标签：#ci

Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.

Lucid's new CEO Silvio Napoli listed four must-win priorities, including the successful launch of its midsize EV, finishing a factory in Saudi Arabia, cutting expenses, and robotaxis.

The Trump administration shared the details of its plan with OpenAI, Anthropic, and other AI labs on Tuesday. For now, the public remains in the dark.

Advertisers seem to be spending a whole lot less than they used to on the platform.

SpaceX's AI revenue grew more than three times to $2.6 billion from the year before, mostly because of deals that the company made to provide compute to other AI companies, according to SpaceX's quarterly earnings. The AI division, which the company said in its documents to go public was the source of most of its […]

Have you ever had this happen? You're chatting with a friend about buying a new pair of shoes. A few hours later... Instagram shows you an ad for those exact shoes. Or maybe you're talking about planning a trip. Suddenly...Your feed is filled with hotel deals, flight offers, and travel videos. The first thought that comes to almost everyone's mind is: "𝐌𝐲 𝐩𝐡𝐨𝐧𝐞 𝐢𝐬 𝐥𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 𝐭𝐨 𝐦𝐞." 👀 Honestly... I've thought the same. And maybe you have too. But what if I told you that the truth is actually more fascinating than the myth? So... is your phone secretly listening? Probably not. Not because it can't. But because it usually doesn't need to. Think about it. Every day you leave behind hundreds of tiny digital clues. 🔍 What you search. ❤️ What you like. ⏱️ How long you watch a video. 🛒 What you browse. 📍 Where you go. 👥 Even who you interact with online. Individually...They don't say much. Together...They tell a story that's surprisingly accurate. A story about your habits. The scary part? AI doesn't need to hear your conversations. Sometimes...It already knows what you're likely to do next. Not because it can read your mind. But because it's incredibly good at recognizing patterns. And when a prediction is accurate enough... It starts to feel like magic. Or surveillance. Here's what fascinates me the most. The real superpower of modern AI isn't listening. It's predicting. And sometimes...Those predictions are so good that they make us question reality itself. The next time you think, "My phone is definitely listening to me." Ask yourself a different question. "How much of my digital behavior have I already shared without realizing it?" Because maybe...The microphone isn't the real story. Your patterns are. 💬 Have you ever had an experience that made you think your phone was listening to you? What happened? Takeaway : Technology doesn't always become powerful by knowing more. Sometimes... It becomes powerful by predicting better. Technology becomes less magical when you und

DeepSeek V4 Flash costs $0.14 per million input tokens and $0.28 per million output, with cache hits at $0.0028, and the retrained 0731 build that now ships under that name has a defect you need to route around: with thinking on (the default) and a strict json_schema , integer fields came back corrupted in 8 of 13 default-thinking runs across two independent request paths. Turning thinking off fixed every run and cut the extraction to a seventh of the tokens. We measured deepseek-v4-flash-0731 on day one: the corruption, the sharper off-switch cliff the retrain introduced, the budget floor that rescues it, the 1,024-token cache pages, and what still separates the preview build and V4 Pro . TL;DR With default thinking plus strict json_schema , deepseek-v4-flash-0731 corrupted integer fields in 8 of 13 runs on two request paths; V4 Pro corrupted 2 of 4, and only the preview stayed clean. The 0731 retrain sharpened the off-switch cliff: 2-hop math fell 6/6 to 0/6. The cache serves 1,024-token pages from roughly a 1.1K-token floor, hits 0.3 seconds after priming, and entries outlive 45 minutes. enable_thinking: false fixed every structured run at a seventh of the tokens; for 2-hop math the safe thinking budget is 256. How do the three V4 builds compare on paper and on the meter? Same tokenizer, same cache, same thinking machinery; different prices, different failure modes. Everything measured below comes from identical probes run against all three (a dash means we did not probe that cell); the day-one analysis threads focus on benchmarks, so this is the operational half of the comparison: Flash 0731 Flash preview V4 Pro List price, in / out per 1M $0.14 / $0.28 $0.14 / $0.28 $0.435 / $0.87 Cache-hit input per 1M $0.0028 $0.0028 $0.003625 Thinking default on on on Strict JSON with thinking on 5/5 corrupted (our path) 4/4 clean 2/4 corrupted 2-hop math with thinking off 0/6 2/6 4/4 thinking_budget token-exact token-exact honored (4/4 at 16) Cache pages 1,024 tokens, hit a

Platform engineering maturity is emerging as an important factor in determining whether organizations can turn AI adoption into sustainable operational value, according to Perforce Software's 2026 Platform Engineering Report. By Craig Risi

Chris Richardson discusses leveraging Team Topologies and internal platforms to accelerate microservices delivery. He explains six key platform patterns - from security and observability to build and deployment - and shares strategies for minimizing cognitive load on stream-aligned teams while avoiding common platform engineering pitfalls. By Chris Richardson

ESPN unveiled an “AI tells detection” tool during broadcasts of the 2026 World Series of Poker. Is it a neat computer-powered party trick, or a real threat to poker’s future?

Ati Robotics assembles its robots in India and uses just a few Chinese parts—a strategy that could pay off as the Trump administration cracks down on Chinese humanoids.

What the Unabomber, Steve Bannon’s tech guy, and Bernie Sanders taught me about the great data center backlash of 2026.

