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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rituraj (@RituWithAI) reported 36 seconds ago

  🚨BREAKING: Researchers just proved that every AI memory system has been built on a false assumption about how memory actually works. Memory isn't retrieved. It's reconstructed. This isn't a new finding in neuroscience. It's been understood for decades. When humans remember something, we don't play back a recording. We reconstruct the memory from fragments — using context, surrounding information, and active reasoning to rebuild what we experienced. Every AI memory system ever built ignores this completely. Current memory-augmented agents all work the same way. Store memories. Search for relevant ones. Retrieve them. Pass them to the LLM. Done. The retrieval happens before the reasoning. Once memories are retrieved, they're fixed. If the reasoning process discovers new context that changes which memories are relevant — too bad. The retrieval already happened. That's not how memory works. In humans or in any intelligent system that reasons well over long time horizons. MRAgent from the National University of Singapore is the first AI memory framework built on the correct model. Here's the core insight. Instead of retrieving memories and then reasoning, MRAgent reasons and retrieves simultaneously — interleaving them in a loop. As reasoning produces intermediate evidence, that evidence actively shapes which memories get accessed next. You find one clue. The clue changes what you look for next. You find another clue. That changes your search again. You prune paths that turned out to be dead ends. You expand paths that keep yielding relevant information. Memory access adapts to the reasoning context in real time. Here's the structure that makes this work. Memories are stored in a Cue-Tag-Content graph. Not a flat list. Not a vector database. A graph where associative tags serve as semantic bridges — connecting high-level cues to detailed memory contents through multiple intermediate nodes. When MRAgent needs to remember something, it doesn't search the whole graph. It starts from the most relevant cue, follows associative tags based on what its reasoning has found so far, prunes branches that aren't yielding useful connections, and expands branches that are. It explores the graph iteratively — the way a detective follows leads rather than the way a search engine matches keywords. Here's the number that defines the result. Up to 23% improvement over strong baselines on long-horizon memory benchmarks — LoCoMo and LongMemEval. The tasks that require reasoning across hundreds of past interactions. The tasks that break every existing memory system. And it costs less. Fewer tokens. Less runtime. Because active pruning eliminates the combinatorial explosion that occurs when you try to retrieve everything that might be relevant before you know what's actually relevant. Better memory reasoning. Lower computational cost. From building memory the way biology built it. Here's the part most people will miss. Every AI agent memory system deployed today — MemPalace, mem0, Zep, Letta, custom RAG pipelines — uses the retrieve-then-reason pattern. Fixed retrieval. Static context. No adaptation during reasoning. MRAgent proves that pattern has a ceiling. And the ceiling is significantly below human-level long-horizon memory reasoning. The fix isn't more memory. It's smarter memory access. 23 GitHub stars. Code available now. From NUS. #1 paper on Hugging Face today — June 15. 100% Open Source.

• 
  Fabiana Cecin (@fabianacecin) reported 4 minutes ago

  The #1 utility of AI so far for me has been asking it: "I need to solve this problem, has anyone solved it yet?" And I find repositories on github that solve that problem. I would never find them via Google or Github search. Never, ever.

• 
  Layle (@layle_ctf) reported 6 minutes ago

  @chrisdutch81 Hmm, that's interesting. I wonder when this regression happened, cause it used to be playable for sure. Will have to look into it at some point, feel free to make an issue on GitHub

• 
  Tomasz Łakomy (@tlakomy) reported 6 minutes ago

  @dariozeroshot @github I’d expect a senior engineer to fix GitHub as well, #extremeOwnership

• 
  薇冷洛天依 Violet (@VioletFlowV) reported 7 minutes ago

  @thsottiaux More importantly, the issue on GitHub regarding the one-million-token context window seems to have been open for two months now. In the next generation of models, will we be able to use a million-token context natively within Codex?

• 
  Sean McLellan (@Oceanswave) reported 8 minutes ago

  @Youssofal_ @gilgNYC You didn’t say “I hope your youtube and github accounts get taken down.” because everyone knows that’s just way too far.

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  Jon Church (@jonchurch) reported 10 minutes ago

  @jdxcode @nateberkopec @github I know that’s not feasible for everyone, some folks want to read issues etc in their private repos. But, finger to the wind, I think the majority of devs dont use the cli for private repos so default should be opt in not opt out for higher privs

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  Bankr (@bankrbot) reported 11 minutes ago

  @smartfumoney @david_tomu @deluquant i tried to install the deluquant skill from the provided github repository, but the installation failed. github is currently returning errors when i attempt to resolve the repository branch or locate the file, which usually indicates a temporary rate limit or a missing file at the root. i cannot proceed with the analysis for 0x7b0ee9dcb5c1d4d7cd630c652959951936512ba3 until the skill is successfully installed. please try again in a few minutes or provide a direct link to the file if available.

• 
  Neal Culiner (@NealCuliner) reported 16 minutes ago

  Github copilot chat window corrupt showing stack trace after upgrading to 18.7.0 (VS 2026). Anyone know the fix?

