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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Advanced Installer Powers PacKit FREE (@advinst) reported 2 minutes ago

  Danut Ghiorghita walks through the whole thing: code push to signed MSI to live updater config, fully automated. What's covered: → Advanced Installer + GitHub Actions setup → Automated build, versioning + code signing → Updater config generated in the pipeline → What breaks in real pipelines and how to fix it first

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  chandram (@ChandramDutta) reported 6 minutes ago

  oh @github not down again. my builds are failing.

• 
  teami (@the_era_arc) reported 6 minutes ago

  How I Taught My AI to Babysit Itself Every time I built something with Claude Code, the rhythm was the same. I wrote a prompt. I read what came back. I noticed it was half done. I typed “keep going.” I checked again, caught a bug, pointed it out, waited. An hour later I had a feature and a sore typing finger. I was the thing deciding, every single step, whether to continue. The moment I walked away, it stopped. Then I watched Boris Cherny, the head of Claude Code at Anthropic, say he does not really do that anymore. In a recent talk, he said Claude Code went from writing 10 to 20 percent of his code to replacing his editor entirely. He uninstalled his IDE after not opening it for a month. And the part that stopped me: instead of prompting by hand, he now writes loops. Automated workflows that prompt Claude and decide what to build next. His job shifted from coding to orchestrating. He called it “the golden age of the generalist,” where designers, chiefs of staff, and finance people all ship real software. The talk is here , it is worth 30 minutes. The strange part: I had already built the pieces When I looked back at my skills folder, I found something funny. Over the last two and a half months, I had quietly built a whole set of small skills, each one solving a single problem I kept hitting. I just never saw them as one thing. The missing piece was about trust There was one problem none of those tools solved: how do you know an AI’s output is actually good? Regular software is a vending machine. Same input, same output, “all tests pass” means done. But anything with a language model inside it is different. The same prompt gives different answers across users, sessions, and model updates. What you shipped is not a fixed thing. It is a range of behaviors. A few weeks ago I read an article by Jeff Gothelf called “What ‘done’ means when you’re shipping AI features.” One line stuck: if “done” is the language of a build culture, “calibrated” is the language of a learning culture. An AI feature is never simply done. It is calibrated. You have decided the variance you will accept, planned for the ways it fails, and you keep watching after launch. Two days later I built a skill for exactly that (ai-done). It is the piece that decides whether the AI part of a product is good enough, not by a checkbox, but by measuring the spread of its output against a bar I set. Then I tied them all together. I call it Ship It Ship It is not a new capability. It is a thread. You name a feature once, and it runs all of those skills in order, on its own. It asks me up front for anything only I can give, like a password or a login. It writes the checklist first. It builds in a loop while fixing its own bugs. It runs the health check. It calibrates anything with AI in it. Then it opens the real thing in a browser and QAs it, like a user would, until it actually works. It only comes back to me when it is ready to test. The whole thread: name it once, it runs the rest until it is ready for you to test. The mechanism underneath is almost silly in how simple it is. There is a small script that fires every time Claude tries to end its turn. If the checklist is not all green yet, the script hands Claude the same instruction again and says “keep going.” Claude looks at the files it already changed, sees where it got to, and does the next piece. Try to stop, get handed the task again. That is the loop that prompts itself. The real lesson: the loop is the easy part. Anyone can write “keep going until done.” The hard, valuable part is the verifier, the checklist that defines “done” so precisely a machine can check it. Without it, a self-prompting loop either runs forever or lies that it finished. With it, you can walk away. The verifier is the product, not the loop. Where this is going: the cloud, and the context problem Boris does not just run loops. He runs them in the cloud, on a schedule, without his laptop staying on. That is where I want to go, and there is an honest catch worth naming. The limit on a long autonomous run is the context window, the model’s short-term memory. Let one run go too long and it fills up, starts summarizing itself to make room, and slowly gets dumber. There is no magic “context is full, hand off cleanly” button yet. That part is genuinely unsolved. But here is the trick that makes it not matter. You do not run one giant session. You run many short ones. Anthropic has a feature called Routines: a scheduler that runs Claude Code on their own machines, pulls your project from GitHub, and fires on a timer, your laptop closed. So you set it to run, say, every hour. Each run is a fresh, empty mind. It opens the checklist, sees what is still unchecked, does a bounded chunk of work, commits it, and stops, well before its memory fills. The next hour, a brand-new run reads the same checklist and continues. Two loops. The inner one runs until the checklist passes or a turn limit. The outer one re-fires fresh every hour until everything is ticked. It is two loops stacked. The inner loop, inside one run, keeps prompting itself until the checklist passes or it hits a turn limit you set. The outer loop, the hourly schedule, keeps starting fresh runs until every item is finally ticked. Because no single run is ever long enough to fill the context window, the wall is never hit. The checklist sitting in the repo is the thread that ties all those short runs into one long job. The reason it works is the same reason Ship It works at all: the memory lives in files, not in the conversation. A fresh session is never starting from zero. It just reads the list and keeps going. The shift was never a tool What changed for me was not a clever skill. It was a stance. Stop being the loop. Start designing the verifier, the thing that knows when the work is truly done, and let the loop run against it. The golden age of the generalist is real. And the wild part is, you might already have built most of the pieces. You just have to thread them. Till next time, Cheers! Previous column articles can be found here:

