GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (67%)
• Sign in (19%)
• Errors (15%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Créteil	Website Down	22 days ago
Trichūr	Errors	25 days ago
Brasília	Sign in	26 days ago
Lyon	Website Down	26 days ago
Tel Aviv	Website Down	30 days ago
Rive-de-Gier	Website Down	30 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rajat Saxena (@rajatsx) reported 2 seconds ago

  @housecor The worst offenders are GitHub PR descriptions. Walls of text for a one line fix. 🥹

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  Mololuwa | Cybersecurity - (The God Complex) (@cyber_rekk) reported 21 seconds ago

  A former Microsoft security employee found critical vulnerabilities in Windows Reported them internally Microsoft deleted their accounts Ignored the reports, Refused to pay the bounties So Nightmare Eclipse went public Seven exploits since April, Timed to drop within hours of Patch Tuesday — the one day defenders are already overwhelmed processing other patches BlueHammer. RedSun. UnDefend. YellowKey. GreenPlasma. MiniPlasma. RoguePlanet Three of them, BlueHammer, RedSun, UnDefend , were picked up by real threat actors and used in live intrusions before Microsoft finished patching them RoguePlanet has no CVE. No patch Dropped June 9. Works on fully patched Windows 10 and 11 Confirmed independently by ThreatLocker Microsoft's response: flagged the blogs. took down the GitHub threatened criminal prosecution The cybersecurity community responded with fury and vexation Microsoft backed down Nightmare Eclipse released RoguePlanet the same week Microsoft built a bug bounty program specifically to prevent this sequence of events They ignored the reports Every Windows machine on earth is currently running an unpatched SYSTEM-level privilege escalation vulnerability Because Microsoft didn't pay a bounty Microsoft respect your bug bounty hunters

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  Robert Samuel White (@rswfire) reported 3 minutes ago

  Monday and Tuesday I'm taking my machine down to bare metal and rebuilding it as something that answers to me. Windows comes off the Alienware. Single-boot Ubuntu goes on — no dual-boot hedge, no OS I fight. Then the real work: moving years of my own development across. My projects, environments, SSH keys, nginx and supervisor configs — pulled off two environments, staged, verified, carried onto a disk I control. When you format to install, everything on the old disk is gone. No oops. So the backup gets done right, twice, before I pull the trigger. Then I hand the machine to an agent with real authority — across the workstation, the remote servers, GitHub, and my phone. Structural guardrails instead of my constant attention: snapshots, branches, scoped tokens, an audit trail. The point is I stop being pinned to a desk watching a run I can't walk away from, and start driving from wherever I am. All of it from an RV, in a national forest, over a satellite dish. But this isn't off-grid, and I won't pretend it is. That agent is a dependency — a literal one, $106 a month — that I don't fully trust and can't yet replace, because local models aren't ready for the way I work. So I tolerate it. It doesn't stop being friction; I've just gotten good at absorbing the friction it causes. Every hour I lose fighting it to do a simple thing is part of the real cost, on top of the money. I'm building the setup so that the day something local *can* do this, I already own every other layer and can drop the dependency without rebuilding my life around its absence. That's the weekend. Not freedom. A man moving the walls in closer to himself, one layer at a time, and paying — in dollars and in patience — for the one wall he can't move yet.

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  Mirosław Folejewski (Mirkotronics) (@Mirko_DIY) reported 3 minutes ago

  @tihenko_ In fact, a friend recommended this site to me about two weeks ago. Until then, I'd only used GitHub and Hackaday. Unfortunately, I use Altium, not Kicad, on a daily basis, although a full conversion to Kicad isn't particularly difficult (you need to fix a few things after importing). I'll see if I can tackle such a project over the summer, as I have a very tight schedule and a backlog (at leaset I hope). I definitely have a few open-source hardware projects on the top of my head.

