# c/c++ Published articles for c/c++. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Waveshare ESP32-S31-Zero - A tiny ESP32-S31 board with up to 32MB PSRAM, 32MB SPI flash DevFeed: [Waveshare ESP32-S31-Zero - A tiny ESP32-S31 board with up to 32MB PSRAM, 32MB SPI flash]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-11-07T03:00:18Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32](), [waveshare](), [PSRAM](), [RISC-V](), [802.15.4](), [Bluetooth](), [USB](), [ESP32-C6](), [ESP32-S3]() Tags: [320-mhz](), [802-15-4](), [ble](), [bluetooth](), [bluetooth-5-3](), [board](), [c-c-plus-plus](), [development](), [development-board](), [esp32](), [esp32-c6](), [esp32-s3](), [esp32-s31](), [espressif](), [flash](), [gpio](), [hardware](), [iot](), [matter](), [psram](), [spi](), [spi-flash](), [thread](), [tiny](), [waveshare](), [wifi-6](), [zero](), [zigbee]() ### AI overview The Waveshare ESP32-S31-Zero combines a compact 28 x 18 mm board with an ESP32-S31 dual-core RISC-V MCU, USB-C, Wi-Fi 6, Bluetooth, 802.15.4, and up to 32 MB PSRAM and SPI flash. Its 40-pin connector exposes options for an LCD, touchscreen, SD card, USB 2.0, and GPIO; the article notes that Ethernet MAC pins may also be reachable there, enabling a possible expansion-board design, though this is not built into the board. Initial Linux support exists, but software maturity is still developing. Eight memory/header combinations are described in theory; the excerpt says only the N32R16 model (32 MB PSRAM, 16 MB flash, unsoldered headers) was then available. ### Source excerpt Waveshare ESP32-S31-Zero is a small ESP32-S31 development board that follows the form factor of the earlier Waveshare "Zero" boards, such as the ESP32-C6-Zero-B, RA4M1-Zero, and ESP32-S3-Zero, among others. The new tiny board still features a USB-C port for power and programming, as well as eighteen through and castellated holes for GPIO and power signals, but it boasts a more powerful ESP32-S31 dual-core RISC-V MCU clocked at up to 320 MHz, featuring 2.4 GHz WiFi 6, Bluetooth 5.3, and 802.15.4 connectivity, as well as up to 32MB on-chip PSRAM and a 16MB or 32MB SPI flash chip. Since the ESP32-S31 has quite a few extra I/Os, Waveshare also added a 40-pin high-density connector for LCD, touchscreen, SD card, USB 2.0, and extra GPIOs. Waveshare ESP32-S31-Zero specifications: SoC - Espressif Systems ESP32-S31 CPU RISC-V HP (High-performance) RV32IMAFCP CPU @ 320 MHz with FPU, SIMD, etc. RISC-V LP (Low-power) MCU core Memory - [...] The post Waveshare ESP32-S31-Zero - A tiny ESP32-S31 board with up to 32MB PSRAM, 32MB SPI flash appeared first on CNX Software - Embedded Systems News. ## ActQuant: Sub-4-bit Action-Guided Backbone Weight-Only Quantization for Vision-Language-Action Models DevFeed: [ActQuant: Sub-4-bit Action-Guided Backbone Weight-Only Quantization for Vision-Language-Action Models]() Original publisher: [Read original article]() Author: Arash Akbari, Arman Akbari, Masih Eskandar, Qitao Tan, Yixiao Chen, Jingwu Luo, Bertha Pangaribuan, Liyun Zhang, Jennifer Dy, Geng Yuan, Xue Lin, Gaowen Liu, Stratis Ioannidis, Yanzhi Wang Published: 2026-11-06T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [quantization](), [Post-training optimization]() Tags: [6-dof](), [actions](), [agent](), [agentic](), [architectures](), [arm](), [benchmark](), [bits](), [block](), [c](), [c-c-plus-plus](), [c-plus-plus](), [compute](), [cs-ai](), [cs-cv](), [language](), [models](), [quantization](), [vision](), [vision-language-action](), [vision-language-action-models](), [weight]() ### AI overview ActQuant reduces VLA backbone weight size through action-guided mixed-precision post-training quantization. It assigns each weight matrix a bit-width based on its contribution to action prediction, then tunes block-level scales using action-aware curvature. The accompanying OmniModel.cpp pipeline converts models to a native C/C++ runtime with low-bit kernels. On LIBERO, the abstract reports that ActQuant is the only evaluated method at or below 3 bits per weight, retaining 95.0% on OpenVLA-OFT and 94.8% on π₀.₅. At 2.5 bits per weight, OpenVLA-OFT retains 90.1% while its backbone shrinks from 14.3 GB to 2.7 GB. On a physical 6-DoF UR3 arm, quantized π₀.₅ retains baseline success rate with 2.5x lower memory use. The supplied material is an abstract and does not provide further experimental details. ### Source excerpt arXiv:2605.24011v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge platforms impractical. Aggressive, sub-4-bit weight quantization is the natural solution, yet existing post-training quantization (PTQ) methods suffer severe performance degradation in this regime. To address this, we introduce ActQuant, an action-guided mixed-precision PTQ framework that operates in two stages: (1) an inter-tensor bit allocator that assigns each weight matrix a single bit-width based on how much it contributes to predicting the agent's actions; (2) an intra-tensor scale optimizer tunes per-block quantization scales using action-aware curvature, so that dynamic range is concentrated on the weights most influential for control. To deliver the on-device benefits of our aggressive quantization, we further introduce OmniModel.cpp, an agentic conversion pipeline that ports architectures into a native C/C++ runtime with efficient low-bit kernels. We evaluate ActQuant both in simulation and on a real-world 6-DoF UR3 arm, with all models deployed through OmniModel.cpp. On the LIBERO benchmark, ActQuant is the only method that operates at or below 3 bits-per-weight, retaining 95.0% on OpenVLA-OFT and 94.8% on $\pi_{0.5}$. Pushed further, ActQuant reaches 2.5 bpw at 90.1% on OpenVLA-OFT, compressing the backbone from 14.3 GB to 2.7 GB (5.3$\times$). On the physical UR3 arm, $\pi_{0.5}$ quantized with ActQuant retains the baseline's success rate while reducing the memory footprint by 2.5$\times$. ## P4 Launcher offers a PC-like and retro gaming experience on ESP32-P4 boards DevFeed: [P4 Launcher offers a PC-like and retro gaming experience on ESP32-P4 boards]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-11-05T10:27:01Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32-P4](), [Embedded Systems](), [PSRAM](), [m5stack]() Tags: [2600](), [3d](), [app](), [application](), [application-platform](), [applications](), [apps](), [atari](), [atari-2600](), [c-c-plus-plus](), [card](), [clock](), [device](), [display](), [doom](), [esp32](), [esp32-p4](), [espressif](), [experience](), [gaming](), [hardware](), [launcher](), [open-source](), [p4](), [pc](), [retro](), [video]() ### AI overview P4 Launcher turns ESP32-P4 touchscreen boards into a microSD-based app and gaming platform: it boots to an LVGL interface, loads .papp applications into PSRAM, and lets users add or remove apps without reflashing or rebooting. The article reports 21 available apps, mostly emulators and games, and support for three devkits, controller input, up to four input devices, and audio streams. Firmware can be updated from microSD or GitHub, with automatic rollback if an update fails to start. Developers get an SDK and compatibility layers that allow some existing games to run with their original glue code. The project is described as more flexible than ESPBrookesia because apps reside on the card; its current focus is gaming. ### Source excerpt Gil Tal has developed the P4 Launcher, an open-source application platform designed to bring a PC-like experience to ESP32-P4 boards with an attached touchscreen display. The launcher boots directly into its interface, while applications (.papp