Fei He

Product Engineer

[Ryo Lu @ryolu_]

overcooking

you've seen this: someone ships a dashboard that shows every number with a sparkline, every action has a confirmation modal, every empty state has an animated illustration and a tagline. individually each decision made sense to someone. together it feels like chaos. nothing is in focus.

that's overcooking. not one bad decision in isolation, but the accumulation of reasonable ones that no one said no to.

AI makes this worse as the cost of adding dropped to near zero. it can build a feature, even a whole new concept in minutes. so people do. and then they do it again. the thing that started with a clear purpose slowly becomes a collection of additions that are each justifiable but collectively incoherent.

the root problem is that most "new ideas" aren't new. they're repackaging of something that already exists at a more fundamental level. a new sticker on an old concept. it feels like progress because something changed, with a new word and skin – but the thinking didn't go deeper, it just duplicated itself into confusion.

the whole has a core. you feel it once you understand the whole system. everything in it are related and balanced. when you overload it, that gravity weakens. not because any one thing is wrong – but because attention is finite and you force it everywhere.

what we need aren't more tools that make more slop. it's seeing through the chaos, and returning to what the thing actually is, and cutting everything that doesn't serve that. that's harder now, not easier. because there's always something else you could add with one more prompt.

[Perplexity @perplexity_ai]

Announcing Personal Computer.

Personal Computer is an always on, local merge with Perplexity Computer that works for you 24/7.

It's personal, secure, and works across your files, apps, and sessions through a continuously running Mac mini.

[Ryo Lu @ryolu_]

software is still about thinking

software has always been about taking ambiguous human needs and crystallizing them into precise, interlocking systems. the craft is in the breakdown: which abstractions to create, where boundaries should live, how pieces communicate.

coding with ai today creates a new trap: the illusion of speed without structure. you can generate code fast, but without clear system architecture – the real boundaries, the actual invariants, the core abstractions – you end up with a pile that works until it doesn't. it's slop because there's no coherent mental model underneath.

ai doesn't replace systems thinking – it amplifies the cost of not doing it. if you don't know what you want structurally, ai fills gaps with whatever pattern it's seen most. you get generic solutions to specific problems. coupled code where you needed clean boundaries. three different ways of doing the same thing because you never specified the one way.

as Cursor handles longer tasks, the gap between "vaguely right direction" and "precisely understood system" compounds exponentially. when agents execute 100 steps instead of 10, your role becomes more important, not less.

the skill shifts from "writing every line" to "holding the system in your head and communicating its essence":

- define boundaries – what are the core abstractions? what should this component know? where does state live?
- specify invariants – what must always be true? what are the constants and defaults that make the system work?
- guide decomposition – how should this break down? what's the natural structure? what's stable vs likely to change?
- maintain coherence – as ai generates more code, you ensure it fits the mental model, follows patterns, respects boundaries.

this is what great architects and designers do: they don't write every line, but they hold the system design and guide toward coherence. agents are just very fast, very literal team members.

the danger is skipping the thinking because ai makes it feel optional. people prompt their way into codebases they don't understand. can't debug because they never designed it. can't extend because there's no structure, just accumulated features.

people who think deeply about systems can now move 100x faster. you spend time on the hard problem – understanding what you're building and why – and ai handles mechanical translation. you're not bogged down in syntax, so you stay in the architectural layer longer.

the future isn't "ai replaces programmers" or "everyone can code now." it's "people who think clearly about systems build incredibly fast, and people who don't generate slop at scale."

the skill becomes: holding complexity, breaking it down cleanly, communicating structure precisely. less syntax, more systems. less implementation, more architecture. less writing code, more designing coherence.

humans are great at seeing patterns, understanding tradeoffs, making judgment calls about how things should fit together.

ai can't save you from unclear thinking – it just makes unclear thinking run faster.

[Fei He @droxer1985]

@FradSer 赞同👍

[Vercel Developers @vercel_dev]

① Install the skill:

$ npx add-skill vercel-labs/agent-skills

② Paste this prompt:

Assess this repo against React best practices. Make a prioritized list of quick wins and top fixes.

③ Review and prompt to "make the fixes"

[Fei He @droxer1985]

"At Manus, a feature has to be 屌 (diào)—fucking sick or it doesn't ship."