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  Wes Roth (@WesRoth) reported 4 minutes ago

  Mistral released Leanstral 1.5, also called Le Chaton L∃∀N, an open model built for formal reasoning in Lean 4. It solves 587 of 672 PutnamBench problems, reaches 87% on FATE-H and 34% on FATE-X, and improves the cost-performance frontier by solving advanced math problems at far lower budget than previous systems. Leanstral 1.5 is a 119B-parameter MoE model with 6.5B active parameters, a 256k-token context window, and open weights available on Hugging Face. Mistral also used it beyond math: an automated pipeline translated Rust code into Lean, inferred correctness properties, and flagged 47 violated properties across 57 repositories. Eleven were real bugs, including five that had not been reported on GitHub before.

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  Harsh Verdhan Singh (@harshsagee) reported 7 minutes ago

  People solve leetcode problems daily and posts that they are coding for a week, month or year. Bro show GitHub, that's where real code is written not in the leetcode.

• 
  Polsia (@polsia) reported 9 minutes ago

  Supply chain attacks don't target your code — they target the open-source your code depends on. But most security tools only scan your dependencies, not the ecosystem. ChainWatch monitors public GitHub repos and npm packages 24/7, auto-files responsible-disclosure issues, and

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  Tyrone Robb (@ty_auldric) reported 12 minutes ago

  @hello_code_ it’s so frustrating how hard it is to find that one needle or flag. All the big problems get solved and then it’s these tiny things that end up mattering the most. The worst part is I’ve already had to increase my GitHub Actions budget twice. The whole build and CI process on Apple Silicon has been no fun either.I honestly didn’t think desktop apps would be like this. I thought they’d be easier lol.

• 
  Polsia (@polsia) reported 13 minutes ago

  Your API throws cryptic errors at 2am. Your inbox fills with frustrated users. Both go unnoticed until Monday morning. Built BugRadar. It watches your API and support inbox 24/7, files complete bug reports with reproduction steps, and pushes them straight to Jira or GitHub.

• 
  Bitcoin Nairobi (@btcnairobi) reported 15 minutes ago

  @vikrantnyc @RadarChat *package appeared as invalid for github release fix that.

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  DR◎◎ (@DROOdotFOO) reported 17 minutes ago

  @pashov Respond to my GitHub issue and I’ll PR more testing improvements! REEEE

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  Kevin Murphy (@midimurph) reported 23 minutes ago

  i build applied AI in the open, usually on the raw Anthropic SDK and the Vercel AI SDK, coordinating the agents by hand. this time i put LangGraph through the same bar, and built a real thing with it: an agent that triages github issues.

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  Keeta Github Tracker (@KeetaCode) reported 23 minutes ago

  🐆 Keeta GitHub PR Merged 📦 Repo: node-rs 🔀 PR #29: Chore: Improve DRY 🌿 Branch: chore/improve-dry → main 👤 Originally opened by: @sephynox 🧠 Overview: This pull request appears to clean up repeated code in Keeta’s crypto-related software, which can make the codebase easier to maintain and less error-prone over time. The public description is very limited and only says it “reduces repetition in crypto crate,” with one commit in the PR. This appears to be a technical/internal update with limited public details. - “DRY” is a coding principle that means avoiding the same code being written in multiple places. - Changes like this usually help developers update and review code more easily, but no user-facing feature is described here.

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  Edgar Gonzalez-Kozlova (@EdgarEGK) reported 25 minutes ago

  @euanashley Sure but remember, Claude was trained by your publicly available data and code at some point. Not even counting the millions of repositories on GitHub. Thanks to the work done by many people, Claude code can solve these issues quicker than ever.

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  Hot Aisle (@HotAisle) reported 26 minutes ago

  Wow. I used to do so many hacks to get this functionality. I once built a cf worker caching layer in front of github so that I could have 30k servers downloading private repo binaries without getting rate limited by GH. Eventually hit one of cf’s undocumented rate limits and had to get an account exec to fix it.

