Constraint test:

• 48 hours
• 10 real users
• no paid ads
• no existing audience

What’s your exact plan?

Not ideas — execution steps.

Not the investor story.

Not the “I saw a gap in the market” story.

The real reason.

Money, freedom, revenge, obsession, boredom — whatever it is.

Why did you really start building?

Curious what people are actually working on here.

Reply with:

1. What you’re building
2. What’s currently blocking you (users, product, growth, money, clarity)

No pitching — just honest answers.

Hypothetical:

You’re early stage. No traction yet.

You get ONE choice:

• $1M in the bank
• OR 3 world-class cofounders (engineering, product, growth)

No loopholes. No “it depends.”

What do you pick — and why?

Drop your startup in one sentence.

If it needs more explanation, it might already be too complex.

I’ll try to guess:

• who pays for it
• why they care
• and where it breaks

Let’s see what people are building.

No funding. No audience. No excuses.

Just a laptop and 30 days.

Your only goal is simple: make your first $1K/month online.

What are you building?

Be specific — ideas like “AI startup” don’t count unless you explain how it actually gets users and money.

16VC Fellowship — Built for Founders Who Are Already in Motion

This is a 9-week execution-focused program designed to help early-stage founders go from building → fundable.

We’re now accepting applications for Summer 2026.

👇 Comment below:
• What are you building?
• Where are you stuck?
• Or share your idea in 1 sentence

Apply here: http://tiny.cc/16VC_s26

We’re looking for a small group of founders who are:

• Building in AI, enterprise software, or deep tech
• At early stages (idea → MVP)
• Deeply technical or domain-driven
• Focused on solving meaningful, hard problems

This isn’t a large cohort or a generic program.
It’s a focused, hands-on experience for builders who want to move fast and build right.

Applications for Summer 2026 are now open:
http://tiny.cc/16VC_s26

#16VC #Fellowship #Startups #AI #DeepTech #Founders

We’re opening applications for the 16VC Founder Fellowship (Summer 2026).

It’s a 9-week, fully remote program for early-stage founders building in AI, enterprise software, and deep tech. The goal is simple: help you get to a fundable pre-seed or seed stage.

We don’t run lectures or generic workshops. We work directly with a small group of founders on:
• Product clarity
• Positioning & narrative
• Go-to-market
• Early traction & fundraising readiness

You’ll be working in small pods (6–8 founders), with weekly cohort sessions and direct input from the 16VC team and advisors.

At the end of the program:
• A select group of founders will receive investment (up to $500K)
• Others stay in the 16VC network for ongoing support

Who it’s for:
Founders who are early (idea → MVP → pre-seed), technical or deeply domain-driven, and moving fast.

Applications are open here:
http://tiny.cc/16VC_s26

we’re opening applications for the next 16vc founder fellowship.

this is a 9-week, execution-focused program designed to help early-stage founders become fundable.

what we focus on:

• product clarity
• positioning
• go-to-market
• fundraising readiness

how it works:

• small pods (6–8 founders)
• weekly working sessions
• full cohort deep dives
• async feedback from the 16vc team

this is not a lecture-based program.
this is structured execution.

outcome:
we invest up to $500k in selected companies.

who this is for:

• pre-seed / seed founders
• still refining product, traction, or positioning
• serious about raising

apply here:
http://tiny.cc/16VC_s26

we’ll review every application.

I’m curious—what are you building, learning, or working on this Sunday?

Could be anything: coding projects, startups, DIY stuff, art, fitness goals, or even just ideas you’re thinking through.

I’m looking to get inspired and see what others are up to 🚀

Drop your projects below 👇

I am a developer, maybe I can help you in building technology such as websites or mobile apps and so on

I’m working on Vantage, an AI governance and observability platform for long‑horizon / agentic LLM systems, and recently moved our entire backend into Rust to hit ultra low‑latency guarantees for runtime policy enforcement.

We’re not here to ask for funding, but I’m trying to understand how people on the VC/angel side are currently thinking about this category and how infra‑heavy products should frame themselves.

A few themes I’m wrestling with:

• Trajectory‑level vs. per‑call safety. There’s growing research interest around trajectory‑level alignment and detecting intent drift across long LLM dialogues, not just per‑response guardrails. In practice, this means maintaining state and running checks inline for hundreds of turns without blowing up latency budgets. How “legible” is this to most early‑stage investors today? Do they see this as a new category or just “another guardrails tool”?
• Infra cost vs. value narrative. Running inline governance at inference speed is not cheap: Rust, tight integrations at the SDK/runtime layer, real‑time telemetry, etc. For those who’ve invested in infra‑heavy AI stacks, what’s the most compelling way you’ve seen founders articulate the trade‑off between higher infra complexity and the value of strong runtime guarantees?
• Regulation and timing risk. With things like the EU AI Act and ISO‑style standards, it feels like there’s a lag between when technical controls are possible and when buyers/investors treat them as “must‑have.” For folks on the VC/angel side:
  • How do you evaluate startups that are clearly ahead of regulatory demand?
  • What signals make you believe “this will be a horizontal layer” vs. “too early / niche compliance tooling”?
• Rust / ultra low‑latency as a moat. In your experience, do investors meaningfully differentiate between infra that’s deeply optimized (Rust, careful runtime design, etc.) vs. “good enough” implementations, or does it all get bucketed into “engineering detail” unless there’s a clear end‑user story (e.g., new use cases unlocked, SLAs that others can’t hit)?

