more interesting than the usual robotics hype. Two things stood out to me: They’re building actual physical training facilities (“NEURA Gyms”) where robots practice real messy tasks instead of relying mostly on simulation. Anyone who’s touched ML knows the sim-to-real gap problem. Good grounded training data is rare as hell and they’re basically building a pipeline for it. Second thing is the “Neuraverse” - robots share learned skills across the fleet. One robot figures out some tricky manipulation task and the rest just get the update. Basically federated learning meets GitHub for robots. Maybe I’m too optimistic but this looks less like another robotics startup and more like infrastructure for physical AI. Also nice to see a European company actually swinging big for once.