files) are stored on a microSD card and loaded into PSRAM when launched. You can add, remove, and run applications without reflashing the firmware or rebooting the device by simply copying files to the microSD card.Gil says 21 PAPP (PSRAM APP) applications are available now, mostly emulators and games, including Neo Geo and Atari 2600 emulators, Doom, Quake, Duke Nukem 3D and OpenTyrian, plus Clock and Files. Like ESPBrookesia, the P4 Launcher user interface runs on ESP32-P4, but it's more flexible since it supports apps stored on the microSD card and targets mainly gaming for now. The launcher currently works on the Guition ESP32-P4 4.3″ (800x480), the Guition JC1060P470C 7″ (1024x600), and the smaller M5Stack [...] The post P4 Launcher offers a PC-like and retro gaming experience on ESP32-P4 boards appeared first on CNX Software - Embedded Systems News. ## SBVUL: Statement-Based Vulnerability Detection DevFeed: [SBVUL: Statement-Based Vulnerability Detection]() Original publisher: [Read original article]() Author: Martin Pinzger, Katharina Stengg, Christian Macho, Stefan Sch\\"oberl, V\\'ictor Mayoral Vilches Published: 2026-11-05T05:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [threat detection](), [Large Language Model](), [llm-reasoning]() Tags: [c-c-plus-plus](), [cs-se](), [deepseek-v4-1-flash](), [large-language-models-llms](), [opus-5-5](), [qwen-3-8](), [statement](), [vulnerability-detection]() ### AI overview SBVul uses two LLM agents to locate exploitable statements in C/C++ functions: one explores the function and repository to propose candidates, and another checks each candidate's control and data flow, confirming it, ruling it out, or marking it uncertain. In an evaluation on 200 manually annotated PrimeVul function pairs, Phase 1 found ground-truth statements in 56-77% of vulnerable functions; Phase 2 confirmed them with explanations accepted by an LLM judge in 26-73%. Models also confirmed candidates in 23-61% of patched functions. Opus 5.5 (xhigh) achieved the best reported statement-level precision, 0.315 at 0.605 recall, and classified 42.0% of pairs correctly, versus 33.8% for the best prior approach cited. ### Source excerpt arXiv:2611.06135v1 Announce Type: new Abstract: Many recent approaches for vulnerability detection use large language models (LLMs) to classify functions as vulnerable or benign, leaving developers to locate the exploitable statements. Furthermore, while these approaches have shown performance improvements, they still fall short in distinguishing vulnerable from patched real-world functions. This paper presents SBVul, an agentic approach that detects exploitable statements in C/C++ functions. In Phase~1, an LLM agent explores the function and its repository and identifies candidate statements. In Phase~2, a second agent analyzes the control and data flow of each candidate and confirms it, rules it out, or marks it uncertain. We created a manually verified ground truth of exploitable statements for 200 pairs of the PrimeVul Paired test set and ran SBVul with the open-weight models Qwen 3.8 and DeepSeek V4.1 Flash and Anthropic's Sonnet 5.5 and Opus 5.5. Phase~1 identifies ground-truth statements in 56--77% of the vulnerable functions, and Phase~2 confirms such a statement with an explanation accepted by an LLM judge in 26--73%. However, all models also confirm candidates in 23--61% of the patched functions. Across vulnerable and patched functions, the highest statement-level precision is 0.315 at a recall of 0.605 obtained with Opus 5.5 (xhigh). At the function level, SBVul with Opus 5.5 classifies 42.0% of the pairs correctly, compared with 33.8% reported for the best previous approach. ## CodeQL 2.27.4 improves accuracy and query metadata DevFeed: [CodeQL 2.27.4 improves accuracy and query metadata]() Original publisher: [Read original article]() Author: Allison Published: 2026-11-04T20:04:52Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [Static code analysis](), [GitHub]() Tags: [analysis](), [application-security](), [arithmetic](), [blog](), [c](), [c-c-plus-plus](), [c-plus-plus](), [c-sharp](), [changelog](), [changes](), [code-scanning](), [codeql](), [engine](), [github](), [improvement](), [improvements](), [metadata](), [post](), [query](), [security](), [static](), [static-analysis]() ### AI overview CodeQL 2.27.4 improves analysis accuracy across several languages: Java/Kotlin queries recognize more sanitizers and path guards; Rust queries have fewer false positives; and C/C++ queries suppress reports for intentional `std::byte*` pointer arithmetic and variables marked `[[maybe_unused]]`. It also removes CWE-730 metadata from specified regex-related queries across C#, Java/Kotlin, JavaScript/TypeScript, Python, Ruby, and Swift. The CLI sends JVM warnings and initialization errors to stderr, preserving machine-readable output. When bundling or publishing packs, it omits files whose resolved symbolic links point outside the pack root and warns about them. GitHub deploys new CodeQL versions automatically for code scanning on github.com; this release is slated for GHES 3.24, while older GHES versions can be upgraded manually. ### Source excerpt CodeQL is the static analysis engine behind GitHub code scanning, which finds and remediates security issues in your code. We've recently released CodeQL 2.27.4 with query accuracy improvements across several... The post CodeQL 2.27.4 improves accuracy and query metadata appeared first on The GitHub Blog. ## Trellis: Bringing Compiler-Guided Repair into Agentic Code Generation DevFeed: [Trellis: Bringing Compiler-Guided Repair into Agentic Code Generation]() Original publisher: [Read original article]() Author: Alexander Du, Jianjun Ou, Danyang Zhuo, Matthew Lentz Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Software Engineering]() Topics: [agentic-coding](), [AI-assisted coding](), [Compilers and Linkers]() Tags: [agentic](), [asynchronous](), [c-c-plus-plus](), [c-plus-plus](), [clang](), [code-generation](), [compilation](), [compiler](), [costs](), [cs-ai](), [cs-pl](), [cs-se](), [feedback](), [incremental](), [opencode](), [repair](), [tasks](), [test](), [token-consumption](), [trellis](), [typescript]() ### AI overview Trellis uses incremental compiler feedback to repair coding-agent output, based on the observation that many compile errors are small and local to recent edits. It applies rollback-based search for targeted repairs, with asynchronous checking and checkpointing to control execution costs. The authors adapted Clang and tsc to its checker interface and integrated it into OpenCode. On C/C++ and TypeScript software engineering tasks, Trellis improved compilation rates by up to 13% and test pass rates by up to 11%, with token consumption comparable to baseline OpenCode. ### Source excerpt arXiv:2605.15238v2 Announce Type: replace Abstract: Coding agents regularly generate programs that do not compile and require substantial tokens to repair. Yet most repairs are small and local to the edit that caused them. We present Trellis, a system that integrates incremental compiler feedback into code generation to improve code correctness while controlling repair costs. Trellis uses rollback-based search for targeted repair and asynchronous checking and checkpointing for efficient execution. We adapt Clang and tsc to Trellis's checker interface and integrate Trellis transparently into OpenCode. On C/C++ and TypeScript software engineering tasks, Trellis improves compilation rate by up to 13% and test pass rate by up to 11% with comparable token consumption to baseline