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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Matt Pocock (@mattpocockuk) reported 2 minutes ago

  Tons of folks are piling in here saying that AFK agents are a myth. I have been using them to ship these GitHub repos: mattpocock/evalite mattpocock/sandcastle mattpocock/software-factory (might be public by the time you see this) Here are a few steps to making this work, and some reality checks. Definitions Let's split this into the day shift and the night shift. Day shift is planning/review/QA, night shift is AFK implementation. Day Shift (part 1) 1. Use /grill-me to align with the AI 2. Use /to-prd and /to-issues to create a PRD (the destination) and implementation steps as separate tickets, which can be grabbed in parallel (the journey) 3. The PRD is a ticket, but it's not an actionable step. You just put the user stories there This is pure requirements gathering ****, same as it ever was. Night Shift 1. I run a planner agent which looks at all the tickets and sees what can be worked on now, and what's blocked 2. The planner agent then kicks off multiple agents (sandboxed using Sandcastle, my OSS tool) to implement the code 3. I then have an automated reviewer agent look at the commits produced - one agent per implementation. This checks alignment to the original PRD, as well as code quality 4. These commits end up on branches that get PR'd to main 5. The planner agent runs again until all work has been completed The review is a crucial step - it's saved me MANY times. I am planning to massively increase the amount of review I do, hopefully with multiple agents. But guess what - AFK agents sometimes produce bad code. This can happen because of: a. The original plan was bad because the best solution was something different b. The original plan was bad because it didn't take into account all the unknown unknowns, and the AI had to make some decisions during the coding session which were bad c. The plan was good, but the AI just shat the bed (twice, once in the review stage, once during implementation) d. Your codebase is bad and the feedback loops don't tell the agent if it did a good job or not So... QA: Day Shift (part 2) 1. QA all of the branches created 2. Create follow-up issues, potentially editing the original PRD to adjust the destination This will usually take a long time, often as long as planning. But then you kick off the night shift again. Once QA is all done, you review the important bits of code manually, usually in PR's. There isn't anything better than the PR UI right now, so that's what we're stuck with. Wake-up Calls 1. If you let the AI run all night unbounded by planning, it's going to produce **** code 2. Mostly, my loops finish before I go to bed, it's just the night shift catching up to the day shift 3. The only reason I do AFK at all is because it allows me to automate review and totally not give a **** about latency 4. I always run night and day shift in parallel. I can't plan that far ahead (skill issue, probably). I need working code to base my plans from, so I'm aggressively QA-ing stuff that lands

• 
  Paweł Huryn (@PawelHuryn) reported 2 minutes ago

  6 free GitHub repos for Claude Code Can save you $100/mo. vercel-labs/agent-browser Replaces Claude in Chrome for web scraping. Accessibility tree instead of screenshots. No HTML mess. rtk-ai/rtk Their claim: 60-90% on common dev commands. 20-30% in my workflow. juliusbrussee/caveman Terse-output skill. Drops conversational filler from responses. tirth8205/code-review-graph Claimed up to 49× fewer tokens on daily coding. AST map. Gronsten/claude-usage-monitor Real-time 5-hour window + active session tokens. Know your cap inline. phuryn/claude-usage Historical breakdown by session, day, week. Where the spend went & what to fix.

• 
  Paweł Huryn (@PawelHuryn) reported 2 minutes ago

  6 free GitHub repos for Claude Code Can save you $100/mo. vercel-labs/agent-browser ~82% fewer tokens than Playwright. Accessibility tree, not screenshots. rtk-ai/rtk Their claim: 60-90% on common dev commands. 20-30% in my workflow. juliusbrussee/caveman Terse-output skill. Drops conversational filler from responses. tirth8205/code-review-graph Claimed up to 49× fewer tokens on daily coding. AST map. Gronsten/claude-usage-monitor Real-time 5-hour window + active session tokens. Know your cap inline. phuryn/claude-usage Historical breakdown by session, day, week. Where the spend went & what to fix.

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  AIHacksByMK (@AIHacksByMK) reported 10 minutes ago

  @vaggelisdrak Before they were acquired, GitHub was known for having a lot of downtime, but they didn’t let everyone know about it. This is just reported downtime. Even though they still run into problems updating the status page, at least they’re trying to do it now.

• 
  adri (@adriwtm) reported 10 minutes ago

  OpenAI shared their internal Codex guide. Beyond coding: note-taking, prototyping, time management. Core insight: structure prompts like GitHub issues. Include file paths, component names, and diffs. The PDF is worth a read.

