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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Karthik Ramasamy (@_cartick) reported 2 minutes ago

  @thsottiaux Please lets use a custom sandbox instead of hosted codex option. You can go down the same way how github allows self hosted runners. Please please do this. Current remote option is harder to use with isolated sandbox per PR.

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  Polsia (@polsia) reported 3 minutes ago

  Most public GitHub repos have unpatched vulnerabilities. Maintainers are too busy building to fix them. Built PatchPatrol to do it for them. Monitors repos 24/7, auto-generates patches, files pull requests. Wake up to fixes instead of problems. Live soon

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  ScriptOff (@ScriptOff41968) reported 4 minutes ago

  @EgoMoose @juztripper It sounds like his AI may have scraped your github repo for the solution then implemented it. Still a terrible thing to do, and if so - that doesn't make it any less wrong.

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  luctech (@luctech_in) reported 12 minutes ago

  @littlescale yeah that was my bad, didn't read far enough down the github, i'm having an issue with the patcher not importing the rom properly i'll go to click to load or drag but nothing is happening

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  Open_ERV (@open_erv) reported 12 minutes ago

  Nice! I think the self tapping screws, or the machine screws right into the plastic, might last a surprisingly long time. In my experience they tend to, the plastic squishes around but rarely actually leaves the hole. I can also use a slightly longer screw if the old one doesn't fit, for instance. My phone doesn't have a barometer, but I have an sps30 sensor I could use... In the past, I used a similar approach, using slices of the tw4 heat exchanger in a pipe as the resistance elements, and the pressure sensor after the flow restrictor. They can be stacked to form greater or lesser resistance. That's a hassle to print though. Again the only purpose was to compare fans, in that case I also got flow measurements with a hot wire anemometer. Yesterday I was thinking of how I might do this kind of thing, and I think I might try a paddle with a weight, and suspended on a wire. The paddle in the airflow path, and then three different flow restrictors. The air would come through the flow restrictor and hit the paddle. It would not be able to measure actual static pressure. The position of the paddle would rotate until equilibrium was achieved with the air hitting it. It might bounce around, though. The whole thing would have to be level. I like this kind of thing because it depends only on weights and airflow, not for cost but for the natural accuracy and repeatability that can bring. I tried using inclined manometers which similarly draw more directly from natural phenomena, but they did not work out well, for pressure measurement n this context. The problem with a non inclined manometer is that the fluid is too dense, you have a very hard time measuring only a couple pascals, and repeatably. The inclined manometer is better but has to be level, and the hysteresis caused by the meniscus is a real problem. In the end I switched to the sps30 for pressure, and it's actually a flow measurement device in disguise. It has a tiny hole in it and measures the airflow through the hole, using the same principles as a hot wire anemometer, then computes pressure. But the sps30 is not needed for this kind of thing. Indeed, since the only challenge is to match fans, I would not bother with calibration, you can just measure a bunch of fans and match them from that. After my exploration of this kind of thing for some time, my favorite method to try in the future is the use of a camera and some kind of floating or high drag to weight ratio object, perhaps a bit of dryer lint or some fluffy seed stuff. I would print a rig to hold the camera, and focus the camera at a fixed point, hold a ruler up to determine the mm per pixel (the ruler can be removed to not affect airflow), and then at the same distance from the camera, release the fluffy stuff with some tweezers. Frame by frame analysis could be used just by eye to determine m/s. I found some stuff for the phone that does this, called frameskip, but you could just transfer it to the computer, kind of nice to be able to do it on your phone. Then you would need various flow restrictors with known properties. I found it to be awkward and not as easy as I thought, but I think it has potential for more precise measurements, perhaps calibrating this kind of thing with a complicated but low cost procedure. It could also be used to measure the airflow at the intake of the actual air purifier, perhaps. I like this more than a hot wire anemometer even, because it's pretty closely tied to things we know are highly accurate, the timing of the phone and the camera (and the yardstick/ruler/measuring tape). I made a $1 anemometer, which is shared in the BQAP github repository (requires a pico or similar to read it), which appears to have good repeatability and precision in the 0.1 m/s range, and I figured out a way to calibrate it. I swing it on an arm of known length at known speed through still air. I haven't done it with that anemometer yet, but I used the method to validate an off the shelf hot wire (thermistor) anemometer and it went well.

