Artificial Intelligence
AI trends, techniques, and real-world implementations
9 min readUsing AI for Customer Support: What Actually Works in 2026
AI customer support has matured significantly. Here is which tiers to automate, which to assist, and which to keep fully human -- with real implementation steps.
10 min readHow to Measure Whether AI Tools Are Actually Making Your Team More Productive
Feeling more productive is not data. This guide covers real measurement approaches -- output metrics, time tracking comparisons, quality metrics -- and how to set up a 30-day framework.
9 min readAI Image Generation for Non-Designers in 2026
The leading models, prompt techniques, commercial licensing, and honest limitations of AI image generation -- a practical guide for software teams and content creators.
8 min readHow to Add AI to Your Startup Without Overbuilding
A practical guide for startup founders: use existing AI APIs, identify your highest-value use case, and ship the simplest version first. Avoid the most common AI mistakes.
9 min readAI Writing Assistants Compared for Professional Use in 2026
Claude, ChatGPT, Gemini, Jasper, and Copy.ai -- a direct comparison for professional writing. Which wins on long-form, which on marketing copy, and when specialized tools beat general models.
10 min readHow AI Is Used in Recruiting and What Concerns It Raises
AI recruiting tools reduce time-to-contact and standardize job descriptions, but AI screening requires careful oversight. A complete guide to current applications and real risks.
9 min readHow Product Managers Work with AI Features
AI features require a different PM playbook. Define success criteria before building, plan your evaluation methodology, and set up feedback loops from day one.
9 min readWe Replaced 6 SaaS Tools With One: What 6 Months of Real Data Shows
We replaced Slack, Google Meet, TimeCamp, and three other tools with Zlyqor. Here is what the real numbers look like after 6 months.
8 min readThe Hidden Cost of Tool Switching: What We Measured With Our 4-Person Team
Gloria Mark's 23-minute refocus research applied to our real team. The formula for calculating what tool switching actually costs, with our own numbers.
9 min readToolformer: Teaching LLMs to Use Tools Without Human Annotations
Toolformer learns to call external APIs - calculators, search engines, calendars - by self-supervising on when API calls improve prediction, requiring no human-labeled examples of tool use.
5 min readLost in the Middle: Why LLMs Struggle With Long Contexts
Liu et al. 2023 showed that LLM performance on multi-document QA follows a U-shaped curve - models best recall information at the start and end of context, with severe degradation in the middle.
4 min readConstitutional AI: How Anthropic Trains Claude to Be Helpful and Harmless
Constitutional AI replaces human feedback at scale with AI-generated critiques guided by a set of principles, reducing reliance on human annotators while producing safer and more helpful models.
9 min readMixture of Experts: How Mistral, DeepSeek, and Grok Use Sparse Activation
MoE models route each token to only a subset of specialized expert networks, achieving the quality of a large dense model at the compute cost of a much smaller one.
7 min readOpenAI o3 and o3-mini: The Next Generation of Reasoning Models
OpenAI o3 scores 87.5% on ARC-AGI and 96.7% on AIME 2024. Here's what compute-optimal inference scaling means and how to use o3-mini cost-effectively.
9 min readSelf-RAG: Teaching LLMs to Decide When to Retrieve
Self-RAG introduces reflection tokens that let the model decide whether retrieval is needed and evaluate passage relevance and citation support, outperforming standard RAG on factuality benchmarks.
9 min readDPO: The RLHF Alternative That Trains LLMs Without a Reward Model
Direct Preference Optimization eliminates the separate reward model and PPO loop from RLHF, deriving an equivalent alignment objective directly from preference data with a simple classification loss.
7 min readRunway Gen-3 Alpha: Professional Video Generation for AI Filmmakers
Runway Gen-3 Alpha raises the bar for AI video with dramatically improved temporal consistency, a motion brush tool for selective animation, and Act One for transferring facial performances onto generated characters.
9 min readSpeculative Decoding: How to Get 3x LLM Speed With a Smaller Draft Model
Speculative decoding uses a small fast model to draft multiple tokens and a large model to verify them in parallel, achieving 1.5-3x speedups without changing output distribution.
8 min readSora: OpenAI's Text-to-Video Model and What It Means for Creators
Sora generates 1080p video up to 60 seconds from text prompts using a diffusion transformer on spacetime patches - and its consistency across frames marks a genuine leap over prior text-to-video approaches.
7 min readDeepSeek R1: The Open-Source Reasoning Model That Beat o1
DeepSeek R1 achieves 79.8% on AIME 2024 (vs o1-mini at 63.6%) using pure reinforcement learning without supervised fine-tuning, and it's MIT licensed.
9 min readMamba: The State Space Model That Challenges the Transformer
Mamba introduces selective state space models with linear scaling in sequence length and constant-time inference, offering a genuine alternative to the Transformer for long-sequence tasks.
9 min readQLoRA: Fine-Tune a 65B LLM on a Single 48GB GPU
QLoRA combines 4-bit quantization with LoRA to make fine-tuning 65B parameter models accessible on consumer hardware, introducing NF4 quantization and paged optimizers.
9 min readLoRA Explained: How 0.1% of Parameters Can Match Full Fine-Tuning
LoRA fine-tunes LLMs by training tiny low-rank decomposition matrices instead of updating billions of weights, cutting VRAM requirements from hundreds of GB to a few GB.
9 min readAttention Is All You Need: What the 2017 Paper Actually Says and Why It Still Matters
The Transformer paper by Vaswani et al. replaced recurrent networks with self-attention and became the foundation of every modern LLM. Here is what the original paper actually says.