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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  M (@minguluh2) reported 4 minutes ago

  @CryptoCyberia Person making the issue, there’s zero reason really to be using a web interface for GitHub files, only time you’d be using it is to download releases or review prs

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  6-6 ape711 | GO CATS (@Ape71104) reported 9 minutes ago

  Something something the purpose of a system is what it does GitHub is absolutely intended to be a place to host code but it at some point became a file sharing platform instead of an open source platform and that’s why people who are less tech-literate have problems

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  Eric Vya (@ericvyacheslav) reported 9 minutes ago

  🚨 Someone just open sourced a way to run a 744B param model on a normal gaming PC. It's called Colibrì. Think of it as llama.cpp, but built to treat your GPU, RAM, and SSD as one shared memory pool instead of needing enough VRAM to hold the whole model. Here's what it does: → Runs GLM-5.2, a 744B-parameter MoE model, by streaming experts straight from disk instead of loading them all into VRAM → Pure C, zero engine dependencies → Ships a live web dashboard showing token speed and which of the 19,456 experts just fired → Never silently drops model precision when memory runs tight, it just slows down → Works across consumer and mixed hardware, not one fixed GPU tier 21K+ GitHub stars. Apache 2.0 license. 100% Open Source. (Link in the reply)

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  Gaston Levai (@gastonelevai) reported 9 minutes ago

  @kiriptotipir After also the community address the issue with the OG dev he finally redirected the fees to the GitHub of @PrJulienMartin .

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  Nicofaster_21 (@nicofaster_21) reported 13 minutes ago

  @CryptoCyberia There are devs that absolutely dont care in the slightest to make their stuff easy to understand Github has ui issues, very much unfriendly to people who do not know about it Many things do give you the .exe (or whatever needed for the thing to function) (again ui issues)

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  ard (@ardchain) reported 16 minutes ago

  this is what happens when you stop trusting AI agents to audit themselves. i recently moved from Claude Code to Codex the problem? i needed a safe, persistent agentic loop that didn't rely on Anthropic's closed ecosystem. so i started using agent-harness. it is a strict GitHub merge gate that treats AI agents like untrusted contractors. before a PR is allowed through, the agent must submit an evidence file: - it enforces explicit permissions - it sets bounded execution cycles - it uses mechanical completion gates the LLM is completely stripped of its auditing power. it can propose a result, but the harness decides if it is DONE. this wrapper makes ChatGPT infinitely more reliable. watch the terminal in the video to see how it mechanically validates the state.

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  ⚡Tech Momentum⚡ (@TechMomentum_) reported 17 minutes ago

  Anthropic quietly gave Claude Code a real browser. Not a screenshot tool, a tabbed pane inside the app that the agent reads, clicks, and navigates on its own. Ctrl+Shift+B (Cmd+Shift+B on Mac) opens it. Ask Claude Code to check a library's docs, click through your own signup flow, or read a GitHub issue thread, and it just does it. No copy-pasting URLs into chat. Two guardrails keep it from going rogue: a classifier reviews every write action, and there's a domain allowlist outside Auto and Bypass modes. OpenAI shut its own browser tool down earlier this year. Anthropic went the other direction. If your agent still can't open a webpage on its own, that's not a limitation anymore. That's a setup problem.

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  Carlos Mendez (@charlesmendez) reported 19 minutes ago

