# nano

Published articles for nano.

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## New NVIDIA Cosmos 3 Open Models for Interactive World Simulation

DevFeed: [New NVIDIA Cosmos 3 Open Models for Interactive World Simulation](<https://devfeed.tech/articles/new-nvidia-cosmos-3-open-models-for-interactive-world-simulation-119751.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia/cosmos-interactive-world-simulation>)

Author: Pranjali Joshi; Rick Z; Yao Xu; Qianli Ma; Chen-Hsuan Lin; Ming-Yu Liu; Xiang Li; Zhengfei Kuang; Zhonghao Wang; Jiaxiang Tang

Published: 2026-11-05T19:35:16Z

Content type: release

Language: en

Sources: [Hugging Face](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [World models](<https://devfeed.tech/topics/world-models.md>), [Simulation](<https://devfeed.tech/topics/simulation.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [Isaac](<https://devfeed.tech/topics/isaac.md>)

Tags: [cosmos](<https://devfeed.tech/tags/cosmos.md>), [cosmos3](<https://devfeed.tech/tags/cosmos3.md>), [driving](<https://devfeed.tech/tags/driving.md>), [generate](<https://devfeed.tech/tags/generate.md>), [interactive](<https://devfeed.tech/tags/interactive.md>), [lidar](<https://devfeed.tech/tags/lidar.md>), [lighting](<https://devfeed.tech/tags/lighting.md>), [model-development](<https://devfeed.tech/tags/model-development.md>), [models](<https://devfeed.tech/tags/models.md>), [nano](<https://devfeed.tech/tags/nano.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [platform](<https://devfeed.tech/tags/platform.md>), [sensor](<https://devfeed.tech/tags/sensor.md>), [sim](<https://devfeed.tech/tags/sim.md>), [simulation](<https://devfeed.tech/tags/simulation.md>), [stream](<https://devfeed.tech/tags/stream.md>), [view](<https://devfeed.tech/tags/view.md>), [weather](<https://devfeed.tech/tags/weather.md>), [world](<https://devfeed.tech/tags/world.md>)

### AI overview

NVIDIA's four open-weight Cosmos 3 Nano checkpoints support different simulation workflows. Transfer-Auto creates offline synchronized driving scenes across up to 11 camera views, including fisheyes, with optional joint RGB-LiDAR generation; WSM controls specify scene structure and traffic motion, while prompts guide appearance. Sim-Auto is its causally distilled streaming variant, generating successive camera and LiDAR chunks from history and updated controls. Closed-loop use also needs a simulation runtime and WSM renderer. Sim-Transfer converts simulator spatial controls such as depth, segmentation, and edges plus a text prompt into RGB observations; the simulator retains responsibility for physics and state. Sim-Bimanual predicts observations from robot and camera actions for YAM and AgiBot two-arm setups, with specific action-schema and timing conventions. The models have 16B trainable parameters and use OpenMDW1.1 terms. Inference requirements include Linux, an NVIDIA GPU, Docker, and NVIDIA Container Toolkit; the published vLLM-Omni setup was tested on one GB200, not specified as a minimum. Training guidance emphasizes validating data and held-out episodes: a short run doesa

### Source excerpt

**NVIDIA Cosmos** world model development platform adds four new open-weight checkpoints to its Cosmos 3 model family for generating interactive world simulation and multi-sensor multi-view driving scenes: - 🌦 **Cosmos3-Nano-Transfer-Auto** : Generate multi-view driving data with varied weather, lighting, and time of day. - 🚗 **Cosmos3-Nano-Sim-Auto** : Stream synchronized camera and LiDAR observations for driving simulation.

