Product design

I'm Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit. My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to use Claude Code: we intentionally build it in a way that you can use it, customize it, and hack it however you like. Each person on the Claude Code team uses it very differently. So, here goes.

2026 is the GREATEST time to build a startup in 30 years I’m 36. I’ve sold 3 startups, helped build companies that raised billions, and backed teams from seed to unicorn. 20 MEGA shifts that make this the BEST time to build in a GENERATION: 1. Hardware got smart. Download open-source AI models from HuggingFace to cheap robots and they're suddenly smart. Opens up tons of use-cases. 2. SaaS is imploding. AI can replicate $500K software for pennies. Enterprise software that took 30 engineers now requires 1 and a Claude Code subscription. Founders will go more niche and more custom and outprice incumbents. 3. Outcome-based pricing is eating subscriptions. With AI agents handling work automatically, founders can guarantee results instead of selling features. This creates a massive arbitrage opportunity to steal market share from rigid subscription models. 4. Vibe marketing is the new marketing. AI agents/tools like Lindy, Gemini and Claude Code Using agents to do personalized outreach, ads and content creation it’s getting good. This is like getting on social in 2005. 5. Social is FYP-ified. Distribution no longer requires massive followings, just content that hits. Founders can build audience from zero without ads and then convert them to owned media channels (text/email). 6. Interfaces are vanishing. Conversations are replacing dashboards across industries. This removes training barriers and means customers can use sophisticated products immediately. 7. Companies are obsessed with efficiency and cutting costs right now. Corporate budgets are getting reallocated to AI. Companies are cutting traditional software spend to make room for AI-powered alternatives. This creates fast-tracked approvals for startups delivering 10x efficiency. 8. 99% of MVPs won't need VC. Low-cost MVPs combined with creator partnerships and AI automation allow bootstrapped scaling. For most software businesses, outside funding is now unnecessary. 9. Global teams. You don’t need to hire in your own city anymore. Opens up tons of arbitrage opportunities and ways to create products unlike before. 10. Millions of creators want to get paid. If you have the right product, the right network of creators, you can hit scale insanely efficiently. Never before did this exist. Next gen founders are building startups community first, software second. 11. Prototyping is nearly instant. With Lovable, Rork etc, you can test ideas in days, not months. MVP speed is basically 1x/week. This creates room for multiple products from small companies (multipreneurship), helps get to PMF faster, 12. LLM APIs create building blocks weekly. I can’t even keep up with how many new APIs/tools coming out from LLMs weekly. Example: Nano Banana pro comes out, probably 1000 ideas built on top of that can be $5M/year businesses. 13. $1m+ revenue per employee. With the leverage of LLMs, community and agents, employees are way more efficient. It won’t be uncommon to generate $1m per employee. This will lead to a rise of "multipreneurship", small teams owning multiple products /businesses. Holding companies will be as common as startups. 14. Superniche is the new niche. Because costs to create software startups is 1/100th, you can service little niches (i call them superniches) and still have a life-changing business. 15. Mobile app ecosystem about to 10X. 2 reasons. First is, adding AI to apps make apps more useful. More useful apps, make more money. Second, 16. Compliance and boring workflows are suddenly buildable. Permits, audits, insurance, payroll edge cases, filings, RFPs. These were “too annoying” for startups before. Agents thrive on rules, checklists, and repetition. The least sexy problems now have the best unit economics. 17. Claude Code killed the “engineering bottleneck.” The constraint is no longer “can we build it,” it’s “do we understand the workflow deeply enough.” The winning founders are ex-operators who encode tribal knowledge into agents. Code is cheap. Taste + domain insight is scarce. 18. The long tail of software is now profitable. Niches that capped at $200k ARR can clear $5M with near-zero marginal cost. 19. Services are quietly becoming software. Manual agencies are one agent away from product margins. 20. if AI can replicate $500K software for $20/month, what’s your moat? distribution, customer service, brand, data etc. REALLY good time to be a world class designer/marketer. (and even more.... but this is getting long already!) We've entered the rarest of windows... when multiple technological shifts collide at once, creating a brief period where small teams can build things that were previously impossible. THE FUTURE OF BUILDING STARTUPS IS DIFFERENT. I know this... This unique moment won't last forever. Markets will adapt. Giants will respond. The window will close. But right now, a founder with clear vision and bias for action can build more in six months than was previously possible in years. (note: if you need an idea to get creative juices flowing, grab one at @ideabrowser) The next generation of great companies is being created right now, many by founders you've never heard of. Some by people who would never have had a shot in previous cycles. That's the beauty of these rare windows. The playing field briefly levels, and the future belongs to those who see it clearly and move first. It's a sacred time, don't bookmark/share this, build something in 2026, will ya? Happy building, my friends. 2026 is yours. Am I wrong?

