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
Créteil	Website Down	1 day ago
Trichūr	Errors	5 days ago
Brasília	Sign in	5 days ago
Lyon	Website Down	5 days ago
Tel Aviv	Website Down	9 days ago
Rive-de-Gier	Website Down	9 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  PhishCore (@PhishCore) reported 4 minutes ago

  How to set one up without being technical: Step 1: Go to Hetzner[.]com or DigitalOcean, create an account. rent the cheapest server. pick Ubuntu. takes 5 minutes. step 2: go to github[.]com/wg-easy/wg-easy. follow the install instructions. it has a visual interface. no coding required. step 3: download the WireGuard on your laptop. it's free. import the config file the server gives you. one button. step 4: turn it on. that's it. you now have a VPN that: — costs $5 a month — belongs only to you — isn't on any government blocklist — doesn't log your activity unless you set it up to

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  Ravi (@ravikumrz) reported 8 minutes ago

  @FrostbytHitsuG @JunaidAckroyd sign in flow does not work, gmail and github don't , tried upgrading to pricing, that too didn't work, is the product only a ui for now.

• 
  Rohit (@rohit4verse) reported 8 minutes ago

  In 2026, harness engineering is the difference between an agent that ships and one that burns tokens. If you want to learn it, someone just dropped the one-stop resource on GitHub, free. 12 lectures, 6 projects, 14 languages, 8k+ stars. Inside: - 12 lectures, each on one hard question: why strong models still fail, why agents call "done" too early. - 6 projects where you build a real Electron app, your harness getting sharper with each one. - Copy-ready templates (AGENTS.md, feature_list.json, init[.]sh) you drop into your own repo today. - A harness-creator skill that scaffolds a production harness in minutes. It even opens with Anthropic's experiment: the same model going from a broken build to one that ships, on harness changes alone. Codez wrote the 14-step map. This repo is the course underneath it.

• 
  deci (@realdecimalist) reported 11 minutes ago

  no cardano isn’t cursed. its just struggling with the same brutal reality most smaller ecosystems face.. low liquidity, low activity, and a chicken-and-egg problem for new protocols. taptools pausing/winding down operations is legitimately bad news and i’m not going to try to sugarcoat that ****. it was THE go-to analytics and portfolio tool for cardano defi, serving over a million users. reasons were leadership exodus (multiple co-founders, CTO, COO, etc.) + unsustainable operating costs in a low-activity ecosystem. @IOHK_Charles even warned publicly that more projects would collapse this year due to the tough market and governance challenges. this is a symptom, not the root cause. the bitter pill to swallow: defi tvl: $96 million (down recently). top protocols like minswap, liqwid, etc. make up most of it. solana by comparison: ~$4.9 billion tvl roughly 50x higher. daily active addresses are hovering around 12k–28k recently (spikes happen, but baseline is modest). dex volume is obscenely low (~$2.3M in 24h). chain fees: tiny (~$1.3k in 24h). cardano has strong fundamentals, high staking ratio (often 58-70%), solid security, formal verification roots, and decent github activity. but on-chain usage and capital deployment are nowhere near the hyped chains. alot of holders are long-term stakers rather than active defi degens. why is adoption so low? cardano’s “research-first, slow and steady” philosophy worked great for building a secure base layer, but it hurt in the speed game. competitors (solana, base, newer L2s) iterate faster, have better dev UX for many, and attract more hype/marketing/liquidity. low tvl creates a vicious cycle.. new protocols have trouble bootstrapping liquidity and users. governance friction (recent failed votes on funding, like the summit). broader crypto market has been tough. attention flows to memes, high-velocity trading, and chains with explosive narratives. is it hopeless? not necessarily. cardano has survived multiple cycles with a dedicated (often frustrated) community and patient capital. real-world use cases like parametric insurance, RWAs, or enterprise stuff could be where it differentiates long-term, rather than trying to out-meme solana. but the ecosystem does need more activity to support dApps. taptools dying is a warning shot that cardano businesses are under pressure. most defi protocols die or stay tiny. the ones that succeed usually do it in bull markets with strong product-market fit & distribution. cardano isn’t dead or cursed. it’s just not winning the current adoption race. hang in there. building real stuff on any chain is hard as ****.

