Many people think any given ML project is 99% training.
In reality, it’s 50% evaluation, 40% data cleaning, 8% integration, and 2% training.
The first two set the noise floor for learning. No ML magic matters; the model cannot lower the noise floor, as that’s the optimal bound of Shannon encoding of your data.
Thus, not a single day goes by without me thinking about ontology. Even the old labels have to be constantly reviewed.
最近在带入组的本科实习生,发现怎么读论文其实是科研训练里最容易被忽略的一步。
推荐一篇每个科研新人都该读的经典短文:S. Keshav 的 How to Read a Paper。
文章提出了非常实用的“三遍读论文法”:
第一遍,5 到 10 分钟快速扫读:标题、摘要、引言、章节标题、结论和参考文献。
目标是回答 5C:
Category, Context, Correctness, Contributions, Clarity。
也就是判断这篇论文是什么、和谁相关、假设是否合理、贡献是什么、写得清不清楚。
第二遍,认真读论文主线,但先跳过证明细节。重点看图表、实验设置、结果是否清楚、引用了哪些关键工作。
第三遍才进入深度理解:尝试像复现一样重建作者的思路,检��假设、方法、创新点和潜在漏洞。
放在今天看,这个方法和 AI 辅助读论文其实很契合。
第一遍可以让 AI 帮忙快速总结论文的研究问题、核心贡献和主要结论,但自己一定要判断这篇文章是否真的值得继续读。
第二遍可以让 AI 帮忙解释方法、实验设置、图表和不熟悉的概念,但不能只看 AI 总结。关键图表、实验设计和结果数字一定要回到原文核对。
第三遍可以让 AI 扮演 reviewer,帮你追问:这篇文章的假设是否成立?实验是否支持结论?有没有 missing baseline?有没有潜在的数据泄漏、评价偏差或过度 claim?
读论文不是“读完”就行。真正重要的是知道什么时候快速跳过,什么时候认真理解。
尤其在 AI 工具越来越强的情况下,科研新人更需要训练自己的判断力。
AI 可以帮你压缩信息,但不能替你决定一篇论文是否重要、是否可信、是否值得借鉴。
https://t.co/8gUc4HbLwR
Fable 5 is the biggest step up I’ve felt in our models since Opus 4.5 back in November. After 4.5 came out I uninstalled my IDE when I realized that I’d been doing 100% of my coding in a terminal for a few weeks. With Fable, it’s felt like Claude has stepped up from being a coding agent to a thought and design partner in building the product. Fable has judgement, taste, and dimensionality in a way that previous models didn’t, leading me to trust it more with the most complex work.
I think the first time I had this realization was when I asked Fable to debug something. It is the first model I have used that was so methodical and precise, taking measurements and adding logs then verifying that it truly fixed the issue before declaring victory.
There’s nothing in claude code’s prompting telling the model to do that, it’s just part of its personality. It really has this “big model smell” that I haven’t felt before.
The laptop hasn't changed in 30 years. NVIDIA just changed it
RTX Spark is their first PC chip ever.
- RTX 5070 level GPU
- 128GB unified memory
- 1 petaflop of local AI
- thin, light, barely throttles unplugged
Your AI agent lives on the machine. 24/7. No cloud.
This is step one of the agentic AI PC, and everyone else is about to copy it.
在 vibe 了五个多月之后,我不觉得 SOTA 模型公司有能力吃下所有软件,我也不站在使用 FDE 改造现的企业来挣点快钱的那一侧,相反的,我是坚定的 AI Native company 的支持者,我认为所有企业都会被 AI Native 的组织所替代,而我坚信这一变化会持续数十年,这些未来的软件公司,会创造本世纪最伟大的投资机会。
PREDICTION:
Anthropic will surpass Alphabet in revenue by mid-2028.
This is not a bull case or an acceleration scenario — it is a continuation of the curve already in evidence.
Anthropic’s ARR went from $1B (Jan 2025) to $9B (Dec 2025) to $30B (Apr 2026) — a 3.3x step in a single four-month window, and the curve has been steepening, not flattening.
My projection actually assumes deceleration from here: $100B by end of 2026, $340B in 2027, $850B in 2028, $1.4T in 2029, $2T by 2030.
Crossover with Alphabet happens at ~$575B in mid-2028, not because Anthropic accelerates beyond today’s pace, but because Alphabet — locked at ~15% YoY in a mature ads-and-cloud business — cannot match enterprise AI’s adoption physics.
As @rodriscoll intelligently observed recently, Gemini tokens served grew by only 60% in the last quarter … while Anthropic grew by 10X.
Three drivers make the continuation structural, not speculative: customers spending >$1M/year with Anthropic doubled from 500 to 1,000 in under two months post-Series G (these are multi-year expanding contracts with near-zero churn — switching a deployed agent stack mid-flight is operationally untenable);
Claude Code is the wedge, not the product, dragging the rest of the platform — agents, MCP,
healthcare, biotech — into every Fortune 2000 deployment as an attach point;
and compute supply is finally non-binding with the 3.5GW Google + Broadcom deal (2027+), this weeks SpaceX partnership, and 1GW of standing Google capacity for 2026.
For most of 2024–2025 the bottleneck was supply, not demand. That constraint is releasing exactly when the demand curve is steepest.
The standard objection — “no company has ever sustained this at scale” — applies a software-era frame to a labor-era business.
AWS, Azure, and Meta decelerated at $50–100B because they sold tools to the economy.
Anthropic is selling cognitive capacity into the economy.
The TAM isn’t enterprise software ($800B). It’s labor ($50T+).
When the denominator is two orders of magnitude larger, “deceleration at $100B ARR” stops being a law and starts being an assumption.
The crossover isn’t a maybe. It’s a function of timing. Mid-2028 is when I think Anthropic surpasses Google.