מתי להשתמש
"AI for developer", "AI for founder", "Build AI app", "AI MVP".
הוראות עבודה
1. Developer's AI Stack 2026
Coding
- Cursor / Windsurf ($20/m) — IDE.
- GitHub Copilot ($10-39/m).
- Claude Code (Pro plan) — agent CLI.
- v0 / Bolt — UI / app scaffold.
Research / Learning
- Claude Pro — explain code, debug.
- Perplexity — current docs.
Building AI Apps
- Anthropic API — for production.
- OpenAI API — multimodal.
- LangChain / LlamaIndex — frameworks.
- Pinecone / Weaviate — vector DBs.
2. Founder's AI Use Cases
Strategy
- Market research (Perplexity).
- Competitor analysis.
- Investor pitch drafts.
- Hiring screen.
Execution
- Code prototypes (no-code AI tools).
- Marketing content.
- Customer outreach.
- Operations automation.
Time-Savers
- Email assistance.
- Meeting summaries.
- Research speed.
3. Build AI MVP — Speed
Stack for MVP
- Frontend: v0 / Bolt / Lovable.
- Backend: Vercel / Replit.
- AI: Anthropic / OpenAI API.
- DB: Supabase / PlanetScale.
- Vector DB: Pinecone (if RAG).
Time
- Idea → working MVP: 1-3 days.
- Vs traditional: 1-3 months.
Example: AI Document Q&A App
1. v0 — create UI (file upload + chat).
2. Vercel — deploy.
3. Anthropic API + Pinecone — RAG.
4. Supabase — auth + storage.
Total: 1 week, $50/m running.
4. Build with AI APIs — Best Practices
Cost
- Start with cheap models (Haiku/GPT-4o-mini).
- Cache aggressively.
- Use Batch API for async.
Reliability
- Retries with backoff.
- Fallback models.
- Validate outputs.
Latency
- Stream when possible.
- Parallel calls.
- Cache.
Quality
- Eval on test set.
- Monitor in production.
- Iterate prompts.
5. Founder Decision: Build AI or Use Existing?
Build AI Feature
- When AI = your differentiator.
- When existing tools don't fit.
- When you have engineering capacity.
Use Existing AI
- When AI is feature, not core.
- For fast experimentation.
- When non-differentiating.
6. AI in Product — Common Patterns
Pattern 1: AI as Feature
- "Smart" version of existing product.
- Examples: Notion AI, GitHub Copilot.
Pattern 2: AI-Native Product
- Built from ground up around AI.
- Examples: Perplexity, Cursor, Lovable.
Pattern 3: AI-Wrapped
- Repackage existing AI for niche.
- Risk: thin moat, copyable.
7. Fundraising with AI Story
What VCs Want 2026
- Defensibility — what's your moat?
- Data flywheel — your data improves AI.
- Distribution — how do you reach users?
- Unit economics — AI costs at scale.
What to Avoid
- "GPT wrapper" pitch.
- No data advantage.
- High AI cost = no margin.
8. Israeli Founder AI Scene
Notable Israeli AI Startups (2026)
- AI21 Labs — foundation models.
- Run:ai (Nvidia acquired) — GPU orchestration.
- WIZ — security.
- Plus 100+ AI-focused startups.
Resources
- 8200 alumni network.
- TLV TechWeek.
- Israeli Startup Nation Central.
9. AI Hiring
Roles to Hire
- AI Engineer — APIs, prompt eng, RAG.
- ML Engineer — fine-tuning, models.
- AI PM — strategy, prioritization.
- AI Researcher — novel approaches (rare).
Compensation (Israel 2026)
- Junior AI Eng: $150K-200K.
- Senior: $250K-400K.
- Head of AI: $400K+ + equity.
10. Common Pitfalls
❌ AI for AI's sake — no real value. ❌ Over-engineer — start simple. ❌ Ignore costs — bill shock. ❌ No evals — can't improve. ❌ Compete with foundation models — losing battle.
11. MVP Decision Tree
Has clear use case?
├── No → User research first.
└── Yes → Continue.
Existing tool fits 80%?
├── Yes → Use existing, focus on differentiation.
└── No → Continue.
Have engineering capacity?
├── No → Hire / no-code / outsource.
└── Yes → Build with AI APIs.
12. אסיים בהמלצה.
פרומפט לדוגמה
Founder, want to build AI document Q&A SaaS. Stack + 1-month plan.
Solo dev, 2 weeks. Build AI feature MVP for SaaS.
Hire 1st AI engineer in Israel. Profile + comp.
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