This paper from Stanford and Harvard explains why most “agentic AI” systems feel impressive in demos and then completely fall apart in real use. The core argument is simple and uncomfortable: agents don’t fail because they lack intelligence. They fail because they don’t adapt. The research shows that most agents are built to execute plans, not revise them. They assume the world stays stable. Tools work as expected. Goals remain valid. Once any of that changes, the agent keeps going anyway, confidently making the wrong move over and over. The authors draw a clear line between execution and adaptation. Execution is following a plan. Adaptation is noticing the plan is wrong and changing behavior mid-flight. Most agents today only do the first. A few key insights stood out. Adaptation is not fine-tuning. These agents are not retrained. They adapt by monitoring outcomes, recognizing failure patterns, and updating strategies while the task is still running. Rigid tool use is a hidden failure mode. Agents that treat tools as fixed options get stuck. Agents that can re-rank, abandon, or switch tools based on feedback perform far better. Memory beats raw reasoning. Agents that store short, structured lessons from past successes and failures outperform agents that rely on longer chains of reasoning. Remembering what worked matters more than thinking harder. The takeaway is blunt. Scaling agentic AI is not about larger models or more complex prompts. It’s about systems that can detect when reality diverges from their assumptions and respond intelligently instead of pushing forward blindly. Most “autonomous agents” today don’t adapt. They execute. And execution without adaptation is just automation with better marketing.

A few of you asked how to disable auto-generated terminal titles in Claude Code CLAUDE_CODE_DISABLE_TERMINAL_TITLE=1 claude You can also add that env var to the “env” section of your settings.json

we're only building agent-native apps @every now here's what that means: traditional software architecture: you write code that defines what happens. The computer executes your instructions. agent-native architecture: you define outcomes in natural language. the agent figures out how to achieve them using tools. in an agent-native world, features are prompts not code. good agent-native architectures have the following characteristics: - parity. anything the user can do in the app, the agent can do. - granularity. features are prompts, not tools. the agent has access to tools that are more atomic than features so a few tool calls are composed into a single feature. - composability. this enables composability: the agent can combine tool calls in new ways easily. this allows developers to move faster—and allows users to customize the application more easily with prompts. all of the above enables emergent capability in your app—it can do things you didn't plan for. this allows you to discover latent demand from your users that inform your roadmap. this a core way that @bcherny builds features in Claude Code—which is architected with all of the above characteristics

Can someone build this app: It scrolls like social media. The users are books you’ve read on kindle. The posts are the parts you’ve highlighted.

im building a content engine ◆ listens to github releases ◆ turn them into beautiful blogs, changelogs ◆ clean Cursor x Notion interface

BREAKING: Google Research just dropped the textbook killer. Its called "Learn Your Way" and it uses LearnLM to transform any PDF into 5 personalized learning formats. Students using it scored 78% vs 67% on retention tests. The education revolution is here.

NEW: i wrote a complete technical guide to building agent-native software (co-authored with claude) it covers: - the five pillars of agent native design (parity, granularity, composability, emergent capability, self-improvement) - files as the universal interface - agent execution patterns with code samples - mobile agent patterns - advanced patterns like dynamic capability discovery if you want to take full advantage of this moment, it's worth your time: https://every.to/guides/agent-native?source=post_button…

cooking baby cursor 3.0 with @leerob. there's something you can only feel when it feels real. can't wait to ship a Cursor that's both simple and infinitely powerful.

