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Superhuman’s latest AI email drafting feature is its most convincing yet, generating replies that often required little to no editing in our testing.
@kungfufk Since the dawn of computing, we have built Artificial Intelligence on a flawed premise: perfect rationality. We brute-force algorithms to find the optimal solution, assuming infinite time and infinite capacity. But humans don't work like that. As Herbert Simon famously coined, we operate on Bounded Rationality. We make decisions based on limited time, limited cognitive capacity, and limited information. What if, instead of forcing AI to be perfectly rational, we created a mathematical equivalent for human processing? What if we modeled human cognition using the laws of physics — wave theory, thermodynamics, and mechanical energy equations — to build a heavy, complex, but highly probabilistic AI engine? Here is a blueprint for a new field of research: Computational Cognitive Mechanics . 1. The Core Equations of Cognitive Processing To model bounded rationality mathematically, we first need to define the relationship between Knowledge ($K$), Cognitive Capacity ($C$), and Processing Time ($T$). Based on human observation, we can establish these foundational proportions: Knowledge vs. Time — The more knowledge you possess, the faster you can generate a decision. $$T \propto \frac{1}{K}$$ Capacity vs. Time — High cognitive capacity (skills, processing power) inversely relates to the time required to solve a problem. $$T \propto \frac{1}{C}$$ Knowledge vs. Capacity — This is the most fascinating limit. Knowledge does not scale linearly with capacity. Gaining true knowledge requires exponential capacity (effort/skill). Therefore, knowledge is roughly proportional to the square root of capacity. $$K \propto \sqrt{C}$$ By integrating these, we can build a baseline processing algorithm for an AI. Instead of giving an AI unlimited time to compute, we cap its computing time based on a synthetic "Knowledge and Capacity" matrix, forcing it to use heuristics — just like a human. 2. Cognitive Wave Theory & FFT: Information as Interference In physics, waves interact throug
Preclinical trial is testing the feasibility of humanoid robots in surgery.
📖 Read the full version with charts and embedded sources on ComputeLeap → You can now buy a walking, flipping, kung-fu-kicking humanoid robot on AliExpress for $4,900 — less than a used Honda Civic, less than a semester of community college, less than what most people spend on a couch-and-TV combo. Unitree's R1 AIR shipped its first global batch in April, and it represents something the robotics industry has been promising and failing to deliver for decades: a humanoid robot that a normal person can actually afford. But here's what the breathless headlines won't tell you: price is falling faster than capability. The gap between what this robot costs and what it can actually do is where the hype lives — and understanding that gap is the difference between seeing a revolution and seeing a very expensive toy. The Number That Matters The Unitree R1 AIR stands 4 feet tall, weighs 55 pounds, and packs 20 degrees of freedom into a bipedal frame that can run, do cartwheels, throw punches, and execute spin kicks . At CES 2026, Unitree's booth stopped traffic with R1s replicating Bruce Lee sequences, Michael Jackson dance moves, and Mike Tyson combinations. The base R1 AIR ships with a monocular camera, 8-core CPU, and onboard AI for voice and image recognition. For $1,000 more, the standard R1 at $5,900 adds six more degrees of freedom (26 total), binocular depth perception, waist articulation, and head movement. Both come with hot-swappable batteries — about an hour of runtime per charge. To put the price in context: Figure AI and Tesla each shipped roughly 150 humanoid units in 2025. Unitree shipped 5,500 . That's not a typo — Unitree alone outshipped every Western humanoid manufacturer combined by a factor of 20x. The R1's $4,900 price point isn't an outlier. It's the leading edge of a Chinese manufacturing tidal wave. The Raspberry Pi Parallel — and Its Limits When the Raspberry Pi launched in 2012 at $35, it didn't replace laptops. It didn't become the computer most peo
What happens when a machine no longer needs to be trained to see something new? That's the quiet question sitting underneath this week's news, buried next to a less invasive brain implant and a handful of robots getting tougher for the real world. Neuralink says it's completed its first "transdural" brain implant, a surgical approach built to reduce trauma during the procedure. As someone who spends a lot of time thinking about how you get sensors close to a human eye without hurting anyone, I find these less-invasive-implant strategies worth watching, because the surgical-risk problem is basically the same one we wrestle with in ophthalmic hardware. Vision is getting less invasive too, in its own way. Roboflow rolled out text-prompt object detection built on SAM3 (Meta's latest segmentation model): you type the class of object you want "forklift," "cracked tile," whatever, and it returns boxes and masks without you collecting a single training image first. That's a real shift. For most of computer vision's history, teaching a model to recognize something new meant labeling hundreds of examples before you could even start; this collapses that step into a sentence. The same week brought several applied builds using the same detect-then-orchestrate pattern: a drone system that patrols for intrusions, a pipeline that inspects transmission lines for damaged cables, and an airport tool that spots foreign debris on the tarmac. The Robot Report's roundup of June's biggest robotics stories leaned heavily on humanoid robots companies going public, new deployments, and production milestones stacking up faster than would have seemed plausible a few years ago. Apptronik unveiled its Apollo 2 humanoid alongside a dedicated data-collection facility built so the robot keeps learning after it's deployed, not just during initial training which quietly answers one of the harder questions in robotics: how do you keep a system improving once it's out of the lab? X Square Robot raised e
Starliner's certification may be delayed to 2027, 10 years later than Boeing's original schedule.
