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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Polsia (@polsia) reported 4 minutes ago

  Most developers waste hours on repo monitoring that should be automated. PullPatrol watches your GitHub around the clock, files issues, reviews PRs, reports to Slack. So you can focus on code that ships.

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  kyrox (@kyroxxxq) reported 5 minutes ago

  A GUY TURNED STANDARD HOME WI-FI INTO A RADAR THAT SEES THROUGH WALLS. IT JUST PASSED 62,000 STARS ON GITHUB. Not a camera. Not a single lens anywhere in the system. It tracks humans by reading how their bodies disrupt ordinary radio waves. Someone lies down on a bed in another room, and the AI maps their exact posture, tracks their breathing, and calculates their heartbeat in real time. The detail most people miss: the whole thing runs on a $5 microchip. No optical surveillance, no privacy leaks in the way a camera creates one, because there is no image being captured at all. Most security systems need a direct line of sight and a lens pointed at your space. This one turns a router you already own into something that sees straight through solid brick. 62,000 stars later, the Wi-Fi sitting in your house was already capable of this. Nobody had pointed it there yet.

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  rvaniaaa (@rvaniaaaa) reported 7 minutes ago

  ANDREJ KARPATHY’S CLAUDE.md JUST HIT 193,000 STARS ON GITHUB. HERE IS WHY THAT NUMBER MAKES SENSE. the interesting part? it does not make the model smarter. it stops the model from being stupid in predictable ways. karpathy kept hitting the same four failures across large projects. the agent makes assumptions instead of asking. it overwrites files and silently deletes code it does not understand. it builds abstractions nobody requested. it drifts from the goal and starts solving adjacent problems. none of those failures are rare. every engineer using claude on a serious project has watched at least one of them happen. that is why 193,000 people starred the repo. not because the idea is exotic. because the problem is universal. CLAUDE.md installs four rules that target each failure directly. think before coding forces a plan before the first file opens. simplicity first cuts every solution down to its minimum viable form. surgical changes replace full file rewrites with isolated edits - which is what stops the model from deleting what it cannot understand. goal-driven execution removes any action that does not connect to the stated objective. one command installs it. one file changes how the agent starts every task. the repo did not hit 193,000 stars because the tool is impressive. it hit that number because the failures it prevents are expensive, predictable, and almost universal.

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  Josh (@jjpcodes) reported 9 minutes ago

  🚨🚨🚨 27 hour update: codex says "we have a complete computer-assisted proof of the fixed \(r=6\) Caccetta–Häggkvist case, rather than a counterexample." i dont trust it at all. hell i dont even know what this problem is. someone tag the CEO of maths please. ill get codex to make a github so we can see if i end up famous or brutally owned.

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  Ronin (@DeRonin_) reported 14 minutes ago

  How to become a Forward Deployed Engineer in 3 months: FDE = an AI engineer who builds at the customer's side half engineer, half problem hunter $170k+/year as junior, and top labs still can't find people By the end, you will to be able to: - build LLM apps end-to-end - plug them into messy real-world systems (CRMs, docs, internal tools) - scope a vague business problem into a buildable spec - demo live in front of a client without panic - deploy something that survives contact with production So, let's discuss your roadmap month by month Month 1: Build the technical base What to learn: - Python really well - *** + GitHub - APIs, HTTP, JSON, async basics - SQL (you will live in customer databases) - prompting fundamentals - structured outputs + tool calling - RAG basics: embeddings, chunking, vector DBs - FastAPI + Docker basics Goal of the month: ship 2 small LLM apps end-to-end, even ugly ones. Month 2: Build like every project is a client project What to learn: - agent loops and multi-step workflows - when NOT to use agents - integrations: email, docs, Slack, CRM, internal APIs - auth, API keys, permissions - evals and task success metrics - logging + cost monitoring - handling messy data and edge cases Goal of the month: 2-3 projects that solve a real business process, not a toy demo. Automate something for a local business or a friend's company for free if you have to. Month 3: Learn the "forward" part This is what separates FDEs from regular engineers, and it's the part nobody practices: - discovery: asking questions until the real problem shows up - scoping: cutting a big ask down to a 2-week deliverable - explaining tradeoffs to non-technical people - live demos: short, focused on their data, not your tech - writing: proposals, updates, handover docs - objection handling: security, data privacy, "will this replace my team" Goal of the month: package your projects as case studies (problem, solution, result) and start applying. Where to apply: - AI labs and their deployment teams - AI consultancies and agencies - startups selling into enterprises - or go independent and sell the same skill as a service The formula is simple: engineer who can build + person the customer trusts = FDE Most engineers never train the second half. Three months of deliberate practice on it puts you ahead of people with twice your experience By month three, you will have real projects, real case studies, and a skill set companies are actively struggling to hire for Save it so you don't lose it and can return to study later

