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.

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?” ...

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: ...

Speculative decoding is a clever inference optimization technique that exploits a fundamental asymmetry in how LLMs work: verifying a token is much cheaper than generating one. The Basic Setup You run two models simultaneously — a small, fast “draft” model and your large “target” model. The draft model generates several tokens ahead in a single pass (typically 4–8 tokens). The large model then verifies all of those candidate tokens in parallel in one forward pass. If the draft tokens match what the large model would have produced, you accept them all at once. If a token diverges, you reject it (and everything after it) and fall back to the large model’s output for that position. ...

Model weights are the learned numbers inside the neural network. During training, the model adjusts billions of numeric parameters so that, given some input text, it becomes better at predicting the next token. Those parameters are the weights. Short Intuition A useful way to think about it: The model architecture is the blueprint. The weights are the filled-in values that make the blueprint useful. Without weights, the model is just an empty structure. What Weights Do Weights control how information flows through the network. ...

GGUF (GPT-Generated Unified Format) is a binary file format for storing and distributing large language models, designed specifically for efficient local inference. Background Introduced by the llama.cpp project in 2023 as a replacement for the older GGML format. The name reflects its origins but it’s now used broadly across many model families beyond GPT. Key Characteristics Self-contained — A single .gguf file bundles everything needed to run a model: weights, tokenizer vocabulary, metadata, and architecture config. No separate config files needed. ...

Prompt bias is a type of AI bias that comes from how a question or instruction is written, not just from the model itself. In simple terms: The wording, framing, or assumptions in a prompt can push an AI toward a particular answer—even if that answer isn’t neutral or fully accurate. What Prompt Bias Looks Like Here’s a quick comparison: Neutral prompt: “What are the effects of remote work on productivity?” ...

What primacy bias is Primacy bias is the tendency of an AI model to give disproportionate weight to items that appear earlier in a list or prompt. When a model is asked to choose from many options, options shown first can become over-represented in the final answer even when later options are equally or more appropriate. In practical terms, this means that a selector prompt like: style-a style-b style-c … can systematically prefer style-a more often than expected if the candidates are always presented in the same order. ...

Slack MCP Ideas: Using Slack MCP monitor for automation opportunities within the org. Using Slack MCP identify duplicated efforts in the org.

ELO scoring for AI models works the same way it does in chess — it’s a method for ranking competitors based on head-to-head outcomes, where your rating rises or falls depending on whether you beat or lose to opponents of known strength. How it works The core idea: Every model starts with a baseline rating. When two models are compared, the system predicts the expected outcome based on the rating gap. If the actual result matches the prediction, ratings barely move. If an underdog wins, ratings shift dramatically. ...

Distillation in AI (also called knowledge distillation) is a model compression technique where a smaller “student” model is trained to mimic the behavior of a larger, more capable “teacher” model. How it works Instead of training the student on hard labels (e.g., “this image is a cat”), the student learns from the teacher’s soft outputs — the probability distribution the teacher assigns across all classes. These soft outputs carry richer information. For example, knowing a model thinks an image is 70% cat, 20% leopard, and 10% tiger tells the student more about the underlying structure than just “cat.” ...

Paper: Training-Free Group Relative Policy Optimization By: Youtu-Agent Team Publication date: October 9, 2025 The Problem Fine-tuning LLMs with reinforcement learning (RL) to improve agent performance in specialized domains is expensive, data-hungry, prone to overfitting, and kills cross-domain generalization. Most RL approaches are limited to sub-32B models due to compute constraints. The Core Idea Instead of updating model parameters (gradient-based RL), Training-Free GRPO updates model context — building an evolving library of experiential knowledge that gets injected into the prompt. The model weights stay frozen throughout. ...

Attention in AI Attention is a mechanism that allows a model to focus on the most relevant parts of its input when producing an output — much like how humans pay more attention to certain words or objects in a scene than others. The Core Idea Instead of treating all parts of the input equally, attention assigns weights to different elements, so the model can dynamically decide what’s important for each step of its task. ...

The Transformer architecture, introduced in the 2017 paper “Attention Is All You Need” by Vaswani et al., revolutionized AI by replacing recurrent networks with a purely attention-based design. Here’s a breakdown of how it works: Core Idea: Self-Attention Instead of processing sequences step-by-step (like RNNs), Transformers process all tokens in parallel and learn relationships between every pair of tokens simultaneously. This is done via self-attention. For each token, three vectors are computed: ...

RNN stands for Recurrent Neural Network — a type of neural network designed to work with sequential data. Unlike standard feedforward networks, RNNs have a “memory” mechanism: they pass information from one step to the next, making them well-suited for tasks where order and context matter, like text, speech, or time-series data. The key idea is that at each step, the network takes both the current input and a hidden state from the previous step, producing an output and an updated hidden state. ...