ContentsThe Physics: Energy, Probability, and TemperatureThe LLM Equivalency: Vocabulary as StatesSoftmax is Boltzmann in DisguiseHow Temperature Controls the “Heat” of TextA Small but Important Detail: T = 0 Isn’t Really ZeroWatching It Happen: Real Logits from GPT-2Closing the loop The Physics: Energy, Probability, and Temperature that can exist in several different states. Each state has …

How to Decode the Temperature Parameter in LLMs Read More »

  Contents# Introduction# The Architecture and Codebase# The Strategy and Agentic Workflows# The Ecosystem Overview# Where to Go From Here # Introduction  The narrative around generative AI is shifting in 2026. While massive frontier models keep grabbing headlines, the reality of enterprise AI deployment looks very different. Cost constraints, latency limits, and strict data privacy requirements have pushed engineering teams away …

5 Must-Read Resources for Mastering Small Language Models Read More »

Welcome back! our journey of understanding backpropagation in detail. Let’s briefly recall what we covered in Part 1 so far. ContentsA Quick RecapDo We Really Have to Repeat This?The Chain Rule to the RescueFollowing the Dependency PathPartial Derivatives vs. the Chain RuleApplying the Chain Rule to Our Neural NetworkStep 1: ∂L/∂y^partial L/partialhat{y}Step 2: ∂y^/∂a1partialhat{y}/partial a_1Step …

Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way Read More »

  Contents# Introduction# Step 1: Deciding What Your Agent Actually Needs to Do (and What It Shouldn’t)# Step 2: Picking Your Model and Framework# Step 3: Setting Up the Project# Step 4: Building the Core Agent Loop# Step 5: Giving It Memory and a Second Tool# Step 6: Adding Guardrails Before You Trust It# Step 7: Deploying It Somewhere Real# Wrapping Up # Introduction  Most …

7 Steps to Building and Deploying Your First Autonomous Agent Read More »

Developers and customers building production AI agents need higher token efficiency, lower latency, and more reliable performance. Our Flash series of models is built to meet the sweet spot of efficiency and quality to enable scaling agentic workflows. Building on Gemini 3.5 Flash, we’re introducing new Gemini models: 3.6 Flash: Our workhorse model that delivers …

3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber Read More »

model predicts the missing column of any table, zero-shot, the way a language model completes text. On the main community benchmark, every single-model entry above the best tuned gradient-boosted tree is now one of these. This post explains what they are, verifies the strongest one with unrestricted open weights on my own hardware, and maps …

Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet Read More »