Zhemal Khamidun
Head of AI at Alpina Digital, CPO of AlpinaGPT (~30-person engineering & AI team). Founder of Vibe Academy & Hamidun Consulting, author of the AI channel «Готовим ИИшницу» (@jemal_hamidun).
AnyGameRenderer Dresses Gray AI Prototypes in Real Graphics and Keeps 62.9% of Units Intact
A generative renderer built on the 33B MiniMax-H3 model turns blockouts from AI-agent games into polished video; temporal entity match rose from 50.9% to 62.9%.
Claude Proves Safety of 123 Libraries at the RISC-V Machine-Code Level
Anthropic and Theorem used Claude to formally verify 239 entry points across 123 open-source libraries: about 180M lines of proofs, plus bugs that fuzzing missed.
WeChat Open-Sources WeVisOne-VL: Detection, Masks and Matting in a Single VLM
WeChat has open-sourced WeVisOne-VL, a single VLM that finds objects, counts them, reads text and builds masks with alpha mattes. It claims top results on 30+…
UserArena: A 64 729-Session Benchmark Tests Whether Shoppers Can Be Simulated
Echo Teaches VLA Robots to Recognize Success and Failure Inside Their Own Actions
HiSTEVE 1.0 Plays Minecraft With a Mouse and Keyboard: 85.0% on MineStudio Bench
Dual Exam: GPT 6 Astra Scores 31.3 Among Multimodal Agents
DISC Learns to Combine Time-Series Similarity Metrics and Predict Transfer Success
DISC, a new learnable framework, calibrates and combines dataset similarity metrics for time series to estimate in advance how well a forecast will transfer from a…
MMVistaReason: Open Recipe Trains a 4B Model That Beats Qwen3.5-9B on 15 Benchmarks
The MMVistaReason authors released an open post-training recipe: MVR-4B scored 72.8 across 15 multimodal benchmarks, beating Qwen3.5-9B (Instruct).
LeWorldModel Tested for Action Sensitivity: Axes Diverge by 36–42°
FocusVLA Teaches a Robot to Pick a Camera Based on Task Stage: +13.3 pp in Real-World Tests
UCLA Team Pushes the Speed–Quality Frontier in Speculative Diffusion With Two Knobs
PaperFormalizer Turns LaTeX Papers into Verified Lean Code, with the Author in the Loop
Nestor: nested sparsity in attention beats MoE at equal compute
Tianzhe Chu, Druv Pai, and Yi Ma propose the Nestor transformer: attention sparsity nests inside MoE, delivering gains at equal compute and in continual pretraining.
A 17-million-parameter encoder picks the right tool in 14ms on CPU
Tardelli Stekel has released six open encoders ranging from 17M to 1B parameters for closed-set selection. The 68M and 150M models beat the 435M DeBERTa-v3 by 4.9…