Rui Pereira

In a recent batch talk, YC General Partner @t_blom broke down how to build a self-improving, AI-native company. He walks through how to create recursive, self-improving AI loops, and why founders who get this right will run companies that improve while they sleep. 00:00 — Companies Are Roman Legions 00:54 — Copilots Are the Wrong Mental Model 01:55 — Extract the Domain Knowledge 02:24 — The Recursive Self-Improving Loop 04:12 — The Holy Shit Moment at YC 05:50 — Self-Optimizing Product and Support Loops 06:29 — Burn Tokens, Not Headcount 07:23 — Middle Management Is Over 08:05 — Make Everything Legible to AI 09:40 — Regenerating the YC User Manual 11:19 — Software Is Ephemeral, Context Is Valuable 12:18 — Where Humans Still Matter

This is the best site on the internet to learn harness engineering walkinglabs.github.io/learn-harness-…

106 Claude Code skills for 15 professions github.com/mohitagw15856/…

there's definitely a before-river and after-river shopify. a lot of people talk about having fully accessible agents in their organization, but i wish they could sit in and see just how effective it is at shopify. a couple things that made it instantly magical at shopify: 1. shopify is still a fully remote company. everything is documented, structured, categorized, and made for easy read/write. our vault, internal tooling, hell, even perf tools feel second to none. not because we nerd out over it (we do) but because it's a necessity given how spread out the team is. 2. shopify is run by an engineer. culture is set at the top. shopify, at its core, is an infra company. down to the very details of our culture, you'll find nerdy eng tidbits. there are a lot of amazing things about ai, but one of the best is that coding and interacting with files is in its "dna". when you have a company that is designed and operates sorta like well-written software, it enables something like River to have a tidal effect pretty immediately. you can see this with how natural the adoption has been. making it part of public channels in slack and just launching it, you see everyone start playing with it, and witness use cases exploding. it's transformed how people work and, the best part is, it's currently the worst it'll ever be. if you nerd out about this stuff and want a front row seat to see how bleeding edge companies apply ai, i would find a way to get a seat at shopify.

Here are 10 GitHub repos that quietly print money while you sleep. 1. Cal. com Open-source Calendly. Fork it, white-label it, sell to dentists and lawyers for $200/month. The founders hit $5M ARR in 3 years doing exactly this. Repo → github.com/calcom/cal.com 2. Plausible Analytics Privacy-first Google Analytics. Self-host it, resell to agencies for $50/month per client. Two founders bootstrapped this to 7 figures. Repo → github.com/plausible/anal… 3. Ghost Open-source Substack with 100% margin. 1,000 readers at $5/month equals $60,000 a year. Forever. Repo → github.com/TryGhost/Ghost 4. n8n Open-source Zapier. Sell automation services for $500-$2,000 per setup. n8n raised $14M because the agency model behind it works. Repo → github.com/n8n-io/n8n 5. Supabase Free Firebase replacement. Build a SaaS in a weekend, charge $29-$99/month. They raised $116M for a reason. Repo → github.com/supabase/supab… 6. Medusa Open-source Shopify. Take 5% on every sale forever. Zero rev share to Shopify. Repo → github.com/medusajs/medusa 7. AppFlowy Open-source Notion. Sell self-hosted to enterprises worried about data privacy. They raised $30M because this market is massive. Repo → github.com/AppFlowy-IO/Ap… 8. Coolify Open-source Vercel and Heroku. Charge developers $20/month to manage their deployments. Replace their $200 Vercel bill. Repo → github.com/coollabsio/coo… 9. Listmonk Open-source Mailchimp. Send unlimited emails for the cost of an AWS bill. Resell to agencies at 10x markup. Repo → github.com/knadh/listmonk 10. Penpot Open-source Figma. Sell self-hosted design tools to agencies who refuse to upload client files to the cloud. Repo → github.com/penpot/penpot The difference between developers who build features and developers who build businesses is one decision. Pick one of these. Fork it this weekend. Ship it next week. The founders behind these repos already proved the model. Save this. Share it with the developer in your life who deserves to break free. 100% free. 100% open source.

