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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 […]
SpaceX doubled its revenue compared to last year, according to its first quarterly earnings since going public in June.
New findings by the Electronic Frontier Foundation aim to warn app developers that some of the third-party code they place in their apps may also collect their users' location data when they grant permission to the app.
Count every chew with your AirPods Discussion | Link
A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could outpace governance and safeguards.
Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.
Wrinkles, available on both iOS and Android, essentially acts as an AI-powered audio tour guide that reveals hidden history and local stories.
Build powerful AI agents & workflows Discussion | Link
The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.
Telegram CEO Pavel Durov blames an extortionist for planting child sexual abuse material (CSAM) in a public chat to get the app temporarily removed from Apple's App Store on Monday night. "Apple removed Telegram from the App Store before contacting us," Durov says in a post on X. "This creates a potential systemic risk for […]
Learn how to build tools to simplify how you work—without writing a single line of code. The post How the GitHub legal team used Copilot CLI to streamline their workflows appeared first on The GitHub Blog .
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Can MS Paint run Doom? (Yes)
Apple caps bug bounty program due to deluge of AI submissions.
Why Decouple Reasoning from Motor Control General-purpose robots have to pull off two very different jobs at once. They need to read a cluttered, full-room visual scene, hold a multi-minute plan in memory, and converse with a person — and, in the same instant, close a high-frequency control loop that keeps a balancing humanoid upright and moves a delicate hand without dropping whatever it holds. Cramming both jobs into a single end-to-end network forces uncomfortable trade-offs: the large context window you want for reasoning fights the low latency you need for torque control. On July 28, 2026, Google DeepMind pushed directly against that trade-off with Gemini Robotics 2 , followed on July 30 by Gemini Robotics ER 2. Rather than one monolithic network, the suite splits the problem across three specialized models — whole-body vision-language-action (VLA) control, high-level embodied reasoning, and on-device adaptation — each tuned to a different cadence and context size. The same modular thinking is visible across recent robotics and VLA research collected on the arXiv robotics listings and on Hugging Face Papers , where decomposed perception-planning-control stacks have become a recurring pattern. Understanding DeepMind's specific split clarifies why this architecture is gaining traction. The Three-Model Split ER 2: High-Level Task Reasoning Gemini Robotics ER 2 is the cognitive planner of the stack. It is a vision-language model built for embodied reasoning: it ingests the live camera feed and a natural-language instruction, then decomposes a task that may run several minutes into structured sub-goals. Beyond planning, ER 2 manages dialogue with a human supervisor, interprets spatial context, and coordinates multiple robots operating in a shared workspace — deciding which sub-task gets handed to which platform. Operating more slowly than the control layer (roughly a few times per second), ER 2 trades frequency for breadth of context. That separation matters: a reas
Most business prediction problems do not arrive as prose. They arrive as rows: account attributes, transactions, sensor readings, test results, and a target column. For this kind of data, gradient-boosted trees and other conventional methods remain hard to displace. A new paper, Why Large Language Models Fail at Tabular Prediction , asks a much more useful question than “can an LLM classify a table?”: what, specifically, breaks as the task becomes more like ordinary tabular machine learning? The answer from the authors’ controlled experiments is input dimensionality. Their result matters because it separates a real limitation from several explanations that sound plausible but did not hold up in their tests. The experiment was about prediction, not table chat The paper evaluates frontier LLMs in a pure inference setup: a model receives labeled examples and must predict labels for new rows in a single generation pass. There is no fine-tuning, retrieval pipeline, tool calling, or agent loop to compensate for the base model. This is deliberately narrow. It asks whether a general-purpose language model can act as a direct tabular learner. Across 31 benchmark datasets, the authors compare nine methods and 252 configured classical models. That scope is important: a weak result on one CSV is easy to explain away as prompt design or a quirky dataset. A consistent trend across many tasks is harder to dismiss. The headline is not simply that LLMs lose to established tabular baselines. It is that their accuracy declines as the number of input dimensions grows, while the classical baselines in the study stay stable or improve. The paper therefore treats dimensionality as the central failure mode rather than an incidental property of difficult datasets. Four popular explanations did not survive testing There are several standard reasons developers give for poor LLM performance on tables. The researchers turn these into falsifiable hypotheses. “The classes overlap too much.” If th