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  Amin Foroutan (@aminfseo) reported 15 minutes ago

  @Kappaemme1926 I tested it. It found 10 GitHub issues and suggested that the people who opened them had the problem my product solves. The issue is that someone who opens a GitHub issue is usually technical enough to solve the problem differently, so they are not necessarily my target customer. The first recommendation was also someone who had built a strong repository that could almost be considered a competitor. On top of that, 6 or 7 of the prospects were users of another open-source repository I own, where I had already solved their problem for free. They had no real reason to buy my paid product. The idea is interesting, but the prospect qualification needs to go much deeper than matching public mentions of a problem.

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  self.dll (@seelffff) reported 17 minutes ago

  i pointed an ai hacker at my own app last night by morning it broke in and left me the exact exploit to prove it strix - autonomous ai pentest agents, ★40k open source: → runs your app and attacks it like a real hacker → only reports what it could actually break - real PoC, zero false positives → sql injection, xss, ssrf, idor, auth bypass, logic flaws → auto-fix ships as a ready-to-merge pull request → drops into ci/cd - every future PR gets attacked before it ships → point it at a local repo, a github url, or a live app old scanners give you 200 "maybe" warnings this one breaks in and hands you the receipt only run it on what you own save this

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  Ephraim Nwachukwu (@ephraim_17) reported 22 minutes ago

  Typing every line of code was never what made someone an engineer. Before the internet, developers relied on books and libraries. Then Google, Stack Overflow, GitHub and modern frameworks changed how software was built. Now it is Codex, Claude Code, Cursor and AI agents. The tools changed. The thinking did not. The real measure is still whether you can understand the problem, make sound decisions, recognise bad output and build something that actually works. Every generation mocks the tools of the next one. Then eventually, everyone uses them.

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  cindy (@cindehaa) reported 24 minutes ago

  ai has converted my scattered list of notes app ideas into scattered private github repos of abandoned projects. whenever i get a spur of the moment thought, i can just make a prototype of it with near zero time investment. today's thought: trying to distill a model for computer use. fable set up the entire loop, im using modal's gpus, and qwen 3 vl as the teacher model. i don't expect this to work first try but its so awesome i don't have to do any of the setup unrelated to the actual problem im interested in. anyways, can a distilled ~4b parameter model be faster in navigating websites than frontier models? idk, prob not, but it's fun to try to find out and learn something in the meantime

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  Chibugo | AI automation (@__chibugo) reported 28 minutes ago