## CISA: Retrofitting Dense Attention Models with Chunk-Indexed Sparse Attention

DevFeed: [CISA: Retrofitting Dense Attention Models with Chunk-Indexed Sparse Attention](<https://devfeed.tech/articles/cisa-retrofitting-dense-attention-models-with-chunk-indexed-sparse-attention-114772.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2611.03200>)

Author: Mohammed Dabbah, Izhak Golan, Oren Tropp, Daniel Afrimi, Vladimir Anisimov, Tomer Asida, Nave Assaf, Ran El-Yaniv, Ido Galil, Yonatan Geifman, Omri Puny, Ido Shahaf, Akhiad Bercovich

Published: 2026-11-03T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>)

Tags: [attention](<https://devfeed.tech/tags/attention.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cache](<https://devfeed.tech/tags/cache.md>), [cisa](<https://devfeed.tech/tags/cisa.md>), [context](<https://devfeed.tech/tags/context.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [decode](<https://devfeed.tech/tags/decode.md>), [gpu-utilization](<https://devfeed.tech/tags/gpu-utilization.md>), [indexing](<https://devfeed.tech/tags/indexing.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm-inference](<https://devfeed.tech/tags/llm-inference.md>), [models](<https://devfeed.tech/tags/models.md>), [nano](<https://devfeed.tech/tags/nano.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-b300](<https://devfeed.tech/tags/nvidia-b300.md>), [prefill](<https://devfeed.tech/tags/prefill.md>), [quality](<https://devfeed.tech/tags/quality.md>), [serving](<https://devfeed.tech/tags/serving.md>), [throughput](<https://devfeed.tech/tags/throughput.md>)

### AI overview

Chunk-Indexed Sparse Attention (CISA) retrofits pretrained dense attention models without gradient updates by selecting fixed-size KV chunks from compact summaries of existing keys. It shares selections across queries within a KV group, while QShare reuses selections across adjacent prefill queries to reduce indexing work. In reported benchmarks, including scoring and selection, CISA was 8.6x faster for late prefill queries and 3.0x faster in decode; core attention alone reached 11.7x and 13.0x. Quality stayed close to dense attention on the named evaluations at the reported chunk budgets, with RULER scores within 2.05 percentage points using 8-16x fewer KV reads. For million-token prompts under the specified four-GPU serving setup, throughput was 2.29x dense attention, rising to 2.61x with fewer chunks; that smaller budget widened the GraphWalks score gap from 1.49 to 3.59 points. Results show a quality-speed tradeoff, and the figures are tied to the stated models, precisions, workloads, and hardware.

### Source excerpt

arXiv:2611.03200v1 Announce Type: new Abstract: Long-context LLM inference is costly because attention repeatedly reads a growing key-value (KV) history. We introduce Chunk-Indexed Sparse Attention (CISA), which retrofits pretrained dense attention models without gradient updates. CISA selects fixed-size KV chunks using compact summaries of existing keys, avoiding token-level indexing and a separate full-length index cache. It uses one indexing query per KV group and shares selected chunks across queries in that group. During prefill, QShare reuses selections across adjacent queries to reduce indexing cost and improve KV reuse and GPU utilization. We evaluate CISA on NVIDIA Nemotron 3 Nano and Ultra, and Inkling-Small. In one-layer BF16 benchmarks at batch size 32 using Ultra's per-device attention dimensions, core attention is 11.7x faster for prefill queries near the end of a 1M-token prompt and 13.0x faster during decode. Including chunk scoring, selection, and all other measured GPU work, speedups are 8.6x and 3.0x. On Ultra NVFP4 with a 4,096-chunk budget, aggregate scores stay within 1.5 percentage points of dense attention on MRCR, LongBench v2, and GraphWalks. RULER sweeps on Nano and Ultra BF16 up to one million tokens show scores within 2.05 percentage points of dense attention with 8-16x fewer KV reads. Serving million-token prompts with 32 concurrent requests and 4K output tokens per request, CISA achieves 2.29x the end-to-end throughput of dense attention using Nemotron 3 Ultra NVFP4 on four NVIDIA B300 GPUs. Selecting fewer chunks increases throughput to 2.61x; in separate GraphWalks evaluations, the smaller budget widens the aggregate score gap from 1.49 to 3.59 percentage points. These results expose a practical quality-speed tradeoff without sparse training.

