Building Non-Interactive Agentic Coding Workflows with Moonshot AI’s Kimi CLI, JSONL Streaming, Testing, and Session Memory
In this tutorial, we configure and operate Kimi CLI as a fully non-interactive AI coding agent. We install the CLI through uv with an isolated Python 3.13 environment, configure Moonshot API authentication through a TOML-based provider and model definition, and build a reusable Python wrapper for ex...
Fireworks AI Releases Fireworks Nexus: A Drop-In Routing and Cost-Control Layer That Moves Routine Coding Work to Open-Weight Models
Fireworks AI has released Fireworks Nexus, an AI management and routing platform aimed at engineering organizations. It connects the coding tools developers already use to a managed layer of open-weight models. The problem it targets is well documented. Forbes reported that Uber exhausted its entire...
A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.
How Much Does a Local LLM Actually Cost to Run? I Measured Every Watt on Apple Silicon
Five models, sustained generation, real wall-socket energy at $0.31/kWh — and the surprise the RTX-3090 numbers predicted, only bigger.
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Powerful Compute So Compact, It’s Clutch — Build AI in Your Hand With NVIDIA Jetson
Anyone can make a robot move; NVIDIA Jetson makes it think. As a discerning AI investor who values style and substance, Sarah Guo knows this season’s standout accessory isn’t the latest designer purse — but what’s inside it. In a recent video, Guo, founder of AI-native venture capital firm Convicti...
MCP Explained: How Modern AI Agents Connect to the Real World
from custom integrations to a universal standard for tool access
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Fish Audio raises $50M seed to build AI voice models for creators and enterprises
Since launching last year, the startup today has more than 8 million people using the open-source or hosted version of its models, and now generates annual recurring revenue of $21 million.
Microsoft AI Releases MAI-Cyber-1-Flash: A 5B-Active-Parameter Cyber Model That Pushes MDASH to 95.95% on CyberGym
Microsoft AI has released MAI-Cyber-1-Flash, its first model built specifically for cyber defense. It is a 137B total, 5B active sparse MoE fine-tune of MAI-Code-1-Flash with a 256k context window. The model does not ship as a standalone endpoint — it runs inside MDASH, Microsoft's multi-model agent...
Deploying a 1-Bit Bonsai-27B Model with PrismML llama.cpp and OpenAI-Compatible Local Inference Workflows
In this tutorial, we deploy the 1-bit Bonsai-27B language model using the PrismML fork of llama.cpp, which provides the specialized CUDA kernels required to decode the model’s Q1_0_g128 GGUF quantization format
The post Deploying a 1-Bit Bonsai-27B Model with PrismML llama.cpp and OpenAI-Compatible ...
Graph Engineering for AI Agents: Beyond the Single-Agent Loop
AI-agent development has progressed through overlapping phases: prompt engineering, context engineering, tool use, autonomous loops, memory systems, and multi-agent coordination. A newer focus is graph engineering, which treats AI applications as explicitly designed workflows rather than a single au...
QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction
arXiv:2607.22549v1 Announce Type: new
Abstract: Hybrid quantum-classical protein structure prediction depends strongly on Hamiltonian penalty weights, yet existing lattice-based workflows typically fix these coefficients by hand and evaluate only very short fragments in simulation. We present QFold...
Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B
arXiv:2607.22545v1 Announce Type: new
Abstract: Deploying large language models in financial-services and agentic settings requires safety classifiers that simultaneously handle prompt injection, regulatory compliance, and general harm, a combination no existing open guardrail addresses in a single...
CORVUS: Context Optimization and Reduction Via Underlying Synchronization for LLM Coding Agents
arXiv:2607.22711v1 Announce Type: new
Abstract: LLM coding agents operate by constructing trajectories that accumulate reasoning, tool calls, and results to enable multi-step decision-making. However, the conventional append-only trajectory architecture found in practice tightly couples file-read a...
CausalGate: Causal Importance Distillation for Transformer Module Pruning
arXiv:2607.22720v1 Announce Type: new
Abstract: Existing adaptive inference methods for Large Language Models rely on observational heuristics, such as hidden-state similarity or activation magnitudes, to drop redundant modules. However, these correlation-based metrics often fail to capture subtle,...
Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks
arXiv:2607.22724v1 Announce Type: new
Abstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or steps within a group. However, on difficult long-horizon tasks, this comparison can suffer f...
QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation
arXiv:2607.22743v1 Announce Type: new
Abstract: Background and Objective: Automatic polyp segmentation supports computer-aided diagnosis and early colorectal cancer detec- tion. Centralized deep learning requires hospitals to share sensitive medical data, while federated learning preserves privacy ...
SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series
arXiv:2607.22548v1 Announce Type: new
Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typi...
Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
arXiv:2607.22554v1 Announce Type: new
Abstract: Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in different but equivalent ways. In this work, we study how model answers change...