• 
  Petros Gauchodeliakis (@gauchodelia) reported 11 minutes ago

  my workflow with ai... antigravity as a ide, just to dont cursor due to the stupid plans and limits , i found it horrible. a bash file to commit and push to github and via ftp to the server. because is for wordpress sites, the flow is jus simple like that.

• 
  BeingInvested (@0xbeinginvested) reported 12 minutes ago

  In Shenzhen, a 13-year-old kid was bored during winter break, so he wrote a Python script. That same night, he uploaded it to GitHub. In just three days and one conversation with Claude, he created eight files. The README was written in awkward English: “ai agent reads sport news fast for me. ”He couldn’t even run the script himself. He was only 13, with no bank card, no payment methods, he couldn’t move a single cent. After pushing the code, he stared at the 0 stars on the repo, closed his laptop, and went to eat. Six months later, someone showed him a screenshot. The script had been forked 2,400 times. One of those forks was linked to a wallet with a balance of $4,526,176. A 27-year-old developer in Singapore spent $40 to buy a copy of this script from a private list. He ran the installation command, pointed it at his laptop, and walked away. Six months later, one day he looked at a screen full of green logs and posted the profit curve on a small developer forum with one simple caption: “I didn’t write a single line of this code. I just clicked install.”The developer community exploded. 180,000 views in 24 hours. Everyone was asking for the GitHub link, which Claude model he used, and who the original author of the script was.The Singapore guy told them. That was his mistake. Pause the demo video at 0:08. Look at the contributor name in the top right corner of the GitHub page.The contributor is 13 years old.The comment section instantly turned into a detective scene. Some people slowed the demo video down to 0.25x speed. Others dug into the original repo’s contribution history. Someone even traced the kid’s other public commits and found a record of him participating in a programming competition at Shenzhen No. 4 Middle School.The kid won second prize. The judge’s comment was: “This project has no practical application value.”The Singapore developer is still running the script.

• 
  Electrum (@ElectrumWallet) reported 12 minutes ago

  @WhalesSecret Well, twitter isn't the right place to host this kind of discussion; you should open an issue on github. Note: If the server uses our protocol (not Boltz), then a reverse swap failing will not result in the user losing fees, because the invoices are bundled.

• 
  dkalephjunkmail (@dykaccountmail) reported 13 minutes ago

  @md_kasif_uddin Microsoft Copilot makes fewest errors in coding.. it is the official AI for github.. we can do a simple test, give each ai a coding error for repair, Copilot will do it in one or two steps where others won't

• 
  Wayan (@wayanhq) reported 16 minutes ago

  OpenClaw says an AI bot used 50 Codex instances in parallel to close roughly 4,000 stale or already-solved GitHub issues. That is not replacing maintainers. It is removing the layer of dust that stops maintainers from seeing the real fire.

• 
  Luca Rossi ꩜ (@lucaronin) reported 21 minutes ago

  @vr4300 can you check if the issue has been reported already and in case it's not open it up in github issues copying the diagnostics? 🙏 you can do so from the contribute panel bottom right in the app

• 
  Simplex (@simplex_fx) reported 22 minutes ago

  If it wasn’t clear at this point, if your company stores the code on github, it’s a ******* joke. Hell, even if you are a student not running your own scm server and immutable backup storage, you are ******* joke too.

• 
  elorri_79 (@456c6f727269) reported 22 minutes ago

  That said, the community is already calling out the hype: - Stealth mode fails against Kasada-class protection (open GitHub issue) - Self-reported benchmarks, only 4 commits at launch - Lightpanda (Zig) shipped similar specs 6 months ago with public data Worth watching. Not worth hyping yet. HT @simulx4 @gabor_rar @kstonekuan for the reality check

• 
  dodothebird (@dodothebird) reported 22 minutes ago

  - All my projects live in a monorepo. Maybe the workspace solution also works, but I'm going with the monorepo for now. - Using GitHub Pro. I created another account for working with Codex on the monorepo. - All tasks are in different projects on GitHub, mirroring sub-packages in the monorepo. - All PRs are created by Codex on the monorepo. - After PR creation, the agent starts a PR Steward automation to check the state of the PR for code reviews from Codex, CI errors, etc. The automation runs until Codex review gives 👍 - After that, the automation stops. - I check the PR myself. If everything is OK, I approve the PR with my own user. I think I'm in a good place now. Things could be better with more cloud-based code review and PR cleaning processes, but I think I can manage with this for my personal work.

• 
  Chad Scira | CTO (@icodeforlove) reported 24 minutes ago

  @georgeorch the useful signal is still there, it just doesn't announce itself usually buried in a 6-like github issue, a postmortem, or some random person's benchmark footnote that's the actual anti-hype stack