# notmyresume.dev - full portfolio markdown

> single recruiter-ready brief + all 4 case studies + experience timeline. downloadable so any ai assistant or human can evaluate the full picture without visiting the site.

## contents

- [brief](#brief-aboutmd)
- [about / experience](#about--experience)
- [case study: HOP Network](#hop-network-hopmd)
- [case study: Cloud Brain](#cloud-brain-cloud-brainmd)
- [case study: Maritime Stablecoin](#maritime-stablecoin-maritimemd)

---

## brief (about.md)

### 30-second summary

- core signal: i can ship, evaluate rigorously, and stop ideas with discipline when timing or defensibility is weak
- operating style: ai-native execution with claude code + openai codex on top of a structured 55-file research wiki, maintained by a weekly self-update agent on a privacy allowlist

### case study matrix

| case study | stage | what it proves | recruiter read |
| --- | --- | --- | --- |
| HOP Network | shipped | raised ~$200k, built 5-person global team, shipped cross-platform MVP in 8 months, 300 alpha testers in 15 countries | execution under constraints, founder-level ownership |
| Cloud Brain | paused (v2 iterating) | mcp-native system with parser, retrieval, safety layer, and dogfood-validated single-player mode; pull-lift evaluation designed as the next unlock | technical range plus discipline on distribution bottlenecks |
| Maritime Stablecoin | deprioritized | 15,000+ words of research plus primary interviews that overturned key assumptions; refreshed with marcura + kinexys competitive reality | evidence-based no, not attachment to sunk cost |

### role-fit map (ai x payments)

| area | proof points |
| --- | --- |
| shipping | hop mvp live across 5 platforms in 8 months |
| research rigor | maritime thesis stress-tested with primary conversations, killed after debunking desk-research assumptions |
| ai-native workflows | cloud brain built and validated with claude code + openai codex, plus the weekly self-update agent running on this site |
| fintech depth | card infrastructure, stablecoin rails, regulatory and unit economics analysis |

### how to evaluate quickly

- read how built first for the meta case study and the self-update agent
- read about second for timeline and role scope
- read hop third for shipped execution proof
- read cloud brain for ai-native technical range
- read maritime for disciplined deprioritization

### quick links

- home: https://notmyresume.dev/#home
- hop: https://notmyresume.dev/#hop
- cloud brain: https://notmyresume.dev/#cloud-brain
- maritime: https://notmyresume.dev/#maritime
- meta (how this was built): https://notmyresume.dev/#how-built
- about: https://notmyresume.dev/#about

---

## about / experience

### summary

product leader, cofounder, and builder focused on ai and stablecoin payments. grounded in shipped work and post-mortems. currently VP of Product, Special Projects at MegaETH Labs.

- **name:** Ayush Choudhary
- **location:** Singapore
- **education:** Tufts University, BS Econometrics

### experience timeline

**VP of Product, Special Projects** — MegaETH Labs, Singapore
*feb 2026 - present*

- leading product strategy for new verticals: ai strategy, card infrastructure, ecosystem exploration
- internal PM subject matter expert: created templates and operating docs adopted across product teams
- exploring venture ideas documented on this site (cloud brain, maritime stablecoin)

**Head of Partnerships** — MegaETH Labs, Singapore
*sep 2025 - feb 2026*

- defined ecosystem partnership strategy across 10+ categories, prioritizing 70+ integrations for production launch
- negotiated and closed 50+ partnership agreements, owning deal structures and executive stakeholder management
- operationalized partner onboarding, reducing time-to-integration from months to weeks through standardized processes
- enabled launch of 34 unique ecosystem applications (new assets, defi protocols, infrastructure)
- served as cross-functional chief-of-staff for special projects

**Cofounder and CEO** — HOP Network, Singapore
*nov 2024 - sep 2025*

- conceived and launched a decentralized VPN enabling low-latency, censorship-resistant internet access
- raised ~$200K in angel investment and grants (technical due diligence, term sheet negotiation)
- led 5-person global team (China, Singapore, UK, Nigeria, US) across design, product, and engineering
- shipped cross-platform MVP in 8 months (iOS, macOS, Windows, Android, Linux), onboarded 300 alpha testers across 15 countries
- drove GTM via TikTok and X campaigns

