AI Weekly Malaysia

Jeff is an independent open-source project offering fine-tunes of Qwen3.5 (0.8B and 2B) and Gemma 4 (E2B) as tiny zero-shot classification models that reuse Jev's request format and return a calibrated probability per option from a single forward pass instead of generated text. The README reports about 22 ms per decision on an RTX PRO 6000 and 28 ms on an Apple M4 Max via MLX, with the 0.8B training in roughly 2 hours and the 2B in about 3.5 hours on one RTX PRO 6000, using synthetic data written by an open model on two DGX Sparks. It is explicitly not affiliated with or endorsed by TypeSafe, the makers of Jev, and the repo shows 298 stars, 8 forks and 6 commits; the Hacker News thread drew 222 points and 71 comments.

Why: If you currently route simple label decisions — support queues, moderation labels, intents, game moves — through a hosted LLM API, this is a concrete alternative: ~22-28 ms per decision on a single GPU or an M4 Max MacBook, no per-token billing and no data leaving the machine. The reported fine-tune result (held-out accuracy 31.7% to 95.8% for voice navigation in under 30 minutes on one GPU) is the number to test against your own labels, since zero-shot accuracy at 0.8B is the stated weak point and the README itself says reasoning will not match a much larger model. For teams in Malaysia, running this on local or consumer hardware removes cloud GPU spend and cross-border data transfer for classification tasks, though you still need to verify the models' licensing and Jev's own terms before swapping them in.

Cloudflare reports it is now the fastest provider in 74% of the world's 1,000 largest networks, up from 60% at its April 2026 Agents Week update — an additional 150 networks and 38 countries where it now ranks fastest. The measurement ranks the top 1,000 networks by APNIC-estimated user population and compares providers on connection time (time to complete a TCP handshake), ranked by a trimean of the 25th, 50th and 75th percentiles. It also introduces a new methodology that runs background fetches from Cloudflare Challenge Pages to compare connection times against Cloudflare, Amazon CloudFront, Google, Fastly and Akamai.

Why: The headline number is a vendor's own self-reported benchmark, and the metric is TCP handshake time measured partly from Cloudflare-branded error and challenge pages — not your app's p95 latency, TLS setup, or origin fetch time for users in Malaysia. If you are picking or renegotiating an edge/CDN provider on the strength of this claim, treat it as a directional signal only and instrument your own real-user latency for your actual Malaysian traffic before switching; the post does not name which countries or networks improved, so you cannot check whether your users are in the 74%.

Steam has added a new low-latency codec called Pyrowave to its Remote Play game-streaming feature, according to Tom's Hardware. The codec is reported to improve streaming quality on local networks but consumes '5-10 times' more bandwidth than the existing option. The excerpt contains almost no further technical detail — no supported hardware, resolution/framerate figures, rollout date, or opt-in mechanism — because the supplied text is largely subscription boilerplate.

Why: If you stream games or remote sessions across a LAN — or you're building anything that trades bandwidth for latency (remote dev boxes, cloud gaming, thin-client setups) — this is a concrete data point that the low-latency win costs roughly 5-10x the bits, which will break on Wi-Fi or capped connections even when it looks fine on wired Ethernet. Beyond that, the source text doesn't say which GPUs/clients support Pyrowave or whether it's default or opt-in, so there's nothing actionable to configure yet; treat this as a watch item, not a change to make.