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Superintelligence will be built on Self Improvement. Today @hexoai, we’re excited to release ‘SIA’ - an open-source Self-Improving AI, to achieve any goal through recursive self improvement. While trying to solve a problem, SIA doesn't just improve it's abilities by updating it's harness, it updates it's own weights as well.

Intel’s EMIB Packaging Is Growing Rapidly — Silicon Capacitors Are Taking Off Too Silicon capacitors are poised for explosive growth in the AI semiconductor space. Intel has been found to be planning a large-scale adoption of silicon capacitors starting next year, in order to enhance the performance of its in-house 2.5D packaging technology, “EMIB.” The most clearly visible source of demand is Google. Google plans to launch its next-generation AI accelerator, “v8e,” in the second half of next year, and has adopted an EMIB substrate with embedded silicon capacitors for that chip. With other Big Tech companies such as Amazon also currently applying EMIB, analysts say demand could increase sharply. According to industry sources on the 27th, Intel plans to apply silicon capacitors to its 2.5D packaging starting next year. Intel Adopts “Silicon Capacitors” for 2.5D Packaging… Google AI Chip Gets First Application 2.5D is an advanced packaging technology that inserts a thin-film interposer between the semiconductor and the substrate. Because it can connect circuits at higher density compared with conventional packaging that uses only a substrate, demand is rising in the AI and HPC fields. To improve cost efficiency in 2.5D packaging, Intel devised its own technology called EMIB. Rather than using a broad, spread-out interposer, EMIB connects chip to chip using a small silicon bridge. Since bridges only need to be placed where chip-to-chip connections are required, chips can be arranged more flexibly and efficiently. Recently, EMIB has been drawing attention as an alternative to TSMC, which had been leading the existing 2.5D packaging market. This is because TSMC’s 2.5D packaging capacity is suffering from a supply shortage amid the rapid development of the AI industry. Indeed, global Big Tech player Google is also paying attention to EMIB. Google has decided to adopt EMIB for its in-house AI semiconductor “v8e,” which it plans to launch in the second half of next year. Under this structure, TSMC handles chip mass production, MediaTek handles design and manufacturing support, and Intel handles packaging. However, there have been concerns that EMIB is gradually showing limitations in providing stable power supply for AI semiconductors, which consume large amounts of power. Accordingly, Intel plans to introduce new technologies such as silicon capacitors and through-silicon vias (TSV) to ensure stable packaging for the v8e. A capacitor is a component that stores and releases electricity in an electronic circuit. In the case of silicon capacitors, their resistance (ESL/ESR) is more than 100 times lower than that of conventional multilayer ceramic capacitors (MLCC), minimizing the signal loss that occurs in high-performance semiconductors. They can also be designed in an ultra-thin structure based on a silicon wafer, enabling high-density integration. A semiconductor industry official explained, “Because the voltage drop (the phenomenon of voltage decreasing) that occurs in the high-frequency region within AI chips is difficult to solve with MLCC, we understand that Intel is adopting silicon capacitors as a solution,” adding, “The relevant supply chain is now in place, and mass production is set to begin in earnest next year.” EMIB-T Is Already on a Growth Trajectory — The Related Ecosystem and Market Are Expanding Together Intel has also inserted TSVs, which serve as power-delivery channels, into the silicon bridge. The key point is that by using TSVs to shorten the power-delivery path between the substrate and the chip, Intel has improved power efficiency and signal integrity. Intel calls this “EMIB-T.” The industry expects the EMIB-T and silicon capacitor markets to grow rapidly. This is because Japan’s Ibiden — one of the major companies that mass-produces semiconductor substrates for EMIB-T — is aggressively pursuing capital investment. Previously, Ibiden had planned to build its Kawashima (Gama) plant in Gifu Prefecture as a substrate plant for Intel CPUs. However, it postponed that schedule and decided in the first half of this year to officially convert the Gama plant into a mass-production line for EMIB-T substrates. The investment is 220 billion yen (about KRW 2.1 trillion). In its recent earnings announcement, Ibiden stated, “Operation of the Gama plant will begin in 2027 and enter full-scale mass production in 2028,” adding, “EMIB-T substrate capacity is currently far short of demand. However, adding further capacity is quite difficult, so we are discussing options with our customers.” A semiconductor industry official explained, “Ibiden’s EMIB-T-dedicated line is being built with most of the investment coming from customers such as Google, Amazon, and Intel,” adding, “This demonstrates that AI semiconductors based on EMIB-T will grow significantly going forward, and silicon capacitors are likely to expand alongside them.”

Updates since then: * Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( ), and it works, however it's only fast on Apple hardware (I've head ~35 tok/s). On AMD, it's ~7 tok/s. IMO actually taking the effort to properly support more than one hardware manufacturer is a great example of the difference between mere "decentralized AI" and genuine "CROPS AI". I hope we can become better at this. * also has alpha telegram support now. However, the path to adding your account is quite janky * looks promising as a way to run "dense" models (eg. Qwen 27B) more efficiently. It's janky, but on my 5090 laptop it seems to be ~2x more tok/s than llama.cpp * VoxTerm (local AI recording, no third-party servers) continues to be developed And there's a lot more projects coming on the horizon. One other thing that has been on my mind is that there's actually a lot of intersection between "CROPS ethereum access layer" and "CROPS AI". For example, we want a ZK way to make (paid) calls to remote LLMs. But if we have this, then it's just as useful for solving another problem: private RPC reads in Ethereum. Another example: application-specific finetuned LLMs. Leanstral ( ; I get ~38 tok/s on AMD) fits into < 70 GB, but can hold its own against 1T models on writing Lean code. Things like this are a huge boon for writing more secure code ( ). We should have models finetuned for Ethereum-related use cases as well.

