Artificial Intelligence
AI trends, techniques, and real-world implementations
How 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.
AI 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.
AI Ethics for Engineering Teams, Not Philosophers
The concrete ethical decisions engineers face when building AI: logging conversations, user consent for training data, disclosing AI errors, and what to do when AI makes consequential decisions.
How 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.
Accelerating Software Testing with AI Without Sacrificing Test Quality
An engineering reality check on AI in software testing, examining synthetic test case generation, mutation testing, and automated regression validation.
How 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.
AI 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.
Deploying High Impact AI for Small Businesses Without an In-House Technical Team
A pragmatic executive roadmap for small business owners to deploy high-ROI generative AI tools, automate customer support, and streamline daily operations without technical overhead.
AI in Customer Support: Architecture, RAG Pipelines, and Escalation Systems for Engineering Teams
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.
How 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.
Operationalizing Responsible AI Across Enterprise Product Teams
A practical engineering guide to operationalizing responsible AI across enterprise product teams, covering bias mitigation, EU AI Act compliance, and safety guardrails.
We 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.
The 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.
Developer Productivity Benchmarks and Time Tracking Data for Engineering Teams
Explore empirical developer productivity benchmarks from real time-tracking data. Discover how high-performing engineering teams allocate deep work and code time.
30-50 Tasks Per Week: How a 4-Person AI Team Actually Structures Work
How we manage 30-50 tasks per week across phases and modules in Zlyqor, what AI task suggestions actually contributed, and what our task taxonomy looks like.
Toolformer: 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.
Lost 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.
Constitutional 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.
Mixture 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.
OpenAI 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.
Self-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.
DPO: 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.
Runway 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.
Speculative 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.