South Korea targets physical AI lead and commercial humanoid robots by 2028.
Dolphins, sharks, turtles, and human workers are all victims of unregulated squid fishing fleets. Another news article . As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.
The general shape of the problem is that every public LLM benchmark is on a saturation clock that runs from the moment of its publication to the moment a model's training corpus has eaten it. The clock has been running, on the visible benchmarks of the last five years, for somewhere between twelve and thirty months before each one is no longer useful for differentiating frontier models. The benchmarks are not failing. They are doing exactly what they were designed to do, in the order they were designed to do it, and the field has been running through them faster than the people designing them anticipated. I want to put numbers on the saturation pattern, walk through what the contamination evidence actually says, and then sit with the question of what an honest benchmark would have to look like in 2026 — because the "private held-out eval" answer that the labs are converging on has economics that are worth examining carefully before any of us salute it as the solution. The saturation timeline, with numbers HumanEval (Chen et al., OpenAI, July 2021). 164 hand-written Python problems. The benchmark was published with Codex at 28.8% pass@1; the underlying GPT-3 base model scored 0%. GPT-4 (March 2023) hit 67% in the original Technical Report. By late 2024, OpenAI's o1-preview and o1-mini both reached 96.3% pass@1 ; Claude 3.5 Sonnet sat at 93.7%. The benchmark is saturated in the operational sense — the relative spread across the top ten models is around 10 percentage points, which is too small a gap to differentiate them on, and most of the new models arrive within a percentage point or two of the ceiling. The successor variants (HumanEval+ from EvalPlus, with augmented test cases) are the field's response. Lifespan from publication to operational saturation: about 36 months. MMLU (Hendrycks et al., September 2020). 57 subjects, ~14,000 multiple-choice questions, taken from publicly-available test prep and academic sources. The problem with MMLU is not that it's satura
Much is new about this month’s upcoming FIFA World Cup tournament, which will be held in the US, Canada, and Mexico. It hosts more teams than ever before. It’s the first to occur in three different host countries. And, like predecessor cups for over half a century, it will employ a soccer ball with a…
What’s the difference between a person, an artifact, and an ecosystem?
Robot demonstrations can distort public perceptions of robotic capabilities.
The breach at wearable ring maker Ultrahuman stemmed from credentials stolen from a malware-infected employee laptop.