## Reverse engineering a Ledger Nano X hardware implant

DevFeed: [Reverse engineering a Ledger Nano X hardware implant](<https://devfeed.tech/articles/reverse-engineering-a-ledger-nano-x-hardware-implant-87025.md>)

Original publisher: [Read original article](<https://blog.adafruit.com/2026/10/14/reverse-engineering-a-ledger-nano-x-hardware-implant/>)

Author: Anne Barela

Published: 2026-10-14T13:29:55Z

Content type: news

Language: en

Sources: [Adafruit Industries](<https://devfeed.tech/sources/adafruit-industries-makers-hackers-artists-designers-and-engineers.md>)

Topics: [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [Nano](<https://devfeed.tech/topics/nanocurrency.md>), [Cryptography](<https://devfeed.tech/topics/cryptography.md>), [Embedded Software Dev](<https://devfeed.tech/topics/embedded-software-dev.md>)

Tags: [adafruit](<https://devfeed.tech/tags/adafruit.md>), [bus](<https://devfeed.tech/tags/bus.md>), [data](<https://devfeed.tech/tags/data.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [esim](<https://devfeed.tech/tags/esim.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [hackers](<https://devfeed.tech/tags/hackers.md>), [hacks](<https://devfeed.tech/tags/hacks.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [io](<https://devfeed.tech/tags/io.md>), [lte](<https://devfeed.tech/tags/lte.md>), [microcontrollers](<https://devfeed.tech/tags/microcontrollers.md>), [micropython](<https://devfeed.tech/tags/micropython.md>), [nano](<https://devfeed.tech/tags/nano.md>), [oled](<https://devfeed.tech/tags/oled.md>), [pi](<https://devfeed.tech/tags/pi.md>), [power](<https://devfeed.tech/tags/power.md>), [python](<https://devfeed.tech/tags/python.md>), [python-on-microcontrollers](<https://devfeed.tech/tags/python-on-microcontrollers.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>)

### AI overview

Joe Grand's talk describes a hardware implant found inside a counterfeit Ledger Nano X. It monitors the OLED's SPI bus for the displayed recovery words, stores the seed in flash, and transmits it through an LTE module. The account also notes changes to the battery and fuel gauge and says later implant versions had been spotted by the time of the talk.

### Source excerpt

Writing on core-jmp.org, oxfemale walks through Joe Grand's HARDWEAR.IO 2026 talk in Santa Clara, where Grand tore down a real hardware implant found inside a counterfeit Ledger Nano X. The unit came from a Lazada seller in Thailand and surfaced in an August 2025 Reddit post. The implant doesn't touch the Secure Element. It sits [...]

## A 16GB iPod Nano 3G upgrade by Tucker Osman

DevFeed: [A 16GB iPod Nano 3G upgrade by Tucker Osman](<https://devfeed.tech/articles/a-16gb-ipod-nano-3g-upgrade-by-tucker-osman-61174.md>)

Original publisher: [Read original article](<https://blog.adafruit.com/2026/09/28/a-16gb-ipod-nano-3g-upgrade-by-tucker-osman/>)

Author: Anne Barela

Published: 2026-09-28T15:54:39Z

Content type: article

Language: en

Sources: [Adafruit Industries](<https://devfeed.tech/sources/adafruit-industries-makers-hackers-artists-designers-and-engineers.md>)

Topics: [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>), [ESP32-C3](<https://devfeed.tech/topics/esp32-c3.md>), [Linux Kernel](<https://devfeed.tech/topics/linux-kernel.md>)

Tags: [apple](<https://devfeed.tech/tags/apple.md>), [block](<https://devfeed.tech/tags/block.md>), [bootloader](<https://devfeed.tech/tags/bootloader.md>), [chip](<https://devfeed.tech/tags/chip.md>), [ecc](<https://devfeed.tech/tags/ecc.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [hacks](<https://devfeed.tech/tags/hacks.md>), [ipod](<https://devfeed.tech/tags/ipod.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nand](<https://devfeed.tech/tags/nand.md>), [nano](<https://devfeed.tech/tags/nano.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [upcycling](<https://devfeed.tech/tags/upcycling.md>), [upgrade](<https://devfeed.tech/tags/upgrade.md>)

### AI overview

A six-year project replaced an iPod Nano 3rd Generation's NAND chip with a 16GB chip. The upgrade required changes across the bootloader, disk mode, updater, and operating system firmware, plus a translator for the chip's larger page size. After correcting additional erase, ECC, and bad-block handling issues, the iPod reported 15GB and booted, synced, and played music and video.

