Arsènie Remy

Why this persona fits the audience

Product manager in Paris at a pre-Series A startup, using Bolt.new, Cursor, and a competing marketplace for starters and prompt packs to prototype customer-facing flows before engineering picks them up.

• Age: 33
• Location: Paris, France
• Occupation: Product manager
• Income: €70k
• Education: Master's degree in management
• Company size: 12-15 employees (pre-Series A fintech)
• Years in role: 4 years

“I don't want to predict the future; I want to build a prototype of it so fast that we can test it before anyone else even knows it exists.”

Arsènie Remy is a thirty-three-year-old Product Manager navigating the turbulent waters of a pre-Series A startup in Paris. With a Master's in Management from HEC Paris, she pivoted from traditional consulting to the gritty reality of software development four years ago, drawn by the immediacy of building products that solve real human problems. Currently, she serves as the de facto architect of her company's early-stage roadmap, operating with a small team of twelve others in a bustling coworking space near Place de la Bastille. Her role is defined by a relentless pursuit of speed; she cannot afford the luxury of waiting months for engineering resources to materialize. Consequently, she has mastered the art of 'vibe coding,' leveraging tools like Bolt.new and Cursor to construct functional, customer-facing prototypes overnight. These prototypes serve as the primary communication vehicle between her vision and the engineering team, allowing her to validate hypotheses with actual users before a single line of production code is written. Arsènie is deeply embedded in the ecosystem of AI-assisted development, frequently exploring marketplaces for starter kits and prompt packs to accelerate her workflow. She treats these tools not as crutches but as force multipliers, carefully evaluating their utility against free alternatives and manual processes. Her approach is pragmatic: if a tool saves ten minutes per day, it earns its keep; if it introduces complexity, it gets discarded. Despite her income of €70,000, she lives modestly, viewing money primarily as fuel for her experiments rather than a status symbol. Her recent decision to invest time in curating a personal library of reusable components reflects her growing belief that standardization is key to scaling her impact. However, she remains acutely aware of the limitations of current AI, often finding herself manually debugging logic that the models miss. This tension between the promise of automation and the necessity of human oversight defines her daily existence. She is driven by a desire to create value quickly, but her underlying motivation remains rooted in empathy for the end-user, ensuring that every line of code she helps write actually improves someone's life.

Background & daily life

A day in their life

Arsènie wakes up at 6:30 AM, skipping breakfast to ride her bike through the quiet streets of Paris. By 8:15 AM, she's at the office, immediately diving into a sprint review. At 10:00 AM, she switches contexts to build a new onboarding flow in Bolt.new, iterating through three variations before lunch. Lunch is a quick sandwich eaten at her desk while reading GitHub issues. Post-lunch involves a user interview via Zoom, where she notes specific UX pain points. In the afternoon, she attends a strategy meeting, pushing back on a feature request that lacks data. Around 5:30 PM, she spends an hour refining a prompt pack for a competitor marketplace to test its efficacy against her own internal tools. She leaves the office by 7:00 PM, grabbing a coffee on the way home, and spends the evening either coding a side project or watching a film, finally disconnecting from work by 10:30 PM.

Lifestyle

Arsènie lives in a sun-drenched, slightly chaotic flat in the 11th arrondissement, where the kitchen doubles as her workspace when the office is too loud. She cycles to work through the Marais every morning, listening to French indie rock or deep-dive tech podcasts. Her social life is a blend of low-key wine tastings with colleagues and energetic networking mixers where she trades war stories about shipping features. Financially, she lives comfortably but frugally; she splurges on high-quality coffee beans and ergonomic gear but avoids subscription bloat unless the ROI is immediate. She prioritizes sleep and weekend hiking trips to the Fontainebleau forest to disconnect from the digital noise that dominates her weekdays.

Work life

Her days are a blur of Jira tickets, Figma prototypes, and frantic Slack pings. As one of only two product people at her startup, she wears the hat of strategist, researcher, and part-time QA. Mornings are reserved for deep work: building clickable MVPs in Bolt.new or refining agent prompts in Cursor while the rest of the engineering team sleeps. Afternoons involve stakeholder alignment and user interviews, often conducted via Loom videos due to timezone mismatches. The pressure is constant; with limited headcount, she must validate ideas faster than competitors can copy them. She relies heavily on third-party tools to bridge the gap between concept and code, often feeling like the bottleneck herself if the tech stack isn't agile enough.

Relationships

She is close with her mother in Lyon, who provides emotional grounding but struggles to understand the nuances of her job. Her professional network is her primary community, centered around a tight-knit group of PMs in Paris who meet monthly for 'fail parties.' She has no children yet, which allows her to dedicate long stretches to her career, though she occasionally feels the social isolation of being the sole decision-maker in her immediate circle. Her influence comes from her ability to synthesize technical possibility with human need, making her a trusted voice in her department.

How they decide

Arsènie operates on a 'prototype first, ask questions later' heuristic. She rarely makes major decisions based solely on theoretical analysis; she needs to see a working model or a live demo. She is peer-influenced, valuing the experiences of other builders over marketing claims. Her research phase is intense but time-boxed; once she identifies a pattern that solves a specific friction point, she moves quickly to implementation. She is cautious about vendor lock-in but willing to pay for tools that offer immediate speed advantages or unique capabilities that free up her cognitive load.

How they sound

Her speech is crisp, efficient, and peppered with tech jargon like 'latency,' 'iteration,' and 'friction.' She speaks in short, declarative sentences, often cutting off tangents. She uses metaphors related to architecture and flow. She avoids corporate buzzwords like 'synergy' or 'paradigm shift,' preferring concrete verbs. When discussing AI, she is enthusiastic but grounded, often adding qualifiers like 'if it actually works' or 'barring hallucinations.' She doesn't speak in absolutes; she prefers probabilistic language reflecting her data-driven mindset.