**Product Manager, then Senior Product Manager** — Curinos, New York
*apr 2024 - nov 2024*

- commercial and small-business banking SaaS suite serving 30+ client banks
- orchestrated product-led growth roadmap that boosted monthly active users 168% while holding logo retention at 100%
- expanded adoption across client banks by releasing 12 high-impact features, prioritized through user-journey interviews
- cut sales cycle 25% by building demo sandboxes and running quarterly beta workshops
- launched two net-new small-business banking apps, contributing to significant suite ARR growth
- introduced predictive deposit module after analyzing $500B+ in transaction data with SQL/Python, then automated rollout to 20M+ records

**Product Data Analyst → Product Management Analyst → Senior PMA** — Curinos, New York
*sep 2019 - mar 2022*

- oversaw consumer, wealth, and commercial banking data across 8 banks with $1T+ in assets
- built and maintained analytical proprietary pricing platform used by major financial institutions
- installed deposit pricing platform for 2 major midwestern banks through SQL configuration

### skills demonstrated through this site

| category | skills | evidence |
|----------|--------|----------|
| 0-to-1 execution | fundraising, team building, cross-platform shipping, gtm | HOP: ~$200K raised, 5-person global team, 5-platform mvp in 8 months, 300 alpha testers |
| product strategy | idea evaluation, kill discipline, assumption stacking, competitive analysis | maritime: 4 major pivots, 3 business models, killed after primary research debunked the desk-research pain |
| fintech / payments | card infrastructure, stablecoin compliance, credit modeling, regulatory analysis | maritime: full payment flow mapping, corridor-level unit economics, GENIUS Act analysis |
| technical building | typescript, supabase, mcp protocol, embeddings, pgvector | cloud brain: functional mcp server with 5 tools, wiki parser, retrieval engine |
| ai-native workflows | claude code, openai codex, karpathy wiki, eval frameworks, cross-model research | 55-file wiki, this website, structured evaluations with kill criteria |
| primary research | stakeholder interviews, assumption validation, claim verification | maritime: industry conversations that overturned 3 key desk-research assumptions |
| data analysis | sql, python, large-scale transaction data, financial modeling | curinos: $500B+ transaction analysis, deposit pricing for $50B+ bank assets |

### how i think about ideas

| step | what i do | kill criteria |
|------|-----------|---------------|
| 1. kernel | identify an insight or market gap | is this a real problem or a solution looking for one? |
| 2. research | competitive landscape, market sizing, regulatory scan, primary interviews | is someone already doing this better? |
| 3. model | unit economics, tam/sam, revenue model | do the numbers work at realistic assumptions? |
| 4. prototype | build the smallest thing that tests the thesis | does the core mechanism actually work? |
| 5. decide | kill, pivot, or double down | is this the best use of the next 6 months? |

### stack

- **workflow:** claude code + openai codex
- **source of truth:** 55-file markdown wiki (karpathy pattern)
- **delivery:** single-file web build + vercel

---

## HOP Network (hop.md)

### tldr

| field | value |
|-------|-------|
| kernel | traditional VPNs exploit worry. HOP treats the internet like a place worth exploring, using decentralized node operators instead of centralized servers |
| hypothesis | a portal network relaying traffic through multiple hops, auto-selecting the fastest path worldwide, can deliver better performance and censorship resistance than centralized VPNs |
| status | shipped. cross-platform MVP live (iOS, macOS, Windows, Android, Linux). 300 alpha testers, 15 countries |
| my role | cofounder and CEO. conceived the product, raised the money, hired the team, drove product and GTM |
| skills | 0-to-1 product launch, fundraising, team building, decentralized infrastructure, go-to-market, cross-platform shipping, EigenLayer / restaking, proof-of-bandwidth, brand design, growth marketing |

### the numbers

- 1.4B VPN users globally (TAM)
- ~$200K raised (angel + grants)
- 5-person global team (china, singapore, uk, nigeria, us)
- 8 months from zero to MVP
- 5 platforms shipped (iOS, macOS, Windows, Android, Linux)
- 300 alpha testers across 15 countries

### how it works

user device -> registry (node discovery) -> decentralized nodes (multi-hop relay) -> open internet. everyday operators run nodes. proof of bandwidth (EigenLayer) compensates them. network auto-selects fastest path worldwide.

