YC S25 — Coolest software-focused startups (developer-forward review)

Stephen Stilwell — Mon, 11 Aug 2025 04:35:08 +0000

Quick snapshot: what S25 smells like

S25 is saturated with agentic AI, developer tooling, and verticalized AI products for healthcare, finance, and robotics. Practically every demo day pitch now contains some blend of agents + automation + domain data. That means a lot of engineering-heavy problems—and a lot of interesting roles for folks who want to build infra, observability, and trustworthy AI systems.

My top picks (cool / fun / exciting for software people)

Below are the companies I dug into and why they caught my attention. Each entry explains what they build, the engineering hooks, and what I’d watch if I were hiring or thinking about integrating their product.

1) Embedder — "Cursor for embedded"

What it does: AI-first IDE and agent tooling for embedded / firmware engineers: generate drivers, run test/flash cycles, and speed up firmware development.
Why it’s cool: embedded development is still painfully manual—anything that automates driver generation, flashing, and test loops will save weeks per feature. This sits at an interesting intersection of low-level systems, CI for hardware, and agent orchestration.
Engineering hooks: hardware-in-the-loop CI, reproducible cross-compilation, test harnesses that can program physical devices, CLI agents that can flash, run tests, and debug.
What to watch: their CLI + agent integration (if it can actually run end-to-end workflows on real boards, this will be huge for prototyping).

2) Wedge — trust & monitoring layer for health AI

What it does: deployment and monitoring tooling that helps hospitals deploy, observe, and govern AI models safely.
Why it’s cool: real-world ML in hospitals is a nightmare—data drift, auditability, and regulatory needs create demand for robust ML ops and observability.
Engineering hooks: model monitoring, explainability pipelines, secure model serving, and compliance/audit trails. Lots of opportunity for feature flags, canarying, and latency-aware inference.
What to watch: integrations with EHR systems and their approach to privacy-preserving telemetry.

3) April — voice-first AI executive assistant

What it does: voice-powered assistant that manages email and calendar hands-free; live on the App Store.
Why it’s cool: voice-first UIs for productivity are an under-explored UX frontier. Building safe, accurate outbound-email agents (that can match tone and avoid hallucinations) is technically challenging and interesting.
Engineering hooks: NLU for calendar/email intent, secure Gmail + Calendar integrations, short-latency on-device components, and robust user-style personalization.
What to watch: quality of generated replies, failure modes (what happens when it gets it wrong), and privacy practices for email handling.

4) Prism — AI-native product analytics (session-replay agent)

What it does: watches session replays using vision models and surfaces friction points to product & engineering teams.
Why it’s cool: automating qualitative signals from session replay turns hours of manual review into queryable insights—great for small teams that can’t hire a dedicated UX researcher.
Engineering hooks: efficient video/session encoding, vision models tuned to UI, semantic search over user sessions, and integrations with existing observability stacks.
What to watch: model accuracy for detecting true friction vs intentional flows, and retention/privacy guarantees for replay data.

5) Plexe — build predictive ML models from a prompt

What it does: an agentic system that connects to your data, runs modeling experiments, and returns deployable models from a plain-language description.
Why it’s cool: if it works, it dramatically lowers the barrier to traditional ML workflows (feature engineering, model selection, backtesting, deployment).
Engineering hooks: safe data connectors, reproducible experiment pipelines, automated feature engineering, and model packaging for production APIs.
What to watch: how Plexe diagnoses failures and how transparent the generated pipelines are (the trust problem for auto-ML agents).

6) Mbodi AI — teach robots with language and demos

What it does: embodied AI platform that converts language + demos into reliable robot actions.
Why it’s cool: it’s a real fusion of perception, planning, and symbolic reasoning—applies to manufacturing and supply chain automation where scripting robots is still tedious.
Engineering hooks: sim-to-real transfer, robust perception stacks, action-parameterization, and safety checks for physical systems.

7) Aegis & Galen AI — two health AI startups worth scanning

Aegis: automates insurance-denial appeals and documentation; a very practical, revenue-recovery ML application.
Galen AI: personal healthcare agent that aggregates records and wearables; interesting for patient-facing ML and data integration.