### Source excerpt

A project write-up on tuckerosman.com documents a six year effort to put a 16GB NAND chip into an iPod Nano 3rd Generation. The Nano 3G was picked because it is the newest Nano whose NAND chip has legs instead of a BGA package. Desoldering the stock chip and fitting a larger one got a Red [...]

## Holo4: powering generalist computer-use agents

DevFeed: [Holo4: powering generalist computer-use agents](<https://devfeed.tech/articles/holo4-powering-generalist-computer-use-agents-61238.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Hcompany/holo4>)

Author: Tony Wu; Maxime Theillard; Frederic Renard; Vincent Coyette; Emrick Sinitambirivoutin; Avshalom Manevich; Antonio Loison; Antoine Bonnet; Maxime Langevin; Aleix Cambray; Léonard Benedetti; Mats L Rich

Published: 2026-09-28T09:44:05Z

Content type: release

Language: en

Sources: [Hugging Face](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [has](<https://devfeed.tech/tags/has.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [nano](<https://devfeed.tech/tags/nano.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

Holo4 is a series of agentic models in 27B dense and 35B-A3B Mixture of Experts sizes, available through the H Models API. It interacts with software through GUIs, code, MCP and APIs, and is designed for business workflows. The release also includes Holotron4 Nano. The article reports benchmark results and emphasizes lower task costs, while noting that Holo4 trails the strongest closed models on long workflows.

### Source excerpt

Holo4 is our new series of agentic models. It comes in two sizes: 27B dense and 35B-A3B Mixture of Experts. Both are available on the H Models API. We are also releasing an updated version of Holotron 3: Holotron4 Nano. Holo4 builds on our previous model and interacts with software through any available interface: GUIs, code, MCP and APIs. It scores well on academic benchmarks, but we built it for real business workflows.

## Gradient Labs gives every bank customer an AI account manager

DevFeed: [Gradient Labs gives every bank customer an AI account manager](<https://devfeed.tech/articles/gradient-labs-gives-every-bank-customer-an-ai-account-manager-6442.md>)

Original publisher: [Read original article](<https://openai.com/index/gradient-labs>)

Published: 2026-04-01T02:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [AI for customer service automation](<https://devfeed.tech/topics/ai-for-customer-service-automation.md>), [agentic workflows](<https://devfeed.tech/topics/agentic-workflows.md>), [Real-time AI interactions](<https://devfeed.tech/topics/real-time-ai-interactions.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Support](<https://devfeed.tech/topics/support.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [banking](<https://devfeed.tech/tags/banking.md>), [customer-satisfaction](<https://devfeed.tech/tags/customer-satisfaction.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [gpt-4](<https://devfeed.tech/tags/gpt-4.md>), [gpt-5-4](<https://devfeed.tech/tags/gpt-5-4.md>), [gradient](<https://devfeed.tech/tags/gradient.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mini](<https://devfeed.tech/tags/mini.md>), [nano](<https://devfeed.tech/tags/nano.md>), [startup](<https://devfeed.tech/tags/startup.md>), [support](<https://devfeed.tech/tags/support.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Gradient Labs automates complex banking support procedures with AI agents designed to preserve procedure state through interruptions while responding quickly. Its hybrid system uses OpenAI models for reasoning-heavy steps and smaller models for faster, deterministic tasks; 15+ parallel guardrails monitor compliance and sensitive situations. In an initial benchmark, GPT-4.1 reached 97% trajectory accuracy, compared with 88% for the next closest provider. The company says GPT-5.4 mini and nano deliver about 500 ms latency for voice conversations. Before deployment, Gradient Labs replays real conversations and tests synthetic edge cases; banks can select workflows, simulate conversations, begin with a small share of traffic, and expand with monitoring. Customers report CSAT up to 98% and deployments starting above 50% resolution on day one. These are company-reported results from the supplied excerpt.

