(01)
Internal AI assistants — building them right vs the usual mess
Every company in 2026 is building "our internal ChatGPT." Most end up as expensive abandonware within a year. A few become indispensable productivity tools. The difference is in five engineering decisions made at the start.
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(02)
Choosing between open-source LLMs and API providers in 2026
OpenAI, Anthropic, Google APIs vs self-hosted Llama, Mistral, Qwen. The decision used to be mostly about cost. In 2026 it's about latency, privacy, controllability, compliance, and lock-in. Practical framework for choosing.
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(03)
AI code review — does it catch bugs or just add noise
AI code review tools promise to catch bugs and security issues automatically. In practice, half their comments are noise and the other half miss the real problems. When AI code review pays off and when it just trains engineers to ignore PRs.
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(04)
RAG over corporate docs — what teams underestimate
RAG looks simple in demos: index documents, retrieve chunks, ask LLM. Production RAG over real corporate knowledge is harder than demos suggest. Teams underestimate data quality, chunking strategy, evaluation, and ongoing maintenance.
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(05)
LLM-powered customer support without making it worse than humans
AI customer support is everywhere in 2026, and most of it is worse than the human alternative — slower, evasive, hallucinating, frustrating. A short guide to building LLM support that customers actually prefer over hold music.
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(06)
MCP servers for business: connecting Claude to 1C, amoCRM and Bitrix24
Model Context Protocol lets Claude reach into 1C, amoCRM, or Bitrix24 without copying data into the chat. Here is where it pays off and where it stays a toy.
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(07)
Claude Code writes 4% of all public GitHub commits — what it means for teams
SemiAnalysis says 4% of public GitHub commits ran through Claude Code in March 2026. They project 20% by December. Here is what actually changed in hiring and review.
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(08)
SEO in the AI Overview era — what actually changed
Google AI Overview, Yandex Neuro and Perplexity take clicks away from the classic SERP. What to change in your site and content to stay visible.
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(09)
Hermes 3 8B vs OpenAI: cost and quality on typical workloads
When does it make sense to run your own Hermes 3 8B on an A10 vs paying OpenAI for gpt-4o-mini. Real numbers across three workloads: ticket classification, document summaries, function-calling agents.
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(10)
Hermes 3 as an agent: function calling and tool use on your own server
Hermes 3 from Nous Research is a Llama 3.1 fine-tune tuned for function calling and role-based prompting. What it can do as an agent and why teams pick it over OpenAI when data cannot leave the perimeter.
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(11)
AI video generation tools — 2026 review
Sora 2, Veo 3, Kling 2, Runway Gen-4, Pika, Luma. What each tool actually delivers and how much a typical clip costs.
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(12)
Writing scripts for AI video — what changes
A prompt for AI is not a script. It is a technical brief where every word carries weight. What to write and how to break it down.
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(13)
AI video for product marketing — what works
Which AI video formats actually drive installs and sales — and where AI is best avoided because users notice.
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(14)
AI avatars and UGC ads — ethics and effectiveness
A $5 AI creator instead of a $5,000 real influencer. Technically yes. Effectively — not always. Where the line is.
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(15)
Creative velocity with AI video — A/B tests in a day
Creative tests used to drag for months. With AI a team ships 20 variants in a day. What that changes.
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(16)
Where AI video still fails
The tech moved fast but 30% of generations still go to the trash. Concrete failure cases and workarounds.
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(17)
Localizing video with AI: voice, lip-sync, translation
One English clip becomes 12 localizations with proper articulation in an hour. Stack and pitfalls.
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(18)
Anthropic's MCP — a year in, who actually uses it
Model Context Protocol launched as the "USB-C of AI tools". After a year — solid traction with developer tools, slow elsewhere.
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(19)
AI-generated images for ads — Midjourney, Sora, Flux
A year of generative ads. Patterns that work and the visible AI smell that ruins campaigns.
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(20)
Cold email with AI — what works in 2026
GPT can write a cold email in 5 seconds. The hard part is what happens before the writing — research, personalisation, list quality.
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(21)
AGI hype vs ship in 2026
Two years of "AGI is months away" from labs. What actually shipped: better autocomplete, mediocre agents, expensive APIs.
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(22)
AI coding agents — who is worth using
Cursor, Cline, Aider, Copilot. Year of agents. Honest review of who saves time and who wastes it.
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(23)
SEO after AI Overviews — what changed
Google's AI Overviews ate top-of-funnel queries. Click-through rates on informational searches dropped 30-60%. Where SEO still wins.
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(24)
WebGPU finally lands — what to actually build
WebGPU shipped in Chrome, Edge, Firefox and Safari by late 2025. Now what. Browser-side ML inference is the killer app.
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