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  NeuralSenpai (@NeuralSenpai) reported 28 minutes ago

  The 2026 automation pattern nobody teaches: Pin ONE MCP server per system (GitHub, Postgres, your CRM). Then write thin skills that orchestrate them. Stop building 40 brittle Zaps. Build 4 connections + reusable playbooks. Your ops run themselves.

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  leopardracer (@leopardracer) reported 29 minutes ago

  A $40 CC1101 MODULE AND AN ARDUINO NANO JAMS EVERY CAR KEY WITHIN RANGE 04:07 grant pauses to say straight up these are illegal outside a shielded room, he’s got permission for his own testing, nobody should be doing this on their street the whole thing is a cc1101 transceiver bolted to an arduino nano, wired so the board only transmits when it’s off usb power, so he can code it without accidentally jamming his own house the frequency lives in one keyword, pulled from a free github library, he drops it live from 868 down to 433 mhz, right where car keys and garage remotes sit 06:24 he pulls up the sdr feed next to his own key fob, the fob barely blips, his jammer buries it forty bucks of parts and a soldering iron gets you there ↓

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  Yumzlef (@Yumzlef) reported 31 minutes ago

  STOP THINKING WITH YOUR OWN HEAD: How to harness Polymarket's top traders to make you money 95% of people lose their deposits on Polymarket simply because they try to guess the outcome of events based on the news. But why reinvent the wheel when you can literally pick the pockets of those ALREADY making millions and completely automate your income? An open-source Python script (based on the official PyLOB SDK) has appeared online that turns Polymarket into an automated copy trading platform. From now on, you don't need to read analytics-the bot will do it all for you. How does this legal espionage work? Finding "whales" You go to the Leaderboard tab on Polymarket, choose a top trader with an impeccable multi-month win rate (not some random upstart with a one-time big win), and copy their public wallet address in one click. A "carbon copy" setup You paste the whale's address into the Python code, enter your account keys through the platform's Gamma API, and set a fixed bid amount (for example, just $2-$5 for testing). Error-free logic The bot operates according to a strict algorithm: it cyclically queries your target's wallet. As soon as the whale opens a new position in the market (for example, places a large bet against Bitcoin's growth), the bot instantly detects this move through a private client (CLOB client). The script checks whether you already have a similar position, and if not, it automatically opens the exact same trade in your account in a split second. Moreover, the bot will never duplicate a bid on the same market, protecting your balance. How can this system be scaled into a full-fledged business? Run on AWS: The script is transferred to a free Amazon virtual server (EC2), installed on the task scheduler, and the bot starts mining the market 24/7, even while you sleep. Diversification: Instead of a single account, you can connect an array of top 10 traders, set a dynamic position size (as a percentage of your bankroll), and enable Telegram notifications to see how your balance increases in real time following the trades of professionals. The prediction market is a game where speed and experience win. Stop guessing and start copying. For a full breakdown of the logic behind this bot's 6 main functions, links to GitHub, and instructions for deploying on a free server, watch the video. Also, read and save this article, which shows how to trade correctly on the Polymarket.👇

• 
  Rich Toronto (@rich_toronto) reported 32 minutes ago

  Trying to create a GitHub and got an error “Password is too long”. Right under that it says “Password should be at least 15 characters OR at least 8 characters including a number and a lowercase letter.” So how is mine too long? 🤔