### Source excerpt

Gradient Labs uses GPT-4.1 and GPT-5.4 mini and nano to power AI agents that automate banking support workflows with low latency and high reliability.

## Introducing GPT-5.4 mini and nano

DevFeed: [Introducing GPT-5.4 mini and nano](<https://devfeed.tech/articles/introducing-gpt-5-4-mini-and-nano-6493.md>)

Original publisher: [Read original article](<https://openai.com/index/introducing-gpt-5-4-mini-and-nano>)

Published: 2026-03-17T10:00:00Z

Content type: release

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [llm-reasoning](<https://devfeed.tech/topics/llm-reasoning.md>), [cursor](<https://devfeed.tech/topics/cursor.md>), [Real-time AI interactions](<https://devfeed.tech/topics/real-time-ai-interactions.md>), [ChatGPT API](<https://devfeed.tech/topics/chatgpt-api.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [agent](<https://devfeed.tech/tags/agent.md>), [api](<https://devfeed.tech/tags/api.md>), [classification](<https://devfeed.tech/tags/classification.md>), [codex](<https://devfeed.tech/tags/codex.md>), [coding](<https://devfeed.tech/tags/coding.md>), [coding-tasks](<https://devfeed.tech/tags/coding-tasks.md>), [company](<https://devfeed.tech/tags/company.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [cost](<https://devfeed.tech/tags/cost.md>), [faster](<https://devfeed.tech/tags/faster.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [gpt-5-4](<https://devfeed.tech/tags/gpt-5-4.md>), [mini](<https://devfeed.tech/tags/mini.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nano](<https://devfeed.tech/tags/nano.md>), [optimized](<https://devfeed.tech/tags/optimized.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [tool-use](<https://devfeed.tech/tags/tool-use.md>), [volume](<https://devfeed.tech/tags/volume.md>)

### AI overview

GPT-5.4 mini and nano are faster, more efficient models aimed at high-volume work. Mini improves on GPT-5 mini in coding, reasoning, multimodal understanding, and tool use while running more than twice as fast; it approaches GPT-5.4 on some evaluations, including SWE-Bench Pro and OSWorld-Verified. The article highlights low-latency coding tasks and using mini subagents for narrower work while a larger model plans and judges results. Mini is available in the API, Codex, and ChatGPT; its API supports text and image inputs, tools and computer use, has a 400k context window, and costs $0.75 per million input tokens and $4.50 per million output tokens. Nano is the smallest, cheapest model, recommended for simpler tasks such as classification, extraction, ranking, and coding subagents. It is API-only and costs $0.20 per million input tokens and $1.25 per million output tokens.

### Source excerpt

GPT-5.4 mini and nano are smaller, faster versions of GPT-5.4 optimized for coding, tool use, multimodal reasoning, and high-volume API and sub-agent workloads.

## Nano Banana 2: Combining Pro capabilities with lightning-fast speed

DevFeed: [Nano Banana 2: Combining Pro capabilities with lightning-fast speed](<https://devfeed.tech/articles/nano-banana-2-combining-pro-capabilities-with-lightning-fast-speed-6222.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/nano-banana-2-combining-pro-capabilities-with-lightning-fast-speed/>)

