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).