Beau Carnes writes about a new hands-on beginner's course on the freeCodeCamp.org YouTube channel designed to help developers master OpenAI Codex. The tutorial covers essential topics including installation, pricing tiers, and interface navigation, while also exploring advanced workflows like Plan Mode and Go Mode for autonomous software development.
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
- Features demonstrations of building a voice-controlled Flappy Bird clone using only prompts
- Covers managing external context through tools like Notion and Supabase
- Teaches how to convert open-source repositories into native iOS and Android apps via Expo
- Includes instructions on running scheduled background automations and handling GitHub pull requests
Andrew Ng writes that AI engineering is transforming software development by blurring the lines between developers, product managers, and designers. Rather than just implementing predefined specs, skilled AI engineers are increasingly expected to "shape the build" through high-agency ownership, driving rapid iteration loops, making critical product decisions, and communicating effectively across various business functions.
- The role requires a bias for action and moving at a higher velocity facilitated by AI tools.
- Key skills include user empathy, basic design sense, and an understanding of business metrics like unit economics.
- Engineers may need to step into roles involving marketing, finance, or legal coordination to align stakeholders.
- The skill set emphasizes identifying opportunities and executing solutions without waiting for top-down direction.
- The role requires a bias for action and moving at a higher velocity facilitated by AI tools.
- Key skills include user empathy, basic design sense, and an understanding of business metrics like unit economics.
- Engineers may need to step into roles involving marketing, finance, or legal coordination to align stakeholders.
- The skill set emphasizes identifying opportunities and executing solutions without waiting for top-down direction.
Anirudh Ramanathan writes that while Anthropic suggests code is no longer the primary bottleneck in development, organizations cannot adopt a single, rigid software development life cycle (SDLC) for all changes. Instead, effective management requires a variety of processes tailored to the risk and complexity of each change—ranging from simple documentation fixes to high-stakes schema migrations—utilizing state machines that react to external evidence rather than fixed workflows.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
- A spec-driven approach uses written artifacts like intent documents and plans as versioned drivers for development.
- High-velocity code generation necessitates verification mechanisms (like hooks or automated tests) that provide deterministic gates.
- Effective AI governance requires evidence from outside the agent, such as test results from independent systems, to ensure quality at scale.
Jiahe Geng writes about RSM-full, an online clustered-memory pipeline designed to optimize the quality-to-token trade-off for long-horizon LLM deployments with limited prompt budgets. By utilizing a cosine-gated max-member merge rule and atom-aware grouped context packing, the method achieves significant performance gains in compact-memory regimes compared to existing baselines like Online K-Means and A-MEM. The approach is particularly effective when maintaining an answer quality of 83% for Full-Context level tasks while utilizing only 32% of the total token cost within a 4k budget.
- RSM-full outperforms Streaming-Proto by +2.97 percentage points on the RealMem benchmark.
- The performance gain is primarily driven by the merge rule and grouped packing rather than just flat concatenation or simple clustering.
- The method reaches its optimal utility in the 2k to 5k prompt token range.
- RSM-full outperforms Streaming-Proto by +2.97 percentage points on the RealMem benchmark.
- The performance gain is primarily driven by the merge rule and grouped packing rather than just flat concatenation or simple clustering.
- The method reaches its optimal utility in the 2k to 5k prompt token range.
Saga Ishtardottir writes that CyberTiel is an abliterated, quantized MoE model designed for high-speed agentic coding and offensive security tasks. Based on the Ornith-1.5-35B-A3B architecture, this version features a grafted multi-token prediction (MTP) head to enable speculative decoding via llama.cpp and uses a custom cyber-weighted importance matrix to maintain performance in low-bit quantizations. The model is optimized for real-world programming problems, showing significant improvements on benchmarks like SWE-bench-Live compared to its censored counterparts.
- Includes an abliterated (uncensored) base that allows the model to perform offensive security work without refusal.
- Features a grafted MTP head at block 40 specifically designed for speculative decoding speedups in llama.cpp.
- Uses Unsloth Dynamic quantization with a custom imatrix calibrated on specialized cybersecurity and code datasets.
- Capable of vision tasks via the mmproj-BF16.gguf projector from Ornith's original model.
