Summary of "Nasz AI Toolkit 2026 – Budowa produktów i produktywność ⚙️ LIVE AI Product Heroes"

Overview

A live webinar by the AI Product Heroes team presented their “AI toolkit 2026” for building products and improving daily product-team productivity. The session included demos and comparisons of LLMs, low-code/no-code agents, AI builders, image pipelines, automation flows and productivity tooling, illustrated with live use-cases, code/prototype examples and a real mini-product built during the session.

Key technological concepts & takeaways

LLM benchmarking and selection
- Use LLM leaderboards (e.g., LLM Arena / Artificial Analysis) to compare intelligence, speed, cost and specialty (text, code, image, multimodal).
- Practical note: rankings change fast — re-check frequently.
- Observations from their tests:
  - GPT family (ChatGPT / GPT-4.5/5) scores high on text and code tasks.
  - Gemini performs very well with large/contextual image analysis and multimodal tasks.
  - Claude / “Cloud Sonnet” produced more detailed PRD outputs (better structure, tech-stack suggestions, user stories) in their PRD demo versus Gemini.
Prompt engineering & structured prompts
- JSON-style prompts increase precision for image and asset generation; building reusable prompt templates is high ROI.
- Tools (e.g., Prompt Cowboy) help convert weak prompts into production-ready prompts.

Major product / tool demos & features

Cloud / Cowork / Cloud Code (Cloud Coda)
- Agent mode can act directly on files, folders, repos and browser context on your machine (background tasks).
- Use cases demoed:
  - Organize a messy Downloads folder into categories and move files.
  - OCR receipts → generate Excel expense reports.
  - Load CSV customer data → automatic segmentation, charts and insights.
  - Create short social clips from podcast recordings (auto-transcribe → clip generation).
  - Browser agent: scrape job listings and export to a spreadsheet.
- Cloud Code (CLI / terminal / VS Code) can scaffold, inspect and assemble real apps — they used it to compose a production-ready training platform.
Real mini-product built live (training platform)
- Suggested stack assembled by Cloud Code: Astro (SEO-friendly frontend), Sanity (headless CMS), Memberstack (membership), Bunny.net (CDN/hosting), Stripe (payments).
- Demonstrated full flow: idea → PRD → prototype → subscription payments.
Spec-driven development (Speckit, Tracer AI, etc.)
- Use spec-driven tools to constrain generative code: write a product/UX constitution, auto-generate specs, user stories and task breakdowns; iterate with engineers.
- Helps reduce hallucinations and maintain development rails when LLMs produce code.
AI builders & prototyping
- Lovable highlighted as a rapid prototyping, chat-driven builder — presented as the “winner” of their AI builders battle.
- Example: a product compass (OKRs, discoveries, decisions, tasks, roadmaps) created as a high-fidelity prototype via an AI builder.
- Replit recommended as a middle ground for broader language support if Lovable is too opinionated.
Synthetic user for interview practice (GPT-based)
- Configured GPT-based agent that:
  - Generates a realistic persona.
  - Runs voice-mode practice interviews.
  - Produces interview-quality assessments (leading questions, behavior vs opinion, frameworks used) and improvement suggestions.
- Recommended as rehearsal before real user research.
Image generation & workflow
- Tools used: Gemini (personalization), Midjourney, “Jason Banana” / “Nano banana” (app that extracts JSON-like photo descriptors) and a GPT image editing pipeline.
- Workflow: reverse-engineer a photo into structured JSON (appearance, pose, lighting, accessories) → feed JSON into a generative model to recreate/modify images consistently across marketing assets.
- Legal/safety note: models may refuse to create images of public figures (policy limits).
Automation & UX audits with n8n + RAG
- Example workflow: when a design screenshot is added → n8n triggers a retrieval + generation agent that uses a vectorized Baymard guidelines corpus → outputs a prioritized UX audit (score, issues, prioritized fixes).
- Practical for auto-generating backlog items and feeding RAG context into subsequent AI tasks.
Meetings, transcripts & knowledge workflow
- Tools covered: Grain / Granola (meeting recording & summaries — English-first), Fireflies (transcription; noted volume issues), Tana (task/knowledge graph), Sketchpal, Whisperflow (dictation/transcription).
- Scheduling/flow tools: Reclaim / Skpal (automated calendar scheduling, protecting focus time, auto-reschedule).
- Quick actions: Raycast / Alfred for keyboard-driven utilities and clipboard tools.
Small utilities & prompt helpers
- Examples: WhisperFlow (voice-to-text prompts), Prompt Cowboy (prompt refinement), Goblin Tools (task breakdowns for neurodivergent workflows: Magic ToDo, Boss), Raycast, OCR tools.
- Recommendation: use utilities to streamline prompt entry, note-taking and quick editing.