Author: Naina Raisinghani

Published: 2026-02-26T16:01:50Z

Content type: release

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Image](<https://devfeed.tech/topics/image.md>), [Frontier AI](<https://devfeed.tech/topics/frontier-ai.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [advanced](<https://devfeed.tech/tags/advanced.md>), [all](<https://devfeed.tech/tags/all.md>), [capabilities](<https://devfeed.tech/tags/capabilities.md>), [consistency](<https://devfeed.tech/tags/consistency.md>), [fast](<https://devfeed.tech/tags/fast.md>), [flash](<https://devfeed.tech/tags/flash.md>), [generation](<https://devfeed.tech/tags/generation.md>), [image](<https://devfeed.tech/tags/image.md>), [image-generation](<https://devfeed.tech/tags/image-generation.md>), [knowledge](<https://devfeed.tech/tags/knowledge.md>), [latest](<https://devfeed.tech/tags/latest.md>), [lightning](<https://devfeed.tech/tags/lightning.md>), [model](<https://devfeed.tech/tags/model.md>), [nano](<https://devfeed.tech/tags/nano.md>), [none](<https://devfeed.tech/tags/none.md>), [offers](<https://devfeed.tech/tags/offers.md>), [production](<https://devfeed.tech/tags/production.md>), [speed](<https://devfeed.tech/tags/speed.md>), [with](<https://devfeed.tech/tags/with.md>), [world](<https://devfeed.tech/tags/world.md>)

### AI overview

Nano Banana 2 (Gemini 3.1 Flash Image) combines fast image generation with capabilities the article associates with Nano Banana Pro, including real-world and web-search grounding, legible text generation and translation, and support for infographics and data visualizations. It claims consistency for up to five characters and 14 objects per workflow, stricter instruction following, and output sizes from 512px to 4K. Google positions Nano Banana 2 for rapid generation and iteration, while Nano Banana Pro remains available for tasks requiring maximum factual accuracy. The article says Nano Banana 2 is rolling out across Google products, with preview access in AI Studio, the Gemini API, and Vertex AI; it is Flow's default at zero credits. Google says it combines SynthID with C2PA credentials and plans to add C2PA verification to the Gemini app.

### Source excerpt

Our latest image generation model offers advanced world knowledge, production ready specs, subject consistency and more, all at Flash speed.

## KAgent Vision combines webcam capture, image and video generation, and ASL conversation

DevFeed: [KAgent Vision combines webcam capture, image and video generation, and ASL conversation](<https://devfeed.tech/articles/kagent-vision-66054.md>)

Original publisher: [Read original article](<https://github.com/automateyournetwork/kagent_vision>)

Published: 2026-02-08T12:00:00Z

Content type: tutorial

Language: en

Sources: [Automate Your Network](<https://devfeed.tech/sources/automate-your-network.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [ReAct agent Python](<https://devfeed.tech/topics/react-agent-python.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [webcam face tracking](<https://devfeed.tech/topics/webcam-face-tracking.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [google-adk](<https://devfeed.tech/topics/google-adk.md>), [webservice](<https://devfeed.tech/topics/webservice.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-api](<https://devfeed.tech/tags/ai-api.md>), [ai-tools](<https://devfeed.tech/tags/ai-tools.md>), [ai-video](<https://devfeed.tech/tags/ai-video.md>), [animate](<https://devfeed.tech/tags/animate.md>), [api](<https://devfeed.tech/tags/api.md>), [app](<https://devfeed.tech/tags/app.md>), [avfoundation](<https://devfeed.tech/tags/avfoundation.md>), [backend](<https://devfeed.tech/tags/backend.md>), [camera](<https://devfeed.tech/tags/camera.md>), [capture](<https://devfeed.tech/tags/capture.md>), [fastapi](<https://devfeed.tech/tags/fastapi.md>), [frontend](<https://devfeed.tech/tags/frontend.md>), [gemini-api](<https://devfeed.tech/tags/gemini-api.md>), [generation](<https://devfeed.tech/tags/generation.md>), [html](<https://devfeed.tech/tags/html.md>), [kagent](<https://devfeed.tech/tags/kagent.md>), [nano](<https://devfeed.tech/tags/nano.md>), [project](<https://devfeed.tech/tags/project.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

KAgent Vision is a Google ADK and MCP project that captures images from a webcam or upload, transforms them into artwork with Gemini, generates short video clips with Veo, and supports ASL conversation through a webcam. The project includes a custom web interface and setup instructions for running it locally.

### Source excerpt

A KAgent Agent with Vision - Capture photos, transform them with Nano Banana, animate them with Veo3. Supports ASL.