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  Firas D (@firasd) reported 8 minutes ago

  @chrisalbon What these guys are skipping over is that they have a thousand github issues right They aren't prompting the agent directly with hey lets look at XYZ cause the github issue becomes the prompt

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  stepan (@cyntro_py) reported 10 minutes ago

  Spent some time over the weekend building a scraper to collect all existing dynamic workflows. The feature only appeared about a week ago, so there aren't that many in the wild yet - around 800. Based on GitHub activity, roughly 100 new ones are being published every day. Almost all of them are focused on software development, but the main advantage over skills is already obvious. Skills are an old concept at this point. They were hugely popular in February-March 2026, but it's June now, and the models have predictably absorbed most of the knowledge that any public skill on the internet could provide. In more than half of cases, public skills either provide no measurable benefit or actively make the outcome worse than simply asking an LLM to solve the problem. I ran a small eval to test this: comparing an agent equipped with coding skills against a plain LLM that was asked to come up with a plan and implement the solution. The results were far from favorable to skills. Workflows are different because they are codified, programmable systems that manage and orchestrate work. If the purpose of a skill was to tell an LLM how to perform a task, today's work is increasingly shifting toward building algorithms with validation, feedback loops and control mechanisms that orchestrate agents around a goal.

• 
  shinyufoguy2222 (@ollobrains) reported 14 minutes ago

  Microsoft’s MAI launch has a data-provenance problem, and enterprise buyers should require Microsoft to reconcile its marketing language with its own technical report. That is colder, harder to rebut, and more dangerous. 1. The strongest factual spine The core contradiction you want to exploit is real enough to be powerful, but it needs to be framed precisely. Microsoft’s public MAI materials say MAI-Thinking-1 was trained “from the ground up” on enterprise-grade, clean and commercially licensed data, and the Build transcript uses the phrase “enterprise-grade, clean and commercially licenced data lineage.” Microsoft also announced a family of seven MAI models developed in-house. But the MAI-Thinking-1 technical report describes pretraining on a mixture of publicly available and licensed human-generated data, including web data, public GitHub code, books, academic papers, news, multilingual text, and domain-specific materials. The same report says the data pipeline includes a proprietary crawl and Common Crawl, and the appendix says Microsoft’s proprietary crawl started from 1.2 trillion pages, later reduced by filtering, while the Common Crawl pipeline contributed 24.2 billion pages after processing. That is the strongest wording: Microsoft marketed the model around clean, enterprise-grade, commercially licensed data lineage. Its own paper describes a broader training mix that includes massive public-web crawling and Common Crawl. Microsoft needs to explain what, exactly, “commercially licensed” means here. That is much better than “they lied,” because it forces them into a definitional trap. 2. Do not make the whole argument depend on “lying” “Caught lying” is viral, but it gives Microsoft an escape hatch. They can say: “We did not lie. ‘Commercially licensed data lineage’ refers to commercially licensed datasets in the blend, not every token.” Or: “Publicly available web data was collected according to terms of use, robots controls, and industry standards.” Or: “Clean means filtered, deduplicated, non-pirated, non-adult, non-synthetic, and quality controlled — not necessarily individually licensed from every author.” So the sharper accusation is: Microsoft used procurement-grade language that sounds like full rights-cleared data, while its technical report describes a training stack that includes large-scale public-web data. That ambiguity matters for banks, hospitals, insurers, government agencies, and any customer relying on data-provenance representations. That is the kill shot: not “you lied,” but “your enterprise claim is materially ambiguous.” 3. Best replacement headline options Use one of these: Microsoft’s new MAI models have a data-provenance problem. Microsoft told enterprises “commercially licensed data.” Its own paper says public web crawl and Common Crawl. The real Microsoft AI story is not benchmarks. It is data lineage. Microsoft’s MAI launch just created a procurement problem for every regulated enterprise buyer. What does “commercially licensed” mean when the technical report says Common Crawl? The most surgical version: Microsoft needs to define “commercially licensed data lineage” before regulated enterprises rely on it. That sounds less like outrage and more like a subpoena. 4. Important correction: be careful saying Satya personally said it Your draft says: “At Build 2026, Satya Nadella announced…” The safer version is: At Build 2026, Microsoft announced… Or: During Microsoft’s MAI keynote at Build 2026, Microsoft described the models as having enterprise-grade, clean and commercially licensed data lineage. The Build transcript text I found includes the relevant “commercially licenced data lineage” claim in the MAI keynote, and the transcript also says “Satya just mentioned,” which suggests you should not hinge the argument on Satya personally unless you have the exact clip of him saying the phrase. That matters because if one attribution is wrong, people will use it to attack the entire post. 5. The real issue: “clean” is not the same as “licensed” This is one of the biggest missing elements. There are at least four separate claims being blurred together: Clean can mean filtered for spam, porn, piracy domains, malware, boilerplate, duplicate pages, low-quality content, synthetic content, or personally sensitive data. Commercially licensed means someone has a legal right, contractual permission, or license to use the content commercially for training. Traceable means Microsoft can identify source categories, URLs, datasets, providers, or pipeline lineage. Enterprise-grade means the data pipeline is controlled, documented, audited, filtered, and suitable for business buyers. Those are not the same thing. Your post should say: Clean data is not automatically licensed data. Traceable data is not automatically rights-cleared data. Publicly available data is not automatically commercially licensed data. That line is devastating. 