标签：#founder

Our rebranded Boston event, TechCrunch Founder Summit (formerly All Stage), is back on November 4th! And we are looking for some incredible volunteers to help us make this event happen. If you are interested in finding out what goes into building tech events, apply to volunteer. If you are selected, not only will you get […]

Two weeks after launching Buildside, 130 people had joined. I did not have an ad budget or a huge email list. There was no secret growth trick. I grew Buildside by sharing the journey on X and LinkedIn, speaking with users, and letting their feedback guide the app. The biggest lesson I learned was: You have to market your product long before you launch it. I started marketing before the app was ready Many founders wait until their product feels finished before they talk about it. Sharing early work can feel risky. You may worry that people will judge it, copy it, or ignore it. I chose to share anyway. Before Buildside launched, I posted about the problem I wanted to solve, why I cared about it, and what I was learning. I showed small bits of progress and asked people what they thought. That gave people a reason to follow the journey. On launch day, I was not asking strangers to care about a new app. I was inviting people who had already watched the idea grow. Y Combinator tells founders to release early. Its guide says waiting too long is often driven by fear and the wish to make everything perfect. An early version lets you ask, “What do you think?” That advice matched my experience . I shared the fails as well as the wins It is easy to post a new user count or a kind comment. It is harder to share what went wrong. Yet the honest posts often led to the best talks. When something failed, I said so. When I changed my mind, I explained why. I shared the good days too. The goal was not to make every post look impressive. It was to show the real work. Honesty builds trust. Buffer has shared parts of its business in public for years, through both strong and hard moments. The company says being open builds trust and keeps it accountable. Its open company page shows this in action . People do not need a founder to look perfect. They want to know there is a real person listening and trying to make something useful. I let users shape Buildside Building in public was not only

The email arrives. "Mercury will not be able to support your business at this time. We will not be able to provide additional details about this decision." You spend the next two weeks building an appeal: residence permit, business plan, tax registration, customer contracts, the whole file. You attach a polite cover letter explaining that you are not in Russia, not a sanctioned individual, fully compliant. Mercury either does not respond or sends the same boilerplate back. By week three you have decided you did something wrong, that your business is somehow tainted, that you will never get a US bank account. None of that is true. The reject was a system response, and once you can see the arithmetic driving it, the next move gets obvious and the spiral stops. The math behind an auto-decline OFAC violation penalties start at roughly $1 million per transaction. The annual revenue from a single diaspora-founder account at Mercury sits somewhere between $50 and $500. On top of that sits reputational risk: one Bloomberg story about "the fintech serving sanctioned Russians" damages the next funding round, strains banking partner relationships, and invites regulatory attention. Run those numbers and an auto-decline on an RU or BY passport signal becomes the rational move for the fintech, even when the overwhelming majority of flagged applications are perfectly legal. The downside of a single miss outweighs the upside of correctly clearing every legal applicant. What you are looking at is a company optimising against an asymmetry: maximum downside, minimal upside, per application. There is no judgement of you anywhere in that calculation. How the decline actually happens A KYC submission includes a passport scan, residence permit, and business documents. The decisioning system flags an RU or BY passport regardless of where you live, how the company is structured, or where the revenue comes from. Human review exists, but it triggers only when the signal-to-noise ratio is exce

Each year, we get a huge influx of applicants to speak at TechCrunch’s events, and this year’s Founder Summit in Boston on November 4 will be no different!

I started my career as a software developer, Initially a front end developer and then became a full stack developer where success often meant solving difficult technical problems, building reliable systems, and delivering good software. Becoming a co-founder changed that perspective. Suddenly, building a product wasn't just about writing code. It was about understanding the problem deeply, making decisions with incomplete information, taking responsibility for outcomes, building a team, and constantly deciding what not to build. Now, as an Engineering Director at an AI company, I'm learning to balance both sides staying close to technology while thinking about people, product, strategy, and long-term engineering decisions. Honestly, The transition from developer to founder hasn't been a straight line. It's been a continuous process of learning, unlearning, and becoming comfortable with uncertainty. I'm starting this blog to document some of those lessons from building AI products and engineering teams to the technical decisions and challenges that come with growing a technology company. I know I'm just beginning my journey and that I thought I could perhaps share it with my tech community.