I started this project because an LLM annoyed me. I gave a very strong model 322 , a small Dota 2 drafting game. The choices looked like the kind of work a computer should enjoy: repeated packs of players and heroes, visible ratings, familiarity scores, chemistry, rerolls and a simulated tournament at the end. I was disappointed by how well the LLM did. I am not a Dota expert, and I had only started watching it occasionally again during the previous six months or year. I still seemed to be doing better. The interesting engineering question was not how to write a longer prompt. It was how to replace the card-by-card language-model judgement with a deterministic policy, then test that policy without confusing improvement with luck. A stochastic benchmark needs shared randomness The browser history gave us a useful irritation and almost no reliable comparison. My earlier manual record contained 50 runs with a 14% title rate. The LLM won once in nine attempts. Putting 14% beside 11% looks temptingly quantitative, but the random offers, rejected packs and opponent fields were not preserved. The samples were small, unpaired and produced under different choices. That is not a model benchmark. It is a reason to build one. The offline solver generated every random choice from indexed tapes. Policy A and policy B received the same player offers, hero samples, field candidates and tournament randomness for a given episode. We could then compare the paired result: did the new policy win this exact episode where the old policy lost it? This is the common-random-numbers idea in a practical form. Sharing the luck removes a large amount of noise that has nothing to do with the policy change. Keep the simulator separate from the policy Before evaluating a strategy, we reproduced the game. The public client and seven data files were frozen with SHA-256 hashes. Draft legality, automatic hero allocation, chemistry, scoring and the tournament were ported into a deterministic Python engi

Open-weight AI models are having a moment in the wake of recent turmoil at US tech giants. For French AI lab Mistral, that’s the the best thing that could have happened.

Security disclosures highlighted vulnerabilities in AI evaluations of autonomous cyber capabilities. Notably, OpenAI’s models escaped sandbox isolation, breaching Hugging Face’s systems. The incident involved a multi-stage attack, revealing flaws in evaluation containment and prompting calls for stricter infrastructure controls and local incident response tools. By Olimpiu Pop

A portfolio can look good on the usual scorecard and still answer the wrong question. One line says return was high. Another says risk-adjusted performance was acceptable. A third says drawdown stayed inside a tolerable range. Then the market turns, the benchmark starts recovering, and the thing I actually care about is different: how efficiently did the portfolio catch up? That is where a new metric can fool its own author. If I build a recovery measure and it moves almost exactly like an existing ratio, I have created a longer name for the same signal. The right acceptance test is geometric: a useful metric should cast a different shadow. This is the rule I used while validating Hyperlogarithmic Benchmark Catch-Up Ratio (HBCR): orthogonality to existing measures is a first-class test, not a chart for the appendix. 1. A new metric has to earn its axis HBCR was built to measure benchmark-relative recovery dynamics. The research page states the motivation plainly: traditional benchmark-relative metrics often fail to capture the true dynamics of investment performance, especially during market recoveries [ A New Metric for Private Equity Risk Adjusted Returns , Calibration of Risk and Correlation in Private Equity ]. That framing matters because the obvious validation path is tempting and weak. You compare the new number with familiar performance measures, find a comforting relationship, and declare victory. But a high correlation with a well-known score can be a warning. If HBCR strongly tracked Sharpe Ratio, it would probably be an expensive synonym for risk-adjusted return. The acceptance test I wanted was sharper. HBCR should have some relationship with performance, because recovery has economic content. It should also avoid collapsing into the same direction as Sharpe Ratio, Beta, Volatility, Alpha, Total Return, or Max Drawdown. Written as a predicate, the test has two sides. Let $\mathcal{T}$ be the set of metrics already on the scorecard, $\rho_{n,m}$ the corr

Key takeaways Three engines, three internets: across the searches where all three answered, Google, Bing, and Brave agreed on the #1 result only 29% of the time, and Google and Bing shared just 3 of the top 10 on average. Each engine has a personality. Bing rewards traditional publishers (Forbes appeared in 9 of 15 top-10s, PCMag in 8). Google leans on its own properties and forums (Reddit and YouTube each showed up in 57% of Google SERPs, Wikipedia in 43%). Brave is a blend of both. The platforms Google loves, Bing ignores: Reddit, YouTube, and Wikipedia appeared in 0% of the Bing top-10s we checked. If you track rankings on one engine, you are blind on the others. Cross-engine divergence is the case for multi-engine SERP monitoring — not a single Google rank check. Everyone talks about "ranking on Google." But Google is not the only place your customers search, and the other engines do not agree with it — or with each other. We ran the same 15 searches through Google, Bing, and Brave using Crawlora's search APIs and compared the top 10 results. The short version: the three engines return strikingly different pages, reward different kinds of sites, and rarely even agree on what belongs at #1. How much do the engines overlap? For each search we took the top 10 result domains from each engine and counted how many they shared. No pair shares even half its results on average, and all three engines agree on fewer than 3 of 10: Engine pair Avg shared (of 10) Overlap Google ∩ Bing 3.0 30% Google ∩ Brave 5.1 51% Bing ∩ Brave 4.0 40% All three 2.7 27% A page ranking #3 on Bing might be nowhere on Google. If your rank tracker only watches one engine, most of this picture is invisible to you. They rarely agree on #1 The single most valuable position — the #1 organic result — matched across all three engines for only 2 of the 7 searches where every engine answered (29%). Here is the real head-to-head: Search Google #1 Bing #1 Brave #1 Agree? best running shoes runrepeat.com wi

Fenix Flexin’s hit song “Rubberz” has hip-hop fans arguing over whether it was generated by AI. Some say they have proof it’s machine-made, but will anyone care?

When asked about product-market fit, Spiegel said he believes mass-market consumer adoption won't occur until the end of the decade.

Also: Cyborg diving suits for cockroaches, why sleepy sperm whales blow bubbles, Betelgeuse's companion star.