allen

Head of Product Eng @storyprotocol
Building @blueprint_os

ex @atlassian

[allen @alchuang]

🧢

[allen @alchuang]

we’re entering the age of agent-run companies.

not just agents for coding, but agents equipped to operate across the full stack of a business:
▸ product
▸ engineering
▸ marketing
▸ branding
▸ content
▸ and more

that’s the idea behind BlueprintOS, an open source project i’m working on that's compatible with @openclaw

more soon.

[allen @alchuang]

@NotionHQ Worse, you press OK and it doesn't tell you how to import. Ridiculous UX

[allen @alchuang]

SMH. @NotionHQ really need to get with the times. I can't even copy paste a long PRD I created from chatting with agents without seeing this.

Fix your architecture or something, its just text for gods sake🤦🏻‍♂️

[allen @alchuang]

@NotionHQ needs to get with the times. I can't even copy paste a long PRD I created from chatting with agents without seeing this.

Fix your architecture or something, its just text🤦🏻‍♂️

[Charles Patterson @CharlesPattson]

Glass on web has been achieved. The greatest invention since the drop shadow.

[konstantinpaulus @konstipaulus]

Introducing text-to-lottie: an open source skill and harness for generating production ready Lottie animations with codex/claude code.

$ npx skills add diffusionstudio/lottie

Prompts guide and repo in the comments.

[GREG ISENBERG @gregisenberg]

Claude Code just dropped "dynamic workflows" and it's pretty cool.

You type "create a workflow" or turn on "ultracode" in the effort menu and it spins up hundreds of parallel agents that check each other's work.

The unit of work you can hand off jumps from a file to an entire codebase. Migrations, audits, rewrites, framework swaps, stuff you used to plan in sprints now finishes overnight.

The part that got me:....the agents argue with each other before showing you the result. Independent attempts at the same problem, then adversarial agents trying to break the answer. It keeps iterating until they converge. That's how senior engineering teams work. Except this team runs at 3am and never gets tired.

Also if the workflow gets interrupted, it picks up where it left off. That means you can kick off work that runs for days. Not sessions. Days.

Fair warning though: this burns through tokens FAST.

Anthropic says so themselves. But if the task is a codebase migration that would have taken a team 3 months, spending $500 in tokens to do it in a week is the best trade in software.

The ceiling on what one person can build just moved again. Classic.

Going to be playing with this all week.

Pretty cool.

[GREG ISENBERG @gregisenberg]

I just got back from SF and I FEEL INSPIRED.

I spent 5 days with frontier AI model teams, AI startup founders, and 3 billionaires.

My takeaways:

1. I had lunch with 3 billionaires. All of them are buying SaaS companies and rebuilding them agent-first. They were deeply inspired by Bending Spoons and Ryan Cohen's eBay deal. Buy the company, cut the headcount, rebuild the tech, add agents, add features, make more valuable experience, raise prices.

2. The frontier model companies are hungry for usage data from the field. They can see API calls and token counts. They can't see the actual workflows. If you're deep in a niche using these models in ways the model companies haven't seen, that understanding is incredibly valuable. Usage intelligence is the new alpha.

3. Consumer AI is massively underbuilt. Every billboard in SF is either B2B inference infrastructure or vertical agent companies. The entire city is optimized for enterprise. Meanwhile you have companies like Cal AI doing $50M ARR in 18 months as a consumer app. I met with a cool few teams doing consumer AI (@paulscherer / @ekuyda)

4. MCP came up in literally every conversation. The companies exposing their product as MCP endpoints are getting pulled into deals they never pitched for. The ones that aren't are becoming invisible to agents. This is the new SEO. If agents can't find you, you don't exist. Building products for agents is the new zeitgeist in general.

5. Not uncommon for hot seed rounds to be $25-50 million valuations. I saw a Series A at $450 million

6. If I had a dollar every time someone mentioned "forward-deployed engineer" this trip I could have funded a seed round. It's the hottest role in SF right now. The person who sits between the agent and the customer, making sure everything actually works.

7. The mood around open source shifted. A year ago it felt like open source was chasing the frontier models. Now founders are telling me Gemma and DeepSeek are good enough for 80% of what they need at a fraction of the cost. The "which model do you use" conversation is being replaced by "which model for which task." Model loyalty kinda feels dead.

8. Voice agents came up more than I expected. Multiple founders told me voice is the interface for the next billion users. The billion people who will never type a prompt will absolutely talk to one.

9. The Obsidian community in SF is weirdly intense. Multiple founders showed me their vaults unprompted. Like showing someone your home gym. It's a flex now. The quality of your knowledge base (second brain?) is becoming a status symbol among builders.

10. Maybe it was just the people I met but the age of the founders is shifting. I met more founders over 40 this trip than any trip before and more founders under age 21 than ever before. Founders getting older and younger at the same time.

11. I spoke to a lot of fast-growing startups, VCs and frontier models who are hiring content creators right now.

12. The restaurant scene in SF is actually better than it's been in years. Founders are going out more. Alcohol is out, not surprisingly.

13. SF doesn't feel like the only place anymore. We all have access to the same frontier models. We all read the same X feed. A founder in NYC or Lagos is calling the same APIs as a founder in SoMa. So in the past it felt like SF was always lightyears ahead, doesn't feel that way anymore. It's okay not to live in SF and have BIG DREAMS.

14. The coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people. I had a few startup ideas here....

15. Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats, none of them use any AI at all.

