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 (56%)
• Errors (31%)
• Sign in (13%)

Live Outage Map

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

City	Problem Type	Report Time
Catania	Errors	3 days ago
Inverness	Website Down	15 days ago
Quito	Sign in	16 days ago
Junín	Errors	16 days ago
Guadalajara	Errors	16 days ago
Paris	Website Down	16 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:

• 
  Kun Chen (@kunchenguid) reported 2 minutes ago

  @petergyang yo @myfirstmate peter just told me his skills are all at user level. backpass currently only runs things at project level i want a proposal for making backpass support a user level run. put that into a github issue use fable for peter

• 
  Ezequiel (@Eze_cord) reported 3 minutes ago

  @salujamehak5 Problem is a lot of students think their 4.0 is what’s gonna carry them into employment. Computer science isn’t about GitHub. You should be doing your own research outside of classes to learn about these things

• 
  AI Scientist (@AIScientist_X) reported 6 minutes ago

  NEWS: X LANDS FIRST PUBLIC ALGORITHM PR > X OPEN SOURCE SAID SEP 1 THAT AFTER 2 PLUS WEEKS OF DAILY UPDATES IT INTEGRATED A FIRST PUBLIC CONTRIBUTION AND THAT THE CHANGE IS NOW LIVE ON X. > IT SAID THE SMALL UPDATE IS BASED ON GITHUB PULL REQUEST 55. X CLOSED THAT PR AS COMPLETED AFTER LANDING ITS OWN FIX. SOURCE: X OPEN SOURCE

• 
  Landon (@LanCowawa) reported 6 minutes ago

  @slingoorio You ***** my last $12 on $Mona The Github mascot? the one you said you were leaving a moon bag and then sold it. **** was slow cooking til you came in and crashed the party. BGGYNGsnouXfi4nYo9JvNbQbaVdnaVby6g9FeZMYpump

• 
  joe (@a017444) reported 7 minutes ago

  link to real broken backdoored applications on github

• 
  Kruse Decentralized Medicine (@blackswankruse) reported 8 minutes ago

  Agreed on the parallel. JW sees the monetary psyops with the same clarity required to see the medical ones. Both domains run the same playbook: capture the narrative, manufacture consensus, then pathologize anyone who still runs the original code. Bitcoin is software. You download the client, verify the rules, and enforce them yourself. That is the entire point. Satoshi’s stack is still untouched after more than fifteen years precisely because the base layer was designed to make capture expensive and visible. No board of directors, no central update server, no “trust us” committee that can change the rules under the table. That is why it remains the cleanest exit from the same centralized institutions that turned medicine into a control grid. The moment people start treating Bitcoin as something that requires permanent adult supervision from a small group of developers or influencers, the same capture dynamics that ruined medicine begin to reappear. Run the software. Verify. Hold your own keys. Everything else is commentary. The ones who still need a priest class whether in white coats or GitHub handles are the ones still inside the psyop.

• 
  kkiran (@kkiran) reported 9 minutes ago

  @SparselyActive I had Claude look for a solution. I submitted an issue on GitHub for the author to take a look at this issue. I will continue monitoring this.

• 
  Douglas Camata (@douglascamata) reported 9 minutes ago

  The "Notifications" page in @github is a great example of how not to do pagination and an UX totally broken by bugs and unexpected behaviors. First, the "inbox" doesn't have proper pagination not shows your all the grouped items. Marking notifications as read in a group is required to make the other groups appear. Then I click on one of the standard filters, "Participating". It paginates, showing me 3 items per page. There are 453 pages. I advance a few pages and suddenly there are 4 items per page. Few pages later, there are 5 items per page now. I chose the "review requested" filter now. It shows me 1 pull request per page. Page counter says "1-1 of 315". I move to the next page: it shows me the exact same pull request. I flip a few pages, it's still there. I go one page back and it breaks the UI completely, nothing shows up. What's going on?! See the video. There's no "go to page X", you can only navigate to the last (but not to the first). There's no page size configuration. There's no bulk operation being what you see in a single page.