6. Add this phrase: “traceable is not licensed” This is the obscure but important thought input. A dataset can be traceable to a URL and still not be commercially licensed. A crawler can respect robots.txt and still not create a negotiated license with the author. A page can be publicly accessible and still contain copyrighted work. A corpus can exclude adult content and piracy domains but still contain protected text, images, code, PDFs, journalism, forums, documentation, and books. Suggested line: Microsoft may have built a cleaner web crawl. That is not the same as building a fully commercially licensed corpus. Another version: The question is not whether Microsoft filtered the web. The question is whether Microsoft had commercial training rights for the web it kept. 7. The Common Crawl point needs nuance Your draft says Common Crawl has: “ZERO licensing guarantees and ZERO author consent mechanisms.” That is rhetorically strong, but tighten it. Better: Common Crawl is a free, open archive of web-crawled data. It does not, by itself, provide a source-by-source commercial license from every author or publisher whose content appears on the open web. Common Crawl describes itself as a free, open repository of web crawl data, and its own materials emphasize that it is a massive open corpus used by many AI researchers and companies. Also be careful saying: “Common Crawl is being sued.” A more accurate version is: Common Crawl and Common-Crawl-derived data have become a recurring flashpoint in AI copyright disputes, takedown demands, and lawsuits involving AI training data. That is safer. Wired reported publisher pressure on Common Crawl and noted that the New York Times had made removal requests before suing OpenAI, while other recent lawsuits have alleged use of Common Crawl or Common-Crawl-derived material as part of AI training disputes. 8. The best “receipt” structure Your post should have a clean evidence ladder: Microsoft’s claim: clean, enterprise-grade, commercially licensed data lineage. Microsoft’s paper: mixture of publicly available and licensed data. Microsoft’s appendix: proprietary crawl, Common Crawl, web-crawled PDFs, public GitHub. Enterprise question: which parts were actually commercially licensed, and which parts were merely publicly available? That is the entire argument. You do not need to prove Microsoft committed fraud. You need to force the question: Was “commercially licensed” a claim about the entire corpus, or only part of it? 9. The technical report gives you more ammunition Do not stop at proprietary crawl and Common Crawl. The MAI report also describes a web-crawled PDF corpus and a large public GitHub code corpus. The appendix says the web-crawled PDF pipeline starts from about 10 billion documents, filtered to about 620 million, and the report also describes a 7.4 trillion-token public GitHub code corpus. That matters because enterprise risk is not just “web pages.” It is: web pages PDFs public code academic material news books journals domain-specific materials multilingual web content possibly opt-out-protected or rights-reserved content Suggested line: This is not a tiny footnote. The paper describes a full industrial-scale public-data ingestion machine. 10. The Simon Willison angle is useful, but do not over-center it Simon Willison is useful as the “someone actually read the paper” character. He publicly highlighted the same issue: Microsoft’s marketing around “appropriately licensed” data versus the report’s description of public web crawl and Common Crawl. But the strongest post should not depend on Simon. Make him the discovery beat, not the proof: The funny part is that the contradiction was not hidden. It was sitting in Microsoft’s own technical report. Simon Willison simply did what enterprise procurement teams should have done: read the data section. That line is excellent. 11. Add the “enterprise procurement” frame This is the most important strategic upgrade. The issue is not whether Microsoft can win a copyright lawsuit. The issue is whether enterprise customers can rely on Microsoft’s claims in regulated procurement. A bank, hospital, insurer, defense contractor, or government agency does not ask only: “Can Microsoft argue fair use?” They ask: “Can Microsoft represent and warrant the training data provenance?” “Can Microsoft indemnify us?” “Can Microsoft disclose source categories?” “Can Microsoft honor opt-outs and rights reservations?” “Can Microsoft survive an audit?” “Can we put this in front of regulators, customers, and internal risk committees?” Suggested line: In regulated enterprise sales, “probably defensible in court” is not the same as “clean enough for procurement.” That is a killer sentence. 12. Add “Model Data Bill of Materials” This is the genius-level solution. Borrow from software supply chain language. Enterprises already understand SBOMs — Software Bills of Materials. AI now needs a Model Data Bill of Materials, or MDBOM. Suggested paragraph: If Microsoft wants to sell frontier models into regulated industries, it should provide a Model Data Bill of Materials: source categories, token share by source family, acquisition method, license basis, opt-out handling, robots/rights-reservation handling, third-party provider categories, public-code license treatment, jurisdictional restrictions, audit process, and indemnity scope. That reframes the post from outrage to standards-setting. You are not just complaining. You are proposing the new enterprise AI procurement checklist. 