Lessons are broken, you frequently get booted before the lesson ends, or the progress bar stalls

Completing a lesson on legendary completely detaches the review content for the lesson from its place in the course. If I want to review the content of unit 1 lesson 1 after doing it on legendary, I can’t. Instead I see questions with long sentences and complex grammar that fit much later in the course

It’s so broken that it acts as a disincentive for achieving legendary, when doing so would otherwise serve a very useful purpose (reviewing content with enforced lesser handholding)

Detailed rant in comments

Was looking at an Nvidia Spark, and when I heard that about a third of its price is just its connectors, I wondered if something better could be put together for a lower price

I gave a similar prompt to Google and Deepseek

>We want to run the latest top end open models (eg DeepSeek Flash V4), and train models up to 5bn params comfortably, 8-10bn at a push

>Super fast inference is not important. 5 tokens/sec is fine

>We do need to consider the physical properties of the resulting computer. No point having something that sounds like a jet engine, or works for a month before it overheats and breaks

and got the responses below

Google suggests:

The All-Rounder AI Workstation Blueprint (Inference + Scratch Training)

By blending 48GB of ultra-fast VRAM with 192GB of system RAM, this system splits into two distinct, high-end workflows.

Component	Choice for Dual 3090 Setup	Estimated Cost	Why It Is Crucial
GPU 1	NVIDIA RTX 3090 (24GB VRAM - Used/Refurbished)	£650	Supplies massive, fast parallel pipelines needed to hold full training models.
GPU 2	NVIDIA RTX 3090  (24GB VRAM - Used/Refurbished)	£650	Combines with GPU 1 to hit 48GB VRAM.
System RAM	192GB Corsair Vengeance DDR5 (4 x 48GB)	£500	Retained strictly to load massive inference models (DeepSeek V4 Flash).
Processor	AMD Ryzen 9 9900X (12 Cores)	£350	Handles heavy data pipelines and token calculations smoothly.
Motherboard	ASUS ProArt X670E-CREATOR WIFI	£420	Upgraded: Specifically engineered for multi-GPU spacing and reliable x8/x8 lane allocation.
Power Supply	Corsair AX1600i (or Tier-A 1500W-1600W)	£320	Upgraded: Two 3090s spike heavily in power. You need a massive unit to prevent shutdowns.
Storage	4TB Crucial T500 NVMe M.2 SSD	£240	Holds your training datasets alongside frontier inference models.
Case & Cooling	Fractal Design Torrent + Noctua NH-D15	£220	Upgraded: The absolute best airflow case on the market to prevent the stacked 3090s from choking.
Total Hardware	The Ultimate Cross-Discipline AI PC	~£3,350	Still roughly £650 cheaper than the Spark.

Deepseek suggests:

The Silent Frontier AI Workstation

Optimised for quiet operation, massive model inference, and 1-5B training (10B at a push)

Component	Choice	Cost (£)	Why
GPU	NVIDIA RTX 3090 24GB (Used)	650	24GB VRAM handles full fine-tuning up to 5B, QLoRA at 10B. CUDA mature, wide support. Single card keeps noise down.
CPU	AMD Ryzen 9 7950X (16 Cores)	450	AVX-512 accelerates CPU inference. 16 cores for data prep and token processing.
RAM	256GB DDR5 (4 × 64GB)	760	The heart of the inference strategy. Holds 200B+ models at Q6-Q8 with generous context. Populated fully from day one.
Motherboard	ASUS ProArt X670E-CREATOR WIFI	420	x16 PCIe 5.0, 4 RAM slots (now full), robust VRMs, designed for sustained workstation loads.
Storage	4TB WD Black SN850X NVMe	240	Fast model loading, ample space for datasets and multiple frontier model checkpoints.
PSU	Corsair RM1000x (1000W)	170	Overspecced for a single 3090 — runs cool and quiet, fan rarely spins up.
CPU Cooler	Arctic Liquid Freezer III 360	90	360mm AIO — overkill thermals means fans stay at near-silent RPMs under any load.
Case	Fractal Design Define 7 (Solid)	160	Sound-dampened panels. Sealed front for silence, still adequate airflow for a single-GPU setup.
Case Fans	3 × Arctic P14 PWM (Intake) + 1 × P14 (Exhaust)	40	140mm fans move air slowly and silently. Arctic's motors are near-inaudible at low RPM.
Total		~£2,980	£1,020 under the Spark. £370 under the dual-3090 jet engine.

Deepseek saw Google's response and advised that two 3090s in that case would overheat and risk failure, so it's build is aimed at being slightly sleeker and quieter

How did they do? Are these decent builds or do they make mistakes? Can we do better?