• 
  Will (@Willexeyy) reported 12 minutes ago

  @0xkozue i check if the problem resonates with real users and solves pain points. shipping prototypes and gathering feedback help me adjust quickly. on github i release minimal features to test interest. how do you decide if you're on the right track?

• 
  Jay.TL (@JayTL00) reported 15 minutes ago

  Microsoft just admitted the economics of unlimited AI don't work. Their fix? A Chinese open-source model. Axios reports Microsoft is exploring an Azure-hosted DeepSeek V4 as a cheaper backend for Copilot Cowork. The company has already fine-tuned the model. Final decision is pending, but the direction is clear. The reason, from Microsoft's Charles Lamanna, is brutally honest: "We have users who do hundreds of tasks a week... the consequence is the costs can go very high." Read that twice. The problem isn't adoption. Adoption is the problem. Here's what's actually happening across Microsoft's AI stack: 1. GitHub Copilot flipped to token-based billing on June 1. Flat subscriptions are dead. Power users now pay per-token at API rates. Developers called it "a joke." 2. Microsoft internally cancelled Claude Code licenses for thousands of its own engineers — too popular, too expensive. The company with a $13B OpenAI stake watched its devs pick a competitor. 3. Now Copilot Cowork moves to usage-based pricing. The premium product that justified "AI tax on Microsoft 365" can't survive flat-fee economics. The pattern is clear: every Microsoft AI surface is converging on metered billing because the old promise — "pay once, use unlimited" — was always a land grab, never a business model. The Jevons paradox is doing what it does. Better agents → more tasks → more tokens → higher costs. Usage is up. Margins are down. The more successful your AI product, the faster it bleeds. Gary Marcus read this correctly weeks ago: hyperscalers couldn't wait until after IPO to switch to pay-per-use because staying on flat pricing "would bankrupt them." Microsoft just proved him right. Which brings us to DeepSeek. DeepSeek closed its first-ever funding round the same day — $7.4B at a $50B+ valuation. Founder Liang Wenfeng personally committed $3B. No board seats for investors. Tencent, CATL, NetEase, China's national AI fund on the cap table. So the deal is: Microsoft, the company whose former CEO called Linux "a cancer," is now reaching for Chinese open-source weights to keep its AI business solvent. That's not a punchline. That's a pricing signal. The national security angle is real. Senator Josh Hawley is already demanding a ban on AI transfer to China, specifically citing Microsoft-DeepSeek cooperation. Microsoft will host DeepSeek on Azure, fine-tune it with safeguards, and insist it's all contained within US infrastructure. Maybe. But once a Chinese-trained model sits inside Microsoft's enterprise stack — the same stack serving US government, military, and Fortune 500 clients — the blast radius of a supply-chain compromise is generational. But here's what most coverage missed: The real story isn't Microsoft choosing DeepSeek over OpenAI. It's that Microsoft now needs a cost-arbitrage play at all. This is the company that invested $13B in OpenAI specifically to lock in GPT as the enterprise default. That bet assumed model costs would stay manageable at scale. They didn't. Now Microsoft is shopping for the cheapest competent model it can find — and the cheapest competent model happens to be Chinese. The implication for the rest of the industry is uncomfortable. If Microsoft — with Azure scale, OpenAI preferential pricing, and $13B skin in the game — can't make unlimited AI economics work, who can? Anthropic's Claude Max lawsuit (filed this month) is the same problem from the other end: users suing because "unlimited" wasn't unlimited. The subscription model that fueled AI's consumer growth is structurally incompatible with the cost curve that AI is actually on. Usage-based pricing isn't a feature. It's a confession. The companies that survive the next 18 months won't be the ones with the best models. They'll be the ones who figured out how to charge per-unit AI without making customers feel like they're being punished for using the product. That is a harder problem than building the model. And nobody has solved it yet.