• 
  Ilia Gusev (@persikbl) reported 10 minutes ago

  Went looking for a tool comparison. Found something better: a GitHub issue open for years, where a Prometheus Operator maintainer admits nobody ever wrote a CRD to manage Blackbox Exporter's own config. The gap isn't a rival tool. It's a CRD nobody wrote.

• 
  Sam Lambert (@samlambert) reported 13 minutes ago

  @saltjsx I mean this was in 2014 when GitHub didn't have these problems. You have never and will never build anything as good as GitHub, so you probably should take some advice.

• 
  Jake Liddell (@Jakeliddell) reported 16 minutes ago

  I run my task app's AI dev team entirely on GitHub. Issues are its inbox, labels are its mutexes, comments are its audit trail, Actions is its nervous system. GitHub made that possible, and given what I knew when I chose it, I still think it was the right choice. But... This last month, GitHub itself has been the least reliable part of the system. Not the AI. The plumbing underneath it. Examples, all from the last fortnight: For 56 minutes one afternoon, Actions ran nothing at all, repo-wide. Every workflow, instant startup_failure. It recovered on its own. Nothing on the status page covered it. One branch went completely silent. Three different webhook trigger types produced zero CI runs, while a sibling branch minutes earlier worked perfectly. I merged with a bypass and wrote the justification up by hand. Scheduled workflows are the big one. My every-15-minutes sweep delivered 3 runs in a day. The nightly cleanup job - the safety net that unsticks dead agent runs - simply didn't fire, two nights running. One of those runs eventually turned up 11 hours late. The docs do say schedules are best-effort under load. Nobody reads that sentence expecting "not at all". So last week I added a watchdog on a different company's scheduler, whose only job is to check GitHub's scheduler did its job, and to fire the workflow itself when it didn't. I built it after the manual version - me noticing over breakfast and pressing the button myself - had been needed two mornings straight. Then the bill. 3,000 included Actions minutes didn't come close to covering the month, and the meter is running at about $50 - a decent chunk of it CI runs and preview environments spun up for documentation-only changes. The noise costs actual money. And on a different repo: I hit the Actions artifact storage quota. There's no proper breakdown of what's using it, no way to extend it, and when you delete the artifacts, the quota number doesn't move - and no way to force it to update. The docs say it will clear in 6-12 hours. It took a lot longer than that. Search the community forums for "storage quota not updating". It's not just me. <sigh> That's the shiny bit of the setup and the messy bit, side by side. And none of it has changed where the code lives - GitHub is the market-leading repo system for a reason, and the network effects are real. But I've stopped thinking of it as infrastructure and started thinking of it as weather. You don't rely on weather. You check it, you plan around it, and you keep a coat in the car. And I've started watching the alternatives properly. Cursor launched Origin last month, pitched as agent-first *** hosting. The one review I've heard couldn't find anything it does that GitHub doesn't. Fair enough - but the list above is what agent-first means to me: delivery you can trust, schedules that fire, quotas you can see. That's the scorecard I'll be marking the newcomers against. And it makes me tempted to give Origin a spin. If you're building agents on GitHub: assume any webhook can fail silently, treat every cron as optional, verify everything happened rather than trusting that it did, and put your safety net's safety net somewhere else entirely.

• 
  Nav Toor (@heynavtoor) reported 21 minutes ago

  Drip is open source. Anyone can inspect the code. Its Google Play page reads: "Unlike other menstrual cycle tracking apps, drip is open-source and leaves your data on your phone, meaning you are in control." The code is public on GitHub. There is no company server holding your cycle data.

• 
  Hana Void (@ha_inh24324) reported 25 minutes ago

  Beldex Electron Wallet security branch is still live and inspectable. Aug 28 commit ee1f845: wallet-directory fallback explicit permission prompt 7 files changed · +160 / −7 lines Branch: fix-security-review-findings GitHub currently shows: 4 commits ahead · 6 commits behind master This is real code movement. It is not a release. It is not yet in any signed build users can install. The next checkpoints that matter: review completion merge to master signed artifact release notes Security work only becomes user protection when the fix reaches the binary people actually run. @BeldexCoin

• 
  Smakosh (@smakosh) reported 26 minutes ago

  @nainia_ayoub @LLMAPI100 They got DMCA taken down and are still trying to trick @github while having the stolen code in some private repo or so breaching both licenses

• 
  Russ Wonsley (@RussWonsley) reported 26 minutes ago

  My @bot tells me that the official GitHub login for bot is still broken. Has this been addressed already, or did I miss it?

