idea drone

In December, the FCC banned foreign-made drones. Every US government agency must now buy American.

That’s a problem, because 90% of commercial drones are made in China.

The US military, border patrol, emergency services, and infrastructure inspectors all rely on drones. They now need domestic alternatives immediately.

Enter  dpro option

They’re already a trusted supplier to the US Army. Now, with foreign drones banned and defense budgets surging, Draganfly is positioned at the center of a forced reshoring wave.

Here’s what makes DPRO compelling:

  • Currently  for intelligence and reconnaissance operations.
  • Just awarded a contract with the U.S. Air Force Special Operations Command units to supply Flex FPV Drones and training in partnership with DelMar Aerospace Corporation.
  • Clients include Shell, Ford, Red Bull, and emergency services across North America already rely on Draganfly’s systems.
  • Draganfly made the first drone to ever save a human life, which the Smithsonian called “the most significant aeronautical revolution in decades.”

A bicycle for the mind

“A bicycle for the mind” is a famous analogy coined by Steve Jobs to describe the personal computer as a tool that amplifies human cognitive abilities, much like a bicycle amplifies physical locomotion efficiency. Inspired by a Scientific American study on energy efficiency, Jobs argued that computers enable humans to transcend mental limitations just as bicycles allow them to surpass natural physical speeds.

Key aspects of this concept include:

  • The Analogy: A 1973 Scientific American article found humans on bikes were the most efficient travellers, prompting Jobs to view computers as tools to boost mental efficiency.
  • Purpose: Computers act as a “new man-machine partnership,” helping with data organization, calculation, and communication to enhance creativity and productivity.
  • Evolution: While initially referring to personal computers, the concept has evolved, with many now viewing generative AI as the modern “e-bike” of the mind.
  • Human Potential: The metaphor emphasizes humans as tool builders who use technology to augment their capabilities, a vision articulated by Jobs throughout the late 70s and 80s.

A Motorcycle for the Mind

Vibe Coding = New Product Management Modern frontier models (e.g., strong coding agents like Claude) let non-coders or rusty developers describe an app idea in natural language (“vibe”), iterate via feedback, and have the AI scaffold, code, test, debug, and deploy an entire working application. English is effectively the hottest new programming language. Anyone can now go from idea → taste → product without writing code themselves. This democratizes app creation → expect a massive explosion of applications, especially filling long-tail niches that were previously uneconomical.

Training & Tuning Models = New Coding While “vibe coding” opens product creation to everyone, the highest-leverage programming today is training/tuning/fine-tuning the models themselves. This is a new form of programming: instead of hand-writing precise instructions, you design architectures, curate/tokenize massive human-generated datasets, tune hyperparameters, and search for emergent programs inside the weights. It’s programming through data and search rather than explicit logic. Domain-specific models (biology, CAD, video, sensors, games, etc.) will proliferate.

Traditional Software Engineering Is Not Dead — It’s Hyper-Leveraged AI tools make mistakes, produce leaky abstractions, suboptimal architecture, and bugs — especially outside their training distribution. Engineers who deeply understand code can debug, optimize performance, catch architectural issues, and use AI tools far more effectively than non-technical “vibe coders.” The best engineers become even more powerful. Winner-Takes-(Almost)-All + Long Tail Explosion

  • No demand for average: in a world of infinite apps, only the best in each category wins big.
  • More shots on goal → more niches filled, more “best” options appear.
  • Head: a few giant aggregator platforms / super-apps become even larger and more dominant.
  • Tail: enormous long tail of hyper-specific tiny apps.
  • Medium-sized software companies get squeezed hardest.

Broader Philosophical & Practical Takes

  • AI adapts to humans faster than we adapt to it.
  • Entrepreneurs aren’t worried about losing jobs — the goal was never “a job” anyway.
  • AIs aren’t alive and fail the ultimate intelligence test (creating new fundamental knowledge from scratch).
  • Early AI adopters gain enormous, compounding edge.
  • AI meets you exactly where you are — the more you know/bring, the more value you extract.
  • Always leverage the best available intelligence (don’t be loyal to mediocre models/tools).
  • If you can’t clearly define something, you can’t effectively program/AI it.
  • Solution to AI anxiety is action — build, experiment, do. Armchair philosophizing without market/physics feedback turns hollow. Naval emphasizes he’s building again at a startup called Impossible because doing beats commenting.

llm大模型结束

阿里通义千问推出Qwen 3.5中型模型系列金十数据2月25日讯，阿里千问宣布Qwen 3.5中型模型系列正式发布，包含Qwen3.5-Flash、Qwen3.5-35B-A3B、Qwen3.5-122B-A10B，以及Qwen3.5-27B。据介绍，新模型更强智能、更低算力。Qwen3.5-35B-A3B性能已超越Qwen3-235B-A22B-2507与Qwen3-VL-235B-A22B——证明架构、数据质量与强化学习才是智能进步的关键，而非单纯堆参数。Qwen3.5-122B-A10B与27B进一步缩小了中型模型与前沿模型的差距，尤其在复杂智能体任务中表现突出。Qwen3.5-Flash是基于35B-A3B的托管生产版本，默认支持100万tokens上下文长度，内置官方工具集。(金十数据APP)

数据增强+sglang = 90% 稳定性

首次解决率 │ ~70% │ ~95% │ +35% │
│ 对话轮次 │ 3-4 轮 │ 1-2 轮 │ -50% │
│ 用户验证时间 │ 需要查证 │ 可直接用 │ -80% │

训练数据外的知识 (2025-2026) │ 1/5 │ 5/5 │ +400% │
└──────────────────────────────┴────────┴───────────┴───────┘

合成数据的核心价值

 1. 获取训练数据之后的知识 - 2025-2026 年的框架变更
 2. 更准确的细节 - 具体版本号、弃用时间线
 3. 更完整的上下文 - 生态系统变化、最佳实践

报告已保存到 /root/final_comparison_report.md

异构蜂群算法

测试2组任务

同家族模型效果

与异构效果

这个异构“梦之队”（GLM-5/GLM-4.6 主脑 + Kimi K2.5 数据长上下文 + Qwen3.5 局部工具/推理 + DeepSeek V3.2 数学/代码硬核）的成本优势非常夸张，尤其在2026年2月这个时间点，中国模型的API价格已经把闭源前沿模型甩开几条街。

一套“纯Qwen全家桶”本地配置（零成本无限跑）

分工模拟异构，但全用Qwen变体（同构但prompt角色分工）：

  • 主脑/规划：Qwen3.5-Plus 或 Qwen3-Max-Instruct（长链思考强）。
  • 局部推理/工具：Qwen3.5-Coder 或 Qwen2.5-Coder（代码/工具稳）。
  • 数据/多模态：Qwen-VL-Plus 或 Qwen3-VL（视觉/文档提取牛）。
  • 数学/硬核：Qwen3.5-Math 或通用Qwen3.5（补刀）