Notes

If you want to build AI agents in Go, there are a few Agent SDKs and frameworks available in 2026 that make it easier to integrate with LLMs, tools, and multi-agent workflows. Below is a runnable Go example using a modern Agent SDK pattern. I’ll show you a minimal agent that can receive a prompt, call an LLM API, and return a response. Example: Minimal AI Agent in Go package main import ( "context" "fmt" "log" "os" "time" "github.com/ingenimax/agent-sdk-go/agent" "github.com/ingenimax/agent-sdk-go/llm" ) func main() { // Load API key from environment variable apiKey := os.Getenv("OPENAI_API_KEY") if apiKey == "" { log.Fatal("Please set the OPENAI_API_KEY environment variable") } // Create a new LLM client (example: OpenAI GPT model) llmClient, err := llm.NewOpenAI(apiKey, llm.WithModel("gpt-4o-mini")) if err != nil { log.Fatalf("Failed to create LLM client: %v", err) } // Create an agent with a simple reasoning function myAgent := agent.New("helper-agent", agent.WithLLM(llmClient), agent.WithSystemPrompt("You are a helpful assistant that answers concisely."), ) // Context with timeout for safety ctx, cancel := context.WithTimeout(context.Background(), 15*time.Second) defer cancel() // Run the agent with a user query response, err := myAgent.Run(ctx, "Explain the difference between concurrency and parallelism in Go.") if err != nil { log.Fatalf("Agent error: %v", err) } fmt.Println("Agent Response:") fmt.Println(response) } How This Works agent-sdk-go – A Go framework for building AI agents with modular tools, memory, and reasoning loops. LLM Client – Connects to an LLM provider (OpenAI in this example). Agent – Wraps the LLM with a system prompt and optional tools. Run – Executes the reasoning loop and returns the answer. Installation go get github.com/Ingenimax/agent-sdk-go Features of Modern Go Agent SDKs Tool Integration – Agents can call APIs, databases, or custom functions. Multi-Agent Workflows – Agents can hand off tasks to other agents. Memory – Store and recall conversation history. Streaming – Get partial responses in real time. Concurrency – Use Go’s goroutines for parallel tool calls.

Source paper: Learning-based Artificial Intelligence Artwork: Methodology Taxonomy and Quality Evaluation, ACM Computing Surveys (2024). Origin The rubric was built from art vocabulary and traditional principles of painting analysis, then validated through a user study to confirm the weightings felt reasonable across different artwork types. The goal was a consistent, repeatable way to evaluate AI-generated artworks across different styles. The Six Dimensions Beauty (50%) — The dominant criterion. Encompasses overall compositional harmony: balance, proportion, the arrangement of visual elements, and the pleasing relationship between subjects. An image can score well on every other dimension and still feel wrong if the composition is off. This is where Gestalt principles are most directly applied — does the whole hang together? ...

Gestalt principles are a set of rules from psychology describing how the human mind naturally organizes visual information into meaningful wholes rather than perceiving a collection of separate parts. The name comes from the German word Gestalt, meaning “shape” or “form,” and the core idea is captured in the phrase: the whole is greater than the sum of its parts. The Main Principles Proximity — elements that are close together are perceived as belonging to the same group. ...

A rubric is a scoring guide or evaluation framework that breaks down quality into specific, defined criteria. It provides a structured way to assess something by listing what to look for and, often, how much weight each criterion carries — rather than relying on a vague overall impression. In everyday use, rubrics appear most commonly in education (e.g., grading rubrics for essays) and in evaluation contexts where consistent, transparent judgment is needed. ...

A commitment gate is a verification checkpoint between an agent producing a candidate output and that output being “locked in” — emitted as a final answer, written to disk, or used to call an irreversible tool. Instead of running skills along a fixed script and fusing results at the end (where errors propagate silently into late fusion), a harness with commitment gates pauses at each commit point, runs relative checks, and either lets the result through or triggers a targeted recovery loop. ...

Defense-in-depth is a security strategy that uses multiple layers of defenses so that if one layer fails, others still protect the system. The idea comes from military fortification — castles didn’t rely on a single wall; they had moats, outer walls, inner walls, keeps, and so on. Breaching one didn’t mean the attacker won. In Information Security This translates to combining different controls rather than depending on any single one. A typical stack might include: ...

Open-weight models Open-weight models are AI models where the trained parameters (weights) are made publicly available so others can download, run, and often fine-tune them locally. The core idea (plain terms) When an AI model is trained, it learns billions (or trillions) of numbers—these are its weights. An open-weight model gives you access to those numbers. That means you can: Run the model on your own machine or server Fine-tune it with your own data Inspect or modify how it behaves (to some extent) How this differs from other terms 1. Open-weight vs Closed models Open-weight: You get the weights Example: LLaMA 2, Mistral 7B Closed model: You only get API access Example: GPT-4 With closed models, you use them—but you don’t own or inspect them. ...

Cross-Entropy in AI Cross-entropy is a concept from Information Theory that is widely used in machine learning to measure how different two probability distributions are. In AI, it is most commonly used as a loss function to evaluate how well a model’s predicted probabilities match the actual (true) labels. 🧠 Intuition Think of cross-entropy as a way to answer: “How surprised is the model when it sees the true answer?” ...