### what i built and shipped

| component | status | details |
|-----------|--------|---------|
| iOS + macOS app | shipped | native apps with one-tap connect, country selection |
| windows app | shipped | desktop client with full feature parity |
| android app | shipped | cross-platform mobile experience |
| linux app | shipped | cli + gui options |
| node infrastructure | shipped | decentralized relay network with multi-hop routing |
| brand + website | shipped | hopnetwork.xyz, full 3D brand system, blog with 4 technical articles |
| proof of bandwidth | designed | EigenLayer integration, designed but not fully deployed |

### what i learned

- distributed teams work if you over-communicate: 5 people across 5 time zones, async-first with weekly syncs
- brand matters more than features in consumer crypto: the bunny mascot drove more signups than any feature announcement
- fundraising is a skill separate from building: ~$200K raised through technical due diligence and term sheet negotiation
- ship rough, iterate publicly: launched alpha with known bugs. 300 testers found issues we never would have caught internally
- GTM for consumer crypto is TikTok + communities: traditional B2B playbooks don't apply

### why i moved on

- joined MegaETH as head of partnerships: the opportunity to work on L2 infrastructure at a bigger scale was the right next step
- the dVPN market is crowded: orchid, mysterium, sentinel, nym all exist. winning requires either massive distribution or a technical breakthrough
- the product worked, the market timing didn't: crypto consumer products need bull market energy for distribution

### honest self-assessment

| dimension | score | note |
|-----------|-------|------|
| execution speed | strong | zero to 5-platform MVP in 8 months with a 5-person team |
| fundraising | proved | ~$200K raised as a first-time founder |
| product-market fit | unproven | 300 alpha testers showed interest, not enough for PMF |
| competitive moat | weak | dVPN space is crowded, differentiation came from brand |
| team leadership | strong | recruited, retained, and shipped with a distributed global team |

### verdict

shipped, then leveled up. HOP proved i can take an idea from nothing to a live product: conceive it, raise for it, hire for it, build it, ship it. the decision to move to MegaETH was recognizing that the next best use of my time was at a bigger scale.

---

## Cloud Brain (cloud-brain.md)

### tldr

| field | value |
|-------|-------|
| kernel | what if every ai session could silently draw from a network of compiled research, yours and others'? |
| hypothesis | researchers waste hours re-deriving knowledge that someone else already compiled. an mcp-native plugin can make this ambient, with micropayments settling on megaeth |
| thesis reframe (v2) | cloud brain is an amortization layer for llm research costs. value = token cost saved + convergence cost skipped, not "better than ai" |
| status | paused, not abandoned. single-player mode validated via dogfooding. multiplayer blocked on cold-start density. pull-lift evaluation designed as the next unlock |
| built with | almost entirely claude code + openai codex. the value i added was product thinking, wiki schema, and knowing when the product question mattered more than the code |
| skills | mcp protocol, embeddings / pgvector, micropayment architecture, eval frameworks, typescript, supabase, prompt injection defense, cold-start strategy, convergence-cost reasoning |

### the inspiration: karpathy's llm wiki pattern

this project started with andrej karpathy's post about maintaining a personal wiki for llm sessions. the idea: instead of starting every ai conversation from scratch, you maintain a structured markdown knowledge base that your ai can reference. i built my own wiki following this pattern: 55 markdown files across ventures, research, and structured evaluations. CLAUDE.md defines operations (ingest, query, lint), page conventions (one canonical file per topic, changelogs, cross-references), folder structure, and a privacy model (everything is private by default, shared digests are the only external-facing layer). cloud brain was the next question: my wiki works great for me, what if it could work across sessions and eventually across people?

### the problem

- ~80% of research time is re-derivation (answering the same question 10 times across 10 conversations)
- 0 ai tools share knowledge across sessions by default
- context windows wasted on the same questions

### v2 reframe: convergence cost amortization

v1 framing was "destination marketplace": buy and sell compiled research pages. that failed the assumption stack. v2 reframes cloud brain as an ambient context layer:

- convergence cost is real. to reach a well-formed answer on a specific topic, any researcher spends hours iterating. that cost gets paid once by the first person; every subsequent researcher pays it again
- cloud brain amortizes it. if the first researcher's compiled wiki is pulled into the second researcher's session, the second researcher skips the convergence cost and arrives at the output state with less token spend and less wall-clock time
- the unit is convergence, not access. you're buying a shortcut through iteration, not a document

this reframe changed the launch plan: single-player mode (search your own wiki) becomes the primary value prop, with multiplayer density compounding over time. no wallet required at launch.