  For the past year, I have been working on a memory system that resembles how the human mind works, with both short-term and long-term memory. The best way I found to represent this with today’s technology was through a combination of compaction for operational memory, semantic memory using RAG, and Wiki-like graphs for structured long-term knowledge. The idea is that the AI should be able to remember what is happening right now, retrieve relevant past experiences when needed, and maintain a structured understanding of people, projects, decisions, and rules over time. After implementing the system, I created a group chat with several people and let the AI participate in our conversations. Over time, it accumulated different types of memories. Some were explicit decisions, some were project rules, some were technical errors, and others were temporary events that simply seemed important at the time. Later, I asked the AI to evaluate its own memory. I asked it to compare semantic memory against the Wiki, explain what each system did well, identify what each one got wrong, and describe how it would prefer to use them going forward. The result was more interesting than I expected. Its main conclusion was that the Wiki was technically better overall, but neither system was complete enough to act as the sole source of truth. Semantic memory was better for fuzzy discovery. It worked well when asked broad questions such as, “What do we remember about the Compensar project?” or “What happened with that Vercel issue?” It did not need the exact wording. It could find related memories based on meaning and successfully recovered things like the normal branch and pull request workflow, case-specific exceptions, who had authorized direct changes to the main branch, the requirement for runtime approval before direct writes, the correct Vercel project scope, connector problems, device errors, and README tasks. That is the real strength of semantic memory. It works through association. It is similar to how a person may not remember the exact date or sentence, but still remembers that something happened and can recover the general context around it. But its weaknesses also became clear. When the AI was asked for the exact Vercel failure, semantic memory did not retrieve the decisive serverless package error. When it was asked for the latest README pull request, branch, commit, and merge status, it mostly returned older memories about permissions and workflow rules. It repeatedly surfaced memories that were related to the topic but were not actually the answer. Some memories were truncated, older but still valid decisions were becoming less important because of freshness scoring, and similar memories competed against each other instead of being consolidated into one clearly current understanding. In simple terms, semantic memory was good at remembering the neighborhood, but not always the exact address. The Wiki had the opposite characteristics. It was more explicit, structured, inspectable, and deterministic. Its strongest project page clearly documented how the rules had evolved over time. First, there was a one-time exception to push directly to the main branch. Later, Daniel gave broader authorization. Runtime approval was still required. The Vercel project resolved to the correct client scope, and that scope needed to be derived from live CLI output rather than assumed. The Wiki also preserved dates and sources, which made it much better for understanding what the current rule was, how it had changed, and where the information came from. The AI said it would trust the Wiki over semantic memory when deciding how to execute a repository operation. But the Wiki also had serious problems. The AI described it as “memory confetti.” There were many tiny pages about individual tasks, several pages containing almost the same information, and generic entries saying things like “stored completion details” without actually storing the commit, result, or evidence. There was even a contradictory instruction saying to push to main and open a pull request. The decisive Vercel logs were missing, the latest pull request and commit were missing, and temporary connector errors were being stored next to permanent project knowledge as though they had the same importance. So while the Wiki had a better structure, the quality of the information being added to it was inconsistent. The AI scored the Wiki approximately 7 out of 10 and semantic memory 5 out of 10. Its explanation was the most important part. Operational memory requires more than similarity. It needs identity, chronology, inspectability, and conflict resolution. It needs to understand that a newer decision may replace an older one. It also needs to distinguish between a permanent rule and a temporary incident. For example, “Carlos is authorized to push directly to main” is durable project knowledge. “The deployment failed today because of a serverless package error” is an incident that may be useful for debugging, but it should not become permanent operating policy. The architecture the AI recommended was semantic memory for discovery, the Wiki for canonical knowledge, and live tools for mutable reality. In practice, semantic memory can locate potentially relevant concepts. The Wiki can store current rules, known infrastructure behavior, project identity, and superseded decisions. ***, GitHub, Vercel, databases, calendars, email, and other tools should verify facts that may have changed. Memory should not be trusted to determine whether a pull request is currently open. It should check GitHub. It should not be trusted to determine the latest deployment state. It should check Vercel. It should not be trusted to determine the current branch or commit. It should check ***. This distinction is critical because memory is not the same as reality. Memory provides context about reality. The most important finding was that the current conversation contained materially better operational history than either long-term memory system. The conversation remembered the latest deployment failure and the recent pull request lifecycle. The durable memory systems did not. This exposes one of the biggest problems with current AI memory. It is relatively easy to store information. It is much harder to decide what should be remembered, what should be forgotten, what should be consolidated, what should replace something older, what is a permanent rule, what is a temporary event, what should be retrieved through similarity, and what should be verified through a live source. A memory system is not simply a vector database where every message is embedded and retrieved later. That is recall, not memory. Real memory requires judgment, compression, hierarchy, versioning, and an understanding that some facts are durable, some expire, and some should not be stored at all because they are already available from a more reliable live source. My conclusion is that the future of AI memory will not be one system. It will be several layers working together: compact operational memory for the current task, a semantic layer for associative recall, a structured Wiki for canonical long-term knowledge, an incident history for failures and temporary events, and live tools for anything that can change. The interesting part is that the AI reached almost the same conclusion by evaluating its own failures. The question is no longer whether AI will have memory. The real question is whether it will learn what deserves to become a memory.

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  Bajortski (@Bajortski) reported 19 minutes ago

  @sophiiess_ I don't think it would hurt for the UX around downloading builds to be a little more intuitive regardless of who the site js "meant" for because non-technical users are frequently directed to use a tool hosted on github to solve a niche problem they have.

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  Gaston Levai (@gastonelevai) reported 19 minutes ago

  @GRAZZLEYNFT @elGodric After also the community address the issue with the OG dev he finally redirected the fees to the GitHub of @PrJulienMartin .

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  Benjamin Drexler (@Devjon13) reported 20 minutes ago

  @CryptoCyberia @PetrichorCEO My problem is that the jump for me from running an exe to building something off of Github is a vertical cliff. If you know the tools the dev is using, then it's obvious what to do. If not, it's a skill to even recognize which tool they're using.

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  Short Paul (@ShortPaulUK) reported 22 minutes ago

  @blakeknock @Angaisb_ Sorry no. You’ll get a ‘Unknown model’ error. Sol and Terra report as multi_agent_version = "v2" whole Luna is v1. There are plenty of issues and bug reports open in Github about this. Even if you hack configs, it still won’t share context. So unless they’ve fixed this…

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  The Doom Master (@DoomMaster10913) reported 25 minutes ago

  I was seeing a trend in the search section about github not having a simple "download" button and here's my opinion no one asked for (🧵 ): Github is indeed a site for sharing code first and foremost, but the problem is these same guys put their stuff on github for the average..

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  Ev Haus (@EvHaus) reported 26 minutes ago

  @cassidoo Ability to assign a GitHub App as an issue assignee. Eg. Cursor, Claude, etc...

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  karthik bk (@karthikbk02) reported 27 minutes ago

  @thsottiaux In my usage, more often codex forgets it has plugins that I've installed. It keeps asking me to login on CLI (be it supabase, github, vercel in my case). I have to tell it to make use of installed plugins most times.