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开发者

Empathy for the optimizers

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. Bryan Johnson, best known as the man who wants to live forever, has an incurable autoimmune disease. The internet's most […]

2026-07-10 原文 →
AI 资讯

I tracked every trending AI repo's stars daily for 3 weeks. The growth is not where I expected

I run a small AI trends site, and three weeks ago I started doing something simple: every day, snapshot the star count of every repo that crosses my GitHub trending scan for AI. No judgment, no curation, just append-only rows in a database. 611 repos and 2,671 data points later (June 19 to July 10), the picture of what's actually growing looks pretty different from what my feeds told me was hot. Here's what the data says. Before publishing this I re-checked every number below against GitHub's live API. Star counts drift by the hour, so treat them as of July 10. The top 10 risers, by raw stars gained Repo Gained Window From → To calesthio/OpenMontage +30,253 21 days 5,899 → 36,152 DeusData/codebase-memory-mcp +20,483 19 days 7,516 → 27,999 mattpocock/skills +19,053 15 days 137,485 → 156,538 obra/superpowers +16,887 20 days 232,908 → 249,795 NousResearch/hermes-agent +14,896 21 days 197,297 → 212,193 Panniantong/Agent-Reach +14,334 14 days 34,780 → 49,114 usestrix/strix +13,243 12 days 26,363 → 39,606 addyosmani/agent-skills +12,685 21 days 63,156 → 75,841 asgeirtj/system_prompts_leaks +11,720 21 days 43,415 → 55,135 msitarzewski/agency-agents +11,055 10 days 118,241 → 129,296 Windows differ because I only hold snapshots for the days a repo appeared in my scan; each row states its own real window. Three things in this data genuinely surprised me. 1. "Skills" are eating agent frameworks Four of the top ten are not agent frameworks. They are collections of packaged expertise that plug into an existing agent: obra/superpowers (still compounding at roughly 840 stars a day on a 250k base), mattpocock/skills, addyosmani/agent-skills, msitarzewski/agency-agents. A year ago this table would have been full of new frameworks. Now the framework layer looks settled and the growth is in what you load INTO the agent. The moat moved from orchestration code to encoded judgment. 2. The sharpest climbs are applications, not infrastructure The steepest sustained climb from a newcomer in

2026-07-10 原文 →
AI 资讯

Microsoft’s carbon emissions went up 25 percent last year

Microsoft may once again be struggling to keep up with its own climate goals, according to its 2026 sustainability report. As reported by GeekWire, the report states that Microsoft's carbon emissions increased 25 percent in 2025, totalling 34 million metric tons "without select interventions." Microsoft says this was "driven primarily by the expansion of our […]

2026-07-10 原文 →
科技前沿

SpaceX is on track for record-setting Starlink deployments

SpaceX is currently ahead of last year's record-setting pace for Starlink satellite deployments. SpaceX launched 1,589 Starlink satellites into low-Earth orbit in the first half of 2026, according to launch data compiled by Jonathan McDowell's satellite tracker, compared to 1,489 satellites deployed at the same point in 2025. 2025 was already a record year for […]

2026-07-09 原文 →
AI 资讯

Carnot Efficiency: The Hard Ceiling on Every Heat Engine

Picture a power plant burning fuel to spin a turbine. It is tempting to assume that with enough engineering — better seals, smoother bearings, cleaner combustion — the plant could be pushed toward converting nearly all its heat into useful work. It cannot. A large modern thermal power station turns only something like 40 to 45 percent of its fuel energy into electricity, and the missing majority is not lost to sloppy design. It is lost to a law of physics. That law sets a ceiling on every device that turns heat into work, from a car engine to a steam turbine to a jet. The ceiling is called the Carnot efficiency, and the remarkable thing about it is how little it depends on. Not on the working fluid, not on the mechanism, not on the cleverness of the builder — only on two temperatures. This article explains where that limit comes from, how to compute it, and why it reshapes how engineers think about efficiency. Why this calculation matters The Carnot efficiency is the benchmark against which every real engine is judged. When an engineer reports that a gas turbine runs at 38 percent efficiency, that number means little on its own. Compared against the Carnot limit for the same hot and cold temperatures, it suddenly tells you how much room is left — whether the design is already near the physical wall or still has slack worth chasing. It also redirects design effort toward the things that actually matter. Because the Carnot limit depends only on the ratio of cold to hot absolute temperatures, the single most powerful way to raise the ceiling is to raise the temperature at which heat enters the engine, or lower the temperature at which it is rejected. This is why turbine inlet temperatures have climbed for decades, pushing the limits of metallurgy and cooling. Polishing internal friction yields small gains; raising the hot-side temperature raises the ceiling itself. The core formula Sadi Carnot, in 1824, imagined an idealized engine running on a perfectly reversible cyc

2026-07-09 原文 →
开发者

Building malloc from Scratch (Part 1): Architecture & Core Concepts

I wanted to understand how malloc actually works under the hood. Most explanations I found online described what an allocator does, but completely skipped over the "why" behind its design decisions. Rather than stopping at theory, I decided to build a cross-platform allocator in C that implements malloc , calloc , realloc , and free from scratch. This article documents the design of that allocator, the architectural tradeoffs I faced, and the core concepts I had to learn along the way. Table Of Contents Design Decisions Architecture Modern Allocator Strategies Core Concepts What's Next Project Overview A custom memory allocator does not create physical memory. Instead, it requests pages of raw virtual memory from the operating system and manages how that memory is partitioned, reused, resized, and released by the application. The goal of this educational project is to implement C's core memory management API using a modular, cross-platform architecture inspired by design principles found in modern allocators, rather than relying on legacy, single-platform tricks. Design Decisions #1: Why I am Skipping sbrk While many classic tutorials use sbrk for educational implementations, I deliberately chose a 100% mmap -based approach for two major reasons: sbrk is a fragile global bottleneck. It works by moving a single pointer (the program break) up and down. This means the allocator assumes it owns a contiguous line of memory. If a third-party library or another thread in the program secretly calls sbrk behind the scenes, the allocator's memory layout can break instantly. mmap , by contrast, provides isolated, independent chunks of memory. Cross-Platform Symmetry. Windows has absolutely no equivalent to sbrk , but it has a direct equivalent to mmap : VirtualAlloc . If we used sbrk , our architectural abstraction ( os_alloc ) would become awkward because Linux would deal with a moving pointer while Windows dealt with independent pages. Using mmap keeps the abstraction perfec

2026-07-08 原文 →
AI 资讯

First look: Fi Ultra Starlink pet tracker

Fi Ultra is the first Starlink-enabled pet tracker you can buy. It expands on GPS and LTE trackers, adding automatic failover to T-Mobile's T-Satellite-branded direct-to-cell service when venturing into cellular dead zones. That lets owners tap into SpaceX's constellation of low Earth orbit Starlink satellites to track their pets anywhere in the US. But it […]

2026-07-08 原文 →