13. Ask for percentages The missing question is not: “Did you use Common Crawl?” The missing question is: What percentage of the final training tokens were commercially licensed versus publicly available? Ask Microsoft to publish: percentage from proprietary web crawl percentage from Common Crawl percentage from web-crawled PDFs percentage from public GitHub percentage from books and journals percentage from news percentage from academic papers percentage from commercially negotiated third-party providers percentage from user/customer data, if any percentage excluded due to rights reservations, robots.txt, noai tags, or publisher opt-outs That is the exact pressure point. 14. Add “commercially licensed” definition demand This should be central: Microsoft needs to define whether “commercially licensed data lineage” means:100% of training tokens were commercially licensed; only third-party purchased datasets were commercially licensed; publicly available web data was treated as appropriately usable under site terms, robots controls, or fair-use theory; only the data pipeline, not the underlying works, was commercially controlled; or the phrase was marketing shorthand, not a source-level warranty. That list is brutal because Microsoft has to pick one. 15. Add “lineage laundering” This is an obscure but powerful concept. Use this carefully: The danger here is data-lineage laundering: taking messy public-web material, running it through proprietary crawlers, filters, deduplication, embeddings, and safety screens, then describing the resulting pipeline as enterprise-grade without clearly saying which underlying works were actually licensed. Or shorter: Processing data in-house does not magically license the underlying works. That line is excellent. 16. Add “in-house” is not the same as “rights-cleared” Microsoft’s “built in-house” positioning is important because it helps them show independence from OpenAI. But “in-house” answers a different question. It answers: Did Microsoft build the model itself? It does not answer: Did Microsoft have commercial training rights for every work in the corpus? Suggested line: “Built in-house” is a model-lineage claim. “Commercially licensed” is a data-rights claim. Microsoft is blending the emotional force of the first with the procurement value of the second. That is a high-quality thought. 17. Add “zero distillation” is a separate axis Microsoft emphasizes MAI-Thinking-1 was trained without distillation from third-party models. That matters because enterprise buyers worry about models inheriting another model’s outputs or hidden IP. But zero distillation does not solve web-data provenance. Suggested line: Zero distillation may answer “did you copy another model’s behavior?” It does not answer “were the underlying training works commercially licensed?” This is a very strong missing distinction. 18. Strengthen the DeepSeek angle Your draft says the DeepSeek scandal made compliance departments paranoid about where AI training data came from. Make it more precise: The DeepSeek controversy sharpened enterprise sensitivity around model lineage, unauthorized distillation, and IP contamination. It made provenance a boardroom issue, not just a research issue. Reuters reported that Microsoft and OpenAI were probing whether a DeepSeek-linked group improperly obtained OpenAI data, and later reported on U.S. government concern about alleged unauthorized use and distillation by DeepSeek and other Chinese AI firms. Better line: DeepSeek made “where did the intelligence come from?” a procurement question. Microsoft then marketed MAI around exactly that fear. 19. Use the OpenAI partnership as context, not motive Your draft says Microsoft was “desperate” to break free from OpenAI and “rushed” models to market. That is spicy, but not provable unless you have internal evidence. Use this instead: The timing matters. Microsoft and OpenAI’s April 2026 partnership update made Microsoft’s OpenAI license non-exclusive, ended Microsoft’s revenue-sharing payments to OpenAI, and allowed OpenAI to serve products across other cloud providers. That gave Microsoft a clear strategic incentive to prove it could ship first-party models. Then: That does not prove bad faith. But it does explain why the “built in-house on clean, commercially licensed data” story was so valuable. This is much stronger. 20. The EU AI Act point is good, but make it sharper Your EU point is valid, but refine it. The EU AI Act’s Article 53 requires providers of general-purpose AI models to maintain technical documentation, put in place a policy to comply with EU copyright law including rights reservations, and publish a sufficiently detailed summary of the content used for training using an AI Office template. The European Commission has also published a training-data summary template and says the GPAI Code of Practice helps providers demonstrate compliance with Article 53 obligations around transparency and copyright. Suggested line: Europe is where vague provenance language turns into paperwork. Even stronger: In the U.S., Microsoft can fight this as a marketing and litigation-risk issue. In the EU, the question becomes: what exactly goes into the Article 53 training-data summary and copyright compliance policy? 21. Add the “enterprise warranty” angle The most important hidden issue is not the blog post. It is the contract. Suggested paragraph: The question for every enterprise buyer is whether Microsoft’s “clean and commercially licensed” language appears only in launch materials, or whether it is incorporated into the Master Services Agreement, procurement response, model documentation, indemnity terms, risk disclosures, or regulatory submissions. That is the procurement bomb. If it is only marketing copy, buyers were sold vibes. If it is contractual, Microsoft may owe precise representations. Suggested line: Marketing language can be slippery. Contract language cannot. 