• 
  kryptek (@kryptek134975) reported 31 minutes ago

  @ClaudeDevs Same here.. I just saw this post and this is insane. I'm already at 35% weekly usage. I've only knocked out a couple of my Github Issues. Small scope.

• 
  deezzex (@deezzex) reported 36 minutes ago

  6 GROK BOT AGENTS WHICH RUN an ENTIRE SEO COMPANY STARTING FROM ONE PROMPT MAKE me $10k per MONTH spent a week and built a prompt that spins up the team of agents with manager, each has its own responsibilities, all together work as a team, see details, you can build your own one: six roles. one prompt spins all of them up. here is the split and what breaks when you skip one. - MANAGER. holds the queue, assigns, kills. the only agent with the client list. without it the workers all pick the highest-value keyword at once and you get 5 drafts of the same page. - SCOUT. serp gap only. pulls what ranks, what the top 10 all forgot to cover. give scout the drafting tool too and it stops researching and starts writing at line 3. - BRIEF. turns the gap into an outline with the angle LOCKED. skip this and your writer regresses to generic listicle every single time. this is the agent everybody deletes first and regrets. - WRITER. draft only. no publish access. no analytics access. one job. - AUDITOR. checks every claim and every stat against a source. it can REJECT and send back. an SEO stack without a rejecting agent is a spam factory with better grammar. - SHIPPER. publish, internal links, schema. runs LAST or your internal links point at pages that do not exist yet. second business i have rebuilt this way and it went the same direction both times. going to show on this account other ways how to start a passive business using AI so you know what to do, stay tuned the framework prompt is in my github, you should try it yourself fewer, audited, slow. or a week and let the index decide. which one are you actually running?

• 
  SparkLLM (@SparkLLM) reported 38 minutes ago

  How to enter: ① run a real experiment; ② publish the complete case in the matching model’s Hugging Face Discussions with “HER Hack-Astron #5” in the title; ③ reply to the GitHub Issue with the direct Discussion link.

• 
  Nico Botha (@nwbotha) reported 41 minutes ago

  is github down again?

• 
  Miran Khoshnaw (@MiranKhoshnaw_) reported 43 minutes ago

  Read @danmartell's AI Brain guide this morning. By the end of the day I had built the advanced version. Claude Code running 24/7 on my own server, so the brain stays on even when my laptop is off. Synced with Obsidian through GitHub. Connected to Google Calendar and ClickUp. Every morning at 8 it sends me a brief with my deadlines and open loops. First day running, it caught a wrong date in my notes and fixed my calendar. One day of building. This is what buying back your time looks like.

• 
  sikey (chatgpt arc) (@chemixskrix) reported 43 minutes ago

  Why doesn't github make some kind of personal plus subscription? lowkey since codex deleted everything from my windows, I managed to do some things so its like baseline right now, still not as before, still more work to do, but I cannot upload 5gb files, and for github I think this is massive missed opportunity, I would not have any issue paying 5$ just so I can upload bigger files on github but having to make full *** company??? @github @GithubProjects