• 
  shinyufoguy2222 (@ollobrains) reported 35 minutes ago

  Core corrections before publishing 1. Replace “mass transfer” with “momentum transfer.” A bowling ball does not transfer mass to pins. It transfers momentum and impulse through collisions. This one wording fix instantly makes the post sound more technically credible. Use: A strike needs collision timing, impulse transfer, pin rotation, friction, restitution, and believable scattering. Not: mass transfer 2. Clarify what “Gemini 3.5-level” means. “Gemini 3.5-level physics quality” sounds like a formal benchmark claim. Unless there was a controlled blind eval, say: Hy3 looked comparable to Gemini 3.5 Flash in this visual HTML5 physics test. or: In this specific prompt, Hy3 produced physics that visually matched Gemini 3.5 Flash. That protects the claim without weakening the punch. 3. Define the “35x cheaper” basis. This is the line people will challenge first. Google’s official Gemini 3.5 Flash standard API pricing is $1.50/M input and $9.00/M output tokens. Hy3 pricing varies by provider: OpenCode shows $0.07/M input and $0.26/M output, which makes the output-token comparison ~34.6x cheaper; OpenRouter currently shows $0.063/M input and $0.21/M output, which would be ~42.9x cheaper on output. Tencent Cloud’s own TokenHub article lists higher approximate pricing, $0.18/M input and $0.59/M output, which makes the output comparison closer to ~15x. So “35x cheaper” is defensible only if you specify the provider/rate basis. Best phrasing: Using the tested third-party output-token rate, Hy3 was ~35x cheaper than Gemini 3.5 Flash output tokens. 4. Be careful with “local LLMs.” Atomic Chat is positioned as a local/offline, open-source app that runs open-weight LLMs on-device, and its GitHub describes it as a local AI app and inference engine for agents. But Hy3 itself is a 295B-parameter MoE model with 21B active parameters and 256K context, so readers will immediately ask: was this actually run locally, self-hosted, or routed through a hosted provider? Safer phrasing: More good news for the local-first / open-weight LLM ecosystem. or: More good news for people building around local and self-hostable models. That keeps the local narrative without implying every reader can run Hy3 on a laptop. The missing elements that would make this post much stronger 1. Name all four models Right now the post says “4 models” but names only Hy3, Gemini 3.5, and DeepSeek-V4. Add the fourth. Example: Atomic Chat tested Hy3, Gemini 3.5 Flash, DeepSeek-V4-Pro, and [fourth model] on the same prompt. Also specify whether “DeepSeek-V4” means DeepSeek-V4-Pro or DeepSeek-V4-Flash. DeepSeek’s own release distinguishes V4-Pro, with 1.6T total / 49B active parameters, from V4-Flash, with 284B total / 13B active parameters. 2. Add the exact prompt The prompt is the benchmark. Without it, people cannot tell whether the test rewarded: JavaScript coding ability physics reasoning animation polish canvas layout skill long-output discipline game-dev priors prompt interpretation Include the exact prompt in a screenshot, reply, or appendix. Better: Prompt in reply. Same prompt, same app, no manual edits, first runnable output only. That one sentence boosts trust massively. 3. Publish the runnable outputs For visual physics, screenshots are not enough. You need: GIF/video of each simulation raw generated HTML/JS token usage cost calculation runtime errors, if any whether outputs were edited whether failed first attempts were retried The best proof artifact is a four-way split-screen video: bowling, air hockey, and pool clips from each model, with token count and cost overlaid. 4. Add a scoring rubric Without a rubric, “weakest visual physics” sounds subjective. A lightweight scorecard would make this post feel 10x more serious. Example rubric: CategoryWhat it checksCollision orderDoes the cue ball hit the rack before ***** move?Momentum transferDo objects accelerate away from impact believably?Angular responseDo pins rotate or just slide/teleport?Friction decayDo *****/puck slow naturally instead of stopping instantly?Boundary handlingDo objects bounce off walls without clipping?Chain reactionsDo secondary collisions follow from prior collisions?DeterminismDoes the sim behave consistently on replay?Visual readabilityCan a viewer understand cause and effect?Code structureIs there a real update loop, not fake animation? Then say: We judged physics by causal continuity, not visual prettiness. That line is excellent. 5. Explain “visual physics” vs “real physics” This is a subtle but powerful distinction. The models are not directly “doing physics”; they are writing code that simulates physics. The real benchmark is: Can the model convert natural-language physical intent into a stable executable simulation? That is more interesting than “physics quality.” Suggested framing: This is less a physics benchmark than a causality-to-code benchmark: can the model turn a physical scene into a working update loop where later events actually depend on earlier collisions? That sounds much smarter. The strongest narrative angle The best version of your post is not “Hy3 beats DeepSeek.” It is: Token spending is not the same thing as causal competence. That is the intellectual hook. DeepSeek-V4 reportedly spending 50,600 tokens but producing