OpenCode. ## FuzzAgent: Multi-Agent System for End-to-End Evolutionary Library Fuzzing DevFeed: [FuzzAgent: Multi-Agent System for End-to-End Evolutionary Library Fuzzing]() Original publisher: [Read original article]() Author: Yunlong Lyu, Peng Chen, Fengyi Wu, Junzhe Yu, Hao Chen Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Fuzzing/Fuzz testing](), [Multi Agent Systems](), [hierarchical agent systems](), [Library]() Tags: [agent](), [api](), [bugs](), [c](), [c-c-plus-plus](), [c-plus-plus](), [campaign](), [complete](), [coverage](), [cs-cr](), [cs-se](), [dependency](), [diagnosis](), [execution](), [fuzzing](), [generation](), [harness](), [integration](), [knowledge](), [library](), [multi-agent](), [multi-agent-system]() ### AI overview FuzzAgent builds and evolves complete library fuzzing campaigns from a source tree, using hierarchical coverage diagnosis to locate blockers, dependency-aware harness generation to meet API lifecycle requirements, and runtime object-state recovery to distinguish harness misuse from library bugs. In an evaluation on 20 real-world C/C++ libraries, it completed the lifecycle without human intervention at $3.17 per library and reached 179,619 branches, exceeding five named baselines by reported margins of 45.1% to 191.2%. It identified 102 new library bugs; upstream maintainers had acknowledged or fixed 90. ### Source excerpt arXiv:2605.14431v2 Announce Type: replace Abstract: Library fuzzing synthesizes harnesses that expose new API interactions, but deploying it end to end still requires extensive project-specific integration. Existing LLM-based systems automate individual steps, yet they treat execution outcomes as coarse signals: a stalled campaign does not reveal which state blocks progress, and a crash trace does not reveal whether the library or the harness is at fault. We present FuzzAgent, a multi-agent system that builds and evolves a complete fuzzing campaign from a source tree alone. Specialized agents acquire project knowledge and repair campaign artifacts automatically. Hierarchical coverage diagnosis localizes blockers from modules down to branches, and dependency-aware generation converts them into harnesses with the required API lifecycle. Crash validation recovers runtime object states to distinguish harness misuse from library bugs. We evaluate FuzzAgent on 20 real-world C/C++ libraries against five state-of-the-art baselines (OSS-Fuzz, OSS-Fuzz-Gen, PromptFuzz, PromeFuzz, and ProteusFuzz). FuzzAgent completes the full fuzzing lifecycle for all 20 libraries without human intervention at $3.17 per library. On the 20 libraries, it reaches 179619 branches, exceeding OSS-Fuzz, PromptFuzz, ProteusFuzz, PromeFuzz, and OSS-Fuzz-Gen by 45.1%, 73.2%, 87.7%, 92.1%, and 191.2%, respectively. FuzzAgent also identifies 102 new library bugs, 90 of which have already been acknowledged or fixed by upstream maintainers. ## ICALL-BENCH: Benchmarking LLM Interprocedural Reasoning in Binary Programs DevFeed: [ICALL-BENCH: Benchmarking LLM Interprocedural Reasoning in Binary Programs]() Original publisher: [Read original article]() Author: Shenglong Yao, Zirui Liu, Xiangchi Yuan, Wenke Lee Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Reverse Engineering](), [code analysis](), [llm-reasoning](), [Large Language Model](), [Artificial Intelligence]() Tags: [agents](), [bench](), [benchmarking](), [binary-analysis](), [c-c-plus-plus](), [call](), [challenge](), [cs-cr](), [cs-se](), [functions](), [internet](), [llm](), [optimization](), [performance](), [primary](), [reasoning](), [recovery](), [robust](), [semantics](), [static](), [target]() ### AI overview ICALL-BENCH tests whether language models can trace interprocedural semantics in compiled binaries, using indirect-call target recovery as a measurable probe. It covers 50 real-world C/C++ programs across complexity levels and optimization settings, and compares model-only reasoning, static tool-augmented agents, and Internet-enabled agents. Across five models and their agents, program-level verifiable success ranges from 23% to 81%. More information access does not reliably improve results, especially on the hardest cases, where deeper interprocedural reasoning remains a bottleneck. The abstract concludes that robust binary interprocedural reasoning remains an open challenge. ### Source excerpt arXiv:2610.37871v1 Announce Type: new Abstract: Recent benchmarks show that LLMs achieve strong performance in both function-level binary understanding and end-to-end binary reverse engineering tasks, but whether LLMs can reason over interprocedural semantics in compiled binaries remains unclear. Function-level benchmarks offer limited visibility into cross-function relationships, while whole-program benchmarks typically evaluate final outcomes of tasks that may be solvable from local clues alone, making it difficult to disentangle interprocedural reasoning from exploration, tool use, execution, and retrieval. To address this limitation, we introduce ICALL-BENCH, a benchmark that directly evaluates whether LLMs and agents can understand and reason over interprocedural semantics in binary programs, using indirect-call target recovery as a measurable probe. ICALL-BENCH covers 50 complete real-world C/C++ programs spanning different complexity levels and multiple optimization configurations, with an average of 74,746 lines of code and 941 functions per program. We further introduce three analysis regimes spanning model-only reasoning, static tool-augmented agents, and Internet-enabled open-world agents to examine how intrinsic reasoning, active information acquisition, and external knowledge each contribute to interprocedural binary analysis. We evaluate five recent LLMs and their corresponding agents. Our results reveal a wide separation across current models and agents, with program-level verifiable success rates ranging from 23% to 81%. They also show that greater information access does not consistently lead to better results, with particularly limited gains on the hardest cases, where deeper interprocedural reasoning remains the primary bottleneck. Uneven performance across models and frequent verifiable failures even under an optimistic evaluation indicate that robust binary interprocedural reasoning remains an open challenge. ## When Does the Dependency Graph Help Code-to-Architecture Mapping? DevFeed: [When Does the Dependency Graph Help Code-to-Architecture Mapping?]