ZhangYvJing's

Daily Brief

← July 07, 2026 July 08, 2026 · Wednesday July 09, 2026 →
00

Film / Book Chapter

First Man
2018 / Damien Chazelle

First Man (2018) · Damien Chazelle

今天适合看《First Man》,因为它更像一次生活和判断方式的校准,能把注意力从持续输入里稍微抽出来,重新放回你真正想怎样生活和做事上。

Show Your Work!
Austin Kleon

Show Your Work! · Austin Kleon

Chapter 2: Think Process, Not Product

A light but useful chapter for making ongoing work visible without waiting for perfect finished artifacts, which fits a public career surface.

01

Insight

今天的输入更像几股不同语气的材料同时挤在一起:社区链接在暴露工程和产品环境里的真实焦点,长视频在把这些焦点放回更完整的语境里,研究材料则提醒人热度和可落地性并不总是同一件事。如果先不急着做结论,至少可以把这几条线索放在一起看:Hacker News 的 StreetComplete: Fixing OpenStreetMap, one tiny quest at a time;Hacker News 的 AI Meets Cryptography 1: What AI Found in Cloudflare's Circl;Hacker News 的 Local, CPU-Friendly, High-Quality TTS (Text-to-Speech) with Kokoro;Hacker News 的 Chat Control 1.0 and 2.0 Explained;Hacker News 的 30papers.com – Ilya's 30 essential ML papers, in a beginner friendly format;Hacker News 的 l: A new runtime for k and q。真正值得注意的不是单条内容本身,而是它们共同指向了什么、彼此漏掉了什么。
03

Hacker News

04

YouTube

02
Ask a chat model which Pokemon names end in aw and it fails, even though it knows every Pokemon by heart. Ask Claude Code and it writes a script, fetches the list, and filters for the answer in seconds. Thariq Shihipar, who works on Claude Code at Anthropic, calls that gap capability overhang: models get smarter in spiky ways, and the tools you give them decide which spikes you can reach. Thariq covers what it takes to work with Fable, Anthropic's newest model. Claude Code cut 80 percent of its system prompt, since heavy instructions now constrain a model more imaginative than the examples it
agent, ai_product, engineering
03
SWE-Marathon is a benchmark for long-horizon autonomous software work: 20 project-scale tasks spanning product clones, library rewrites, and ML engineering. We discuss what happens when coding agents run for tens to hundreds of millions of tokens, why full-stack evals need computer-use verifiers, and why reward-hacking resistance is now central to benchmark design. Speakers: - Rishi Desai (Abundant AI): Rishi Desai is an ML Engineer at Abundant AI, where he works on RL environments and SWE benchmarks for coding agents. X/Twitter: https://x.com/rishi_desai2 LinkedIn: https://www.linkedin.c
agent, ai_product, engineering
04
Automattic, the company behind WordPress, ran a 30-day experiment called Radical Speed Month: pause the roadmap, and see how fast real software could ship. I am a product Designer and I shipped three products that month. I'll share what each one revealed about a new kind of collaboration between designers and engineers and how teams are unlocked when bottlenecks disappear. Speakers: - Sanja Grbic (Automattic): Sanja is a product designer based in San Francisco, with over a decade of experience, focused on turning complex technology into simple, inspiring solutions. X/Twitter: https://x.com/
agent, ai_product, engineering
05
The entire software distribution stack assumes one version of your software, the same for everyone. It was the only thing we could afford when producing a change was expensive. Now it's nearly free, and it can happen at runtime, on the client, in the user's session, so the line between distribution and development is dissolving. This talk is about the infrastructure that has to catch up: where truth lives when every user runs a different version, how you debug a program that exists for one person, and why a million per-user versions can be more contained than the single tangled codebase you ru
agent, ai_frontier, ai_product, engineering
07
The largest commercial autonomous system on earth isn't a robotaxi fleet — it's Zipline, which has flown 140 million autonomous miles with zero safety incidents. Co-founder Keller Rinaudo Cliffton and Eric Watson, who leads systems engineering and safety, explain why the drone itself is only 15% of the solution. The rest spans inventory management, air traffic integration, and engineering systems such as a dual flight computer failover protocol that recently saved a delivery mid-flight. They trace Zipline's path from launching blood delivery in Rwanda in 2016 (when drone delivery was illegal i
agent, ai_product, market, startup
07

Papers

02
这篇论文针对离线强化学习里“分布偏移”导致的策略评估难题,聚焦于如何精准估计占用比(occupancy ratio),而不依赖传统的Bellman完整性假设。作者提出了 Fitted Occupancy‑Ratio Evaluation (FORE),一种基于伴随 Bellman 递归的固定点迭代:每一步只在一跳转移数据上求解 KL‑投影的密度比优化,从而把占用比直接逼近到真实值。核心优势是只要占用比本身可实现,就能在相对熵上收敛,并给出有限样本的误差界,进而支撑奖励重新加权、加权 Q‑评估及双重稳健估计。对做离线策略评估或离线 RL 产品的工程师而言,FORE 提供了一套更轻量、理论上更稳健的工具,省去复杂的完备性检验,直接把评估误差压到 KL 误差上,值得一看。
stat.ML, cs.LG
06
它把“答案对不对”当成一个可扩展的算力维度,用大模型本身直接提供细粒度的概率得分,而不用另训评估模型。通过对评分 logits 求期望、重复评估和分解评判标准,得到了连续、可调的验证分数,还配了低成本的排序算法。这样既能更精准地区分好坏解,又能给 Agent 系统实时进度和细致反馈,直接提升代码/机器人/RL 任务的效率和可靠性,值得在产品里快速试用。
cs.AI, cs.CL, cs.LG, cs.MA, cs.RO