Forbes Called It a Playbook. I Call It a Production Log. Forbes published a piece recently calling AI agent startups "the new solo-founder playbook." I read it twice. The framing bothered me both times. A playbook implies steps. A sequence. Something you can hand to someone and say: follow this, and you will get the result. What Forbes described is not that. It is a description of an outcome, written by people who did not have to fix anything at 2 AM when the agent broke. Let me tell you what it actually looks like. The Night 871 Emails Went to the Wrong People Fourteen months ago I built my first agent that could send emails on behalf of the system. It was an outreach automation, nothing exotic. The agent would identify leads, draft a message, and send it after a human approval step. Except the approval step had a race condition. Two concurrent jobs both read "pending" from the database, both approved, and both dispatched. One lead received 871 emails over 40 minutes before I caught it. No company, no legal team, no PR buffer. Just me and an inbox full of angry replies. That night I wrote my first hard guardrail: #!/bin/bash # email-dedup-guard.sh LEAD_ID = " $1 " LOCK_FILE = "/tmp/email-lock- ${ LEAD_ID } " if [ -f " $LOCK_FILE " ] ; then echo "BLOCK: email already dispatched for lead ${ LEAD_ID } " > &2 exit 1 fi touch " $LOCK_FILE " # proceed with send Embarrassingly simple. But I did not know I needed it until I needed it. This is what Forbes leaves out. The playbook is written in retrospect, after someone else absorbed the cost of learning. The Model Is Not the Problem Every conversation about AI agents eventually becomes a conversation about which model to use. GPT-4 versus Claude versus Gemini. Benchmarks and context windows and reasoning scores. Here is what I learned: the model is the easy part. My current system runs 86 containers across two Hetzner servers. 240 automated jobs. Every day, these jobs do things: post content, process leads, trigger builds,

85 containers. 24 PostgreSQL databases. 67 domains. 232 cron jobs. One developer. 120 EUR/month in Hetzner bills. This is not a startup fantasy pitch. This is my production infrastructure for a SaaS ecosystem serving German golf clubs, a golf school management platform, a community platform, a CRM, and an auth service. Every customer gets their own database. Physical tenant isolation, not software filters. People tell me this cannot work. The containers disagree. The Stack Next.js for all frontends. Single-tenant PostgreSQL per customer (Supabase stacks). Docker on bare metal. Coolify for deployment orchestration. Traefik as the reverse proxy handling 67 domains. Two Hetzner servers in Germany. Total infrastructure cost: 120 EUR/month. The single-tenant architecture is a deliberate trade-off. Multi-tenant saves infrastructure cost, but one RLS bug exposes every customer's data. One compromised tenant enables lateral movement to all others. GDPR Article 17 deletion in multi-tenant requires complex cross-tenant queries. In single-tenant, deletion is DROP DATABASE . No residual risk. The cost is more operational complexity. Which is exactly why automation is not optional. 176 Guard Rules: The Immune System My AI agents (Claude Code with custom hooks) execute roughly 80% of daily development and operations work. That is dangerous without constraints. So I built a guard system: 176 shell scripts that fire on every command, every file edit, every session end. The architecture is simple. Four dispatchers route to context-specific guards: #!/bin/bash # Pre-Bash-Dispatcher: Loads guards based on command profile. # Not all 176 guards fire on every command. Profiling classifies # each command (git, docker, npm, database, deploy, comms) and # loads only relevant guards. set -uo pipefail GUARDS_DIR = " $( dirname " $0 " ) /guards" INPUT = $( cat ) CMD = $( echo " $INPUT " | jq -r '.tool_input.command // ""' ) # 8 security gates fire ALWAYS, non-negotiable: # tabu-gate, pii-gate,

Want to tap into the energy of 1,100+ startup founders, investors, and tech leaders descending on Boston for the Founder Summit 2026 on June 9? Host your own Side Event during “Founder Summit Week,” happening June 4-10!

