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An open-source project porting Ogre Battle 64: Person of Lordly Caliber from N64 to native PC using static recompilation via N64Recomp has fully converted the game's main code segment of 807 functions into C. The team, which used the DeepSeek v4/v4.1 Flash AI model for much of the work, now moves to building the runtime layer covering rendering, input, and audio.
Cactus launched Needle 3, a foundation model small enough to run as a single 8-29MB binary on phones, wearables, robots, smart home hubs and cars. Built on what the company calls a Simple Attention Network, it sacrifices general chat ability to specialize in three tasks: text embedding for local search, structured data extraction from messy text, and tool calling that maps spoken requests to app functions. The company claims it outperforms models ten times larger on mobile tool calls and matches models two to three times bigger on extraction.
DeepSeek released V4.1 Flash, a model initially mistaken for a minor update but revealed via its technical report to be a significant architectural overhaul, effectively a V5-class release. It achieves near 420 tokens/second throughput while compressing KV cache by 4x through techniques including cross-layer compression, sparse attention indexing optimizations, and FP4 precision, alongside a YOCO-inspired prefill design that only activates 8B parameters during prefill versus 16B during decode across its 40 layers.
DeepSeek V4.1 Flash achieved code execution on all 11 vulnerable systems in an AI hacking benchmark while leaving four patched systems untouched, at a total cost of just $4.65 for accepted runs. A manual review found the model discovered five novel attack paths beyond the six expected solutions, including a faster exploit against Grafana that bypassed the intended vulnerability entirely.
DeepSeek has released V4.1-Flash on its API, replacing the earlier V4-Flash and V4-Flash-Vision-Exp models. The new model adds native multimodal capabilities and can be accessed by setting the model parameter to deepseek-flash.
A follow-up experiment tested four open models—DeepSeek V4 Flash, Inkling, Kimi K3, and Qwen3.8 A95B—by inserting the first 1% of GPT-5.5 Pro's reasoning trace into each model's own reasoning channel before letting it generate answers freely. Researchers then measured how much of GPT-5.5 Pro's visible answer text overlapped with each model's output. Qwen3.8 showed the largest jump, with overlap rising from 33.92% unprefilled to 54.50% with the GPT-5.5 Pro prefill, a 20.58 percentage-point increase, while other models showed much smaller shifts.
DeepSeek confirmed it will officially release its V4.1 Flash model on September 10, 2026 (Beijing Time), stating internal testing shows it outperforms V4 Pro on performance, cost, speed, and task completion time. Until V4.1 Pro arrives, all Pro-model requests will be automatically redirected to V4.1 Flash and charged at the cheaper Flash pricing tier.
The developer behind the iMessage AI assistant Olly, which has processed over 18 million messages with roughly a third routed through OpenRouter's open-source models, published a technical breakdown of problems encountered at scale. The core issue: OpenRouter can send identical model requests to any of about 20 different hosting providers, each running the same weights but with different precision, optimizations and tool parsers, producing measurably different benchmark results. For DeepSeek V4 Flash, GPQA Diamond and TAU-Bench Airline scores varied by several points across providers on the same day.
A research team reports developing a pretraining method that matches DeepSeek V4 Pro Base's performance using roughly 50 times less compute, at an estimated cost of about $0.5 million on GB200 chips. Scaling the same recipe up 10x further, to roughly $4 million, reportedly surpassed all publicly available open base models on perplexity benchmarks.
A newcomer called StartLux, founded by veteran programmer Chen Danyan and formerly known as Yuandian Xinghui, placed second in China's CAICT MCP specialized benchmark with its StartLux-V1.0-27B-Preview model. The 27-billion-parameter model trailed DeepSeek-V4-Pro, a 1.6-trillion-parameter system, by just 1.3 percentage points, despite running locally on consumer-grade PCs rather than in the cloud. Chen has argued that local models will eventually outcompete cloud-based ones on efficiency and market share.
The vLLM project released version 0.28.0, combining 584 commits from 270 contributors. The update centers on deep performance work for Kimi-K3, including new decode context parallelism, fused kernels, memory-saving expert sharding, and ROCm support, alongside DeepSeek V4 improvements such as end-to-end sparse MLA and AMD Quark NVFP4 support.