Cross-cutting technical themes (why devs should care)

Agent orchestration: many companies are stitching together multi-agent flows—one agent to fetch data, another to preprocess, another to call models, and another to evaluate. Building reliable orchestration primitives is a hot problem.
Trust, observability, and safe-deploy: audit logging, human-in-the-loop checks, and rollback primitives are becoming standard parts of the stack.
Hardware + software CI: embedded and robotics startups are trying to replicate software CI for physical devices—this is where a lot of smart engineering will happen.
Low-latency & on-device components: voice agents and safety-critical integrations need predictable performance and hybrid on-device/cloud models.

Hiring / partnership signal — where to look for senior roles

Platform engineers for model serving and observability
SRE / infra with hardware CI experience
ML infra (feature stores, experiment tracking, deployment) engineers
Data privacy / compliance engineers for healthcare integrations
Frontend engineers who can integrate UI session replay + model insights

Final takeaways

S25 is an engineer-forward batch. If you’re a software builder, there’s a lot to love: from firmwarish tooling (Embedder) to safety/observability (Wedge, Aegis), to human-facing agent UIs (April), to new ML productivity primitives (Plexe, Prism). These companies are building the scaffolding future teams will use to ship agentic AI and trustworthy systems.

Feeling Stuck? 4 Common Jobs Where Many Seek Fulfillment

Stephen Stilwell — Sun, 10 Aug 2025 03:14:44 +0000

Grinding through work feeling unfulfilled? You're not alone: many in popular roles feel drained by routines, no autonomy, or mismatched passions. Your expertise can spot real problems to solve. From 2025 trends, four roles with burnout:

Software Developer: Deadlines, code maintenance, tool changes make devs feel like cogs.
Registered Nurse: Shortages mean long shifts, overloads, bureaucracy causing burnout.
Sales Rep: Quotas, rejection, scripted pitches crush those wanting real connections.
Financial Manager: Repetitive compliance, volatility stress, no creativity fuel entrepreneurial dreams.

These are launchpads—use frustrations for startups.

Job Rut to Startup: Build YC-Worthy Solutions

Y Combinator (Airbnb, Stripe backer) says thrive on "good problems": personal, urgent, underserved, large market, user-validated. "Good solutions": fast MVPs with core value, feedback iteration, skill leverage, focused scale.

Pivot if unsatisfied. For each role: YC-based problems/solutions. Validate with 10 peers, prototype simply for early user love.

Software Dev: Tool Fragmentation & Burnout
Juggling tools fragments workflows? YC problem: personal, urgent (20-30% turnover), underserved, $700B market.
Solution: MVP AI integrator dashboard (GitHub+Amazon Web Services (AWS) start). Code fast—90/10 value. A/B betas, scale enterprises. Like Zapier to $5B.
Nurse: Staffing Shortages & Care Inefficiencies
Understaffing rushes care, exhausts amid woes. YC: personal, urgent (10-15% vacancies), underserved, $4T scale.
Solution: MVP AI staffing forecaster (data/real-time). Unscalable start (SMS), iterate feedback, integrate. Matches YC RFS; telemedicine grew big.
Sales Rep: Cold Outreach Inefficiency
Cold calls/emails waste time (<2% convert). YC: personal fatigue, urgent (lost billions), CRM gaps, $1T market.
Solution: MVP AI lead qualifier (LinkedIn analysis). Sales edge: simple scoring. Unscalable vet, A/B tests. Vertical focus (SaaS), profit growth. Gong-style AI.
Financial Manager: Compliance Complexity
Regs/manuals stifle; $14K/employee non-compliance cost. YC: personal, urgent, underserved, $200B fintech.
Solution: MVP LLM dashboard flags risks, reports (taxes). Expertise scales. Test/refine, freemium. Like Brex from frustrations.

Channel pains into YC ideas:
Escape rut, create change.
Start small, obsess users.
Prototype now; startup.

DEV Community: Stephen Stilwell