Reviews, comparisons & recommendations

Choose LLMs by use-case (text generation, code, image analysis, multimodal). Test specific prompts/models side-by-side with LLM Arena.
For PRDs and strategic product docs: Cloud Sonnet gave richer output in the demo; Gemini produced shorter but workable outputs.
For prototype-first development: Lovable is strong for rapid prototyping; Replit is more flexible for multi-language coding; Cloud Code is best for production-ready integration and assembling a tech stack.
For spec-driven engineering: Speckit / spec tools (and Tracer AI) help keep generated code aligned with product intent.
For automation: n8n and agent + vectorized corpora are powerful for report generation and audit automation.

Guides, tutorials & resources mentioned

Prior webinars and workshops:
- AI builders battle (deep review/comparison of AI builder tools).
- Assistant configuration workshop (building an assistant/digital twin with memory/system prompt).
- Cloud Code masterclass (promised as part of the cohort).
They offered to share prompts/configs (synthetic user prompt, JSON-photo prompts, Lovable recipes) via email or LinkedIn.
Tools / sites to explore:
- LLM Arena (text/image leaderboards and head-to-head comparisons).
- Vector DBs + RAG (Chroma mentioned) for domain knowledge augmentation.
Offers: course buyers (before midnight at the time of the webinar) would receive bonuses (hat + access to “Jason Banana” reverse-engineer app and prompts).

Course offering — AI Product Heroes (cohort 2)

Format: 5-week cohort
Price (pre‑sale): 1,990 PLN; starts April 20 (presale limited)
Curriculum highlights:
1. Week 1: Superhero product sprint (idea → fast validation)
2. Week 2: Discovery & research (user interviews, data collection; building agents for research)
3. Week 3: Prototyping & Cloud Code masterclass (build prototypes and production integrations)
4. Week 4: Launch, testing, analytics, A/B tests, production learnings (Cloud Code and practical analytics)
5. Week 5: Strategy, roadmap, scaling product in production
Extras: community, team options, company discounts, live feedback sessions for teams

Practical cautions & operational notes

Cost control: token and cloud costs can grow — track spending and prefer cheaper models for non-critical tasks.
Data security & compliance: avoid uploading sensitive company data into third-party LLMs unless contracts/controls are in place; consider local LLM/vector setups for sensitive document search.
Tool churn: many tools evolve fast; pick tools that fit your stack and re-evaluate periodically.
Human roles: roles may blur (builders combining design/dev/PM skills), but experienced engineers remain essential for complex systems.

Practical note: re-check LLM rankings and tool choices often — the landscape changes quickly.

Examples & demos (concise list)

PRD generation: Cloud Sonnet 4.5 vs Gemini 3 Pro (Cloud Sonnet produced a richer PRD).
Cloud Cowork / Coda demos: Downloads cleanup; receipts → Excel; customer CSV segmentation; podcast clip generation; job scraper.
Cloud Code product: full training platform (Astro + Sanity + Memberstack + Bunny.net + Stripe).
AI builder: Lovable prototype — product compass (OKRs, discoveries, roadmaps).
Spec-driven dev: Speckit demo generating specs → user stories → tasks; Tracer AI / Shotgun mentioned.
Synthetic user: GPT-based voice-mode interview practice & assessment.
Image pipeline: Gemini personalization → JSON-photo reverse engineering (Jason Banana) → image generation and animation.
Automation: n8n flow + vectorized Baymard guidelines → automated UX audit report.
Productivity combo: Whisperflow (dictation) + Reclaim/Skpal (calendar auto-scheduling) + Tana (notes/tasks).

Where to get demos, prompts or follow-up

Prompts, assistant configs and the Jason Banana app were offered to course buyers and by request via email (aiproductheroes.pl) or LinkedIn.
Prior webinars (AI builders battle, assistant workshop) are available on the AI Product Heroes YouTube channel.

Main speakers / sources

Piotr (Piotrek) — co-host (former CTO/CPO experience; product/engineering demos and product examples).
Wojtek (Wojtek Strzałkowski) — co-host (head of product at GOG; demos on Cloud Code/Cowork, automation, UX/analytics).
Tools / references mentioned: Cloud (Claude / “Cloud Sonnet”), ChatGPT / GPT family, Gemini, Midjourney, Lovable, Replit, Cloud Coda/Cloud Code, Cowork, LLM Arena, Speckit, n8n, Whisperflow, Tana, Granola/Grain, Fireflies, Reclaim / Skpal, Raycast, Alfred, Prompt Cowboy, Goblin Tools, “Jason Banana” (photo→JSON utility).