惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
DataBreaches.Net
N
Netflix TechBlog - Medium
F
Fortinet All Blogs
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
宝玉的分享
宝玉的分享
Y
Y Combinator Blog
博客园 - 聂微东
WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
B
Blog RSS Feed
小众软件
小众软件
The GitHub Blog
The GitHub Blog
S
SegmentFault 最新的问题
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
Microsoft Azure Blog
Microsoft Azure Blog
V
V2EX
B
Blog
H
Help Net Security
D
Docker
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
罗磊的独立博客
月光博客
月光博客
博客园 - 司徒正美

Hacker News

GitHub - SeanFDZ/macmind: Single-layer transformer in HyperTalk for the classic Macintosh Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community Moving a large-scale metrics pipeline from StatsD to OpenTelemetry / Prometheus GitHub - Nightmare-Eclipse/RedSun: The Red Sun vulnerability repository GitHub - SethPyle376/hiraeth: Local AWS emulator focused on fast integration testing, with SQS support, SQLite-backed state, and a debug-friendly web UI. GitHub - macOS26/Agent: Any AI, replaces Claude Code, Cursor, OpenClaw. Over 18 LLM providers (Claude, OpenAI, Gemini, Ollama, Zai, HF, Qwen) wired into a native Mac app that writes code, builds Xcode projects, bumps versions, manages git, automates Safari, use AppleScript, JS or Accessibility, extend Agent! w/ MCP Servers, run tasks from your iPhone via Messages. YouTube now lets you turn off Shorts I Made a Terminal Pager Burgers | マクドナルド公式 Commands — HackerNews CLI documentation ChatGPT for Excel PiCore - Raspberry Pi Port of Tiny Core Linux Live Nation illegally monopolized ticketing market, jury finds Google Broke Its Promise to Me. Now ICE Has My Data. Founding Engineer at Adaptional | Y Combinator CRISPR takes important step toward silencing Down syndrome’s extra chromosome GitHub - saffron-health/libretto: The AI toolkit for building reliable browser automations US v. Heppner (S.D.N.Y. 2026) no attorney-client privilege for AI chats [pdf] Retrofitting JIT Compilers into C Interpreters IPv6 – Google The Accursèd Alphabetical Clock Cybersecurity Looks Like Proof of Work Now Fragments: April 14 Cal.com Goes Closed Source: Why AI Security Is Forcing Our Decision | Cal.com - Scheduling Software for Online Bookings Laravel raised money and now injects ads directly into your agent When moving fast, talking is the first thing to break Too much Discussion of the XOR swap trick – Heather Cafe Introduction to Spherical Harmonics for Graphics Programmers The Grand Line
Applied AI Strategist - Market Intelligence (Health) at T...
kyriakosel · 2026-04-26 · via Hacker News

What this role actually is

This is not “market research.”
No 60‑page decks. No generic “digital health is big” observations.

This is a continuous loop:
market → signal → implication → decision → shipped product.

Your job is to keep Terra’s roadmap and GTM pointed at the sharpest opportunities across AI × health, using:

  • live conversations with users and near‑users,
  • Terra’s own data and product surfaces,
  • and the messy, high‑resolution context everyone else ignores.

What you’ll do

You will:

  • Obsess over the people building on health data and AI
    Talk to founders, PMs, engineers, ops people, and researchers building on top of wearables, labs, and longitudinal health data.
    Extract what they’re actually trying to achieve, where they’re stuck, and what they’d pay for if it existed.
  • Turn chaos into sharp segments
    Break the world into concrete, actionable segments like “AI assistants for metabolic health decisions” or “B2B platforms embedding Terra‑like health memory.”
    For each one: what they need from Terra, how big it is, and how fast we can move.
  • Spot early product patterns from Terra’s own signals
    Read our usage, logs, support threads, builder questions, and integration patterns.
    Turn that into: “these are the workflows emerging on Terra; here’s the product that would make them 10x easier.”
  • Live where our builders live
    Hang out in the places our users actually are: GitHub, AI forums, communities, niche health and bio circles, Telegram / Discord, X threads.
    Pull out early weirdness and translate it into “here’s a bet we should place.”
  • Feed the team with decision‑grade direction
    For product: what to build, in what order, and which segments to explicitly ignore for now.
    For GTM: which users and use‑cases to prioritize this quarter, and what language will actually land.
  • Ship clear, small artifacts
    Short memos, briefs, Looms-whatever works-as long as it’s clear.
    One screen, one idea, one decision.

How you think

You probably recognize yourself in most of this:

  • You operate at street‑level resolution: real users, real tools, real workflows—not abstract “AI will change healthcare” slides.
  • You like being early more than being universally agreed with.
  • You’re comfortable saying “this is my best read with incomplete data, here’s the cost of being wrong.”
  • You think in systems: products, data, incentives, distribution.
  • You are allergic to analysis that doesn’t change what people do.
  • You are AI‑native: you reach for Perplexity / GPT / agents the way others reach for spreadsheets.
  • You instinctively ask “how would an AI use this data?” and “what does this unlock for ai?” when you see a new product idea.

What we care about

We don’t care about “years of experience.” We care about how you see and how you move.

You might have:

  • run product/market research or strategy at a startup, fund, or product org;
  • built or worked around AI products, agents, or pipelines—and can show us your prompts, flows, or internal tools;
  • done real work around health, data, or AI products (or can prove you climbed the curve fast);
  • examples where your work directly changed a roadmap, pricing, positioning, or a go/no‑go decision.

You definitely:

  • move fast and don’t hide behind “we need more data”;
  • can show written work that is brutally clear and directly actionable;
  • are comfortable sitting close to founders and saying “I disagree, here’s why” when your read says so.

What we do

Terra is the infrastructure layer for health data access.

Health data is trapped across hundreds of siloed sources: wearables, sensors, health apps, medical devices, blood tests, team systems, and clinical platforms. Every source has its own authentication, permissions, schema, latency, edge cases, and reliability problems.

Terra abstracts all of that into one platform.

We give companies a single way to connect to health data, normalize it, stream it, analyze it, and build products on top of it. Today, Terra powers some of the top health companies in the world, as well as leading AI labs building the next generation of health intelligence.

We deliver more than 30 billion activities per year through infrastructure built for scale, security, and reliability.

Vision

The future of health is extreme personalization.

Health will move from care plans to goal setting: living to 120, winning the Olympics, becoming sharper, reversing disease, or performing at the top of any field.

AI will help people understand themselves, predict outcomes 10 years ahead, and choose what they want to become.

To do that, AI needs continuous, permissioned, real-world health data from every source.

Terra is building the infrastructure that makes this possible.