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.
Supreeth Koundinya writes that Spotify has successfully reduced token consumption for its internal coding agent, Claude Code, by approximately 90% through a model routing approach. By using their "Portal" developer platform and "AiKA Modes," the company directs repetitive or I/O-intensive tasks—such as file reading and basic code generation—to cheaper models like Google's Gemini 2.5 Flash, reserving Anthropic's frontier Claude models for complex reasoning and debugging.
- Routing is facilitated by a plugin called Shunt using PreToolUse hooks to intercept large file reads.
- The system uses ephemeral runtimes via AiKA modes so developers don't have to manage infrastructure or API keys manually.
- To prevent context bloat, Claude Code does not directly consume the output of generated code written by worker models to disk.
- Current limitations include a 30-second invocation limit and potential loss of line-level detail during delegated analysis.
- Routing is facilitated by a plugin called Shunt using PreToolUse hooks to intercept large file reads.
- The system uses ephemeral runtimes via AiKA modes so developers don't have to manage infrastructure or API keys manually.
- To prevent context bloat, Claude Code does not directly consume the output of generated code written by worker models to disk.
- Current limitations include a 30-second invocation limit and potential loss of line-level detail during delegated analysis.
Carl Franzen writes that DeepSeek has launched V4.1-Flash, a model featuring a 552-billion-parameter mixture-of-experts backbone designed to drastically reduce costs for long-context workflows through specialized caching and architecture. The model offers extremely low off-peak rates of $0.003 per million cached input tokens, making it highly competitive against frontier models like GPT-5.6 Sol and Claude Opus 5 when used in repetitive agentic loops. While its total parameter count has increased significantly compared to previous versions, its Causal Encoder-Decoder architecture aims to minimize compute requirements during the prefill stage of inference.
- V4.1-Flash utilizes a Causal Encoder-Decoder architecture that activates 8 billion parameters during prefill and 16 billion during decoding.
- The model features an extremely high context window of up to 1 million tokens.
- DeepSeek's technical report highlights the use of FP4 KV caching, which reduces global KV cache size by approximately one-quarter compared to its predecessor.
- Off-peak hours for lower pricing are scheduled from Monday through Friday, specifically between 01:00–04:00 UTC and 06:00–10:00 UTC.
- V4.1-Flash utilizes a Causal Encoder-Decoder architecture that activates 8 billion parameters during prefill and 16 billion during decoding.
- The model features an extremely high context window of up to 1 million tokens.
- DeepSeek's technical report highlights the use of FP4 KV caching, which reduces global KV cache size by approximately one-quarter compared to its predecessor.
- Off-peak hours for lower pricing are scheduled from Monday through Friday, specifically between 01:00–04:00 UTC and 06:00–10:00 UTC.
Paul Sawers writes that Amazon Web Services (AWS) has released an open-source application called Pizza Bot, which provides developers with an email-inspired inbox to manage autonomous AI agents. Designed specifically for tasks that continue after a user has finished their session, the tool moves away from chat interfaces toward an asynchronous model where completed jobs arrive as threads and urgent decisions are surfaced for human triage.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