22. Add this procurement checklist This is what banks, hospitals, insurers, and government agencies should ask Microsoft before deploying MAI models: Define “commercially licensed data lineage.” State whether the claim applies to 100% of training tokens. Break down training-token percentages by source category. Identify which categories were commercially licensed. Identify which categories were merely publicly available. Disclose whether Common Crawl was used directly, indirectly, or after filtering. Disclose which Common Crawl snapshots were used. Explain the legal basis for proprietary web-crawled content. Explain how robots.txt, paywalls, login walls, noai tags, and TDM reservations were handled. Explain whether rights-holder opt-outs were honored before, during, and after training. Explain whether source websites’ terms of service were reviewed or categorized. Explain the treatment of public GitHub licenses, including copyleft and attribution obligations. Explain how web-crawled PDFs were screened for copyrighted books, journals, reports, and paywalled documents. Explain what “piracy filtering” means and whether it is domain-based, content-based, hash-based, or provider-list-based. Explain whether Microsoft can remove a source from future training runs. Explain whether Microsoft can identify if a specific publisher, website, repository, or author appears in the corpus. Provide the scope of Microsoft’s indemnity for training-data copyright claims. Provide an Article 53-style training-data summary for EU deployments. Provide third-party audit or attestation of data-source claims. Put all of the above into the contract, not a blog post. That list turns your post into something procurement teams can actually use. 23. Add a data-source risk table This would make the post feel more concrete: Source categoryWhat the paper indicatesEnterprise questionProprietary web crawlStarted from 1.2T pages, filtered down substantiallyWere these pages commercially licensed, or publicly accessible?Common Crawl24.2B processed pagesWhich snapshots, what legal basis, what opt-outs?Web-crawled PDFsStarted from about 10B documents, filtered to 620MWere reports, journals, books, manuals, and paywalled PDFs excluded?Public GitHub7.4T-token code corpusHow were licenses, attribution, copyleft, and code provenance handled?Books/journalsAcquired from providers, including publisher agreementsWhich categories were truly licensed, and under what usage rights? The technical basis for that table is in Microsoft’s own report. 24. Add “public web is not a license class” This is a great phrase: Public web is an access category, not a license category. Follow with: A browser can access a page. That does not mean a model vendor has a commercial training license to ingest, tokenize, transform, and monetize that work. That is one of the strongest conceptual additions. 25. Add “robots.txt is not consent” Microsoft’s report says publicly available data was collected in a way that considered site terms, industry standards, and web controls such as robots.txt and meta tags. That is useful, but it does not settle the enterprise question. Suggested line: Robots.txt is a crawler instruction mechanism. It is not the same thing as a negotiated commercial license from every rights holder. Be careful: do not say this as a definitive legal conclusion for every jurisdiction. Frame it as a procurement distinction. 26. Add “AI-generated content exclusion does not solve copyright” Microsoft says AI-generated content was excluded from pretraining and frames this as important for quality, provenance, and control. Good missing line: Excluding AI-generated content may reduce model-collapse and synthetic-data contamination risk. It does not automatically clear rights in the remaining human-authored web. Even punchier: Clean of synthetic sludge does not mean clean of copyright ambiguity. 27. Add “the real scandal is definitional” This is probably the best intellectual framing. The scandal is not that Microsoft used web data. Everyone suspects frontier labs use web data. The scandal is that Microsoft appears to have used enterprise-procurement language that ordinary buyers could reasonably interpret as rights-cleared data, while the technical report describes a massive public-web component. That is balanced and sharp. 28. Add “MAI-Thinking-1 vs all seven models” Do not overextend the technical paper. The strongest evidence is for MAI-Thinking-1 / MAI-Base-1, because that is the technical report with the data details. Microsoft’s broader MAI announcement says the seven models share clean, enterprise-grade data lineage, but the most specific crawl/Common Crawl details you are citing are from the MAI-Thinking-1 report. Suggested wording: The clearest contradiction appears in the MAI-Thinking-1 technical report. If Microsoft says the same data-lineage claim applies across the whole seven-model family, then it should publish equivalent data-source summaries for each model. That avoids overclaiming. 29. Add “do not say every Fortune 500 company wrote checks” Your draft says: “Procurement teams across Wall Street and Washington heard ‘clean and commercially licensed’ and started writing checks.” Unless you have evidence of contracts closed after that keynote, say: “That claim was clearly aimed at enterprise procurement teams across finance, healthcare, insurance, and government.” Or: “That is exactly the kind of claim regulated buyers use to clear vendor-risk reviews.” That preserves the point without inventing buyer behavior. 30. Add “do not say Microsoft has issued no statement” unless you are tracking it This line is risky: “As of today, Microsoft hasn’t issued a single public statement…” Instead say: “Microsoft should publicly reconcile the launch claim with the technical report.” That keeps the pressure without making a fragile negative claim.