() Original publisher: [Read original article]() Author: Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist, Anna Wingkvist Published: 2026-10-28T04:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning](), [arXiv: Software Engineering]() Topics: [Graphs](), [mlp](), [code analysis](), [model architecture]() Tags: [22](), [abstract](), [aggregation](), [all](), [architecture](), [arxiv](), [attention](), [bayes](), [benefit](), [c](), [c-c-plus-plus](), [c-plus-plus](), [chrome](), [classification](), [code](), [cs-lg](), [cs-se](), [dependency](), [dependency-graph](), [graph](), [help](), [mapping]() ### AI overview Across ten open-source Java and C/C++ systems, graph-based models and a graph-free MLP all beat the Naive Bayes reference on both F1 metrics. GAT had the highest average system-level median scores (83.5 macro-F1 and 92.2 micro-F1), though the MLP matched or surpassed learned graph models on several systems. On Chrome's 18,343 files, GAT and GCN exceeded MLP by 22 and 19 macro-F1 points and had fewer persistently misclassified files. For both GCN and GAT, preserving dependency types for multi-relational aggregation outperformed merging edges into one type on every system. Connectivity and homophily were associated with where message passing helped; some rankings varied by training seed, but the large Chrome advantage remained. ### Source excerpt arXiv:2610.19769v2 Announce Type: replace Abstract: Architectural technical debt arises when a system's implementation drifts from its inten ded modular decomposition, while the mapping between source files and architectural modu les is missing, incomplete, or costly to maintain manually. We formulate code-to-archite cture mapping as semi-supervised node classification over a directed multi-relational de pendency graph, with files as nodes, typed dependencies as edges, and lexical features d erived from code identifiers and directory layout. Starting from a small labeled sample, an iterative self-training procedure expands the mapping to the remaining files. We com pare a graph convolutional network (GCN), a graph attention network (GAT), and a graph-f ree multi-layer perceptron (MLP), using identical features and training procedures for t he learned models, against a Naive Bayes reference. Across 100 runs on ten open-source J ava and C/C++ systems, all three neural models outperform the reference on both F1 metri cs. GAT achieves the highest averages of system-level median macro and micro F1, at 83.5 and 92.2, respectively, whereas MLP remains competitive or superior on several systems. On Chrome, the largest system with 18,343 files, GAT and GCN exceed MLP by 22 and 19 ma cro-F1 points, respectively, and have substantially fewer persistently misclassified fil es. Connectivity and homophily are associated with where neural message passing helps, w hile multi-relational aggregation outperforms a single merged edge type on every system for both GCN and GAT. A follow-up study finds that several model rankings depend on the training seed, qualifying smaller advantages without removing the large graph benefit ob served on Chrome. ## Generation and Verification: Human-Agent Collaboration for Safety-Critical Automotive Software Engineering DevFeed: [Generation and Verification: Human-Agent Collaboration for Safety-Critical Automotive Software Engineering]() Original publisher: [Read original article]() Author: Elizaveta Zinovyeva, Aiham Taleb, Nikita Kozodoi, Daniel Schleicher, Amir Mahdi Namazi, Martin Kraus, Levent Kent, Brian Jensen Published: 2026-10-28T04:00:00Z Content type: news Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Software Engineering]() Topics: [coding assistant](), [Enhancing AI model performance](), [Software Testing](), [engineering-culture]() Tags: [agent](), [agents](), [architecture](), [architectures](), [automotive](), [automotive-software](), [automotive-software-engineering](), [autosar](), [behavior](), [c](), [c-c-plus-plus](), [c-plus-plus](), [collaboration](), [critical](), [cs-ai](), [cs-se](), [engineering](), [generation](), [safety](), [software](), [software-engineering](), [verification]() ### AI overview The assistant for automotive C/C++ makes verification a user-invoked step: developers can generate, edit, or run verify on any code through the Model Context Protocol, including their own code. Its automotive customization uses prompt engineering or fine-tuning and a dataset pairing AUMOVIO production functions with requirements and UML behavior. In a two-week pilot, developers said verify was the most-used tool; 75% preferred the faster prompt-engineered configuration. MISRA- and AUTOSAR-inspired checks found fewer C violations with prompt engineering, while fine-tuning better matched C++ architecture patterns. ### Source excerpt arXiv:2610.35982v1 Announce Type: cross Abstract: Research on coding agents mainly focuses on generation quality. Yet, safety-critical automotive software makes two additional demands. It prioritizes verification, since every change is reviewed, traced to a requirement, and checked against industry standards such as MISRA and AUTOSAR before it can be committed. It is also a narrow domain, written against layered architectures, embedded resource constraints, and organization-specific interface conventions that are scarce in the data general-purpose LLMs are trained on. We present a coding assistant for automotive C/C++ that addresses both demands. First, it exposes three tools to the user over the Model Context Protocol (generate, edit, and verify), allowing developers to run verify on any code on demand rather than relying on the agent to check its own output. Second, rather than using a general-purpose model, we customize the underlying LLM for the automotive domain through prompt engineering and fine-tuning. We construct a dataset from automotive production code of AUMOVIO, where each function is paired with the requirement texts and Unified Modeling Language (UML) behavior it must satisfy. In a two-week pilot on active production projects, developers report verify as the most-used tool and confirm using it on their own code and not only on generated output. Comparing model customization strategies, 75% prefer the faster prompt-engineered configuration. MISRA- and AUTOSAR-inspired pattern checkers indicate that prompt engineering achieves a lower number of violations on C, while fine-tuning shows better conformance with architecture patterns for C++. ## Synergy Logic SLM-RP4 - A Raspberry Pi 4 PLC with modular industrial I/O DevFeed: [Synergy Logic SLM-RP4 - A Raspberry Pi 4 PLC with modular industrial I/O]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-10-27T09:00:19Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Raspberry Pi](), [Modbus](), [Bluetooth]() Tags: [acquisition](), [automation](), [bluetooth](), [broadcom-bcmxxxx](), [c-c-plus-plus](), [debian](), [din-rail](), [fanless](), [gigabit-ethernet](), [hardware](), [hmi](), [i-o](), [industrial](), [industrial-automation](), [industrial-iot](), [iot](), [linux](), [modbus](), [modular](), [plc](), [python](), [raspberry-pi](), [raspberry-pi-4](), [raspberry-pi-os](), [scada](), [spi](), [video](), [wifi-5]() ### AI overview The Synergy Logic SLM-RP4 is a DIN-rail Raspberry Pi 4 PLC with a built-in SPI backplane for up to eight SLM modules and 128 I/O points. Its modules cover digital and relay I/O, analog signals, and thermocouples; they connect without inter-module cables, but are not hot-swappable. It runs Raspberry Pi OS with OpenPLC Runtime and supports IEC 61131-3 programming, plus Python and C++ function blocks. The backplane allows configurable 1-1000 ms scan intervals (10 ms by default) and can stop PLC execution and reset outputs after excessive communication errors. Python and C++ libraries can control the backplane without OpenPLC, but cannot be used alongside it. The controller also includes a hardware watchdog and documentation for FUXA dashboards over Modbus TCP. It is listed at $249, with I/O modules sold separately. ### Source excerpt Synergy Logic SLM-RP4 industrial PLC is built around a Raspberry Pi 4 SBC and designed for industrial automation, machine control, data acquisition, and Industrial IoT (IIoT) applications. The DIN-rail mountable controller supports 24V DC input and an SPI-based backplane that connects up to eight Synergy Logic I/O modules, providing up to 128 I/O points. It also features Gigabit Ethernet, WiFi 5, Bluetooth 5.0, USB ports, two micro-HDMI outputs, and a fanless aluminum enclosure. The system runs Raspberry Pi OS with OpenPLC pre-installed for standard IEC 61131-3 logic programming. Synergy Logic SLM-RP4 specifications: SBC - Raspberry Pi 4 Model B (2GB RAM) Storage - 16GB microSD card Video - 2x micro-HDMI ports Audio - 3.5mm audio jack Networking Gigabit Ethernet port Dual-band WiFi 5 (802.11ac) Bluetooth 5.0/BLE USB 2x USB 3.0 Type-A ports 2x USB 2.0 Type-A ports 1x USB-C port for auxiliary power I/O Expansion - SPI-based backplane supporting up [...] The post Synergy Logic SLM-RP4 - A Raspberry Pi 4 PLC with modular industrial I/O appeared first on CNX Software - Embedded Systems News. ## Adafruit Metro ESP32-P4 Hydra - An Arduino UNO-shaped ESP32-P4 + ESP32-C6 board with WiFi 6, MIPI interfaces DevFeed: [Adafruit Metro ESP32-P4 Hydra - An Arduino UNO-shaped ESP32-P4 + ESP32-C6 board with WiFi 6, MIPI interfaces]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-10-26T09:00:37Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32-P4](), [ESP32-C6](), [Embedded Systems](), [RISC-V]() Tags: [40-mhz](), [adafruit](), [ai](), [arduino](), [battery](), [bluetooth](), [bluetooth-5-3](), [board](), [c-c-plus-plus](), [camera](), [circuitpython](), [development-board](), [esp32](), [esp32-c6](), [esp32-p4](), [espressif](), [hardware](), [hmi](), [hydra](), [interfaces](), [iot](), [metro](), [metro-esp32-p4-hydra](), [mipi](), [raspberry-pi](), [uno](), [wifi](), [wifi-6]() ### AI overview The Metro ESP32-P4 Hydra pairs an ESP32-P4 with an ESP32-C6 wireless module in an Arduino UNO-shaped board. It provides 16MB PSRAM and flash, WiFi 6, Bluetooth LE 5.3, and 802.15.4 for Thread and Zigbee, though the latter may require additional firmware integration. Two-lane Raspberry Pi-format MIPI connectors support the Raspberry Pi Touch Display 2 and OV5647-based Zero-style cameras; the IMX708 Camera Module 3 is not supported. Other hardware includes a USB 2.0 High-Speed host port, microSD, UNO headers, STEMMA QT, and LiPo charging. Programming is supported through ESP-IDF and TinyUF2; CircuitPython support is in alpha, and Adafruit says Arduino support is in development. The board ships with the original ESP32-P4 v1 chipset, and the excerpt says detailed documentation was not available. Listed price: $34.95. ### Source excerpt Adafruit has launched the Metro ESP32-P4 Hydra, an Arduino UNO-shaped development board with ESP32-P4, 16MB PSRAM, and 16MB NOR flash, and an ESP32-C6 for WiFi 6, Bluetooth 5.3 LE, Thread, and Zigbee connectivity. Other features include Raspberry Pi-compatible MIPI DSI and CSI interfaces, a USB 2.0 High-Speed host port, a microSD card slot, and Arduino UNO expansion headers. The board also includes a STEMMA QT connector, LiPo battery charging, and multiple power input options, making it suitable for HMI, camera, and IoT applications. Adafruit Metro ESP32-P4 Hydra specifications: Microcontroller - Espressif Systems ESP32-P4 (v1 chipset) CPU Dual-core 32-bit RISC-V HP (High-performance) CPU @ up to 360 MHz with AI instructions extension and single-precision FPU Single-RISC-V LP (Low-power) MCU core @ up to 40 MHz with 8KB of zero-wait TCM RAM Memory 768 KB HP L2MEM (for dual-core CPU), 32 KB LP SRAM, 8 KB TCM (for LP MCU core) 16MB PSRAM GPU [...] The post Adafruit Metro ESP32-P4 Hydra - An Arduino UNO-shaped ESP32-P4 + ESP32-C6 board with WiFi 6, MIPI interfaces appeared first on CNX Software - Embedded Systems News. ## Deki Engine - ESP32 gets an open source Unity-style editor and C++ 2D game engine DevFeed: [Deki Engine - ESP32 gets an open source Unity-style editor and C++ 2D game engine]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-10-25T03:00:49Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Game engine](), [Open Source](), [Unity]() Tags: [apache-2-0](), [c-c-plus-plus](), [c-plus-plus](), [editor](), [elecrow](), [engine](), [esp32](), [espressif](), [game](), [gaming](), [graphics](), [lilygo](), [open-source](), [unity](), [video]() ### AI overview Deki Engine is an open-source modular C++ 2D game engine with a Unity-style visual editor, aimed at ESP32 devices. Developers can build scenes from sprites, text, particles, tilemaps, state machines and tweens, select a board in the editor, and flash the project. The editor and compiler are available for Windows and Linux; projects can also run on desktop through SDL3 or headlessly without a renderer. The excerpt reports 60 FPS on ESP32-S3 and lists support for LilyGO T-Deck Plus and T-Display S3 AMOLED, with CrowPanel Advanced 7-inch ESP32-P4 support upcoming. Packages add hardware features and software capabilities including audio over I2S, Wi-Fi, BLE, IMU, RTC, a Tiled importer and software 3D rendering. Games can run on Tactility OS, and the project is released under Apache 2.0. The source says the documentation is difficult to navigate and notes uncertainty about software 3D performance on ESP32. ### Source excerpt Deki Engine is an open-source modular C++ engine with a Unity-style visual editor for embedded devices, currently optimized for ESP32 platforms such as LilyGO T-Deck Plus, T-Display S3 AMOLED, and soon Elecrow CrowPanel Advanced 7″ ESP32-P4. The editor is available for Windows and Linux with a compiler for the 2D graphics engine, and everything is released under an Apache 2.0 license on GitHub. The project also works on desktop through SDL3, which may be convenient during development. Some of the highlights of the Deki Engine project: Workflow Build scenes visually (sprites, text, particles, tilemaps, state machines, tweens) Pick a board in the editor's Build panel Flash it. The project also runs on desktop through SDL3, or headless with no renderer Performance - 60 FPS on ESP32-S3 (See demo video below) Package-based solution Hardware packages LilyGO T-Deck Plus with support for display, touch, keyboard, trackball, SD card, GPS and LoRa (SX1262) [...] The post Deki Engine - ESP32 gets an open source Unity-style editor and C++ 2D game engine appeared first on CNX Software - Embedded Systems News. ## JANUS: Agentic Cross-Language Fuzzing Harness Generation for Android Native Libraries DevFeed: [JANUS: Agentic Cross-Language Fuzzing Harness Generation for Android Native Libraries]() Original publisher: [Read original article]() Author: Xiangyu Zhang, Yucheng Su, Lingling Fan, Yinan Li, Sen Chen Published: 2026-10-23T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Fuzzing/Fuzz testing](), [Agent Harness]() Tags: [agentic](), [android](), [api](), [attack-surface](), [automatic](), [autonomous](), [bridging](), [c](), [c-c-plus-plus](), [c-plus-plus](), [call](), [complex](), [cross](), [cs-cr](), [cs-se](), [fuzzing](), [generation](), [harness](), [janus](), [language](), [libraries](), [native]() ### AI overview JANUS generates JNI-aware fuzzing harnesses for Android native libraries by iteratively resolving explicit and implicit cross-language calls to recover app runtime context, then recursively assembling composite arguments bottom-up. The approach targets gaps in existing tools: precise JNI-crossing analysis may not scale, lightweight approximations can miss invocation patterns, and prior tools do not construct complex composite objects. Across 44 popular apps and 21,386 JNI methods, JANUS produced 6,094 valid harnesses, over 5.9 times as many as state-of-the-art automatic harnessing tools. Tests on pre-installed apps from five Android vendors also uncovered six previously unknown vulnerabilities in closed-source native libraries. ### Source excerpt arXiv:2610.30218v1 Announce Type: cross Abstract: Modern Android apps extensively rely on native libraries, bridging Java/Kotlin and C/C++ through the Java Native Interface (JNI). This cross-language interoperation, however, exposes the memory-safe Android Runtime to the unsafe C/C++ world, creating an