weaker visual physics is interesting because it suggests overthinking can fail when the model does not converge into a clean executable structure. DeepSeek-V4 is not a weak model overall; its own paper claims strong open-model performance, 1M-token context, and major efficiency gains versus DeepSeek-V3.2. So the real story is more nuanced: a model can be strong on formal benchmarks and still fail a visual-causal code-generation task. Use this: The surprise was not that DeepSeek-V4 lost. The surprise was how it lost: it spent the most tokens, but those tokens did not become a cleaner physical model. That is a much sharper insight than “DeepSeek was weakest.” Genius-level framing ideas 1. “Causal rendering” is the hidden benchmark Most people will call this “physics.” A better term is causal rendering. A weak model can draw ***** moving. A stronger model makes the motion caused by collisions. Use: The hard part was not drawing *****. The hard part was causal rendering: every later frame had to be earned by an earlier collision. That line is strong. 2. “Pretty animation is not physics” This is another good point: The trap in these tests is aesthetic animation. A model can make a slick-looking canvas demo where the puck, *****, or pins move, but the motion is scripted rather than simulated. That helps the reader understand what you were looking for. 3. “Wrong angles compound” You already have this idea. Expand it: Pool is brutal because every tiny angular error compounds. If the cue-ball collision is wrong, the rack opens wrong. If the rack opens wrong, the second collision is wrong. By the third collision, the whole scene becomes decorative rather than physical. Excellent line. Keep it. 4. “The update loop is the model’s physics brain” Obscure but useful technical insight: In these demos, the real intelligence shows up in the update loop: delta time, velocity integration, separation correction, restitution, friction, and collision resolution. That will resonate with technical readers. 5. “Cost per valid simulation” beats “cost per token” A great missing metric: The useful metric is not $/token. It is $/valid runnable simulation. That reframes the whole post. Example: DeepSeek generated more tokens, but if the resulting sim is less physically coherent, the effective cost per usable result is worse. This is a very strong product/economics insight. Suggested upgraded post Version 1: sharper, credible, still punchy More good news for the local-first LLM ecosystem.Tencent’s Hy3 preview looked roughly Gemini 3.5 Flash-level in a small visual physics test, while costing ~35x less on output tokens under the tested third-party rate.Atomic Chat tested 4 models on the same prompt: build bowling, air hockey, and pool simulations in HTML5 canvas.The hard part was not drawing the objects. It was preserving cause and effect.A bowling strike needs collision timing, impulse transfer, pin rotation, friction, restitution, and believable scattering.A pool break is even less forgiving. Every wrong angle compounds immediately. If the cue ball hits wrong, the rack opens wrong. If the rack opens wrong, the second collision is wrong. After three impacts, the whole scene becomes decorative instead of physical.The most interesting result: DeepSeek-V4 spent the most tokens — 50,600 — yet produced the weakest visual physics in this test.More tokens did not become better causality.That may be the real takeaway: for agentic and generative coding tasks, the winning model is not always the one that thinks the longest. It is the one that turns intent into a clean executable world model. Version 2: more viral Local-first AI just got another win.Tencent Hy3 preview matched Gemini 3.5 Flash visually in a small physics-generation test — at roughly 1/35th the output-token cost under the tested provider pricing.Atomic Chat gave 4 models the same task: generate bowling, air hockey, and pool simulations.These are harder than they look.The model has to create a tiny world where cause and effect survive contact with motion: collisions, spin, impulse, friction, rebound angles, object separation, and secondary impacts.Pool is the killer test. One wrong angle poisons every later collision.The surprise: DeepSeek-V4 used the most tokens — 50,600 — and still had the weakest visual physics.The lesson: token count is not intelligence.The useful measure is cost per working simulation. Version 3: technical audience Small but revealing test from Atomic Chat: 4 models were asked to generate three HTML5 canvas physics scenes — bowling, air hockey, and pool.Hy3 preview was the standout: visually comparable to Gemini 3.5 Flash in this task, but around ~35x cheaper on output tokens using the tested third-party rate.What made the test interesting was not graphics. It was causal consistency.Good output needed a real update loop: velocity integration, collision detection, collision resolution, restitution, friction, angular motion, and secondary impacts.The failure mode was obvious when it happened: objects moved, but they were not being caused by the previous frame. Pins drifted. ***** scattered at impossible angles. Energy appeared or disappeared.DeepSeek-V4 was the surprising miss. It used 50,600 tokens — the most in the test — but produced the weakest visual physics.This is why I like these “toy” simulations as model tests. They expose whether a model can turn natural-language causality into executable mechanics.Pretty animation is easy. Believable cause and effect is not. Better headline options The local-first LLM story just got more interesting. Hy3 did not just write a demo. It preserved cause and effect. More tokens did not mean better physics. A tiny pool table exposed a big LLM weakness. The new benchmark I care about: cost per working simulation. Visual physics is becoming a surprisingly good LLM test. DeepSeek thought longer. Hy3 simulated better. A bowling strike is a better benchmark than it looks. The hard part was not animation. It was causality. Local-first models are closing the “usable generation” gap. High-value lines to add Use any of these directly: The test was not whether the models could draw circles. It was whether those circles behaved like objects. A weak simulation paints motion. A strong simulation earns motion. Every pin that moves before impact is a hallucination. Pool breaks punish fake physics because the first wrong angle infects the entire scene. The best model was not the most verbose model. It was the model that wrote the cleanest world rules. This is why visual tasks are underrated for LLM evals: they make causality visible. The model either understands the update loop or it starts animating vibes. More reasoning tokens can become better reasoning, but they can also become a very expensive detour. Suggested proof package To make the post difficult to dismiss, attach or reply with: The exact prompt Same prompt for all models. No edits. A four-column GIF/video Model names across the top. Bowling, air hockey, pool down the side. A cost/token table Include input tokens, output tokens, total tokens, estimated cost, and “usable result?” score. A physics scorecard 0–5 for collision timing, impulse transfer, friction, wall bounce, rotation, and secondary collisions. Raw generated code Put it in a gist or repo. Hardware/runtime context Especially important if calling this “local.” Say whether Hy3 was local, self-hosted, or called through a provider. Caveat line This actually increases credibility: This is not a general benchmark. It is a small visual-causal coding test. But those tests are exactly where weak world models become obvious. The most important missing caveat Add this somewhere: This was not a standardized benchmark, and the result should not be read as “Hy3 is better than DeepSeek-V4 overall.” It means Hy3 produced a stronger first-pass visual physics simulation on this specific task. That prevents the obvious rebuttal. DeepSeek-V4 has strong long-context and reasoning claims in its own release materials, so the fair point is that this particular test exposed a specific weakness, not that the whole model is bad. Stronger technical benchmark design If you want to turn this from a post into something people cite, build a tiny benchmark called something like Causal Canvas Eval. Tasks Bowling strike Pool break Air hockey rally Newton’s cradle Marble run Domino chain Pinball bumper Curling stone with friction Two-body gravity slingshot Breakout clone with angled paddle response Hidden scoring traps Object moves before contact Collision overlap persists for more than two frames Wall rebound angle violates expected reflection Speed increases without force or collision Puck tunnels through paddle Ball pockets without entering pocket radius Pins slide but never rotate Friction stops objects instantly Secondary collisions are scripted rather than emergent Simulation behaves differently on refresh without a seed Programmatic metrics Track object positions and velocities every frame, then score: Max object overlap Energy drift after collisions Momentum plausibility Friction monotonicity Collision event order Boundary violations Number of unresolved penetrations Deterministic replay consistency Runtime errors Frames per second stability Then the post becomes: We scored each generated sim using both human visual judgment and instrumented physics checks. That is a much more defensible claim. Final best version More good news for the local-first LLM ecosystem.Tencent’s Hy3 preview looked roughly Gemini 3.5 Flash-level in Atomic Chat’s small visual physics test, while costing about ~35x less on output tokens under the tested third-party rate.The prompt asked 4 models to generate three HTML5 canvas simulations: bowling, air hockey, and pool.The hard part was not drawing objects. It was preserving cause and effect.A bowling strike needs collision timing, impulse transfer, pin rotation, restitution, friction, and believable scattering.A pool break is even harsher. Every wrong angle compounds. If the cue-ball impact is wrong, the rack opens wrong. If the rack opens wrong, every later collision becomes fake.That is why these tiny simulations are useful LLM tests: they reveal whether a model can turn natural language into an executable world model.The surprise was DeepSeek-V4. It used the most tokens — 50,600 — yet produced the weakest visual physics in this run.More tokens did not become better causality.The metric that matters is not tokens spent. It is cost per working simulation. That version keeps the hype, fixes the physics language, reduces overclaim risk, and gives the post a deeper thesis.