- Includes an abliterated (uncensored) base that allows the model to perform offensive security work without refusal.
- Features a grafted MTP head at block 40 specifically designed for speculative decoding speedups in llama.cpp.
- Uses Unsloth Dynamic quantization with a custom imatrix calibrated on specialized cybersecurity and code datasets.
- Capable of vision tasks via the mmproj-BF16.gguf projector from Ornith's original model.
Benjamin Marie writes that while Qwen3.8 27B demonstrates superior accuracy across various tasks compared to the recently released Muse Glimmer—particularly in long-horizon agentic coding—Muse Glimmer offers significant advantages in memory efficiency due to its lower KV-cache consumption and shorter reasoning traces.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
Benjamin Marie explores the trade-offs between accuracy and token efficiency when adjusting the reasoning effort settings in Qwen3.8 27B. By comparing configurations where thinking is disabled, set to low, medium, or xhigh, he examines whether increasing a model's "thinking" time provides significant performance gains relative to the added computational cost and memory usage.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
Benjamin Marie writes that the effectiveness of an LLM in long-horizon agentic coding tasks depends heavily on the harness used to drive it rather than just the model itself. Through testing Qwen3.8 27B across three different interfaces—Mini-SWE Agent, Claude Code, and Pi—the author found that while specific configurations like "benchmaxxed" Pi can solve the highest number of tasks, other setups like Claude Code achieve better functional coverage (F2P). The study highlights how critical engineering choices, such as preserving reasoning traces or managing output token limits, are essential for successful agentic performance.
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
This guide provides instructions for setting up the reTerminal Sticky, a magnetic ePaper display device designed for calm information display. Users can power on the device, connect it to 2.4GHz Wi-Fi via the Seeedash App, and begin using features like AI voice input for quick note creation.
- The device includes an integrated touchscreen, physical page buttons, and a dedicated AI voice button.
- It supports mounting on non-metal surfaces using the included adhesive magnetic ring.
- Users can customize display fonts by adding .ttf or .otf files via a microSD card in a specific folder structure.
- The Seeedash App allows for remote management of device settings, Wi-Fi configuration, and content syncing like photos and weather updates.
- The device includes an integrated touchscreen, physical page buttons, and a dedicated AI voice button.
- It supports mounting on non-metal surfaces using the included adhesive magnetic ring.
- Users can customize display fonts by adding .ttf or .otf files via a microSD card in a specific folder structure.
- The Seeedash App allows for remote management of device settings, Wi-Fi configuration, and content syncing like photos and weather updates.
Dmitry Soldatkin, Andrew Smith, and Vinay Arora write about deploying the open-weight Qwen3.8-2.4T-A95B model on Amazon SageMaker HyperPod using vLLM to support demanding agentic and reasoning workloads. The article provides a technical walkthrough for hosting this massive 2.4 trillion parameter mixture-of-experts (MoE) model on NVIDIA B300 Blackwell Ultra GPUs, covering infrastructure sizing with NVFP4 quantization, configuration of features like Multi-Token Prediction (MTP), and performance optimization through expert parallelism and prefix caching.
- The model uses a hybrid architecture combining Gated DeltaNet layers for linear attention and Gated Attention layers for full quadratic attention to manage long context windows up to 1 million tokens.
- NVFP4 quantization reduces the model's weight footprint to ~1.2 TB, allowing it to fit on a single node with 8× NVIDIA B300 GPUs.
- Enabling Multi-Token Prediction (MTP) speculative decoding can reduce Time-To-First-Token (TTFT) by nearly 60%.
- Deployment is managed via the SageMaker HyperPod Inference Operator using Kubernetes (EKS) for automated lifecycle management and resilience.
- The model uses a hybrid architecture combining Gated DeltaNet layers for linear attention and Gated Attention layers for full quadratic attention to manage long context windows up to 1 million tokens.
- NVFP4 quantization reduces the model's weight footprint to ~1.2 TB, allowing it to fit on a single node with 8× NVIDIA B300 GPUs.
- Enabling Multi-Token Prediction (MTP) speculative decoding can reduce Time-To-First-Token (TTFT) by nearly 60%.
- Deployment is managed via the SageMaker HyperPod Inference Operator using Kubernetes (EKS) for automated lifecycle management and resilience.