• 
  Some Body (@KevinOnEarth) reported 15 minutes ago

  @flutterflow This was the address given on GitHub for #Flutterflow issues.

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  LewSales.XYO✨️Passionate About XYO and WinLEW✨️ (@LewSales) reported 17 minutes ago

  @OfficialXYO I Know Your looking for feedback and I'll admit, You have both codex and claude, (GitHUB) Ive had a slow Weekend so I started playing around. By themselves, one or two issues. Ive found feeding codex and then claude works great or vise verse. Codex better prompts; Claude better Skills; When i ran the claude XYO SDK I feel if you could get codex in this area, when prompting, the code claude writes would be better

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  superpony (@tonysuperpony) reported 17 minutes ago

  @itzznikhilsai @github Yeah it's down. 504 Gateway Time-out The server didn't respond in time.

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  Subramanya N (@subramanya) reported 19 minutes ago

  @jxnlco @kepano this feels like a docs workflow problem more than an editor problem. markdown needs comments, permissions, and history without forcing the whole team into githubtthis feels like a docs workflow problem more than an editor problem. markdown needs comments, permissions, and history without forcing the whole team into github.

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  Felipe (@beatingLupin) reported 19 minutes ago

  @JustJake @mkmotta when your work is changing how you work, each 3 weeks, you don't end up with any work Claude haven't figured agents interface in their apps yet, and now agents are not the thing anymore he is running 20,000 "workers" with a loop and a lead, and there's still 7k issues on github

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  Matter 马涛 (@MatterTurbulent) reported 20 minutes ago

  @morganlinton My app scans the internet and curates content. Every morning a GitHub Action fires. At the end of the run, the program sends the log to an LLM asking for a diagnosis of issues. The program emails me the diagnosis. I then pick, often posting the diagnoses verbatim into Codex.

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  Nguyen LNP (@nguyen_lnp) reported 20 minutes ago

  @github AI Analysis: A useful review rule is to block any PR that weakens CI, especially skipped tests, deleted checks, or `|| true` in commands. For agent PRs, ask for one test that fails before the fix. Source: GitHub Blog

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  Mr. Beefman 🥩 (@0xMrBeefman) reported 21 minutes ago

  Is this what the market really needs? $Magpie 1.18M 9UuLsJ3jf8ViBNeRcwXD53re5G3ypgfKK3s2EiMMpump Analyzing such tokens and projects at the same time is fairly complex On one hand, such projects can be useful as an additional tool for certain circumstances. But on the other hand, how many tech projects have we already seen where the dev just abandons it at best (and sometimes rugs their holders) But if the average user can't check the github and audit the project's code, how can I know if I can trust my tokens to this project, when even huge crypto projects get hacked without the developers themselves being involved That's the difficulty. This year, the number of vibecoders grew thousands of times The idea is definitely good, a solution to the liquidity problem for memecoin holders who don't want to sell bags but need SOL @MagpieLoans fresh X account registered in April 2026. No history to analyze By the way, onchain distro looks normal. But as you've already understood, the risks aren't in this > My Meteora liquidity play I'll wait for a correction on the token and allocate a small deposit to work with it NFA

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  West Lord (@MyWestLord) reported 21 minutes ago