16. I heard the phrase "agent debt" for the first time. Like technical debt but for agents. When you hack together an agent workflow fast and never clean it up, the system prompts conflict, the memory gets polluted, the tools overlap. 6 months later the agent is doing weird things and nobody knows why lol.

17. Met a few people who carry two phones now. One for personal. One that's basically an agent terminal running Telegram or iMessage connections to their agent fleet.

It's always amazing to get that dose of inspiration in SF. I FEEL INSPIRED.

But I'm so happy to be back home, locked in and building.

We're 12-18 months into a shift that will take 15 years to play out. The urgency in every conversation was real.

What an incredible time to be building.

[allen @alchuang]

@theallinpod @chamath Retardmaxxing

[Dhravya Shah @DhravyaShah]

the team cooked an insane demo on SMFS dot ai

just put in the files

(made with css and claude!)

[allen @alchuang]

@psdnai NUMO 👀

[isabelle @isareksopuro]

i made a map to monitor data centers all around the world

tracks construction + nearby power plants + local AI legislation, and follows the politicians behind their bans (+ if they're getting paid to do so!)

[Viv @Vtrivedy10]

Harness, Memory, Context Fragments, & the Bitter Lesson

this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape

this is v30+, HTML diagrams help me iteratively refine + chat to roughly “see” and alter the mental model

Harnesses & Context Fragments:
a very important job of the harness is to efficiently & correctly route data within its boundaries into the context window boundary for computation to happen

the context window is a precious artifact. Harnesses make decisions on how to populate, manage, edit, and organize it so agents can do work. Each loaded object can be thought of as a Context Fragment and represents an explicit decision by the user and harness designer of what needs a model needs to do work at any given time.

many ideas on externalizing objects + loading into the context window are pioneered and very well described by @a1zhang with RLMs

Experiential Memory:
we’re in the very early days of deploying agents and agents produce massive amounts of data in every interaction they have. this is akin to humans doing things and remembering things they did.

however agent memory has a massive advantage as it can be accumulated across all agents which are easily forked and duplicated (unlike humans). @dwarkesh_sp does a good talking about this massive benefit of artificial systems

memory can be treated as an externalized object. the harness is tasked with doing good contextualized retrieval which means pulling in the right data from accumulated memories across all agent interactions

Search & The Bitter Lesson:
As we deploy agents in our world over year timescales, there is going to be a hyper-exponential in the amount of data produced by those agents. We should want to:
1. Own that data for ourselves. Open ecosystems are important here
2. Use that data

This means that we’ll have to search over, distill, and organize massive amounts of data. Our brain is exceptional at doing this. Both contextually using prior experience and mostly committing the right stuff to memory with enough intentional practice.

Our current infrastructure systems and algorithms will be put to the test and often break as we get used to this new data regime

some open questions:
- how do we efficiently distill experiences (Traces) into higher level memory primitives that capture the important parts? How do we do this over ultra long time horizons?

- How much of the future is Search just-in-time vs Search that gets integrated into model weights?

- How do we make models much better at self-managing their context window? How do we reduce error rates in recursively allowing agents to operate over external objects?

i’ll be expanding on, altering, and adjusting these mental models but these feel like an important subset to me on the future of designing agents practically

[allen @alchuang]

wow! big news for crypto eco

[Garry Tan @garrytan]

YC will invest in any YC company in stablecoins

The new financial rails of the revolution will not be over ACH or wire

[allen @alchuang]

@rds_agi @vercel the point of UX is for it to be easier for humans to see things at a glance. i think if its for agents, then wouldn't it be easier done through agents with access to vercel cli commands?

[allen @alchuang]

@Tocelot @andrewchen @speedrun @blueprint_os AI Operating System that automates your company and ideas. It is a harness with strict rules built in including everything from running your socials, to building out the app

[basement.studio @basementstudio]

gm, today we're launching Shader Lab, like photoshop but for shaders

• design slick layered shader compositions
• export high-quality assets or shaders
• OSS package to plug & play

↳ https://t.co/5FjvLy8UIQ

[Farza 🇵🇰🇺🇸 @FarzaTV]

I built this thing called Clicky.

It's an AI teacher that lives as a buddy next to your cursor.

It can see your screen, talk to you, and even point at stuff, kinda like having a real teacher next to you.

I've been using it the past few days to learn Davinci Resolve, 10/10.

[Pahud Hsieh @pahudnet]

一位 AI 開發者 @outsource_ 表示，由於 Anthropic 停止支援 Claude Opus 4.6 的 OAuth 登入方式，他在 OpenClaw 等工具中無法繼續輕鬆使用該模型，因此決定轉用 OpenAI 的 GPT-5.4。

他在轉換前，讓 Opus 4.6 自行設計一套認知框架（Cognitive Framework），包含思考步驟、輸出規則、問題解決模式與反模式清單，直接放入 system prompt 使用，無需 fine-tuning 或 RAG。

測試同一任務「建立 2 週 sprint plan」時，原生 GPT-5.4 得分僅 6.5/10（泛化、無結構），加入框架後提升至 9.5/10，甚至發現 Opus 未察覺的程式 bug。

這顯示在當前 AI 發展階段，優質的 system prompt 與認知框架對輸出品質的影響，可能大於模型本身的基礎差異，強調「prompt 工程仍是關鍵杠杆」。

該框架已被公開分享，供其他開發者直接套用在 agent 系統中，反映社群正積極探索跨模型的效能優化方法。