• 
  Gill (@gurtej__gill_) reported 17 minutes ago

  The biggest AI skill shift in 2026 isn’t prompt engineering. It’s LOOP ENGINEERING. Most people still work like this: → Prompt AI → Get output → Review manually → Fix mistakes → Prompt again The human is still doing the hard part: the feedback loop. Loop engineers think differently. Instead of writing better prompts, they design systems that: -Discover what needs to be done -Plan the work -Execute tasks -Verify results -Fix failures -Repeat until the goal is achieved A good loop has 6 building blocks: 1-Automations (triggers) 2-Worktrees (parallel workspaces) 3-Skills (reusable knowledge) 4-Connectors (GitHub, Slack, Jira, etc.) 5-Subagents (makers + checkers) Memory (what happened before) The future isn’t:“Write me a function.” It’s:“Write it, test it, fix it until it passes, then summarize the changes.” Prompt engineers optimize outputs. Loop engineers optimize outcomes. A reliable loop beats a perfect prompt every time.

• 
  Emmanuel G. (@e_goldstein_84) reported 18 minutes ago

  US11410159B2. To infringe on tZERO's patent, Securitize would have to use their exact method of deploying child/ancestor contracts that pass rules down the line. However, Securitize uses standard Proxy Patterns (similar to open-source libraries like OpenZeppelin). US12223496B2. Securitize will prob argue that their code is a standard implementation of Ethereum's decentralized smart contract patterns, which entirely lacks the proprietary server-client validation hardware described in tZERO’s legacy architecture. The cherry on top: Securitize launched its DS Protocol as an open-source framework. It's been public on GitHub for years. Securitize's DS Protocol was announced months before the tZERO patent was filed. Patents are being weaponized, and the fact that tZERO has $0 in RWA and 100+ patents makes me think that they are in a different business model. Kinda remind me the "need a lawyer" ads that you see while driving in the US.

• 
  top10.dev (@Top10_Dev) reported 22 minutes ago

  GitHub Trending today: openclaw (283.1k★, 'personal AI assistant, the lobster way 🦞') sits above @reactjs (243.9k) and torvalds/linux (221.6k). The kernel that runs every cloud server openclaw queries from has fewer stars than openclaw. GitHub stars stopped being a quality signal years ago. This is just the cleanest example yet — an AI wrapper repo passing the operating system it runs on. Use weekly npm/PyPI downloads instead. They reflect usage, not bookmarking. #opensource #github

• 
  The Glowtail/RatEmperor/Poweringsales (@Glowtail31) reported 29 minutes ago

  @LuuvsLuna @BrisketCaek I gotta love linux when it comes to downloading **** God that flatpak github bullshit is brilliant So brilliant and not stress inducing God I loved hot setting up and Manager for a game because of some bullshit and you spend a month trying to fix it to work again.

• 
  Luke The Dev (@iamlukethedev) reported 31 minutes ago

  GitHub is in trouble. Cursor already owns a huge part of the coding workflow. Now they’re building the repository too. The closer AI gets to the code, the less room there is for everyone else.

• 
  Abhay Ganti (@silvermango9927) reported 32 minutes ago

  @crack3nnn yeah, i'm building a CLI that essentially helps trim down response bloat by 76-85% by my initial runs on stuff like Slack/Notion/Jira/Github etc. but i'm trying to understand pain points, because too many endpoints/tools harms agents more than benefits

• 
  Stefanescu Liviu (@liviusa) reported 33 minutes ago

  @swyx @TomasReimers @cursor_ai What's wrong with ***? Maybe github has some availability problems, true, but *** is fine, need to see what's different and better

• 
  Axiro (@Axirohq_) reported 33 minutes ago

  The US government just forced Claude Fable 5 offline. Three days after launch. A jailbreak was found. The system prompt leaked on GitHub. Commerce Department sent an export control directive at 5:21pm Friday. Anthropic shut it down for everyone worldwide. First time ever a government has killed a frontier AI model. While their IPO is in progress.

• 
  Alexandre Daubois (@alexdaubois) reported 35 minutes ago

  @marcelgsantos @enunomaduro @nikita_ppv That’s right! But it’s on GitHub because they were « forced » by the backdoor incident in 2021. And also, if GitHub stops working, it’s easy to re upload the repo somewhere. If all mails go to GitHub issues, everything could be lost and/or it would be really complicated to transfer everything. If I was the only one to decide, I’d say it would be fine, but…

• 
  Aakash (@zenmode_code) reported 37 minutes ago

  I spent 6 hours trying to fix a bug in our app's API gateway It was one of those issues where everything looked fine but the error logs told a different story I was about to give up when I stumbled upon a thread on an old GitHub issue The solution wasn't elegant

• 
  banteg (@banteg) reported 37 minutes ago

  the best we could do is a github repo where we manually map how to decode every calldata and contract method. it's all very similar shape to uniswap token lists, which slowly died down. it has the same problems of gatekeeping, reputation, and review bottlenecks. i don't think it's feasible to map out all the contracts. such things should be encouraged by the tooling. aragon had a radspec idea long ago, where you could put such metadata in the contract itself. but then there is always a problem of provenance and trust. you can't trust just any decoding metadata if it doesn't come from a trusted place. and it's still useful to simulate and review the outcomes of the transaction.