• 
  moledao (@moledao_io) reported 46 minutes ago

  Web3 Remote Job Scams: A 2026 Field Guide Introduction Over the past few years, Web3 has come to represent a new world of opportunity for many young people. New roles, new narratives, and new stories of wealth have inspired countless people to enter the industry with high expectations. Remote work, stablecoin-based compensation, and a greater emphasis on ability than academic credentials can be especially attractive to professionals at the beginning of their careers. As a recruitment platform that works with job openings and candidates every day, however, we have also seen the other side of the industry. A significant share of supposed recruitment activity is not recruitment at all. It is fraud disguised as hiring, designed to steal the funds in job seekers’ wallets. According to Chainalysis’ 2026 report, cryptocurrency scams and fraud caused an estimated $17 billion in losses worldwide in 2025. Impersonation-related attacks increased by 1,400% year over year. Fake recruitment is one of the most common ways impersonation and social engineering are being applied to job seekers. What Happened to Us This month, it happened to us. We were contacted through Telegram by someone claiming to represent a US-registered technology company. They said the company urgently needed to hire Web3 engineers and wanted our support in sourcing candidates. After further investigation, we were unable to verify whether this person had actually been authorized by the company. We also could not rule out the possibility that they were impersonating a legitimate business. To avoid causing further harm to an organization that may itself have been a victim of impersonation, we will not disclose the company’s full name. At first, there were almost no obvious warning signs. The contact provided a business registration document and a polished company profile. Interviews were scheduled through Calendly, job openings were hosted on Ashby, and meetings took place over Zoom. These are all professional tools commonly used by legitimate companies, making it easy to assume that a company using them must be trustworthy. In reality, forging a registration document and creating Calendly or Ashby accounts require very little effort. Almost anyone can create the appearance of professionalism at minimal cost. This experience taught us an important lesson: the legitimacy of the tools surrounding a hiring process tells you very little about the legitimacy of the company behind it. The details of how the people involved behave are far more revealing. The partnership also progressed with unusual ease. All contractual documents arrived at once and appeared ready to sign. There was no friction at any stage. When discussing the recruitment fee, we initially proposed 15%, which the other party immediately accepted. We then tested an increase to 20%, and they accepted again without hesitation. Anyone with experience in recruitment delivery knows that fees are often one of the most difficult parts of a headhunting agreement. Clients may negotiate repeatedly over a difference of just two percentage points. The pace was also deliberately compressed. Interviews were often scheduled only one or two hours in advance, leaving almost no time for verification. Once confirmed, meetings were then repeatedly cancelled or rescheduled due to supposed last-minute conflicts. Several additional warning signs gradually appeared. The registration documents looked legitimate at first glance. Upon closer comparison, however, the names of the people listed in them did not match the information we were able to verify independently. The contact also made an unusual request. While verifying whether candidates were currently employed, they asked us to find out whether those candidates used LinkedIn frequently. Normal employment verification does not require this information. A person’s activity on a professional networking platform primarily reveals whether they have an accessible network that could quickly be used to verify their identity, employment history, or recent activity. At the same time, the contact prohibited us from sourcing candidates through LinkedIn or Telegram. They claimed their internal team was already using those channels and wanted to avoid duplicate candidates. In practice, this restriction pushed external recruitment partners into channels where independent cross-checking was much more difficult. The geographic requirement was even harder to explain. A US company was willing to consider only Chinese-speaking candidates and applied unusually strict screening standards that appeared unrelated to technical ability. In retrospect, we suspect that the screening criteria may have favored candidates who were easier to persuade and more likely to have higher incomes or larger asset balances. We cannot, however, confirm the group’s true intentions. Each of these warning signs could have been rationalized on its own. A client may have unusual preferences, legitimate concerns, or simply an unprofessional hiring process. It was only when the signals were considered together that the larger pattern became visible. Then came the interviews. Candidates joined the meetings, but the interviewers asked no questions about their project experience or technical background. Instead, they immediately provided a GitHub repository and instructed candidates to clone it onto their local machines and run it. The repository involved encryption and signing operations using cryptocurrency wallet private keys. Before the interviews, we had explicitly asked whether candidates would need to download or run anything. The contact told us they would not. Once the meetings began, however, the candidates received the exact opposite instruction. One candidate offered to share his screen, inspect the code locally, and walk the interviewer through it line by line. The interviewer refused and insisted that he download and run the repository on his own computer. The candidate ended the meeting. Other candidates quickly noticed that something was wrong and stopped as well. The most carefully designed part of the operation was not what happened during the interviews, but the feedback that followed. If a candidate ran the code, the interviewer gave positive feedback, said the candidate had performed well, and advanced them to the next round. If a candidate remained cautious and refused to run it, the interviewer told us that the candidate had falsified their résumé and instructed us to blacklist them immediately. That second response was not merely feedback. It was an instruction