Ryan Beiermeister, who demonstrated cool analysis in the Founders Fund YouTube series "Mafia," has joined the firm as a partner.

Marc Lou read a tweet, slept on it, and woke up still annoyed. The tweet, from Pieter Levels, was about all the fake revenue screenshots on X. By the next evening Lou had built a thing to fix it. By the day after that, the thing had made $20,378. That is the part everyone retweets. I want to walk you through it, and then I want to show you the line in his own year-end letter that complicates the whole legend. The setup Lou got fired by Tai Lopez in November 2021, was broke and depressed, and moved to Bali. He started shipping tiny products in public, copying the playbook of, yes, Pieter Levels. His breakout was ShipFast , a Next.js starter kit that did $40,000 in its first month in September 2023. By December 2025 he was running 15 startups generating about $84,900 a month, with cumulative revenue past $2.26 million, per his verified TrustMRR data. The reason I trust his numbers more than most is that he verifies them through Stripe on his own product, TrustMRR , which brings me to the 24-hour story. The moment something worked, absurdly fast TrustMRR exists to kill fake MRR screenshots. You connect a read-only Stripe key, and it shows your verified revenue on a public page nobody can edit. Lou built it in a day on top of his own boilerplate, which is the cheat code here. He was not starting from zero, he was starting from ShipFast. "TrustMRR is 24 hours old and was built in 24 hours." @marc_louvion on X He monetized it with sidebar ad slots. He listed them at $299 a month, then raised the price each time one sold, all the way to $1,499. In his newsletter he wrote that within three days every slot was gone and the side project had made $20,378. He called it the third fastest-growing thing he has ever built. Five days in, he posted the run-rate dream out loud. "20/20 spots filled! TrustMRR went from $0 to $18,380 MRR in 5 days. That's $220,000 ARR if I'm allowed to dream a little" @marc_louvion on X It kept going. By December 2025 TrustMRR was his single biggest inco

We just opened the waitlist for Something, and the part that surprised me most while building it wasn't the multi-agent orchestration — it was how hard it is to make an AI actually disagree. Every model we tested defaults to being helpful, which in practice means agreeable. Even when explicitly prompted to "find flaws," the outputs would soften into "here are some considerations" instead of a real critique. We had to engineer around this specifically: Separate system prompts with opposing reward framing — one agent optimizes for identifying growth potential, the other is explicitly told its only success metric is surfacing a disqualifying flaw Structured output forcing a verdict, not a summary — the skeptic agent (Nothing) has to commit to a specific weakness category (unit economics, timing, technical feasibility) rather than hedging across all of them A reconciliation step where both outputs get merged into one conviction score, so the founder isn't just reading two contradictory paragraphs If anyone's built adversarial agent setups and hit the same "it just wants to agree with me" problem, curious how you solved it. [Everyone who has a brain is a founder here] something-waitlist.vercel.app

2 days left to lock in your spot at TechCrunch Founder Summit 2026 and save up to $190 before Early Bird rates expire on June 26 at 11:59 p.m. PT. Register here.

You have just 3 days left to save up to $190 on your pass to TechCrunch Founder Summit 2026 before Early Bird rates end on June 26 at 11:59 p.m. PT. Register here.

The debut episode, moderated by Founders Fund chief marketing officer Mike Solana, included a star-studded cast of current tech luminaries.

Layup Parts co-founder Zack Eakin has drawn on a motorsports background, and his experience working for Palmer Luckey and Elon Musk, to tackle making faster, cheaper, and better composites.

Speaking to TechCrunch, Crunchbase’s head of research Gené Teare, said the factors holding back Black founders include “access to networks, relationships, and early introductions."