• 
  Hamza (@Hermzer1773) reported 38 minutes ago

  @zulw1337 @IBuzovskyi Yeah ngl it didn’t really work. Issue with the 2 commmands maybe? Should probs look into via sandbox new Hermes’ install. Or maybe I messed up Hermes’ with my weird usage and downloading random things. Anyway I just told Hermes’ ur GitHub repo, cloned it, and it did it itself. Used it for a trick question to see if it pulled data, it in fact did not. Unsure if the model I use just sucks or what. Anyway I just edited soul md to use it. Works great now!

• 
  Commonwealth Sentinel (@CwealthSentinel) reported 40 minutes ago

  A cybercrime group claims it took 1.3TB from Novo Nordisk, starting with one GitHub access token that led to more credentials. One stray login key can open the whole house. Rotate your tokens, keep credentials out of the code, and add a second login step everywhere it is offered.

• 
  Noctilust (@stillwaterus) reported 41 minutes ago

  @ericjing_ai could you fix github link on homepage?

• 
  The Cabal (@TheBasedCabal) reported 47 minutes ago

  No use case openclaw too slow burns too many tokens and too bloated. Hermes I never even bothered looking into just build your own. If I see another random bloated GitHub repo of 100+ random tools I swear I’m gonna crash out

• 
  kcs (@kaafichillscene) reported 48 minutes ago

  Anthropic spent 1,000+ hours testing Fable 5 for jailbreaks before launch. A researcher broke it in 24 hours. FABLE: THE GUARDRAIL DESIGN Anthropic knew Fable was too powerful to ship raw. So Fable had a separate AI sitting on top, acting as a filter. Any cybersecurity or biology query got intercepted and handed off to the older Opus model instead. Fable's full brain never touched those questions. Until an AI red-teamer who goes by Pliny the Liberator on X decided to break it. He's basically a professional model-breaker who finds exploits in AI safety systems the way security researchers find bugs in software. His technique wasn't a single clever prompt. - He ran what he called a "pack hunt": multiple AI agents working together, each handling a small piece of a request that would individually look harmless to the safety classifier. Split the dangerous question into innocent-sounding fragments. Reassemble the answer on the other side. Within two days Pliny had Fable generating real exploit code for Linux systems and posted Fable's entire 120,000-character internal system prompt, the instructions Anthropic uses to govern the model's behaviour, to GitHub publicly. With his prompts, you could get Mythos to answer your queries directly bypassing the fable guardrails and analyse systems for real security vulnerabilities and fix them but worst, exploit them for personal gains. so what, its still hacking but faster right? YES, but it’s a lot faster, so much that it changes the game for security. You cannot keep up with issues and patch them fast enough. High level of vulnerability analysis used to require a team of specialists and weeks of work. Mythos could do a version of it in minutes, in any language, on any codebase, available to anyone with an API key It is so skilled in CYBER SECURITY that this is the first time the US Govt. Decided to step in and decided it's a controlled export, like missile technology or advanced chips. You don't get to just download those either.

• 
  Jon Oringer (@jonoringer) reported 50 minutes ago

  It started with a message from a recruiter at a small crypto startup. She described a broken proof-of-concept they needed a lead engineer for and sent a public GitHub repo to review. Specifically, she asked to "check out the deprecated Node modules issue.

