OVERTRADING October 10, 2025 i had what would become the single biggest one-day loss of my career i lost eight figures in minutes due to systemic failures on the biggest CEXs, and to this day, no one has taken accountability for what happened my losses made headlines and became one of the biggest stories to come out of that day but what many people don't know is what happened in the weeks and months that followed i lost several million more and i did it by making one of the biggest mistakes traders like me tend to make: OVERTRADING in hindsight, i would have been far better off shutting everything down for the next few months or even quarters i probably would have become a much better trader if i had simply stayed away from the computer for at least six months but a part of me was desperate for action a part of me wanted to make it all back a part of me wanted to prove that i still had what it takes and that i would get back on my feet i wanted to 'work' and sitting back, doing nothing, and staying away from the computer for a prolonged period didn't feel like work except that, for a trader, being AFK can sometimes be the most important work you do one of the biggest mistakes i made — and that many traders make — is assuming that skill, experience, and process are enough to consistently outperform the market they aren't believe it or not, successful trading is often a combination of skill, favorable market conditions, timing, and luck as a Christian, i attribute the 'luck' component to GOD's blessings and favor rather than sheer luck but the underlying principle remains the same: our biggest wins often come from combining a solid process with being in the right place at the right time sometimes, that means catching a single exceptional trade other times, it means being positioned during a streak of opportunities that emerge in quick succession because market conditions are overwhelmingly in your favor for example, you could have been a memecoin god simply by being active in the market during the explosive memecoin era of 2021, which produced $DOGE, $SHIB, $FLOKI, and several other billion-dollar runners many traders made life-changing money during that period but the same sophisticated processes that helped some of us make 7, 8, or even 9 figures during the bull market would have failed miserably in the bear market that followed in 2022 the same principle applies to the 2024–early 2025 era, which produced several multibillion-dollar runners like $WIF, $POPCAT, and $FARTCOIN and then the prolonged dearth of opportunities after $TRUMP leading to 10/10 and the almost one-year period following the point is simple: the edge that makes you money in one market environment won't necessarily make you money in another and one of the most important skills you can develop as a trader is recognizing when your edge is no longer there sometimes, the best trade is no trade at all sometimes, the smartest thing you can do is take a bow, step away, and wait for the next opportunity even if only temporarily it's almost impossible to consistently beat the market over a sufficiently long timeframe but as crypto traders, we have one significant advantage: we don't need to outperform the market every single day, week, or month to generate exceptional returns historically, crypto's bull phases have tended to occupy a disproportionate share of the cycle, with bear phases accounting for a smaller portion using one historical framework, the bull phase averages approximately 1,060 days, or 74% of the cycle, while the bear phase averages around 380 days, or 26% but if your definition of a bull run is narrower — i.e. a period of explosive momentum when $BTC trades above its 200-day moving average and much of the cycle's biggest price action occurs — then the window becomes significantly shorter under that framework, we're talking about approximately 237 days, or around 16% of the cycle these figures are estimates based on particular definitions of market phases, not universal constants but the broader lesson remains: you don't need to trade every market. you need to recognize the windows when your edge is strongest and capitalize on them for the rest of the time, there's nothing wrong with stepping away entirely or trading only with a tiny fraction of your portfolio that you can comfortably afford to lose preserving your capital, your mental clarity, and your ability to recognize the next major opportunity is far more important than forcing trades in unfavorable conditions because the goal isn't to be active all the time the goal is to be positioned when it matters most and to have the discipline to do nothing when it doesn't to conclude, i only wish i had followed this advice myself hopefully, i'll do a better job of applying it going forward, GOD WILLING

Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.

Over the past five years the combined market value of the West’s ten largest listed alcoholic-drinks companies has fallen dramatically. Register to discover how they’re responding

Over $83M traded through Rialto in the past 7 days. The most popular assets were NVDA, MU, and SPCX. NVDA accounted for 36.6% of total trading volume, followed by MU at 17.9% and SPCX at 14.1%.

Over the next week, more than 25 of the companies behind perp markets listed on Omni are expected to report earnings. Whether earnings are released before, during, or after market hours, you can trade them 24/7 on Variational.

Over 7.2 million $INJ has been burned to date 🔥 Every month, Injective revenue flows into the Community BuyBack. INJ is burned and users earn a share of revenue & onchain stocks. More usage → More revenue → More INJ burned → More rewards. This is the INJ Revenue Flywheel.

Over 90% of crimes involve a digital trail, yet the UK's 43 police forces each hold their data in separate, incompatible systems. Elastic just laid out a decentralized prescription for that boundary problem. Title: Making Data Work Across Boundaries: The Next Challenge for Policing URL: 🧩 The data mesh idea Instead of centralizing everything into one platform, each force runs its own Elastic instance under local governance, connected only through common, open interfaces, making information available to those who need it without making everything available to everyone. 📈 Concrete efficiency numbers Pre-preparing context cut input processing by 58% and response time by 40%. Agent steps dropped from 33 to 9, and response time fell from 213 seconds to 64 seconds. ⚖️ Three principles shaped by past failures Separate the data foundation from applications, make openness the default, and centralize only when operationally justified. These lessons echo past large-scale failures like the NHS National Programme (£9.8 billion lost) and FiReControl's shutdown (£469 million lost). Connecting data across organizational boundaries without centralizing it feels like a pattern that could extend well beyond policing to many public-sector systems. #DataMesh# #Elastic#