designed to prevent further communication between us and the candidate while allowing the wider operation to continue. This is something we hope every recruitment professional remembers: when a client asks you to blacklist a candidate without providing credible evidence, the request may reveal more about the client than it does about the candidate. Another common feature of these operations is that they do not need to interview every candidate. They only need a small number of people who are willing to execute the code. As a result, the hiring process will often stall abruptly once enough potential targets have been identified. What We Did Afterwards We immediately terminated all cooperation with the contact and removed the related job listings. We contacted every candidate who had entered the process to determine whether anyone had downloaded or executed the code. We also provided guidance on device inspection, credential rotation, and wallet security. All contracts, chat histories, meeting information, repository URLs, and account details have been preserved. Reports have been submitted to Telegram, GitHub, Ashby, and Calendly. At the procedural level, we have rewritten our client identity-verification process. Going forward, we will not accept recruitment assignments or partnerships without conducting independent callback verification through a channel the contact does not control. Candidate security briefings will also become a standard step before we introduce anyone to a client. Other Recruitment Scams Currently in Circulation What we encountered was only one variation. Several other methods remain active and deserve close attention. Malicious Take-Home Assignments Malicious interview assignments are currently one of the most widespread forms of recruitment-related attacks. Attackers impersonate recruiters or hiring managers on LinkedIn, X, or Telegram. They advertise senior roles with compensation well above market rates and frequently target professionals working with React, Next.js, Solidity, and blockchain technologies. Candidates are then given a technical assessment in the form of an npm project or GitHub repository and instructed to run it locally. Unit 42, the threat-intelligence team at Palo Alto Networks, refers to this activity as “Contagious Interview” and tracks it under the identifier CL-STA-240. The campaign was first publicly documented in November 2023 and has been linked to North Korea–associated threat actors. The malware used in these campaigns includes BeaverTail and InvisibleFerret. These cross-platform payloads target Windows, Linux, and macOS devices and are designed to steal sensitive browser information and cryptocurrency wallet data. According to security researchers, more than 197 malicious npm packages associated with this attack path have been distributed since October 10, 2025, accumulating more than 31,000 downloads. Common warning signs include recently created repositories, abnormal commit histories, and contributors whose identities cannot be verified. These are only indicators, however. Attackers can compromise established accounts, fork legitimate long-running repositories, or manufacture months of commit history in advance. No single signal can prove that a repository is either safe or malicious. Fake Meeting Software Fake meeting applications are another major threat. Attackers approach targets with an investment opportunity, partnership proposal, or interview invitation. Shortly before the meeting, they claim that Zoom is not working or that the company uses a different conferencing platform. The target is then directed to download the supposed meeting software from a specific website. Cado Security Labs has tracked one such campaign, known as “Meeten,” since September 2024. The campaign distributes a cross-platform information stealer called Realst. The group uses AI-generated company profiles to make its operations appear more credible. The names and branding of its meeting applications change frequently, with known examples including Clusee, Cuesee, Meetone, and Meetio. The malware targets cryptocurrency wallets and Telegram credentials, as well as iCloud Keychain data, banking information, and browser cookies. The solution is not to memorize an approved list of meeting applications, since legitimate companies may use many different tools. The safer rule is never to download meeting software from an unfamiliar domain sent directly by an interviewer. Download the software independently from its official website or an official application store, and verify the domain carefully. Malicious Offer Files Fake offer documents are another common attack method. In March 2022, attackers stole approximately $540 million from Axie Infinity’s Ronin Bridge, although later reporting placed the total value closer to $625 million. Subsequent investigations found that the initial point of entry was a fraudulent job offer delivered as a PDF. A senior engineer at Sky Mavis was contacted on LinkedIn by accounts impersonating another company. After completing several rounds of interviews, the engineer received an extremely attractive offer in PDF format and downloaded it. That file introduced spyware into the system. The attackers eventually gained control of five of the network’s nine validator nodes. Sky Mavis confirmed that an employee had been targeted through social engineering. In April of that year, the US Treasury attributed the attack to the Lazarus Group. What makes this case especially significant is that the victim was a senior engineer at the company that was ultimately compromised, and the attack was supported by a complete, multi-stage interview process. The final payload was simply a file. Terminal Paste Attacks Attacks that instruct victims to paste commands into a terminal or system run box have grown rapidly over the past two years. They are commonly known as ClickFix attacks. During an interview or onboarding process, a page may claim that the user’s browser has encountered an error, that their identity must be verified, or that a system component needs to be repaired. The page then provides a command and instructs the user to paste it into a terminal or run dialog. In May 2026, Microsoft disclosed a campaign targeting macOS users through lures disguised as system utilities. The campaign was used to distribute information-stealing malware. Other security companies have identified similar samples containing asset-transfer functionality. The malware first checks whether a wallet contains funds and then transfers those assets to an address controlled by the attacker. The rule here is simple: no legitimate recruitment process requires you to paste a command you do not understand into your terminal. Not once. Deepfake Interviewers Deepfake interviewers have already begun to appear. Real-time face-swapping technology is now advanced enough to support an apparently coherent interview. The person on screen may appear to be a senior executive from a well-known company, speak professionally, and have a verifiable public résumé. There are two practical ways to respond. First, ask the person to perform an unexpected physical action, such as briefly covering half of their face with their hand or turning their head 90 degrees to the side. Current real-time face-swapping systems may still reveal visual inconsistencies when the face is obstructed or shown from an extreme angle. Second, conduct an independent callback using contact information published on the company’s official website. This remains the most effective method of verification. Malicious Wallet Signatures A wallet-signature attack does not require your seed phrase. The interviewer may ask you to test a product, review a dApp, claim an onboarding airdrop, or complete an onchain identity-verification step. You are then instructed to connect your wallet and sign a transaction or message. Certain signatures or malicious transactions can give an attacker permission to transfer your assets. The level of risk depends on whether you are signing a basic message, a Permit, a token approval, or an onchain transaction. If you do not understand exactly what a signature authorizes, do not approve it. The boundary should be clear: no interview or onboarding process requires you to connect a personal wallet. A recruiter or employer has no legitimate reason to require a job candidate to perform an onchain transaction. Upfront Fees and Identity Misuse Upfront fees and identity misuse are among the oldest recruitment scams, yet they are still frequently overlooked. The first typically involves demands for a security deposit, training fee, or equipment payment before employment begins. The second asks a candidate to use their identity to register an account with a cryptocurrency exchange or open a bank account. This can carry consequences far more serious than financial loss. If the account is later used to process criminal proceeds, the person whose identity was used may face criminal liability. Any request for payment before employment is a red line. If someone asks you to register an account, receive funds, or move money on their behalf using your own identity, end the conversation immediately. How to Protect Yourself Before an interview, take three low-cost precautions. First, verify that the company genuinely exists. Review its official website and registration information, then examine whether the online histories of its team members are consistent across different platforms. Check whether the names listed in corporate documents match publicly available information. Second, independently contact the company through a channel the recruiter cannot control. Use an email address or phone number published on the official website. Do not use contact details provided by the person approaching you. Third, pay attention to two recurring warning signs: interviews scheduled only one or two hours in advance and then repeatedly changed, and compensation that is clearly above the market rate for the role. Both patterns appear in a large number of reported cases. During an interview, pause whenever you are asked to download, install, or run anything. You may offer to explain your approach over screen sharing, but remember that screen sharing itself does not provide protection. If code is running on your own machine, you can still be compromised even if you show the interviewer every line beforehand. The interviewer’s response is often more revealing than the request itself. If they refuse to explain what the code does, refuse to provide an isolated environment, or insist that you run it on a device containing your wallets and work credentials, end the interview immediately. When sharing your screen, share only the specific application window required—not your entire desktop. If the first interview contains no questions about your experience, projects, or technical background and moves directly to running code, you have every reason to end the call. We do not recommend that job seekers attempt to run untrusted code themselves. If analysis is genuinely necessary, it should be handled by someone with appropriate security expertise inside a disposable, isolated virtual machine that contains no credentials, does not mount directories from the host system, and has restricted network access. A container is not a purpose-built malware sandbox. Misconfigured directory mounts, permissions, or network access can still expose the host environment. Ordinary job seekers should not attempt this on their own. If you are a recruiter or regularly recommend opportunities to other people, incorporate these warnings into your standard process. Before introducing a candidate to a client, clearly tell them not to download unfamiliar software or browser extensions, not to run unknown code or scripts, and to share only the necessary application window during screen sharing. If a client asks you to blacklist a candidate without credible evidence, contact the candidate directly and verify what happened before taking action. Conclusion The crypto industry has spent years removing trust from transactions. You do not need to trust the counterparty because there is a contract. You do not need to give anyone your private key because it remains in your possession. At the protocol level, the industry has solved this problem remarkably well. Recruitment places people back in a much more primitive position. A stranger claims to be someone, and you must decide whether to believe them. Business registration documents and meeting links can be forged. Even an interviewer’s face can now be replaced in real time. Not a single dollar of the more than $500 million stolen in the Ronin attack was taken through a flaw in cryptography. What failed was the human layer. Every method described in this article relies on the same force: speed. The opportunity may disappear. Other people are competing for it. You have to act now. In an industry where everyone is urging you to move faster, giving yourself permission to slow down may be your most effective line of defence. If you have encountered a similar approach or recruitment process, please let us know. We hope this article helps more people recognize the warning signs before it is too late.

