AI Weekly Malaysia

In this How I AI episode, John Lindquist (creator of egghead.io, now running mega.dev) demos eight uses of "Jev" — a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, a live presentation coach and more — arguing it should be treated as a fast, cheap decision engine rather than a chatbot, since it returns scores, classifications, probabilities and function calls instead of prose. Concrete cost figures: 73 cents across 23 development runs, and a separate run where Claire processed 5 GB of JSON for 40 cents. In a chess benchmark, Jev analysed a full game in under a second — 10x faster and 4x cheaper than a low-reasoning LLM with no accuracy loss. The excerpt also teases an OpenAI ChatGPT Sites segment and a DevDay recap, but gives no details on either.

Why: If your agents spend tokens on classification, routing or branch-selection calls, this is a concrete cost argument for re-pricing those paths: 73 cents over 23 dev runs and 5 GB of JSON for 40 cents is a different order of magnitude from per-token LLM calls, and the chess benchmark (10x faster, 4x cheaper, same accuracy) is the one directly comparable number. The recommended mental model — put Jev wherever a traditional program would have an if/else, switch or branch, and layer multiple cheap classifications instead of chasing one perfect prompt — is something you can apply this week. Caveat worth stating on air: the excerpt never says who makes Jev, what it costs in production, or how to access it, so treat this as a pattern to test, not a product to adopt. There is no Malaysia or Southeast Asia angle in the text.

Reflection AI, a Brooklyn-based startup founded in 2024, unveiled Beam, its first open-weight frontier model: a text-only mixture-of-experts with 501B total parameters, 23B active, pre-trained on 23.8T tokens, and a 1M-token context window. Reflection claims Beam matches Z.ai's GLM-5.2 (roughly 744B total / 40B active) on advanced reasoning benchmarks while using 3-4x less inference compute, and that it outscores Thinking Machines Lab's Inkling on four coding tests where both report results, though Inkling is multimodal and Beam is text-only. The benchmarks are self-reported and have not been independently verified.

Why: The 23B-active-of-501B design and 1M-token context are the concrete numbers to check before assuming Beam is cheap to serve: if the 3-4x-lower-inference-compute claim survives independent testing, agent pipelines that currently pay per-token to closed APIs have a credible open-weight swap, but the benchmarks are vendor-reported, so treat Beam as a candidate to benchmark on your own eval set rather than a reason to migrate now. Note it is text-only, so anything relying on vision or audio input is unaffected by this launch.

CNBC reports that public backlash against power-hungry data centers is spreading from the U.S. to Europe and Asia, with South Korea seeing local opposition and proposals for tighter restrictions even as the national government pushes faster development. Citing STL Partners, CNBC says about $42 billion of European data center investments have been affected by delays and cancellations, versus roughly $77 billion in the U.S. The tension is between government AI ambitions and local concerns over electricity, water, and land use.

Why: For Malaysian and Southeast Asian builders, the concrete risk is not a named local project but the cost and availability of AI/cloud infrastructure: if similar opposition delays capacity, GPU/cloud pricing and regional latency could worsen. The text gives no Malaysia-specific detail, so treat the $42 billion Europe figure as a leading indicator to watch, not a local forecast.

US Census construction-spending data shows spending on data center buildings running at a record $85 billion annual pace, up 73% year over year. The article notes the Census figure covers only the buildings themselves and excludes the servers and racks inside them, so total data center investment is larger than the headline number.

Why: The $85B figure is buildings-only, so it understates real data center capex — but it also gives no Malaysia or SEA numbers, and nothing here tells a builder what to change this week. The one usable read: if US building spend is up 73% in a year while the compute inside is excluded from the count, capacity is still being added, so don't plan around a near-term collapse in cloud or GPU rental pricing. Treat this as background context for budgeting, not a decision input on its own.