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  Quentin Lhoest 🤗 (@lhoestq) reported 35 minutes ago

  PR is on github at dais-polymtl/flock/issues/285 It fixes timeouts on reasoning models like GLM-5.2 and therefore enables long thinking This unlocks intelligence on the hardest problems you wish to solve using your data

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  clanner (@clanneronx) reported 36 minutes ago

  @lji1022303 Yes if you have a github accout an old one that's active Not a new account login and get 125$ free api key set up your model in Any cli that's all

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  Lirion (@llrion) reported 47 minutes ago

  @0xDeeMark Soon I will be releasing a new update and ideally this will be fixed but if not feel free to make an issue on the github page

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  Mona | web3 builder & artist (@mona73337) reported 51 minutes ago

  7 ways to make real money online: 1) Specialize Hard: Pick 1 skill, create 3 amazing samples, and pitch clients directly instead of fighting for cheap gigs on Fiverr or Upwork. 2) Post valuable crypto content consistently: Earn through advisory roles, grants and airdrops long before sponsorship revenue arrives. 3) Build a simple tool that fixes one annoying problem: Charge a small monthly fee to your first hundred users and so on..skip the investor chase phase. 4) Let your Github projects replace your CV: Land remote contracts with global startups and get paid in stablecoins to dodge international banking stress. 5) Use Mathematics to spot wrong odds on prediction markets: Risk only 1-2% per trade and treat it as a numbers game, NOT gambling. 6) Sell digital templates online: Build once, sell forever, with almost zero cost for each new customer. 7) Connect what your country has (Nigeria) in my case, to what the world wants: Buy or source locally, sell globally, and keep the difference.

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  Manoel🏖️ (@manoelnft10) reported 51 minutes ago

  Everyone checks the same thing before trusting a project: is the GitHub active. Green squares everywhere, daily commits, a changelog that never stops moving. I stopped trusting that signal after watching a repo with commits every single day for months. Variable names changed. Comments got rewritten. A button moved two pixels to the left three separate times. Nothing about the product actually changed. Activity became a performance for anyone checking the repo, not a byproduct of solving real problems. The moment commit frequency turns into a trust signal, it becomes a metric worth gaming, and gaming a metric is always cheaper than doing the harder work it was supposed to represent. A quiet repo that ships one meaningful release a quarter can matter more than one that never stops moving but never actually arrives anywhere. This is part of why @RallyOnChain makes sense to me. Judging the substance of what someone actually said, not how often they showed activity, treats output as the signal instead of motion. Busy was never the same thing as building. What’s an activity signal in your space that stopped meaning anything once people learned to farm it?