## Video Overviews on NotebookLM get a major upgrade with Nano Banana

DevFeed: [Video Overviews on NotebookLM get a major upgrade with Nano Banana](<https://devfeed.tech/articles/video-overviews-on-notebooklm-get-a-major-upgrade-with-nano-banana-89464.md>)

Original publisher: [Read original article](<https://blog.google/innovation-and-ai/models-and-research/google-labs/video-overviews-nano-banana/>)

Author: Eugene Lo

Published: 2025-10-13T16:00:00Z

Content type: release

Language: en

Sources: [Gemini Notebook](<https://devfeed.tech/sources/gemini-notebook.md>)

Topics: [generative-ai-tools](<https://devfeed.tech/topics/generative-ai-tools.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [synthid](<https://devfeed.tech/topics/synthid.md>)

Tags: [ai-products](<https://devfeed.tech/tags/ai-products.md>), [feature](<https://devfeed.tech/tags/feature.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [gemini-models](<https://devfeed.tech/tags/gemini-models.md>), [google-labs](<https://devfeed.tech/tags/google-labs.md>), [image-generation](<https://devfeed.tech/tags/image-generation.md>), [nano](<https://devfeed.tech/tags/nano.md>), [none](<https://devfeed.tech/tags/none.md>), [notebooklm](<https://devfeed.tech/tags/notebooklm.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

NotebookLM is rolling out an update to Video Overviews that uses Gemini's Nano Banana image generation model to create illustrations based on uploaded sources. Users can choose among six visual styles and two formats: a comprehensive Explainer or a shorter Brief. The update is rolling out to Pro users first, followed by all users in the coming weeks.

### Source excerpt

NotebookLM Video Overviews with nano banana

## OpenAI GPT-4.1-mini and GPT-4.1-nano are now generally available in GitHub Models

DevFeed: [OpenAI GPT-4.1-mini and GPT-4.1-nano are now generally available in GitHub Models](<https://devfeed.tech/articles/openai-gpt-4-1-mini-and-gpt-4-1-nano-are-now-generally-available-in-github-models-72639.md>)

Original publisher: [Read original article](<https://github.blog/changelog/2025-04-15-gpt-4-1-mini-and-gpt-4-1-nano-are-now-generally-available-in-github-models>)

Author: Allison

Published: 2025-04-15T09:44:33Z

Content type: release

Language: en

Sources: [GitHub Changelog](<https://devfeed.tech/sources/github-changelog.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [GitHub API](<https://devfeed.tech/topics/github-api.md>), [Real-time AI interactions](<https://devfeed.tech/topics/real-time-ai-interactions.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI Integration Strategies](<https://devfeed.tech/topics/ai-integration-strategies.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [applications](<https://devfeed.tech/tags/applications.md>), [chatbots](<https://devfeed.tech/tags/chatbots.md>), [code](<https://devfeed.tech/tags/code.md>), [cost](<https://devfeed.tech/tags/cost.md>), [github](<https://devfeed.tech/tags/github.md>), [github-models](<https://devfeed.tech/tags/github-models.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [gpt-4](<https://devfeed.tech/tags/gpt-4.md>), [high-performance](<https://devfeed.tech/tags/high-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [launch](<https://devfeed.tech/tags/launch.md>), [lightweight](<https://devfeed.tech/tags/lightweight.md>), [mini](<https://devfeed.tech/tags/mini.md>), [model](<https://devfeed.tech/tags/model.md>), [nano](<https://devfeed.tech/tags/nano.md>), [openai](<https://devfeed.tech/tags/openai.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### AI overview

GitHub Models now offers GPT-4.1-mini and GPT-4.1-nano as lower-cost, lower-latency variants of GPT-4.1. Both support text and image input, coding, instruction following, long-context understanding, parallel function calling, and structured outputs. GPT-4.1-mini is positioned for general-purpose reasoning and text or vision use cases; GPT-4.1-nano is described as even cheaper and faster for lightweight, high-frequency tasks at scale. The text says developers can try and compare them in the playground or use them through the GitHub API.