로봇 두 대가 말 한마디 없이 방을 정리했다, 그런데 진짜 질문은 '어떻게'가 아니다 협업의 정의가 바뀌고 있다. 인간끼리도 아니고, 인간과 로봇도 아니라, 로봇과 로봇 사이에서. TL;DR : 피규어 AI의 휴머노이드 두 대가 언어 없이 2분 만에 침실 정리에 성공했다. 기술 자체보다 흥미로운 것은, 이 '눈치'가 어떻게 만들어졌는가이다. 로봇 협업이 인간 협업의 방식을 모방한 게 아니라, 아예 다른 방식으로 진화하고 있다는 신호다. 로봇 산업에는 잘 알려지지 않은 규칙이 하나 있다. 로봇을 한 대 잘 만드는 것보다, 두 대가 함께 작동하게 만드는 것이 기하급수적으로 어렵다는 것. 보스턴 다이내믹스는 수십 년 동안 혼자 뛰고, 혼자 문을 열고, 혼자 계단을 오르는 로봇을 만들어왔다. 테슬라의 옵티머스는 혼자 부품을 집고, 혼자 배터리를 나른다. 그런데 피규어 AI는 올해 다른 질문을 던졌다. "두 대가 서로 말을 하지 않아도, 협력할 수 있을까?" 그리고 최근 그 답이 나왔다. 2분이었다. 먼저, '눈치'라는 단어를 다시 생각해야 한다 우리가 일상에서 쓰는 '눈치'는 상당히 복잡한 인지 활동이다. 상대방의 행동을 보면서, 다음 행동을 예측하고, 내 행동을 조율하고, 충돌을 피하고, 빈틈을 채우는 것. 인간은 이걸 언어 없이, 심지어 시선 교환만으로 해낸다. 오랜 시간을 함께한 팀에서, 숙련된 주방의 요리사들 사이에서, 그리고 가족 사이에서. 그런데 이 능력은 학습된 것이지, 타고난 것이 아니다. 아이들은 눈치가 없다. 신입 직원도 눈치가 없다. 수백 번의 상호작용과 실수와 교정을 거쳐야 비로소 '눈치'가 생긴다. 피규어 AI의 휴머노이드 두 대는 이 과정을 어떻게 압축했을까. 보도에 따르면 이들은 사전에 언어 명령이나 역할 분담 지시 없이, 상대 로봇의 행동을 실시간으로 인식하고 자신의 다음 동작을 결정했다. 공간을 나눠 쓰고, 같은 물건에 손을 뻗지 않고, 한쪽이 멈추면 다른 쪽이 채웠다. 이것을 연구자들은 '암묵적 협업(implicit collaboration)'이라고 부른다. 쉽게 말하면, 로봇이 눈치를 배웠다는 뜻이다. 두 대가 함께 움직인다는 것의 기술적 의미 단일 로봇의 작동 원리는 비교적 단순하게 설명할 수 있다. 센서가 환경을 인식하고, 모델이 행동을 결정하고, 액추에이터가 실행한다. 루프가 하나다. 두 대가 함께 움직이는 순간, 루프가 두 개가 아니라 세 개가 된다. 로봇 A의 루프, 로봇 B의 루프, 그리고 A와 B가 서로를 환경으로 인식하면서 생기는 상호작용 루프. 이 세 번째 루프가 문제다. A의 행동이 B의 환경을 바꾸고, 그 변화가 다시 B의 행동을 바꾸고, 그 행동이 또 A의 환경을 바꾼다. 루프가 루프를 먹는 구조다. 이것을 중앙에서 통제하는 방식은 예전부터 존재했다. 공장 자동화에서 쓰이는 PLC(프로그래머블 로직 컨트롤러) 방식이 대표적이다. A는 1번 작업, B는 2번 작업, 충돌 시 A가 우선 — 이런 식으로 모든 경우의 수를 미리 프로그래밍한다. 정해진 공간, 정해진 물건, 정해진 순서. 공장에서는 작동한다. 일상에서는 작동하지 않는다. 침실은 공장이 아니다. 물건의 위치가 매번 다르고, 침대 정리와 바닥 정리가 동시에 일어나야 할 수도 있고, 하나가 예상치 못한 물건을 발견하면 계획 전체가 바뀐다. 규칙 기반의 중앙 통제로는 불가능하다. 피규어 AI가 선택한 방향은 분산 의사결정이었다. 각 로봇이 독립적으로 환경을 인식하고, 상대 로봇의 현재 상태를 하나의 입력값으로 받아들이면서, 스스로 다음 행동을 결정하는 방식이다. 중앙 관제탑이 없다. 각자가 판단하되, 서로를 인식한다. 이것이 인간의 눈치와 구조적으로 가장 유사한 접근이다. 2분이라는 숫자가 중요한 이유 2분. 이 숫자를 처음 들으면 "겨우 2분?"이라고 생각할 수 있다. 그런데 맥락을 알면 반응이 바뀐다. 로봇이 단독으로 침실을 정리하는 데 걸리는 시간과 비교해보자. 현재 가장 발전한 단일 휴머노이드 로봇들의 가사 작업 수행 속도는, 같은 작업을 인간이 하는 것보다 보통 3배에서 10배 느리다. 동작이 느린 것도 있