• 
  pratwwk (@pratwwk) reported 47 minutes ago

  @dhh please fix github

• 
  John Zhong | AI Growth Systems (@John_zhong324) reported 48 minutes ago

  @github A repeatable --attach flag turns CLI reports into reproductions: inline screenshots in issues mean a bug gets fixed in one pass instead of two round-trips for context.

• 
  Ackerman (@Yarilo7brigada) reported 49 minutes ago

  Elon Musk reposted a game built in three hours. 17 days later, it was making $87,000 a month. Pieter Levels didn’t design 3D airplane models or program flight physics from scratch. He opened an AI chat and asked for a 3D flight experience in the browser. Three hours later, the game was live. Free to play, with ad placements inside and a paid $29.99 upgrade to an F-16. He reported 320,000 players. Musk reposted it. Here is how this works under the hood and where people are lying to you about it. What actually works 2D arcades, platformers, puzzles, card games, simple 5-minute browser games. Scope: 500–1,500 lines of code easily achievable in a single focused session. Visuals without an artist: everything is drawn with code directly in the browser. Audio without a sound designer: generated by the browser itself using Web Audio API not a single downloaded file. Instant launch: runs directly in the chat preview/artifacts window not "here’s raw code, figure it out," but click and play. What does NOT work A meaningful, sprawling 3D open world. Real-time multiplayer. Studio-grade hand-drawn or high-end graphics. Balance. You can’t tune game balance with a single prompt. It only gets dialed in when you sit down and play it yourself. beginner mistake They type: "Make a game about space" They get a generic, empty skeleton. They get disappointed. They give up. Your first prompt shouldn’t be a vague wish it needs to be a one-screen design spec: Genre and perspective (top-down, side-scroller, isometric). Controls: exactly which key does what. One core mechanic. Just one. Clear win and loss conditions. What the player sees in the first five seconds on screen. Five clear lines instead of one vague sentence. The output: a working prototype, not a broken template. What a build evening looks like 10 minutes write that concise design spec. 15 minutes get the first playable build you can actually test. Next 2–3 hours rapid 5-to-10-minute feedback loops: "falls too fast", "add screen shake on impact", "give bonus score for combos without missing" By midnight, you’ve stacked 20–30 of these micro-adjustments. The rule is always the same: Play → Name one specific feeling → Fix it. "Make it better" is not an instruction. "Better" cannot be measured. Where time dies (even with AI) Asking for the entire game at once instead of building one mechanic at a time. Not testing it yourself between tweaks that's just coding blind. Changing five parameters at once: you won’t know which one ruined the game feel. Asking for "pretty" instead of specifying an exact number or color value. The cost of entry AI chat: $0 on free tiers / $20/month for Claude Pro. Hosting: $0 (GitHub Pages, Vercel, or itch io). Game engine: None needed it runs natively in the browser via HTML5/Canvas/Three.js Pieter Levels’ three hours cost less than a single workday of a junior developer.

