HW6 Problem 4 (20 points) takes apart the cost of generating one token at a time in three questions: picking the most likely token at each step doesn't give the most likely sequence; recomputing every key at every step makes cost quadratic, and a KV cache brings it back to linear; speculative decoding lets a small model guess and a large model verify in one pass. It is all pencil-and-paper, and every question maps onto a real design choice in today's LLM inference systems.
HW6 Problem 1 (15 pts) swaps the discrete HMM from lecture for a continuous state: the state drifts by Gaussian noise each step, each observation adds more noise, and you derive the mean and variance of the filtering distribution p(zₜ | x₀…xₜ). That is a one-dimensional Kalman filter. The solution is two moves, predict with the transition and then correct with the observation, and the problem hands you both Gaussian identities you need.
HW6 Problem 2 (15 pts) hands you a 4×5 Gridworld where moves can slip and rewards arrive only when you leave a cell. You write one step each of policy evaluation, policy iteration, and value iteration in the notebook, watch how the discount factor γ reshapes the policy, and finally ask whether this is a sensible model of the robot task at all. The rules of the world are fully known, so this is planning, not learning yet.
HW6 Problem 3 (20 pts) has you write a tabular Q-learning agent for Swingy Monkey, a Flappy Bird-like game. The minimum bar is scoring over 50 at least once within 100 epochs, plus one improvement of your choice. Problem 5 (10 pts) is a 250-word ethics question: assuming a social platform's users grow more politically extreme, use RL concepts to explain how the choice of reward function might have contributed.
The CS181 Spring 2026 midterm is in class on Mar 10, worth 15% of the grade, closed-book with one double-sided note sheet. The official midterm checklist has four blocks: regression, classification, neural networks and model selection, and SVMs. This post maps each block to HW0–HW3 problem numbers, flags what the 2026 homework never drilled, and explains how to use the 2025 practice exam, review session, and concept checks.
There is no 'never comment' school — only 'comment by exception' (Uncle Bob) versus 'comments are part of the design' (Ousterhout, antirez). In their 2024–2025 public debate, both agree on why-comments and against noise comments; the real fights are over interface comments for internal methods, long names as a substitute, and whether comments can be trusted. Controlled studies say quality decides: the same comments moved performance anywhere from -30% to +34% depending on the snippet.
Gemma is Google's open-weights family paired with the closed-source Gemini flagship; Gemma 4 (E2B / E4B / 26B-MoE / 31B) released in April 2026 switched its license from Google Gemma Terms of Use to Apache 2.0 — the single most important change in this article.
Laguna is Poolside's agentic coding model family: XS 2.1 packs 33B-A3B into a 36GB Mac, while S 2.1 brings 118B-A8B with 1M context to 70.2% on Terminal-Bench 2.1 and 40.4% on DeepSWE, both open under OpenMDW-1.1.
Ling separates execution, Ring handles deep reasoning, and Ming covers multimodal tasks; the family prioritizes intelligence per unit of compute, so selection starts with workload, deployment budget, and modality.
Muse Spark is Meta's closed-source agentic model line: version 1.3 combines a 1M-token context, multimodal inputs, and long-horizon tool loops. Standard pricing is $1.25/$4.25 per 1M input/output tokens, while Contributor drops to $0.10/$0.20 in exchange for training rights. It is not the next Llama; it is a separate product line built around models, APIs, and coding agents.
Nex-N2.5 is Nex AGI's open agentic model family: mini scores 82.9 on OSWorld-G at 35B-A3B, Pro tops Claude Opus 5 with 87.4 at 397B-A17B, and Max leads the whole official table on BrowseComp with 92.6 at 1.6T, all open under Apache-2.0.
GPUtw AI Skill is an official Agent Skill pack plus MCP server that lets Claude Code, Codex, Cursor, Copilot, and Gemini CLI manage GPUtw's Taiwan-based GPU cloud using natural language. 18 MCP tools cover catalog queries, instance lifecycle, status monitoring, and Vault operations. MIT licensed.
document_parse gains an effort parameter (fast/standard/deep) unifying anydoc WASM, OCR.space, and Docling VLM into a single dial; document_chunk's fieldAware mode extracts field names from table headers and metadata to solve RAG attribute conflation; document_smart_parse now returns a confidence score so agents decide whether to upgrade.