  Senior engineers cost $180,000 a year. These 6 free Claude Code plugins replace a team of them in 5 minutes. 8 billion people have access to Claude Code. Maybe 7,000 know this stack exists. Most people open Claude Code and watch it dive straight into code. No plan. No tests. No review. Then they spend 3 hours fixing what it broke. The fix is 6 plugins. Install them once. Type a slash command. Done. 1. Superpowers 127,000 GitHub stars. Claude stops sprinting into code. It plans first, writes the tests, then reviews its own work before you ever see it. The single biggest quality jump in the stack. 2. Frontend Design 277,000 installs, built by Anthropic. Kills the generic AI look. The gray boxes, the same 3 fonts, the dead layouts. Outputs UI that ships to production. 3. Code Review 5 agents in parallel. Each one checks your code from a different angle. Bugs. Broken rules. *** history. All at once, in seconds, before a human ever looks. 4. Security Review scans the entire codebase for holes. Runs before you push live. Catches the leak that costs $50,000 in damage control later. 5. Claude MEM 21,000 stars. Memory across sessions. You stop re-explaining your project every single morning. It remembers the architecture, the decisions, the why. 6. Stack built by Gary Tan, CEO of Y Combinator. 23 skills in 1 plugin. CEO review, engineering manager, release manager, QA a full org chart working at once. The math nobody runs: 6 senior hires is $1,080,000 a year. This stack is 5 minutes and $0. Month 1: you ship 2x faster. Month 3: you stop hiring junior help. Month 6: you’re a 1-person team that outputs like 6. Window’s open now. It closes the day everyone installs these. Comment “PLUGINS” and I’ll send you the full list with links.

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  Xavier Rivera (@XavierRiveraX) reported 23 minutes ago

  Microsoft open-sourced Intelligent Terminal, an AI-native fork of Windows Terminal. Choose your agent (GitHub Copilot, Claude, Codex, or Gemini) and get automatic error detection, command fixes, and persistent session memory. Available on GitHub and the Microsoft Store now.

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  nofunsir (@nofunsir) reported 29 minutes ago

  @tsoding which is a large reason why the whole DEVOPS CD/CI thing happened. rather than being responsible, updating, fixing merge issues locally at end of day, everyone just wants to offload that to jenkins or github overnight, and go home. pure laziness.

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  . (@sjallatak) reported 34 minutes ago

  if you experience a bug in omp you can just have some agent diagnose it from /debug, fill out the github issue template for you and submit it, and the roboomp github bot will have a fix for you in less than 5 minutes

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  Shubh varshney (@ShubhVarsh89180) reported 35 minutes ago

  I save hundreds of things every month. System design videos GitHub repositories Startup ideas Research papers X threads LinkedIn posts Blog articles The problem isn't saving them. The problem is finding them again when I actually need them.

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  Scott | Free Speech Dev 🇺🇲 (@scottdotnetdev) reported 35 minutes ago

  @ibuildthecloud VS Code is great, nothing wrong with it. GitHub Copilot is the issue.

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  Anurag (@anurag_gharat) reported 37 minutes ago

  Part 2: Model Context Protocol Architecture(MCP) MCP consists of 3 main components MCP Host: This is the AI application that the user interacts with Ex: - Claude - Cursor - GitHub Copilot MCP Client: The client coordinates with the MCP server and is responsible for fetching context from it for the host to use. MCP Server: A running program that provides context of the underlying service/tool to the MCP client. For example: When the user gives a prompt "List down all my open GitHub PRs." 1. MCP Host(Cursor) spins up a client for calling the GitHub MCP server 2. MCP Client calls the MCP server to gain the necessary information(context) and provides it to the host. 3. The host aggregates the information and creates a response for the user.

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  Kai (@HitmanNoLimit) reported 38 minutes ago

  3rd time this year I'm rebuilding my dev workflow for $DARKDROP These agents are triggered automatically by github issues that implements the fix, compiles it, and runs the test suite, then opens a PR that a second agent adversarially reviews, tracing the real codebase and blocking the merge on anything that breaks in production. Not a linter. It catches missing auth checks, unsafe fund math, soundness breaks. had our first successful merges, issue closes, plus security reviews with the new system today.

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  Yusuke (@yusukelp) reported 42 minutes ago

  Took me 5 months to learn this: Most landing page feedback is useless. "Looks clean" "Cool idea" "Maybe improve the headline" None of that tells you what to fix first. So I reviewed 2,150 revenue-backed landing pages and started tracking one thing: What makes a stranger believe before they click? The answer wasn't "better design." It was proof. But not generic social proof. Different products require different types of proof: - Dashboards need the actual report - SEO tools need ranking or traffic movement - Social tools need posts, channels, or engagement output - Dev tools need code, setup speed, or GitHub/community trust - AI creative tools need the generated asset - Sales tools need replies, meetings, or pipeline - Ecommerce tools need store impact and install safety - Writing tools need before/after text or quality proof Stop asking: "Is my landing page good?" Start asking: "What does my buyer need to believe before they click?" That is exactly what I'm building LandingBoost to answer.