attack surface that has been repeatedly exploited for full device compromise. While fuzzing is a proven technique for vetting native libraries, state-of-the-art harness generation tools struggle to faithfully capture closed-source cross-language API usage: (i) precise program analysis across the JNI boundary does not scale, whereas lightweight approximations miss critical invocation patterns; and (ii) none tackles the construction of complex composite objects consumed by JNI methods. In this paper, we propose JANUS, the first agentic approach to synthesizing cross-language JNI-aware fuzzing harnesses for Android native libraries. JANUS iteratively resolves both explicit and implicit cross-language call dependencies to recover app-specific runtime context, and recursively assembles cross-language argument objects in a bottom-up manner. JANUS leverages an autonomous agentic workflow to reduce reliance on hand-crafted heuristics, capturing the vast landscape of hidden API usage knowledge embedded in closed-source native libraries. Evaluation on 44 popular real-world apps (such as Google and YouTube Music, covering 21,386 JNI methods total) shows that JANUS produces over 5.9 times as many valid harnesses (6,094) as state-of-the-art automatic harnessing tools. On pre-installed apps from five mainstream Android vendors (e.g., Xiaomi, Vivo, Samsung), JANUS further uncovers 6 previously unknown vulnerabilities in closed-source native libraries. ## Rotary encoder with 1.8-inch touch display gets upgrade to ESP32-S31 SoC DevFeed: [Rotary encoder with 1.8-inch touch display gets upgrade to ESP32-S31 SoC]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-10-22T00:00:24Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [touchscreen](), [RISC-V](), [PSRAM](), [Bluetooth LE](), [802.15.4](), [Lvgl]() Tags: [320-mhz](), [802-15-4](), [also](), [arduino](), [audio](), [battery](), [bluetooth](), [bluetooth-5-4](), [board](), [built-in](), [c-c-plus-plus](), [card](), [configuration](), [core](), [coverage](), [cpu](), [development](), [devkit](), [display](), [esp32](), [esp32-s31](), [espressif](), [graphics](), [hardware](), [jpeg](), [smart-speaker](), [soc](), [touchscreen](), [upgrade](), [with]() ### AI overview The GUITION JC3636F118C-I-YR1 combines a 1.8-inch round touchscreen and haptic rotary knob with an ESP32-S31NRV16, 16 MB PSRAM, Wi-Fi 6, Bluetooth 5.4, and 802.15.4. It also has a speaker, microphone, MicroSD slot, and optional rechargeable battery. Preloaded functions include clock and media playback; USB-C orientation determines whether it is used for file transfer or firmware programming. Although GUITION mentions Arduino IDE, ESP-IDF, MicroPython, and its own programming environment, the supplied development archive contains ESP-IDF 6.1.0 samples and an empty Arduino folder. The excerpt describes faster cores, multimedia features, broader wireless options, and faster USB as benefits over the earlier ESP32-S3 model, while noting less mature firmware and slightly higher cost. Listed prices are $40.09 with battery and $38.49 without, before taxes. ### Source excerpt Following our coverage of a 4.3-inch ESP32-S31 display development board, a Facebook reader pointed out the GUITION JC3636F118C-I-YR1 rotary encoder with a built-in 1.8-inch touchscreen display. It appears to be an update to an earlier ESP32-S3 model, now equipped with an ESP32-S31NRV16 WiFi 6, Bluetooth 5.4, and 802.15.4 SoC with 16MB of PSRAM. The rotary knob also includes 16MB SPI flash, a speaker, a microphone, and an optional rechargeable battery. GUITION JC3636F118C-I-YR1 specifications: SoC - ESP32-S31NRV16 CPU RISC-V HP (High-performance) RV32IMAFCP CPU @ 320 MHz with FPU, SIMD, etc. RISC-V LP (Low-power) MCU core Memory - 512 KB SRAM, 32 KB low-power SRAM, 16MB PSRAM Wireless 2.4 GHz WiFi 6 (802.11ax) Bluetooth 5.4 with support for LE Audio, direction finding, Bluetooth Mesh 1.1, and Classic (BR/EDR) 802.15.4 radio for Zigbee, Thread, and Matter Storage 16 MB flash MicroSD card slot (for WiFi configuration, music or MJPEG video playback, etc.) Display 1.8-inch [...] The post Rotary encoder with 1.8-inch touch display gets upgrade to ESP32-S31 SoC appeared first on CNX Software - Embedded Systems News. ## Raise or Check? Predicting the Necessity of Additional Fuzzing for Continuous Fuzzing DevFeed: [Raise or Check? Predicting the Necessity of Additional Fuzzing for Continuous Fuzzing]() Original publisher: [Read original article]() Author: Riku Kato, Tatsuya Shirai, Olivier Nourry, Yutaro Kashiwa, Kenji Fujiwara, Yasutaka Kamei, Hajimu Iida Published: 2026-10-21T04:00:00Z Content type: article Language: en Sources: [arXiv: Cryptography and Security](), [arXiv: Software Engineering]() Topics: [Fuzzing/Fuzz testing](), [Open Source](), [scikit-learn](), [aws inspector]() Tags: [c-c-plus-plus](), [classification](), [commits](), [continuous](), [cost](), [coverage](), [cross](), [cs-cr](), [cs-se](), [daily](), [dataset](), [days](), [detection](), [efficiently](), [fuzzing](), [introduced](), [model](), [open-source](), [osv](), [random](), [vulnerabilities](), [vulnerability-detection]() ### AI overview RAISE prioritizes extra continuous fuzzing for project-days predicted to be at higher risk of introducing a vulnerability. It classifies project-days using a dataset of 15 C/C++ projects and 15,210 days, including 347 vulnerability-introducing days. In cross-project validation, a Random Forest combining VCCFinder static features with dynamic fuzzing data reached ROC-AUC 0.74; coverage metrics, especially PatchCoverage, ranked as the most important features. In a simulation covering 968 vulnerabilities, Simple-Regression used 91.60 additional fuzzing sessions per detected vulnerability, about half the Median heuristic's 171.89. These results suggest risk estimates can guide resource allocation, though the evidence reported here is limited to the described dataset and simulation. ### Source excerpt arXiv:2610.27828v1 Announce Type: new Abstract: Continuous fuzzing is used for vulnerability detection in Open Source Software (OSS). Large-scale services such as Google's OSS-Fuzz fuzz over 1,300 projects and have revealed more than 10,000 vulnerabilities. Despite this, a considerable time lag often exists between when a vulnerability is introduced and when it is detected (i.e., the window of exposure). Given the computational cost of additional fuzzing, we aim to allocate fuzzing resources efficiently using vulnerability-risk estimates. We propose RAISE (Risk-Aware Intensive Supplemental Execution Framework), a Just-In-Time (JIT) framework that prioritizes additional fuzzing for high-risk project-days. RAISE performs binary classification at the project-day granularity to estimate whether a vulnerability is introduced on a given day. We construct a dataset combining vulnerability-introducing commits from the OSV database with daily coverage and change metrics from OSS-Fuzz, covering 15 C/C++ projects and 15,210 project-days, of which 347 (2.28%) are vulnerability-introducing. In a cross-project validation setting, our best model, a Random Forest combining VCCFinder-based static features with dynamic fuzzing features, achieves a ROC-AUC of 0.74, substantially outperforming a random baseline; coverage metrics, led by PatchCoverage, are the most important features. In a simulation over 968 vulnerabilities comparing five allocation strategies, Simple-Regression was significantly more cost-efficient than the prediction-agnostic heuristics, spending 91.60 additional fuzzing sessions per detected vulnerability, roughly half the