Groundlane v0.1.0 adds four quality mechanisms: web_extract selector healing (three-layer deterministic fallback, no LLM), corpus_retrieval_test (RAG recall verification inspired by RAGFlow), a document benchmark CLI (character-level F1), and a search benchmark framework (ground truth corpus with multi-provider comparison). Tool count goes from 54 to 55.
Four AI security certs, four different bets: SecAI+ ($298) is CompTIA's mid-level expansion play, CAISP ($1,099 all-in) has the strongest hands-on labs and fullest OWASP LLM Top 10 coverage, GAIPS ($999/$9K) is the SANS gold-standard defender cert with CyberLive exams, and AAISM ($459+) is governance-layer but requires CISM or CISSP first. Under $400 → SecAI+. Want to actually hack and fix → CAISP. Company paying → GAIPS. For the offensive side (AI red teaming), OSAI+, HTB COAE, and COASP launched in 2026 and form their own group; see the section near the end.
10+ new AI security certifications launched in just 18 months (2025–2026), while the classic trio — Security+ ($439), AWS Security Specialty ($300), CISSP ($749) — remains foundational. For AI platform developers who aren't security specialists, a three-phase path works best: Security+ → AWS Security → SecAI+ or CAISP → CISSP, totaling $1,780–$2,700.
Security+ ($439) takes 2–3 months, no published pass rate, +$10K–$20K salary premium. AWS Security Specialty ($300) takes 2–3 months, 45–55% pass rate, highest ROI at 40–67x. CISSP ($749) takes 3–6 months, 50–60% pass rate, +$25K–$35K premium but requires 5 years of experience. Five-year cost to earn and maintain (including estimated study materials): Security+ about $619, AWS Security $200–$350 (no annual fee), CISSP about $1,524.
Taiwan's Cybersecurity Act 2.0 passed and FSC's three-tier system is in place, but neither mandates specific certifications — real demand comes from the job market. Taiwan has 7+ training providers: Uuu has the widest catalog (classroom courses for all three of SecAI+, COASP, and OSAI+), WUSON is the CISSP legend (monthly cohorts sold out through mid-2027), and DEVCORE was the first to bring OffSec factory instructors. ISC2 test centers in Taipei and Kaohsiung; most certs also support online proctoring from home.
Marker (Datalab open source, Apache 2.0 license, v2.0.0 released 2026-07-20, 39.5k stars) is a pipeline-style document parsing standard library that outputs Markdown and JSON, supports optional LLM boost (`--use_llm`, default `gemini-3.5-flash`), custom formatting logic, table/formula/inline-math/link/reference/code formatting, image extraction and preservation, header/footer removal, and runs on pure CPU, GPU, or MPS (`Apple Silicon`). Unlike [Docling](/posts/tech/2026-09-06-docling-document-parsing) (structured JSON core, dedicated XML exports, pure MIT), Marker centers on Markdown/JSON under `Apache 2.0` with a separate model-weight license (`AI Pubs Open Rail-M`, $5M commercial threshold); unlike [MinerU](/posts/tech/2026-09-05-mineru-ocr-doc-parsing) (custom agreement with MAU/revenue thresholds + attribution obligations), Marker offers a simpler licensing story (`Apache 2.0` code) but requires accepting a separate model-weight license for weights. The series framework ([three-layer model](/posts/ai/2026-08-06-document-parsing-three-layers)) positions all three (`MinerU`, `Docling`, `Marker`) as pipeline-based parsing-layer options with distinct licensing, output, and speed trade-offs.
Docling (IBM Research Zurich, now governed by the Linux Foundation AI & Data, MIT license, v2.100.0 released 2026-06-09) is an open-source document parsing standard library with structured JSON (DoclingDocument) as its core output. It supports PDF, DOCX, PPTX, XLSX, HTML, EPUB, Apple Pages, video (MP4/AVI/MOV with ASR transcription and keyframes), audio (WAV/MP3), email (EML/MSG), ODF, and XBRL financial reports, with a swappable-stage pipeline parser (pure CPU or GPU-accelerated), VlmPipeline option (GraniteDocling 258M VLM), MCP server and API server (docling-serve), and native integrations with LangChain, LlamaIndex, Crew AI, and Haystack.