BREAKING: ANTHROPIC JUST OPEN SOURCED THE ENTIRE WALL STREET WORKFLOW. DCF models. LBO models. Equity research reports. Merger analysis. KYC checks. All of it. Free. On GitHub. It connects Claude directly to: -> Bloomberg, FactSet, S&P Global, Morningstar, PitchBook -> Builds real Excel models with live formulas and sensitivity tables -> Drafts CIMs, IC memos, earnings reports, and buyer lists -> Runs PE due diligence, GL reconciliation, and NAV tie-outs This is not a chatbot wrapper. These are production agents that own entire financial workflows. The kind firms pay $50,000 to $500,000 per year in software to run. Now it is a one-line Claude Code plugin install. 19.8K GitHub stars. Apache-2.0 License. 100% Open Source.

99.8% of bun’s pre-existing test suite passes on Linux x64 glibc in the rust rewrite

Every firm will need to reconceptualize work as they build agentic systems. As AI and agents take on more of the execution, the opportunity is to expand human agency and redesign how work gets done. An in-depth look from the team at what this shift means and key considerations for every business: microsoft.com/en-us/worklab/…

Boris Cherny of Anthropic on $NOW: "If I'm doing something and I don't have the context, I'm not going to do a great job... ServiceNow is a really a great way to bring in that context that it needs to do the job."

First coding, now finance. The playbook is super clear: go deep into massive verticals, ship workflow-specific agents, wrap them with services/SI partners for enterprise last mile, and keep buying or partnering for proprietary data. AI platforms are becoming vertical operating systems. What vertical is next? Frontier labs will only go deeper and deeper as they get more data from customers and the flywheel spins...

I think everyone in marketing/PR at an AI company (& especially at large model companies) should watch this short piece of advice from @dylan522p w/ @patrick_oshag. Just a few minutes. Crisp and to the point. youtube.com/watch?v=LF3aUI…

@dan__rosenthal github.com/chenhg5/agency… I create agencycli to help people build an agent company quickly.

Love the workflows diagram you made they are inspiring for how much gtm directions you can take. Consider using cabinet for viewing your company OS and manage sub gtm projects. You will need much more than MD files to be productive, and us human love webapps and adaptive UI to see our work

‘Service-as-a-software’ is here... We moved our entire company brain to GitHub and wired 25+ tools through MCPs. Any one of our 20+ team members can now spin up a contextualized AI assistant in seconds. The system has 5 layers: 1. Markdown company OS ↳ SOPs and campaign playbooks converted into .md files using research agents ↳ Most SOPs turned into agents that handle 70% of the task ↳ Output: 50+ actionable Claude skills 2. Context environment ↳ One Company OS GitHub repo propagated to every session via org-wide plugin ↳ Each client gets their own repo with Slack DMs, call transcripts, GDrive changes, and campaign data auto-synced through n8n ↳ Zero configuration needed per session 3. MCPs ↳ 25+ tools connected including InstantlyAI, HeyReach, Apollo, HubSpot, Slack, Notion, n8n, Supabase, Pinecone, Browserbase, Apify ↳ Not just research. Action through AI. ↳ We went from researching work to actually doing it 4. Self-improvement engines ↳ Pinecone database stores 1000s of LinkedIn posts and outbound campaigns with performance metrics ↳ Copywriting skills query this data to find winning formats to reuse ↳ Human corrections get fed back in so the system gets sharper over time 5. Operating principles ↳ Every repo has a safeguard file that prevents certain operations ↳ 100% AI outputs are not acceptable, everyone owns their work and every mistake ↳ Agent swarms split one task into 5-20 sub-agents when needed Our goal is to become the most advanced AI-native services company for our niche (GTM).

This is a masterclass on the map Dan Koe uses to reach the top 1% in every area of life:

we held our quarterly AI session with LPs last week where we go over ai trends and our experiments sharing an abbreviated version here for anyone interested 🧵

RIP document extractors. Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools. Here’s what it does: 🧵