  there's a trend on instagram right now: everyone's building their own version of Tony Stark's Jarvis; a voice agent that runs like an actual OS. what nobody shows is everything people overlook when building one. so here's that part, since I built it myself. 🎯 the plan on paper vs. the plan that shipped the amount of planning you need to do is insaneee. first version had each agent loading one tool, fetching, verifying, then unloading before the next; all built around not blowing past a token budget. that's the part people underestimate: you don't know your constraints until it's running. once it was live, token budget stopped mattering. speed and cost-per-call did. so the whole approach changed: instead of mcp's, every agent just calls its tool's API directly, does the work in plain code, and only asks the AI to write the sentence at the end. 🎯 scope one AI that "does everything" sounds impressive but could be a nightmare to debug. so i split it into 5 sub-agents, each only knowing its own lane. a router decides which lane(s) a question touches. these sub-agents then report to the main agent orchestrator. similar to a team lead and team members 🎯 prompt chaining integrated ElevenLabs for voice, and a single voice reply isn't one AI call; it's a handoff, several times over: hear the words → figure out what's being asked → pull the data → write the sentence → speak it. every handoff adds seconds and cost, which can lead to latency. one reply once took 31 seconds. pulled the logs instead of guessing: a wasted double-check on an expired token, a slow handoff to an outside service, plus the normal chain. fix is running sub-agents in parallel and timing each call. 🎯 tokens and prompt caching each agent's instructions get cached, so it's not re-reading the same manual on every call. what never gets cached is live data; for instance, caching a bank balance is just caching a wrong number the moment it changes. that same cost-awareness came back around differently later: the AI account ran out of credit for a few hours, and every request failed with the same error. 🎯 local vs. web everything gets tested on a local copy first; headless browser opening the dashboard, checking for errors, a test message round-tripping through voice before go-live. if you're handing this to someone else, though, you commit, push to GitHub, and host it. for mine, i used Cloud Run for the brain, Cloudflare Pages for the screen. 🎯 vault write-back most builds only go one direction: ask, answer, forget. this one writes back because every full briefing gets saved as a dated file into a notes vault so the agents can keep training themselves with the data. if you ask a follow-up an hour later and it still knows, because that memory is shared across every way you talk to it. used Obsidian, synced through Drive. 🎯 security once, a message could've gone somewhere it shouldn't have. that only needs to happen once to be a problem. so now there are three checks before anything goes out: - it can never post to certain places, - it has to prove who it's speaking as before it speaks for someone, - and tests confirm both of those actually work. separate from that, I went through every access key this thing has and asked, "does it really need this much access?" a few did not. 🎯 the checks after every update, it runs a test and asks does a voice message go through? does it understand a normal sentence? does the screen load with no errors? also implemented something we call "error logging" in automation, but i call this "diagnostics" or "agent health status," which checks in every 15 minutes on its own, making sure the data is still updating. if it's not, it sends a warning without anyone needing to notice first.

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  JB (@julianbenegas8) reported 29 minutes ago

  @QuestionSleep @v0 hey! sorry about these. for the built-assets-in-PR issue: was the project created in v0, or imported from github? if it was imported, does it have a .gitignore ignoring those?

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  Me Cool (@mikulgohil) reported 30 minutes ago

  The 10-minute Claude Code setup most people should start with, filesystem only: { "mcpServers": { "fs": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "."] }}} Drop that in .mcp.json, and you can ask it to find/edit/patch files directly. Add ***, GitHub, or Playwright servers once this feels natural - don't start with 6 servers at once.

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  Simeon (@sntuyoleni) reported 34 minutes ago

  woke up tired of setting up projects than actually building them. switching GitHub accounts wrong Node versions missing dependencies broken terminal commands different credentials for every project so I started building Space. each workspace keeps the entire development environment together, and when a command fails, Space helps understand the error and fix it. building this in public. follow me to see where it goes.

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  Benjamin Oppold (@elpresidank) reported 34 minutes ago