cost of the Median heuristic (171.89). RAISE shows that JIT vulnerability prediction with coverage-aware fuzzing features can effectively guide risk-aware allocation of continuous fuzzing resources. ## VIEWE launches 4.3-inch ESP32-S31 display development board DevFeed: [VIEWE launches 4.3-inch ESP32-S31 display development board]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-10-14T00:00:23Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32-C5](), [RISC-V](), [Bluetooth LE](), [Wi-Fi](), [zigbee]() Tags: [802-15-4](), [audio](), [bluetooth-5-4](), [c-c-plus-plus](), [can-bus](), [development-board](), [display](), [embedded-systems](), [esp32](), [esp32-s31](), [espressif](), [graphics](), [hardware](), [hmi](), [i2c](), [iot](), [launches](), [mcu](), [rs485](), [smart-speaker](), [touchscreen](), [usb](), [usb-c](), [wi-fi-6](), [wifi-6]() ### AI overview VIEWE introduced a 4.3-inch display development board built around the ESP32-S31, described as the first third-party board using the preview MCU. It combines an 800 x 480 touchscreen with Wi-Fi 6, Bluetooth 5.4, Zigbee and Thread support, audio hardware, storage, and RS485/CAN interfaces. Programming currently requires ESP-IDF master with the preview flag; example projects are available for display, touch, audio, storage, and interfaces. ### Source excerpt VIEWE has introduced the PLCM-UES31S043H800V480C-U, a 4.3-inch smart display module that appears to be the first third-party development board powered by the new ESP32-S31 MCU. The board includes a 4.3-inch display with 2.4 GHz Wi-Fi 6, Bluetooth 5.4 LE and Classic, and IEEE 802.15.4 for Zigbee and Thread. It also features USB-C and USB-A ports, a microSD card slot, RS485 and CAN interfaces, and a 20-pin GPIO expansion header with UART and I2C. The board also includes an ES8389 audio codec, two NS4150B amplifiers, two onboard microphones, four ADC buttons, a WS2812B RGB LED, and BOOT/RESET buttons. It operates on a 5V power supply and supports an operating temperature range of -20°C to +70°C. VIEWE PLCM-UES31S043H800V480C-U Specifications: Wireless module - ESP32-S31-WROOM-3-N16R16V SoC - ESP32-S31NRV16 CPU RISC-V HP (High-performance) RV32IMAFCP CPU @ 320 MHz with FPU, SIMD, etc. RISC-V LP (Low-power) MCU core Memory - 512 KB SRAM, 32 KB low-power SRAM, [...] The post VIEWE launches 4.3-inch ESP32-S31 display development board appeared first on CNX Software - Embedded Systems News. ## Q8botOne - A palm-sized open-source quadruped robot with ESP32-C3, DYNAMIXEL smart actuators (Crowdfunding) DevFeed: [Q8botOne - A palm-sized open-source quadruped robot with ESP32-C3, DYNAMIXEL smart actuators (Crowdfunding)]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-10-08T09:00:08Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32-C3](), [Open Source](), [RISC-V](), [Modulino Nodes]() Tags: [adafruit](), [arduino](), [c-c-plus-plus](), [crowd-supply](), [embedded-systems](), [esp32](), [esp32-c3](), [espressif](), [hardware](), [open-source](), [python](), [raspberry-pi](), [risc-v](), [robotics](), [video]() ### AI overview ZeroWire Robotics' Q8botOne is a compact, open-source quadruped built around an ESP32-C3 and DYNAMIXEL smart actuators. Its cable-light design integrates components on a central PCB, and its controller communicates with the robot over ESP-NOW. The article describes its hardware, firmware and control software, compares it with other small quadruped robots, and reports crowdfunding prices and expected delivery timing. ### Source excerpt ZeroWire Robotics Q8botOne is an open-source, palm-sized quadruped robot built around an ESP32-C3 MCU and ROBOTIS DYNAMIXEL smart actuators. Designed with a unique wire-free architecture, the robot integrates all components onto a central printed circuit board also used as the structural chassis. The robot is roughly the size of a smartphone and can jog, jump, and carry up to twice its 250-gram weight. The main PCB uses a dedicated 6V/16A step-down converter for motor power and includes a battery management system for two 14500 Li-ion cells. For control, the kit includes a separate ESP32-C3 controller PCB with an Adafruit Mini I2C Gamepad. Q8botOne specifications: Wireless module - Espressif Systems ESP32-C3-MINI-1-N4 module with: ESP32-C3 (ESP32-C3FN4) SoC CPU - 32-bit RISC-V single-core processor up to 160 MHz Memory - 400 KB SRAM (16 KB for cache), 8 KB SRAM in RTC Storage - 4 MB embedded flash, 384 KB ROM Connectivity - [...] The post Q8botOne - A palm-sized open-source quadruped robot with ESP32-C3, DYNAMIXEL smart actuators (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## DSPi firmware turns Raspberry Pi RP2040/RP2350 into a USB sound card with an onboard DSP engine DevFeed: [DSPi firmware turns Raspberry Pi RP2040/RP2350 into a USB sound card with an onboard DSP engine]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-10-07T09:00:08Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Software Dev](), [Embedded Systems](), [Raspberry Pi RP2350]() Tags: [audio](), [c-c-plus-plus](), [development-board](), [dsp](), [firmware](), [raspberry-pi](), [raspberry-pi-rp2350](), [rp2040](), [rp2350](), [usb]() ### AI overview DSPi firmware turns Raspberry Pi RP2040 and RP2350 boards into USB sound cards with integrated digital signal processing. It supports audio features such as parametric EQ, crossovers, time alignment, multiple output channels, and configuration through GUI and command-line applications. ### Source excerpt DSPi is a full-featured audio DSP firmware for the Raspberry Pi Pico, Pico 2, and other Raspberry Pi RP2040/RP2350 boards, which turns them into a USB sound card with an integrated DSP. It allows users to make use of essential tools like room correction, active crossovers, parametric EQ, time alignment, and more, and essentially works as a regular sound card on your computer. DSPi firmware's capabilities: Dual-Core DSP - EQ processing is split across both cores on both RP2040 and RP2350. USB Audio Interface - Supports 16-bit and 24-bit PCM input at 44.1, 48, and 96 kHz, working on macOS, Windows, Linux, and iOS. S/PDIF Input - 24-bit PCM stereo audio at 44.1 or 48kHz. 24-bit S/PDIF or I2S Outputs - Up to 4x independent stereo output slots (8x channels on RP2350, 4x channels on RP2040). Subwoofer Output - Dedicated mono PDM output channel with a high-performance 2nd-order delta-sigma modulator, [...] The post DSPi firmware turns Raspberry Pi RP2040/RP2350 into a USB sound card with an onboard DSP engine appeared first on CNX Software - Embedded Systems News. ## OpenArm 2.0 - An open-source 7-DOF robot arm with QDD joints, bilateral force feedback, in-hand camera DevFeed: [OpenArm 2.0 - An open-source 7-DOF robot arm with QDD joints, bilateral force feedback, in-hand camera]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-30T09:00:23Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Robot Manipulation](), [lerobot](), [Isaac](), [Moleculer]() Tags: [2](), [arm](), [c-c-plus-plus](), [can-bus](), [firmware](), [hardware](), [linux](), [open-source](), [physical-ai](), [python](), [robot](), [robot-arm](), [robotics](), [ros](), [ros-2](), [software](), [teleoperation](), [ubuntu](), [video]() ### AI overview Enactic's OpenArm 2.0 is an open-source, seven-degree-of-freedom robot arm for physical AI research, teleoperation, and contact-rich data collection. Its backdrivable joints enable compliant motion and bilateral force feedback; the platform also includes an in-hand camera, firmware and control software, ROS 2 support, and tools for robotics learning and evaluation. ### Source excerpt