Fine-tuning techniques can be grouped along a few axes: what you optimize (full weights vs. small additions), what signal you train on (labels, instructions, preferences, rewards), and how the data is generated (human, synthetic, AI-judged). Full Fine-Tuning (FFT) Update every parameter in the model on a target dataset. Highest capacity, but expensive in memory and prone to catastrophic forgetting. Mostly reserved for smaller models or when you have lots of high-quality data and compute. ...

Unsloth Studio supports several dataset formats depending on your fine-tuning goal. Files can be uploaded directly as JSONL, JSON, CSV, Parquet, PDF, or DOCX. Format Overview 1. Raw Text (Continued Pretraining) Used to inject domain knowledge without any structure. The model learns from continuous prose. T h e m i t o c h o n d r i a i s t h e p o w e r h o u s e o f t h e c e l l . A T P s y n t h e s i s o c c u r s v i a o x i d a t i v e p h o s p h o r y l a t i o n . . . Best for: books, articles, documentation dumps, codebases. ...

Mixture of Experts is an architecture pattern in machine learning where a model is divided into many specialized sub-networks (“experts”), with a routing mechanism that selectively activates only a subset of them for any given input. Core Idea Instead of passing every input through all parameters of a model, MoE routes each token (or input) to only a few relevant experts. This decouples total parameter count from compute per forward pass — you can have a massive model that’s still fast and efficient to run. ...

Chain of Thought is a prompting technique where an AI model is guided — or learns — to reason through a problem step by step before arriving at a final answer, rather than jumping straight to the conclusion. The core idea is that breaking down complex reasoning into intermediate steps leads to more accurate and reliable outputs, much like how a person might work through a math problem by showing their work. ...

Visual chain-of-thought (CoT) reasoning is the extension of standard chain-of-thought prompting to multimodal settings — where the model reasons step-by-step over both visual and textual information together. Core Idea In standard CoT, a language model breaks a problem into intermediate reasoning steps before arriving at a final answer. Visual CoT does the same, but the reasoning chain involves interpreting, referencing, and drawing inferences from images, diagrams, charts, or visual scenes alongside text. ...

Agent harness engineering is the practice of building the scaffolding, infrastructure, and tooling that surrounds an AI agent — everything that isn’t the model itself but makes the model useful, reliable, and safe in production. The model is the engine; the harness is the chassis, controls, safety systems, and instrumentation around it. Core Components Execution Environment The runtime that manages how the agent runs — process lifecycle, sandboxing, resource limits, timeouts, and isolation between agent instances. ...

Diffusion models are a class of generative AI models that learn to create data (images, audio, video, etc.) by learning to reverse a gradual noising process. The Core Idea The training process has two phases: Forward process (destroying data): Take a real image and progressively add Gaussian noise over many steps (say, 1000 steps) until it becomes pure random noise. This is fixed and requires no learning. Reverse process (learning to reconstruct): Train a neural network (usually a U-Net or Transformer) to predict and remove the noise at each step — essentially learning to “denoise.” At inference time, you start from pure noise and repeatedly apply this denoising to generate a new sample. ...

System Prompt A system prompt is a set of instructions given to an AI model before any conversation begins. It’s written by the developer or application builder (not the end user) and sets the AI’s behavior, persona, tone, rules, and constraints for the entire session. The user typically doesn’t see it. Think of it like a job briefing you give an employee before they meet a customer — it shapes how they behave without the customer knowing the specifics. ...

Elastic Looped Transformers (ELT) are a recent architectural innovation that rethinks how transformer layers are applied — moving from a fixed, one-pass stack to a dynamic, recurrent execution model. The Standard Transformer Problem In a conventional transformer, you have a fixed stack of N layers (say, 96 layers in a large model). Every input always passes through all 96 layers exactly once. This is rigid in two ways: Every input gets the same compute budget, regardless of whether it’s a trivial question or a complex reasoning problem. Depth is fixed at architecture design time — you can’t adapt it post-training without retraining. The Core Idea: Looping ELT takes a shallower set of transformer layers and runs them multiple times in a loop — hence “looped.” Instead of having 96 distinct layers, you might have 12 layers that execute 8 times, with hidden states passed from one loop iteration to the next. ...

Tempo is a framework designed to solve one of the hardest problems in multimodal AI: understanding very long videos without blowing up your context window or compute budget. The Core Problem It Solves Videos are brutally expensive for transformers. A 1-hour video at even 1 frame per second gives you 3,600 frames. At typical vision encoding resolutions, each frame becomes hundreds of tokens — potentially millions of tokens total, far beyond what any current model can process in a single context window. And even if it could, the attention computation would be prohibitively expensive (attention is O(n²) in sequence length). ...

Memory-augmented architectures are neural network designs that give a model access to an explicit, addressable memory store that exists separately from the model’s weights. Standard transformers have two forms of “memory” baked in — the weights (long-term parametric knowledge frozen at training time) and the context window (short-term working memory limited to the current input). Memory-augmented architectures add a third, dynamic layer in between. Why It Matters Standard transformers are stateless between calls. Everything the model “knows” about your session either lives in the weights or gets re-fed through the context window every time. This creates hard limits: context windows are expensive to fill, they get stale, and they can’t persist knowledge across sessions without explicit engineering workarounds. ...

These terms are fundamental to understanding how LLMs work under the hood. Forward Pass A forward pass is a single run of data through a neural network, from input to output. In an LLM, it means feeding a sequence of tokens into the model and computing a probability distribution over the vocabulary for the next token (or all token positions simultaneously). Here’s what actually happens during a forward pass in a transformer: ...