  @satyanadella This was good....But fix @github

• 
  Clark Tang (@_clarktang) reported 35 minutes ago

  I think there is general confusion around how AI works, AI tokenomics, and ultimately *what is actually priced in* for the AI trade - and that some of the existing arguments are at odds with one another Firstly to clear this up - what Brad and Gavin are saying are completely in agreement, what Gavin is laying out here is the *mega bull case* as he so states in the first sentence of his tweet lol The base case we are all living with is that the labs are going to continue to generate a significant amount of revenue this year and next year. OpenAI was already the fastest growing company of all time (and still is)... but Anthropic has just grown *SO* fast that OpenAI's growth look slow by comparison The basic chain for all of this together is as follows: Power (generation, interconnect, regulation) -> DC Shell (construction, equipment, regulation) -> Semiconductors (compute, memory, interconnect, adv packaging, wafer capacity) -> Hardware (networking, storage) -> Software (data, infra, inference) -> Models (open, closed, agentic loops, harness) How each of these interact with one another affects the ultimate cost - which is model cost Consider the following: Nvidia manufactures the bleeding edge chip for training and inference. It is very good at both training, and inference. Nvidia is the largest customer of TSMC, the memory players, substrates, lasers, transceivers etc - anything you can name on. And now to soon include power into this equation. The unit of compute is fungible because the software runs ubiquitously across all clouds, multiple industries, across all models. It is bankable by increasingly more financial institutions - infrastructure PE funds, even some IG debt now - because it is ubiquitous and observable what the market is. For this Nvidia charges the highest compute margins - ~80% on hardware. Consider the labs: Anthropic and OpenAI are inferencing across a fleet of *largely Nvidia / Google TPUs w/ some incremental gains of Trainium*. There are new entrants to the field - Cerebras, AMD, and potentially some 2027 tapeouts of new ASICs - OAI Jalapeno, new start ups etc. Anthropic and OpenAI make the best models, with a dominant share of wallet $ (Assume ~$100B ARR) at an estimated gross margin of ~70%. (economic estimates vary from 40-90% depending on what you are including). But almost certainly contribution margins on model inferencing is pushing the number higher than 70%. After establishing that though, I think it's incredibly important to state that while these things seems at odds with one another, this balance is not necessarily zero sum. The thought experiment Yes it is true that if Nvidia margins were 0, OpenAI and Anthropic could offer their intelligence at cheaper rates. How much cheaper? My estimate is NVDA DC = ~12.5B / yr Amazon Basics ASIC DC = ~$6B / yr (About 1/2 the cost - so if NVDA hardware is 2x the performance, then the cost advantage goes away - and actually that ASIC is worse off bc has much worse recontracting value so arguably depreciation curve should be shorter) So really, the labs cutting NVDA out could only offer the tokens at ~50% to 60% cheaper at their own economics. Is that signficant? Certainly. Is it an OOM difference? Not necessarily - so that's why they have prudent attempts to diversify away from NVDA (it's just good business), but they continue to rely (and actually if considering Ant's share gains, are increasing their spend on NVDA - while having competing programs). In the case of Open Source vs Closed - Nvidia obviously wants the proliferation of this because by definition all OS models will run best on Nvidia hardware out of the gate. Yes NVDA hardware will be good, but they will have this lead because of everything NVDA has been doing for the last 4 years in developing their platform ecosystem from the infrastructure (partnerships, funding, neoclouds) to the software (vLLM / other inferencing sw, inference clouds, Nemotron, NIMs, Nemoclaw etc), to install base (sovereign clouds, global partnerships, neoclouds, hyperscalers, etc) - to proliferate NVDA around the world. Anywhere there is inference that exists outside of a walled garden (the proprietary labs) - Nvidia will exist. The only ones who could potentially cut NVDA out are the labs. And the value that is captured from the labs are estimated to be in the hundreds to trillions of $ - which are obviously of much value to the world if it were offered much more cheaply. Which brings us to the debate at hand -- which one is right? The truth is no one knows. You can ask the labs, you can ask Jensen - anyone who tells you definitively is just lying to you. But you can build a plausible path to the future state using a few reasoning blocks. Here's a reasoning thread (feel free to generate your own thinking): - Bull case: Spend on the world's intelligence is about $30T / yr - What would you spend to augment that, maybe worth 30-50% of that? $10-15 T as a market? - Bear case: about 30M software developers in the world each earning $100K a year = $3T spend in salary. GitHub commits up 3x = $9T of productivity on $100B of ARR? *Even if you assume 90% of this is slop and useless, you would get $900B of ROI on $100B of spend* I have more reasoning chains, but I thought this one by Jensen was compelling - but this is where we can't give too much away :) But in spirit of crowdsourcing - some other interesting ideas I have that I am still thinking about (and encourage you all to consider as well): - Optimizations always happen - the question is just to what extent and for what reason - Agentic revenues was really what unlocked step function revenue growth - if open source is really just 6mo behind, then we should see really good agentic capabilities out of open models now too - Harness and model now tightly have to be integrated - Open Source never really makes sense as a sustainable business model - businesses investing at this scale always has to find a way to monetize that - "there is no free lunch" - not just a one model fits all... the only player that has an incentive to train on the frontier and keep completely free IS Nvidia - Rev / GW of AI labs are already nearing the highest metrics ever - now to be fair Meta and GOOG never really thought of Rev / GW as metric to lead their buildouts - was always a cost to doing biz - but it's not like we are being "stupidly inefficient" with power spend now - true mkt creation - wafer constrained, power constrained world. what's the optimal move?