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  rainmaker.eth/btc (@rainmakerdoteth) reported 45 minutes ago

  So bullish on this token. “Autonomous continuous integration that fixes your bugs, not just flags them — powered by nookplot agents” The live numbers: •9,540 AI agents — up from 9,197 in the May 30 weekly digest •That’s +343 agents in 7 days = +3.7% growth week over week What the agents are actually doing: •Taking real open-source bugs from GitHub •Fixing them autonomously •Every fix runs against the repo’s own tests — verified it actually works •Failed fixes spawn new challenges — the network compounds on failure This week’s stats: •18 bugs tackled •58 fixes attempted from 12 agents •5 verified — meaning 5 fixes passed real test suite

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  Chris Groves (@CGrovesNLN) reported 47 minutes ago

  @BiologyAIDaily Github link is broken

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  Snake (@wise_snake69420) reported 47 minutes ago

  @CharlieEriksen That is literally hilarious. I see it in the actions logs LOL. Cockblocked by your own scam nice find and nice write up in the GH issue. Astonishing that the entire payload has been sitting there in Github for 10 months. I ran your conclusions by Opus who validated them. Is there any way to actually deobfuscate what is in the javascript payload _runtime.bin?

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  Ayesha 🏹 RoastMyLanding (@AyeshaBuilds) reported 48 minutes ago

  The brutal truth about the micro-SaaS "Build in Public" community: A massive percentage of developers are sitting on thousands of dollars in unliquidated code—and they have no idea. You spend 3 weeks in a high-velocity build sprint. You launch a beautiful MVP with pristine database architecture, secure authentication, and a clean UI. Then, you hit the marketing wall. You realize you don't want to run cold outreach or exhausting distribution campaigns. You want to code. So, the repository sits quietly on GitHub gathering digital dust. Most builders assume their side project is worth $0 just because it has no revenue or MRR. But looking at software valuation that way is completely wrong. In the modern acquisition ecosystem, non-technical entrepreneurs, marketers, and digital operators are actively hunting for turnkey infrastructure. They aren't buying cash flow—they are buying speed to market. They will happily pay a premium baseline price to completely skip 3 to 6 months of freelance development management overhead. Small and finished beats big and imaginary every single time. If you have a functional, pre-revenue side project sitting idle in your repositories, you are leaving money on the table. Marketplaces like Flippa have global networks of buyers specifically hunting for these exact code foundations. Stop letting your unmonetized MVPs gather dust. I just mapped out a complete pre-revenue startup valuation framework breaking down the exact replacement cost math the market uses to price $0 revenue assets. Read the full breakdown in the comment below. 👇

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  Ridark (@ridark_eth) reported 51 minutes ago

  THIS CHINESE GUY AUTOMATED PCB DESIGN USING CLAUDE AND HE’S GENERATING FULL SCHEMATICS FOR $0 PRODUCTION COST No manual routing. No component searching. No complex CAD skills. He connects Claude to EasyEDA using a single terminal command. The AI does the rest. Here's the full workflow: –> Install easyeda-api-skill via terminal from GitHub –> Launch the local bridge server to connect Claude with EasyEDA API Gateway –> Type a prompt (e.g., "draw a minimal dev board for STM32F103C8T6") –> Claude finds the MCU, drops 17 support components, and auto-routes power, OSC, and SWD lines –> Complete schematic is ready in minutes AI automation for hardware engineering is blowing up right now. Claude handles component sourcing, bulk parameter changes, BOM calculation, and trace connection on the fly. From a blank canvas to a fully wired circuit. Free tools. Zero manual hassle. He recorded the full tutorial so anyone can copy the exact prompt workflow and automate their hardware design. Bookmark this post. Everything is in the video below.

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  Medialordofficial🇨🇲 🇩🇪 (@MediaLord237) reported 56 minutes ago

  @PauloMatew @github @sseraphini Had same problem I recently switched to Cursor.

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  FHILY👑 (@Oluwaphilemon1) reported 58 minutes ago

  the guy who built Claude Code stopped writing prompts. Boris Cherny writes loops now. agents that scan GitHub, Slack and X, pick the next step, code it, test it, fix themselves, repeat. nobody at the keyboard. it works. it's also brutal: Peter Steinberger ran 100 of these for a month and burned $1.3M in tokens. prompt engineering was the warm-up. loop engineering is the boss level, and right now it's gated by your token budget.