Elon Musk’s goal for The Boring Company is to solve one of the most miserable daily experiences on Earth: traffic Cities are three-dimensional But transportation is still mostly trapped in a two-dimensional surface network Roads, intersections, bottlenecks, traffic lights, accidents, construction, weather - everything gets stacked on the same flat layer until the entire system chokes The Boring Company’s answer is simple but radical: Go underground Build fast, low-cost tunnel networks under major cities and turn transportation into true 3D infrastructure Right now, the focus is on making tunneling dramatically faster and cheaper with machines like Prufrock, which is designed to mine continuously while installing tunnel liner at the same time But the long-term vision goes much further Local Loop tunnels could move people across cities without surface traffic, while future Hyperloop-style systems could connect entire cities at ultra-high speed Imagine going from Los Angeles to San Francisco, New York to Washington D.C., or Dubai to Abu Dhabi in a fraction of today’s travel time - underground, electric, direct, and protected from surface congestion That is the real mission: Building the missing third dimension of transportation This is how you actually attack soul-destroying traffic at civilization scale

吴说获悉，Ethereum Foundation 表示，Open Intents Framework 旨在作为跨链 Intent 的共享基础设施，为生态提供可部署、发现和执行 Intent 的模块化开放框架。与此同时，LI FI 宣布推出 LI FI Intents，这是一套面向同链与跨链转账的全栈执行引擎，通过专业 Solver 网络竞争报价并完成订单执行。LI FI 文档显示，其 Intent/Solver Marketplace 是 Open Intents Framework 的官方基础之一，Ethereum Foundation 称该框架正进入规模化采用阶段。

Today on “The Intelligence”: the persistent confusion surrounding a potential Iran deal, agentic AI comes to China’s superapps, and how to solve the great ski-resort grouse mystery

Credit card interest rates are way too high. What if you could create a credit card that had an interest rate below 10% to solve the problem. This is what @SadiSKhan and Aven have done. Fascinating business & discussion.

General Matter CEO @ScottNolan's biggest lessons from working as an early employee at SpaceX: "Pick the right thing to work on. Pick the problem no one else is working on, that really needs to be solved." "Lesson two is—clean slate. Start from scratch. Question everything."

In 1990, the World Wide Web was invented on Steve Jobs' computer. Steve ignored it. This is the story I tell in my new book Steve Jobs in Exile. Here is what it should tell the rest of us about the moment we are in now. Steve was running NeXT, an unsuccessful computer company. He had been pushed out of Apple five years earlier and was burning his fortune trying to build a successor to the Macintosh. The machine NeXT sold was a matte-black magnesium cube -- expensive and beautiful and not selling. In October of that year, on the other side of the Atlantic, a British physicist named Tim Berners-Lee took delivery of a NeXT Cube at CERN, the physics laboratory on the Swiss-French border. He used it to invent the World Wide Web. The web ran on the Cube for its first year of existence. The revolution was happening on Steve's hardware, and yet Steve ignored it. Here is the question I keep thinking about from my book. If Steve Jobs, the most visionary tech mind of his generation, missed the Web, the most civilization-shaping tech of his lifetime, how are the rest of us supposed to see anything coming? Berners-Lee had been asking his boss at CERN for a NeXT Cube for months. His boss finally signed off, hoping to test the exotic Cube. "He suggested that I should buy one of these NeXT machines I'd been talking about so enthusiastically," Berners-Lee later told Fresh Air. "And if we needed a sort of test project to run on the NeXT machine ... 'Why not just do this hypertext thing you're talking about?'" The "test project" evolved into the World Wide Web. The problem Berners-Lee was trying to solve was not a glamorous one. CERN employed thousands of scientists from over a hundred countries, most cycling through on short assignments and taking their knowledge with them when they left. Berners-Lee was trying to keep institutional knowledge from walking out the door. He wanted a system that worked the way human memory does, where any piece of information could connect to any other without permission or central control. Through late 1990, he coded in his gray-floored office. The Cube's object-oriented system let him build in months what would have taken a year on anything else. By December, the first website went online. The World Wide Web now existed, running on a single black NeXT Cube in CERN's Building 31. Berners-Lee scrawled a warning on it in red ink: "This machine is a server. DO NOT POWER IT DOWN!!" Underneath the elegant interface he was building HTTP, HTML, and the server software that would deliver web pages. These three inventions would form much of the invisible plumbing of our modern connectivity. When a colleague of Berners-Lee's brought a demo of the Web to NeXT's headquarters in California, he could not get anyone there to pay attention. Nobody even dared show it to Steve, afraid he would dismiss it. NeXT was busy with its own internet plans, which Steve eventually killed. So back to the question. If Steve Jobs missed the web, how are the rest of us supposed to see whatever comes next? The honest answer is that we cannot. Nobody can. The rest of us are not going to outpattern-match Steve Jobs. But here is what I learned writing Steve Jobs in Exile. Transformations almost always begin in obscurity, on the margins, solving boring problems with boring tools. The web did not look revolutionary in 1990. It looked like a tool for sharing physics papers. We are in another such moment now. AI is the obvious changemaker. But the biggest transformations are rarely the obvious ones. The next one is happening somewhere right now, and it is trickier to spot than any sweeping proclamation about AI. We will recognize it, if we recognize it at all, from the unglamorous work few people are focused on. I will not speculate on what Steve would have made of AI today. But if he could miss the Web, the rest of us are going to have to look harder. Photo of the original CERN NeXT Cube courtesy of Robert Scoble.