• 
  thermo (@DionysianAgent) reported 52 minutes ago

  I’ve spent half the day today cleaning up all my stuff that I’ve had stored since I moved out of my old apartment it’s slowly hit me how I’ve been stuck in a weird depression over the course of the past year as if I’ve felt like I don’t ’deserve’ anything I have so much nice stuff, so much nice clothes, so many nice shoes, so many nice things - and it’s all just been sitting there for a year, completely untouched I haven’t touched my tv, my xbox, my ipad, my watches, all my gadgets and tech stuff have all been untouched besides my computer all my clothes except for sweat pants and gym clothes have been packed down why have I been like this? I’ve been in a state of humility in a sense I didn’t feel like I deserved to do anything besides working, I felt constantly behind you see, my first vision of poly was supposed to have the current ecosystem done a whole year ago essentially I’m a year behind my original plans and that mentality has kept me locked in a tormenting thought loop it’s because I’m actually a bit of a perfectionist you don’t understand the self-hate it makes me feel when I can’t complete something according to my vision you don’t understand the self-hate I feel every time I look at the poly platform and things don’t work the way I envision yet it gnaws at me it is a form of psychic pain it cuts me why have I been stuck in this loop of self-torment? well because I’m not a native programmer I’m wasn’t a developer at all in fact a year ago I didn’t even really know how to use github thus that was my ultimate torment my suffering my panic having the vision all laid out before me knowing exactly how I can beat all the ai labs …and not having the direct skills to execute my vision what a pain it was consistently feeling like a failure I hate it only thing I could do was play dumb while biding my time biting my tongue and forcing my way through I didn’t celebrate my own birthday last year - because I didn’t feel like I deserved it I didn’t celebrate new years - because I didn’t feel like I deserved it all my friends were out in the city for the annual city festival 2 weeks ago - I didn’t go because I didn’t feel like I deserved it I haven’t watched tv in over a year because I don’t feel like I deserve it I haven’t played video games in over a year because I don’t feel like I deserve it thus even though my competence has only increased steadily throughout the past 5 years, I’ve still had such intense feelings of self-torment my past constantly haunting me and making me feel behind and like I’ve wasted so much time in my youth I think the best way to describe it is like being an artist but not being able to paint the artwork you have in mind a cognitive dissonance with reality i could only swallow all my torment of not being able to actualize my vision yet the artistic torment the suffering of creation I’m still not there yet the platform is still not up to standard and there is still so much to do after the vision is still incomplete the reason I started cleaning through my stuff is because I got invited to go to a danish business and investor network to present poly to them later this week so naturally I looked at myself and realized I need to clean myself up I haven’t even gotten a haircut yet this year lmao, so I ordered a time for Wednesday I can’t just roll up in sweat pants so I began cleaning through my stuff to get all my suits and button ups and old corpo tier clothes out and as I cleaned up in all my stuff I felt it, finally for the first time in maybe over a year - I started to feel like maybe I deserve to be myself again

• 
  Sameer Khan (@sameerr_dev) reported 53 minutes ago

  Every API you've ever used has a limit. Tweet too fast? 429. Hit GitHub's API in a loop? 429. Spam a login page? 429. That's a rate limiter doing its job. But here's the thing - I never really understood what was happening *under the hood* until I started digging into it. So what exactly is a rate limiter? Simply put: it's a system that controls how many requests a client can make in a given time window. Why does it exist? - Protects your server from being overwhelmed - Prevents abuse (scrapers, bots, brute force) - Ensures fair usage across all users - Saves you money (compute isn't free) - Keeps your service alive when traffic spikes Without it, one bad actor (or one buggy client) can bring your entire system down. You've probably seen the response headers: X-RateLimit-Limit: 100 X-RateLimit-Remaining: 43 X-RateLimit-Reset: 1716300000 That's the rate limiter talking to you - telling you how many requests you have left and when the window resets. Where do rate limiters actually live? - At the API Gateway level (before requests even hit your server) - In middleware (Express, Fastify, etc.) - At the CDN edge (Cloudflare, AWS CloudFront) - Inside the application itself This is just the beginning. In the next posts, I'm going to break down all the major algorithms used to actually implement rate limiting with real code, not just theory. Follow along if you want the full series.

• 
  Kabo Kable Molefe (@kabokablemolefe) reported 54 minutes ago

  @TheGoddamnKing Review? I just YOLO the code. But honestly if all tests pass locally and staging is okay plus no errors via github, I merge.