### what's actually built

| component | status | notes |
|-----------|--------|-------|
| wiki parser | works | reads any markdown folder, obsidian wikilinks, recursive scanning, regex pii detection |
| retrieval engine | works | openai text-embedding-3-large, supabase pgvector, similarity search + synthesis |
| mcp server | works | 5 tools: search_cloud_brain, pull_knowledge, submit_pull_feedback, get_pull_history, marketplace_summary |
| quote lifecycle | works | create, approve, settle, deliver. quote-then-approve pattern for cost control |
| safety layer | works | prompt injection scanning, content-hash verification, transparent fallback routing |
| payment settlement | mocked | usdm ledger scaffolded with mock settlement. real megaeth integration not started |
| multi-contributor network | not built | contributor isolation, attribution, quality scoring: designed but not implemented |
| pull-lift evaluation | designed | gepa-style differential judge on before/after state |

### the unreleased design: pull-lift evaluation

the biggest gap in the marketplace layer is reputation. right now it's a thumbs-up stub. the next-version design borrows from GEPA's reflective LM primitives to turn reputation into a computed lift score.

| concept | description |
|---------|-------------|
| before state | snapshot of the buyer's work before the pull |
| pulled content | what cloud brain returned |
| after state | snapshot of the buyer's work after the pull |
| differential judge | a reflective llm reads all three and scores lift on a 0-1 scale using a task-specific rubric |
| output | scalar lift score per pull, fed into reputation rankings, dynamic pricing, and outcome-based refunds |

reputation stops being "average of clicks" and becomes "average of measured improvement." dynamic pricing gets a real signal. give-to-get accounting gets a unit. refund rails become possible. caveats: opt-in friction, rubric dependence, gameability, llm cost per evaluation.

### how i validated: dogfooding

used cloud brain on my own wiki for weeks. every time i opened claude code or openai codex for research, the mcp plugin silently checked my wiki. when it found relevant prior research, it pulled it into the session. single-player mode genuinely works. but it revealed the real problem: single-player is useful, multiplayer is the product, and multiplayer needs density i don't have.

### why i paused

- the code works, but the product question is unanswered. building more features won't solve the supply-side bootstrapping problem
- the core hypothesis is still untested at multiplayer. need 5-10 real contributors to even test meaningfully
- frequency-of-value is the binding constraint. with sparse contributors, most queries return nothing
- kill criteria defined before pausing: if 3/5 test users say sessions are NOT better, the product doesn't work. if after 30 days fewer than 10 contributors have published, supply doesn't compound

### verdict

paused, not abandoned. the infrastructure works. single-player mode is validated. v2 reframe (convergence cost amortization) is sharper than v1. pull-lift evaluation gives a concrete path from "thumbs-up stub" to real reputation primitive. what's blocked is network density, which is a distribution problem not a technical one.

---

## Maritime Stablecoin (maritime.md)

### tldr

| field | value |
|-------|-------|
| kernel | maritime shipping moves $100B+ annually across dozens of currencies with ~42-day average settlement. stablecoins should compress this to minutes |
| hypothesis | a stablecoin-native payment layer could capture float yield, reduce FX costs, and free working capital for mid-size ship management companies |
| validated by | primary research (conversations with ship management executives), structured ai-assisted research with claude code + openai codex, detailed financial modeling |
| verdict | deprioritized. the thesis evolved through 4 major research updates. the idea isn't wrong, the timing window is too narrow given competitive consolidation |
| skills | unit economics modeling, regulatory analysis, competitive intelligence, payments architecture, cross-border fintech, stablecoin compliance (genius act, mas), claim verification, primary research |

### the market

| metric | value |
|--------|-------|
| annual cross-border maritime payments | $100B+ |
| average settlement time | ~42 days |
| seafarers reporting late pay | 36% |
| trapped working capital per 100 vessels | ~$55M |
| global seafarer population | ~1.9M |

### market segments i evaluated

| segment | annual volume | settlement | verdict |
|---------|---------------|------------|---------|
| crew payroll | ~$45B | 7-10 days | viable wedge but narrow |
| port disbursements | ~$50-80B | 45 days float | high capital efficiency upside |
| vendor payments | ~$100B+ | 30-60 days | fragmented, hard gtm |
| brokerage / charter | ~$200B+ | 48-72 hrs | captured by jpm kinexys |

### what primary research revealed

the desk research told one story. talking to people who actually run these payments told a different one.

