Research Blog
Technical articles and accessible explanations from across our five research domains.
How compere uses bandit algorithms to rank items with minimal pairwise comparisons — applications in search evaluation, recommendation, and tournament design, vs Bradley-Terry and TrueSkill.
A practical comparison of embedcache, Redis vector cache, and GPTCache for eliminating redundant embedding recomputation in RAG pipelines.
A practical comparison of l0l1 with generic Text2SQL tools — when the privacy guarantee matters, when the learning from query patterns matters, and when a vanilla LLM SQL bridge is enough.
A practical comparison of llamafu, llama.rn, and flutter_llama_cpp for running LLMs on Flutter — what each does well, what each doesn't, and which to pick.
A practical comparison of memista, Pinecone, Qdrant, Weaviate, Milvus, and Chroma for vector search — and why the answer is usually 'no, you need SQLite'.
A practical comparison of mullama, Ollama, vLLM, LocalAI, and LM Studio for local LLM serving — when to use which, and why you might pick the research-instrumented option.
A practical comparison of perishable, Cloudflare API Gateway, and HashiCorp Vault for ephemeral LLM API credentials — when zero-trust LLM access needs its own purpose-built tooling, and when a general-purpose solution is enough.
A practical comparison of polymathy, Haystack, and LangChain for building RAG pipelines — when you want a Rust-native async chunking + embedding service, and when the Python frameworks are still the right answer.
A practical comparison of route-switch, LiteLLM, Portkey, and OpenRouter for LLM routing — when you need a gateway, when you need a learned router, and when you need both.
How savanty turns English problem descriptions into mathematically guaranteed solutions. The case for LLM-to-formal-solver pipelines over pure LLM output.
How Skelf Research compares to Big Tech AI labs, AI startups, and individual researchers — what 'independent AI research lab publishing open-source software' means in 2026, and why the methodology is the moat.
A practical comparison of slorg, Algolia, Meilisearch, and Typesense for search — when deliberative search (reasoning before retrieval) is the right answer, and when traditional search is still the right answer.
A practical comparison of ukkin, ChatGPT Operator, and Anthropic Computer Use for autonomous mobile and desktop AI agents — privacy, autonomy, and the on-device vs cloud trade-off.
How waremax gives warehouse-robotics research a deterministic simulator, a Gymnasium RL interface, and instrumented delay attribution. Why deterministic beats plausible for reproducible research.
A practical comparison of zviz, gVisor, and Firecracker for sandboxing untrusted AI-generated code — performance, security, and operational trade-offs.
A practical comparison of memorg and Mem0 for agent memory — when structured schemas beat vector recall, when they don't, and how to combine them.
MCP defines how agents talk. mpl defines what correct looks like. Why production agentic systems need both — and how to compose them.
A practical comparison of promptel and DSPy for prompt engineering — when to use a declarative specification language, when to use a programmatic compiler, and how they compose.
The story of building a Lua-native database in Lua, then rewriting it in Rust with RocksDB — and what the performance tells us.
Why traditional search retrieves first and ranks later — and how slorg inverts this by understanding intent before fetching results.
What happens when LLM agents need to remember across sessions — structured memory schemas, retrieval strategies, and the memory-context distinction.
Building approximate nearest-neighbour search on SQLite in pure Rust — and why you might not need a dedicated vector database.
Why AI agents need scoped, time-limited credentials — and how perishable implements zero-trust patterns for LLM API access.
A practical framework for managing prompts as versioned dependencies — tackling drift, regression, and reproducibility.
How compere uses MAB algorithms to rank items effectively with minimal pairwise feedback — applications in search and recommendation.
Why treating prompts as typed, portable artefacts changes how we reason about LLM behaviour — and how promptel implements this idea.
Describe optimisation problems in plain English and receive mathematically guaranteed solutions — no PhD required.
How route-switch uses MIPROv2 to automatically select the right model for each query — balancing cost, quality, and latency.
Building AI agents that browse, observe, and automate tasks entirely on-device — the autonomy-safety spectrum on mobile.
A post-mortem on building a local LLM serving layer — llama.cpp integration, model management, and where existing tools constrain research.
What happens when you run a full LLM on mobile hardware with zero cloud dependency — memory, latency, and model quality on consumer devices.
How zviz uses Zig's comptime capabilities to build gVisor-inspired sandboxing with near-zero runtime cost.
Language choice as research methodology — how memory-safe, deterministic-performance languages produce falsifiable systems claims.
From visual signal specification to verified Rust executable — how sigc turns alpha hypotheses into production-ready code in minutes.
The case for radical openness in AI research — reproducibility, falsifiability, and community trust through 25 open-source projects.