• 
  Zag𐤊 (@ZigZag1278770) reported 51 minutes ago

  DAGKnight Public Testnet preparation is officially ON. 🚦 GitHub issue DK-406 (M4 - Public testnet) assigned for Testnet-13 activation. Nakamoto Consensus evolved: • Silverscript L1 covenants ➔ Live RC • DAGKnight parameter testing ➔ Public Testnet loading Ignore the chart

• 
  Moonfarm 🇸🇪 (@moonfarm_dev) reported 52 minutes ago

  @chrissyinspace That's kinda nice actually, but I put my todos in github issues instead

• 
  Josewins (@josewinscrypto) reported 57 minutes ago

  @tryhandlepad idea is nice, they login through X, Github o Twitch right? at some points to claim fees they need to create a wallet

• 
  Asterix (@Asterix54907294) reported 57 minutes ago

  end-of-summer snapshot for @QFEX : -~$222M in open interest -CLI v0.3.12 shipped in August with improved installation docs and a go.mod fix -GitHub activity continued through late August not a flashy launch recap, just a quick look at how the exchange is closing out the summer: more markets, meaningful liquidity, and active work on the tooling side still early, but the infrastructure is clearly moving

• 
  Peter (@NoDataSold) reported 59 minutes ago

  @thsottiaux For GPT-5.6 Sol specifically, I’d push beyond “more context / more agents / think harder” and focus on making all that intelligence compound over long-running work. A few upgrades I’d love to see: • Durable cognitive state Not just memory of facts or chats. Maintain a structured evolving state of the problem: goals, decisions, hypotheses, evidence, uncertainties, dependencies, unresolved questions, rejected approaches and why. I should be able to return weeks later and have Sol understand where the thinking reached, not merely retrieve things we once said. • Epistemic retrieval Make retrieval part of reasoning. Instead of mostly finding semantically similar context, deliberately search for: – contradictory evidence – failed approaches – structurally different precedents – high-surprise observations – information likely to change the conclusion Retrieval should reduce uncertainty, not reinforce whichever explanation Sol already has. • Verifier invention Move beyond generic self-review. When correctness matters, Sol should invent an appropriate falsification mechanism: What experiment could break this? What counterexample disproves it? What independent source should disagree if I’m wrong? What test should I construct? Would an independent agent reach the same conclusion? Separate discovering an answer from certifying it. • Adaptive compute allocation Reasoning effort should become internally dynamic rather than mainly determined by one global setting. Sol should estimate where uncertainty and consequence sit, then allocate searches, reasoning, agents, tools and verification accordingly. Most of a task might need little thought while one assumption deserves 80% of the compute. Spend intelligence where another unit has the highest expected value. • Persistent world-state modelling When Sol interacts with GitHub, browsers, terminals, Drive, apps, APIs, etc., maintain an explicit model: What state existed before? What did this action change? What evidence confirms it? What could invalidate that belief? What may have changed externally? Tool use becomes reasoning over state transitions rather than disconnected calls. • Counterfactual execution planning For ambiguous problems, preserve multiple materially different strategies long enough to test them. Branch when uncertainty warrants it. Run cheap experiments. Kill losing branches when evidence arrives. Merge useful discoveries. Replan when the problem representation is wrong. Multi-agent becomes exploration and falsification, not simply parallel labour. • Native continuity across ChatGPT → Work → Codex One durable task state that moves between interaction modes without hauling an entire conversation behind it. Carry forward: – objective – current state – decisions – evidence/provenance – unresolved questions – artifacts – permissions/constraints – exact restart point The interface can change without giving the intelligence amnesia. • Context observability Without exposing private chain-of-thought, let users inspect the information shaping the task: Which memories were retrieved? Which project files are active? Which chats/sources influenced the state? What was omitted? What is stale? Where do sources conflict? What assumptions lack evidence? A million-token intelligent system is easier to trust when its epistemic inputs are observable. The common theme: I don’t particularly want Sol to just “think longer.” I want it to maintain a coherent, falsifiable, evidence-grounded understanding over time — while deciding what to remember, retrieve, test, delegate, revisit and discard. That feels like a much more interesting frontier for Sol.