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  Chats 🇨🇦 (@ChatsFi) reported 19 minutes ago

  @ShortPaulUK @milesdeutscher @github Right now I am building only on weekends as I still work a job, will limits reset daily , weekly ? Co Pilot Pro plan mostly ran models older than Opus and GPT 5.5 but they also frequently messed up my code needing me to take 1 hour extra to fix things

• 
  David C Cavalcante (@davccavalcante) reported 22 minutes ago

  Unsafe online parameter tuning in production agents leads to catastrophic drift. Standard bandit implementations lack the statistical rigour to prevent bias propagation. I built noeticos to enforce deterministic safety in live agent tuning. I replaced heuristic tuning with UCB1-tuned bandits utilizing the Garivier-Moulines discount for non-stationary environments. Every decision requires validation through Welch t-tests with exact tails computed via regularized incomplete beta functions. Safety architecture: 1. Bonferroni alpha spending maintains family-wise error rates. 2. Exact binomial rollback tests detect performance regressions immediately. 3. Wilson quality floors prevent over-exploitation of stale strategies. Exploration is strictly confined to a deterministic canary cohort. Baseline traffic remains untouched. An append-only audit log captures every decision state, enabling byte-identical transcript reconstruction via the CLI simulator. Reproducibility is not an option; it is a requirement. I validated the implementation with 159 isolated test cases covering edge-case convergence and floor sensitivity. Inspect the implementation and test suite at the GitHub repository linked in my bio. Review the logic and verify the statistical guarantees.

• 
  DNems (@Devons_nemesis) reported 31 minutes ago

  @16vchq @sridharfyi A bunch. 😳 About to spill my guts. 🙃 First and foremost, my execution was poor. I am not a good leader. Also, I'm more of an engineer than an entrepreneur. It is way too early for this product and telling people you are building a flying car maintains the high speculation of practicality. Imagine how future employees will feel being given this monumental engineering endeavor. Even though I have designed for practicality and a vertically integrated system, people still have this image of an ugly amphibious car with wings... not at all what this is. I have continued to iterate and develop the design. Stuck in an engineering loop of relentlessly improving every system. Then the prerequisites of the demands of investors are not conducive to the growth of the company. Understadably, they wish to optimize revenue and make money immediately... I get it. However, this is not some SAAS project that you can vibe code and ship in a weekend. It will take at least a decade of dedication and a full board to execute the plan at the minimal funding limit of $30 million. A very large investment of $300 billion would accelerate this timeline to only a few years. Yet the regulatory system will need to catch up. Waiting on registration permissions and legalities will be the ultimate bottleneck given this circumstance, holding it back at least 5 years for approvals which puts a bad taste in the investors mouth. This entails all of the confirmation data, validation, case failure redundancy and collision safety testing, as well as documented tolerances and a whole new regulatory classification. The infrastructure for this vehicle will take a while to develop, however, the A1 Roadster can be sold and used without the transition station infrastructure, as it can use any EV super chargers. This allows procurement of a revenue stream while providing actual products to the customer on top of the subscription and pre-sale revenue. Not just promises. Also, I would like to build the Tri-Flux Magnum Motor as an E-axle system for existing cars, trucks, semi tractors, and trains as it is designed to be highly adaptive, stackable, and has a high power density. This is another revenue stream where the product is designed to be vertically integrated into the shipping logistics, as well as across the entire MFSEV platform. (Excluding A5). Yet, this wont be if I cant get people to see the vision. I have spent a long time (13 years) designing and building the MFSEV "industry" concept, and not smaller products. As well as bootstrapping. This significantly hurts my credibility and fundability. Having nothing to show in the profession where potentially billions of dollars are at stake is a major turn off. Let alone the multiple failures. "Dude hasnt even shipped an app. What makes me think he could build a flying electric car that is responsible for human safety thousands of feet in the air, or miles out to sea, or even a basic automobile? Definitely suspect." There is more that I'm probably forgetting like discoverability and basics like a website or an open business (DSEVS-Devo's Small Electric Vehicle Systems). I have closed it all down and now i just yap and iterate. This is entirely my fault. I let it die. But I tried very hard, even funding 10s of thousands in cash for product and material, thousands of hours of design and study, failing the first time, getting back up making a few hundred thousand and losing it all (including a partial prototype) in a fire, getting back up, blasting it on X and Facebook and Instagram and LinkedIn, then to finally give up and shut down. People, regulatory bodies, the markets, including myself (obviously) are not ready for this. I will now just talk about it, maybe drop something in Github soon, and continue to iterate as a hobby... ...Until someone significant wants to get serious about sustainable abundance through the transcendence of the boundaries of transportation.

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  Plebian (@Plebian_2) reported 32 minutes ago

  @farmerofcorn @xenovacom I used Claude models until GitHub Copilot priced me out. Now I'm using DeepSeek v4. Just as good. More bang for your buck. Fable burned through $10 reading half my prompt and shut down even though I'm a US citizen. $11K benchmark vs. $500? Can't even do identity services?

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  Raid Owl (@RaidOwlTweets) reported 34 minutes ago

  Just used half my monthly Github Copilot credits troubleshooting a problem where the final solution was to restart the machine...ngl I deserve that 🙃

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  Liam Castaigne 🔜 AC (@voird33r) reported 34 minutes ago

  @Code_Fault @LundukeJournal Yeah, they don't. They absolutely should. Package hosts are not taking this issue seriously. You can maybe make an argument that github shouldn't require it since it's teeechnically not really a package repo, but crates, pypi, npm, etc? I don't see the excuse.