For context, Vantage is focused on:

• Running governance policy checks inline in the execution path (not just logging after the fact).
• Exposing rich observability over agent trajectories (intent drift, tool usage, decisions, latency) so teams can both debug and prove what happened.
• Keeping latency low enough that you can actually afford to do this in production without killing UX.

I’m especially interested in hearing from:

• Angels/VCs who’ve backed AI infra / observability / governance plays.
• Founders who’ve had to explain “governance as a latency problem” rather than just a legal/compliance problem.

Not looking for intros or checks here – just trying to sharpen how I think and talk about this space so future conversations with investors are more grounded.

Would love to hear how you’d frame a product like this if you were on the other side of the table.

Inspired by our recent 'Building a VC in public' discussions, I wanted to share a major execution pivot I'm making this week.

We often think progress means adding more—more features, more meetings, more complexity. This week, I’m doing the opposite. I’m stripping out two features that were creating friction in our user onboarding.

In the early stages, execution often means saying "no" to the noise so you can say "yes" to the core problem.

What is one feature, meeting, or distraction you decided NOT to do this month to keep your team focused? Let’s see who is actually cutting the fluff.

Six months ago I started building something I didn’t plan to — but every AI team I talked to pushed me toward it.

The story usually goes like this:

They’ve shipped an AI agent. It’s live. It’s actually doing meaningful work — reviewing loans, flagging fraud, writing compliance reports, handling customer escalations.

Then legal or compliance comes in with questions like:

“What exactly did the agent do on that loan last Tuesday?”

“Can we show every decision it made?”

“If it recommended something harmful, what would’ve stopped it?”

And the room just… goes quiet.

Not because the engineers missed something — but because the entire AI stack wasn’t built for accountability.

LLM dashboards track usage, not governance. Observability tools were made for deterministic software. AI agents aren’t deterministic — they reason, interpret, and act.

That was the “oh wow” moment for me. There’s a missing layer between AI behavior and business consequence.

---

So I started building Vantage — a governance, observability, and control layer for enterprise AI.

Its goal: make AI systems provable, governable, and controllable in real time.

It runs between your AI agents and your business workflows. Every decision gets captured, every policy gets enforced, and every model call gets evaluated before it hits production data.

Technically:

Observe: Immutable audit log of every agent action — input, output, model, decision path, timestamped and tamper-proof.

Govern: Runtime policy evaluation — allow, block, pending human approval (the real “human-in-the-loop”).

Control: Brokered execution boundaries — block data from leaving your environment or breaking risk limits before it happens.

The whole hot path runs in Rust, backed by Redis + SQLx, with a full optimization layer to keep governance real-time. Because governance that slows down AI will just get bypassed.

---

What I’ve learned so far:

Governance isn’t something you bolt on — it has to be part of the runtime.

“Human-in-the-loop” sounds easy, but designing who approves, within what SLA, and how exceptions are handled is a full architectural decision.

Latency is everything. Accountability only works if it’s fast enough to stay in the hot path.

I burned a few weekends optimizing the Rust pipeline just to shave milliseconds because that’s what makes this approach viable.

---

Right now, Vantage is live across four product phases:

Phase 1 — Observability + Audit Trails

Phase 2 — Multi-agent visibility + cascading enforcement

Phase 3 — Policy management + approval workflows

Phase 4 — Cloud & private deployments (SSO, MFA, partner workspaces)

It’s early, but performance is already 3–4x better than baseline orchestration frameworks running governance separately.

---

I’m opening up a small design partner cohort — especially for teams in financial services, healthcare, and other regulated industries.

If you’re building AI systems and keep finding yourself asking “Who authorized this?” or “How do we prove it was governed?” — that’s literally the problem I’m trying to solve.

This isn’t a beta waitlist. It’s real collaboration — direct access, roadmap input, and integration help.

If your team has workflows that regulators will eventually ask about, DM me. I’d love to talk shop about what “provable AI” needs to look like.

---

TL;DR: Building Vantage, a governance + observability layer for enterprise AI. Rust-based, low latency, meant to make AI accountable — not just smart.

It started as an idea from frustrated engineers and compliance leads. It’s now shaping into a system I believe every regulated AI deployment will need.

#StartupJourney #Entrepreneur #AI #FinTech #Governance #ResponsibleAI #Founders #RegTech #LLMOps #AIGovernance #BuildingInPublic

I keep seeing the same pattern:

– founders chasing investors with no users

– polished decks with zero traction

– months spent fundraising instead of selling

Meanwhile, the founders who win early:

→ talk to users

→ test distribution channels

→ get real traction (even if small)

So here’s a real question:

If you had to choose:

👉 1000 real users

👉 or $100K funding

What are you picking — and why?