Ali Syed

For the first time in 50 years, humans are leaving Earth orbit and this time, we’re not just visiting. @TheShadowObserver/we-are-going-back-to-the-moon-after-50-years-but-this-time-its-different-38d1b64de42c" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

Your organization's data cannot be pasted here. rystadenergy.com/insights/oil-m…

The End of the Monolithic AI: Why the Future of Intelligence Is Social #ArtificialIntelligence #AgenticAI #GenAI @TheShadowObserver/the-end-of-the-monolithic-ai-why-the-future-of-intelligence-is-social-573f74e21959" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

Oil & gas insiders are unanimous: “The world still hasn’t grasped the severity of the situation.” bloomberg.com/graphics/2026-… #OilShock #Hormuz #EnergyCrisis

40% of global helium is offline. It's critical for computer chips and there's only a few weeks of inventory. ft.com/content/2c5068…

The AI boom wasn’t built for the polycrisis. theatlantic.com/technology/202…

The Crude Reality #EnergyMarkets #OilMarket #OilPrices @TheShadowObserver/the-crude-reality-1684ad21c44c" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

Gemini 3.1 Flash Live is Google's highest-quality audio model, designed for natural and reliable real-time dialogue. Developers can access it through the Gemini Live API in Google AI Studio, while enterprises can use it for customer experience. blog.google/innovation-and…

Sustained choke in the Strait of Hormuz and prolonged supply annihilation. That’s when the system reprices structurally. How long the oil actually stays dark is the signal.

This Is No Fleeting Market Glitch. It Is a Profound Rupture. #EnergyMarkets #energycrisis @TheShadowObserver/this-is-no-fleeting-market-glitch-it-is-a-profound-rupture-20198348c499" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

ALS has gradually taken away Kenneth’s ability to speak. Through Neuralink’s VOICE clinical trial, he’s exploring how a brain-computer interface designed to translate thought to speech could help restore autonomy in his daily life. Watch to learn more:

Tesla plans to start producing Gen 3 robots this summer, with goals for mass production and potential public availability by the end of 2027.

How a Poisoned Security Scanner Became the Key to Backdooring LiteLLM snyk.io/articles/poiso…

Understanding Bear Traps and Bull Traps in Trading @TheShadowObserver/understanding-bear-traps-and-bull-traps-in-trading-an-educational-guide-with-a-recent-real-world-da645d65ea73" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

#TERAFAB The 1-Terawatt Chip Factory That Will Make Humanity a Galactic Civilization. @TheShadowObserver/terafab-the-1-terawatt-chip-factory-that-will-make-humanity-a-galactic-civilization-503bf9c6c592" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

A shipping shock delays supply. A capacity shock destroys it. @TheShadowObserver/the-war-that-rewired-the-world-economy-from-regional-conflict-to-a-global-capacity-crisis-b025075011c7" target="_blank" rel="nofollow noopener">medium.com/@TheShadowObse…

Introducing the new @stitchbygoogle, Google’s vibe design platform that transforms natural language into high-fidelity designs in one seamless flow. 🎨Create with a smarter design agent: Describe a new business concept or app vision and see it take shape on an AI-native canvas. ⚡️ Iterate quickly: Stitch screens together into interactive prototypes and manage your brand with a portable design system. 🎤 Collaborate with voice: Use hands-free voice interactions to update layouts and explore new variations in real-time. Try it now (Age 18+ only. Currently available in English and in countries where Gemini is supported.) → stitch.withgoogle.com

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

“Agents of Chaos” The problem with AI agents now is: once you give LLMs tools + persistence, AI's mistakes become real-world security incidents, and standard chatbot evals would easily miss that. This paper red-teams autonomous agents in realistic setups and shows they can be socially hijacked into leaking secrets, taking destructive actions, and getting stuck in harmful loops. Basically AI autonomy is a HUGE risk multiplier, and agents need built-in identity/auth, oversight, and governance before deployment, not after.

Demis Hassabis just defined the real test for AGI. It’s more brutal than anyone expected. Train AI on all human knowledge. Cut it off at 1911. See if it independently discovers general relativity like Einstein did in 1915. If it can, we have AGI. If not, we’re still building pattern matchers. Hassabis: “My definition of AGI has never changed. A system that can exhibit all the cognitive capabilities that humans can.” Not bar exams. Not coding competitions. All cognitive capabilities. Hassabis: “The brain is the only existence proof we have, maybe in the universe, of a general intelligence.” That’s why DeepMind studies neuroscience. Not for inspiration. For data. The human brain is the only confirmed evidence that general intelligence is physically possible. If you want to build it, you study the only example that exists. Hassabis: “True creativity, continual learning, long-term planning. They’re not good at those things.” Current systems are impressive and broken simultaneously. Hassabis: “They can get gold medals in international math olympiad questions, but they can still fall over on relatively simple math problems if you pose it in a certain way.” Jagged intelligence. Brilliant in narrow domains. Incompetent when approached differently. That inconsistency is the tell. A true general intelligence doesn’t spike in one direction and collapse in another. The Einstein test cuts through all of it. No benchmarks. No leaderboards. No carefully curated evals. Just a model, a knowledge cutoff, and the question of whether it can do what one human did alone in 1915. Hassabis: “Training an AI system with a knowledge cutoff of 1911 and seeing if it could come up with general relativity like Einstein did in 1915. That’s the true test of whether we have a full AGI system.” Current models can’t. They remix brilliantly. They don’t generate paradigm-shifting theories from first principles. Hassabis: “I think we’re still a few years away from that.” A few years. Not decades. The system that can be Einstein once can be Einstein a thousand times simultaneously across every domain. That’s not AGI anymore. That’s the beginning of something we don’t have words for yet. When that test gets passed, we won’t need a press release to know what happened.