I really appreciate how the Claude Code team is building in public. By openly sharing their prompts, agents, and commands, @bcherny, @trq212, @amorriscode, and the whole team send a clear message: Claude Code is extremely easy to use and extremely powerful. That level of transparency matters. It lowers the barrier to entry, accelerates learning, and shows real confidence in the product. That’s it. The repository with the prompts and commands used by the team is public, and you can check it out here: https://github.com/anthropics/claude-code/tree/main/plugins…

Introducing Pickle 1, the first soul computer. Order batch 1 today at http://pickle.com

Currently I have two prototype features on my phone that solve the two largest problems of X. Can’t wait this to get this out. Almost there.

The real moats in 2025: specific workflows, proprietary data with real switching costs, distribution, and UX that makes AI disappear into the job-to-be-done. Simultaneously: we are early (only a % are using AI properly) so this is an amazing time to start a startup.

Design is the hardest part of vibe coding for me The strategy I've been using lately is collecting any beautiful designs I find in a Figma board. Whenever I want to build out some UI, I include a screenshot of one of those designs in my prompt as a reference for the LLM to use.

If want to use Claude Code but are don't like the terminal interface, you can now use local Claude Code from Claude Desktop! To do so: 1. download Claude Desktop 2. open the sidebar and click 'Code' toggle 3. select the folder that you want Claude Code to have access to 4. submit your prompt!

BREAKING: Within the past 72 hours: - Apple's AI Chief steps down - Apple's Head of UI Design leaves to Meta - Apple's Policy Chief steps down - Apple's Head of General Counsel steps down

I just nearly fell into a 50-year-old trap: waterfall. "I'll plan everything upfront, feed the spec to the LLM, ship it all." But specs→code isn't like a compiler. Some questions only get answered in code. Note to self: Validate the risky parts first.

Good Products are Opinionated. “Every great founder I’ve seen up close, or even from afar, is highly opinionated and they’re almost dictatorial in how they run things. Also, early-stage teams are opinionated. And the products they build are opinionated. Opinionated means they have a strong vision for what it should and should not do. If you don’t have a strong vision of what it should and should not do, then you end up with a giant mess of competing features. @Jack Dorsey has a great phrase: “Limit the number of details and make every detail perfect.” And that’s especially important in consumer products. You have to be extremely opinionated. All the best products in consumer-land get there through simplicity. You could argue the recent success of ChatGPT and similar AI chatbots is because they’re even simpler than Google. Google looked like the simplest product you could possibly build. It was just a box. But even that box had limitations in what you could do. You were trained not to talk to it conversationally. You would enter keywords and you had to be careful with those keywords. You couldn’t just ask a question outright and get a sensible answer. It wouldn’t do proper synonym matching, and then it would spit you back a whole bunch of results. That was complicated. You’d have to sift through and figure out which ones were ads, which ones were real, were they sorted correctly, and then you’d have to click through and read it. ChatGPT and the chatbot simplified that even further. You just talk to it like a human—use your voice or you type and it gives you back a straight answer. It might not always be right, but it’s good enough, and it gives you back a straight answer in text or voice or images or whatever you prefer. So it simplifies what we looked at as the simplest product on the Internet, which was formerly Google, and makes it even simpler. And you just cannot make a product that’s simple enough. To be simple, you have to be extremely opinionated. You have to remove everything that doesn’t match your opinion of what the product should be doing. You have to meticulously remove every single click, every single extra button, every single setting. In fact, things in the settings menu are an indication that you’ve abdicated your responsibility to the user. Choices for the user are an abdication of your responsibility. Maybe for legal or important reasons, you can have a few of these, but you should struggle and resist against every single choice the user has to make. In the age of TikTok and ChatGPT, that’s more obvious than ever. People don’t want to make choices. They don’t want the cognitive load. They want you to figure out what the right defaults are and what they should be doing and looking at, and they want you to present it to them.”

linktree is a billion-dollar company????? a billion???????? dollars??????????????? for literally... links. in. a. tree.