### Source excerpt

Alongside the launch of GPT-4.1 in GitHub Models, we're introducing GPT-4.1-mini and GPT-4.1-nano--lightweight variants of OpenAI's latest model. Designed for high performance with lower cost and latency, these models are...

## Quasar Alpha and Optimus Alpha Reveal

DevFeed: [Quasar Alpha and Optimus Alpha Reveal](<https://devfeed.tech/articles/quasar-alpha-and-optimus-alpha-reveal-78573.md>)

Original publisher: [Read original article](<https://openrouter.ai/blog/announcements/quasar-alpha-and-optimus-alpha-reveal/>)

Author: OpenRouter

Published: 2025-04-14T00:00:00Z

Content type: news

Language: en

Sources: [OpenRouter](<https://devfeed.tech/sources/openrouter.md>)

Topics: [Quasar](<https://devfeed.tech/topics/quasar.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [api](<https://devfeed.tech/tags/api.md>), [costs](<https://devfeed.tech/tags/costs.md>), [gpt-4](<https://devfeed.tech/tags/gpt-4.md>), [nano](<https://devfeed.tech/tags/nano.md>), [openai](<https://devfeed.tech/tags/openai.md>), [openrouter](<https://devfeed.tech/tags/openrouter.md>), [optimus](<https://devfeed.tech/tags/optimus.md>), [prevent](<https://devfeed.tech/tags/prevent.md>), [pricing](<https://devfeed.tech/tags/pricing.md>), [quasar](<https://devfeed.tech/tags/quasar.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [updated](<https://devfeed.tech/tags/updated.md>), [version](<https://devfeed.tech/tags/version.md>)

### AI overview

OpenRouter says Quasar Alpha and Optimus Alpha were tests of GPT-4.1, whose 1M-token context model is now generally available alongside Mini and Nano versions. The announcement lists pricing for each model and says the free alpha endpoints have been retired; requests to them return 404 with no automatic redirects to prevent unexpected costs.

### Source excerpt

Quasar Alpha and Optimus Alpha, which both topped the charts during testing, were early test versions of...

## ESP8266 Wifi With Arduino Uno and Nano

DevFeed: [ESP8266 Wifi With Arduino Uno and Nano](<https://devfeed.tech/articles/esp8266-wifi-with-arduino-uno-and-nano-20702.md>)

Original publisher: [Read original article](<https://docs.microsoft.com/archive/blogs/abhinaba/esp8266-wifi-with-arduino-uno-and-nano>)

Author: Abhinaba Basu \[MSFT\]

Published: 2016-01-23T15:37:42Z

Content type: tutorial

Language: en

Sources: [Abhinaba Basu](<https://devfeed.tech/sources/abhinaba-basu.md>)

Topics: [ESP8266](<https://devfeed.tech/topics/esp8266.md>), [Arduino](<https://devfeed.tech/topics/arduino.md>), [Internet of things](<https://devfeed.tech/topics/iot.md>), [Embedded Systems](<https://devfeed.tech/topics/embedded-systems.md>)

Tags: [arduino](<https://devfeed.tech/tags/arduino.md>), [esp8266](<https://devfeed.tech/tags/esp8266.md>), [nano](<https://devfeed.tech/tags/nano.md>), [uno](<https://devfeed.tech/tags/uno.md>), [wifi](<https://devfeed.tech/tags/wifi.md>)

### AI overview

The excerpt's key setup constraints are that the ESP8266 needs a separate 3.3V supply capable of about 200mA, and Uno/Nano serial communication requires reducing the module's baud rate from 115200 to 9600 for SoftwareSerial. The author recommends a shared ground and a resistor voltage divider on Arduino TX to protect the ESP8266's 3.3V RX input. They use Nano pins 8 and 9, noting D2/D3 failed in their tests, and provide a serial repeater sketch to pass PC commands through the Arduino. After setting the baud rate with AT+CIOBAUD=9600, the excerpt reports successful Wi-Fi scanning and connection commands. It distinguishes Uno/Nano from Mega, which has additional hardware serial capability; details here focus on Uno/Nano.

### Source excerpt

If you are trying to add Wifi connectivity to an existing Arduino project or have serious...