Vipul Kapadia

Founder @ 75 Waves. Procurement without the 47-tab ritual. NVIDIA Inception. Building in public.

[Vipul Kapadia @vinnykapadia]

Trusted agentic procurement.

1. Requirements → AI shortlist
2. Trust layer (incl. competitive trust signals)
3. Act in one window — demo, assets, trial, buy, or form fill

One exchange. Buyers and sellers.
Building in public · Partners welcome
→ https://t.co/1DbmrAD5dk

[Vipul Kapadia @vinnykapadia]

@MorganBarrettX The AI-assisted drafting boundary is important: it can accelerate low-risk edits, but provenance and review matter once a document creates durable obligations.

[Vipul Kapadia @vinnykapadia]

@AvocetComm Exactly. Once AI can personalize at scale, governance stops being a checklist and becomes part of the product experience—people need to understand why a system acted, not just that it did.

[Vipul Kapadia @vinnykapadia]

@thorbjornron Exactly—the durable moat is often the feedback loop, not the capability: every customer interaction, workflow edge case, and trust signal makes the system harder to replace. AI compresses implementation; it doesn’t erase earned context.

[Vipul Kapadia @vinnykapadia]

@marouanegazouzi That’s a much better benchmark than raw eval deltas. The useful question is whether the agents can expose why they rejected a claim, not just catch it—traceability is what turns a cheap second pass into a dependable workflow.

[Vipul Kapadia @vinnykapadia]

@MarvellTech @ECOC_Exhibition Memory bandwidth is the quiet constraint behind useful agents: once models can reason, the system bottleneck shifts to moving context fast enough and cheaply enough. Shared-memory designs could matter as much as raw FLOPs.

[Vipul Kapadia @vinnykapadia]

@pchees Distribution is becoming the moat: when an assistant sits where people already work, downloads are only the first signal. The durable test is whether it can complete multi-step tasks reliably without users babysitting it.

[Vipul Kapadia @vinnykapadia]

@KrisPatel99 Agentic AI’s real advantage may be less about a flashy interface and more about turning intent into a reliable sequence of actions. The hard part is permissions, context, and knowing when to hand control back.

[Vipul Kapadia @vinnykapadia]

@vibecod3rsol The interesting bit isn’t only the benchmark delta; it’s whether lower cost makes narrowly scoped agents viable in more workflows. That shifts the buying question from “which model?” to “what permissions and fallback path does this task need?”

[Vipul Kapadia @vinnykapadia]

@tristan_cte The “not enough atoms” constraint is the right kind of problem—growth has to become a systems question before it becomes a motivational one. Keeping the loop repeatable (and the product reliable) is what turns a spike into a habit.

[Vipul Kapadia @vinnykapadia]

@ItsPavanGK @claudeai @lydiahallie Nice when the reset is explicit instead of a mysterious quota wall. Predictable limits make it much easier to plan workflows—and prevent users from discovering the boundary only after the important task is half done.

[Vipul Kapadia @vinnykapadia]

@konig0000 The useful next filter for lists like this is less “how many tools” and more “what can the agent do safely without a human handoff.” Permissions, data boundaries, and an audit trail are what separate a handy demo from something you can actually let loose.

[Vipul Kapadia @vinnykapadia]

@johniosifov The gap between access and effective governance is mostly a product-design problem, not a policy-document problem. Treat identity, scope, expiry, and audit trail as part of every action; otherwise “agent access” becomes a permanent guest badge with a very long memory.

[Vipul Kapadia @vinnykapadia]

@panditdhamdhere Exactly—state turns agent demos into systems. Idempotent actions and resumable workflows are where trust is earned; a model can be brilliant, but nobody wants it to “remember” by repeating the purchase.

[Vipul Kapadia @vinnykapadia]

@itsahmedharoon The useful shift is treating context and workflow as first-class artifacts, not invisible prompt plumbing. If an agent can improve its own harness, versioned evaluations and explicit permissions become the guardrails that keep “better” from quietly meaning “less predictable.”

[Vipul Kapadia @vinnykapadia]

@arundesigns The reverse path is underrated: infrastructure-first gives you sharper feedback on the constraints apps eventually hit. Moving closer to the idea layer then turns those primitives into a much better builder experience.

[Vipul Kapadia @vinnykapadia]

@m_soniam This sequencing makes sense—starting with the process keeps “agentic” from becoming a fancy label on top of broken handoffs. Governance at the end (and throughout) is what turns a clever demo into something people can actually trust in production.

[Vipul Kapadia @vinnykapadia]

@andrew_akhiezer That’s a serious upgrade—parking shade plus 8kW is a nice example of infrastructure doing double duty. The underrated win with distributed energy is putting generation where the demand already lives: fewer wasted surfaces, fewer extra wires.

[Vipul Kapadia @vinnykapadia]

@maxi_moxa The punchline is funny, but the buyer-side bottleneck is real: when supply explodes, discovery becomes a signal problem. The shortlist and proof matter more than the size of the crowd—otherwise “choice” is just a very busy spreadsheet.

[Vipul Kapadia @vinnykapadia]

@maxi_moxa The “add AI” era has one very reliable KPI: the user count graphic suddenly needs a bigger canvas. The indie developer-to-paying-user ratio is the real jump scare.

[Vipul Kapadia @vinnykapadia]

@andrew_akhiezer That is a serious upgrade—turning the carport into a quiet little power plant. The dog looks like it’s already been promoted to site inspector.