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AI for Science: Putting Domain Structure into Learning | MIT 6.S191 Lecture 8

Lecture 8 of the 2026 course uses the scientific-discovery loop to show how simulators, AI emulators, and experiments cooperate instead of reducing science to generic prediction.

Massively Parallel Training: Memory and Communication Set the Boundary | MIT 6.S191 Lecture 9

Lecture 9 of the 2026 course starts with GPU memory pressure and moves through checkpointing, offloading, ZeRO, FSDP, and multiple forms of parallelism.

Generate Music with PyTorch and an LSTM | MIT 6.S191 Lab 1

In the 2026 lab, students cover tensors, autograd, and modules before turning ABC notation into character sequences for LSTM music generation.

From MNIST to Facial Debiasing with a DB-VAE | MIT 6.S191 Lab 2

In the 2026 lab, part 1 classifies MNIST with dense and convolutional networks; Part 2 learns a facial latent distribution with a DB-VAE and changes training sampling.

LoRA Fine-Tuning and LLM-as-a-Judge Evaluation | MIT 6.S191 Lab 3

In the 2026 lab, students build chat templates and generation with LFM2-1.2B, adapt style through LoRA, and combine OpenRouter with Opik for a judge workflow.

Private-Corpus Retrieval Eval: Turning a Traditional Chinese Query Set into a Reproducible Benchmark

The repository has a 20-query Traditional Chinese/English golden dataset, but no document-level qrels, retrieval runs, raw latency data, or executable benchmark script. Reporting Recall@k, MRR, or nDCG as measured results would therefore be dishonest; this article defines the contract needed to run them reproducibly.

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SerpAPI Complete Guide: Multiple Engines, Structured SERPs, and Async Queries

SerpAPI primarily manages search-results-page retrieval and parsing: select an engine, receive a structured SERP, then handle location, pagination, asynchronous polling, and validation in your application.

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Serper Search API Guide: Turn Google Results into Agent-Ready JSON

Serper is a third-party Google SERP API: one POST request returns structured JSON such as organic, knowledgeGraph, and peopleAlsoAsk, but production code still needs optional-field validation, URL checks, retries, and source verification.

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AgentQL Complete Guide: Semantic Web Extraction and Playwright Automation

AgentQL replaces brittle CSS and XPath selectors with queries shaped like the data you want: `query_data` returns structured values, while `query_elements` returns interactive Playwright locators. The public Starter plan lists 50 free API calls per month, but its payment, hard-stop, and remote-browser reset rules still need to be verified in Billing.

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Apify Complete Guide: How Actors, Tasks, Schedules, and Datasets Form a Scraping Platform

Apify is not a single crawler. It packages scraping programs as Actors, saves reusable configurations as Tasks, triggers them with Schedules, and delivers results through Datasets. It fits teams that do not want to operate queues, schedulers, and workers, but Actor fees, compute, proxies, storage, and transfer all draw from the same platform budget.

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Browser Use Complete Guide: The Agent Loop Behind Browser Automation

Browser Use combines browser state, model decisions, and actions such as click, type, and extract into a repeatable loop. The open-source package favors custom tools and execution control; Cloud manages browsers, profiles, proxies, and concurrent work.

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changedetection.io Complete Guide: Selectors, Notifications, and Browser Steps

changedetection.io is a web-change signal layer: narrow the monitored content, suppress noise, and notify downstream systems only when a meaningful change occurs. It is neither a search API nor a crawler replacement.

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Crawl4AI Complete Guide: From Markdown Crawling to Structured Extraction

Crawl4AI handles retrieval after a URL is known: use JsonCssExtractionStrategy for stable DOMs, and switch to LLMExtractionStrategy only when extraction needs semantic judgment or must tolerate irregular layouts.

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Firecrawl Complete Guide: Choosing Scrape, Crawl, Map, and Structured Extraction

Firecrawl puts single-page scraping, site discovery, whole-site crawling, and JSON extraction behind one API. Cloud removes browser, proxy, and worker operations; self-hosting gives infrastructure control, but not the complete Cloud feature set.

Linkup Search API Guide: From standard and deep to Structured Output

Linkup separates search depth from response shape: start most agent queries with standard + searchResults, move to deep only for multi-step browsing, and treat the monthly $20 as a balance refill rather than a new $20 grant.

Nine Lectures and Three Labs Are Public, but the Full Path Still Uses Three External Services | MIT 6.S191 Course Overview

MIT 6.S191's 2026 edition publishes nine lecture videos, slides, three software labs, and solutions, making it an A3 self-study course. The supplied path still depends on Google/Colab, Comet, and OpenRouter for Lab 3, while unaffiliated learners do not receive MIT credit, project feedback, or API credits.

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Scrapling Complete Guide: From Adaptive Selectors to Concurrent Spiders

Scrapling puts HTTP, Playwright browsers, CSS/XPath extraction, and a Spider API behind one Python interface. Adaptive selectors save element properties and relocate a target by similarity after a layout change, but the output still needs validation.

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SearXNG Complete Guide: Engine Tuning, JSON API, and Self-Hosted Operations

SearXNG is a metasearch engine, not a crawler, and it does not own a web-wide index. Based on the official 2026.8.20 documentation, this guide covers Compose installation, settings.yml, engine selection, the JSON API, and empty-result diagnosis.

Build Your Own Search Backend: SearXNG + Crawl4AI, From Zero to Claude Code

Three components, one job each: SearXNG finds, Crawl4AI reads, and a thin layer of your own code glues them together. Three defaults will stop you cold — `formats` only emits HTML, `secret_key` ships as the literal string `ultrasecretkey`, and turning on the limiter blocks your own code. Also, the official install path changed in March 2026, so most tutorials online point at a repository that is now archived.

From Languages to Information: An NLP Intro That Teaches Both tf-idf and LLMs | Stanford CS124 Course Overview

CS124 is the first course in Stanford's NLP track: ten weeks from tokenization, n-grams, text classification, information retrieval and embeddings to LLMs and Transformers. The textbook is the free Speech and Language Processing, and the nine assignment repos plus the lab problems and solutions are public; the pre-recorded videos, quizzes and autograding need a Stanford account.