Ai News

TL;DR: The Fly Brain Story in 30 Seconds September 3, 2026: Google Research and HHMI Janelia published the complete nervous system map of an adult male fruit fly in Cell. 166,700 neurons, 125 million synapses, the largest brain map by neuron count so far. The map, a connectome, is a wiring diagram: which neuron connects to which. Nobody “uploaded” a brain. The netlist was extracted and released under CC-BY. Within a week developers put a simple neuron model on top of the wiring and fed game frames into the fly’s eye neurons: Doom (Sept 6), Minecraft and Mario 64 (Sept 7), Beat Saber (Sept 9, 22 million views), crypto trading (Sept 10). Videos below. DOOMFLY’s own validation report is blunt: the fly has not learned to survive. It failed all three gates, vision, conditioning and survival. What you see on screen is reflex from the wiring, not learning. You can run it yourself: browsing neuron by neuron in neuPrint needs zero install, a full-brain simulation with flypoke runs on a laptop from an 850 MB download, and DOOMFLY or StonkFly need 16 GB of RAM and Python 3.11. “A fly brain was uploaded to a computer” was everywhere this week. Half of that sentence is true. The true half: an animal’s entire nervous system has been mapped neuron by neuron, synapse by synapse, and the map sits on a server as a file anyone can download. The false half is the idea that the file is a fly. ...

Summary: DeepSeek V4.1 Flash in 30 Seconds DeepSeek-V4.1-Flash shipped on September 10, 2026. License is MIT, weights are on Hugging Face. 552B backbone parameters, but only 8B active while reading input and 16B while generating output. That asymmetry is the whole point of the model. New architecture: Causal Encoder-Decoder (CED). 40 layers, 20 causal encoder plus 20 decoder. The decoder’s global KV cache is projected from the encoder’s final hidden states instead of from each decoder layer’s own. Result: a global KV cache of 890 bytes per token. One quarter of V4-Flash, and 1/437 of DeepSeek-V1. Natively multimodal: images go through DeepSeek-ViT, a vision encoder trained from scratch, from the very start of language-model pre-training. Pre-training corpus is 45T tokens. Beats Claude Opus 5 on several agentic benchmarks: 90.6 vs 89.1 on Terminal-Bench 2.1, 74.2 vs 74.0 on DeepSWE v1.1, 54.8 vs 50.3 on AutomationBench. Reasoning effort is a continuous 1-100 dial, not an on/off thinking toggle. API pricing is $0.15 in / $0.60 out per million tokens off-peak, double that at peak. Roughly one twentieth of Claude Opus 5’s output price. DeepSeek is retiring V4 Pro: from September 14, 2026, deepseek-v4-pro requests get routed to V4.1 Flash and billed at Flash rates. Everyone in the open-weight race this year is chasing the same two numbers: active parameters and KV cache. The first decides what each token costs, the second decides how much memory a long context eats. GLM-5.3-Flash answered with hybrid attention, Qwen3.8-Flash-Next with 6B active parameters. ...

Meta shipped Muse Spark 1.3 on September 2, 2026: a closed multimodal reasoning model built for long-running agentic workflows, multi-agent setups and coding. Two numbers made the headlines. It scores 75.4 on DeepSWE v1.1, edging past Claude Opus 5, and it hits 98.1% retrieval across a full million tokens of context. Both are real. Both come with a footnote, and the footnotes are what this post is about. What Is Muse Spark 1.3? 🧠 Muse Spark is Meta Superintelligence Labs’ closed flagship series. Version 1.3 succeeds 1.2, and the pitch is not producing one good answer but carrying a long task all the way to the end. ...

Two models, one price tag: $10 per million input tokens, $50 per million output tokens. GPT-6 Astra shipped on September 3, Claude Fable 5.1 on September 1. Identical sticker prices, so the comparison looks simple. It is not. The sticker is the same, the bill is not. Run the same work through both and what you pay can double or halve depending on the shape of your workload. This post starts with where that gap comes from, then walks the benchmark table, and finishes with three concrete billing scenarios. ...

Summary: ChatGPT Images 2.5 in 30 Seconds OpenAI announced ChatGPT Images 2.5 on 8 September 2026. Sharper detail, more precise editing, faster generation. Generation latency is down by up to 50% compared with Images 2.0. Sketch is new: draw inside ChatGPT and use the drawing as the reference for the final image. Type @Sketch to open it. Also new: templates for popular formats, comments placed on the image itself for focused edits, and the option to share the prompt alongside the image. Rolling out to all ChatGPT, ChatGPT Work and Codex users, on every plan, across desktop, mobile and web. Two new API models: GPT-Image-2.5 Flare (the fast default) and GPT-Image-2.5 Sunburst (slower, more precise). OpenAI says more than 3 billion images a week are already created across ChatGPT Images and the GPT-Image models in the API. Whatever else Images 2.5 is, it is an update to one of the most heavily used products the company ships. ...