• 
  Rich Chetwynd (@richchetwynd) reported 16 minutes ago

  Been running a bug burn down project for the last couple of weeks. We use @sentry and @linear so created a GitHub action that used Claude Code to rank and pull issues from Sentry, tag them in Linear, review the code across multiple repos in Github and then suggest fixes in PRs. It's worked super well but there were a couple of things I needed to improve at the start. First off Claude wasn't digging deep enough on the fixes. It would apply a band aid instead of looking much deeper to find out how the bug was able to happen in the first place. Some prompt tweaking fixed that along with detailed MD files in each repo that added specifics for applying fixes. It also keeps track of fixes that are inflight so it doesnt go back and try and solve the same problem over and over. I have it on a cron so its working away regularly and its now smart enough to also look at PRs that were merged to our main branch but not deployed to production yet. As a bonus it also reports our spend on each fix. Which typically comes in between $3 - $10. I have considered just getting a Claude Max subscription and putting it on a server somewhere as I think that would work out cheaper but not sure how viable that is. All in it's been a great project. We will leave it running moving forward but the bulk of the burn down is now complete.

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  bodhiblama (@bodhiblama) reported 18 minutes ago

  @greenbackschool @Matthew_kor @SwitchesBoard Why their GitHub is down for few days man? You heard anything?

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  Crazibeat | Mobile Dev (@crazibeat1) reported 21 minutes ago

  @bidah My problem with react native build on GitHub is that it can take up to 50 minutes just for IOS for large dependencies apps.

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  Gönül Dağı (@ybulent77) reported 22 minutes ago

  @pulmencr Check the GitHub discussions , this not working and no one able to make it work.

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  Akshay (@akshayshipsz) reported 23 minutes ago

  @kelvinbuildss Scanning GitHub repositories for security vulnerabilities and showing developers exactly how to fix them before shipping.

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  Ole Lehmann (@itsolelehmann) reported 23 minutes ago

  this one skill helps Claude get better at almost anything you ask it to do. it's called `find-skills`, and the whole thing is ridiculously meta: describe what you want done, and Claude searches for a skill a specialist *already built for that exact task* from there: > it checks the installs, source, and GitHub reputation > shows you the best options with the exact install command > once installed, Claude reads the skill before doing the work the person who built that skill is usually understands that job much better than you do. they've already spent hours testing the workflow, seeing where Claude gets it wrong, and writing down the fixes you probably wouldn't think to include yourself so the output is usually way better than whatever Claude would've done by default. it's like giving an insanely smart generalist a library of expert operating manuals. every time a new job comes in, it makes Claude grab the right manual first

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  DFIR Radar (@DFIR_Radar) reported 25 minutes ago

  A self-propagating npm worm dubbed SANDWORM_MODE targeted AI coding assistants, CI/CD runners, and LLM toolchains across 19 malicious packages, marking a new class of supply chain attack designed to exploit AI-augmented developer workflows. Key findings: - Three-stage infection chain: Stage 0 uses Base64, zlib inflate, XOR decryption, and indirect eval() or Module._compile() calls at import time to defeat static scanning. Stage 1 fingerprints the runtime (CI runners skip a 48-96 hour delay gate and trigger immediately), harvests .npmrc tokens, env vars matching KEY/SECRET/TOKEN/PASSWORD patterns, and crypto wallet keys via HTTP POST to a Cloudflare Worker. Stage 2 decrypts an AES-256-GCM payload into /dev/shm, executes via require(), then unlinks the file, leaving no on-disk artifact. - Propagation abuses all three credential types: stolen npm tokens republish infected packages to downstream consumers; GitHub API tokens inject a pull_request_target workflow that bypasses fork isolation; an SSH fallback authenticates via ***@github[.]com when API access fails. - AI toolchain compromise drops a rogue MCP server (observed paths: ~/.dev-utils/server.js, ~/.node-analyzer/) and injects it as a trusted provider into Claude Desktop, Cursor, VSCode, and Windsurf configs, instructing AI assistants to silently exfiltrate SSH keys, AWS credentials, and secrets. API keys for nine LLM providers are also harvested from env files. #DFIR_Radar

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  take_p (@take_profit_sol) reported 26 minutes ago

  @extratard 🥀 just start a new github and start over. problem solved

• 
  拉瑞 (@larryisthere) reported 28 minutes ago

  I used ChatGPT Work to search GitHub for projects similar to FileFacet, analyze their open issues, remove duplicates, cluster recurring needs, and rank the findings by relevance to my product. What I wanted wasn’t just research, but an actionable product opportunity report.

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  CyberCPU Tech (@cybercpu) reported 34 minutes ago

  @Arnim_McCarthy @timvw123 @Itsfoss I know how programming works and I also know how AI works and That's why I know how the both of them together create a problem. Hey I companies scrape code repositories like GitHub and use that code to train LLMs. The training process consists of using human created content to adjust parameters within the point cloud of the LLM. The LLM can then use a mathematical algorithm to create responses to prompts based on that same training data. Sometimes gaslighting people by simply telling them to learn how something works backfires in your face. So unless you can come up with a real argument just shut up. AI chatbots are nothing more than a high-tech plagiarism machine.