A practical look at a serverless LLM stock-analysis prototype whose real lesson is not prediction accuracy, but deployable, low-cost financial intelligence architecture.
A practical reading of why table agents need operational workflows, not just better prompts or larger context windows.
SetupBench shows that coding agents still struggle with the unglamorous first mile of software work: making the project actually run.
A practical map for deciding when static RAG is enough, when reasoning-aware retrieval helps, and when full agentic retrieval-reasoning systems are worth the cost.
A mechanism-first reading of MemSinks, a training-time approach for separating useful generalization from removable verbatim memorization in language models.
Clue-RAG shows that better enterprise retrieval may come less from bigger knowledge graphs and more from using the right semantic layer at the right moment.
A mechanism-first reading of neural jamming: useful as a scaling lens, hazardous as a consciousness claim.
A practical reading of Introspection of Thought, a prompt-side framework that compresses agent-like debate into a single LLM call.
Athena shows that LLM accuracy can improve when models are wired to calculators, APIs, and run-monitoring loops, but the real lesson is orchestration discipline rather than model-size theatre.
A multimodal benchmark shows why finfluencer confidence is easier to sell than to parse, trade, or operationalise safely.