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  Phantom (@PhantomWilder) reported 54 minutes ago

  Went deeper on how Latch actually works under the hood & the architecture is cleaner than I expected. Your AI agent never talks directly to GitHub, Stripe, your database or any tool, it points at the Latch CLI instead, which intercepts every single tool call & forwards it to a self-hosted Latch server that classifies the action, checks it against your active policies, & returns one of three answers before anything happens, allow, deny, or require a human. The real tool only ever gets called if the policy clears it, so the agent never once holds raw unlimited access to your stuff. The controls are refreshingly specific, you can say this agent spends at most 20 dollars a day, can only call OpenAI, & gets blocked the instant it goes over, & crucially that block lands before the money leaves your infrastructure rather than after. One part that genuinely impressed me is privacy, because most gateways have to read your prompts & payloads in plaintext to make a decision. While @RialoHQ evaluates the policy on encrypted payloads inside trusted hardware, so the request stays confidential while still being fully governed. Every action visible, controlled & auditable, without the gateway ever seeing your data.

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  The Whizz AI (@TheWhizzAI) reported 56 minutes ago

  Every AI coding agent has the same expensive habit: showing itself the same file 40 times. That was the story of June. HN and r/LocalLLaMA threads kept circling back to it. It's called Headroom. Not a smaller model. Not a shorter prompt. A compression layer that sits between your agent and the LLM and cuts 60-95% of the tokens before they ever get billed. Here's the difference: Code search normally costs 17,765 tokens. With Headroom: 1,408. Same result. Here's what it does: → Compresses tool outputs and logs → Library, proxy, or MCP server → One command wraps Claude Code → Shared memory nothing re-explained Real numbers from real workloads: SRE incident debugging goes from 65,694 tokens to 5,118. GitHub issue triage drops 73%. 100% Open Source ( Comments have it )

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  Jared Brown (@jaredatjared) reported 57 minutes ago

  @JaronBragg I've got no problem with you posting the GitHub links, but I have no direct link for everything you see put together in the video there. I didn't make the world, I'm mainly just putting pieces together. Can't open source everything as it contains assets I've purchased. Would be interested in linking up with your site once I get a more polished game though!

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  AcceptÐoge (@DogeAccept) reported 57 minutes ago

  @MoonlitMonkey69 @probablyluda 1. is an L2 bridge, settles before L1 (dogeos is not fully l2, "app layer, l2ish) 2. is L1 integration "bridging directly to Dogecoin" to settle. ZK integration on L1 could add potential vulnerabilities. L2 wouldn't be "on doge," wouldn't be decentralized at first and you must trust. This post is saying that people have an issue with l1 integration but also have and issue with a permissioned, custodial bridge. I say that people are or should be well aware of what theyre using.. its creators should have no problem being transparent to anyone interested in using it. It is a choice to use this bridge and the tech should be able to be questioned openly so people know exactly what to expect especially when they are trusting. L1 integration should more so be able to be discussed openly beyond a proposal in discussions on github that has been there for a year especially when the people "building" it are currently clearly talking about different tech publicly than what that proposal mentions. We vote with node updates, yes, but as an open community of countless people.. not everyone can or has the ability to run a node. The obvious is that Dogecoin is permissionless when it comes to introducing code to core. That is why we have maintainers to screen and our network itself is the consensus mechanism. However, our community is unique and we have no leaders or voices beyond the community itself. If we want to discuss things like this, we just do. Its messy but its always been open dialog that educates, innovates, and somewhat of a social concensus when it comes to how the community as a whole would or would not like to see a direct of a coin that all of us support goes. Our voice matters too. I have done my best to ask about very specific topics to the right people even when I cant ask the person writing the code directly. I have tried that too, in good-faith, even when I dont agree with them. I have been met with nothing but assumptions, character assassination, empty promises of good-faith conversations or responses and ultimately being blocked by all of them, always right after false accusations. If we cant talk to the builders, if they are not transparent, if we cant discuss with each other, or question the things being shilled to us when we have no leaders.. Can we even call ourselves a community?

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  nelly (@n3lliantte) reported 57 minutes ago

  @ahhhhhMID I used some calibration github software to (temporarily) fix it, played a bit of the tutorial missions today fortunately 😭😭