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
I built a debugging-practice site where student code runs entirely in the browser . Python via Pyodide , JavaScript in a worker. No server executes anything. No execution bill, no queue, no sandbox to maintain. But four things bit me hard. 1. Your arguments aren't Python objects Pass a JS object into Python and you get this: TypeError: 'pyodide.ffi.JsProxy' object is not subscriptable It's not a dict . It's a live view of the JS object, and it supports neither obj[key] nor .get() . Convert explicitly: const pyArgs = input . map (( arg ) => pyodide . toPy ( arg )); const result = fn (... pyArgs ); 2. null is not None This one passed my entire test suite while being broken in production. pyodide . toPy ( null ) check result type(v) JsNull bool(v) False ✅ falsy, as expected v is None False ❌ the surprise It's falsy, so truthiness checks work fine. But is None fails — which was exactly what my code was checking. Why my tests missed it: the harness used json.loads . The app used toPy . Different conversion paths, different answers. If you need a real None , create it in Python. Don't pass one across. 3. sys.settrace is a free step debugger Want to show users their code running line by line? Python basically hands it to you: def _tracer ( frame , event , arg ): if frame . f_code . co_name != target : return None # skip library frames if event == " line " : steps . append ({ " line " : frame . f_lineno , " locals " : dict ( frame . f_locals ), }) return _tracer Two things this naive version gets wrong: Add a step cap. A tight loop generates steps faster than it burns a 5-second timeout. You need both guards. Handle exception . During unwinding, the return event still fires with arg=None . Miss it and your trace says "returned None" for code that crashed. 4. Your snapshots are lying A user screenshot exposed this one. Every step in the trace showed the final state of a list. Step 1 included mutations that hadn't happened yet. tracing: nums = []; nums.append(1); nums.append(
The USB port in your car is likely not powerful enough to charge your laptop, but there are other options.
Hay muchísimo escrito sobre qué tiene que reportar una organización: guías de Supersalud, de Supersociedades, de SAGRILAFT, de PTEE, de SST, de reportes ambientales. Todas contestan la misma pregunta — ¿qué me aplica? — y la contestan bien. Casi nadie escribe sobre la pregunta que de verdad hace fallar a las organizaciones: ¿cómo no perder ninguno, todos los años, cuando son treinta? Porque los incumplimientos que he visto de cerca casi nunca vienen de que alguien ignorara la obligación. Vienen de que la obligación se conocía perfectamente y aun así se pasó la fecha. Este artículo va del problema operativo —fechas, evidencia, responsables— no de cuáles normas le aplican a su entidad. Eso es otra conversación, y no es esta. Por qué el archivo de Excel deja de servir Con tres obligaciones, una hoja de cálculo sobra. El dolor no empieza por el número: empieza cuando el calendario hay que derivarlo . 1. Las fechas no son fechas, son reglas. Muchos vencimientos no están escritos como un día del calendario: dependen del último dígito del NIT, de días hábiles, o de un plazo contado desde un hecho. Eso significa que alguien recalcula el calendario entero cada año, a mano . Cada enero se reintroduce la misma oportunidad de equivocarse, y basta con un festivo mal contado. 2. El calendario vive en una persona. Casi siempre hay alguien que "sabe cómo es la cosa". Mientras esté, funciona. Cuando se va de vacaciones —o se va de la empresa— se va con ella el contexto que nunca estuvo escrito. La hoja sobrevive; el criterio para llenarla, no. 3. La hoja dice que se entregó, no lo prueba. La celda en verde es una afirmación de alguien. La evidencia real —el radicado, el archivo exacto que se subió, la hora— está en el correo de alguien. El día que hay que demostrarlo, empieza la arqueología en bandejas de entrada. 4. Los terceros que le reportan a usted. Si recibe información de contratistas, sedes o filiales, ahora administra dos problemas: sus propios vencimientos y los de ellos.