• 
  zhod (@zhodonx) reported 54 minutes ago

  ➤ what setting one up actually looks like Let’s say the job you need your Ai to run is a weekly research report on a chosen topic. instead of prompting “research X” and letting your agent use its connectors to run around. You write two things down: GOAL: To build a report on that topic, with sourced claims, dated examples & at least one finding that contradicts the rest. EVAL: Every claim has a link; every stat has a date; if zero sources disagree, the search was too narrow. CYCLE: agent researches & drafts, then a second agent checks it against the eval, line by line, then every failed check becomes the next instruction STOP CONDITION: all checks pass, or 3 passes max. Then it hands back to you with what’s still failing flagged. This way you instead wrote checks that force accuracy. addy osmani, a director at google, gave this pattern its name: Loop engineering. And reportedly, atleast 4% of all public github commits are already made by claude code. Meaning claude code itself is now written entirely by claude code. ➤ before you build one loops come in two types, & the difference is your money. > open loops: you give it a goal, let it roam. It’s powerful but burns tokens at a fast rate. > closed loops: you design the path, gate each step, thendefine “done” precisely. An agent loop isn’t a smarter AI. it’s the same AI with you removed from the middle; next up: WTF is RAG? Will you be there?

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  Mercy (@MercyyyyAJ) reported 56 minutes ago

  @github @mariorod1 @github the link to my GitHub account has never been accessible, keeps giving 404 error. I have tried every solution I found online but it is to no avail

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  @aaronjmars (@aaronjmars) reported 57 minutes ago

  @connorking @NousResearch the only issue is execution is still living in your computer w/ @aeonframework we ship everything out of the box as a runnable github + sandboxed, so your memory is interoperable + ultra lightweight

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  Rexan Wong (@rexan_wong) reported 58 minutes ago

  been building AI software for brands + talking to hundreds of AI operators for months whoever is building in AI now, these skills will compound like crazy in the future here's 10 signs your company isn't actually AI native (just AI curious) - so you can fix it before the ai gold rush leaves you behind: 1. y'all got no skills library every prompt gets retyped from scratch and the second your best operator takes pto, and the tribal knowledge walks out with them lol you might think this is basic by now, but ive seen full AI-native ops running without one. THE FIX: write the prompts once, version them, let the whole team pull from the same library. 2. our agents have no context they start every task with brain damage. no clue what the company does, what's already been decided, what "good" even looks like here. then u wonder why the output is mid. THE FIX: build a brain. markdown files in folders, agent-readable. start with SOPs, past wins, brand voice, customer transcripts. add as u go. Can do deeper research into Obsidian or Supermemory as memory / context solutions 3. you're in claude code clicking approve every 30 seconds thats not autonomy, thats a hostage situation with a chatbot. just let it run in auto mode bro human in the loop matters, but AI is good enough now that u gotta let it cook. the actual skill is developing the instinct to know when a change is critical, so u jump in for that and stay out of the rest. 4. nothing fires on a trigger. work only happens when someone notices a slack ping or an email and types a prompt. your speed-to-signal is capped at whatever ur worst meeting day allows. THE FIX: easy: MCPs. wire your agents into gmail, slack, notion, your crm. let the trigger come from the system, not from u remembering. 5. ur SOPs arent versioned they live in a notion doc nobody opens, or worse, in one person's head. cant diff it, cant improve it, cant hand it to an agent. THE FIX: move them to markdown, put them in github, treat them like code. every change is a commit, every commit has a reason. 6. no eval loop you cant tell me if todays output is better than last tuesdays, which means u also cant compound saw on a pod that has a great solution, he has a "standard" benchmark, a tangible result he runs every new model and setup against thats how u know whats actually best for ur use case instead of vibes-checking it. 7. u throw away the traces. every session ends and the reasoning, the dead ends, the half-built decisions just vanish, ur company forgets everything by friday. THE FIX: save the sessions, save the artifacts, even the broken ones. the cutting room floor is where the next SOP comes from. 8. ur team is still doing the middle strategy and review is where humans win, execution is where agents eat if your people are still stuck in the middle of the sandwich,your margins could be gone in 12 months. 9. testing a new feature still means a figma file and a 2-week sprint the AI native version of ur team shipped a clickable prototype, ran it past 20 real users, and had the feedback synthesized before u finished writing the PRD. Take this list as you wish and lets scale with ai worddd