Summary of "9 AI-навыков, которые должен освоить каждый в 2026 году"

Overall thesis

A forecast‑based practical checklist: nine AI skills to master in 2026 so you stay ahead of most AI users. Recommendations are grounded in recent forecasts and research (subtitles reference UC Berkeley and a research firm). The skills respond to trends such as demand for verifiable AI outputs, multi‑model workflows, multimodal I/O, low‑code/no‑code generation, exploding AI content, AI‑powered fraud, and new checks that require human‑only performance.

Key trends driving these skills

Higher demand for verifiable, source‑referenced AI outputs (reduce hallucinations).
No single “best” model — multi‑model workflows and cross‑checking are becoming standard.
Multimodal input/output (text, audio, image, video) is increasingly important.
Low‑code and no‑code generation tools are ubiquitous and maturing.
Explosion of AI content increases noise; human curation matters more.
Growth of AI‑powered fraud (deepfakes, personalized scams) requires digital awareness.
Some checks and gatekeeping will require human‑only performance; alternate work modes preserve cognitive skills.

The nine AI skills (with techniques, tools, examples)

Context & source management (reduce hallucinations)
- Technique: provide the model with your own documents (PDFs, transcripts); require answers “based only on this source”; add confidence labels (high/medium/low); list uncertainties or unverifiable claims.
- Tools & tips: text expanders for standardized prompt templates; Google’s LM Notebook / “LM laptop” to build a dataset and connect it to Gemini so answers cite sources.
Building an AI council (multi‑model cross‑checking)
- Technique: send the same task to multiple models, compare answers, ask models to critique each other, or have one model synthesize a final answer as a “chairperson.”
- Benefit: more balanced outputs, less risk of missing important points or accepting hallucinations.
Orchestration (connecting and sequencing tools)
- Process: map repetitive tasks into discrete steps, assign the best tool/model to each step, and connect them into a pipeline.
- Example pipeline (video/content production): GPT chat for initial research → LM Notebook for deep source work → human writer for scripts → Gemini for SEO → Nanoban for visuals/covers.
- Emphasis: practical experimentation to discover which models handle which steps best.
Automation & AI agents
- Goal: convert orchestrated steps into automated agents so you don’t repeat prompts manually.
- Platforms cited: Make (MA), n8n, ManyChat (constructors for bots/automation).
- Training: hands‑on courses and YouTube tutorials available for building and deploying agents.
Multimodal fluency (working across text, audio, image, video)
- Input: pick the most efficient input format (e.g., film a room instead of describing it). Gemini reportedly accepts video/audio and can analyze frame‑by‑frame.
- Output: convert results into the most effective medium (audio → short text summary; idea → infographic or podcast).
- Tools: Gemini, Nanoban (visual generation).
“Wipe‑coding” / vibecoding (AI‑driven no‑code / prompt‑based app creation)
- Definition: describe the desired functionality to AI and get working mini‑apps (forms, trackers, bots) without writing traditional code.
- Example platforms: Lovable (accessible, award‑winning), Google AI Studio (dialogue with Gemini to build tools), and more advanced environments like Cursor.
- Advice: start with simple builders, then progress to flexible tools; the core skill is structuring requests effectively.
Curatorial taste / critical evaluation of AI output
- Problem: massive volume of AI content increases noise; human taste and judgment determine quality and differentiation.
- Practice: train visual and editorial acuity through study (design analyses, guided museum visits, critique practice).
- Outcome: ability to select, edit, and elevate AI outputs will be a high‑value skill.
Critical thinking & digital awareness (defense against AI‑enabled fraud)
- Threats: more convincing phishing, voice/video deepfakes, and AI‑written personalized scams with higher engagement rates.
- Defensive habits: question urgency, tone, and context; verify via alternate channels; maintain skepticism and consistent verification routines.
Alternating work modes to preserve cognitive skills
- Trend: employers may require AI‑free checks; overreliance on AI erodes reasoning.
- Practice: alternate between AI‑assisted tasks and fully independent tasks (write without prompts, form arguments manually) to keep reasoning “muscles” active.

Product & platform features called out

Google Gemini (Gemini 3): multimodal (video/audio input); can connect to LM Notebook datasets for source‑referenced answers; used for SEO tasks.
Google LM Notebook / “LM laptop”: assemble personal datasets (articles, audio, transcripts) so chat models answer based only on those docs and provide references.
Text expanders: insert standardized prompt templates quickly.
Nanoban (Nanobanana / variations in subtitles): generate visuals, covers, and infographics.
Make (MA), n8n, ManyChat: platforms for building automations and agents.
Lovable: accessible no‑code/vibecoding tool mentioned as award‑winning.
Google AI Studio: build mini‑apps through dialogue with Gemini.
Cursor and other advanced tools: for deeper no‑code development.
BL VPN (promoted in subtitles): claimed features include discounts, unlimited devices/traffic, up to 10 Gbps speeds, 50+ locations, 30‑day money‑back.

Guides, tutorials and reviews referenced

Detailed guide on using the LM Notebook (the presenter’s video).
Previous video covering text expanders and AI techniques.
Ongoing YouTube series and training programs for automation, agents, and “wipe coding.”
Multi‑month paid trainings (offered by the channel/author) for AI agents and orchestrations.
Recommendation: experiment with free resources before buying orchestration courses — many skills are learnable through practice.

Practical templates & recommended habits

End each AI task with a standard template:

Answer based only on this source. If unsure, say “I don’t know.” For each main statement add confidence: high/medium/low. List what you couldn’t verify.
Build and maintain a personal knowledge base (LM Notebook) and connect it to chat models.
Map complex tasks into steps, test multiple models per step, then automate with agents where appropriate.
Regularly perform tasks without AI to preserve reasoning ability.
Use multimodal inputs when they save time or communicate meaning better (e.g., video for spatial tasks, audio for nuance).

Notes about subtitle accuracy

Several proper names in the subtitles may be mistranscribed (examples: “LM laptop,” “GMI/GN,” “Nanoban,” “Lovable,” “Andriy Karpatyy / Andrew Ying,” “Gther”). The summary preserves subtitle names as they appear but they may correspond to:
- Google’s LM Notebook, Gemini, ManyChat / Runway / Nanobase, Lovelace / Vev / Bubble, Andrew Ng, etc.

Main speakers & sources (as listed in subtitles)

Primary presenter / channel author (first‑person speaker and course creator).
Cited researchers/projects in subtitles: Andriy Karpatyy, Andrew Ying (possibly variant spellings); Andrey Karpatov (credited with the “AI council” concept/service).
Institutions mentioned: University of California, Berkeley; a research firm listed as “Gther” in subtitles.

Optional next outputs available

One‑page printable checklist (prompts, templates, tools organized by skill).
Concrete how‑to mapping of the example orchestration pipeline with step‑by‑step connector recommendations.
Verification and cleanup of ambiguous product names from the transcript (cross‑referencing likely real‑world names).