A recent study reveals that large language models, when tasked with generating strategic advice, tend to produce generic, consensus-aligned recommendations rather than context-specific insights. This phenomenon, dubbed …
A benchmark of CPU-only embedding inference from low cost DigitalOcean droplets to Apple Silicon, focusing on runtime overhead, CPU tier effects, and scaling limits.
A practical approach to securing LLM agents by treating tool invocation as a control boundary. Why fail-closed gates, monotonic risk checks, and adversarial benchmarks are more robust than prompt-only defenses for …
Best practices for sampling parameters and system prompt design to optimize style compliance and throughput with local LLMs like GPT-OSS-20B.
LLMs have collapsed the cost of execution, but verification, comprehension, and ownership remain the binding constraints in software engineering and adjacent knowledge work.
The latest nullbench run shows no step-function gains in base model capability. Instead, fine-tuning, abliteration, and long-context extensions primarily redistribute behavior rather than raise ceilings. The real …
AI agents make easy software tasks cheaper but don’t automate hard engineering. This post tries to explain why autonomy claims overreach and where AI actually helps.
2025 suggests that the gap between expectations and measured outcomes around LLMs in software engineering is no longer subtle. Large investments and confident narratives imply broad productivity gains or partial task …
MXFP4, aka Microscaling Format for 4-bit Floating-Point, is essentially a very small floating-point format that borrows one big trick from signal processing and older “block floating point” designs, in that …
This post reports a small, benchmark run on a Raspberry Pi 4B (8GB RAM) device using llama.cpp across five compact GGUF models spanning ~270M to ~1.2B parameters, with aggressive quantization. The aim was to measure how …