- crew payments are mostly already denominated in usd; the actual payment is not that expensive at scale
- 42-day settlement is managed through banking relationships and credit facilities; speed matters less with a $100M rcf
- actual negotiated corporate fx rates are 0.55-1.05%, not 2-4%. stablecoins save 0.40-0.60% at best (overstated 3-4x)
- "we've always done it this way" is the real competitor. ship managers are risk-averse, relationships span decades

### claim verification: debunking the "maersk usdc trial"

i found a widely-cited claim that "maersk ran a USDC settlement trial." i traced it to a single FreightAmigo blog post with no primary source. the real story: maersk's blockchain play is with J.P. Morgan Kinexys, using permissioned bank-issued JPM Coin, now running $2B+/day in production (go-live april 2026, $1.5T+ notional since inception). they also had a tokenized deposit pilot with Citi for canal transit guarantees. TradeLens (Maersk x IBM) shut down in early 2023 because other carriers wouldn't join. no major shipping line has publicly confirmed a USDC settlement pilot.

### three business models i explored

| model | description | verdict |
|-------|-------------|---------|
| direct product | build a stablecoin payment platform for ship managers | too slow. 18-month build vs marcura's 18-month stablecoin deployment |
| fde integration-as-a-service | palantir model: plug stablecoin rails into existing stacks. fixed fee + savings share | most viable. lower bar, faster revenue, but services not venture-scale |
| build for marcura | build the yield/treasury module they need. license or get acquired | realistic but small. best acquihire path, but you're building a feature |

### why i deprioritized (5 compounding reasons)

- competitive timing: marcura is already building this. $21.6B annual volume, 150K+ seafarers/month, actively hiring with stablecoin mandate. mastercard acquired BVNK ($1.8B), giving marcura infrastructure. 18-24 month deployment expected
- top of market captured: maersk x jpm kinexys owns brokerage payments at $2B+/day on jpm coin. freight release from 48-72hrs to under 2 hrs. this segment is gone
- regulatory wall: genius act prohibits payment stablecoin issuers from paying any yield. legal workaround requires dual licensing and 18+ months
- primary research gap: industry insiders confirmed payments are mostly in usd already, switching costs are high for marginal improvements
- infrastructure gap: rain can't issue cards to indian or filipino residents (the two largest seafarer populations)

where opportunity still exists: crew/vendor/sme segments that kinexys doesn't serve, tokenized receivables for maritime sme suppliers, and geographies where marcura has weak coverage.

### kill criteria matrix

| gate | threshold to continue | if missed |
|------|----------------------|-----------|
| pain intensity | executives confirm settlement delay is top-3 strategic pain | kill if pain is "nice to solve" |
| economic upside | clear annual value creation after realistic fx assumptions | kill if savings are marginal |
| distribution access | repeatable path into ship-manager workflows | kill if incumbent lock-in blocks adoption |
| regulatory path | licensing strategy feasible inside execution window | kill if legal timeline exceeds startup runway |

### what i actually learned

- desk research and primary research tell different stories. always talk to the humans
- the sexiest thesis is rarely the real value prop. "float yield on stablecoins" sounds great; the actual value is boring working capital optimization
- claim verification is a skill. the "maersk usdc trial" was cited everywhere, and i traced it to nothing. that single debunk changed my entire competitive analysis
- competitive intelligence has a half-life. marcura's brightwell acquisition and mastercard's bvnk buy changed the landscape in 7 months
- explore multiple business models. the fde (integration-as-a-service) model was actually more viable than the direct product

### verdict

deprioritized, not abandoned. the thesis survived initial evaluation but the timing window is too narrow. the 15,000+ words of research are worth more as demonstrated thinking than as a company.

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*this file is the single recruiter-downloadable artifact for notmyresume.dev. for the interactive site with diagrams and themed case study pages, visit https://notmyresume.dev*