1. Your Responsibility Doesn't End When You Ship
2. Prototypes over Plans
3. Don't Pigeonhole Yourself

https://t.co/a5zEOnEtDE

[Parker Lyman @parker_lyman]

"At Manus, a feature has to be 屌 (diào)—fucking sick or it doesn't ship."

@ivanleomk wrote about what it's like on our team: https://t.co/z2T5ttczfT

[Fei He @droxer1985]

Would you push a piece of code into production if you yourself couldn’t clearly explain how it works? #vibecoding

[Fei He @droxer1985]

Vibe Coding didn’t fundamentally change how we build software; it remains grounded in Component-Oriented Programming. In fact, modularization and componentization have become even more essential—for system consistency, lower maintenance costs, and better team collaboration.

[Weaviate AI Database @weaviate_io]

We just released our complete guide to Context Engineering.
(These 6 components are the future of production AI apps)

Every developer hits the same wall when building with Large Language Models: the model is brilliant but fundamentally disconnected. It can't access your private documents, has no memory of past conversations, and is limited by its context window.

The solution isn't better prompts. It's 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - the discipline of architecting systems that feed LLMs the right information in the right way at the right time.

Our new ebook is the blueprint for building production-ready AI applications through 6 core components:

1️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀: The decision-making brain that orchestrates information flow and adapts strategies dynamically
2️⃣ 𝗤𝘂𝗲𝗿𝘆 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Techniques for transforming messy user requests into precise, machine-readable intent through rewriting, expansion, and decomposition
3️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Strategies for chunking and retrieving the perfect piece of information from your knowledge base (semantic chunking, late chunking, hierarchical approaches)
4️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: From Chain of Thought to ReAct frameworks - how to guide model reasoning effectively
5️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆: Architecting short-term and long-term memory systems that give your application a sense of history and the ability to learn
6️⃣ 𝗧𝗼𝗼𝗹𝘀: Connecting LLMs to the outside world through function calling, the Model Context Protocol (MCP), and composable architectures

We're not just teaching you to prompt a model - we're showing you how to architect the entire context system around it. This is what is going to take AI from demo status to actual useful production applications.

Each section includes practical examples, implementation guidance, and real-world frameworks you can use today.

Download it here: https://t.co/Z7IRWNGMZc

[Fei He @droxer1985]

@dotey 架构设计不只是简单的知识学习，更需要是长时间的积累和试错。

- 设计模式的书的随手可得，但是真正理解和正确使用却很少。
- SOLID 原则大家都知道，但真正在架构设计做的好的很少。

[Aaron Epstein @aaron_epstein]

More designers should be founders. And we want to back them at YC.

Over the next decade, as new coding tools make it easier than ever to build and ship products quickly, great design is going to matter even more.

[Fei He @droxer1985]

@zanbuilds CODE

[Andrew Ng @AndrewYNg]

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful!

In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.

As coding becomes easier, more people should code, not fewer!

Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step.

I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively.

One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that.

When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result.

Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do.

[Original text: https://t.co/HdI3Jb9HmF ]

[Mario Nawfal @MarioNawfal]

🇺🇸 STEVE JOBS: THE BEST MANAGERS ARE GREAT CONTRIBUTORS... WHO NEVER WANTED TO BE MANAGERS

"At Apple, we thought hiring professional managers would make us a big company.

It didn’t work—most were bozos who could manage but couldn’t do anything else.

The best managers?

Great individual contributors who never wanted the job but took it because they knew no one else could do it as well."

Source: BusinessX on IG

[Ben Lang @benln]

Paul Graham one-pager on how to start a startup:

[Fei He @droxer1985]

@zacker330 去那裡了？

[Fei He @droxer1985]

Product Teams: The Best vs. The Rest | Marty Cagan - Partner at Silicon ... https://t.co/eNEmtgaR3V via @YouTube

[Fei He @droxer1985]

What is an empowered product team? https://t.co/euLQBTsXmr via @YouTube

[Fei He @droxer1985]

"Product is Hard" by Inspired Author Marty Cagan of SVPG at Lean Product... https://t.co/wHdHeZn1YF via @YouTube