Tokyo-based Enactic OpenArm 2.0 is an open-source hardware 7-DOF robot arm designed for physical AI research, teleoperation, and contact-rich data collection. It uses quasi-direct-drive (QDD) backdrivable joints to support compliant motion and bilateral force-feedback control. The arm supports 4.1 kg nominal and 6 kg peak payload, features 1 kHz CAN-FD control, and a compact parallel gripper with an in-hand camera and replaceable fingers. It supports bilateral force-feedback teleoperation, gravity compensation, and compliant interaction, with an aluminum and stainless-steel structure and MISUMI-frame base. The ecosystem also includes the OpenArm Cell for standardized evaluation and OpenArm KER, a motorless replica for teleoperation and data collection. Open-source CAD, BOM, firmware, control software, ROS 2, MuJoCo, Isaac Lab, and dataset tools support applications such as imitation learning, reinforcement learning, sim-to-real, and robotic manipulation. OpenArm 2.0 specifications: Communication CAN-FD (recommended): nominal 1 Mbps, data 5 Mbps, control loop 1 kHz Classic CAN 2.0 fallback @ [...] The post OpenArm 2.0 - An open-source 7-DOF robot arm with QDD joints, bilateral force feedback, in-hand camera appeared first on CNX Software - Embedded Systems News. ## ESP32-C5 devkit features 3.5-inch touch LCD, optional camera and battery DevFeed: [ESP32-C5 devkit features 3.5-inch touch LCD, optional camera and battery]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-30T00:00:38Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [ESP32-C5](), [ESP32](), [Embedded Systems]() Tags: [arduino](), [battery](), [bluetooth](), [c-c-plus-plus](), [camera](), [devkit](), [display](), [esp32](), [esp32-c5](), [espressif](), [hardware](), [sensors](), [smart-speaker](), [touchscreen](), [waveshare](), [wifi-6]() ### AI overview The Waveshare ESP32-C5-Touch-LCD-3.5 is a development board with a 3.5-inch touchscreen, optional camera and battery, sensors, audio features, microSD storage, and GPIO expansion. Its ESP32-C5 module supports dual-band Wi-Fi 6, Bluetooth LE, Zigbee, and Thread. ### Source excerpt Waveshare ESP32-C5-Touch-LCD-3.5 is another ESP32-C5 devkit with a built-in display, but equipped with a larger 3.5-inch color touch LCD and an optional BF3901 low-resolution (0.08 MP) camera and/or a 500 mAh battery. It also features a speaker connector and a built-in microphone for voice interaction, a 6-axis IMU and a temperature and humidity sensor, a microSD card slot for additional storage, and a 32-pin GPIO connector for expansion. ESP32-C5-Touch-LCD-3.5 specifications: Wireless module - Espressif Systems ESP32-C5-WROOM-1U-N32R8 SoC - ESP32-C5HR8 CPU Single-core 32-bit RISC-V processor @ up to 240 MHz Low-power RISC-V core @ 40 MHz acting as the main processor for power-sensitive applications Memory - 384 KB SRAM on-chip, 8MB PSRAM Storage - 320 KB ROM Wireless Connectivity Dual-band (2.4GHz/5 GHz) 802.11ax WiFi 6, with 802.11b/g/n WiFi 4 standard fallback Bluetooth 5.0 Low Energy (LE) 802.15.4 radio for Zigbee 3.0 and Thread 1.3 Storage - 32MB flash Antenna - IPEX [...] The post ESP32-C5 devkit features 3.5-inch touch LCD, optional camera and battery appeared first on CNX Software - Embedded Systems News. ## CodeQL 2.27.1 adds C and C++ query and Kotlin 2.4.20 support DevFeed: [CodeQL 2.27.1 adds C and C++ query and Kotlin 2.4.20 support]() Original publisher: [Read original article]() Author: Allison Published: 2026-09-25T09:55:23Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [Static code analysis](), [c/c++](), [Kotlin]() Tags: [c-c-plus-plus](), [c-plus-plus](), [c-sharp](), [code-scanning](), [codeql](), [ghes](), [github-actions](), [github-enterprise-server](), [improvements](), [kotlin](), [queries](), [static-analysis]() ### AI overview CodeQL 2.27.1 expands security analysis models and adds query and accuracy improvements across several languages. C/C++ gains flow models for Boost.Asio, Bloomberg's Blob buffer, and Protocol Buffers, plus a query for ambiguous comparison assignments. Go gains standard-library data-flow coverage; Kotlin 2.4.20 is supported, with a K2 extraction fix that reduces false positives. Other changes improve Fastify route attribution, Rust taint-flow detection and path resolution, C# LINQ and antiforgery analysis, and GitHub Actions pinning checks. Private NuGet registries configured to replace base feeds now take precedence during dependency downloads. GitHub deploys the release automatically on github.com; GHES 3.24 will include it, while older GHES versions can be upgraded manually. ### Source excerpt CodeQL 2.27.1 adds new queries for C/C++ and C#, support for Kotlin 2.4.20, and query-accuracy improvements. CodeQL is the static analysis engine behind GitHub code scanning, which helps you find... ## NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU DevFeed: [NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-12T02:19:36Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Microcontroller](), [Wi-Fi](), [Hardware](), [object-detection]() Tags: [c-c-plus-plus](), [camera](), [computer-vision](), [edge-ai](), [embedded-systems](), [hardware](), [mcu](), [object-detection](), [open-source](), [stm32](), [stmicro-stm32](), [vision](), [wifi-6]() ### AI overview The article reports on CamThink's NeoEyes NE302, a compact USB-C-powered Wi-Fi 6 edge AI vision camera built around the STM32N6 MCU. It describes the device's reduced memory and feature set compared with the NE301, along with its camera, wireless, debugging, video-encoding, object-detection, networking, OTA, and security capabilities. ### Source excerpt CamThink NeoEyes NE302 is a tiny WiFi 6 Edge AI camera based on an STM32N6 Arm Cortex-M55 MCU with Neural-ART NPU which the company says is "especially suitable for developers and makers". I initially thought about it as a smaller version of the CamThink NeoEyes NE301 based on the same STM32N6 MCU and 4MP OS04C10 camera sensor. But it's quite a different device. First, it doesn't have a battery, and the USB-C is only used for power. Memory and storage capacities have been reduced to 32 MB PSRAM and 64 MP SPI flash, and a range of built-in and optional features have been removed, including 4G LTE module (global or US), audio wafers, PIR motion connector, and a 16-pin GPIO header, as well as support for PoE power. CamThink NeoEyes NE302 specifications: Vision MCU STMicro STM32N6 MCU Core - Arm 32-bit Cortex-M55 CPU @ up to 800MHz with Arm Helium [...] The post NeoEyes NE302 - A tiny USB-C-powered WiFi 6 Edge AI Vision camera based on STM32N6 MCU appeared first on CNX Software - Embedded Systems News. ## CodeQL 2.27.0 adds support for Linux ARM64 DevFeed: [CodeQL 2.27.0 adds support for Linux ARM64]() Original publisher: [Read original article]() Author: Allison Published: 2026-09-09T21:45:06Z Content type: release Language: en Sources: [GitHub Changelog]() Topics: [CVSS v4.0]() Tags: [arm64](), [asp-net-core](), [c-c-plus-plus](), [c-sharp](), [codeql](), [github](), [kotlin](), [rust](), [security]() ### AI overview CodeQL 2.27.0 adds native Linux ARM64 support, a Rust command-line injection query, expanded Java/Kotlin and C# framework coverage, and analysis improvements across multiple languages. It also updates code scanning authentication options and announces Java support and distribution deprecations. ### Source excerpt CodeQL 2.27.0 is now available on Linux ARM64, adds a new Rust security query, expanded framework coverage for Java/Kotlin and C#, and analysis accuracy improvements across multiple languages. CodeQL is... [Next page]()