LLMs: guides & tutorials

Gemini 3.1 Flash-Lite (March 2026) is Google’s preview Gemini 3–series API model positioned for high-volume, latency- and cost-sensitive workloads; Workspace AI updates in the same window push Gemini deeper into Docs, Sheets, Slides, and Drive for Google AI Ultra and Pro subscribers, with English-first rollout and region limits on some Drive features.

TL;DR: According to Google’s March 2026 announcement, Gemini 3.1 Flash-Lite is the Gemini 3–series option Google positions as fastest and most cost-efficient on the API for developers—listed at $0.25 / 1M input tokens and $1.50 / 1M output tokens, in preview through Google AI Studio and Vertex AI. On a separate track, Gemini in Docs, Sheets, Slides, and Drive is gaining beta capabilities for Google AI Ultra and Pro subscribers: English for Docs, Sheets, and Slides globally; Drive-related features first in the U.S., with more languages and product polish planned.

If you run high-volume LLM backends or spend your day in Workspace, both tracks matter: lower per-token cost for apps and APIs, and deeper Gemini inside the files teams already share. For surrounding news, see Top 10 AI and Tech Stories This Week (March 17–24, 2026).

GPT-5.4 native computer use (OpenAI, 2026) means the model drives GUIs the way a user would: it reads screen state (e.g. screenshots), issues clicks and keyboard input, and loops until a task completes—paired with a very large API context for long traces. It is not a drop-in replacement for structured web extraction at scale; pair it with scrapers when pages are stable data sources.

Vendor-reported highlights​

OpenAI reports 75% on the OSWorld-Verified benchmark versus a published 72.4% human baseline on that suite, and positions GPT-5.4 as one stack for reasoning, coding, and agentic flows across ChatGPT, the API, and Codex.

At a glance​

• What it is (vendor framing): GUI-grounded control: perceive screen state, propose actions, loop until done—not a universal substitute for structured data pipelines.
• Headline benchmark (vendor-reported): 75% OSWorld-Verified vs. 72.4% human baseline on the same benchmark; your internal apps still need their own validation.
• API scale (vendor-reported): Up to 1M tokens in the API (922K input / 128K output, per OpenAI)—useful for traces, not a reason to skip log compression.
• Where it usually loses to scraping: High-volume, repeatable HTML/JSON extraction; pair computer use with Apify Actors when the page is a stable data source.

Below: what “native computer use” implies operationally, how benchmarks map to production risk, a straight comparison to scraping, a loop teams actually ship, builder checklists, and compliance guardrails. For the week’s news context, see Top 10 AI and tech stories this week (March 17–24, 2026).

Model Context Protocol (MCP) is an open standard for how an AI host (IDE, desktop assistant, or similar) discovers and calls tools in a separate MCP server over transports such as stdio (local) or HTTP (remote). It is a JSON-RPC bridge that standardizes the boundary where models meet APIs, files, and automation. It does not replace your databases or scrapers. The payoff is one reusable integration surface across MCP-capable hosts, provided you still own auth, timeouts, and least privilege.

Below we split what the spec actually defines from what each vendor ships, sketch how the ecosystem shows up in scraping and data workflows, and outline adoption so a chat session does not become root access. For headline news context, see Top 10 AI and tech stories this week (March 17–24, 2026).

The n8n + Dify + Ollama stack is a common 2026 self-hosted pattern for teams that want open-source control over automation, LLM apps, and where models run: n8n runs event-driven workflows across SaaS and APIs; Dify ships LLM apps, agents, and knowledge bases; Ollama exposes a local OpenAI-compatible inference API on your hardware. None fully replaces the others, but teams often blur which layer owns which job.

This article maps each tool’s role, where capabilities overlap, how they compose into one architecture versus compete for the same budget and headcount, and a practical deployment roadmap you can adapt from a laptop toward production.

For how agent runtimes, MCP, and infra headlines affect wiring choices like these, see Top 10 AI and tech stories this week (March 17–24, 2026).

This weekly AI and tech briefing (March 17–24, 2026) is a curated index of ten major stories: each item states what happened, why it matters for builders, and links to a full on-site analysis with sources and tradeoffs.

This week in one sentence: the loudest headlines mattered less than where teams are settling—agent runtimes, tool protocols like MCP, and infrastructure economics—and that keeps narrowing the gap between demo and production.

Below are ten short takes you can skim in a few minutes. Each one links to a full on-site deep dive with sources, caveats, and how to think about tradeoffs. When something matches your roadmap, jump to that piece.

If you have spent time on classic scraping, you are used to mapping .css selectors or XPath until every field lands in JSON. RAG and agent workflows push a different need: lots of clean text in Markdown, ready for chunking and embeddings, without hand-maintaining a schema for every page layout.

Firecrawl is built around that workflow. You send a /scrape request; it runs headless Chromium, gets past many WAFs, strips the obvious chrome (<nav>, <footer>, and similar), and hands back Markdown instead of making you wire proxies and parsers yourself first.

Below we walk through how it is put together, how it plugs into tools like MCP, and where it starts to hurt when you push volume or need surgical control.

Feeding live web pages into an LLM or a RAG index sounds simple until you look at the token math. Raw HTML for a typical docs or product page can blow past 100,000 tokens once you count inline CSS, SVG, trackers, and nested <div>s—while the sentences you actually care about might sit closer to 3,000.

Shipping that HTML straight into inference burns latency and context budget, and it tends to bury the important lines—the familiar “lost in the middle” problem.

This guide compares the old playbook (sanitize HTML yourself) with API-first Markdown extraction aimed at chunking and embeddings, without changing the underlying tradeoffs.

Most production AI systems still depend on large, diverse corpora. For many teams, web scraping—automated, structured extraction from public pages and feeds—is the primary way to collect training and evaluation data at scale, especially when no tidy vendor dataset exists. The hard part is not “download HTML”; it is quality, legal alignment, and repeatable pipelines.

This guide covers types of training data, how teams collect it, Apify Store Actors that map to common needs, and quality controls before you label, embed, or train.